Animal models and conserved processes

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Animal models and conserved processes
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Background: The concept of conserved processes presents unique opportunities for using nonhuman animal models in biomedical research. However, the concept must be examined in the context that humans and nonhuman animals are evolved, complex, adaptive systems. Given that nonhuman animals are examples of living systems that are differently complex from humans, what does the existence of a conserved gene or process imply for inter-species extrapolation?
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Publication of this article was funded in part by the University of Florida Open-Access publishing Fund. In addition, requestors receiving funding through the UFOAP project are expected to submit a post-review, final draft of the article to UF's institutional repository at the University of Florida community, with research, news, outreach, and educational materials.
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Greek and Rice Theoretical Biology and Medical Modelling 2012, 9:40 http://www.tbiomed.com/content/9/1/40; Pages 1-33
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doi:10.1186/1742-4682-9-40 Cite this article as: Greek and Rice: Animal models and conserved processes. Theoretical Biology and Medical Modelling 2012 9:40.

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Greek and Rice Theoretical Biology and Medical Modelling 2012, 9:40
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.- THEORETICAL BIOLOGY AND
MEDICAL MODELLING
^'


[RESEARCH Op


Animal models and conserved processes

Ray Greek" and Mark J Rice2


I AC* D R k


seIreopon fence r ay reey @
gmail com
Americans For Medical Advancement
(wwwAFMA-curediseaseorg), 2251
Refugio Rd, Goleta, CA 93117, USA
Full list of author information is
available at the end of the article


0 2012 Greek and Rice, licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Biolled Central Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.


Abstract

Background: The concept of conserved processes presents unique opportunities for
using nonhuman animal models in biomedical research. However, the concept must
be examined in the context that humans and nonhuman animals are evolved,
complex, adaptive systems. Given that nonhuman animals are examples of living
systems that are differently complex from humans, what does the existence of a
conserved gene or process imply for inter-species extrapolation?
Methods: We surveyed the literature including philosophy of science, biological
complexity, conserved processes, evolutionary biology, comparative medicine,
anti-neoplastic agents, inhalational anesthetics, and drug development journals in
order to determine the value of nonhuman animal models when studying conserved
processes.
Results: Evolution through natural selection has employed components and
processes both to produce the same outcomes among species but also to generate
different functions and traits. Many genes and processes are conserved, but new
combinations of these processes or different regulation of the genes involved in
these processes have resulted in unique organisms. Further, there is a hierarchy of
organization in complex living systems. At some levels, the components are simple
systems that can be analyzed by mathematics or the physical sciences, while at
other levels the system cannot be fully analyzed by reducing it to a physical system.
The study of complex living systems must alternate between focusing on the parts
and examining the intact whole organism while taking into account the connections
between the two. Systems biology aims for this holism. We examined the actions of
inhalational anesthetic agents and anti-neoplastic agents in order to address what
the characteristics of complex living systems imply for inter-species extrapolation of
traits and responses related to conserved processes.
Conclusion: We conclude that even the presence of conserved processes is
insufficient for inter-species extrapolation when the trait or response being studied is
located at higher levels of organization, is in a different module, or is influenced by
other modules. However, when the examination of the conserved process occurs at
the same level of organization or in the same module, and hence is subject to study
solely by reductionism, then extrapolation is possible.
Keywords: Anesthesia, Animal models, Cancer, Complexity, Conserved processes,
Systems biology






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Background
Marc Kirschner and John Gerhart introduced the concept of facilitated variation and
conserved core processes in their book, The Plausibility of Life [1], in order to explain
how novelty arises in evolution. Motivated by advances in evolutionary and developmental
biology (evo devo), these investigators proposed that conserved processes are ubiquitous in
eukaryotes but pointed out that by using conserved processes differently, for example by
differently regulating the genes that code for the processes, expressing the genes differently,
varying the sequences or combination of genes or transcription factors, novelty can arise.
Mutations in the genes that regulate the conserved processes can accomplish this novelty.
Moreover, by adjusting the regulatory genes, the organism can evolve with fewer mutations
than would be the case if a trait had to arise de novo or from mutations in structural genes.
This has implications for using nonhuman animals (hereafter referred to simply as animals)
as models for humans in biomedical research. One should expect to discover information
regarding conserved processes in humans by studying animal models. We sought to deter-
mine whether limits exist on this method and if so what those limits are.


Methods
We surveyed the relevant literature including philosophy of science, biological complexity,
conserved processes, evolutionary biology, comparative medicine, anti-neoplastic
agents, inhalational anesthetics, and drug development journals in order to determine
the appropriate role for animal models when studying conserved processes. Philosophy
of science is relevant to our discussion as it includes the premises and assumptions on
which research is then based. A study or method can be methodologically sound but if
the premises are incorrect, then the study loses much if not all of its value. The drug
development literature was searched because the final application of much research is
targeted intervention via drugs hence that literature can inform regarding the success of
a practice or modality. The literature concerning biological complexity and conserved
processes was surveyed as it directly relates to the issue being explored. All of this must
be placed into the context of evolutionary biology in order to better explain the findings.
We chose inhalational anesthetics and anti-neoplastic agents as examples because of the
well-known conserved nature of these agents.


Results
Animal models
The use of models has a long history in science, which led philosopher of science Richard
Braithwaite to warn that: "The price of employment of models is eternal vigilance" [2]. In
this section, we will explore what animal models are, how they can be used in scientific
investigation, including biomedical research, and discuss classification schemes. In this art-
icle, we will address the use of predictive animal models in light of the concepts of complex
systems, personalized medicine and pharmacogenomics, and evolutionary biology. We will
then explore what this implies when using animal models to study conserved processes.
Models are important for scientific pursuits and can take the form of abstract models,
computational models, heuristic models, mathematical models, physical models such as
scale models, iconic models, and idealized models. Models can also be divided on the
basis of whether they are used to replicate a portion of the item being modeled or are used






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to test hypotheses or interpret aspects of a theory. Examples of historically important
models include Watson and Crick's physical model of DNA, Pauling's model of chemical
bonds, Bohr's solar system model of the atom, and the billiard ball model of gases. More
recent models include the computer model of the brain, mathematical models of disease
spread, and Lorenz's model of the atmosphere.
Robert Hinde observed that models:

Should be different from the thing being modeled, because if it is not, the modeler
might assume that all properties demonstrated by the model exist in the thing being
modeled;
Are usually less complicated than the thing being modeled;
Are more readily available than the thing being modeled, and;
"pose questions, suggest relations, or can be manipulated in ways not possible with
the original" [3].

In light of the importance of models, some philosophers of science assert that the
study of models per se has been neglected by the philosophy of science community.
Frigg and Hartmann [4] state: "What fills in the blank in 'M represents T if and only
if __; where M is a model and T a target system?" Moreover, how one classifies
models and what criteria must be fulfilled in order for M to be considered a specific
type of model has arguably not been adequately addressed by the philosophy of science
community. Yet another problem with the philosophy of models is the relationship between
theory and model [4]. We maintain that this lack of scholarly attention to models has
played a role in what we see as the confusion surrounding the use of animals as models.
Animal models are physical models and can be further classified based on various
features and uses. For example, they can be distinguished by the phylogenetic distance
of the model species from humans. Animal models can also be classified based on fidel-
ity-how well the model resembles humans-as well as based on validity-how well
what you think you are measuring corresponds to what you really are measuring.
Animal models can also be considered based on reliability-the precision and accuracy
of the measurement [5]. Hau explains that animal models can be categorized as spon-
taneous, induced, transgenic, negative and orphan. Hau states: "The majority of labora-
tory animal models are developed and used to study the cause, nature, and cure of
human disorders" [[6] p3]. This is important as Hau further states that animal models
can be used to predict human responses: "A third important group of animal models is
employed as predictive models. These models are used with the aim of discovering and
quantifying the impact of a treatment, whether this is to cure a disease or to assess
toxicity of a chemical compound. The appropriateness of any laboratory animal model
will eventually be judged by its capacity to explain and predict the observed effects in
the target species" [6]. Others agree that predicting human response is a common use
for animal models [7-12]. For example, Heywood stated: "Animal studies fall into two
main categories: predictive evaluations of new compounds and their incorporation into
schemes designed to help lessen or clarify a recognized hazard" [13].
Animals are utilized for numerous scientific purposes (see ]Table 1) and one of the
authors (Greek) has addressed these various uses in previous publications [14-20]. One
cannot have a meaningful discussion regarding the utility of animal models unless one







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Table 1 Categories of animal use in science and research [16]
1. As predictive models for human disease
2. As predictive models to evaluate human exposure safety in the context of pharmacology
and toxicology (e.g., in drug testing)
3. As sources of 'spare parts' (e.g., aortic valve replacements for humans)
4. As bioreactors (e.g., as factories for the production of insulin, or monoclonal antibodies, or
the fruits of genetic engineering)
5. As sources of tissue in order to study basic physiological principles
6. For dissection and study in education and medical training
7. As heuristic devices to prompt new biological/biomedical hypotheses
8. For the benefit of other nonhuman animals
9. For the pursuit of scientific knowledge in and of itself


specifies the category under discussion. For example, areas in which animal models
have been successfully employed include the evaluation of a phenomenon that can be
described by the physicochemical properties of the organism, the study of basic physiologic
functions, and the study of other traits that can be described by the use of conversion
factors based on the body surface area of the organism. In general, animal models can be
successfully employed in categories 3-9 in Table 1. However, animal models have failed to
be predictive modalities for human response to drugs and disease [13-16,18,21-41], depicted
by categories 1 and 2 in Table 1. (The authors have addressed this failure in numerous
publications and, because an exploration for this failure is not the purpose of the article, we
refer the reader to those publications [14-20,23] even though we realize that some view this
position as controversial [7,11,42-44].) This is not to say that a species can never be found
in retrospect that mimics an outcome in humans. Such a species usually can be identified,
however retrospective correlation is obviously not the same as prediction [45-47]. Moreover,
any process or modality claiming to be predictive can be evaluated by use of the binomial
classification table and equations in Table 2 (as illustrated in Table 3 [48]). Such calculations
are commonly used in science [49-53].

Table 2 Binary classification test
Gold standard
GS+ GS-
Test T+ TP FP
T- FN TN
Sensitivity TP/(TP + FN)
Specificity TN/(FP + TN)
Positive Predictive Value -TP/(TP+ FP)
Negative Predictive Value -TN/(FN+TN)
T- -Test negative
T + Test positive
FP -False positive
TP True positive
FN False negative
TN -True negative
GS- Gold standard negative
GS+- Gold standard positive
The binary classification test allows calculations for determining how well a test or practice compares with reality or the
gold standard.







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Table 3 Example of binary classification values
Gold standard (human)
GS+ GS-
Test T+ 22 26
T- 22 30
Sensitivity 22/(22 + 22) 0.5
Specificity 30/(26 + 30) 0.54
Positive Predictive Value -22/(22+26) -0.46
Negative Predictive Value 30/(22 + 30) 0.58
Binary classification values for cardiovascular toxicity test in monkeys from 25 compounds also tested in humans [48].
Note the values are approximately what would be expected from a coin toss.


When judging the predictive value of a modality, one is not using the term predict
in the same sense as when describing how hypotheses generate predictions to be
tested. The predictive value of a commonly used modality usually is known, or can be
ascertained, for example the positive and negative predictive value of x-ray computed
tomography (commonly referred to as a CT scan) for diagnosing pneumothorax (a rupture
of, or interference in, the pleural membrane which allows air to enter the pleural space and
thus interferes with breathing) approaches 1.0 (is accurate for diagnosing the condition in
100% of cases).
Animal models as used in biomedical research, can also be categorized as causal
analogical models (CAMs) or as heuristic or hypothetical analogical models (HAMs)
[54-59]. The use of animal models to predict human response to drugs and disease, in
accordance with categories 1 and 2 in Table 1, would be an example of using animals
as CAMs. Analogical models in general include the hydraulic model of economies and
the computer model of the brain and can be further divided based on various criteria [4].
Causalism or causal determinism dates to Aristotle who stated: "what is called Wisdom is
concerned with the primary causes and principles." Causalism can be summarized
succinctly, as "everything has a cause." This notion of causation was the basis for animal
models as can be appreciated by the writings of Claude Bernard [60], considered the father
of animal modeling since the 19th century. Bernard's thoughts on animal models are an
extension of Aristotle via the determinism of Descartes and Newton [61]. Causal deter-
minism and the principle of uniformity led to the concept, still accepted by many animal
modelers today, that the same cause would result in the same effect in qualitatively similar
systems. This line of thinking was in keeping with the creationist thinking of 19th century
French physiologists, including Bernard, who rejected Darwin's Theory of Evolution
[60,62,63]. The notion of causal determinism and the principle of uniformity combined
with the rejection of evolution led to the belief in the interchangeability of parts. There-
fore, if one ascertained the function of the pancreas in a dog, he could directly extrapolate
that knowledge to the function of the pancreas in humans, once scaling for size had been
factored in [14,63,64]. Unfortunately, this linear thinking persists as the baboon heart
transplant to Baby Fae illustrates. The operation was performed by the creationist surgeon
Leonard Bailey of Loma Linda University in 1984 [[65] p162-3].
We acknowledge that the concept of causation is problematic [66]. Russell suggested
it be abandoned in 1913 [67] and it is clearly more useful for linear systems than
complex systems. While an exhaustive explanation and discussion of the controversies
surrounding causation would occupy more space than is available for this article







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(see Bunge [61] for such an analysis) we should note that a more current explanation for
causation is that of a "first order approximation." Causation is usually discussed in the
context of a chain of causes. Bunge summarizes current thinking: "neodeterminism ..
asserts in this connection that causation is only one among several interrelated categories
concurring in real processes" [61]. This principle is appreciated even more fully in
complex systems. Current thinking notwithstanding, the use of animal models assumes
the Cartesian concept of causation in that a causal model assumes a deterministic causal
relationship between variables. We will explore this thinking and show that even in the
traditional context there are problems with using animal models to discover "causal"
relationships. These problems are increased exponentially when placed in the context of
complex systems.
Based on the writings of LaFollette and Shanks [[58]p63], we suggest the following in
order for a model to be considered a CAM. X (the model) and Y (the subject being
modeled) share properties {a.. .e}. In X, these properties are associated with, and
thought relevant to, state Sl. S1 has not been observed directly in Y, but Y likely also
has would exhibit S1 under the same conditions as X. This concept is illustrated in
Table 4. LaFollette and Shanks [58] state that, "there should be no causally-relevant
disanalogies between the model and the thing being modeled." Unfortunately, causally
relevant disanalogies do exist among species and even within a species, which leads to
different states or outcomes, as illustrated in Table 4. We again paraphrase LaFollette
and Shanks [[58] p112] and suggest that two more conditions must be met for a model
to qualify as a CAM: the shared properties {a,...,e} must have a causal relationship with
state S1 and be the only causally relevant properties associated with Sl. As Table 4
illustrates, the commonalities between the humans and chimpanzees are insufficient to
qualify chimpanzees as CAMs for human response to HIV infection. (For more on


Table 4 Causal analogical models
X, the model Y, the system Shared properties Perturbation Outcome Outcome in system
being modeled between X and Y to the model in model being modeled
Animal system Human system
(for example,
Pan troglodytes)
a. Genes. >90% of Exposure State St. AIDS. State S1 is
nucleotide sequences to HIV. Mild illness not shared despite
identical, of limited the presence of
duration, shared, relevant
properties.
b. Immune system.
Many commonalities.
Constructed on
generally the same
plan.
c. White blood cells
present and function
similarly.
d. Receptors on white
blood cells also present
and function similarly.
e. Shared intracellular
components of white
blood cells.
Shared properties a ... e for humans and chimpanzee do not result in state S7 also being shared.






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animal models of HIV/AIDS see [14,68].) As we will show, animals and humans are
evolved complex systems and as such exhibit the properties of robustness and redundancy;
hence numerous "causes" can result in the same effect and the same perturbation can
result in different outcomes. Because of this and other properties of complex systems,
we should expect different species to exhibit different causal relationships.
Correspondingly, Giere, Bickle, and Mauldin [69] note that some question the use of
causal models in the study of humans because humans are complex systems whereas
casual models assume a deterministic system: an outcome in a simple system is fixed by
the variables. The problems of determining causation are further explored by Bunge [61]
in his neodeterminism explanation alluded to above and his analysis is highly relevant to
this discussion. While we will attempt to contrast the traditional deterministic view of
causality in light of complexity science, this article will not do justice the current thinking
on causation and we refer the reader to Bunge [61] for a fuller explanation.
Giere, Bickle, and Mauldin suggest a probabilistic relationship instead of a 100%
causal relationship for the model: "C is a positive causal factor (probabilistic) for E in
an individual, I, characterized by residual state, S, if in I the probability of E given C is
greater than the probability of E given Not-C." Likewise, LaFollette and Shanks raise
the question as to whether animal models can be weak CAMs: "Begin with two systems
Si and S2. Si has causal mechanisms {a,b,c,d,e}, S2 has mechanisms {a,b,c,x,y}. When
we stimulate sub-system {a,b,c} of Si with stimuli sf response rf regularly occurs. We
can therefore infer that were we to stimulate sub-systems {a,b,c}of S2 with sf rf would
probably occur" [[58] p141]. LaFollette and Shanks then explain that this outcome will
be highly probable if and only if {a,b,c} are causally independent of {d,e} and {x,y}.
Again we anticipate problems in using animal models as weak CAMs, even in the
traditional deterministic-causation view, because, as we shall discuss, various properties
of complex systems will likely give rise to difficulties in isolating subsystems, which
would be required for an animal model to be a weak CAM. These problems have been
referred to as causal/functional asymmetry and mandates caution in extrapolating data
between species. Kirschner and Gerhart give an example of this:

The case of the octopus and the human camera eye has been looked into, and the
lessons are clear. Underneath the gross anatomical similarities are many differences.
The eye derives from different tissues by different developmental means. Although
both structures use the same pigment (rhodopsin) for photoreception, and both send
electrical signals to the brain, we now know that the intervening circuitry is
completely different [[1] p240-01].

Independent evolution has also produced spindle neurons in species as diverse as
humans and cetaceans. Spindle neurons connect parts of the brain involved in higher
cognition and were thought to only occur in primates but have recently been discovered
in cetaceans, such as humpback whales and fin whales, as well as elephants [70-72].
Convergent evolution, the acquisition of the same trait in different lineages, is also
important when considering the role of animal models.

Evolved complex systems
Reductionism is a method of study that seeks to break a system down into its compo-
nent parts, study each part individually, and then reach a conclusion about the system






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as a whole or at least the role of the individual part. Descartes introduced the concept
and it has proven effective for ascertaining many facts about the material universe.
Conversely, the clockwork universe of Descartes has not held up to scrutiny on all
levels. Quantum mechanics, relativity, chaos, and complexity have revealed the stochastic
nature of the supposedly clockwork, deterministic universe. Regrettably, while physicists
recognized the limitations of reductionism, biologists were uncritically embracing it. Francis
Crick extended reductionism to all aspects of biology when he stated: "The ultimate aim of
the modern movement in biology is to explain all biology in terms of physics and chemistry"
[73]. Biological reductionism arguably reached its zenith in the Human Genome Project
(HGP) [74,75] and, ironically, the consequences of the HGP-that humans have a relatively
small number of genes-have, in large part, been responsible for a re-examination of the
role of reductionism in biology. This has been especially true for human pathophysiology
where animals are used as models for humans.
Systems can be categorized as simple or complex. The world of Newton and Descartes
was largely confined to simple systems hence reductionism functioned well for discovery.
At some levels, the components of a complex system can be simple systems and thus are
subject to study by reductionism while at other levels these simple systems combine to
make complex systems thus necessitating study of the intact whole. Mazzocchi
points out that when reductionism takes a component out of its natural environment
it has consequences for extrapolating the results back to the organism as a whole:
"But this extrapolation is at best debatable and at worst misleading or even hazardous. The
failure of many promising drug candidates in clinical research shows that it is not always
possible to transfer results from mice or even primates to humans" [76].
While evolution is defined as a change in allele frequency over time, complexity
science can be defined as "the study of the behaviour of large collections of simple,
interacting units, endowed with the potential to evolve with time" [77,78]. Living
organisms are complex systems that have highly variable evolutionary histories and as
such are best modeled using nonlinear differential equations. The difficulty with this
approach is that the values for many of the factors are unknown; hence solving the
equation is impossible [49,77].
Animals and humans are examples of living complex adaptive systems and as such
exhibit the following properties [79-97]:

1. Complex systems are composed of many components. Some of these components
may be simple systems, but many are complex systems. These components exist on many
scales and interact extensively with each other. A complex system is a "system of systems."
2. The components can be grouped as modules. For example, the following could be
considered as modules: the cell; the various processes in a cell; gene networks; gene-
gene interactions; gene-protein interactions; protein-protein interactions; organs; and
all the factors that influence the natural history of a disease. However, failure in one
module does not necessarily spread demise to the system as a whole as redundancy
and robustness (see #s 5 and 6 below) also exist and the various modules also
communicate with each other.
3. The different components of a complex system are linked to and affect one
another in a synergistic manner. There is positive and negative feedback in a complex
system [93].






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4. A complex system demonstrates hierarchal levels of organization [98,99]. These
levels range from the subatomic to the molecular to the whole individual to collections
of individuals [100]. Emergence (see # 13 below) occurs at each level; therefore, even a
complete understanding of the lower level is insufficient for explaining the upper level.
The various levels interact such that there is both upward causation and downward
causation. In order to understand a particular level, one must alternate between
looking at the components and looking at the whole while taking into account the
connections between each [76,101]. Moreover, the various levels may respond
differently to the same perturbation.

The various levels of organization are important when considering which responses
to specific perturbations can be extrapolated among species. Living complex systems
have numerous properties that can be studied without consideration of the fact that the
whole, intact organism is a complex system. Some systems or components follow only
the laws of physics, or even simple geometry, while others are best described by their
physicochemical properties or just by chemistry. Some properties of complex systems
can be described simply by math formulas. Growth, for example, can be described as
geometrical in some cases and exponential in others. The surface area of a body
increases by the square of the linear dimensions while the volume increases by the
cube. This is a consequence of geometry and is important in physiology, in part,
because heat loss is proportional to surface area while heat production is proportional
to volume. Haldane stated: "Comparative anatomy is largely the story of the struggle to
increase surface in proportion to volumes" [102]. For example, chewing increases the
surface area of food, the rate the small bowel absorbs nutrients and other chemicals
depends in part on the surface area of the small bowel, and air sacs in the lungs rely on
surface area for gas exchange, as do capillaries.
Allometry is the study of the relationship of body size to shape. Examples of allometric
laws include Kleiber's law: qo -~M where qo is metabolic rate and is proportional to M,
body mass, raised to the % power. The rate t, of breathing and heart contractions are
proportional to M, body mass, raised to % power: t -M". Further, many physiological
functions affect or depend on surface area.
Levels of organization can also be described based on whether they are primarily
chemical reactions and hence subject to analysis by chemistry. Reactions or perturba-
tions that involve the denaturation of proteins should affect all systems, be they simple
or complex, similarly because at this level of organization other factors do not come
into play. Exactly what effects sulfuric acid would have in a person over an extended
period of time are irrelevant as it denatures protein more or less immediately. Perhaps
species differences would manifest if small amounts of H2SO4 were infused over long
periods of time, but the immediate effects are the same across species because of the
chemical properties of the acid.
Animals can be successfully used for numerous purposes in science (see 3-9 in
Table 1). One of the purposes for which animals can be successfully used is to evaluate
phenomena that can be described by the physicochemical properties of the organism.
The same applies to basic physiologic functions. There are physiological parameters
that can be applied across species lines by the use of conversion factors based on the
weight or surface area of the organism. There are also properties of organisms that can






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be anticipated by the physical or chemical properties of the substance acting on the
organism. All of these are instances of successfully treating a complex system as if it
were a simple system. Problems arise however, as the level of organization under study
increases. Allometric scaling based on body surface area (BSA), for example, does not
include differences that manifest at higher levels of organization for example in the
elimination or metabolism of drugs. Different levels of organization can be acted on by
single factors or many factors but perturbations of simple systems, or systems that can
be described as simple on the level or organization being affected, should produce
similar results.

5. Complex systems are robust, meaning they have the capacity to resist change.
This can be illustrated by the fact that knocking out a gene in one strain of mouse
may produce negligible effects while being lethal to another strain. Gene pleiotropy is
an additional example [103].
6. Complex systems exhibit redundancy. For example, living systems exhibit
redundancy of some genes and proteins [103].
7. Complex systems are dynamic. They communicate with, and are acted on by,
their environment.
8. Complex systems exhibit self-organization, which allows adaptation to the
environment [85,104-106]. The intact cell is a prime example of this property.
9. Complex systems are dependent on initial conditions. The well-known example of
the butterfly flapping its wings and causing a weather catastrophe on the other side of
the earth-the butterfly effect-is an example of dependence on initial conditions. An
example in living complex systems would be that very small differences in genetic
makeup between two systems could result in dramatic differences in response to the
same perturbation. For example, monozygotic twins raised in the same environment
may have different predispositions to diseases such as multiple sclerosis and
schizophrenia [107-110]. Additionally, the above-mentioned observation that knocking
out a gene results in different outcomes in two stains of mice illustrates the concept
that small differences in initial conditions-genetic makeup-can mean the difference
between life and death [93,103,111,112].
10. The initial conditions of a complex living system are determined, in part, by
evolution. Various species have different evolutionary histories and thus are differently
organized complex systems. Initial conditions can be different, despite the exact same
genes, secondary to modifier genes, differences in regulation or expression of genes,
epigenetics, and mutations among others factors. For example, small epigenetic
changes probably account for the dissimilarities between monozygotic twins in terms
of disease susceptibility [107,108,113-116].
11. Perturbations to complex systems result in effects that are nonlinear [99]. Large
disturbances may result in no change to the system while minor perturbations may
cause havoc [76,105]. Efforts to describe complex systems in terms of linear cause
and effect relationships are prone to failure [117]. Extrapolating among complex
systems is even more problematic because of nonlinearity, along with the other
factors described.
12. The whole of a complex system is greater than the sum of the parts; hence, some
processes and or perturbations are not amenable to study by reductionism.






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13. Complex systems have emergent properties that cannot be predicted even in light
of full knowledge of the component parts.

Animal models have historically been utilized for the prediction of human responses
to drugs and disease and this use has also been the justification for animal use in
research in general [118,119]. But because various levels of organization and different
modules can be acted on by the same perturbation, in order to evaluate whether an
animal model can be used as a predictive modality, one needs to understand the levels
affected by the perturbation, what rules are being followed at those levels, and whether
the system is simple or complex at the respective levels. Empirical evidence, explained
and placed in context by theory developed from complexity science and evolutionary
biology, suggests animal models cannot predict human responses to drugs and disease
[14-16,18,57,58,120], despite the presence of shared physicochemical properties and
conserved processes.

Conserved processes
Theodosius Dobzhansky famously stated: "Nothing in biology makes sense except in
the light of evolution." We want to examine the consequences that various characteristics
of evolved complex systems, such as modules and different levels of organization, have
on processes conserved by evolution in terms of determining the response of whole
organisms to perturbations. Conserved processes and genes are the subject of much
interest today [1,121-133]. Kirschner and Gerhardt state: "all organisms are a mixture of
conserved and nonconserved processes (said otherwise, or changing and unchanging
processes)" [[1] p34-35]. Conserved processes are not reactions to the laws of physics or
the determination of properties of an organism as they relate to chemistry or geometry.
Nevertheless, conservation reaches across phyla and even kingdoms. Kirschner and
Gerhardt have pointed out that processes conserved include those involved in cell function
and organization, development, and metabolism and that these processes are similar in
animals, yeast, and bacteria. They note that novelty has been the result of using the
conserved processes in different ways rather than inventing completely new processes
[[1] p34-35]. This has critical implications for what can be learned from interspecies study.
Housekeeping genes in general perform the same function; make the same proteins,
in mice, frogs or humans. The role of FOX transcription factors is conserved among
species [134] as is the role of Sarco(endo)plasmic reticulum (SER) Ca2' ATPases
(SERCA) pumps [135]. Modules have also been conserved. The fin module of the
modern fish for example, arose roughly 400 million years ago and has been conserved
ever since [[1] p65].
Conserved processes include core genes like those in the homeobox that are involved in
the same developmental processes. Because these processes and genes are conserved
among species, we could reasonably expect the same outcome from the same perturbation,
regardless of the species containing these processes. But is this the case? In 1978 Lewis
[136] published his seminal work on the anterior-posterior layout of Drosophila. This was
followed in 1984 by the discovery of the homeobox by McGinnis et al. [137]. The field of
evo devo developed in large part from this work. In the last decade, enormous strides have
been made as a result of research in evo devo and the various genome projects. The results
of such research have revealed an enormous genetic similarity among mammals. At the






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level of the genes centrally involved in development, e.g., the homeobox genes, bilaterians
are virtually identical. The homeobox class of genes [138] are conserved across species
lines, functioning in early cellular organization and anterior-posterior body plan
layout [139]. There are important differences however. For example, there are nine
Hox genes in flies but thirty-nine in mammals. Pertinently, we understand how
modifications (gene duplications, deletions, changes in the regulatory processes and
so forth) to these conserved processes have resulted in the evolution of different
body types and indeed different species [138,140-142].
MicroRNA (miRNA) has been found in essentially all species from Caenorhabditis
elegans to humans and plays a large role in gene regulation [143-145]. Apparently, over
50% of miRNAs are conserved across species lines in vertebrates [145]. An important
consideration for drug development, however, is the fact that even though miRNA is
conserved, up to 50% of miRNAs differs among vertebrates. This is important when
considering the use of animals as predictive human models. Furthermore, miRNA
expression levels change when tissues deteriorate from a healthy state to a diseased
state [146-152]. Thus the exact role of miRNA may differ intra-individually depending
on age and disease. Hence, we see both inter-species and intra-individual differences
with respect to this conserved process.
It is well known that humans and nonhuman primates respond differently to infec-
tions. For example, untreated humans usually progress to AIDS when infected with
HIV, are susceptible to malaria (except those with sickle cell anemia), have different
reactions to hepatitis B and C than nonhuman primates and, appear more susceptible
to many cancers and Alzheimer's disease [153-155]. Barreiro et al. [154] studied gene
expression levels in monocytes from humans, chimpanzees, and rhesus macaques and
found that all three species demonstrated "the universal Toll-like receptor response"
when stimulated with lipopolysaccharide (LPS). However they also discovered that only
58% of genes identified in the Toll-like receptor response "showed a conserved regula-
tory response to stimulation with LPS," and only 31% of those genes demonstrated the
same conserved regulatory response when exposed to viruses or bacteria. Barreiro et al.
also discovered that 335 genes in humans are unique among the species in responding
to LPS, with 273 genes responding only in chimpanzees, and 393 only in rhesus maca-
ques [154]. Even in conserved processes, there are going to be significant differences
that influence the outcomes from disease perturbations. Significant differences in the
details of conserved processes (also illustrated by Figure 1 [156]) mean that there are
differences in the initial conditions of the complex system and this has major implica-
tions for inter-species extrapolation.
The implications of the various properties of complex systems also become apparent
when scientists study processes such as preimplantation embryonic development
(PED). PED is thought to be highly conserved among species which led Xie et al. [157]
to study gene expression profiles in embryos from humans, mice, and cows. They
found that: "40.2% orthologous gene triplets exhibited different expression patterns
among these species." Differences in expression profiles have implications for drug and
disease response.
The Cdcl4 gene was discovered in the yeast Saccharomyces cerevisiae and is classified
as a dual-specificity phosphatase. It has since been found in many organisms including
humans. Human Cdcl4B fulfills the role, in yeast, of the yeast gene Cdcl4. Because the






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A S
as


iglec
671 910


3

01 1 1 1
4





-










1 E2E

I 3l.I. i J


4


1 ru rm
2^ J
3~J


Siglec
3 6 6 7 8 9 10













01
1 1 ~ 1 U

H2 -- .






2 I LLL__

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4



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HS *4TIT


Human


Fluorescence Intensity


0 1o1 l 10e 10i 10e I
Fluorescence Intensity


% Positive
Figure 1 Variation in sialic acid (Sia)-recognizing Ig-superfamily lectins among primates. "Expression
of CD33rSiglecs on human and great ape lymphocytes. (A) Percentage of positive lymphocytes for each
Siglec Ab (staining above negative controls) for 16 chimpanzees, 5 bonobos, and 3 ,. are shown, as
well as data for 8 humans (the latter were tested on one or more occasions). Examples of flow cytometry
histograms of human (B) and chimpanzee (C) lymphocytes using Abs recognizing Siglec-3, Siglec-5, Siglec-7,
and Siglec-9 (y axis: normalized cell numbers expressed as percent of maximum cell number detected). In later
samples examined, low levels of Siglec-11 staining (<5% positive) were occasionally detected on lymphocytes
in both great apes and humans (data not shown)" [156].


yeast gene plays a role in regulating late mitosis, it was assumed the gene would have
the same role in mammals. In actuality, neither Cdcl4A nor Cdcl4B are necessary for
cell-cycle progression in humans [158]. Thus, we have a conserved gene but not a
conserved function.
Pyrin proteins have been found to be ubiquitous in mammals. Pyrin-only protein 2
(POP2) was found in humans and thought to be important in inflammatory diseases.
Atianand et al. [159] studied mice but did not find POP2. They then discovered that
POP2 was not in rodents or many other mammals but was present in chimpanzees
(Pan troglodytes) and rhesus macaques (Macaca mulatta). Moreover, the chimpanzee


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i






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POP2 was identical to humans POP2 at both the DNA and protein levels but the
macaque POP2 was not.
Conserved processes act, are affected by, or interact at multiple levels of organization.
As Cairns-Smith points out, proteins, catalysts, nucleic acids, membranes, and lipids
are interlocked and all are dependent on the others for their production. Cairns-Smith
summarizes by stating: "Subsystems are highly interlocked ... The inter-locking is tight
and critical. At the centre everything depends on everything" [[81] p39]. The same per-
turbation may result in different effects or outcomes for different levels of organization
in the same intact system. This further complicates our ability to predict outcomes
between two intact living complex systems. Thus it appears that a perturbation of
complex system S containing conserved process Pi resulting in outcome 01 will not
necessarily result in 01 in the very similar complex system S2 that also has Pi.
We will now examine in more detail the response of organisms to inhalational anesthetics
and anti-neoplastic agents in order to illustrate what can and cannot be extrapolated
between species knowing that species are acted on and affected by the fundamental
principles of geometry, chemistry, and physics as well as shared conserved processes.

Conserved processes in anesthesia
General anesthesia by means of inhalational anesthetics (IAs) provides us with an excellent
opportunity to examine where the effects of conserved processes can and cannot be extra-
polated between species. We expect to see various effects at different levels of organization
and in different modules. We also anticipate effects on emergent properties. Because
IAs act on the system as a whole, we expect to see effects that cannot be predicted from
reductionism. This has implications for what can be expected in terms of predicting human
response by studying a different species or perhaps even a different individual. Therefore,
both the primary effect of the anesthetic agent as well as the side effects may vary.
Broadly speaking, general anesthesia in humans and animals is defined by amnesia,
controlled insensitivity and consciousness, and immobility. It has been observed that most,
if not all, extant vertebrate species exhibit an anesthetic-like response to a wide variety of
chemicals that seemingly have little in common. This has been termed the universal
response. Multiple mechanisms for the universal response have been postulated and this is
an area of intense current research [160-164]. There seems to be general agreement that
ligand gated ion channel (LGIC) protein receptors are involved as well as possible effects on
the cellular membrane. Regardless of the exact details, the conservation of mechanisms can
be seen in that inhalational anesthetics (IAs) have observable effects on motor or motility
responses in vertebrates and invertebrates [165-168], tactile plants [169] and ciliated protests
[170]. (We note that this is probably an example of an exaptation, specifically a spandrel,
rather than an adaptation [171-173].) Interestingly, effects have even been observed in S.
cerevisiae (Baker's yeast) [174], suggesting that crucial aspects of the universal response go
beyond metazoans to include Eukaryotes. Moreover, IAs have been shown to have effects
on membrane composition in prokaryote species [163,175] e.g., A. laidlawii [176,177],
Bacillus halodurans [175] and E. coli [178] and the single-celled eukaryote tetrahymena
[179,180] (a ciliated protozoan). The universal response appears to date far back in evolu-
tionary time and strongly suggests that the mechanism has been conserved among species.
However, there are differences in outcomes with respect to IAs. Humphrey et al.
[181] studied genes in Caenorhabditis elegans and Drosophila melanogaster in order to






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assess the function of genes thought involved in the response to IAs. They found that a
gene in C elegans, unc-79, and a gene in Drosophila, narrow abdomen (na), were
related to each other and play a conserved role in response to anesthetics. However,
mutations in each gene produced unique changes in sensitivity to IAs. The sensitivity
to halothane, an IA, was increased but the sensitivity to enflurane, a different IA, was
unchanged or perhaps even lowered. This is perplexing because one would have
expected the two inhalational agents to be affected in a similar fashion by the mutation.
The gene unc-79 appears to be a post-transcriptional regulator of na, thus the genes
operate in the same pathway. Interestingly, both genes are also associated with similar
phenotypes regarding locomotion: "fainting" in C elegans and "hesitant walking" in
Drosophila.
Stimulation of the conserved processes controlling the universal response results in
clinically significant variability among humans, even though the minimum alveolar
concentration (MAC) for IAs for most species is approximately the same. MAC is the
most often used metric to assess IA potency. However, the concept of MAC implies
variability. MACso, simply called MAC in anesthesiology, is the minimum alveolar
concentration necessary to suppress movement in response to painful stimuli in 50% of
subjects [182]. MAC is significantly variable among humans depending on a number of
factors including age and sex. Why is this the case?
Sonner et al. reported, "one hundred forty-six statistically significant differences
among the 15 strains [of mice] were found for the three inhaled anesthetics (isoflurane,
desflurane, and halothane)" [164]. They concluded that multiple genes must be
involved in anesthetic potency. Wang et al. developed two strains of mice that mani-
fested different sensitivities to isoflurane [183]. MAC is an example of a phenomenon
controlled by quantitative trait loci [184], which may explain in part why, while one
can obtain a rough approximation of MAC by studying other species, there will still be
clinically significant differences.
IAs also function at different levels of organization and on modules in addition to the
one involved in the universal response. The side effects of the same chemical that
produce an effect on the conserved receptors or other processes vary greatly from
species to species and in some cases, even from person to person. A good example is
the case of isoflurane and coronary steal. In the 1980s, there was heated controversy
regarding the administration of the inhalation anesthetic isoflurane to patients with
heart disease. The controversy centered on research using canines that indicated that
the drug caused myocardial ischemia during certain situations in patients with coronary
disease. The phenomenon appeared to result from isoflurane causing dilation of the
normal coronary arteries, and thus blood being shunted away from the occluded coron-
ary arteries; the arteries and tissues that most needed it. This was called coronary steal.
Further, this situation was worsened by a decrease in blood pressure; a condition that
often occurs during general anesthesia with IAs. This supposed danger, based almost
entirely on studies in canines, was seized on by many in the anesthesiology community
as dogma [185,186].
This was an interesting reaction from clinicians for two reasons. First, experiments
with other species had failed to demonstrate coronary steal [187,188] and second,
anesthesiologists had not noticed ischemic changes associated with isoflurane despite
much use of the agent. The situation was also troublesome because isoflurane was a






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needed addition to an anesthesiologist's armamentarium when initially approved for
clinical practice. Six years after its introduction, it was the most frequently used IA, in
part because of the favorable properties of the drug [185]. Further studies continued to
demonstrate varying effects intra- and inter-species [189,190]. Ultimately, studies began
to appear that suggested isoflurane was in fact cardio-protective. The mechanism for
this protection was called preconditioningg and involves the opening of adenosine
triphosphate-dependent potassium channels" [191]. Isoflurane went from being contrain-
dicated in patients with coronary artery disease to being the drug of choice in such
patients. Studies from animals, specifically dogs, figured heavily in forming both, mutually
exclusive, conclusions.
Just as with the homeobox, miRNAs, and the response to inflammation, there are
differences among species in how the conserved process known as the universal
response to anesthesia manifests. Clinically, these differences are significant and limit
the amount of information that can be extrapolated between species even when the
underlying process is conserved. Inhalation anesthetics are also a good example of why,
when the level of organization or module being examined changes, extrapolation breaks
down. The same chemical that induces general anesthesia in a dog will probably result
in the same effect in humans but the dose may vary in a clinically significant fashion
and the side effects will most likely vary, as the conserved process does not dictate the
side effects. Differences in outcomes from perturbations like the ones we have seen
above have been explained by evolution-based species-specific differences, for example
background genes, mutations, expression levels, and modifier genes [192-209].

Anti-neoplastic drugs acting on mitosis
As discussed, a relationship exists between BSA and many physiological parameters
[210]. For example, Reagan-Shaw, Nihal, and Ahmad state: "BSA correlates well across
several mammalian species with several parameters of biology, including oxygen
utilization, caloric expenditure, basal metabolism, blood volume, circulating plasma
proteins, and renal function" [211]. Dosing algorithms for first-in-man (FIM) trials are
based on the assumption that there is a one-to-one dose scale between humans and
animals when BSA is taken into account [212]. The first study suggesting a relationship
between dose and body surface area was performed by Pinkel in 1958 [210] involving
anti-neoplastic agents, drugs where the effects and side effects are largely the same-
cell death. Subsequently, Freireich et al., [213] studied 18 anti-neoplastic drugs in six
animal species and concluded that the maximum tolerated dose (MTD) for humans
was 1/12 of the dose in mice that resulted in the death of 10% of the mice (LD10). They
also noted that the MTD was 1/7 of the LD10 in rats. These were also the ratios for
converting from a mg/kg dose to a dose based on BSA. Fifty anti-neoplastic drugs were
then studied using this formula and all were reportedly introduced into human trials
without incident [214,215]. The standard for FIM doses then became the 1/10th the
LD10 for mice. Actually Freireich recommended a starting dose of 1/3rd the LD10 not
1/10th but that changed over time. The 1/3rd recommendation was found to be too
large for FIM and was changed to 1/10th [216]. More studies appeared to confirm the
1/10th value [217].
The above makes a prima facie case that animal models can predict a starting dose
for humans in clinical trials for anti-neoplastics. Further substantiating this is the fact






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that anti-neoplastics are not always metabolized by the liver [218], thus possibly elimin-
ating a complex system from consideration. Cell division by mitosis is arguably the
most conserved process one can find in biology and the traditional drugs for treating
cancer act by interfering with mitosis. (Newer drugs act on targeted pathways as
opposed to the cell cycle.) Anti-neoplastics kill the cells that are dividing most rapidly
-the cancer cells. However, hair cells, cells in the bone marrow, and cells in the gut
also divide at a similar rate such that anti-neoplastics can affect them. Thus, in part,
the effects and side effects of anti-neoplastics are the same-cell death. The problem
with traditional anti-neoplastics is that they do not discriminate adequately.
Anti-neoplastic drugs are unique in medicine in that: 1) they are nonspecific; 2) long
term toxicities are anticipated and accepted because the patient frequently does not
have any other viable options; 3) the effects and side effects of the drugs are the same
-cell death; and 4) they act on a universally conserved process-mitosis. This is why
body surface area appears to be so effective for calculating FIM dose. Whereas, when
one is examining effects and side effects of drugs based on interactions at the level of
organization where complexity is relevant, for example metabolism [219-229], there are
simply too many other factors to allow for the expectation of one-to-one correlations.
Species-specific differences create perturbations in the complex system thus the differ-
ences among species outweigh the similarities [13-16,18,21-41].
However, in the final analysis even the FIM dose of the anti-neoplastic agents cannot
be reliably ascertained based on BSA. Most anti-neoplastics are effective only at doses
near the maximum tolerated dose and the drugs are given in an escalating fashion
during clinical trials. "Patients treated at the lower end of the dose escalation strategy
are unlikely to receive even a potentially therapeutic dose since most cytotoxic drugs
are only active at or near the MTD" [217]. Differences among species in dose response
for anti-neoplastics are due in part to differences in pharmacokinetics [217,230-232],
which cannot be accounted for based on BSA. Brennan et al. state that: "While proper
determination of drug doses can be complicated within the same species, it can be an
incredible challenge and burden between species" [233]. Brennan et al. continue by
pointing out that metabolism and clearance differ among species and that "...the liver,
kidneys and hematopoietic system between species may have significant differences in
their sensitivity to chemotherapeutic agents. None of these factors are taken into
account with the use of the species-specific dose calculations" [233]. They recommend
area under the curve (AUC) for calculating FIM dose but then concede: "However,
there are numerous examples in which the species-specific conversion dose varies
significantly from the AUC guided dose and/or far exceeds the animal's maximum
tolerated dose." They then list examples from pediatrics where the recommended and
actual doses differ significantly [233].
Horstmann et al. [234] reviewed 460 Phase I National Cancer Institute trials involving
11,935 adults that occurred between 1991 and 2002. Approximately 25% of the trials
were FIM trials. Horstmann et al. found that serious nonfatal effects occurred in 15%
of the patients undergoing single chemotherapy, with 58 deaths that were probably
treatment-related [234,235]. Concern has also been expressed that animal models have
derailed anti-neoplastics that would have been successful in humans [30,235-239].
FIM dose based on animal models is ineffective for predicting dose for other drug
classes as well-TGN1412 being a recent notable example [26,240,241]. An unnamed






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clinician, speaking of toxicity trials for new drugs in general in humans, was quoted in
Science, stating, "If you were to look in [a big company's] files for testing small-
molecule drugs you'd find hundreds of deaths" [242]. Chapman reinforced this stating:
". but other incidents of harm [besides TGN1412], even death, to participants in
Phase I trials, some then known and other unpublicized, had taken place" [235]. It is
also important to note that the 1/3rd or 1/10th safety factor is fabricated. Perlstein et al.
state: "Due to uncertainty in translating animal model findings to humans, particularly
for unprecedented mechanisms, a wide dose range (1000-fold) is expected to cover the
entire exposure-response curve" [243]. Extrapolating from species to species should
not require fudge factors if the process is truly science-based. In Phase I trials, where
FIM or first in human (FIH) occurs, scientists want to characterize the drug's PK prop-
erties and safety margins [244]. Wexler and Bertelsen summarize the situation when
they state: "Although allometric scaling techniques continue to provide poor predictive
estimates for human pharmacokinetic parameters, FIH starting doses are selected with
substantial safety factors applied to human equivalent dose, often in excess of regulatory
guidelines. Approaches that could enhance the predictive nature of a compound's
disposition and adaptive nature of FIH studies could provide a tremendous benefit for
drug development" [245]. FIM for all classes of drug could be easily accomplished using
microdosing [246-248] with the first dose of one nanogram [249,250] and increasing
subsequent doses to the desired endpoint.
Finally, one must recall that 95% [31,251,252] of anti-neoplastic agents fail in clinical trials.
Oncology drugs fail more frequently in clinical trials than most other categories [253,254]
and a higher percentage of anti-neoplastic drugs fail in Phase III trials than drugs from any
other category [255]. Reasons for the attrition include the fact that most of the effects and
side effects, even of the anti-neoplastic agents, when placed into the context of a complex
system, are not predicted from animal studies. Interfering in mitosis is a universal
phenomenon but the degree and success of that interference varies. The FIM dose estima-
tion is apparently successful because the level of organization in question is very basic and
conserved and because the dose is lowered even further by fudge factors. Picking a starting
dose based on the most toxic substances in nature [249,250] would be more scientific. The
apparent success also breaks down because the types of cancers in humans differ from those
in animals, the genetic background of humans varies from that in animals, and because the
reality of a complex system-the interactions of all the other systems (for example how the
drugs are eventually metabolized and eliminated and how those metabolites interact with
other systems and so on)-eventually appear. These are the problems that cannot be solved
by animal models and are why the attrition rate is 95%. Weinberg stated: "it's been well
known for more than a decade, maybe two decades, that many of these preclinical human
cancer models have very little predictive power in terms of how actual human beings-actual
human tumors inside patients-will respond ... preclinical models of human cancer, in large
part, stink ... hundreds of millions of dollars are being wasted every year by drug companies
using these [animal] models" [236]. Others have also pointed out the inadequacy of animal
models of cancer, including genetically modified animal models [41,214,252,256-261].

Conserved processes in light of systems biology
As the level of organization in a complex system increases, we expect to see an increase
in the number of emergent properties as well as more overall interactions. A gene or






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process that has been conserved will interact with the intact whole organism yielding
new processes and states. Perturbations of these conserved genes or processes will thus
likely result in new states not seen in other organisms that share the conserved
processes; perhaps not even in organisms of the same lineage (clade) or species.
The lack of appreciation for the differences between levels of organization and other
properties of complex systems is apparent in the following from Kardong [[262] p2],
writing in his textbook of comparative vertebrate anatomy: "For example, by testing a
few vertebrate muscles, we may demonstrate that they produce a force of 15 N newtonss)
per square centimeter of muscle fiber cross section. Rather than testing all vertebrate
muscles, a time-consuming process, we usually assume that other muscles of similar cross
section produce a similar force (other things being equal). The discovery of force production
in some muscles is extrapolated to others. In medicine, the comparative effects of drugs on
rabbits or mice are extrapolated to tentative use in humans." At the level of organization
where one studies the force generated by muscle fibers, no doubt inter-species extrapolation
is useful, but that is an entirely different level from where drug actions occur.
Indeed the successes from using animal models have been examples of perturbations
occurring at subsystems that can be described as simple systems and or outcomes or
characteristics that apply on the gross level of examination. For example, the Germ
Theory of Disease applies to humans and animals. The immune system reacts to
foreign entities in a manner that is grossly similar across species lines. The details of
immunity are clinically very different, for example HIV infection leads to AIDS in
humans but not chimpanzees [263-265]. Nevertheless, grossly, inflammation, white
blood cells, and antibodies are identifying characteristics of the immune system in the
phylum Chordata. Likewise, while the heart functions to circulate the blood in
mammals, the diseases various mammalian hearts are subject to differ considerably
[266-272]. The failures of animal models have occurred when attempting to extrapolate
data from higher levels of organization, levels where complexity is an important com-
ponent in the system or subsystem under consideration. For example, a drug that has
passed animal tests and is in Phase I human clinical trials has only an 8% chance of
making it to market [273]. Over 1,000 drugs have been shown to improve outcomes in
cerebral ischemia in animal models but none, save aspirin and thrombolysis, which
were not animal-based discoveries, have been successful in humans [35,274-277]. The
animal model for polio, monkeys, revealed a pathophysiology that was very different
from that of humans [278-281]. Extracranial-intracranial bypass for inoperable carotid
artery disease was successful in animals but results in net harm for humans [282-285].
Most diseases are multifactorial hence it should come as no surprise that conserved
processes play a small, although at times important role, in major diseases like heart
disease, cancer and stroke. The field of systems biology was formed in part in an
attempt to place the parts of molecular biology and genetics in the larger context of the
human system; the system that actually responds to drugs and disease. An editorial in
Nature asks: "What is the difference between a live cat and a dead one? One scientific
answer is 'systems biology'. A dead cat is a collection of its component parts. A live cat
is the emergent behaviour of the system incorporating those parts" [286]. According to
the Department of Systems Biology at Harvard Medical School: "Systems biology is the
study of systems of biological components, which may be molecules, cells, organisms
or entire species. Living systems are dynamic and complex, and their behavior may be






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hard to predict from the properties of individual parts" [287]. Systems biology [288]
takes a top-down approach as opposed to reductionism, which evaluates organisms
from the bottom-up. Systems biology is concerned more with networks than individual
components, although both are studied. It also recognizes the importance of emergent
phenomena. (See Figure 2 [79]). Such top-down approaches are used by the fields com-
monly referred to as "Omics," for example: interactomics, metabolomics, proteomics,
transcriptomics, and even fractalomics [289].
Nobel laureate Sydney Brenner, in 1998, emphasized that the interactions of components
was important in understanding an organism [290]. Only by studying proteins and
processes in the context of their systems can we expect to understand what happens to the
intact organisms as a result of these processes and genes. Further, evolution uses old
pathways and processes in different ways to create novelty [1,133]. Everything is context
dependent. Noble stresses that in order to predict how drugs will act, one must understand
"how a protein behaves in context" at higher levels of organization [291].
Heng [292], writing in JAMA states that, because of reductionism, biological scientists
have sought individual components in a disease process so they could intervene. A linear
cause and effect relationship was assumed to exist. Heng cites diabetes intervention in an
attempt to control blood glucose and cancer therapies as examples. He points out that
while this has worked well in many cases, very tight control of blood glucose was recently
found to increase the risk of death [293]. Along the same lines, chemotherapies for cancer
have decreased the size of the tumors but at the expense of an increase in frequency of
secondary tumors and a very adversely affected lifestyle. Furthermore, most chemotherapy
does not prolong life or result in a longer, high quality life [294-296]. Instead of focusing on
small modules or components of a system, complexity theory mandates that biomedical
science look at the system as a whole.
Closely related to systems biology are the concepts of personalized medicine and
pharmacogenomics [226,297-305]. It has long been appreciated that humans respond
differently to drugs and have different susceptibilities to disease. Based on studies of




Reductionism Systems Science


000
o % <10 =>

Components 0 Interrelationships,
Time / Dynamics
Space
Context

Medical Treatments Medical Treatments
Disease-driven Individualized
Disease-driven Multidimensional use of drugs
Aimed for normalcy (normal range) Time-sensitive
Additive Space-sensitive
Synergistic


Figure 2 Reductionism versus systems biology.






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twins, there appears to be a genetic component to susceptibility to leprosy, poliomyelitis
and hepatitis B, as well as response to opioids [306-309]. Other infectious diseases that
appear to have a genetic component to susceptibility include HIV, Hepatitis C, malaria,
dengue, meningococcal disease, variant Creutzfeldt-Jakob disease and perhaps tuberculosis
among others [310]. Differences in drug and disease response are manifest among ethnic
groups [311-319] and sexes [320-326]. Even monozygotic twins manifest differences in
response to such perturbations [107,108,113-116]. Rashmi R Shah, previous Senior Clinical
Assessor, Medicines and Healthcare products Regulatory Agency, London stated in 2005:
"During the clinical use of a drug at present, a prescribing physician has no means of
predicting the response of an individual patient to a given drug. Invariably, some patients
fail to respond beneficially as expected whereas others experience adverse drug reactions
(ADRs)" [327].
Similarly, Allen Roses, then-worldwide vice-president of genetics at GlaxoSmithKline
(GSK), said fewer than half of the patients prescribed some of the most expensive drugs
derived any benefit from them: "The vast majority of drugs more than 90% only
work in 30 or 50% of the people." Most drugs had an efficacy rate of 50% or lower
[328]. Because of differences in genes, like SNPs, all children may not currently be
protected by the same vaccine [329,330]. It is estimated that "between 5 and 20 per
cent of people vaccinated against hepatitis B, and between 2 and 10 per cent of those
vaccinated against measles, will not be protected if they ever encounter these viruses"
[330]. In the future such children may be able to receive a personalized shot. Currently,
numerous drugs have been linked to genetic mutations and alleles. See Table 5 [303]
and Table 6 [331]. The number of personalized medicine products has increased from
13 in 2006 to 72 as of 2012 [332].
When animals were being used as models in the 19th century, many of the scientists
who were using them had not accepted evolution and believed that animal parts were
interchangeable with their human counterparts [60,62,63]. Given that we now under-
stand that intra-human variation results in such markedly different responses to drugs
and disease, attempting to predict human response from animal models, even for
perturbations acting on conserved processes, seems unwarranted. Yet, despite the
implications of personalized medicine [22], some scientists continue to commit the
fallacy described by Burggren and Bemis: "Yet the use of 'cockroach as insect; 'frog as
amphibian; or 'the turtle as reptile' persists, in spite of clear evidence of the dangers of
this approach. Not surprisingly, this type of comparative physiology has neither
contributed much to evolutionary theories nor drawn upon them to formulate and test
hypotheses in evolutionary physiology" [[333] p206]. Comparative research will yield a
nice comparison of the trait or process among species or phyla. However, one simply
cannot assume that the outcome from a specific perturbation in, say the cockroach, will
be seen in insects in general and this concept becomes even more important when
relying on animal models for medical interventions in humans.

Conclusion
A perturbation of living complex system S containing conserved process P1 resulting
in outcome 01 will not result in 01 in the very similar living complex system S2 that
also has Pi often enough to qualify S as a predictive modality for S2 when the trait or
response being studied is located at higher levels of organization, is in a different







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Table 5 Examples of drugs with genetic information in thier labels
Drug Sponsor Indication Gene or Effect of
genotype genotype
Abacavir GlaxoSmithKline HIV-1 HLA-B*5701 Hypersensit
(Ziagen)


Clinical directive on
label
ivity Black-box warning.
"Prior to initiating


therapy with abacavir,
screening for the HLA-
B*5701 allele is
recommended." "Your
doctor can determine
with a blood test if
you have this gene
variation."


Azathioprine
(Imuran)


Prometheus Renal allograft
transplantation,
rheumatoid


Carbamazepine Novartis
(Tegretol)


Epilepsy,
trigeminal
neuralgia


TPT 2TPT*3Aand Severe
TPMT*3C myeloxicity









HLA-B*1502 Stevens-
Johnson
syndrome c
toxic epider
necrolysis


"TPTgenotyping or
phenotyping can help
identify patients who
are at an increased
risk for developing
Imuran toxicity."
"Phenotyping and
genotyping methods
are commercially
available."
Black-box warning:
"Patients with
,r ancestry in genetically
mal at-risk populations
should be screened
for the presence of
HLA-B*;502 prior to
initiating treatment
with Tegretol. Patients
testing positive for
the allele should not
be treated with
Tegretol." "For
genetically at-risk
patients, high-
resolution HLA-B*1502
typing is
recommended."


KRAS mutations Efficacy "Retrospective subset
analyses of metastatic
or advanced
colorectal cancer trials
have not shown a
treatment benefit for
Erbitux in patients
whose tumors had
KRAS mutations in
codon 12 or 13. Use
of Erbitux is not
recommended for the
treatment of
colorectal cancer with
mutations."


Clopidogrel Bristol-Myer
(Plavix) Squibb


Anticoaculation CYP2C19*2*3


:acy "Tests are a available tc
identify a patient's
CYP2C19 genotype;
these tests can be
used as an aid in
determining
therapeutic strategy.
Consider alternative
treatment or
treatment strategies
in patients identified


Page 22 of 33


Cetuximab
(Erbitux)


Imclone


Metastatic
colorectal
cancer







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Table 5 Examples of drugs with genetic information in thier labels (Continued)
as CYP2C19 poor
metabolizer."
Irinotecan Pfizer Metastatic UGTIA *28 Diarrhea "A reduction in the
(Camptosar) colorectal neutropenia starting dose by at
cancer least one level of
Camptosar should be
consider for patients
knows to be
homozygous for the
UGT1 Al*28 allele. "A
laboratory test is
available to determine
the UGT1A1 status of
patients."
Pantumumab Amgen Metastatic KRAS mutations Efficacy "Retrospective subset
(Vectibix) colorectal analyses of metastatic
cancer colorectal cancer trials
have not shown a
treatment benefit for
Vectibix in patients
whose tumors had
KRAS mutations in
codon 12 or 13. Use
of Vectibix is not
recommended for the
treatment of
colorectal cancer with
these mutations."
Transtuzumab Genetech HER2-positive HER2 expression Efficacy "Detection of HER2
(Herceptin) breastcancer protein
overexpression is
necessary for
selection of patients
appropriate for
Herceptin therapy
because these are the
only patients studied
and for whom benefit
has shown." "Several
FDA-approved
commercial assays are
available to aid in the
selection of breast
cancer and metastatic
cancer patients for
Herptin therapy."
Wafarin Bristol-Myer Venous CYP2C9*2*3 and Bleeding Includes the following
(Coumadin) Squibb thrombosis VKORC1 variants complications table: Range of
Expected Therapeutic
Warfarin Doses Based
on CYP2CP and
VKORC1 Genotypes.
*AII drug labels were accessed through Drugs @FDA at www.accessdata.fda.gov/scripts/cder/drugsatfda. HIV-1 denotes
human immunodeficiency virus type 1, TPMTthiopurine methyltransferase, UGTIA UDP glucuronosyltransferanse 1
family polypeptide Al, and VKORCI vitamins K epoxide reductase complex subunit 1


module, or is influenced by other modules. However, when the examination of the
conserved process occurs at the same or lower level of organization or in the same
module, and hence is subject to study solely by reductionism, then extrapolation is
possible. We believe this is a valuable principle.
Our current understanding of evo devo, evolution in general, complexity science, and
genetics allows us to generalize regarding trans-species extrapolation, even when
conserved processes are involved. Shanks and Greek:







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Table 6 The most significant genetic predictors of drug response
Organ or system involved Associated gene/allele Drug/drug response phenotype
RInod


Red blood cells
Neutrophils


Plates
Coagulation
Brain and peripheral nerve
CNS depression
Anaesthesia
Peripheral nerves
Drug hypersesitivity







Drug-induced liver injury


G6PD
TMPT*2
UGTIA 1 28
CYP2C 1972
CY2C9*2, *3, VKORC1
us system
CYP2D6*N
Butyrylcholinesterase
NAT-2
HLA-B*5701
HLA-B*1502

HLA-A*3101

HLA-B*5801
HLA-B*5701
HLA-DR81* 1501-DQ81 70602
HLA-DR81* 1501-DQ81 70602
HLA-BR81 07-DOAI 02
HLA-DQA1 0201


Primaquine and others
Azathioprine/6MP-induced neutropenia
Irintotecan-induced neutropenia
Stent thrombusis
Warfarin dose-requirement


Codeine-related sedation and respiratory depression
Prolonged apnoea
Isoniazid-induced peripheral neuropathy
Abacavir hypersensitivity
Carbamazepine-induced Steve Johnson syndrome
(in some Asian groups )
Carbamazepine-induced hypersensitivity in Causians
and Japanese
Allopurinol-induced serious cutaneous reactions
Flucloxacillin
Co-amoxiclav
Lumiracoxib
Ximelagatran
Lapatinib


Infection
HIV-1 infection
Hepatitis C infection
Malignancy
Breast cancer
Chronic myeloid leukaemia
Colon cancer
GI stromal tumours
Lung cancer


Malignant melanoma


Maraviroc efficacy
Interferon-alpha efficacy


CYP2DA
BCR-ABL
KRAS
c-kit
EGFR
EML4-ALK
BRAF V600E


Response to tamoxifen
Imatinib and other tyrosine kinase inhibitors
Cetuximab efficacy
Imatinib efficacy
Gefinib efficacy
Crizotinib efficacy
Vemurafenib efficacy


Living complex systems belonging to different species, largely as a result of the
operation of evolutionary mechanisms over long periods of time, manifest different
responses to the same stimuli due to: (1) differences with respect to genes
present; (2) differences with respect to mutations in the same gene (where one
species has an ortholog of a gene found in another); (3) differences with
respect to proteins and protein activity; (4) differences with respect to gene
regulation; (5) differences in gene expression; (6) differences in protein-protein
interactions; (7) differences in genetic networks; (8) differences with respect to
organismal organization (humans and rats may be intact systems, but may be
differently intact); (9) differences in environmental exposures; and last but not
least; (10) differences with respect to evolutionary histories. These are some of
the important reasons why members of one species often respond differently to


Page 24 of 33







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drugs and toxins, and experience different diseases. Immense empirical evidence
supports this position ([14] p358).


The failures of animal models as a predictive modality for human response to disease
and drugs, even when such perturbations are acting on conserved processes, can be
explained in the context of evolved complex systems. One does not need to study every
such perturbation in every species in order to conclude that the animal model will not
be a predictive modality for humans when perturbations occur at higher levels of
organization or involve different modules or affect the system as a whole. This is not to
deny that animal models, as characterized by 3-9 in Table 1, have contributed and will
continue to contribute to scientific advancements.


Competing interests
The authors declare that they have no competing interests

Authors' contributions
The authors contributed equally to this paper

Authors' information
Ray Greek, MD has been on faculty in the Department of Anesthesiology at the University of Wisconsin-Madison and
at Thomas Jefferson University in Philadelphia He is currently president of the not-for-profit Americans For Medical
Advancement (www AFMA-curediseaseorg)
Mark Rice, MD is currently on faculty at the University of Florida (UF) He is chief of the liver transplant division at UF
Department of Anesthesiology, has seven US patents, and reviews for several major journals

Acknowledgements
None

Author details
Americans For Medical Advancement (wwwAFMA-curediseaseorg), 2251 Refugio Rd, Goleta, CA 93117, USA 2Department of
Anesthesiology, University of Florida College of Medicine, PO Box 100254, Gainesville, FL 32610-0254, USA

Received: 30 July 2012 Accepted: 31 August 2012 Published: 10 September 2012

References
1 Kirschner MW, Gerhart JC The Plausibility of Life New Haven Yale University Press; 2006
2 Braithwaite RB Scientific explanation a study of the function of theory, probability and law in science Cambridge
Cambridge University Press; 1953
3 Hinde R Animal-Human Comparisons In The Oxford Companion to the Mind Edited by Gregory RL Oxford
Oxford University Press; 198725-27
4 Frigg R, Hartmann S Scientific Models In The Philosophy of Science An Encyclopedia Volume 2 N-Z Edited by
Sarkar S, Pfeifer J New York Routledge; 2012740-749
5 Shapiro K Animal Model Research. The Apples and Oranges Quandry. ATLA 2004, 32:405-409
6 Hau J In Animal Models, Handbook of Laboratory Animal Science Second Edition Animal Models, Volume II 2nd
edition Edited by Hau J, van Hoosier GK Jr Boca Rotan CRC Press; 2003'1-9
7 Gad S Preface In Animal Models in Toxicology Edited by Gad S Boca Rotan CRC Press; 2007'1-18
8 Longer Tests on Lab Animals Urged for Potential Carcinogens i i ... .. r 11172 html
9 Huff J, Jacobson MF, Davis DL The limits of two-year bioassay exposure regimens for identifying chemical
carcinogens. Environ Health Perspect 2008, 116:1439-1442
10 Devoy A, Bunton-Stasyshyn RKA, Tybulewicz VU, Smith AJH, Fisher EMC Genomically humanized mice:
technologies and promises. Nat Rev Genet 2012, 13:14-20
11 Vassar R Alzheimer's therapy: a BACE in the hand? Nat Med 2011, 17:932-933
12 THS CEO criticized for links to animal testing http//m torontosuncom/2011/09/23/ths-ceo-criticized for-links-to-
animal-testingnoimage
13 Heywood R Clinical Toxicity--Could it have been predicted? Post-marketing experience In Animal Toxicity
Studies Their Relevance for Man Edited by Lumley CE, Walker S Lancaster Quay; 1990'57-67
14 Shanks N, Greek R Animal Models in Light of Evolution Boca Raton Brown Walker; 2009
15 Greek R, Greek J Is the use of sentient animals in basic research justifiable? Philos Ethics Humanit Med 2010, 5:14
16 Greek R, Shanks N FAQs About the Use of Animals in Science A handbook for the scientifically perplexed Lanham
University Press of America; 2009
17 Shanks N, Greek R Experimental use of nonhuman primates is not a simple problem. Nat Med 2008, 14:807-808
18 Shanks N, Greek R, Greek J Are animal models predictive for humans? Philos Ethics Humanit Med 2009, 4:2
19 Shanks N, Greek R, Nobis N, Greek J Animals and Medicine: Do Animal Experiments Predict Human Response?
Skeptic 2007, 13:44-51
20 Greek R Letter. Dogs, Genes and Drugs. Am Sci 2008, 96:4








Greek and Rice Theoretical Biology and Medical Modelling 2012, 9:40 Page 26 of 33
http://www.tbiomed.com/content/9/1/40





21 Greek R, Hansen LA, Menache A An analysis of the Bateson Review of research using nonhuman primates.
Medicolegal Bioethics 2011, 1:3-22
22 Greek R, Menache A, Rice MJ Animal models in an age of personalized medicine. Personalized Med 2012, 9:47-64
23 Greek R, Shanks N, Rice MJ' The History and Implications of Testing Thalidomide on Animals. The Journal of
Philosophy, Science & Law 2011, 11 http//www6 miami edu/ethics/jpsl/archives/all/TestingThalidomide html
24 Collins FS Reengineering Translational Science: The Time Is Right. Sc Transi Med 2011, 3:90cm 7
25 Cook N, Jodrell DI, Tuveson DA Predictive in vivo animal models and translation to clinical trials. Drug Discov
Today 2012, 17:253-260
26 Dixit R, Boelsterli U Healthy animals and animal models of human diseases) in safety assessment of human
pharmaceuticals, including therapeutic antibodies. Drug Discov Today 2007, 12:336-342
27 Drake DR III, Singh I, Nguyen MN, Kachurin A, Wittman V, Parkhill R, Kachurina 0, Moser JM, Burdin N, Moreau M,
et al In Vitro Biomimetic Model of the Human Immune System for Predictive Vaccine Assessments. Disruptive
Sc Technol 2012, 1:28-40
28 FDA Issues Advice to Make Earliest Stages Of Clinical Drug Development More Efficient i ii
NewsEvents/Newsroom/PressAnnouncements/2006/ucm 08576 htm
29 Fletcher AP Drug safety tests and subsequent clinical experience. J R Soc Med 1978, 71:693-696
30 Horrobin DF Modern biomedical research: an internally self-consistent universe with little contact with
medical reality? Nat Rev Drug Discov 2003, 2:151-154
31 Kola I, Landis J Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov 2004, 3:711-715
32 Lumley C Clinical toxicity: could it have been predicted? Premarketing experience In Animal Toxicity Studies
Their Relevance for Moan Edited by Lumley C, Walker S Quay; 199049-56
33 M E This Issue In Models that better mimic human cancer, Nat Biotechnol, Volume 28; 2010'vii
34 Markou A, Chiamulera C, Geyer MA, Tricklebank M, Steckler T Removing obstacles in neuroscience drug
discovery: the future path for animal models. Neuropsychopharmacol Offic PubliAm Coll Neuropsychopharmacol
2009, 34:74-89
35 O'Collins VE, Macleod MR, Donnan GA, Horky LL, van der Worp BH, Howells DW 1,026 experimental treatments
in acute stroke. Ann Neurol 2006, 59:467-477
36 Sharp PA, Langer R Promoting Convergence in Biomedical Science. Science 2011, 333:527
37 Sietsema WK. The absolute oral bioavailability of selected drugs. Int J Clin Pharmacol Ther Toxicol 1989, 27:179-211
38 Suter K What can be learned from case studies? The company approach In Animal Toxicity Studies Their
Relevance for Man Edited by Lumley C, Walker S Lancaster Quay; 199071-78
39 Wall RJ, Shani M Are animal models as good as we think? 2008, 69:2-9
40 Weaver JL, Staten D, Swann J, Armstrong G, Bates M, Hastings KL' Detection of systemic hypersensitivity to
drugs using standard guinea pig assays. Toxicology 2003, 193:203-217
41 Zielinska E Building a better mouse. Scientist 2010, 24:34-38
42 Ringach DL' The use of nonhuman animals in biomedical research. Am J Med Sd 2011, 342:305-313
43 Rudczynski AB Letter to the editor New Haven Register, 2011 Available at )11/
03/25/opinion/doc4d8bb9186a82b265857273 txt
44 Fomchenko El, Holland EC Mouse models of brain tumors and their applications in preclinical trials.
Clin Cancer Res 2006, 12:5288-5297
45 Litchfield JT Jr Predictability of Conventional Animal Toxicity Tests. Ann N YAcad Sc 1965, 123:268-272
46 Lasagna L Regulatory agencies, drugs, and the pregnant patient In Drug use in pregnancy Edited by Stern L
Sydney ADIS Health Science Press; 1984
47 Lin JH Species similarities and differences in pharmacokinetics. Drug Metab Dispos 1995, 23:1008-1021
48 Dixon RL Toxicology of environmental agents: a blend of applied and basic research. Environ Health Perspect
1972, 2:103-116
49 Zhang S, Wang Y-M, Sun C-D, Lu Y, Wu L-Q Clinical value of serum CA19-9 levels in evaluating resectability of
pancreatic carcinoma. World J Gastroenterol 2008, 14:3750-3753
50 Sasson C, Hegg AJ, Macy M, Park A, Kellermann A, McNally B Prehospital Termination of Resuscitation in Cases
of Refractory Out-of-Hospital Cardiac Arrest. JAMA 2008, 300:1432-1438
51 Salekin RT, Rogers R, Ustad KL, Sewell KW Psychopathy and recidivism among female inmates. Law Hum Behav
1998, 22:109-128
52 Mayanja BN, Baisley K, Nalweyiso N, Kibengo FM, Mugisha JO, Paal LV, Maher D, Kaleebu P Using verbal autopsy
to assess the prevalence of HIV infection among deaths in the ART period in rural Uganda: a prospective
cohort study, 2006-2008. Population Health Metrics 2011, 9:36 doi'10 1186/1478-7954-9-36
53 Santos G, Souza A, Virtuoso J, Tavares G, Mazo G Predictive values at risk of falling in physically active and no
active elderly with Berg Balance Scale. Rev Bras Fisioter 2011, 15:95-101
54 Committee on Models for Biomedical Research Board on Basic Biology Committee on Models for Biomedical
Research Board on Basic Commission on Life Scence National Research Council Models for Biomedical
Research' A New Perspective Washington, DC National Academy Press; 1985
55 Tkacs NC, Thompson HJ From bedside to bench and back again: research issues in animal models of human
disease. Biol Res Nurs 2006, 8:78-88
56 Overmier JB, Carroll ME Basic Issues in the Use of Animals in Health Research In Animal Research and Human
Health Edited by Carroll ME, Overmier JB Washington DC American Psychological Association; 2001'5
57 LaFollette H, Shanks N Two Models of Models in Biomedical Research. Phil 1995, 45:141-160
58 LaFollette H, Shanks N Brute Science Dilemmas of animal experimentation London and New York Routledge; 1996
59 Schaffner KF Theories, Models, and Equations in Systems Biology In Systems Philosophical Foundations
Edited by Boogerd F, Bruggeman FJ, Hofmeyr J-HS, Westerhoff HV Netherlands Elsevier; 2007 145-162
60 Bernard C An Introduction to the Study of Experimental Medicine New York Dover; 1957
61 Bunge M Causality And Modern Science 3rd edition New York Dover; 1979








Greek and Rice Theoretical Biology and Medical Modelling 2012, 9:40 Page 27 of 33
http://www.tbiomed.com/content/9/1/40





62 Elliot P Vivisection and the Emergence of Experimental Medicine in Nineteenth Century France In Vivisection
in Historical Perspective Edited by Rupke N New York Croom Helm; 198748-77
63 LaFollette H, Shanks N Animal Experimentation: The Legacy of Claude Bernard. Int Stud Philos Sd 1994, 8:195-210
64 Klaassen CD, Eaton DL' Principles of Toxicology In Casarett and Doulls Toxicology 4th edition Edited by Amdur
MO, Doull J, Klaassen C New York McGraw-Hill; 1993
65 Milner R Darwins Universe Evolution from A to Z Berkeley University of California Press; 2009
66 Wagner A Causality in Complex Systems. Bio Philos 1999, 14:83-101
67 Russell B On the Notion of Cause. Proceedings of the Aristotelian Society. New Set 1913, 13:1-26
68 Greek R Animal Models and the Development of an HIV Vaccine. J AIDS Clin Res 2012, S8:001
69 Giere RN, Bicde J, Mauldin RF Understanding Scientific Reasonoing 5th edition Toronto Thomson Wadsworth; 2006
70 Holden C Random Samples. Well-Wired Whales. Science 2006, 314:1363
71 Hof PR, Van der Gucht E Structure of the cerebral cortex of the humpback whale, Megaptera novaeangliae
(Cetacea, Mysticeti, Balaenopteridae). Anat Rec (Hoboken) 2007, 290:1-31
72 Hakeem AY, Sherwood CC, Bonar CJ, Butti C, Hof PR, Allman JM Von Economo neurons in the elephant brain.
Anat Rec (Hoboken) 2009, 292:242-248
73 Crick F Of Molecules and Man Seattle University of Washington Press; 1966
74 Venter JC, Adams MD, Myers EW, Li PW, Mural RJ, Sutton GG, Smith HO, Yandell M, Evans CA, Holt RA, et ao The
sequence of the human genome. Science 2001, 291:1304-1351
75 McPherson JD, Marra M, Hillier L, Waterston RH, Chinwalla A, Wallis J, Sekhon M, Wylie K, Mardis ER, Wilson RK, et
al A physical map of the human genome. Nature 2001, 409:934-941
76 Mazzocchi F Complexity in biology. Exceeding the limits of reductionism and determinism using complexity
theory. EMBO Rep 2008, 9:10-14
77 Coveney PV, Fowler PW Modelling biological complexity: a physical scientist's perspective. J R Soc Interface
2005, 2:267-280
78 Coveney PV, Highfield RR Frontiers of complexity London Faber and Faber; 1996
79 Ahn AC, Tewari M, Poon CS, Phillips RS The limits of reductionism in medicine: could systems biology offer an
alternative? PLoS Med 2006, 3:e208
80 Aim E, Arkin AP Biological networks. Curr Opin Struct Biol 2003, 13:193-202
81 Cairns-Smith AG Seven Clues to the Origin of Life A Scientific Detective Story Cambridge Cambridge University
Press; 1986
82 Csete ME, Doyle JC Reverse engineering of biological complexity. Science 2002, 295:1664-1669
83 Goodwin B How the Leopard Changed Its Spots The Evolution of Complexity Princeton Princeton University Press; 2001
84 Jura J, Wegrzyn P, Koj A Regulatory mechanisms of gene expression: complexity with elements of
deterministic chaos. Acta Biochim Pol 2006, 53:1-10
85 Kauffman SA he Origins of Order Self Organization and Selection in Evolution Oxford University Press; 1993
86 Kitano H Computational systems biology. Nature 2002, 420:206-210
87 Kitano H Systems biology: a brief overview. Science 2002, 295:1662-1664
88 Definitions, Measures, and Models of Robustness in Gene Regulatory Network Report of research work for CSSS05
http//wwwsantafeedu/education/csss/csss05/papers/monteet al cssssf05pdf
89 Morowitz HJ The Emergence of Everything How the World Became Complex Oxford Oxford University Press; 2002
90 Novikoff AB The Concept of Integrative Levels and Biology. Science 1945, 101:209-215
91 Ottino JM Engineering complex systems. Nature 2004, 427:399
92 Sole R, Goodwin B Signs of Life How Complexity Pervades Basic Books; 2002
93 Van Regenmortel M Reductionism and complexity in molecular biology. Scientists now have the tools to
unravel biological complexity and overcome the limitations of reductionism. EMBO Rep 2004, 5:1016-1020
94 van Regenmortel M Biological complexity emerges from the ashes of genetic reductionism. J Mol Recognit
2004, 17:145-148
95 Van Regenmortel MH, Hull DL' Promises and Limits of Reductionism in the Biomedical Scences (Catalysts for Fine
Chemical Synthesis) West Sussex Wiley; 2002
96 Vicsek T The bigger picture. Nature 2002, 418:131
97 Woodger JH Biological Principles New York Humanities Press; 1967
98 Kola I The state of innovation in drug development. Clin Pharmacol Ther 2008, 83:227-230
99 de Haan J How emergence arises. Ecol Complex 2006, 3:293-301
100 Southern J, Pitt-Francis J, Whiteley J, Stokeley D, Kobashi H, Nobes R, Kadooka Y, Gavaghan D Multi-scale
computational modelling in biology and physiology. Prog Biophys Mol Biol 2008, 96:60-89
101 Morin E Introduction d la Pensee Complexe Paris ESF; 1990
102 Haldane JBS On Being the Right Size New York Harper's; 1926
103 Morange M The misunderstood gene Cambridge Harvard University Press; 2001
104 Kauffman S Theoretical Biology In Epigenetic and Evolutionary Order from Complex Systems Edited by Goodwin B,
Saunders P Edinburgh Edinburgh University Press; 1990
105 Coffey DS Self-organization, complexity and chaos: the new biology for medicine. Nat Med 1998, 4:882-885
106 Misteli T The concept of self-organization in cellular architecture. J Cell Biol 2001, 155:181-185
107 Bruder CE, Piotrowski A, Gijsbers AA, Andersson R, Erickson S, de Stahl TD, Menzel U, Sandgren J, von Tell D,
Poplawski A, et al Phenotypically concordant and discordant monozygotic twins display different DNA copy-
number-variation profiles. Am J Hum Genet 2008, 82:763-771
108 Fraga MF, Ballestar E, Paz MF, Ropero S, Setien F, Ballestar ML, Heine-Suner D, Cigudosa JC, Urioste M, BenitezJ,
et al Epigenetic differences arise during the lifetime of monozygotic twins. Proc Nat Acad Sd USA 2005,
102:10604-10609
109 Javierre BM, Fernandez AF, Richter J, Al-Shahrour F, Martin-Subero JI, Rodriguez-Ubreva J, Berdasco M, Fraga MF,
O'Hanlon TP, Rider LG, et al Changes in the pattern of DNA methylation associate with twin discordance in
systemic lupus erythematosus. Genome Res 2010, 20:170-179








Greek and Rice Theoretical Biology and Medical Modelling 2012, 9:40 Page 28 of 33
http://www.tbiomed.com/content/9/1/40





110 von Herrath M, Nepom GT Remodeling rodent models to mimic human type 1 diabetes. Eur J Immunol 2009,
39:2049-2054
111 Pearson H Surviving a knockout blow. Nature 2002, 415:8-9
112 Morange M' A successful form for reductionism. Biochem 2001, 23:37-39
113 Dempster EL, Pidsley R, Schalkwyk LC, Owens S, Georgiades A, Kane F, Kalidindi S, Picchioni M, Kravariti E,
Toulopoulou T, et al Disease-associated epigenetic changes in monozygotic twins discordant for
schizophrenia and bipolar disorder. Hum Mol Genet 2011, 20:4786-4796
114 Javierre BM, Fernandez AF, Richter J, Al-Shahrour F, Martin-Subero JI, Rodriguez-Ubreva J, Berdasco M, Fraga MF,
O'Hanlon TP, Rider LG, et al Changes in the pattern of DNA methylation associate with twin discordance in
systemic lupus erythematosus. Genome Res 2010, 20:170-179
115 Maiti S, Kumar KHBG, Castellani CA, O'Reilly R, Singh SM Ontogenetic De Novo Copy Number Variations (CNVs)
as a Source of Genetic Individuality: Studies on Two Families with MZD Twins for Schizophrenia. PLoS One
2011, 6:e17125
116 Wong AH, Gottesman II, Petronis A Phenotypic differences in genetically identical organisms: the epigenetic
perspective. Hum Mol Genet 2005, 14(1) 11-18
117 Kellenberger E The evolution of molecular biology. EMBO Rep 2004, 5:546-549
118 Giles J Animal experiments under fire for poor design. Nature 2006, 444:981
119 Editorial: A slippery slope. Nature 2009, 462:699
120 LaFollette H, Shanks N Animal models in biomedical research: some epistemological worries. PublAff 0 1993,
7:113-130
121 Ache BW, Young JM Olfaction: diverse species, conserved principles. Neuron 2005, 48:417-430
122 Bennett CN, Green JE Unlocking the power of cross-species genomic analyses: identification of evolutionarily
conserved breast cancer networks and validation of preclinical models. Breast Concer Res 2008, 10:213
123 Czyz A, Wegrzyn G The Obg subfamily of bacterial GTP-binding proteins: essential proteins of largely unknown
functions that are evolutionarily conserved from bacteria to humans. Act Biochim Pol 2005, 52:35-43
124 Docampo R, de Souza W, Miranda K, Rohloff P, Moreno SN Acidocalcisomes conserved from bacteria to man.
Nat Rev Microbiol 2005, 3:251-261
125 Erol A Insulin resistance is an evolutionarily conserved physiological mechanism at the cellular level for
protection against increased oxidative stress. Bioessays 2007, 29:811-818
126 Hayakawa A, Hayes S, Leonard D, Lambright D, Corvera S Evolutionarily conserved structural and functional
roles of the FYVE domain. Biochem Soc Symp 2007, 74:95-105
127 Miyoshi T, Ishikawa F [Conserved telomeric-end structures among fission yeast and humans]. Tanpokushitsu
Kakusan Koso 2008, 53:1850-1857
128 Saenko SV, French V, Brakefield PM, Beldade P Conserved developmental processes and the formation of
evolutionary novelties: examples from butterfly wings. Philos Trans R Soc Lond B Biol Sci 2008, 363:1549-1555
129 Sumimoto H, Kamakura S, Ito T Structure and function of the PB1 domain, a protein interaction module
conserved in animals, fungi, amoebas, and plants. Sci STKE 2007, 401 2007:re6
130 Tucker RP, Chiquet-Ehrismann R Teneurins: a conserved family of transmembrane proteins involved in
intercellular signaling during development. Dev Biol 2006, 290:237-245
131 van den Heuvel S, Dyson NJ Conserved functions of the pRB and E2F families. Nat Rev Mol Cell Biol 2008,
9:713-724
132 Wang K, Degerny C, Xu M, Yang XJ YAP, TAZ, and Yorkie: a conserved family of signal-responsive
transcriptional coregulators in animal development and human disease. Biochem Cell Biol 2009, 87:77-91
133 Gerhart J, Kirschner M The Theory of Facilitated Variation In the Light of Evolution, Adaptation and Complex
Design, Volume I Edited by Avise JC, Ayala FJ Washington DC National Acdemy of Sciences; 200745-64
134 Arden KC FOXO animal models reveal a variety of diverse roles for FOXO transcription factors. Oncogene
2008, 27:2345-2350
135 Hovnanian A SERCA pumps and human diseases. Subcell Biochem 2007, 45:337-363
136 Lewis EB A gene complex controlling segmentation in Drosophila. Nature 1978, 276:565-570
137 McGinnis W, Hart CP, Gehring WJ, Ruddle FH Molecular cloning and chromosome mapping of a mouse DNA
sequence homologous to homeotic genes of Drosophila. Cell 1984, 38:675-680
138 Gellon G, McGinnis W Shaping animal body plans in development and evolution by modulation of Hox
expression patterns. Bioessays 1998, 20:116-125
139 Slack JM, Holland PW, Graham CF The zootype and the phylotypic stage. Nature 1993, 361:490-492
140 Wagner GP, Amemiya C, Ruddle F Hox cluster duplications and the opportunity for evolutionary novelties.
Proc NatAcod Scid USA 2003, 100:14603-14606
141 Amores A, Force A, Yan YL, Joly L, Amemiya C, Fritz A, Ho RK, Langeland J, Prince V, Wang YL, et al Zebrafish hox
clusters and vertebrate genome evolution. Science 1998, 282:1711-1714
142 Garcia-Fernandez J' Hox, ParaHox, ProtoHox: facts and guesses. Heredity 2005, 94:145-152
143 Lee RC, Feinbaum RL, Ambros V The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense
complementarity to lin-14. Cell 1993, 75:843-854
144 Lau NC, Lim LP, Weinstein EG, Bartel DP An abundant class of tiny RNAs with probable regulatory roles in
Caenorhabditis elegans. Science 2001, 294:858-862
145 Lagos-Quintana M, Rauhut R, Meyer J, Borkhardt A, Tuschl T New microRNAs from mouse and human. RNA
2003, 9:175-179
146 Calin GA, Croce CM MicroRNA signatures in human cancers. Nat Rev Cancer 2006, 6:857-866
147 Ling HY, Ou HS, Feng SD, Zhang XY, Tuo QH, Chen LX, Zhu BY, Gao ZP, Tang CK, Yin WD, et al Changes in
microRNA profile and effects of miR-320 in insulin-resistant 3T3-L1 adipocytes. Clin Exp Phormocol Physio/
2009, doi'101111/j 1440-1681 200905207x
148 Lu J, Getz G, Miska EA, Alvarez-Saavedra E, Lamb J, Peck D, Sweet-Cordero A, Ebert BL, Mak RH, Ferrando AA, et o/
MicroRNA expression profiles classify human cancers. Nature 2005, 435:834-838








Greek and Rice Theoretical Biology and Medical Modelling 2012, 9:40 Page 29 of 33
http://www.tbiomed.com/content/9/1/40





149 Stark A, Brennecke J, Bushati N, Russell RB, Cohen SM Animal MicroRNAs confer robustness to gene expression
and have a significant impact on 3'UTR evolution. Cell 2005, 123:1133-1146
150 Rukov JL, Vinther J, Shomron N Pharmacogenomics genes show varying perceptibility to microRNA
regulation. Phormacogenet Genomics 2011, 21:251-262
151 Provost P MicroRNAs as a molecular basis for mental retardation, Alzheimer's and prion diseases. Brain Res
2010, 1338:58-66
152 Cheng Y, Zhang C MicroRNA-21 in cardiovascular disease. J Cardiovasc Trans/ Res 2010, 3:251-255
153 Varki A, Altheide TK Comparing the human and chimpanzee genomes: searching for needles in a haystack.
Genome Res 2005, 15:1746-1758
154 Barreiro LB, Marioni JC, Blekhman R, 5tephens M, Gilad Y Functional Comparison of Innate Immune Signaling
Pathways in Primates. PLoS Genet 2010, 6:e 001249
155 Varki A A chimpanzee genome project is a biomedical imperative. Genome Res 2000, 10:1065-1070
156 Nguyen DH, Hurtado-Ziola N, Gagneux P, Varki A Loss of Siglec expression on T lymphocytes during human
evolution. Proc Natl Acad Sc USA 2006, 103:7765-7770
157 Xie D, Chen CC, Ptaszek LM, Xiao 5, Cao X, Fang F, Ng HH, Lewin HA, Cowan C, Zhong Rewirable gene
regulatory networks in the preimplantation embryonic development of three mammalian species. Genome
Res 2010, 20:804-815
158 Mocciaro A, Schiebel E Cdc14: a highly conserved family of phosphatases with non-conserved functions? J CelSd
2010, 123:2867-2876
159 Atianand MK, Fuchs T, Harton JA Recent evolution of the NF-kappaB and inflammasome regulating protein
POP2 in primates. BMC Evol Biol 2011, 11:56
160 Eckenhoff RG Why can all of biology be anesthetized? Anesth Analg 2008, 107:859-861
161 Lynch C 3rd Meyer and Overton revisited. Anesth Analg 2008, 107:864-867
162 Sedensky MM, Morgan PG Genetics and the evolution of the anesthetic response. Anesth Analg 2008, 107:855-858
163 Sonner JM A hypothesis on the origin and evolution of the response to inhaled anesthetics. Anesth Analg
2008, 107:849-854
164 Sonner JM, Gong D, Eger El 2nd Naturally occurring variability in anesthetic potency among inbred mouse
strains. Anesth Analg 2000, 91:720-726
165 Olver A, Deamer D Sensitivity to anesthesia by pregnenolone appears late in evolution In Molecular and
Cellular Mechanisms of Alcohol and Anesthetics Edited by Rubin E, Miller K, Roth S New York Annals of the New
York Academy of Sciences; 1991 561-565
166 Morgan PG, Kayser EB, Sedensky MM C. elegans and volatile anesthetics. WormBook 2007'1-11 http//www ncbi
nlm nih gov/entrez/queryfcgi7cmd Retrieve&db PubMed&dopt=Citation&list_uids=18050492
167 Crowder CM, Shebester LD, Schedl T Behavioral effects of volatile anesthetics in Caenorhabditis elegans.
Anesthesiology 1996, 85:901-912
168 Gamo S, Ogaki M, Nakashima-Tanaka E Strain differences in minimum anesthetic concentrations in Drosophila
melanogaster. Anesthesiology 1981, 54:289-293
169 Milne A, Beamish T Inhalational and local anesthetics reduce tactile and thermal responses in mimosa pudica.
Can J Anaesth 1999, 46:287-289
170 Nunn JF, Sturrock JE, Wills EJ, Richmond JE, McPherson CK The effect of inhalational anaesthetics on the
swimming velocity of Tetrahymena pyriformis. J Cell Sc 1974, 15:537-554
171 Gould SJ, Vrba ES Exaptation a missing term in the science of form. Paleobiology 1982, 8:4-15
172 Gould SJ The exaptive excellence of spandrels as a term and prototype. Proc Nat Acad Sci USA 1997,
94:10750-10755
173 Gould SJ, Lewontin RC The spandrels of San Marco and the Panglossian paradigm: a critique of the
adaptationist programme. Proc R Soc Lond B Bio Sci 1979, 205:581 -598
174 Keil RL, Wolfe D, Reiner T, Peterson CJ, Riley JL' Molecular genetic analysis of volatile-anesthetic action. Mol Cell
Biol 1996, 16:3446-3453
175 Ouyang W, Jih T-Y, Zhang T T, Correa AM, Hemmings HC Jr Isoflurane Inhibits NaChBac, a Prokaryotic Voltage-
Gated Sodium Channel. J Pharmacol Exp Ther 2007, 322:1076-1083
176 Wieslander A, Rilfors L, Lindblom G Metabolic changes of membrane lipid composition in Acholeplasma
laidlawii by hydrocarbons, alcohols, and detergents: arguments for effects on lipid packing. Biochemistry 1986,
25:7511-7517
177 Koblin DD, Wang HH Chronic exposure to inhaled anesthetics increases cholesterol content in Acholeplasma
laidlawii. Biochim Biophys Acta 1981, 649:717-725
178 Ingram LO Adaptation of membrane lipids to alcohols. J Bacteriol 1976, 125:670-678
179 Nandini-Kishore SG, Mattox SM, Martin CE, Thompson GA Jr Membrane changes during growth of
Tetrahymena in the presence of ethanol. Biochim Biophys Acta 1979, 551:315-327
180 Nandini-Kishore SG, Kitajima Y, Thompson GA Jr Membrane fluidizing effects of the general anesthetic
methoxyflurane elicit an acclimation response in Tetrahymena. Biochim Biophys Acta 1977, 471:157-161
181 Humphrey JA, Hamming KS, Thacker CM, Scott RL, Sedensky MM, Snutch TP, Morgan PG, Nash HA A putative
cation channel and its novel regulator: cross-species conservation of effects on general anesthesia. Curr Biol
CB 2007, 17:624-629
182 Eger El 2nd, Saidman LJ, Brandstater B Minimum alveolar anesthetic concentration: a standard of anesthetic
potency. Anesthesiology 1965, 26:756-763
183 Wang Q, Zheng Y, Lu J, Chen L, Wang J, Zhou JX Selective breeding of mice strains with different sensitivity
to isoflurane. Chin Med J (Engl) 2010, 123:1315-1319
184 Cascio M, Xing Y, Gong D, Popovich J, Eger El 2nd, Sen 5, Peltz G, Sonner JM Mouse chromosome 7 harbors a
quantitative trait locus for isoflurane minimum alveolar concentration. Anesth Analg 2007, 105:381-385
185 Buffington CW, Romson JL, Levine A, Duttlinger NC, Huang AH Isoflurane induces coronary steal in a canine
model of chronic coronary occlusion. Anesthesiology 1987, 66:280-292








Greek and Rice Theoretical Biology and Medical Modelling 2012, 9:40 Page 30 of 33
http://www.tbiomed.com/content/9/1/40





186 Becker LC Is isoflurane dangerous for the patient with coronary artery disease? Anesthesiology 1987, 66:259-261
187 Lundeen G, Manohar M, Parks C Systemic distribution of blood flow in swine while awake and during 1.0 and
1.5 MAC isoflurane anesthesia with or without 50% nitrous oxide. Anesth Anoalg 1983, 62:499-512
188 Manohar M, Parks C Regional distribution of brain and myocardial perfusion in swine while awake and during 1.0
and 1.5 MAC isoflurane anaesthesia produced without or with 50% nitrous oxide. Caordiovosc Res 1984, 18:344-353
189 Leung JM, Goehner P, O'Kelly BF, Hollenberg M, Pineda N, Cason BA, Mangano DT Isoflurane anesthesia and
myocardial ischemia: comparative risk versus sufentanil anesthesia in patients undergoing coronary artery
bypass graft surgery. The SPI (Study of Perioperative Ischemia) Research Group. Anesthesiology 1991, 74:838-847
190 Sear JW Practical treatment recommendations for the safe use of anaesthetics. Drugs 1992, 43:54-68
191 Agnew NM, Pennefather SH, Russell GN Isoflurane and coronary heart disease. Anaesthesio 2002, 57:338-347
192 Agarwal S, Moorchung N Modifier genes and oligogenic disease. J Nippon Med Sch 2005, 72:326-334
193 Dowell RD, Ryan 0, Jansen A, Cheung D, Agarwala S, Danford T, Bernstein DA, Rolfe PA, Heisler LE, Chin B, et od
Genotype to Phenotype: A Complex Problem. Science 2010, 328:469
194 Editorial: Deconstructing Genetic Contributions to Autoimmunity in Mouse Models. PLoS Biol 2004, 2:e220
195 Friedman A, Perrimon N Genetic screening for signal transduction in the era of network biology. Cell 2007,
128:225-231
196 Hunter K, Welch DR, Liu ET Genetic background is an important determinant of metastatic potential. Nat
Genet 2003, 34:23-24 author reply 25
197 Liu Z, Maas K, Aune TM Comparison of differentially expressed genes in T lymphocytes between human
autoimmune disease and murine models of autoimmune disease. Clin Immunol 2004, 112:225-230
198 Thein SL Genetic modifiers of beta-thalassemia. Haoemtologico 2005, 90:649-660
199 Pai AA, Bell iT, Marioni JC, Pritchard K, Gilad Y A Genome-Wide Study of DNA Methylation Patterns and Gene
Expression Levels in Multiple Human and Chimpanzee Tissues. PLoS Genet 2011, 7:e 001316
200 Morley M, Molony CM, Weber TM, Devlin JL, Ewens KG, Spielman RS, Cheung VG Genetic analysis of genome-
wide variation in human gene expression. Nature 2004, 430:743-747
201 Rosenberg NA, Pritchard JK, Weber JL, Cann HM, Kidd KK, Zhivotovsky LA, Feldman MW Genetic structure of
human populations. Science 2002, 298:2381 -2385
202 Storey JD, Madeoy J, Strout JL, Wurfel M, Ronald J, Akey JM Gene-expression variation within and among
human populations. Am J Hum Genet 2007, 80:502-509
203 Zhang W, Duan S, Kistner EO, Bleibel WK, Huang RS, Clark TA, Chen TX, Schweitzer AC, Blume JE, Cox NJ, Dolan
ME Evaluation of genetic variation contributing to differences in gene expression between populations.
Am J Hum Genet 2008, 82:631-640
204 Pritchard C, Coil D, Hawley S, Hsu L, Nelson PS The contributions of normal variation and genetic background
to mammalian gene expression. Genome Biol 2006, 7:R26
205 Rifkin SA, Kim J, White KP Evolution of gene expression in the Drosophila melanogaster subgroup. Naot Genet
2003, 33:138-144
206 Sandberg R, Yasuda R, Pankratz DG, Carter TA, Del Rio JA, Wodicka L, Mayford M, Lockhart DJ, Barlow C Regional and
strain-specific gene expression mapping in the adult mouse brain. Proc NatlAcod Sci USA 2000, 97:11038-11043
207 Suzuki Y, Nakayama M Differential profiles of genes expressed in neonatal brain of 129X1/SvJ and C57BL/6 J
mice: A database to aid in analyzing DNA microarrays using nonisogenic gene-targeted mice. DNA Res 2003,
10:263-275
208 Gibbs RA, Rogers J, Katze MG, Bumgarner R, Weinstock GM, Mardis ER, Remington KA, Strausberg RL, Venter JC,
Wilson RK, et oal Evolutionary and biomedical insights from the rhesus macaque genome. Science 2007,
316:222-234
209 Enna SJ, Williams M' Defining the role of pharmacology in the emerging world of translational research. Adv
Phormocol 2009, 57:1-30
210 Pinkel D The use of body surface area as a criterion of drug dosage in cancer chemotherapy. Cancer Res 1958,
18:853-856
211 Reagan-Shaw S, Nihal M, Ahmad N Dose translation from animal to human studies revisited. FASEB J Offic Pub/
Fed Am Soc Exp Biol 2008, 22:659-661
212 Teague SJ Learning lessons from drugs that have recently entered the market. Drug Discov Today 2009,
16:398-411
213 Freireich EJ, Gehan EA, Rail DP, Schmidt LH, Skipper HE Quantitative comparison of toxicity of anticancer
agents in mouse, rat, hamster, dog, monkey, and man. Conc Chemother Rep 1966, 50:219-244
214 Talmadge JE, Singh RK, Fidler IJ, Raz A Murine Models to Evaluate Novel and Conventional Therapeutic
Strategies for Cancer. Am J Pothol 2007, 170:793-804
215 Burtles SS, Newell DR, Henrar RE, Connors TA Revisions of general guidelines for the preclinical toxicology of
new cytotoxic anticancer agents in Europe. The Cancer Research Campaign (CRC) Phase 1/11 Clinical Trials
Committee and the European Organization for Research and Treatment of Cancer (EORTC) New Drug
Development Office. Eur J Concer 1995, 31A:408-410
216 Goldsmith MA, Slavik M, Carter SK Quantitative prediction of drug toxicity in humans from toxicology in small
and large animals. Cancer Res 1975, 35:1354-1364
217 Newell DR Phase I clinical studies with cytotoxic drugs: pharmacokinetic and pharmacodynamic
considerations. Br J Concer 1990, 61:189-191
218 Goodman G, Wilson R Quantitative prediction of human cancer risk from rodent carcinogenic potencies: a
closer look at the epidemiological evidence for some chemicals not definitively carcinogenic in humans.
Regul Toxicol Phormocol RTP 1991, 14:118-146
219 Paxton JW The allometric approach for interspecies scaling of pharmacokinetics and toxicity of anti-cancer
drugs. Clin Exp Phormocol Physiol 1995, 22:851-854
220 Abelson PH Exaggerated carcinogenicity of chemicals. Science 1992, 256:1609








Greek and Rice Theoretical Biology and Medical Modelling 2012, 9:40 Page 31 of 33
http://www.tbiomed.com/content/9/1/40





221 Bonati M, Latini R, Tognoni G, Young JF, Garattini S Interspecies comparison of in vivo caffeine
pharmacokinetics in man, monkey, rabbit, rat, and mouse. Drug Metab Rev 1984, 15:1355-1383
222 Caldwell J Problems and opportunities in toxicity testing arising from species differences in xenobiotic
metabolism. Toxicol Lett 1992, 64:651-659
223 Capel ID, French MR, Millburn P, Smith RL, Williams RT Species variations in the metabolism of phenol. Biochem
J 1972, 127:25P-26P
224 Capel ID, French MR, Millburn P, Smith RL, Williams RT The fate of (14C)phenol in various species. Xenobiotica,
Fate Foreign Compounds Biol Syst 1972, 2:25-34
225 Parkinson C, Grasso P The use of the dog in toxicity tests on pharmaceutical compounds. Hum Exp Toxicol
1993, 12:99-109
226 Serrano D, Lazzeroni M, Zambon CF, Macis D, Maisonneuve P, Johansson H, Guerrieri-Gonzaga A, Plebani M, Basso
D, Gjerde J, et oal Efficacy of tamoxifen based on cytochrome P450 2D6, CYP2C19 and SULT1A1 genotype in
the Italian Tamoxifen Prevention Trial. Phormacogenomics J 2011, 11:100-107
227 Smith RL, Caldwell J Drug metabolism in non-human primates In Drug metabolism from microbe to man
Edited by Parke DV, Smith RL London Taylor & Francis; 1977331-356
228 Walker RM, McElligott TF Furosemide induced hepatotoxicity. J Pothol 1981, 135:301-314
229 Weatherall M An end to the search for new drugs? Nature 1982, 296:387-390
230 Collins JM, Zaharko DS, Dedrick RL, Chabner BA Potential roles for preclinical pharmacology in phase I clinical
trials. Cancer Treat Rep 1986, 70:73-80
231 Strolin Benedetti M, Fraier D, Pianezzola E, Castelli MG, Dostert P, Gianni L Stereoselectivity of iododoxorubicin
reduction in various animal species and humans. Xenobioticoa Fate Foreign Compounds Biol Syst 1993, 23:115-121
232 Gianni L, Capri G, Greco M, Villani F, Brambilla C, Luini A, Crippa F, Bonadonna G Activity and toxicity of
4'-iodo-4'-deoxydoxorubicin in patients with advanced breast cancer. Ann Oncol 1991, 2:719-725
233 Brennan R, Federico S, Dyer MA The war on cancer: have we won the battle but lost the war? Oncotorget 2010,
1:77-83
234 Horstmann E, McCabe MS, Grochow L, Yamamoto S, Rubinstein L, Budd T, Shoemaker D, Emanuel EJ, Grady C
Risks and benefits of phase 1 oncology trials, 1991 through 2002. N Eng J Med 2005, 352:895-904
235 Chapman AR' Addressing the Ethical Challenges of First-in-Human Trials. J Clin Res Bioeth 2011, 2:113
236 Leaf C Why we ore losing the war on cancer Fortune; 200477-92
237 Dresser R First-in-human trial participants: not a vulnerable population, but vulnerable nonetheless. J Loaw
Med Ethics 2009, 37:38-50
238 Young M Prediction v Attrition Drug Discovery World; 2008'9-12
239 Gura T Cancer Models: Systems for identifying new drugs are often faulty. Science 1997, 278:1041-1042
240 Cohen AF Developing drug prototypes: pharmacology replaces safety and tolerability? Not Rev Drug Discov
2010, 9:856-865
241 Hansel TT, Kropshofer H, Singer T, Mitchell JA, George AJT The safety and side effects of monoclonal
antibodies. Not Rev Drug Discov 2010, 9:325-338
242 Marshall E Gene therapy on trial. Science 2000, 288:951-957
243 Perlstein I, Bolognese JA, Krishna R, Wagner JA Evaluation of agile designs in first-in-human (FIH) trials-a
simulation study. AAPS J 2009, 11:653-663
244 Buoen C, Bjerrum OJ, Thomsen MS How first-time-in-human studies are being performed: a survey of phase I dose-
escalation trials in healthy volunteers published between 1995 and 2004. J Clin Phormoco 2005, 45:1123-1136
245 Wexler D, Bertelsen KM A Brief Survey of First-in-Human Studies. J Clin Phormocol 2011, 51:988-993
246 Lappin G, Garner RC Big physics, small doses: the use of AMS and PET in human microdosing of
development drugs. Nat Rev Drug Discov 2003, 2:233-240
247 Lappin G, Garner RC The utility of microdosing over the past 5 years. Expert Opin Drug Metob Toxicol 2008, 4:1499-1506
248 Lappin G, Kuhnz W, Jochemsen R, Kneer J, Chaudhary A, Oosterhuis B, Drijfhout WJ, Rowland M, Garner RC Use
of microdosing to predict pharmacokinetics at the therapeutic dose: experience with 5 drugs. Clin Phormocol
Ther 2006, 80:203-215
249 Gill DM Bacterial toxins: a table of lethal amounts. Microbiol Rev 1982, 46:86-94
250 National Institute of Occupational Safety and Health Registry of Toxic Effects of Chemicol Substonces (R-TECS)
Cincinnati National Institute of Occupational Safety and Health; 1996
251 Giri S, Bader A Foundation review: Improved preclinical safety assessment using micro-BAL devices: the
potential impact on human discovery and drug attrition. Drug Discov Today 2011, 16:382-397
252 Wade N New Treatment for Cancer Shows Promise in Testing New York Times; 2009 June 29, 2009
253 DiMasi JA, Grabowski HG Economics of new oncology drug development. J Clin Oncol OfficJ Am Soc Clin Oncol
2007, 25:209-216
254 DiMasi JA, Feldman L, Seckler A, Wilson A Trends in risks associated with new drug development: success
rates for investigational drugs. Clin Phormocol Ther 2010, 87:272-277
255 Kola I, Landis J Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov 2004, 3:711-715
256 Frese KK, Tuveson DA Maximizing mouse cancer models. Nat Rev Cancer 2007, 7:645-658
257 Kerbel RS Human tumor xenografts as predictive preclinical models for anticancer drug activity in humans:
better than commonly perceived-but they can be improved. Cancer Biol Ther 2003, 2:5134-139
258 Singh M, Lima A, Molina R, Hamilton P, Clermont AC, Devasthali V, Thompson JD, Cheng JH, Reslan HB, Ho CCK,
et al Assessing therapeutic responses in Kras mutant cancers using genetically engineered mouse models.
Nat Biotechnol 2010, 28:585-593
259 Peterson JK, Houghton PJ Integrating pharmacology and in vivo cancer models in preclinical and clinical drug
development. Eur J Cancer 2004, 40:837-844
260 Francia G, Kerbel RS Raising the bar for cancer therapy models. Nat Biotech 2010, 28:561-562








Greek and Rice Theoretical Biology and Medical Modelling 2012, 9:40 Page 32 of 33
http://www.tbiomed.com/content/9/1/40





261 Johnson Jl, Decker S, Zaharevitz D, Rubinstein LV, Venditti JM, Schepartz S, Kalyandrug S, Christian M, Arbuck S,
Hollingshead M, Sausville EA Relationships between drug activity in NCI preclinical in vitro and in vivo models
and early clinical trials. Br J Concer 2001, 84:1424-1431
262 Kardong KV Vertebrotes Comparative Anotomy, Function, Evolution International Edition 6th edition Singapore
McGraw-Hill; 2012
263 Johnson BK, Stone GA, Godec MS, Asher DM, Gajdusek DC, Gibbs CJ Jr Long-term observations of human
immunodeficiency virus-infected chimpanzees. AIDS Res Hum Retroviruses 1993, 9:375-378
264 Nath BM, Schumann KE, Boyer JD The chimpanzee and other non-human-primate models in HIV-1 vaccine
research. Trends Microbiol 2000, 8:426-431
265 Stump DS, VandeWoude S Animal models for HIV AIDS: a comparative review. Comp Med 2007, 57:33-43
266 Schmitz W, Scholz H, Erdmann E Effects of a- and 3-adrenergic agonists, phosphodiesterase inhibitors and
adenosine on isolated human heart muscle preparations. Trends Phormocol Sci 1987, 8:447-450
267 Howard AN, Blaton V, Vandamme D, Van Landschoot N, Peeters H Lipid changes in the plasma lipoproteins of
baboons given an atherogenic diet. 3. A comparison between lipid changes in the plasma of the baboon
and chimpanzee given atherogenic diets and those in human plasma lipoproteins of type II
hyperlipoproteinaemia. Atherosclerosis 1972, 16:257-272
268 Piper PJ, Antoniw JW, Stanton AW Release of leukotrienes from porcine and human blood vessels by
immunological and nonimmunological stimuli. Ann N YAcd Sci 1988, 524:133-141
269 Gross DR Animol Models in Cordiovosculor Research The Hague Martinus Nijhoff; 1985
270 Wadman M When the party's over. Nature 2007, 445:13
271 Peters J, Van_Slyke D Quaontitotive Clinical Chemistry, Interpretations, Volume I Secondth edition Baltimore
Williams &Wilkins; 1948
272 Nishina PM, Schneeman BO, Freedland RA Effects of dietary fibers on nonfasting plasma lipoprotein and
apolipoprotein levels in rats. J Nutr 1991, 121:431-437
273 Innovotion or Stgntion? Challenge oand Opportunity on the Critical Path to New Medicol Products http'//www
nipte org/docs/Critical_Path pdf
274 van der Worp HB, Macleod MR Preclinical studies of human disease: Time to take methodological quality
seriously. Journal of molecular oand cellular cardiology 2011, 51 (4) 449-50
275 Jonas S, Aiyagari V, Vieira D, Figueroa M The failure of neuronal protective agents versus the success of
thrombolysis in the treatment of ischemic stroke. The predictive value of animal models. Ann N YAcod Sc
2001, 939:257-267
276 Mullane K, Williams M Translational semantics and infrastructure: another search for the emperor's new
clothes? Drug Discov Today 2012, 17:459-468
277 Kaste M Use of animal models has not contributed to development of acute stroke therapies: pro. Stroke
2005, 36:2323-2324
278 Horstmann D The Poliomyelitis Story; a scientific hegira. Yale J Biol Med 1985, 58:79-90
279 Oshinsky DM Polio An Americon Story Oxford Oxford University Press; 2005
280 Paul JR A History of Poliomyelitis New Haven Yale University Press; 1971
281 Sabin A Testimony before the subcommittee on Hospitals and Health Care, Committee on Veterans Affair's,
House of Representatives, April 26, 1984 serial no. 98-48 In Book Testimony before the subcommittee on
Hospitals oand Heoalth Cre, Committee on Veterons Affoir's, House of Representotives, April 26, 1984 serial no 98-48
(Editor ed Aeds) Washington DC; 1984
282 Broderick JP The Challenges of Intracranial Revascularization for Stroke Prevention. N Eng J Med 2011,
365:1054-1055
283 Chimowitz MI, Lynn MJ, Derdeyn CP, Turan TN, Fiorella D, Lane BF, Janis LS, Lutsep HL, Barnwell SL, Waters MF, et al
Stenting versus aggressive medical therapy for intracranial arterial stenosis. N Eng J Med 2011, 365:993-1003
284 The EC/IC Bypass Study Group Failure of extracranial-intracranial arterial bypass to reduce the risk of ischemic
stroke. Results of an international randomized trial. The EC/IC Bypass Study Group. N Eng/ J Med 1985,
313:1191-1200
285 Powers W, Clarke W, Grubb R, Videen T, Adams H, Derdeyn C Results of the Carotid Occlusion Surgery Study
(COSS) In International Stroke Conference (COSS) Los Angeles; 2011
286 Editorial In pursuit of systems. Nature 2005, 435:1
287 Systems https'//sysbio med harvard edu/
288 Vidal M A unifying view of 21st century systems biology. FEBS Lett 2009, 583:3891 -3894
289 Losa GA The fractal geometry of life. Riv Biol 2009, 102:29-59
290 Brenner Biological computation. Novartis Found Symp 1998, 213:106-111 discussion 111-106
291 Noble D From genes to whole organs: connecting biochemistry to physiology. Novartis Found Symp 2001,
239:111-123 doi'discussion 123-118, 150-119
292 Heng HH The conflict between complex systems and reductionism. JAMA 2008, 300:1580-1581
293 Gerstein HC, Miller ME, Byington RP, Goff DC Jr, Bigger JT, Buse JB, Cushman WC, Genuth S, Ismail-Beigi F, Grimm
RH Jr, et al Effects of intensive glucose lowering in type 2 diabetes. N Eng J Med 2008, 358:2545-2559
294 Bear HD Earlier chemotherapy for breast cancer: perhaps too late but still useful. Ann Surg Oncol 2003,
10:334-335
295 Savage L High-Intensity Chemotherapy Does Not Improve Survival in Small Cell Lung Cancer. J Natd Cancer
Inst 2008, 100:519
296 Mittra I The disconnection between tumor response and survival. Nat Clin Pract Oncol 2007, 4:203
297 Bates S Progress towards personalized medicine. Drug Discov Today 2010, 15:115-120
298 Bhathena A, Spear BB Pharmacogenetics: improving drug and dose selection. Curr Opin Pharmacol 2008, 8:639-646
299 Blair E Predictive tests and personalised medicine In Drug Discovery World; 2009'27-31
300 Dolgin E Big pharma moves from 'blockbusters' to 'niche busters'. Nat Med 2010, 16:837








Greek and Rice Theoretical Biology and Medical Modelling 2012, 9:40 Page 33 of 33
http://www.tbiomed.com/content/9/1/40





301 Flaherty KT, Puzanov I, Kim KB, Ribas A, McArthur GA, Sosman JA, O'Dwyer PJ, Lee RJ, Grippo JF, Nolop K,
Chapman PB Inhibition of mutated, activated BRAF in metastatic melanoma. N EnglJ Med 2010, 363:809-819
302 Froehlich TE, Epstein JN, Nick TG, Melguizo Castro MS, Stein MA, Brinkman WB, Graham AJ, Langberg JM, Kahn RS
Pharmacogenetic Predictors of Methylphenidate Dose-response in Attention-Deficit/Hyperactivity Disorder.
J Am Acad Child Adolesc Psychiatry 2011, 50:1129-1139 el 122
303 Hudson KL' Genomics, Health Care, and Society. N Eng J Med 2011, 365:1033-1041
304 Hughes AR, Spreen WR, Mosteller M, Warren LL, Lai EH, Brothers CH, Cox C, Nelsen AJ, Hughes S, Thorborn DE, et
ao Pharmacogenetics of hypersensitivity to abacavir: from PGx hypothesis to confirmation to clinical utility.
Pharmacogenomics J 2008, 8:365-374
305 Wang D, Guo Y, Wrighton SA, Cooke GE, Sadee W Intronic polymorphism in CYP3A4 affects hepatic expression
and response to station drugs. Pharmacogenomics J 2011, 11:274-286
306 Misch EA, Berrington WR, Vary JC Jr, Hawn TR Leprosy and the human genome. Microbiol Mol Biol Rev 2010,
74:589-620
307 Herndon CN, Jennings RG A twin-family study of susceptibility to poliomyelitis. Am J Hum Genet 1951, 3:17-46
308 Lin TM, Chen CJ, Wu MM, Yang CS, Chen JS, Lin CC, Kwang TY, Hsu ST, Lin SY, Hsu LC Hepatitis B virus markers
in Chinese twins. Anticancer Res 1989, 9:737-741
309 Angst MS, Lazzeroni LC, Phillips NG, Drover DR, Tingle M, Ray A, Swan GE, Clark JD Aversive and Reinforcing
Opioid Effects: A Pharmacogenomic Twin Study. Anesthesiology 2012, 117:22-37 doi 10 1097/
ALN 1090b013e31825a31822a31824e
310 Chapman SJ, Hill AVS Human genetic susceptibility to infectious disease. Nat Rev Genet 2012, 13:175-188
311 Cheung DS, Warman ML, Mulliken JB Hemangioma in twins. Ann Plast Surg 1997, 38:269-274
312 Couzin J Cancer research. Probing the roots of race and cancer. Science 2007, 315:592-594
313 Gregor Z, Joffe L Senile macular changes in the black African. Br J Ophthalmol 1978, 62:547-550
314 Haiman CA, Stram DO, Wilkens LR, Pike MC, Kolonel LN, Henderson BE, Le Marchand L Ethnic and racial
differences in the smoking-related risk of lung cancer. N Eng/ J Med 2006, 354:333-342
315 Kalow W Interethnic variation of drug metabolism. Trends Pharmacol Sci 1991, 12:102-107
316 Kopp JB, Nelson GW, Sampath K, Johnson RC, Genovese G, An P, Friedman D, Briggs W, Dart R, Korbet S, et al
APOL1 Genetic Variants in Focal Segmental Glomerulosclerosis and HIV-Associated Nephropathy. Journal of
the American Society of Nephrology 2011, 22(11)'2129-37
317 Spielman RS, Bastone LA, Burdick JT, Morley M, Ewens WJ, Cheung VG Common genetic variants account for
differences in gene expression among ethnic groups. Nat Genet 2007, 39:226-231
318 Stamer UM, Stuber F The pharmacogenetics of analgesia. Expert Opin Pharmacother 2007, 8:2235-2245
319 Wilke RA, Dolan ME Genetics and Variable Drug Response. JAMA 2011, 306:306-307
320 Canto JG, Rogers WJ, Goldberg RJ, Peterson ED, Wenger NK, Vaccarino V, Kiefe CI, Frederick PD, Sopko G, Zheng
Z J Association of Age and Sex With Myocardial Infarction Symptom Presentation and In-Hospital Mortality.
JAMA 2012, 307:813-822
321 Holden C Sex and the suffering brain. Science 2005, 308:1574
322 Kaiser J Gender in the pharmacy: does it matter? Science 2005, 308:1572
323 Klein S, Huber S Sex differences in susceptibility to viral infection In Sex hormones and immunity to infection
Edited by Klein S, Roberts C Berlin Springer; 2010'93-122
324 Simon V Wanted: women in clinical trials. Science 2005, 308:1517
325 Wald C, Wu C Of Mice and Women: The Bias in Animal Models. Science 2010, 327:1571-1572
326 Willyard C HIV gender clues emerge. Nat Med 2009, 15:830
327 Shah RR Pharmacogenetics in drug regulation: promise, potential and pitfalls. Philos Trans R Soc Lond B Biol Sc
2005, 360:1617-1638
328 Roses AD Pharmacogenetics and the practice of medicine. Nature 2000, 405:857-865
329 Yucesoy B, Johnson VJ, Fluharty K, Kashon ML, Slaven JE, Wilson NW, Weissman DN, Biagini RE, Germolec DR,
Luster MI Influence of cytokine gene variations on immunization to childhood vaccines. Vaccine 2009,
27:6991-6997
330 King C Personalised vaccines could protect all children. New Sc 2009, (2737)11
331 Pirmohamed M Pharmacogenetics: past, present and future. Drug Discov Today 2011, 16:852-861
332 The Case for Personalized Medicine i i i, i, i .. i i ... i i i 1.1 '
Case for PM_3rd edition pdf
333 Burggren WW, Bemis WE Studying Physiological Evolution: Paradigms and Pitfalls In Evolutionary Innovations
Edited by Nitecki MH Chicago University of Chicago Press; 1990'191-228

doi:10.1186/1742-4682-9-40
Cite this article as: Greek and Rice Animal models and conserved processes. Theoretical Biology and Medical
Modelling 2012 940




Full Text

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RESEARCHOpenAccessAnimalmodelsandconservedprocessesRayGreek1*andMarkJRice2*Correspondence: DrRayGreek@ gmail.com1AmericansForMedicalAdvancement (www.AFMA-curedisease.org),2251 RefugioRd,Goleta,CA93117,USA Fulllistofauthorinformationis availableattheendofthearticleAbstractBackground: Theconceptofconservedprocessespresentsuniqueopportunitiesfor usingnonhumananimalmodelsinbiomedicalresearch.However,theconceptmust beexaminedinthecontextthathumansandnonhumananimalsareevolved, complex,adaptivesystems.Giventhatnonhumananimalsareexamplesofliving systemsthatare differentlycomplex fromhumans,whatdoestheexistenceofa conservedgeneorprocessimplyforinter-speciesextrapolation? Methods: Wesurveyedtheliteratureincludingphilosophyofscience,biological complexity,conservedprocesses,evolutionarybiology,comparativemedicine, anti-neoplasticagents,inhalationalanesthetics,anddrugdevelopmentjournalsin ordertodeterminethevalueofnonhumananimalmodelswhenstudyingconserved processes. Results: Evolutionthroughnaturalselectionhasemployedcomponentsand processesbothtoproducethesameoutcomesamongspeciesbutalsotogenerate differentfunctionsandtraits.Manygenesandprocessesareconserved,butnew combinationsoftheseprocessesordifferentregulationofthegenesinvolvedin theseprocesseshaveresultedinuniqueorganisms.Further,thereisahierarchyof organizationincomplexlivingsystems.Atsomelevels,thecomponentsaresimple systemsthatcanbeanalyzedbymathematicsorthephysicalsciences,whileat otherlevelsthesystemcannotbefullyanalyzedbyreducingittoaphysicalsystem. Thestudyofcomplexlivingsystemsmustalternatebetweenfocusingontheparts andexaminingtheintactwholeorganismwhiletakingintoaccounttheconnections betweenthetwo.Systemsbiologyaimsforthisholism.Weexaminedtheactionsof inhalationalanestheticagentsandanti-neoplasticagentsinordertoaddresswhat thecharacteristicsofcomplexlivingsystemsimplyforinter-speciesextrapolationof traitsandresponsesrelatedtoconservedprocesses. Conclusion: Weconcludethateventhepresenceofconservedprocessesis insufficientforinter-speciesextrapolationwhenthetraitorresponsebeingstudiedis locatedathigherlevelsoforganization,isinadifferentmodule,orisinfluencedby othermodules.However,whentheexaminationoftheconservedprocessoccursat thesameleveloforganizationorinthesamemodule,andhenceissubjecttostudy solelybyreductionism,thenextrapolationispossible. Keywords: Anesthesia,Animalmodels,Cancer,Complexity,Conservedprocesses, Systemsbiology 2012GreekandRice;licenseeBioMedCentralLtd.ThisisanOpenAccessarticledistributedunderthetermsoftheCreative CommonsAttributionLicense(http://creativecommons.org/licenses/by/2.0),whichpermitsunrestricteduse,distribution,and reproductioninanymedium,providedtheoriginalworkisproperlycited.GreekandRice TheoreticalBiologyandMedicalModelling 2012, 9 :40 http://www.tbiomed.com/content/9/1/40

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BackgroundMarcKirschnerandJohnGerhartintroducedtheconceptoffacilitatedvariationand conservedcoreprocessesintheirbook, ThePlausibilityofLife [1],inordertoexplain hownoveltyarisesinevolution.Motivatedbya dvancesinevolutionaryanddevelopmental biology(evodevo),theseinvestigatorsproposedthatconservedprocessesareubiquitousin eukaryotesbutpointedoutthatbyusingconservedprocessesdifferently,forexampleby differentlyregulatingthegenesthatcodeforthe processes,expressingthegenesdifferently, varyingthesequencesorcombinationofgenesortranscriptionfactors,noveltycanarise. Mutationsinthegenesthatregulatetheconservedprocessescanaccomplishthisnovelty. Moreover,byadjustingtheregulatorygenes,theorganismcanevolvewithfewermutations thanwouldbethecaseifatraithadtoarise denovo orfrommutationsinstructuralgenes. Thishasimplicationsforusingnonhumananimals(hereafterreferredtosimplyasanimals) asmodelsforhumansinbiomedicalresearch.Oneshouldexpecttodiscoverinformation regardingconservedprocessesinhumansbystudyinganimalmodels.Wesoughttodeterminewhetherlimitsexistonthismethodandifsowhatthoselimitsare.MethodsWesurveyedtherelevantliteratureincluding philosophyofscience,biologicalcomplexity, conservedprocesses,evolutionarybiology,comparativemedicine,anti-neoplastic agents,inhalationalanesthetics,anddrugdevelopmentjournalsinordertodetermine theappropriateroleforanimalmodelswhenstudyingconservedprocesses.Philosophy ofscienceisrelevanttoourdiscussionasitincludesthepremisesandassumptionson whichresearchisthenbased.Astudyormethodcanbe methodologically soundbutif thepremisesareincorrect,thenthestudylosesmuchifnotallofitsvalue.Thedrug developmentliteraturewassearchedbecausethefinalapplicationofmuchresearchis targetedinterventionviadrugshencethatliteraturecaninformregardingthesuccessof apracticeormodality.Theliteratureconcerningbiologicalcomplexityandconserved processeswassurveyedasitdirectlyrelatestotheissuebeingexplored.Allofthismust beplacedintothecontextofevolutionarybiologyinordertobetterexplainthefindings. Wechoseinhalationalanestheticsandanti-neoplasticagentsasexamplesbecauseofthe well-knownconservednatureoftheseagents.ResultsAnimalmodelsTheuseofmodelshasalonghistoryinscience, whichledphilosopherofscienceRichard Braithwaitetowarnthat: “ Thepriceofemploymentofmo delsiseternalvigilance ” [2].In thissection,wewillexplorewhatanimalmodelsare,howtheycanbeusedinscientific investigation,includingbiomedicalresearch,a nddiscussclassificationschemes.Inthisarticle,wewilladdresstheuseofpredictiveanimalmodelsinlightoftheconceptsofcomplex systems,personalizedmedicineandpharmac ogenomics,andevolutionarybiology.Wewill thenexplorewhatthisimplieswhenusinga nimalmodelstostudyconservedprocesses. Modelsareimportantforscientificpursuitsandcantaketheformofabstractmodels, computationalmodels,heuristicmodels,mathematicalmodels,physicalmodelssuchas scalemodels,iconicmodels,andidealizedmodels.Modelscanalsobedividedonthe basisofwhethertheyareusedtoreplicateaportionoftheitembeingmodeledorareusedGreekandRice TheoreticalBiologyandMedicalModelling 2012, 9 :40Page2of33 http://www.tbiomed.com/content/9/1/40

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totesthypothesesorinterpretaspectsofatheory.Examplesofhistoricallyimportant modelsincludeWatsonandCrick ’ sphysicalmodelofDNA,Pauling ’ smodelofchemical bonds,Bohr ’ ssolarsystemmodeloftheatom,andthebilliardballmodelofgases.More recentmodelsincludethecomputermodelofthebrain,mathematicalmodelsofdisease spread,andLorenz ’ smodeloftheatmosphere. RobertHindeobservedthatmodels:Shouldbedifferentfromthethingbeingmodeled,becauseifitisnot,themodeler mightassumethatallpropertiesdemonstratedbythemodelexistinthethingbeing modeled;Areusuallylesscomplicatedthanthethingbeingmodeled;Aremorereadilyavailablethanthethingbeingmodeled,and;“ posequestions,suggestrelations,orcanbemanipulatedinwaysnotpossiblewith theoriginal ” [ 3 ]. Inlightoftheimportanceofmodels,somephilosophersofscienceassertthatthe studyofmodels perse hasbeenneglectedbythephilosophyofsciencecommunity. FriggandHartmann[4]state: “ Whatfillsintheblankin ‘ M represents T ifandonly if____, ’ where M isamodeland T atargetsystem? ” Moreover,howoneclassifies modelsandwhatcriteriamustbefulfilledinorderfor M tobeconsideredaspecific typeofmodelhasarguablynotbeenadequatelyaddressedbythephilosophyofscience community.Yetanotherproblemwiththe philosophyofmodels istherelationshipbetween theoryandmodel[4].Wemaintainthatthislackofscholarlyattentiontomodelshas playedaroleinwhatweseeastheconfusionsurroundingtheuseofanimalsasmodels. Animalmodelsarephysicalmodelsandcanbefurtherclassifiedbasedonvarious featuresanduses.Forexample,theycanbedistinguishedbythephylogeneticdistance ofthemodelspeciesfromhumans.Animalmodelscanalsobeclassifiedbasedonfidelity — howwellthemodelresembleshumans — aswellasbasedonvalidity — howwell whatyouthinkyouaremeasuringcorrespondstowhatyoureallyaremeasuring. Animalmodelscanalsobeconsideredbasedonreliability — theprecisionandaccuracy ofthemeasurement[5].Hauexplainsthatanimalmodelscanbecategorizedasspontaneous,induced,transgenic,negativeandorphan.Haustates: “ Themajorityoflaboratoryanimalmodelsaredevelopedandusedtostudythecause,nature,andcureof humandisorders ” [[6]p3].ThisisimportantasHaufurtherstatesthatanimalmodels canbeusedtopredicthumanresponses: “ Athirdimportantgroupofanimalmodelsis employedas predictive models.Thesemodelsareusedwiththeaimofdiscoveringand quantifyingtheimpactofatreatment,whetherthisistocureadiseaseortoassess toxicityofachemicalcompound.Theappropriatenessofanylaboratoryanimalmodel willeventuallybejudgedbyitscapacitytoexplainandpredicttheobservedeffectsin thetargetspecies ” [6].Othersagreethatpredictinghumanresponseisacommonuse foranimalmodels[7-12].Forexample,Heywoodstated: “ Animalstudiesfallintotwo maincategories:predictiveevaluationsofnewcompoundsandtheirincorporationinto schemesdesignedtohelplessenorclarifyarecognisedhazard ” [13]. Animalsareutilizedfornumerousscientificpurposes(see]Table1)andoneofthe authors(Greek)hasaddressedthesevarioususesinpreviouspublications[14-20].One cannothaveameaningfuldiscussionregardingtheutilityofanimalmodelsunlessoneGreekandRice TheoreticalBiologyandMedicalModelling 2012, 9 :40Page3of33 http://www.tbiomed.com/content/9/1/40

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specifiesthecategoryunderdiscussion.Forexample,areasinwhichanimalmodels havebeensuccessfullyemployedincludetheevaluationofaphenomenonthatcanbe describedbythephysicochemicalpropertieso ftheorganism,thestudyofbasicphysiologic functions,andthestudyofothertraitsth atcanbedescribedbytheuseofconversion factorsbasedonthebodysurfaceareaoftheorganism.Ingeneral,animalmodelscanbe successfullyemployedincategories3 – 9inTable1.However,animalmodelshavefailedto bepredictivemodalitiesforhumanresponseto drugsanddisease[13-16,18,21-41],depicted bycategories1and2inTable1.(Theauthors haveaddressedthisfailureinnumerous publicationsand,becauseanexplorationfort hisfailureisnotthepurposeofthearticle,we referthereadertothosepublications[14-20,23]eventhoughwerealizethatsomeviewthis positionascontroversial[7, 11,42-44].)Thisisnottosaythataspeciescanneverbefound inretrospectthatmimicsanoutcomeinhuman s.Suchaspeciesusuallycanbeidentified, howeverretrospectivecorrelationisobviouslynotthesameasprediction[45-47].Moreover, anyprocessormodalityclaimingtobepredict ivecanbeevaluatedbyuseofthebinomial classificationtableandequationsinTable2(as illustratedinTable3[48]).Suchcalculations arecommonlyusedinscience[49-53]. Table1Categoriesofanimaluseinscienceandresearch[16]1.Aspredictivemodelsforhumandisease 2.Aspredictivemodelstoevaluatehumanexposuresafetyinthecontextofpharmacology andtoxicology(e.g.,indrugtesting) 3.Assourcesof ‘ spareparts ’ (e.g.,aorticvalvereplacementsforhumans) 4.Asbioreactors(e.g.,asfactoriesfortheproductionofinsulin,ormonoclonalantibodies,or thefruitsofgeneticengineering) 5.Assourcesoftissueinordertostudybasicphysiologicalprinciples 6.Fordissectionandstudyineducationandmedicaltraining 7.Asheuristicdevicestopromptnewbiological/biomedicalhypotheses 8.Forthebenefitofothernonhumananimals 9.Forthepursuitofscientificknowledgeinandofitself Table2BinaryclassificationtestGoldstandard GS+GSTestT+TPFP T-FNTN Sensitivity=TP/(TP+FN) Specificity=TN/(FP+TN) PositivePredictiveValue=TP/(TP+FP) NegativePredictiveValue=TN/(FN+TN) T-=Testnegative T+=Testpositive FP=Falsepositive TP=Truepositive FN=Falsenegative TN=Truenegative GS-=Goldstandardnegative GS+=GoldstandardpositiveThebinaryclassificationtestallowscalculationsfordetermininghowwellatestorpracticecompareswithrealityorthe goldstandard.GreekandRice TheoreticalBiologyandMedicalModelling 2012, 9 :40Page4of33 http://www.tbiomed.com/content/9/1/40

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Whenjudgingthepredictivevalueofamodality,oneisnotusingtheterm predict inthesamesenseaswhendescribinghowhypothesesgeneratepredictionstobe tested.Thepredictivevalueofacommonl yusedmodalityusuallyisknown,orcanbe ascertained,forexamplethepositiveandne gativepredictivevalueofx-raycomputed tomography(commonlyreferredtoasaCTscan )fordiagnosingpneumothorax(arupture of,orinterferencein,thepleuralmembrane whichallowsairtoenterthepleuralspaceand thusinterfereswithbreathing )approaches1.0(isaccuratefordiagnosingtheconditionin 100%ofcases). Animalmodelsasusedinbiomedicalresearch,canalsobecategorizedascausal analogicalmodels(CAMs)orasheuristicorhypotheticalanalogicalmodels(HAMs) [54-59].Theuseofanimalmodelstopredicthumanresponsetodrugsanddisease,in accordancewithcategories1and2inTable1,wouldbeanexampleofusinganimals asCAMs.Analogicalmodelsingeneralincludethehydraulicmodelofeconomiesand thecomputermodelofthebrainandcanbefurtherdividedbasedonvariouscriteria[4]. CausalismorcausaldeterminismdatestoAristotlewhostated: “ whatiscalledWisdomis concernedwiththeprimarycausesandprinciples. ” Causalismcanbesummarized succinctly,as “ everythinghasacause. ” Thisnotionofcausationwasthebasisforanimal modelsascanbeappreciatedbythewritingsofClaudeBernard[60],consideredthefather ofanimalmodelingsincethe19thcentury.Bernard ’ sthoughtsonanimalmodelsarean extensionofAristotleviathedeterminismofDescartesandNewton[61].Causaldeterminismandtheprincipleofuniformityledtotheconcept,stillacceptedbymanyanimal modelerstoday,thatthesamecausewouldresultinthesameeffectinqualitativelysimilar systems.Thislineofthinkingwasinkeepingwiththecreationistthinkingof19thcentury Frenchphysiologists,includingBernard,whorejectedDarwin ’ sTheoryofEvolution [60,62,63].Thenotionofcausaldeterminismandtheprincipleofuniformitycombined withtherejectionofevolutionledtothebeliefintheinterchangeabilityofparts.Therefore,ifoneascertainedthefunctionofthepancreasinadog,hecoulddirectlyextrapolate thatknowledgetothefunctionofthepancreasinhumans,oncescalingforsizehadbeen factoredin[14,63,64].Unfortunately,thislinearthinkingpersistsasthebaboonheart transplanttoBabyFaeillustrates.Theoperationwasperformedbythecreationistsurgeon LeonardBaileyofLomaLindaUniversityin1984[[65]p162-3]. Weacknowledgethattheconceptof causation isproblematic[66].Russellsuggested itbeabandonedin1913[67]anditisclearlymoreusefulforlinearsystemsthan complexsystems.Whileanexhaustiveexplanationanddiscussionofthecontroversies surroundingcausationwouldoccupymore spacethanisavailableforthisarticle Table3ExampleofbinaryclassificationvaluesGoldstandard(human) GS+GSTestT+2226 T-2230 Sensitivity=22/(22+22)=0.5 Specificity=30/(26+30)=0.54 PositivePredictiveValue=22/(22+26)=0.46 NegativePredictiveValue=30/(22+30)=0.58Binaryclassificationvaluesforcardiovasculartoxicitytestinmonkeysfrom25compoundsalsotestedinhumans[ 48 ]. Notethevaluesareapproximatelywhatwouldbeexpectedfromacointoss.GreekandRice TheoreticalBiologyandMedicalModelling 2012, 9 :40Page5of33 http://www.tbiomed.com/content/9/1/40

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(seeBunge[61]forsuchananalysis)weshouldnotethatamorecurrentexplanationfor causationisthatofa “ firstorderapproximation. ” Causationisusuallydiscussedinthe contextofa chain ofcauses.Bungesummarizescurrentthinking: “ neodeterminism... assertsinthisconnectionthatcausationisonlyoneamongseveralinterrelatedcategories concurringinrealprocesses ” [61].Thisprincipleisappreciatedevenmorefullyin complexsystems.Currentthinkingnotwithstanding,theuseofanimalmodelsassumes theCartesianconceptofcausationinthatacausalmodelassumesadeterministiccausal relationshipbetweenvariables.Wewillexplorethisthinkingandshowthateveninthe traditionalcontextthereareproblemswithusinganimalmodelstodiscover “ causal ” relationships.Theseproblemsareincreasedexponentiallywhenplacedinthecontextof complexsystems. BasedonthewritingsofLaFolletteandShanks[[58]p63],wesuggestthefollowingin orderforamodeltobeconsideredaCAM. X (themodel)and Y (thesubjectbeing modeled)shareproperties{ a ... e }.InX,thesepropertiesareassociatedwith,and thoughtrelevantto,state S1 S1 hasnotbeenobserveddirectlyin Y ,but Y likelyalso haswouldexhibit S1 underthesameconditionsasX.Thisconceptisillustratedin Table4.LaFolletteandShanks[58]statethat, “ thereshouldbenocausally-relevant disanalogiesbetweenthemodelandthethingbeingmodeled. ” Unfortunately,causally relevantdisanalogiesdoexistamongspeciesandevenwithinaspecies,whichleadsto differentstatesoroutcomes,asillustratedinTable4.WeagainparaphraseLaFollette andShanks[[58]p112]andsuggestthattwomoreconditionsmustbemetforamodel toqualifyasaCAM:thesharedproperties{ a, ... ,e }musthaveacausalrelationshipwith state S1 andbetheonlycausallyrelevantpropertiesassociatedwith S1 .AsTable4 illustrates,thecommonalitiesbetweenthehumansandchimpanzeesareinsufficientto qualifychimpanzeesasCAMsforhumanresponsetoHIVinfection.(Formoreon Table4CausalanalogicalmodelsX,themodelY,thesystem beingmodeled Sharedproperties betweenXandY Perturbation tothemodel Outcome inmodel Outcomeinsystem beingmodeled Animalsystem (forexample, Pantroglodytes ) Humansystem a.Genes.>90%of nucleotidesequences identical. Exposure toHIV. State S1 Mildillness oflimited duration. AIDS.State S1 is not shareddespite thepresenceof shared,relevant properties. b.Immunesystem. Manycommonalities. Constructedon generallythesame plan. c.Whitebloodcells presentandfunction similarly. d.Receptorsonwhite bloodcellsalsopresent andfunctionsimilarly. e.Sharedintracellular componentsofwhite bloodcells.Sharedproperties a...e forhumansandchimpanzeedonotresultinstate S1 alsobeingshared.GreekandRice TheoreticalBiologyandMedicalModelling 2012, 9 :40Page6of33 http://www.tbiomed.com/content/9/1/40

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animalmodelsofHIV/AIDSsee[14,68].)Aswewillshow,animalsandhumansare evolvedcomplexsystemsandassuchexhibitthepropertiesofrobustnessandredundancy; hencenumerous “ causes ” canresultinthesameeffectandthesameperturbationcan resultindifferentoutcomes.Becauseofthisandotherpropertiesofcomplexsystems, weshouldexpectdifferentspeciestoexhibitdifferentcausalrelationships. Correspondingly,Giere,Bickle,andMauldin[69]notethatsomequestiontheuseof causalmodelsinthestudyofhumansbecausehumansarecomplexsystemswhereas casualmodelsassumeadeterministicsystem:anoutcomeinasimplesystemisfixedby thevariables.TheproblemsofdeterminingcausationarefurtherexploredbyBunge[61] inhisneodeterminismexplanationalludedtoaboveandhisanalysisishighlyrelevantto thisdiscussion.Whilewewillattempttocontrastthetraditionaldeterministicviewof causalityinlightofcomplexityscience,thisarticlewillnotdojusticethecurrentthinking oncausationandwereferthereadertoBunge[61]forafullerexplanation. Giere,Bickle,andMauldinsuggestaprobabilisticrelationshipinsteadofa100% causalrelationshipforthemodel: “ Cisapositivecausalfactor(probabilistic)forEin anindividual,I,characterizedbyresidualstate,S,ifinItheprobabilityofEgivenCis greaterthantheprobabilityofEgivenNot-C. ” Likewise,LaFolletteandShanksraise thequestionastowhetheranimalmodelscanbe weakCAMs : “ Beginwithtwosystems S1andS2.S1hascausalmechanisms{a,b,c,d,e},S2hasmechanisms{a,b,c,x,y}.When westimulatesub-system{a,b,c}ofS1withstimulisfresponserfregularlyoccurs.We canthereforeinferthatwerewetostimulatesub-systems{a,b,c}ofS2withsfrfwould probablyoccur ” [[58]p141].LaFolletteandShanksthenexplainthatthisoutcomewill behighlyprobableifandonlyif{a,b,c}arecausallyindependentof{d,e}and{x,y}. AgainweanticipateproblemsinusinganimalmodelsasweakCAMs,eveninthe traditionaldeterministic-causationview,because,asweshalldiscuss,variousproperties ofcomplexsystemswilllikelygiverisetodifficultiesinisolatingsubsystems,which wouldberequiredforananimalmodeltobeaweakCAM.Theseproblemshavebeen referredtoas causal/functionalasymmetry andmandatescautioninextrapolatingdata betweenspecies.KirschnerandGerhartgiveanexampleofthis: Thecaseoftheoctopusandthehumancameraeyehasbeenlookedinto,andthe lessonsareclear.Underneaththegrossanatomicalsimilaritiesaremanydifferences. Theeyederivesfromdifferenttissuesbydifferentdevelopmentalmeans.Although bothstructuresusethesamepigment(rhodopsin)forphotoreception,andbothsend electricalsignalstothebrain,wenowknowthattheinterveningcircuitryis completelydifferent[[1]p240-01]. Independentevolutionhasalsoproducedspindleneuronsinspeciesasdiverseas humansandcetaceans.Spindleneuronsconnectpartsofthebraininvolvedinhigher cognitionandwerethoughttoonlyoccurinprimatesbuthaverecentlybeendiscovered incetaceans,suchashumpbackwhalesandfinwhales,aswellaselephants[70-72]. Convergentevolution,theacquisitionofth esametraitindifferentlineages,isalso importantwhenconsideringtheroleofanimalmodels.EvolvedcomplexsystemsReductionismisamethodofstudythatseekstobreakasystemdownintoitscomponentparts,studyeachpartindividually,andthenreachaconclusionaboutthesystemGreekandRice TheoreticalBiologyandMedicalModelling 2012, 9 :40Page7of33 http://www.tbiomed.com/content/9/1/40

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asawholeoratleasttheroleoftheindividualpart.Descartesintroducedtheconcept andithasproveneffectiveforascertainingmanyfactsaboutthematerialuniverse. Conversely,theclockworkuniverseofDescarteshasnothelduptoscrutinyonall levels.Quantummechanics,relativity,chaos,andcomplexityhaverevealedthestochastic natureofthesupposedlyclockwork,determini sticuniverse.Regrettably,whilephysicists recognizedthelimitationsofreductionism,bio logistswereuncriticallyembracingit.Francis Crickextendedreductionismtoall aspectsofbiologywhenhestated: “ Theultimateaimof themodernmovementinbiologyistoexplaina llbiologyintermsofphysicsandchemistry ” [73].Biologicalreductionism arguablyreacheditszenithintheHumanGenomeProject (HGP)[74,75]and,ironically ,theconsequencesoftheHGP — thathumanshavearelatively smallnumberofgenes — have,inlargepart,beenresponsibleforare-examinationofthe roleofreductionisminbiology.Thishasbeenespeciallytrueforhumanpathophysiology whereanimalsareusedasmodelsforhumans. Systemscanbecategorizedassimpleorcomplex.TheworldofNewtonandDescartes waslargelyconfinedtosimplesystemshencereductionismfunctionedwellfordiscovery. Atsomelevels,thecomponentsofacomplexsystemcanbesimplesystemsandthusare subjecttostudybyreductionismwhileatotherlevelsthesesimplesystemscombineto makecomplexsystemsthusnecessitatin gstudyoftheintactwhole.Mazzocchi pointsoutthatwhenreductionismtakesacomponentoutofitsnaturalenvironment ithasconsequencesforextrapolatingth eresultsbacktotheorganismasawhole: “ Butthisextrapolationisatbestdebatablea ndatworstmisleadingorevenhazardous.The failureofmanypromisingdrugcandidatesinc linicalresearchshowsthatitisnotalways possibletotransferresultsfrommiceorevenprimatestohumans ” [76]. Whileevolutionisdefinedasachangeinallelefrequencyovertime,complexity sciencecanbedefinedas “ thestudyofthebehaviouroflargecollectionsofsimple, interactingunits,endowedwiththepotentialtoevolvewithtime ” [77,78].Living organismsarecomplexsystemsthathavehighlyvariableevolutionaryhistoriesandas sucharebestmodeledusingnonlineardifferentialequations.Thedifficultywiththis approachisthatthevaluesformanyofthefactorsareunknown;hencesolvingthe equationisimpossible[49,77]. Animalsandhumansareexamplesoflivingcomplexadaptivesystemsandassuch exhibitthefollowingproperties[79-97]: 1.Complexsystemsarecomposedofmanycomponents.Someofthesecomponents maybesimplesystems,butmanyarecomplexsystems.Thesecomponentsexistonmany scalesandinteractextensivelywitheachother.Acomplexsystemisa “ systemofsystems. ” 2.Thecomponentscanbegroupedasmodules.Forexample,thefollowingcouldbe consideredasmodules:thecell;thevariousprocessesinacell;genenetworks;genegeneinteractions;gene-proteininteractions;protein-proteininteractions;organs;and allthefactorsthatinfluencethenaturalhistoryofadisease.However,failureinone moduledoesnotnecessarilyspreaddemisetothesystemasawholeasredundancy androbustness(see#s5and6below)alsoexistandthevariousmodulesalso communicatewitheachother. 3.Thedifferentcomponentsofacomplexsystemarelinkedtoandaffectone anotherinasynergisticmanner.Thereispositiveandnegativefeedbackinacomplex system[ 93 ].GreekandRice TheoreticalBiologyandMedicalModelling 2012, 9 :40Page8of33 http://www.tbiomed.com/content/9/1/40

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4.Acomplexsystemdemonstrateshierarchallevelsoforganization[ 98 99 ].These levelsrangefromthesubatomictothemoleculartothewholeindividualtocollections ofindividuals[ 100 ].Emergence(see#13below)occursateachlevel;therefore,evena completeunderstandingofthelowerlevelisinsufficientforexplainingtheupperlevel. Thevariouslevelsinteractsuchthatthereisbothupwardcausationanddownward causation.Inordertounderstandaparticularlevel,onemustalternatebetween lookingatthecomponentsandlookingatthewholewhiletakingintoaccountthe connectionsbetweeneach[ 76 101 ].Moreover,thevariouslevelsmayrespond differentlytothesameperturbation. Thevariouslevelsoforganizationareimportantwhenconsideringwhichresponses tospecificperturbationscanbeextrapolatedamongspecies.Livingcomplexsystems havenumerouspropertiesthatcanbestudiedwithoutconsiderationofthefactthatthe whole,intactorganismisacomplexsystem.Somesystemsorcomponentsfollowonly thelawsofphysics,orevensimplegeometry,whileothersarebestdescribedbytheir physicochemicalpropertiesorjustbychemistry.Somepropertiesofcomplexsystems canbedescribedsimplybymathformulas.Growth,forexample,canbedescribedas geometricalinsomecasesandexponentialinothers.Thesurfaceareaofabody increasesbythesquareofthelineardimensionswhilethevolumeincreasesbythe cube.Thisisaconsequenceofgeometryandisimportantinphysiology,inpart, becauseheatlossisproportionaltosurfaceareawhileheatproductionisproportional tovolume.Haldanestated: “ Comparativeanatomyislargelythestoryofthestruggleto increasesurfaceinproportiontovolumes ” [102].Forexample,chewingincreasesthe surfaceareaoffood,theratethesmallbowelabsorbsnutrientsandotherchemicals dependsinpartonthesurfaceareaofthesmallbowel,andairsacsinthelungsrelyon surfaceareaforgasexchange,asdocapillaries. Allometryisthestudyoftherelationshipofbodysizetoshape.Examplesofallometric lawsincludeKleiber ’ slaw: q0~ Mwhere q0ismetabolicrateandisproportionalto M, bodymass,raisedtothepower.Therate t, ofbreathingandheartcontractionsare proportionalto M ,bodymass,raisedtopower: t ~ M.Further,manyphysiological functionsaffectordependonsurfacearea. Levelsoforganizationcanalsobedescribedbasedonwhethertheyareprimarily chemicalreactionsandhencesubjecttoanalysisbychemistry.Reactionsorperturbationsthatinvolvethedenaturationofproteinsshouldaffectallsystems,betheysimple orcomplex,similarlybecauseatthisleveloforganizationotherfactorsdonotcome intoplay.Exactlywhateffectssulfuricacidwouldhaveinapersonoveranextended periodoftimeareirrelevantasitdenaturesproteinmoreorlessimmediately.Perhaps speciesdifferenceswouldmanifestifsmallamountsofH2SO4wereinfusedoverlong periodsoftime,buttheimmediateeffectsarethesameacrossspeciesbecauseofthe chemicalpropertiesoftheacid. Animalscanbesuccessfullyusedfornumerouspurposesinscience(see3 – 9in Table1).Oneofthepurposesforwhichanimalscanbesuccessfullyusedistoevaluate phenomenathatcanbedescribedbythephysicochemicalpropertiesoftheorganism. Thesameappliestobasicphysiologicfunctions.Therearephysiologicalparameters thatcanbeappliedacrossspecieslinesbytheuseofconversionfactorsbasedonthe weightorsurfaceareaoftheorganism.TherearealsopropertiesoforganismsthatcanGreekandRice TheoreticalBiologyandMedicalModelling 2012, 9 :40Page9of33 http://www.tbiomed.com/content/9/1/40

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beanticipatedbythephysicalorchemicalpropertiesofthesubstanceactingonthe organism.Alloftheseareinstancesofsuccessfullytreatingacomplexsystemasifit wereasimplesystem.Problemsarisehowever,astheleveloforganizationunderstudy increases.Allometricscalingbasedonbodysurfacearea(BSA),forexample,doesnot includedifferencesthatmanifestathigherlevelsoforganizationforexampleinthe eliminationormetabolismofdrugs.Differentlevelsoforganizationcanbeactedonby singlefactorsormanyfactorsbutperturbationsofsimplesystems,orsystemsthatcan bedescribedassimpleonthelevelororganizationbeingaffected,shouldproduce similarresults. 5.Complexsystemsarerobust,meaningtheyhavethecapacitytoresistchange. Thiscanbeillustratedbythefactthatknockingoutageneinonestrainofmouse mayproducenegligibleeffectswhilebeinglethaltoanotherstrain.Genepleiotropyis anadditionalexample[ 103 ]. 6.Complexsystemsexhibitredundancy.Forexample,livingsystemsexhibit redundancyofsomegenesandproteins[ 103 ]. 7.Complexsystemsaredynamic.Theycommunicatewith,andareactedonby, theirenvironment. 8.Complexsystemsexhibitself-organization,whichallowsadaptationtothe environment[ 85 104 106 ].Theintactcellisaprimeexampleofthisproperty. 9.Complexsystemsaredependentoninitialconditions.Thewell-knownexampleof thebutterflyflappingitswingsandcausingaweathercatastropheontheothersideof theearth — thebutterflyeffect — isanexampleofdependenceoninitialconditions.An exampleinlivingcomplexsystemswouldbethatverysmalldifferencesingenetic makeupbetweentwosystemscouldresultindramaticdifferencesinresponsetothe sameperturbation.Forexample,monozygotictwinsraisedinthesameenvironment mayhavedifferentpredispositionstodiseasessuchasmultiplesclerosisand schizophrenia[ 107 110 ].Additionally,theabove-mentionedobservationthatknocking outageneresultsindifferentoutcomesintwostainsofmiceillustratestheconcept thatsmalldifferencesininitialconditions — geneticmakeup — canmeanthedifference betweenlifeanddeath[ 93 103 111 112 ]. 10.Theinitialconditionsofacomplexlivingsystemaredetermined,inpart,by evolution.Variousspecieshavedifferentevolutionaryhistoriesandthusaredifferently organizedcomplexsystems.Initialconditionscanbedifferent,despitetheexactsame genes,secondarytomodifiergenes,differencesinregulationorexpressionofgenes, epigenetics,andmutationsamongothersfactors.Forexample,smallepigenetic changesprobablyaccountforthedissimilaritiesbetweenmonozygotictwinsinterms ofdiseasesusceptibility[ 107 108 113 116 ]. 11.Perturbationstocomplexsystemsresultineffectsthatarenonlinear[ 99 ].Large disturbancesmayresultinnochangetot hesystemwhileminorperturbationsmay causehavoc[ 76 105 ].Effortstodescribecomplexsystemsintermsoflinearcause andeffectrelationshipsarepronetofailure[ 117 ].Extrapolating among complex systemsisevenmoreproblematicbecauseofnonlinearity,alongwiththeother factorsdescribed. 12.Thewholeofacomplexsystemisgreaterthanthesumoftheparts;hence,some processesandorperturbationsarenotamenabletostudybyreductionism.GreekandRice TheoreticalBiologyandMedicalModelling 2012, 9 :40Page10of33 http://www.tbiomed.com/content/9/1/40

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13.Complexsystemshaveemergentpropertiesthatcannotbepredictedeveninlight offullknowledgeofthecomponentparts. Animalmodelshavehistoricallybeenutilizedforthepredictionofhumanresponses todrugsanddiseaseandthisusehasalsobeenthejustificationforanimalusein researchingeneral[118,119].Butbecausevariouslevelsoforganizationanddifferent modulescanbeactedonbythesameperturbation,inordertoevaluatewhetheran animalmodelcanbeusedasapredictivemodality,oneneedstounderstandthelevels affectedbytheperturbation,whatrulesarebeingfollowedatthoselevels,andwhether thesystemissimpleorcomplexattherespectivelevels.Empiricalevidence,explained andplacedincontextbytheorydevelopedfromcomplexityscienceandevolutionary biology,suggestsanimalmodelscannotpredicthumanresponsestodrugsanddisease [14-16,18,57,58,120],despitethepresenceofsharedphysicochemicalpropertiesand conservedprocesses.ConservedprocessesTheodosiusDobzhanskyfamouslystated: “ Nothinginbiologymakessenseexceptin thelightofevolution. ” Wewanttoexaminetheconsequencesthatvariouscharacteristics ofevolvedcomplexsystems,suchasmodulesanddifferentlevelsoforganization,have onprocessesconservedbyevolutionintermsofdeterminingtheresponseofwhole organismstoperturbations.Conservedprocessesandgenesarethesubjectofmuch interesttoday[1,121-133].KirschnerandGerhardtstate: “ allorganismsareamixtureof conservedandnonconservedprocesses(saidotherwise,orchangingandunchanging processes) ” [[1]p34-35].Conserved processes arenotreactionstothelawsofphysicsor thedeterminationofpropertiesofanorganismastheyrelatetochemistryorgeometry. Nevertheless,conservationreachesacrossphylaandevenkingdoms.Kirschnerand Gerhardthavepointedoutthatprocessesconservedincludethoseinvolvedincellfunction andorganization,development,andmetabolismandthattheseprocessesaresimilarin animals,yeast,andbacteria.Theynotethatnoveltyhasbeentheresultofusingthe conservedprocessesindifferentwaysratherthaninventingcompletelynewprocesses [[1]p34-35].Thishascriticalimplicationsfo rwhatcanbelearnedfrominterspeciesstudy. Housekeepinggenesingeneralperformthesamefunction;makethesameproteins, inmice,frogsorhumans.TheroleofFOXtranscriptionfactorsisconservedamong species[134]asistheroleofSarco(endo)plasmicreticulum(SER)Ca2+ATPases (SERCA)pumps[135].Moduleshavealsobeenconserved.Thefinmoduleofthe modernfishforexample,aroseroughly400millionyearsagoandhasbeenconserved eversince[[1]p65]. Conservedprocessesincludecoregeneslikethoseinthehomeoboxthatareinvolvedin thesamedevelopmentalprocesses.Becausetheseprocessesandgenesareconserved amongspecies,wecouldreasonablyexpectthesameoutcomefromthesameperturbation, regardlessofthespeciescontainingtheseprocesses.Butisthisthecase?In1978Lewis [136]publishedhisseminalworkontheanterior-posteriorlayoutof Drosophila .Thiswas followedin1984bythediscoveryofthehomeo boxbyMcGinnisetal.[137].Thefieldof evodevodevelopedinlargepartfromthiswork.Inthelastdecade,enormousstrideshave beenmadeasaresultofresearchinevodevoandthevariousgenomeprojects.Theresults ofsuchresearchhaverevealedanenormousgeneticsimilarityamongmammals.AttheGreekandRice TheoreticalBiologyandMedicalModelling 2012, 9 :40Page11of33 http://www.tbiomed.com/content/9/1/40

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levelofthegenescentrallyinvolvedindevelo pment,e.g.,thehomeoboxgenes,bilaterians arevirtuallyidentical.Thehomeoboxclasso fgenes[138]areconservedacrossspecies lines,functioninginearlycellularorganizationandanterior-posteriorbodyplan layout[139].Thereareimportantdifferenc eshowever.Forexample,therearenine Hoxgenesinfliesbutthirty-nineinmammals.Pertinently,weunderstandhow modifications(geneduplicat ions,deletions,changesintheregulatoryprocessesand soforth)totheseconservedprocesseshav eresultedintheevolutionofdifferent bodytypesandindeeddifferentspecies[138,140-142]. MicroRNA(miRNA)hasbeenfoundinessentiallyallspeciesfrom Caenorhabditis elegans tohumansandplaysalargeroleingeneregulation[143-145].Apparently,over 50%ofmiRNAsareconservedacrossspecieslinesinvertebrates[145].Animportant considerationfordrugdevelopment,however,isthefactthateventhoughmiRNAis conserved,upto50%ofmiRNAsdiffersamongvertebrates.Thisisimportantwhen consideringtheuseofanimalsaspredictivehumanmodels.Furthermore,miRNA expressionlevelschangewhentissuesdeterioratefromahealthystatetoadiseased state[146-152].ThustheexactroleofmiRNAmaydifferintra-individuallydepending onageanddisease.Hence,weseebothinter-speciesandintra-individualdifferences withrespecttothisconservedprocess. Itiswellknownthathumansandnonhumanprimatesresponddifferentlytoinfections.Forexample,untreatedhumansusuallyprogresstoAIDSwheninfectedwith HIV,aresusceptibletomalaria(exceptthosewithsicklecellanemia),havedifferent reactionstohepatitisBandCthannonhumanprimatesand,appearmoresusceptible tomanycancersandAlzheimer ’ sdisease[153-155].Barreiroetal.[154]studiedgene expressionlevelsinmonocytesfromhumans,chimpanzees,andrhesusmacaquesand foundthatallthreespeciesdemonstrated “ theuniversalToll-likereceptorresponse ” whenstimulatedwithlipopolysaccharide(LPS).Howevertheyalsodiscoveredthatonly 58%ofgenesidentifiedintheToll-likereceptorresponse “ showedaconservedregulatoryresponsetostimulationwithLPS, ” andonly31%ofthosegenesdemonstratedthe sameconservedregulatoryresponsewhenexposedtovirusesorbacteria.Barreiroetal. alsodiscoveredthat335genesinhumansareuniqueamongthespeciesinresponding toLPS,with273genesrespondingonlyinchimpanzees,and393onlyinrhesusmacaques[154].Eveninconservedprocesses,therearegoingtobesignificantdifferences thatinfluencetheoutcomesfromdiseaseperturbations.Significantdifferencesinthe detailsofconservedprocesses(alsoillustratedbyFigure1[156])meanthatthereare differencesintheinitialconditionsofthecomplexsystemandthishasmajorimplicationsforinter-speciesextrapolation. Theimplicationsofthevariouspropertiesofcomplexsystemsalsobecomeapparent whenscientistsstudyprocessessuchaspreimplantationembryonicdevelopment (PED).PEDisthoughttobehighlyconservedamongspecieswhichledXieetal.[157] tostudygeneexpressionprofilesinembryosfromhumans,mice,andcows.They foundthat: “ 40.2%orthologousgenetripletsexhibiteddifferentexpressionpatterns amongthesespecies. ” Differencesinexpressionprofileshaveimplicationsfordrugand diseaseresponse. The Cdc14 genewasdiscoveredintheyeast Saccharomycescerevisiae andisclassified asadual-specificityphosphatase.Ithassincebeenfoundinmanyorganismsincluding humans.HumanCdc14Bfulfillstherole,inyeast,oftheyeastgene Cdc14 .BecausetheGreekandRice TheoreticalBiologyandMedicalModelling 2012, 9 :40Page12of33 http://www.tbiomed.com/content/9/1/40

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yeastgeneplaysaroleinregulatinglatemitosis,itwasassumedthegenewouldhave thesameroleinmammals.Inactuality,neither Cdc14A nor Cdc14B arenecessaryfor cell-cycleprogressioninhumans[158].Thus,wehaveaconservedgenebutnota conservedfunction. Pyrinproteinshavebeenfoundtobeubiquitousinmammals.Pyrin-onlyprotein2 (POP2)wasfoundinhumansandthoughttobeimportantininflammatorydiseases. Atianandetal.[159]studiedmicebutdidnotfindPOP2.Theythendiscoveredthat POP2wasnotinrodentsormanyothermammalsbutwaspresentinchimpanzees ( Pantroglodytes )andrhesusmacaques( Macacamulatta). Moreover,thechimpanzee Figure1 Variationinsialicacid(Sia)-recognizingIg-superfamilylectinsamongprimates. “ Expression ofCD33rSiglecsonhumanandgreatapelymphocytes.( A )Percentageofpositivelymphocytesforeach SiglecAb(stainingabovenegativecontrols)for16chimpanzees,5bonobos,and3gorillasareshown,as wellasdatafor8humans(thelatterweretestedononeormoreoccasions).Examplesofflowcytometry histogramsofhuman( B )andchimpanzee( C )lymphocytesusingAbsrecognizingSiglec-3,Siglec-5,Siglec-7, andSiglec-9(yaxis:normalizedcellnumbersexpressedaspercentofmaximumcellnumberdetected).Inlater samplesexamined,lowlevelsofSiglec-11staining(<5%positive)wereoccasionallydetectedonlymphocytes inbothgreatapesandhumans(datanotshown) ” [156]. GreekandRice TheoreticalBiologyandMedicalModelling 2012, 9 :40Page13of33 http://www.tbiomed.com/content/9/1/40

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POP2wasidenticaltohumansPOP2atboththeDNAandproteinlevelsbutthe macaquePOP2wasnot. Conservedprocessesact,areaffectedby,orinteractatmultiplelevelsoforganization. AsCairns-Smithpointsout,proteins,catalysts,nucleicacids,membranes,andlipids areinterlockedandallaredependentontheothersfortheirproduction.Cairns-Smith summarizesbystating: “ Subsystemsarehighlyinterlocked...Theinter-lockingistight andcritical.Atthecentreeverythingdependsoneverything ” [[81]p39].Thesameperturbationmayresultindifferenteffectsoroutcomesfordifferentlevelsoforganization inthesameintactsystem.Thisfurthercomplicatesourabilitytopredictoutcomes betweentwointactlivingcomplexsystems.Thusitappearsthataperturbationof complexsystemS1containingconservedprocessP1resultinginoutcomeO1willnot necessarilyresultinO1intheverysimilarcomplexsystemS2thatalsohasP1. Wewillnowexamineinmoredetailtherespons eoforganismstoinhalationalanesthetics andanti-neoplasticagentsinordertoillu stratewhatcanandcannotbeextrapolated betweenspeciesknowingthatspeciesareactedonandaffectedbythefundamental principlesofgeometry,chemistry,andphys icsaswellassharedconservedprocesses.ConservedprocessesinanesthesiaGeneralanesthesiabymeansofinhalationalane sthetics(IAs)providesuswithanexcellent opportunitytoexaminewheretheeffectsofc onservedprocessescanandcannotbeextrapolatedbetweenspecies.Weexpecttoseevariouseffectsatdifferentlevelsoforganization andindifferentmodules.Wealsoanticipateeffectsonemergentproperties.Because IAsactonthesystemasawhole,weexpecttoseeeffectsthatcannotbepredictedfrom reductionism.Thishasimplicationsforwhatcanbeexpectedintermsofpredictinghuman responsebystudyingadifferentspeciesorperhapsevenadifferentindividual.Therefore, boththeprimaryeffectoftheanestheticagentaswellasthesideeffectsmayvary. Broadlyspeaking,generalanesthesiainhumansandanimalsisdefinedbyamnesia, controlledinsensitivityandc onsciousness,andimmobility.Ithasbeenobservedthatmost, ifnotall,extantvertebratespeciesexhibita nanesthetic-likeresponsetoawidevarietyof chemicalsthatseeminglyhavelittleincommon.Thishasbeentermedthe universal response .Multiplemechanismsfortheuniversalresponsehavebeenpostulatedandthisis anareaofintensecurrentresearch[160-164 ].Thereseemstobegeneralagreementthat ligandgatedionchannel(LGIC)proteinreceptorsareinvolvedaswellaspossibleeffectson thecellularmembrane.Regardlessoftheexactdetails,theconservationofmechanismscan beseeninthatinhalationalanesthetics(IAs) haveobservableeffectsonmotorormotility responsesinvertebratesandinvertebrates[1 65-168],tactileplants[169]andciliatedprotists [170].(Wenotethatthisisprobablyanexampleofanexaptation,specificallyaspandrel, ratherthananadaptation[171-173].)Inter estingly,effectshaveevenbeenobservedin S. cerevisiae (Baker ’ syeast)[174],suggestingthatcrucialaspectsoftheuniversalresponsego beyondmetazoanstoincludeEukaryotes.M oreover,IAshavebeenshowntohaveeffects onmembranecompositioninprokaryotespecies[163,175]e.g., A.laidlawii [176,177], Bacillushalodurans [175]and E.coli [178]andthesingle-celle deukaryotetetrahymena [179,180](aciliatedprotozoan).Theuniversa lresponseappearstodatefarbackinevolutionarytimeandstronglysuggeststhatthem echanismhasbeenconservedamongspecies. However,therearedifferencesinoutcomeswithrespecttoIAs.Humphreyetal. [181]studiedgenesin Caenorhabditiselegans and Drosophilamelanogaster inordertoGreekandRice TheoreticalBiologyandMedicalModelling 2012, 9 :40Page14of33 http://www.tbiomed.com/content/9/1/40

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assessthefunctionofgenesthoughtinvolvedintheresponsetoIAs.Theyfoundthata genein Celegans unc-79 ,andagenein Drosophila narrowabdomen(na) ,were relatedtoeachotherandplayaconservedroleinresponsetoanesthetics.However, mutationsineachgeneproduceduniquechangesinsensitivitytoIAs.Thesensitivity tohalothane,anIA,wasincreasedbutthesensitivitytoenflurane,adifferentIA,was unchangedorperhapsevenlowered.Thisisperplexingbecauseonewouldhave expectedthetwoinhalationalagentstobeaffectedinasimilarfashionbythemutation. Thegene unc-79 appearstobeapost-transcriptionalregulatorof na ,thusthegenes operateinthesamepathway.Interestingly,bothgenesarealsoassociatedwithsimilar phenotypesregardinglocomotion: “ fainting ” in C.elegans and “ hesitantwalking ” in Drosophila Stimulationoftheconservedprocessescontrollingtheuniversalresponseresultsin clinicallysignificantvariabilityamonghumans,eventhoughtheminimumalveolar concentration(MAC)forIAsformostspeciesisapproximatelythesame.MACisthe mostoftenusedmetrictoassessIApotency.However,theconceptofMACimplies variability.MAC50,simplycalledMACinanesthesiology,istheminimumalveolar concentrationnecessarytosuppressmovementinresponsetopainfulstimuliin50%of subjects[182].MACissignificantlyvariableamonghumansdependingonanumberof factorsincludingageandsex.Whyisthisthecase? Sonneretal.reported, “ onehundredforty-sixstatisticallysignificantdifferences amongthe15strains[ofmice]werefoundforthethreeinhaledanesthetics(isoflurane, desflurane,andhalothane) ” [164].Theyconcludedthatmultiplegenesmustbe involvedinanestheticpotency.Wangetal .developedtwostrainsofmicethatmanifesteddifferentsensitivitiestoisoflurane[183].MACisanexampleofaphenomenon controlledbyquantitativetraitloci[184] ,whichmayexplaininpartwhy,whileone canobtainaroughapproximationofMACbystudyingotherspecies,therewillstillbe clinicallysignificantdifferences. IAsalsofunctionatdifferentlevelsoforganizationandonmodulesinadditiontothe oneinvolvedintheuniversalresponse.Thesideeffectsofthesamechemicalthat produceaneffectontheconservedreceptorsorotherprocessesvarygreatlyfrom speciestospeciesandinsomecases,evenfrompersontoperson.Agoodexampleis thecaseofisofluraneandcoronarysteal.Inthe1980s,therewasheatedcontroversy regardingtheadministrationoftheinhalationanestheticisofluranetopatientswith heartdisease.Thecontroversycenteredonresearchusingcaninesthatindicatedthat thedrugcausedmyocardialischemiaduringcertainsituationsinpatientswithcoronary disease.Thephenomenonappearedtoresultfromisofluranecausingdilationofthe normalcoronaryarteries,andthusbloodbeingshuntedawayfromtheoccludedcoronaryarteries;thearteriesandtissuesthatmostneededit.Thiswascalled coronarysteal Further,thissituationwasworsenedbyadecreaseinbloodpressure;aconditionthat oftenoccursduringgeneralanesthesiawithIAs.Thissupposeddanger,basedalmost entirelyonstudiesincanines,wasseizedonbymanyintheanesthesiologycommunity asdogma[185,186]. Thiswasaninterestingreactionfromcliniciansfortworeasons.First,experiments withotherspecieshadfailedtodemonstratecoronarysteal[187,188]andsecond, anesthesiologistshadnotnoticedischemicchangesassociatedwithisofluranedespite muchuseoftheagent.ThesituationwasalsotroublesomebecauseisofluranewasaGreekandRice TheoreticalBiologyandMedicalModelling 2012, 9 :40Page15of33 http://www.tbiomed.com/content/9/1/40

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neededadditiontoananesthesiologist ’ sarmamentariumwheninitiallyapprovedfor clinicalpractice.Sixyearsafteritsintroduction,itwasthemostfrequentlyusedIA,in partbecauseofthefavorablepropertiesofthedrug[185].Furtherstudiescontinuedto demonstratevaryingeffectsintra-andinter-species[189,190].Ultimately,studiesbegan toappearthatsuggestedisofluranewasinfactcardio-protective.Themechanismfor thisprotectionwascalled “ preconditioningandinvolvestheopeningofadenosine triphosphate-dependentpotassiumchannels ” [191].Isofluranewentfrombeingcontraindicatedinpatientswithcoronaryarterydiseasetobeingthedrugofchoiceinsuch patients.Studiesfromanimals,specificallydogs,figuredheavilyinformingboth,mutually exclusive,conclusions. Justaswiththehomeobox,miRNAs,andtheresponsetoinflammation,thereare differencesamongspeciesinhowtheconservedprocessknownastheuniversal responsetoanesthesiamanifests.Clinically,thesedifferencesaresignificantandlimit theamountofinformationthatcanbeextrapolatedbetweenspeciesevenwhenthe underlyingprocessisconserved.Inhalationanestheticsarealsoagoodexampleofwhy, whentheleveloforganizationormodulebeingexaminedchanges,extrapolationbreaks down.Thesamechemicalthatinducesgeneralanesthesiainadogwillprobablyresult inthesameeffectinhumansbutthedosemayvaryinaclinicallysignificantfashion andthesideeffectswillmostlikelyvary,astheconservedprocessdoesnotdictatethe sideeffects.Differencesinoutcomesfromperturbationsliketheoneswehaveseen abovehavebeenexplainedbyevolution-basedspecies-specificdifferences,forexample backgroundgenes,mutations,expressionlevels,andmodifiergenes[192-209].Anti-neoplasticdrugsactingonmitosisAsdiscussed,arelationshipexistsbetweenBSAandmanyphysiologicalparameters [210].Forexample,Reagan-Shaw,Nihal,andAhmadstate: “ BSAcorrelateswellacross severalmammalianspecieswithseveralparametersofbiology,includingoxygen utilization,caloricexpenditure,basalmetabolism,bloodvolume,circulatingplasma proteins,andrenalfunction ” [211].Dosingalgorithmsforfirst-in-man(FIM)trialsare basedontheassumptionthatthereisaone-to-onedosescalebetweenhumansand animalswhenBSAistakenintoaccount[212].Thefirststudysuggestingarelationship betweendoseandbodysurfaceareawasperformedbyPinkelin1958[210]involving anti-neoplasticagents,drugswheretheeffectsandsideeffectsarelargelythesame — celldeath.Subsequently,Freireichetal.,[213]studied18anti-neoplasticdrugsinsix animalspeciesandconcludedthatthemaximumtolerateddose(MTD)forhumans was1/12ofthedoseinmicethatresultedinthedeathof10%ofthemice(LD10).They alsonotedthattheMTDwas1/7oftheLD10inrats.Thesewerealsotheratiosfor convertingfromamg/kgdosetoadosebasedonBSA.Fiftyanti-neoplasticdrugswere thenstudiedusingthisformulaandallwerereportedlyintroducedintohumantrials withoutincident[214,215].ThestandardforFIMdosesthenbecamethe1/10ththe LD10formice.ActuallyFreireichrecommendedastartingdoseof1/3rdtheLD10not 1/10thbutthatchangedovertime.The1/3rdrecommendationwasfoundtobetoo largeforFIMandwaschangedto1/10th[216].Morestudiesappearedtoconfirmthe 1/10thvalue[217]. Theabovemakesa primafacie casethatanimalmodelscanpredictastartingdose forhumansinclinicaltrialsforanti-neoplastics.FurthersubstantiatingthisisthefactGreekandRice TheoreticalBiologyandMedicalModelling 2012, 9 :40Page16of33 http://www.tbiomed.com/content/9/1/40

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thatanti-neoplasticsarenotalwaysmetabolizedbytheliver[218],thuspossiblyeliminatingacomplexsystemfromconsideration.Celldivisionbymitosisisarguablythe mostconservedprocessonecanfindinbiologyandthetraditionaldrugsfortreating canceractbyinterferingwithmitosis.(Newerdrugsactontargetedpathwaysas opposedtothecellcycle.)Anti-neoplasticskillthecellsthataredividingmostrapidly — thecancercells.However,haircells,cellsinthebonemarrow,andcellsinthegut alsodivideatasimilarratesuchthatanti-neoplasticscanaffectthem.Thus,inpart, theeffectsandsideeffectsofanti-neoplasticsarethesame — celldeath.Theproblem withtraditionalanti-neoplasticsisthattheydonotdiscriminateadequately. Anti-neoplasticdrugsareuniqueinmedicineinthat:1)theyarenonspecific;2)long termtoxicitiesareanticipatedandacceptedbecausethepatientfrequentlydoesnot haveanyotherviableoptions;3)theeffectsandsideeffectsofthedrugsarethesame — celldeath;and4)theyactonauniversallyconservedprocess — mitosis.Thisiswhy bodysurfaceareaappearstobesoeffectiveforcalculatingFIMdose.Whereas,when oneisexaminingeffectsandsideeffectsofdrugsbasedoninteractionsatthelevelof organizationwherecomplexityisrelevant,forexamplemetabolism[219-229],thereare simplytoomanyotherfactorstoallowfortheexpectationofone-to-onecorrelations. Species-specificdifferencescreateperturbationsinthecomplexsystemthusthedifferencesamongspeciesoutweighthesimilarities[13-16,18,21-41]. However,inthefinalanalysiseventheFIMdoseoftheanti-neoplasticagentscannot bereliablyascertainedbasedonBSA.Mostanti-neoplasticsareeffectiveonlyatdoses nearthemaximumtolerateddoseandthedrugsaregiveninanescalatingfashion duringclinicaltrials. “ Patientstreatedatthelowerendofthedoseescalationstrategy areunlikelytoreceiveevenapotentiallytherapeuticdosesincemostcytotoxicdrugs areonlyactiveatorneartheMTD ” [217].Differencesamongspeciesindoseresponse foranti-neoplasticsaredueinparttodifferencesinpharmacokinetics[217,230-232], whichcannotbeaccountedforbasedonBSA.Brennanetal.statethat: “ Whileproper determinationofdrugdosescanbecomplicatedwithinthesamespecies,itcanbean incrediblechallengeandburdenbetweenspecies ” [233].Brennanetal.continueby pointingoutthatmetabolismandclearancedifferamongspeciesandthat “ ... theliver, kidneysandhematopoieticsystembetweenspeciesmayhavesignificantdifferencesin theirsensitivitytochemotherapeuticagents.Noneofthesefactorsaretakeninto accountwiththeuseofthespecies-specificdosecalculations ” [233].Theyrecommend areaunderthecurve(AUC)forcalculatingFIMdosebutthenconcede: “ However, therearenumerousexamplesinwhichthespecies-specificconversiondosevaries significantlyfromtheAUCguideddoseand/orfarexceedstheanimal ’ smaximum tolerateddose. ” Theythenlistexamplesfrompediatricswheretherecommendedand actualdosesdiffersignificantly[233]. Horstmannetal.[234]reviewed460PhaseINationalCancerInstitutetrialsinvolving 11,935adultsthatoccurredbetween1991and2002.Approximately25%ofthetrials wereFIMtrials.Horstmannetal.foundthatseriousnonfataleffectsoccurredin15% ofthepatientsundergoingsinglechemotherapy,with58deathsthatwereprobably treatment-related[234,235].Concernhasalsobeenexpressedthatanimalmodelshave derailedanti-neoplasticsthatwouldhavebeensuccessfulinhumans[30,235-239]. FIMdosebasedonanimalmodelsisineffectiveforpredictingdoseforotherdrug classesaswell-TGN1412beingarecentnotableexample[26,240,241].AnunnamedGreekandRice TheoreticalBiologyandMedicalModelling 2012, 9 :40Page17of33 http://www.tbiomed.com/content/9/1/40

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clinician,speakingoftoxicitytrialsfornewdrugsingeneralinhumans,wasquotedin Science, stating, “ Ifyouweretolookin[abigcompany ’ s]filesfortestingsmallmoleculedrugsyou ’ dfindhundredsofdeaths ” [242].Chapmanreinforcedthisstating: “ ...butotherincidentsofharm[besidesTGN1412],evendeath,toparticipantsin PhaseItrials,somethenknownandotherunpublicized,hadtakenplace ” [235].Itis alsoimportanttonotethatthe1/3rdor1/10thsafetyfactorisfabricated.Perlsteinetal. state: “ Duetouncertaintyintranslatinganimalmodelfindingstohumans,particularly forunprecedentedmechanisms,awidedoserange(1000-fold)isexpectedtocoverthe entireexposure – responsecurve ” [243].Extrapolatingfromspeciestospeciesshould notrequire fudgefactors iftheprocessistrulyscience-based.InPhaseItrials,where FIMorfirstinhuman(FIH)occurs,scientistswanttocharacterizethedrug ’ sPKpropertiesandsafetymargins[244].WexlerandBertelsensummarizethesituationwhen theystate: “ Althoughallometricscalingtechniquescontinuetoprovidepoorpredictive estimatesforhumanpharmacokineticparameters,FIHstartingdosesareselectedwith substantialsafetyfactorsappliedtohumanequivalentdose,ofteninexcessofregulatory guidelines....Approachesthatcouldenhancethepredictivenatureofacompound ’ s dispositionandadaptivenatureofFIHstudiescouldprovideatremendousbenefitfor drugdevelopment ” [245].FIMforallclassesofdrugcouldbeeasilyaccomplishedusing microdosing[246-248]withthefirstdoseofonenanogram[249,250]andincreasing subsequentdosestothedesiredendpoint. Finally,onemustrecallthat95%[31,251,252]ofa nti-neoplasticagentsfailinclinicaltrials. Oncologydrugsfailmorefrequentlyinclinical trialsthanmostothercategories[253,254] andahigherpercentageofanti-neoplasticdrugsfailinPhaseIIItrialsthandrugsfromany othercategory[255].Reasonsfortheattritionincludethefactthatmostoftheeffectsand sideeffects,evenoftheanti-neoplasticagents,whenplacedintothecontextofacomplex system,arenotpredictedfromanimalstud ies.Interferinginmitosisisauniversal phenomenonbutthedegreeandsuccessofthatinterferencevaries.TheFIMdoseestimationisapparentlysuccessfulbecausetheleve loforganizationinquestionisverybasicand conservedandbecausethedoseisloweredevenfurtherbyfudgefactors.Pickingastarting dosebasedonthemosttoxicsubstancesinnature[249,250]wouldbemorescientific.The apparentsuccessalsobreaksdownbecausethetypesofcancersinhumansdifferfromthose inanimals,thegeneticbackgroundofhumans variesfromthatinanimals,andbecausethe realityofacomplexsystem — theinteractionsofalltheothersystems(forexamplehowthe drugsareeventuallymetabolizedandeliminat edandhowthosemetabolitesinteractwith othersystemsandsoon) — eventuallyappear.Thesearetheproblemsthatcannotbesolved byanimalmodelsandarewhytheattritionrateis95%.Weinbergstated: “ it ’ sbeenwell knownformorethanadecade,maybetwodecades,thatmanyofthesepreclinicalhuman cancermodelshaveverylittlepredictivepowerintermsofhowactualhumanbeings — actual humantumorsinsidepatients — willrespond...preclinicalmodelsofhumancancer,inlarge part,stink...hundredsofmillionsofdollarsarebeingwastedeveryyearbydrugcompanies usingthese[animal]models ” [236].Othershavealsopointedouttheinadequacyofanimal modelsofcancer,includinggeneticallymodifiedanimalmodels[41,214,252,256-261].ConservedprocessesinlightofsystemsbiologyAstheleveloforganizationinacomplexsystemincreases,weexpecttoseeanincrease inthenumberofemergentpropertiesaswellasmoreoverallinteractions.AgeneorGreekandRice TheoreticalBiologyandMedicalModelling 2012, 9 :40Page18of33 http://www.tbiomed.com/content/9/1/40

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processthathasbeenconservedwillinteractwiththeintactwholeorganismyielding newprocessesandstates.Perturbationsoftheseconservedgenesorprocesseswillthus likelyresultinnewstatesnotseeninotherorganismsthatsharetheconserved processes;perhapsnoteveninorganismsofthesamelineage(clade)orspecies. Thelackofappreciationforthedifferencesbetweenlevelsoforganizationandother propertiesofcomplexsystemsisapparentinthefollowingfromKardong[[262]p2], writinginhistextbookofcomparativevertebrateanatomy: “ Forexample,bytestinga fewvertebratemuscles,wemaydemonstratethattheyproduceaforceof15N(newtons) persquarecentimeterofmusclefibercrosssection.Ratherthantestingallvertebrate muscles,atime-consumingprocess,weusuallyassumethatothermusclesofsimilarcross sectionproduceasimilarforce(otherthingsbe ingequal).Thediscoveryofforceproduction insomemusclesisextrapolatedtoothers.Inmedicine,thecomparativeeffectsofdrugson rabbitsormiceareextrapolatedtotentativeuseinhumans. ” Attheleveloforganization whereonestudiestheforcegeneratedbymuscle fibers,nodoubtinter-speciesextrapolation isuseful,butthatisanentirelydifferentlevelfromwheredrugactionsoccur. Indeedthesuccessesfromusinganimalmodelshavebeenexamplesofperturbations occurringatsubsystemsthatcanbedescribedassimplesystemsandoroutcomesor characteristicsthatapplyonthegrosslevelofexamination.Forexample,theGerm TheoryofDiseaseappliestohumansandanimals.Theimmunesystemreactsto foreignentitiesinamannerthatisgrosslysimilaracrossspecieslines.Thedetailsof immunityareclinicallyverydifferent,forexampleHIVinfectionleadstoAIDSin humansbutnotchimpanzees[263-265].Nevertheless,grossly,inflammation,white bloodcells,andantibodiesareidentifyingcharacteristicsoftheimmunesysteminthe phylumChordata.Likewise,whiletheheartfunctionstocirculatethebloodin mammals,thediseasesvariousmammalianheartsaresubjecttodifferconsiderably [266-272].Thefailuresofanimalmodelshaveoccurredwhenattemptingtoextrapolate datafromhigherlevelsoforganization,levelswherecomplexityisanimportantcomponentinthesystemorsubsystemunderconsideration.Forexample,adrugthathas passedanimaltestsandisinPhaseIhumanclinicaltrialshasonlyan8%chanceof makingittomarket[273].Over1,000drugshavebeenshowntoimproveoutcomesin cerebralischemiainanimalmodelsbutnone,saveaspirinandthrombolysis,which werenotanimal-baseddiscoveries,havebeensuccessfulinhumans[35,274-277].The animalmodelforpolio,monkeys,revealedapathophysiologythatwasverydifferent fromthatofhumans[278-281].Extracranial-intracranialbypassforinoperablecarotid arterydiseasewassuccessfulinanimalsbutresultsinnetharmforhumans[282-285]. Mostdiseasesaremultifactorialhenceitshouldcomeasnosurprisethatconserved processesplayasmall,althoughattimesimportantrole,inmajordiseaseslikeheart disease,cancerandstroke.Thefieldofsystemsbiologywasformedinpartinan attempttoplacethepartsofmolecularbiologyandgeneticsinthelargercontextofthe humansystem;thesystemthatactuallyrespondstodrugsanddisease.Aneditorialin Nature asks: “ Whatisthedifferencebetweenalivecatandadeadone?Onescientific answeris'systemsbiology'.Adeadcatisacollectionofitscomponentparts.Alivecat istheemergentbehaviourofthesystemincorporatingthoseparts ” [286].Accordingto theDepartmentofSystemsBiologyatHarvardMedicalSchool: “ Systemsbiologyisthe studyofsystemsofbiologicalcomponents,whichmaybemolecules,cells,organisms orentirespecies.Livingsystemsaredynamicandcomplex,andtheirbehaviormaybeGreekandRice TheoreticalBiologyandMedicalModelling 2012, 9 :40Page19of33 http://www.tbiomed.com/content/9/1/40

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hardtopredictfromthepropertiesofindividualparts ” [287].Systemsbiology[288] takesatop-downapproachasopposedtoreductionism,whichevaluatesorganisms fromthebottom-up.Systemsbiologyisconcernedmorewithnetworksthanindividual components,althoughbotharestudied.Italsorecognizestheimportanceofemergent phenomena.(SeeFigure2[79]).Suchtop-downapproachesareusedbythefieldscommonlyreferredtoas “ Omics, ” forexample:interactomics,metabolomics,proteomics, transcriptomics,andevenfractalomics[289]. NobellaureateSydneyBrenner,in1998,emphasizedthattheinteractionsofcomponents wasimportantinunderstandinganorgan ism[290].Onlybystudyingproteinsand processesinthecontextoftheirsystemscanweexpecttounderstandwhathappenstothe intactorganismsasaresultoftheseproces sesandgenes.Further,evolutionusesold pathwaysandprocessesindifferentwaystocr eatenovelty[1,133].Everythingiscontext dependent.Noblestressesthatinordertopr edicthowdrugswillact,onemustunderstand “ howaproteinbehavesincontext ” athigherlevelsoforganization[291]. Heng[292],writingin JAMA statesthat,becauseofreductionism,biologicalscientists havesoughtindividualcomponentsinadiseaseprocesssotheycouldintervene.Alinear causeandeffectrelationshipwasassumedtoexist.Hengcitesdiabetesinterventioninan attempttocontrolbloodglucoseandcancertherapiesasexamples.Hepointsoutthat whilethishasworkedwellinmanycases,verytightcontrolofbloodglucosewasrecently foundtoincreasetheriskofdeath[293].Alongthesamelines,chemotherapiesforcancer havedecreasedthesizeofthetumorsbutattheexpenseofanincreaseinfrequencyof secondarytumorsandaveryadverselyaffectedlifestyle.Furthermore,mostchemotherapy doesnotprolonglifeorresultinalonger,highqualitylife[294-296].Insteadoffocusingon smallmodulesorcomponentsofasystem,co mplexitytheorymandatesthatbiomedical sciencelookatthesystemasawhole. Closelyrelatedtosystemsbiologyaretheconceptsofpersonalizedmedicineand pharmacogenomics[226,297-305].Ithaslongbeenappreciatedthathumansrespond differentlytodrugsandhavedifferentsusceptibilitiestodisease.Basedonstudiesof Figure2 Reductionismversussystemsbiology. GreekandRice TheoreticalBiologyandMedicalModelling 2012, 9 :40Page20of33 http://www.tbiomed.com/content/9/1/40

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twins,thereappearstobeageneticcomponenttosusceptibilitytoleprosy,poliomyelitis andhepatitisB,aswellasresponsetoopioids[306-309].Otherinfectiousdiseasesthat appeartohaveageneticcomponenttosusceptibilityincludeHIV,HepatitisC,malaria, dengue,meningococcaldisease,variantCreutzfeldt – Jakobdiseaseandperhapstuberculosis amongothers[310].Differencesindruganddiseaseresponsearemanifestamongethnic groups[311-319]andsexes[320-326].Evenmonozygotictwinsmanifestdifferencesin responsetosuchperturbations[107,108,113 -116].RashmiRShah,previousSeniorClinical Assessor,MedicinesandHealthcareproductsRegulatoryAgency,Londonstatedin2005: “ Duringtheclinicaluseofadrugatpresent,aprescribingphysicianhasnomeansof predictingtheresponseofanindividualpatienttoagivendrug.Invariably,somepatients failtorespondbeneficiallyasexpectedwhereasothersexperienceadversedrugreactions (ADRs) ” [327]. Similarly,AllenRoses,then-worldwidevice-presidentofgeneticsatGlaxoSmithKline (GSK),saidfewerthanhalfofthepatientsprescribedsomeofthemostexpensivedrugs derivedanybenefitfromthem: “ Thevastmajorityofdrugs-morethan90%-only workin30or50%ofthepeople. ” Mostdrugshadanefficacyrateof50%orlower [328].Becauseofdifferencesingenes,likeSNPs,allchildrenmaynotcurrentlybe protectedbythesamevaccine[329,330].Itisestimatedthat “ between5and20per centofpeoplevaccinatedagainsthepatitisB,andbetween2and10percentofthose vaccinatedagainstmeasles,willnotbeprotectediftheyeverencountertheseviruses ” [330].Inthefuturesuchchildrenmaybeabletoreceiveapersonalizedshot.Currently, numerousdrugshavebeenlinkedtogeneticmutationsandalleles.SeeTable5[303] andTable6[331].Thenumberofpersonalizedmedicineproductshasincreasedfrom 13in2006to72asof2012[332]. Whenanimalswerebeingusedasmodelsinthe19thcentury,manyofthescientists whowereusingthemhadnotacceptedevolutionandbelievedthatanimalpartswere interchangeablewiththeirhumancounterparts[60,62,63].Giventhatwenowunderstandthatintra-humanvariationresultsinsuchmarkedlydifferentresponsestodrugs anddisease,attemptingtopredicthumanresponsefromanimalmodels,evenfor perturbationsactingonconservedprocesses,seemsunwarranted.Yet,despitethe implicationsofpersonalizedmedicine[22],somescientistscontinuetocommitthe fallacydescribedbyBurggrenandBemis: “ Yettheuseof ‘ cockroachasinsect, ’‘ frogas amphibian, ’ or ‘ theturtleasreptile ’ persists,inspiteofclearevidenceofthedangersof thisapproach.Notsurprisingly,thistypeofcomparativephysiologyhasneither contributedmuchtoevolutionarytheoriesnordrawnuponthemtoformulateandtest hypothesesinevolutionaryphysiology ” [[333]p206].Comparativeresearchwillyielda nicecomparisonofthetraitorprocessamongspeciesorphyla.However,onesimply cannotassumethattheoutcomefromaspecificperturbationin,saythecockroach,will beseenininsectsingeneralandthisconceptbecomesevenmoreimportantwhen relyingonanimalmodelsformedicalinterventionsinhumans.ConclusionAperturbationoflivingcomplexsystemS1containingconservedprocessP1resulting inoutcomeO1willnotresultinO1intheverysimilarlivingcomplexsystemS2that alsohasP1oftenenoughtoqualifyS1asapredictivemodalityforS2whenthetraitor responsebeingstudiedislocatedathigherlevelsoforganization,isinadifferentGreekandRice TheoreticalBiologyandMedicalModelling 2012, 9 :40Page21of33 http://www.tbiomed.com/content/9/1/40

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Table5ExamplesofdrugswithgeneticinformationinthierlabelsDrugSponsorIndicationGeneor genotype Effectof genotype Clinicaldirectiveon label Abacavir (Ziagen) GlaxoSmithKlineHIV-1 HLA-B*5701 HypersensitivityBlack-boxwarning. ” Priortoinitiating therapywithabacavir, screeningforthe HLAB*5701 alleleis recommended. ”“ Your doctorcandetermine withabloodtestif youhavethisgene variation. ” Azathioprine (Imuran) PrometheusRenalallograft transplantation, rheumatoid TPT*2TPT*3A and TPMT*3C Severe myeloxicity “ TPT genotypingor phenotypingcanhelp identifypatientswho areatanincreased riskfordeveloping Imurantoxicity. ” “ Phenotypingand genotypingmethods arecommercially available. ” Carbamazepine (Tegretol) NovartisEpilepsy, trigeminal neuralgia HLA-B*1502 StevensJohnson syndromeor toxicepidermal necrolysis Black-boxwarning: “ Patientswith ancestryingenetically at-riskpopulations shouldbescreened forthepresenceof HLA-B*1502 priorto initiatingtreatment withTegretol.Patients testingpositivefor thealleleshouldnot betreatedwith Tegretol. ”“ For geneticallyat-risk patients,highresolution HLA-B*1502 typingis recommended. ” Cetuximab (Erbitux) ImcloneMetastatic colorectal cancer KRAS mutationsEfficacy “ Retrospectivesubset analysesofmetastatic oradvanced colerectalcancertrials havenotshowna treatmentbenefitfor Erbituxinpatients whosetumorshad KRASmutationsin codon12or13.Use ofErbituxisnot recommendedforthe treatmentof colorectalcancerwith mutations. ” Clopidogrel (Plavix) Bristol-Myer Squibb AnticoagulationCYP2C19*2*3Efficacy “ Testsareavailableto identifyapatient ’ s CYP2C19genotype;thesetestscanbe usedasanaidin determining therapeuticstrategy. Consideralternative treatmentor treatmentstrategies inpatienrsidentified GreekandRice TheoreticalBiologyandMedicalModelling 2012, 9 :40Page22of33 http://www.tbiomed.com/content/9/1/40

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module,orisinfluencedbyothermodules.However,whentheexaminationofthe conservedprocessoccursatthesameorlowerleveloforganizationorinthesame module,andhenceissubjecttostudysolelybyreductionism,thenextrapolationis possible.Webelievethisisavaluableprinciple. Ourcurrentunderstandingofevodevo,evolutioningeneral,complexityscience,and geneticsallowsustogeneralizeregardingtrans-speciesextrapolation,evenwhen conservedprocessesareinvolved.ShanksandGreek: Table5Examplesofdrugswithgeneticinformationinthierlabels (Continued)asCYP2C19poor metabolizer. ” Irinotecan (Camptosar) PfizerMetastatic colorectal cancer UGT1A1*28 Diarrhea neutropenia “ Areductioninthe startingdosebyat leastonelevelof Camptosarshouldbe considerforpatients knowstobe homozygousforthe UGT1A1*28allele. “ A laboratorytestis availabletodetermine theUGT1A1statusof patients. ” Pantumumab (Vectibix) AmgenMetastatic colorectal cancer KRAS mutationsEfficacy “ Retrospectivesubset analysesofmetastatic colorectalcancertrials havenotshowna treatmentbenefitfor Vectibixinpatients whosetumorshad KRASmutationsin codon12or13.Use ofVectibixisnot recommendedforthe treatmentof colorectalcancerwith thesemutations. ” Transtuzumab (Herceptin) GenetechHER2-positive breastcancer HER2expressionEfficacy “ DetectionofHER2 protein overexpressionis necessaryfor selectionofpatients appropriatefor Herceptintherapy becausethesearethe onlypatientsstudied andforwhombenefit hasshown. ”“ Several FDA-approved commercialassaysare availabletoaidinthe selectionofbreast cancerandmetastatic cancerpatientsfor Herptintherapy. ” Wafarin (Coumadin) Bristol-Myer Squibb Venous thrombosis CYP2C9*2*3and VKORC1variants Bleeding complications Includesthefollowing table:Rangeof ExpectedTherapeutic WarfarinDosesBased onCYP2CPand VKORC1Genotypes.*AlldruglabelswereaccessedthroughDrugs@FDAatwww.accessdata.fda.gov/scripts/cder/drugsatfda.HIV-1denotes humanimmunodeficiencyvirustype1, TPMT thiopurinemethyltransferase, UGT1A1 UDPglucuronosyltransferanse1 familypolypeptideA1,and VKORC1 vitaminsKepoxidereductasecomplexsubunit1GreekandRice TheoreticalBiologyandMedicalModelling 2012, 9 :40Page23of33 http://www.tbiomed.com/content/9/1/40

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Livingcomplexsystemsbelongingtodifferentspecies,largelyasaresultofthe operationofevolutionarymechanismsoverlongperiodsoftime,manifestdifferent responsestothesamestimulidueto:(1)differenceswithrespecttogenes present;(2)differenceswithrespectt omutationsinthesamegene(whereone specieshasanorthologofagenefoundinanother);(3)differenceswith respecttoproteinsandproteinactivity;(4)differenceswithrespecttogene regulation;(5)differencesingeneexpres sion;(6)differencesinprotein-protein interactions;(7)differencesingeneticn etworks;(8)differenceswithrespectto organismalorganization(humansandratsmaybeintactsystems,butmaybe differentlyintact);(9)differencesinen vironmentalexposures;andlastbutnot least;(10)differenceswithrespecttoevo lutionaryhistories.Thesearesomeof theimportantreasonswhymembersofone speciesoftenresponddifferentlyto Table6ThemostsignificantgeneticpredictorsofdrugresponseOrganorsysteminvolvedAssociatedgene/alleleDrug/drugresponsephenotype Blood Redbloodcells G6PD Primaquineandothers Neutrophils TMPT*2 Azathioprine/6MP-inducedneutropenia UGT1A1*28 Irintotecan-inducedneutropenia Plates CYP2C19*2 Stentthrombusis Coagulation CY2C9*2,*3,VKORC1 Warfarindose-requirement Brainandperipheralnervoussystem CNSdepressionCYP2D6*NCodeine-relatedsedationandrespiratorydepression Anaesthesia Butyrylcholinesterase Prolongedapnoea Peripheralnerves NAT-2 Isoniazid-inducedperipheralneuropathy Drughypersesitivity HLA-B*5701 Abacavirhypersensitivity HLA-B*1502 Carbamazepine-inducedSteveJohnsonsyndrome (insomeAsiangroups) HLA-A*3101 Carbamazepine-inducedhypersensitivityinCausians andJapanese HLA-B*5801 Allopurinol-inducedseriouscutaneousreactions Drug-inducedliverinjury HLA-B*5701 Flucloxacillin HLA-DR81*1501-DQ81*0602 Co-amoxiclav HLA-DR81*1501-DQ81*0602 Lumiracoxib HLA-BR81*07-DOA1*02 Ximelagatran HLA-DQA1*0201 Lapatinib Infection HIV-1infection CCRS Maravirocefficacy HepatitisCinfection IL288 Interferon-alphaefficacy Malignancy Breastcancer CYP2D^ Responsetotamoxifen Chronicmyeloidleukaemia BCR-ABL Imatinibandothertyrosinekinaseinhibitors Coloncancer KRAS Cetuximabefficacy GIstromaltumours c-kit Imatinibefficacy Lungcancer EGFR Gefinibefficacy EML4-ALK Crizotinibefficacy Malignantmelanoma BRAFV600E Vemurafenibefficacy GreekandRice TheoreticalBiologyandMedicalModelling 2012, 9 :40Page24of33 http://www.tbiomed.com/content/9/1/40

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drugsandtoxins,andexperiencedifferen tdiseases.Immenseempiricalevidence supportsthisposition([14]p358). Thefailuresofanimalmodelsasapredictivemodalityforhumanresponsetodisease anddrugs,evenwhensuchperturbationsareactingonconservedprocesses,canbe explainedinthecontextofevolvedcomplexsystems.Onedoesnotneedtostudyevery suchperturbationineveryspeciesinordertoconcludethattheanimalmodelwillnot beapredictivemodalityforhumanswhenperturbationsoccurathigherlevelsof organizationorinvolvedifferentmodulesoraffectthesystemasawhole.Thisisnotto denythatanimalmodels,ascharacterizedby3 – 9inTable1,havecontributedandwill continuetocontributetoscientificadvancements.Competinginterests Theauthorsdeclarethattheyhavenocompetinginterests. Authors'contributions Theauthorscontributedequallytothispaper. Authors ’ information RayGreek,MDhasbeenonfacultyintheDepartmentofAnesthesiologyattheUniversityofWisconsin-Madisonand atThomasJeffersonUniversityinPhiladelphia.Heiscurrentlypresidentofthenot-for-profitAmericansForMedical Advancement(www.AFMA-curedisease.org). MarkRice,MDiscurrentlyonfacultyattheUniversityofFlorida(UF).HeischiefofthelivertransplantdivisionatUF DepartmentofAnesthesiology,hassevenUSpatents,andreviewsforseveralmajorjournals. Acknowledgements None. Authordetails1AmericansForMedicalAdvancement(www.AFMA-cured isease.org),2251RefugioRd,Goleta,CA93117,USA.2Departmentof Anesthesiology,UniversityofFloridaCollegeofMedicine,POBox100254,Gainesville,FL32610-0254,USA. Received:30July2012Accepted:31August2012Published:10September2012 References1.KirschnerMW,GerhartJC: ThePlausibilityofLife .NewHaven:YaleUniversityPress;2006. 2.BraithwaiteRB: Scientificexplanation:astudyofthefunctionoftheory,probabilityandlawinscience .Cambridge: CambridgeUniversityPress;1953. 3.HindeR: Animal-HumanComparisons .In TheOxfordCompaniontotheMind .EditedbyGregoryRL.Oxford: OxfordUniversityPress;1987:25 – 27. 4.FriggR,HartmannS: ScientificModels .In ThePhilosophyofScience:AnEncyclopediaVolume2N-Z .Editedby SarkarS,PfeiferJ.NewYork:Routledge;2012:740 – 749. 5.ShapiroK: AnimalModelResearch.TheApplesandOrangesQuandry. ATLA 2004, 32: 405 – 409. 6.HauJ:In AnimalModels ,HandbookofLaboratoryAnimalScienceSecondEditionAnimalModels,VolumeII.2nd edition.EditedbyHauJ,vanHoosierGKJr.BocaRotan:CRCPress;2003:1 – 9. 7.GadS: Preface .In AnimalModelsinToxicology .EditedbyGadS.BocaRotan:CRCPress;2007:1 – 18. 8. LongerTestsonLabAnimalsUrgedforPotentialCarcinogens .http://www.cspinet.org/new/200811172.html. 9.HuffJ,JacobsonMF,DavisDL: Thelimitsoftwo-yearbioassayexposureregimensforidentifyingchemical carcinogens. EnvironHealthPerspect 2008, 116: 1439 – 1442. 10.DevoyA,Bunton-StasyshynRKA,TybulewiczVLJ,SmithAJH,FisherEMC: Genomicallyhumanizedmice: technologiesandpromises. NatRevGenet 2012, 13: 14 – 20. 11.VassarR: Alzheimer'stherapy:aBACEinthehand? NatMed 2011, 17: 932 – 933. 12. THSCEOcriticizedforlinkstoanimaltesting .http://m.torontosun.com/2011/09/23/ths-ceo-criticized-for-links-toanimal-testing?noimage. 13.HeywoodR: ClinicalToxicity--Couldithavebeenpredicted?Post-marketingexperience .In AnimalToxicity Studies:TheirRelevanceforMan .EditedbyLumleyCE,WalkerS.Lancaster:Quay;1990:57 – 67. 14.ShanksN,GreekR: AnimalModelsinLightofEvolution .BocaRaton:BrownWalker;2009. 15.GreekR,GreekJ: Istheuseofsentientanimalsinbasicresearchjustifiable? PhilosEthicsHumanitMed 2010, 5: 14. 16.GreekR,ShanksN: FAQsAbouttheUseofAnimalsinScience:Ahandbookforthescientificallyperplexed .Lanham: UniversityPressofAmerica;2009. 17.ShanksN,GreekR: Experimentaluseofnonhumanprimatesisnotasimpleproblem. NatMed2008, 14: 807 – 808. 18.ShanksN,GreekR,GreekJ: Areanimalmodelspredictiveforhumans? PhilosEthicsHumanitMed 2009, 4: 2. 19.ShanksN,GreekR,NobisN,GreekJ: AnimalsandMedicine:DoAnimalExperimentsPredictHumanResponse? Skeptic 2007, 13: 44 – 51. 20.GreekR: Letter.Dogs,GenesandDrugs. AmSci 2008, 96: 4.GreekandRice TheoreticalBiologyandMedicalModelling 2012, 9 :40Page25of33 http://www.tbiomed.com/content/9/1/40

PAGE 26

21.GreekR,HansenLA,MenacheA: AnanalysisoftheBatesonReviewofresearchusingnonhumanprimates. MedicolegalBioethics 2011, 1: 3 – 22. 22.GreekR,MenacheA,RiceMJ: Animalmodelsinanageofpersonalizedmedicine. PersonalizedMed 2012, 9: 47 – 64. 23.GreekR,ShanksN,RiceMJ: TheHistoryandImplicationsofTestingThalidomideonAnimals. TheJournalof Philosophy,Science&Law 2011, 11 .http://www6.miami.edu/ethics/jpsl/archives/all/TestingThalidomide.html. 24.CollinsFS: ReengineeringTranslationalScience:TheTimeIsRight. SciTranslMed 2011, 3: 90cm17. 25.CookN,JodrellDI,TuvesonDA: Predictiveinvivoanimalmodelsandtranslationtoclinicaltrials. DrugDiscov Today 2012, 17: 253 – 260. 26.DixitR,BoelsterliU: Healthyanimalsandanimalmodelsofhumandisease(s)insafetyassessmentofhuman pharmaceuticals,includingtherapeuticantibodies. DrugDiscovToday 2007, 12: 336 – 342. 27.DrakeDRIII,SinghI,NguyenMN,KachurinA,WittmanV,ParkhillR,KachurinaO,MoserJM,BurdinN,MoreauM, etal : InVitroBiomimeticModeloftheHumanImmuneSystemforPredictiveVaccineAssessments. Disruptive SciTechnol 2012, 1: 28 – 40. 28. FDAIssuesAdvicetoMakeEarliestStagesOfClinicalDrugDevelopmentMoreEfficient .http://www.fda.gov/ NewsEvents/Newsroom/PressAnnouncements/2006/ucm108576.htm. 29.FletcherAP: Drugsafetytestsandsubsequentclinicalexperience. JRSocMed 1978, 71: 693 – 696. 30.HorrobinDF: Modernbiomedicalresearch:aninternallyself-consistentuniversewithlittlecontactwith medicalreality? NatRevDrugDiscov 2003, 2: 151 – 154. 31.KolaI,LandisJ: Canthepharmaceuticalindustryreduceattritionrates? NatRevDrugDiscov 2004, 3: 711 – 715. 32.LumleyC: Clinicaltoxicity:couldithavebeenpredicted?Premarketingexperience .In AnimalToxicityStudies: TheirRelevanceforMan .EditedbyLumleyC,WalkerS.:Quay;1990:49 – 56. 33.M.E: ThisIssue .In Modelsthatbettermimichumancancer ,NatBiotechnol,Volume28.;2010:vii. 34.MarkouA,ChiamuleraC,GeyerMA,TricklebankM,StecklerT:Removingobstaclesinneurosciencedrug discovery:thefuturepathforanimalmodels. NeuropsychopharmacolOfficPublAmCollNeuropsychopharmacol 2009, 34: 74 – 89. 35.O'CollinsVE,MacleodMR,DonnanGA,HorkyLL,vanderWorpBH,HowellsDW: 1,026experimentaltreatments inacutestroke. AnnNeurol 2006, 59: 467 – 477. 36.SharpPA,LangerR: PromotingConvergenceinBiomedicalScience. Science 2011, 333: 527. 37.SietsemaWK: Theabsoluteoralbioavailabilityofselecteddrugs. IntJClinPharmacolTherToxicol 1989, 27: 179 – 211. 38.SuterK: Whatcanbelearnedfromcasestudies?Thecompanyapproach .In AnimalToxicityStudies:Their RelevanceforMan .EditedbyLumleyC,WalkerS.Lancaster:Quay;1990:71 – 78. 39.WallRJ,ShaniM: Areanimalmodelsasgoodaswethink? Theriogenology 2008, 69: 2 – 9. 40.WeaverJL,StatenD,SwannJ,ArmstrongG,BatesM,HastingsKL: Detectionofsystemichypersensitivityto drugsusingstandardguineapigassays. Toxicology 2003, 193: 203 – 217. 41.ZielinskaE: Buildingabettermouse. Scientist 2010, 24: 34 – 38. 42.RingachDL: Theuseofnonhumananimalsinbiomedicalresearch. AmJMedSci 2011, 342: 305 – 313. 43.RudczynskiAB: Lettertotheeditor.NewHavenRegister ;2011.Availableathttp://www.nhregister.com/articles/2011/ 03/25/opinion/doc4d8bb9186a82b265857273.txt. 44.FomchenkoEI,HollandEC: Mousemodelsofbraintumorsandtheirapplicationsinpreclinicaltrials. ClinCancerRes 2006, 12: 5288 – 5297. 45.LitchfieldJTJr: PredictabilityofConventionalAnimalToxicityTests. AnnNYAcadSci 1965, 123: 268 – 272. 46.LasagnaL: Regulatoryagencies,drugs,andthepregnantpatient .In Druguseinpregnancy .EditedbySternL. Sydney:ADISHealth.SciencePress;1984. 47.LinJH: Speciessimilaritiesanddifferencesinpharmacokinetics. DrugMetabDispos1995, 23: 1008 – 1021. 48.DixonRL: Toxicologyofenvironmentalagents:ablendofappliedandbasicresearch. EnvironHealthPerspect 1972, 2: 103 – 116. 49.ZhangS,WangY-M,SunC-D,LuY,WuL-Q: ClinicalvalueofserumCA19-9levelsinevaluatingresectabilityof pancreaticcarcinoma. WorldJGastroenterol 2008, 14: 3750 – 3753. 50.SassonC,HeggAJ,MacyM,ParkA,KellermannA,McNallyB: PrehospitalTerminationofResuscitationinCases ofRefractoryOut-of-HospitalCardiacArrest. JAMA 2008, 300: 1432 – 1438. 51.SalekinRT,RogersR,UstadKL,SewellKW: Psychopathyandrecidivismamongfemaleinmates. LawHumBehav 1998, 22: 109 – 128. 52.MayanjaBN,BaisleyK,NalweyisoN,KibengoFM,MugishaJO,PaalLV,MaherD,KaleebuP: Usingverbalautopsy toassesstheprevalenceofHIVinfectionamongdeathsintheARTperiodinruralUganda:aprospective cohortstudy,2006 – 2008. PopulationHealthMetrics 2011, 9: 36.doi:10.1186/1478-7954-9-36. 53.SantosG,SouzaA,VirtuosoJ,TavaresG,MazoG: Predictivevaluesatriskoffallinginphysicallyactiveandno activeelderlywithBergBalanceScale. RevBrasFisioter 2011, 15: 95 – 101. 54.CommitteeonModelsforBiomedicalResearchBoardonBasicBiology: CommitteeonModelsforBiomedical Research.BoardonBasicBiology.CommissiononLifeScience.NationalResearchCouncil.ModelsforBiomedical Research:ANewPerspective .Washington,DC:NationalAcademyPress;1985. 55.TkacsNC,ThompsonHJ: Frombedsidetobenchandbackagain:researchissuesinanimalmodelsofhuman disease. BiolResNurs 2006, 8: 78 – 88. 56.OvermierJB,CarrollME: BasicIssuesintheUseofAnimalsinHealthResearch .In AnimalResearchandHuman Health .EditedbyCarrollME,OvermierJB.WashingtonDC:AmericanPsychologicalAssociation;2001:5. 57.LaFolletteH,ShanksN: TwoModelsofModelsinBiomedicalResearch. PhilQ 1995, 45: 141 – 160. 58.LaFolletteH,ShanksN: BruteScience:Dilemmasofanimalexperimentation .LondonandNewYork:Routledge;1996. 59.SchaffnerKF: Theories,Models,andEquationsinSystemsBiology .In SystemsBiology:PhilosophicalFoundations EditedbyBoogerdF,BruggemanFJ,HofmeyrJ-HS,WesterhoffHV.Netherlands:Elsevier;2007:145 – 162. 60.BernardC: AnIntroductiontotheStudyofExperimentalMedicine .NewYork:Dover;1957. 61.BungeM: CausalityAndModernScience.3rdedition.NewYork:Dover;1979.GreekandRice TheoreticalBiologyandMedicalModelling 2012, 9 :40Page26of33 http://www.tbiomed.com/content/9/1/40

PAGE 27

62.ElliotP: VivisectionandtheEmergenceofExperimentalMedicineinNineteenthCenturyFrance .In Vivisection inHistoricalPerspective .EditedbyRupkeN.NewYork:CroomHelm;1987:48 – 77. 63.LaFolletteH,ShanksN: AnimalExperimentation:TheLegacyofClaudeBernard. IntStudPhilosSci 1994, 8: 195 – 210. 64.KlaassenCD,EatonDL: PrinciplesofToxicology .In CasarettandDoull'sToxicology .4thedition.EditedbyAmdur MO,DoullJ,KlaassenC.NewYork:McGraw-Hill;1993. 65.MilnerR: Darwin'sUniverse:EvolutionfromAtoZ .Berkeley:UniversityofCaliforniaPress;2009. 66.WagnerA: CausalityinComplexSystems. BiolPhilos 1999, 14: 83 – 101. 67.RussellB: OntheNotionofCause. ProceedingsoftheAristotelianSociety NewSer 1913, 13: 1 – 26. 68.GreekR: AnimalModelsandtheDevelopmentofanHIVVaccine. JAIDSClinRes 2012, S8: 001. 69.GiereRN,BickleJ,MauldinRF: UnderstandingScientificReasonoing .5thedition.Toronto:ThomsonWadsworth;2006. 70.HoldenC: RandomSamples.Well-WiredWhales. Science 2006, 314: 1363. 71.HofPR,VanderGuchtE: Structureofthecerebralcortexofthehumpbackwhale,Megapteranovaeangliae (Cetacea,Mysticeti,Balaenopteridae). AnatRec(Hoboken) 2007, 290: 1 – 31. 72.HakeemAY,SherwoodCC,BonarCJ,ButtiC,HofPR,AllmanJM: VonEconomoneuronsintheelephantbrain. AnatRec(Hoboken) 2009, 292: 242 – 248. 73.CrickF: OfMoleculesandMan .Seattle:UniversityofWashingtonPress;1966. 74.VenterJC,AdamsMD,MyersEW,LiPW,MuralRJ,SuttonGG,SmithHO,YandellM,EvansCA,HoltRA, etal : The sequenceofthehumangenome. Science 2001, 291: 1304 – 1351. 75.McPhersonJD,MarraM,HillierL,WaterstonRH,ChinwallaA,WallisJ,SekhonM,WylieK,MardisER,WilsonRK, et al : Aphysicalmapofthehumangenome. Nature 2001, 409: 934 – 941. 76.MazzocchiF:Complexityinbiology.Exceedingthelimitsofreductionismanddeterminismusingcomplexity theory. EMBORep 2008, 9: 10 – 14. 77.CoveneyPV,FowlerPW: Modellingbiologicalcomplexity:aphysicalscientist'sperspective. JRSocInterface 2005, 2: 267 – 280. 78.CoveneyPV,HighfieldRR: Frontiersofcomplexity .London:FaberandFaber;1996. 79.AhnAC,TewariM,PoonCS,PhillipsRS: Thelimitsofreductionisminmedicine:couldsystemsbiologyofferan alternative? PLoSMed 2006, 3: e208. 80.AlmE,ArkinAP: Biologicalnetworks. CurrOpinStructBiol 2003, 13: 193 – 202. 81.Cairns-SmithAG: SevenCluestotheOriginofLife:AScientificDetectiveStory .Cambridge:CambridgeUniversity Press;1986. 82.CseteME,DoyleJC: Reverseengineeringofbiologicalcomplexity. Science 2002, 295: 1664 – 1669. 83.GoodwinB: HowtheLeopardChangedItsSpots:TheEvolutionofComplexity .Princeton:PrincetonUniversityPress;2001. 84.JuraJ,WegrzynP,KojA: Regulatorymechanismsofgeneexpression:complexitywithelementsof deterministicchaos. ActaBiochimPol 2006, 53: 1 – 10. 85.KauffmanSA: heOriginsofOrder:Self-OrganizationandSelectioninEvolution .:OxfordUniversityPress;1993. 86.KitanoH: Computationalsystemsbiology. Nature 2002, 420: 206 – 210. 87.KitanoH: Systemsbiology:abriefoverview. Science 2002, 295: 1662 – 1664. 88. Definitions,Measures,andModelsofRobustnessinGeneRegulatoryNetwork. ReportofresearchworkforCSSS05 http://www.santafe.edu/education/csss/csss05/papers/monte_et_al._cssssf05.pdf. 89.MorowitzHJ: TheEmergenceofEverything:HowtheWorldBecameComplex .Oxford:OxfordUniversityPress;2002. 90.NovikoffAB: TheConceptofIntegrativeLevelsandBiology. Science 1945, 101: 209 – 215. 91.OttinoJM: Engineeringcomplexsystems. Nature 2004, 427: 399. 92.SoleR,GoodwinB: SignsofLife:HowComplexityPervadesBiology,BasicBooks.;2002. 93.VanRegenmortelM: Reductionismandcomplexityinmolecularbiology.Scientistsnowhavethetoolsto unravelbiologicalcomplexityandovercomethelimitationsofreductionism. EMBORep 2004, 5: 1016 – 1020. 94.vanRegenmortelM: Biologicalcomplexityemergesfromtheashesofgeneticreductionism. JMolRecognit 2004, 17: 145 – 148. 95.VanRegenmortelMH,HullDL: PromisesandLimitsofReductionismintheBiomedicalSciences(CatalystsforFine ChemicalSynthesis) .WestSussex:Wiley;2002. 96.VicsekT: Thebiggerpicture. Nature 2002, 418: 131. 97.WoodgerJH: BiologicalPrinciples .NewYork:HumanitiesPress;1967. 98.KolaI: Thestateofinnovationindrugdevelopment. ClinPharmacolTher 2008, 83: 227 – 230. 99.deHaanJ: Howemergencearises. EcolComplex 2006, 3: 293 – 301. 100.SouthernJ,Pitt-FrancisJ,WhiteleyJ,StokeleyD,KobashiH,NobesR,KadookaY,GavaghanD: Multi-scale computationalmodellinginbiologyandphysiology. ProgBiophysMolBiol 2008, 96: 60 – 89. 101.MorinE: IntroductionlaPenseComplexe .Paris:ESF;1990. 102.HaldaneJBS: OnBeingtheRightSize .NewYork:Harper's;1926. 103.MorangeM: Themisunderstoodgene .Cambridge:HarvardUniversityPress;2001. 104.KauffmanS: TheoreticalBiology .In EpigeneticandEvolutionaryOrderfromComplexSystems .EditedbyGoodwinB, SaundersP.Edinburgh:EdinburghUniversityPress;1990. 105.CoffeyDS: Self-organization,complexityandchaos:thenewbiologyformedicine. NatMed 1998, 4: 882 – 885. 106.MisteliT: Theconceptofself-organizationincellulararchitecture. JCellBiol 2001, 155: 181 – 185. 107.BruderCE,PiotrowskiA,GijsbersAA,AnderssonR,EricksonS,deStahlTD,MenzelU,SandgrenJ,vonTellD, PoplawskiA, etal : PhenotypicallyconcordantanddiscordantmonozygotictwinsdisplaydifferentDNAcopynumber-variationprofiles. AmJHumGenet 2008, 82: 763 – 771. 108.FragaMF,BallestarE,PazMF,RoperoS,SetienF,BallestarML,Heine-SunerD,CigudosaJC,UriosteM,BenitezJ, etal: Epigeneticdifferencesariseduringthelifetimeofmonozygotictwins. ProcNatlAcadSciUSA 2005, 102: 10604 – 10609. 109.JavierreBM,FernandezAF,RichterJ,Al-ShahrourF,Martin-SuberoJI,Rodriguez-UbrevaJ,BerdascoM,FragaMF, O'HanlonTP,RiderLG, etal : ChangesinthepatternofDNAmethylationassociatewithtwindiscordancein systemiclupuserythematosus. GenomeRes 2010, 20: 170 – 179.GreekandRice TheoreticalBiologyandMedicalModelling 2012, 9 :40Page27of33 http://www.tbiomed.com/content/9/1/40

PAGE 28

110.vonHerrathM,NepomGT: Remodelingrodentmodelstomimichumantype1diabetes. EurJImmunol 2009, 39: 2049 – 2054. 111.PearsonH: Survivingaknockoutblow. Nature 2002, 415: 8 – 9. 112.MorangeM: Asuccessfulformforreductionism. Biochem 2001, 23: 37 – 39. 113.DempsterEL,PidsleyR,SchalkwykLC,OwensS,GeorgiadesA,KaneF,KalidindiS,PicchioniM,KravaritiE, ToulopoulouT, etal : Disease-associatedepigeneticchangesinmonozygotictwinsdiscordantfor schizophreniaandbipolardisorder. HumMolGenet 2011, 20: 4786 – 4796. 114.JavierreBM,FernandezAF,RichterJ,Al-ShahrourF,Martin-SuberoJI,Rodriguez-UbrevaJ,BerdascoM,FragaMF, O'HanlonTP,RiderLG, etal : ChangesinthepatternofDNAmethylationassociatewithtwindiscordancein systemiclupuserythematosus. GenomeRes 2010, 20: 170 – 179. 115.MaitiS,KumarKHBG,CastellaniCA,O'ReillyR,SinghSM: OntogeneticDeNovoCopyNumberVariations(CNVs) asaSourceofGeneticIndividuality:StudiesonTwoFamilieswithMZDTwinsforSchizophrenia. PLoSOne 2011, 6: e17125. 116.WongAH,GottesmanII,PetronisA: Phenotypicdifferencesingeneticallyidenticalorganisms:theepigenetic perspective. HumMolGenet 2005, 14 (1):11 – 18. 117.KellenbergerE: Theevolutionofmolecularbiology. EMBORep 2004, 5: 546 – 549. 118.GilesJ: Animalexperimentsunderfireforpoordesign. Nature 2006, 444: 981. 119.: Editorial:Aslipperyslope. Nature 2009, 462: 699. 120.LaFolletteH,ShanksN: Animalmodelsinbiomedicalresearch:someepistemologicalworries. PublAffQ 1993, 7: 113 – 130. 121.AcheBW,YoungJM: Olfaction:diversespecies,conservedprinciples. Neuron 2005, 48: 417 – 430. 122.BennettCN,GreenJE:Unlockingthepowerofcross-speciesgenomicanalyses:identificationofevolutionarily conservedbreastcancernetworksandvalidationofpreclinicalmodels. BreastCancerRes 2008, 10: 213. 123.CzyzA,WegrzynG: TheObgsubfamilyofbacterialGTP-bindingproteins:essentialproteinsoflargelyunknown functionsthatareevolutionarilyconservedfrombacteriatohumans. ActaBiochimPol 2005, 52: 35 – 43. 124.DocampoR,deSouzaW,MirandaK,RohloffP,MorenoSN: Acidocalcisomes-conservedfrombacteriatoman. NatRevMicrobiol 2005, 3: 251 – 261. 125.ErolA: Insulinresistanceisanevolutionarilyconservedphysiologicalmechanismatthecellularlevelfor protectionagainstincreasedoxidativestress. Bioessays 2007, 29: 811 – 818. 126.HayakawaA,HayesS,LeonardD,LambrightD,CorveraS: Evolutionarilyconservedstructuralandfunctional rolesoftheFYVEdomain. BiochemSocSymp 2007, 74: 95 – 105. 127.MiyoshiT,IshikawaF: [Conservedtelomeric-endstructuresamongfissionyeastandhumans]. Tanpakushitsu KakusanKoso 2008, 53: 1850 – 1857. 128.SaenkoSV,FrenchV,BrakefieldPM,BeldadeP: Conserveddevelopmentalprocessesandtheformationof evolutionarynovelties:examplesfrombutterflywings. PhilosTransRSocLondBBiolSci 2008, 363: 1549 – 1555. 129.SumimotoH,KamakuraS,ItoT: StructureandfunctionofthePB1domain,aproteininteractionmodule conservedinanimals,fungi,amoebas,andplants. SciSTKE 2007, 401 2007: re6. 130.TuckerRP,Chiquet-EhrismannR: Teneurins:aconservedfamilyoftransmembraneproteinsinvolvedin intercellularsignalingduringdevelopment. DevBiol 2006, 290: 237 – 245. 131.vandenHeuvelS,DysonNJ: ConservedfunctionsofthepRBandE2Ffamilies. NatRevMolCellBiol 2008, 9: 713 – 724. 132.WangK,DegernyC,XuM,YangXJ: YAP,TAZ,andYorkie:aconservedfamilyofsignal-responsive transcriptionalcoregulatorsinanimaldevelopmentandhumandisease. BiochemCellBiol 2009, 87: 77 – 91. 133.GerhartJ,KirschnerM: TheTheoryofFacilitatedVariation .In theLightofEvolution ,AdaptationandComplex Design,Volume1.EditedbyAviseJC,AyalaFJ.WashingtonDC:NationalAcdemyofSciences;2007:45 –64. 134.ArdenKC: FOXOanimalmodelsrevealavarietyofdiverserolesforFOXOtranscriptionfactors. Oncogene 2008, 27: 2345 – 2350. 135.HovnanianA: SERCApumpsandhumandiseases. SubcellBiochem 2007, 45: 337 – 363. 136.LewisEB: AgenecomplexcontrollingsegmentationinDrosophila. Nature 1978, 276: 565 – 570. 137.McGinnisW,HartCP,GehringWJ,RuddleFH: MolecularcloningandchromosomemappingofamouseDNA sequencehomologoustohomeoticgenesofDrosophila. Cell 1984, 38: 675 – 680. 138.GellonG,McGinnisW: ShapinganimalbodyplansindevelopmentandevolutionbymodulationofHox expressionpatterns. Bioessays 1998, 20: 116 – 125. 139.SlackJM,HollandPW,GrahamCF: Thezootypeandthephylotypicstage. Nature 1993, 361: 490 – 492. 140.WagnerGP,AmemiyaC,RuddleF: Hoxclusterduplicationsandtheopportunityforevolutionarynovelties. ProcNatlAcadSciUSA 2003, 100: 14603 – 14606. 141.AmoresA,ForceA,YanYL,JolyL,AmemiyaC,FritzA,HoRK,LangelandJ,PrinceV,WangYL, etal : Zebrafishhox clustersandvertebrategenomeevolution. Science 1998, 282: 1711 – 1714. 142.Garcia-FernandezJ: Hox,ParaHox,ProtoHox:factsandguesses. Heredity 2005, 94: 145 – 152. 143.LeeRC,FeinbaumRL,AmbrosV: TheC.elegansheterochronicgenelin-4encodessmallRNAswithantisense complementaritytolin-14. Cell 1993, 75: 843 – 854. 144.LauNC,LimLP,WeinsteinEG,BartelDP: AnabundantclassoftinyRNAswithprobableregulatoryrolesin Caenorhabditiselegans. Science 2001, 294: 858 – 862. 145.Lagos-QuintanaM,RauhutR,MeyerJ,BorkhardtA,TuschlT: NewmicroRNAsfrommouseandhuman. RNA 2003, 9:175 – 179. 146.CalinGA,CroceCM: MicroRNAsignaturesinhumancancers. NatRevCancer 2006, 6: 857 – 866. 147.LingHY,OuHS,FengSD,ZhangXY,TuoQH,ChenLX,ZhuBY,GaoZP,TangCK,YinWD, etal : Changesin microRNAprofileandeffectsofmiR-320ininsulin-resistant3T3-L1adipocytes. ClinExpPharmacolPhysiol 2009,doi:10.1111/j.1440-1681.2009.05207.x. 148.LuJ,GetzG,MiskaEA,Alvarez-SaavedraE,LambJ,PeckD,Sweet-CorderoA,EbertBL,MakRH,FerrandoAA, etal : MicroRNAexpressionprofilesclassifyhumancancers. Nature 2005, 435: 834 – 838.GreekandRice TheoreticalBiologyandMedicalModelling 2012, 9 :40Page28of33 http://www.tbiomed.com/content/9/1/40

PAGE 29

149.StarkA,BrenneckeJ,BushatiN,RussellRB,CohenSM: AnimalMicroRNAsconferrobustnesstogeneexpression andhaveasignificantimpacton3'UTRevolution. Cell 2005, 123: 1133 – 1146. 150.RukovJL,VintherJ,ShomronN: PharmacogenomicsgenesshowvaryingperceptibilitytomicroRNA regulation. PharmacogenetGenomics 2011, 21: 251 – 262. 151.ProvostP: MicroRNAsasamolecularbasisformentalretardation,Alzheimer'sandpriondiseases. BrainRes 2010, 1338: 58 – 66. 152.ChengY,ZhangC: MicroRNA-21incardiovasculardisease. JCardiovascTranslRes 2010, 3: 251 – 255. 153.VarkiA,AltheideTK: Comparingthehumanandchimpanzeegenomes:searchingforneedlesinahaystack. GenomeRes 2005, 15: 1746 – 1758. 154.BarreiroLB,MarioniJC,BlekhmanR,StephensM,GiladY: FunctionalComparisonofInnateImmuneSignaling PathwaysinPrimates. PLoSGenet 2010, 6: e1001249. 155.VarkiA: Achimpanzeegenomeprojectisabiomedicalimperative. GenomeRes 2000, 10: 1065 – 1070. 156.NguyenDH,Hurtado-ZiolaN,GagneuxP,VarkiA: LossofSiglecexpressiononTlymphocytesduringhuman evolution. ProcNatlAcadSciUSA 2006, 103: 7765 – 7770. 157.XieD,ChenCC,PtaszekLM,XiaoS,CaoX,FangF,NgHH,LewinHA,CowanC,ZhongS: Rewirablegene regulatorynetworksinthepreimplantationembryonicdevelopmentofthreemammalianspecies. Genome Res 2010, 20: 804 – 815. 158.MocciaroA,SchiebelE: Cdc14:ahighlyconservedfamilyofphosphataseswithnon-conservedfunctions? JCellSci 2010, 123: 2867 – 2876. 159.AtianandMK,FuchsT,HartonJA: RecentevolutionoftheNF-kappaBandinflammasomeregulatingprotein POP2inprimates. BMCEvolBiol 2011, 11: 56. 160.EckenhoffRG: Whycanallofbiologybeanesthetized? AnesthAnalg 2008, 107: 859 – 861. 161.LynchC3rd: MeyerandOvertonrevisited.AnesthAnalg 2008, 107: 864 – 867. 162.SedenskyMM,MorganPG: Geneticsandtheevolutionoftheanestheticresponse. AnesthAnalg 2008, 107: 855 – 858. 163.SonnerJM: Ahypothesisontheoriginandevolutionoftheresponsetoinhaledanesthetics. AnesthAnalg 2008, 107: 849 – 854. 164.SonnerJM,GongD,EgerEI2nd: Naturallyoccurringvariabilityinanestheticpotencyamonginbredmouse strains. AnesthAnalg 2000, 91: 720 – 726. 165.OlverA,DeamerD: Sensitivitytoanesthesiabypregnenoloneappearslateinevolution .In Molecularand CellularMechanismsofAlcoholandAnesthetics .EditedbyRubinE,MillerK,RothS.NewYork:AnnalsoftheNew YorkAcademyofSciences;1991:561 – 565. 166.MorganPG,KayserEB,SedenskyMM: C.elegansandvolatileanesthetics. WormBook 2007:1 – 11.http://www.ncbi. nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=18050492. 167.CrowderCM,ShebesterLD,SchedlT: BehavioraleffectsofvolatileanestheticsinCaenorhabditiselegans. Anesthesiology 1996, 85: 901 – 912. 168.GamoS,OgakiM,Nakashima-TanakaE: StraindifferencesinminimumanestheticconcentrationsinDrosophila melanogaster. Anesthesiology 1981, 54: 289 – 293. 169.MilneA,BeamishT: Inhalationalandlocalanestheticsreducetactileandthermalresponsesinmimosapudica. CanJAnaesth 1999, 46: 287 – 289. 170.NunnJF,SturrockJE,WillsEJ,RichmondJE,McPhersonCK: Theeffectofinhalationalanaestheticsonthe swimmingvelocityofTetrahymenapyriformis. JCellSci 1974, 15: 537 – 554. 171.GouldSJ,VrbaES: Exaptation — amissingterminthescienceofform. Paleobiology 1982, 8: 4 – 15. 172.GouldSJ: Theexaptiveexcellenceofspandrelsasatermandprototype. ProcNatlAcadSciUSA 1997, 94: 10750 – 10755. 173.GouldSJ,LewontinRC: ThespandrelsofSanMarcoandthePanglossianparadigm:acritiqueoftheadaptationistprogramme. ProcRSocLondBBiolSci 1979, 205: 581 – 598. 174.KeilRL,WolfeD,ReinerT,PetersonCJ,RileyJL: Moleculargeneticanalysisofvolatile-anestheticaction. MolCell Biol 1996, 16: 3446 – 3453. 175.OuyangW,JihT-Y,ZhangT-T,CorreaAM,HemmingsHCJr: IsofluraneInhibitsNaChBac,aProkaryoticVoltageGatedSodiumChannel. JPharmacolExpTher 2007, 322: 1076 – 1083. 176.WieslanderA,RilforsL,LindblomG: MetabolicchangesofmembranelipidcompositioninAcholeplasma laidlawiibyhydrocarbons,alcohols,anddetergents:argumentsforeffectsonlipidpacking. Biochemistry 1986, 25: 7511 – 7517. 177.KoblinDD,WangHH: ChronicexposuretoinhaledanestheticsincreasescholesterolcontentinAcholeplasma laidlawii. BiochimBiophysActa 1981, 649: 717 – 725. 178.IngramLO: Adaptationofmembranelipidstoalcohols. JBacteriol 1976, 125: 670 – 678. 179.Nandini-KishoreSG,MattoxSM,MartinCE,ThompsonGAJr: Membranechangesduringgrowthof Tetrahymenainthepresenceofethanol. BiochimBiophysActa 1979, 551: 315 – 327. 180.Nandini-KishoreSG,KitajimaY,ThompsonGAJr: Membranefluidizingeffectsofthegeneralanesthetic methoxyfluraneelicitanacclimationresponseinTetrahymena. BiochimBiophysActa 1977, 471: 157 – 161. 181.HumphreyJA,HammingKS,ThackerCM,ScottRL,SedenskyMM,SnutchTP,MorganPG,NashHA: Aputative cationchannelanditsnovelregulator:cross-speciesconservationofeffectsongeneralanesthesia. CurrBiol: CB 2007, 17: 624 – 629. 182.EgerEI2nd,SaidmanLJ,BrandstaterB: Minimumalveolaranestheticconcentration:astandardofanesthetic potency. Anesthesiology 1965, 26: 756 – 763. 183.WangQ,ZhengY,LuJ,ChenL,WangJ,ZhouJX: Selectivebreedingofmicestrainswithdifferentsensitivity toisoflurane. ChinMedJ(Engl) 2010, 123: 1315 – 1319. 184.CascioM,XingY,GongD,PopovichJ,EgerEI2nd,SenS,PeltzG,SonnerJM: Mousechromosome7harborsa quantitativetraitlocusforisofluraneminimumalveolarconcentration. AnesthAnalg2007, 105: 381 – 385. 185.BuffingtonCW,RomsonJL,LevineA,DuttlingerNC,HuangAH: Isofluraneinducescoronarystealinacanine modelofchroniccoronaryocclusion. Anesthesiology 1987, 66: 280 – 292.GreekandRice TheoreticalBiologyandMedicalModelling 2012, 9 :40Page29of33 http://www.tbiomed.com/content/9/1/40

PAGE 30

186.BeckerLC: Isisofluranedangerousforthepatientwithcoronaryarterydisease? Anesthesiology 1987, 66: 259 – 261. 187.LundeenG,ManoharM,ParksC: Systemicdistributionofbloodflowinswinewhileawakeandduring1.0and 1.5MACisofluraneanesthesiawithorwithout50%nitrousoxide. AnesthAnalg 1983, 62: 499 – 512. 188.ManoharM,ParksC: Regionaldistributionofbrainandmyocardialperfusioninswinewhileawakeandduring1.0 and1.5MACisofluraneanaesthesiaproducedwithoutorwith50%nitrousoxide. CardiovascRes 1984, 18: 344 – 353. 189.LeungJM,GoehnerP,O'KellyBF,HollenbergM,PinedaN,CasonBA,ManganoDT: Isofluraneanesthesiaand myocardialischemia:comparativeriskversussufentanilanesthesiainpatientsundergoingcoronaryartery bypassgraftsurgery.TheSPI(StudyofPerioperativeIschemia)ResearchGroup. Anesthesiology 1991, 74: 838 – 847. 190.SearJW: Practicaltreatmentrecommendationsforthesafeuseofanaesthetics. Drugs 1992, 43: 54 – 68. 191.AgnewNM,PennefatherSH,RussellGN: Isofluraneandcoronaryheartdisease. Anaesthesia 2002, 57: 338 – 347. 192.AgarwalS,MoorchungN: Modifiergenesandoligogenicdisease. JNipponMedSch 2005, 72: 326 – 334. 193.DowellRD,RyanO,JansenA,CheungD,AgarwalaS,DanfordT,BernsteinDA,RolfePA,HeislerLE,ChinB, etal : GenotypetoPhenotype:AComplexProblem. Science 2010, 328: 469. 194. Editorial:DeconstructingGeneticContributionstoAutoimmunityinMouseModels. PLoSBiol 2004, 2: e220. 195.FriedmanA,PerrimonN: Geneticscreeningforsignaltransductionintheeraofnetworkbiology. Cell 2007, 128: 225 – 231. 196.HunterK,WelchDR,LiuET: Geneticbackgroundisanimportantdeterminantofmetastaticpotential. Nat Genet 2003, 34: 23 – 24.authorreply25. 197.LiuZ,MaasK,AuneTM: ComparisonofdifferentiallyexpressedgenesinTlymphocytesbetweenhuman autoimmunediseaseandmurinemodelsofautoimmunedisease. ClinImmunol 2004, 112: 225 – 230. 198.TheinSL: Geneticmodifiersofbeta-thalassemia.Haematologica 2005, 90: 649 – 660. 199.PaiAA,BellJT,MarioniJC,PritchardJK,GiladY: AGenome-WideStudyofDNAMethylationPatternsandGene ExpressionLevelsinMultipleHumanandChimpanzeeTissues. PLoSGenet 2011, 7: e1001316. 200.MorleyM,MolonyCM,WeberTM,DevlinJL,EwensKG,SpielmanRS,CheungVG: Geneticanalysisofgenomewidevariationinhumangeneexpression. Nature 2004, 430: 743 – 747. 201.RosenbergNA,PritchardJK,WeberJL,CannHM,KiddKK,ZhivotovskyLA,FeldmanMW: Geneticstructureof humanpopulations. Science 2002, 298: 2381 – 2385. 202.StoreyJD,MadeoyJ,StroutJL,WurfelM,RonaldJ,AkeyJM: Gene-expressionvariationwithinandamong humanpopulations. AmJHumGenet 2007, 80: 502 – 509. 203.ZhangW,DuanS,KistnerEO,BleibelWK,HuangRS,ClarkTA,ChenTX,SchweitzerAC,BlumeJE,CoxNJ,Dolan ME: Evaluationofgeneticvariationcontributingtodifferencesingeneexpressionbetweenpopulations. AmJHumGenet 2008, 82: 631 – 640. 204.PritchardC,CoilD,HawleyS,HsuL,NelsonPS: Thecontributionsofnormalvariationandgeneticbackground tomammaliangeneexpression. GenomeBiol 2006, 7: R26. 205.RifkinSA,KimJ,WhiteKP: EvolutionofgeneexpressionintheDrosophilamelanogastersubgroup. NatGenet 2003, 33: 138 – 144. 206.SandbergR,YasudaR,PankratzDG,CarterTA,DelRioJA,WodickaL,MayfordM,LockhartDJ,BarlowC: Regionaland strain-specificgeneexpressionmappingintheadultmousebrain. ProcNatlAcadSciUSA 2000, 97: 11038 – 11043. 207.SuzukiY,NakayamaM: Differentialprofilesofgenesexpressedinneonatalbrainof129X1/SvJandC57BL/6J mice:AdatabasetoaidinanalyzingDNAmicroarraysusingnonisogenicgene-targetedmice. DNARes 2003, 10: 263 – 275. 208.GibbsRA,RogersJ,KatzeMG,BumgarnerR,WeinstockGM,MardisER,RemingtonKA,StrausbergRL,VenterJC, WilsonRK, etal : Evolutionaryandbiomedicalinsightsfromtherhesusmacaquegenome. Science 2007, 316: 222 – 234. 209.EnnaSJ,WilliamsM: Definingtheroleofpharmacologyintheemergingworldoftranslationalresearch. Adv Pharmacol 2009, 57: 1 –30. 210.PinkelD: Theuseofbodysurfaceareaasacriterionofdrugdosageincancerchemotherapy. CancerRes 1958, 18: 853 – 856. 211.Reagan-ShawS,NihalM,AhmadN: Dosetranslationfromanimaltohumanstudiesrevisited. FASEBJOfficPubl FedAmSocExpBiol 2008, 22: 659 – 661. 212.TeagueSJ: Learninglessonsfromdrugsthathaverecentlyenteredthemarket. DrugDiscovToday 2009, 16: 398 – 411. 213.FreireichEJ,GehanEA,RallDP,SchmidtLH,SkipperHE: Quantitativecomparisonoftoxicityofanticancer agentsinmouse,rat,hamster,dog,monkey,andman. CancChemotherRep 1966, 50: 219 – 244. 214.TalmadgeJE,SinghRK,FidlerIJ,RazA: MurineModelstoEvaluateNovelandConventionalTherapeutic StrategiesforCancer. AmJPathol 2007, 170: 793 – 804. 215.BurtlesSS,NewellDR,HenrarRE,ConnorsTA: Revisionsofgeneralguidelinesforthepreclinicaltoxicologyof newcytotoxicanticanceragentsinEurope.TheCancerResearchCampaign(CRC)PhaseI/IIClinicalTrials CommitteeandtheEuropeanOrganizationforResearchandTreatmentofCancer(EORTC)NewDrug DevelopmentOffice. EurJCancer 1995, 31A: 408 – 410. 216.GoldsmithMA,SlavikM,CarterSK: Quantitativepredictionofdrugtoxicityinhumansfromtoxicologyinsmall andlargeanimals. CancerRes 1975, 35: 1354 – 1364. 217.NewellDR: PhaseIclinicalstudieswithcytotoxicdrugs:pharmacokineticandpharmacodynamic considerations. BrJCancer 1990, 61: 189 – 191. 218.GoodmanG,WilsonR: Quantitativepredictionofhumancancerriskfromrodentcarcinogenicpotencies:a closerlookattheepidemiologicalevidenceforsomechemicalsnotdefinitivelycarcinogenicinhumans. RegulToxicolPharmacol:RTP 1991, 14: 118 – 146. 219.PaxtonJW: Theallometricapproachforinterspeciesscalingofpharmacokineticsandtoxicityofanti-cancer drugs. ClinExpPharmacolPhysiol 1995, 22: 851 – 854. 220.AbelsonPH: Exaggeratedcarcinogenicityofchemicals. Science 1992, 256: 1609.GreekandRice TheoreticalBiologyandMedicalModelling 2012, 9 :40Page30of33 http://www.tbiomed.com/content/9/1/40

PAGE 31

221.BonatiM,LatiniR,TognoniG,YoungJF,GarattiniS: Interspeciescomparisonofinvivocaffeine pharmacokineticsinman,monkey,rabbit,rat,andmouse. DrugMetabRev 1984, 15: 1355 – 1383. 222.CaldwellJ: Problemsandopportunitiesintoxicitytestingarisingfromspeciesdifferencesinxenobiotic metabolism. ToxicolLett 1992, 64: 651 – 659. 223.CapelID,FrenchMR,MillburnP,SmithRL,WilliamsRT: Speciesvariationsinthemetabolismofphenol. Biochem J 1972, 127: 25P – 26P. 224.CapelID,FrenchMR,MillburnP,SmithRL,WilliamsRT: Thefateof(14C)phenolinvariousspecies. Xenobiotica; FateForeignCompoundsBiolSyst 1972, 2: 25 – 34. 225.ParkinsonC,GrassoP: Theuseofthedogintoxicitytestsonpharmaceuticalcompounds. HumExpToxicol 1993, 12: 99 – 109. 226.SerranoD,LazzeroniM,ZambonCF,MacisD,MaisonneuveP,JohanssonH,Guerrieri-GonzagaA,PlebaniM,Basso D,GjerdeJ, etal : EfficacyoftamoxifenbasedoncytochromeP4502D6,CYP2C19andSULT1A1genotypein theItalianTamoxifenPreventionTrial. PharmacogenomicsJ 2011, 11: 100 – 107. 227.SmithRL,CaldwellJ: Drugmetabolisminnon-humanprimates .In Drugmetabolism-frommicrobetoman EditedbyParkeDV,SmithRL.London:Taylor&Francis;1977:331 – 356. 228.WalkerRM,McElligottTF: Furosemideinducedhepatotoxicity. JPathol 1981, 135: 301 – 314. 229.WeatherallM: Anendtothesearchfornewdrugs? Nature 1982, 296: 387 – 390. 230.CollinsJM,ZaharkoDS,DedrickRL,ChabnerBA: PotentialrolesforpreclinicalpharmacologyinphaseIclinical trials. CancerTreatRep 1986, 70: 73 – 80. 231.StrolinBenedettiM,FraierD,PianezzolaE,CastelliMG,DostertP,GianniL: Stereoselectivityofiododoxorubicin reductioninvariousanimalspeciesandhumans. Xenobiotica;FateForeignCompoundsBiolSyst 1993, 23: 115 – 121. 232.GianniL,CapriG,GrecoM,VillaniF,BrambillaC,LuiniA,CrippaF,BonadonnaG: Activityandtoxicityof 4'-iodo-4'-deoxydoxorubicininpatientswithadvancedbreastcancer.AnnOncol 1991, 2: 719 – 725. 233.BrennanR,FedericoS,DyerMA: Thewaroncancer:havewewonthebattlebutlostthewar? Oncotarget 2010, 1: 77 – 83. 234.HorstmannE,McCabeMS,GrochowL,YamamotoS,RubinsteinL,BuddT,ShoemakerD,EmanuelEJ,GradyC: Risksandbenefitsofphase1oncologytrials,1991through2002. NEngJMed 2005, 352: 895 – 904. 235.ChapmanAR: AddressingtheEthicalChallengesofFirst-in-HumanTrials. JClinResBioeth 2011, 2: 113. 236.LeafC: Whywearelosingthewaroncancer .Fortune;2004:77 – 92. 237.DresserR: First-in-humantrialparticipants:notavulnerablepopulation,butvulnerablenonetheless. JLaw MedEthics 2009, 37: 38 – 50. 238.YoungM: PredictionvAttrition DrugDiscoveryWorld ;2008:9 – 12. 239.GuraT: CancerModels:Systemsforidentifyingnewdrugsareoftenfaulty. Science 1997, 278: 1041 – 1042. 240.CohenAF: Developingdrugprototypes:pharmacologyreplacessafetyandtolerability? NatRevDrugDiscov 2010, 9: 856 – 865. 241.HanselTT,KropshoferH,SingerT,MitchellJA,GeorgeAJT: Thesafetyandsideeffectsofmonoclonal antibodies. NatRevDrugDiscov 2010, 9: 325 – 338. 242.MarshallE: Genetherapyontrial. Science 2000, 288: 951 – 957. 243.PerlsteinI,BologneseJA,KrishnaR,WagnerJA: Evaluationofagiledesignsinfirst-in-human(FIH)trials – a simulationstudy. AAPSJ 2009, 11: 653 – 663. 244.BuoenC,BjerrumOJ,ThomsenMS: Howfirst-time-in-humanstudiesarebeingperformed:asurveyofphaseIdoseescalationtrialsinhealthyvolunt eerspublishedbetween1995and2004. JClinPharmacol 2005, 45: 1123 –1136. 245.WexlerD,BertelsenKM: ABriefSurveyofFirst-in-HumanStudies. JClinPharmacol 2011, 51: 988 – 993. 246.LappinG,GarnerRC: Bigphysics,smalldoses:theuseofAMSandPETinhumanmicrodosingof developmentdrugs. NatRevDrugDiscov 2003, 2: 233 – 240. 247.LappinG,GarnerRC: Theutilityofmicrodosingoverthepast5years. ExpertOpinDrugMetabToxicol 2008, 4: 1499 – 1506. 248.LappinG,KuhnzW,JochemsenR,KneerJ,ChaudharyA,OosterhuisB,DrijfhoutWJ,RowlandM,GarnerRC: Use ofmicrodosingtopredictpharmacokineticsatthetherapeuticdose:experiencewith5drugs. ClinPharmacol Ther 2006, 80: 203 – 215. 249.GillDM: Bacterialtoxins:atableoflethalamounts. MicrobiolRev 1982, 46: 86 – 94. 250.NationalInstituteofOccupationalSafetyandHealth: RegistryofToxicEffectsofChemicalSubstances(R-TECS) Cincinnati:NationalInstituteofOccupationalSafetyandHealth;1996. 251.GiriS,BaderA: Foundationreview:Improvedpreclinicalsafetyassessmentusingmicro-BALdevices:the potentialimpactonhumandiscoveryanddrugattrition. DrugDiscovToday 2011, 16: 382 – 397. 252.WadeN: NewTreatmentforCancerShowsPromiseinTesting .NewYork:Times;2009.June29,2009. 253.DiMasiJA,GrabowskiHG: Economicsofnewoncologydrugdevelopment. JClinOncol:OfficJAmSocClinOncol 2007, 25: 209 – 216. 254.DiMasiJA,FeldmanL,SecklerA,WilsonA: Trendsinrisksassociatedwithnewdrugdevelopment:success ratesforinvestigationaldrugs. ClinPharmacolTher 2010, 87: 272 – 277. 255.KolaI,LandisJ: Canthepharmaceuticalindustryreduceattritionrates? NatRevDrugDiscov 2004, 3: 711 – 715. 256.FreseKK,TuvesonDA: Maximizingmousecancermodels. NatRevCancer 2007, 7: 645 – 658. 257.KerbelRS: Humantumorxenograftsaspredictivepreclinicalmodelsforanticancerdrugactivityinhumans: betterthancommonlyperceived-buttheycanbeimproved. CancerBiolTher 2003, 2: S134 –139. 258.SinghM,LimaA,MolinaR,HamiltonP,ClermontAC,DevasthaliV,ThompsonJD,ChengJH,ReslanHB,HoCCK, etal : AssessingtherapeuticresponsesinKrasmutantcancersusinggeneticallyengineeredmousemodels. NatBiotechnol 2010, 28: 585 – 593. 259.PetersonJK,HoughtonPJ: Integratingpharmacologyand invivo cancermodelsinpreclinicalandclinicaldrug development. EurJCancer 2004, 40: 837 – 844. 260.FranciaG,KerbelRS: Raisingthebarforcancertherapymodels. NatBiotech 2010, 28: 561 – 562.GreekandRice TheoreticalBiologyandMedicalModelling 2012, 9 :40Page31of33 http://www.tbiomed.com/content/9/1/40

PAGE 32

261.JohnsonJI,DeckerS,ZaharevitzD,RubinsteinLV,VendittiJM,SchepartzS,KalyandrugS,ChristianM,ArbuckS, HollingsheadM,SausvilleEA: RelationshipsbetweendrugactivityinNCIpreclinicalinvitroandinvivomodels andearlyclinicaltrials. BrJCancer 2001, 84: 1424 – 1431. 262.KardongKV: Vertebrates.ComparativeAnatomy,Function,Evolution.InternationalEdition .6thedition.Singapore: McGraw-Hill;2012. 263.JohnsonBK,StoneGA,GodecMS,AsherDM,GajdusekDC,GibbsCJJr: Long-termobservationsofhuman immunodeficiencyvirus-infectedchimpanzees. AIDSResHumRetroviruses 1993, 9: 375 – 378. 264.NathBM,SchumannKE,BoyerJD: Thechimpanzeeandothernon-human-primatemodelsinHIV-1vaccine research. TrendsMicrobiol 2000, 8: 426 – 431. 265.StumpDS,VandeWoudeS: AnimalmodelsforHIVAIDS:acomparativereview. CompMed 2007, 57: 33 – 43. 266.SchmitzW,ScholzH,ErdmannE: Effectsof -and -adrenergicagonists,phosphodiesteraseinhibitorsand adenosineonisolatedhumanheartmusclepreparations. TrendsPharmacolSci 1987, 8: 447 – 450. 267.HowardAN,BlatonV,VandammeD,VanLandschootN,PeetersH: Lipidchangesintheplasmalipoproteinsof baboonsgivenanatherogenicdiet.3.Acomparisonbetweenlipidchangesintheplasmaofthebaboon andchimpanzeegivenatherogenicdietsandthoseinhumanplasmalipoproteinsoftypeII hyperlipoproteinaemia. Atherosclerosis 1972, 16: 257 – 272. 268.PiperPJ,AntoniwJW,StantonAW: Releaseofleukotrienesfromporcineandhumanbloodvesselsby immunologicalandnonimmunologicalstimuli. AnnNYAcadSci 1988, 524: 133 – 141. 269.GrossDR: AnimalModelsinCardiovascularResearch .TheHague:MartinusNijhoff;1985. 270.WadmanM: Whentheparty'sover. Nature 2007, 445: 13. 271.PetersJ,Van_SlykeD: QuantitativeClinicalChemistry ,Interpretations,VolumeI.Secondthedition.Baltimore: Williams&Wilkins;1948. 272.NishinaPM,SchneemanBO,FreedlandRA: Effectsofdietaryfibersonnonfastingplasmalipoproteinand apolipoproteinlevelsinrats. JNutr 1991, 121: 431 – 437. 273. InnovationorStagnation?ChallengeandOpportunityontheCriticalPathtoNewMedicalProducts .http://www. nipte.org/docs/Critical_Path.pdf. 274.vanderWorpHB,MacleodMR: Preclinicalstudiesofhumandisease:Timetotakemethodologicalquality seriously.Journalofmolecularandcellularcardiology 2011, 51 (4):449 – 50. 275.JonasS,AiyagariV,VieiraD,FigueroaM: Thefailureofneuronalprotectiveagentsversusthesuccessof thrombolysisinthetreatmentofischemicstroke.Thepredictivevalueofanimalmodels. AnnNYAcadSci 2001, 939: 257 – 267. 276.MullaneK,WilliamsM: Translationalsemanticsandinfrastructure:anothersearchfortheemperor ’ snew clothes? DrugDiscovToday 2012, 17: 459 – 468. 277.KasteM: Useofanimalmodelshasnotcontributedtodevelopmentofacutestroketherapies:pro. Stroke 2005, 36: 2323 – 2324. 278.HorstmannD: ThePoliomyelitisStory;ascientifichegira. YaleJBiolMed 1985, 58: 79 – 90. 279.OshinskyDM: Polio:AnAmericanStory .Oxford:OxfordUniversityPress;2005. 280.PaulJR: AHistoryofPoliomyelitis .NewHaven:YaleUniversityPress;1971. 281.SabinA: TestimonybeforethesubcommitteeonHospitalsandHealthCare,CommitteeonVeteransAffair ’ s, HouseofRepresentatives,April26,1984serialno.98 – 48 .In BookTestimonybeforethesubcommitteeon HospitalsandHealthCare,CommitteeonVeteransAffair ’ s,HouseofRepresentatives,April26,1984serialno.98 – 48 (Editored.^eds.) .WashingtonDC:;1984. 282.BroderickJP: TheChallengesofIntracranialRevascularizationforStrokePrevention. NEngJMed 2011, 365: 1054 – 1055. 283.ChimowitzMI,LynnMJ,DerdeynCP,TuranTN,FiorellaD,LaneBF,JanisLS,LutsepHL,BarnwellSL,WatersMF, etal : Stentingversusaggressivemedicaltherapyforintracranialarterialstenosis. NEngJMed 2011, 365: 993 – 1003. 284.TheEC/ICBypassStudyGroup: Failureofextracranial-intracranialarterialbypasstoreducetheriskofischemic stroke.Resultsofaninternationalrandomizedtrial.TheEC/ICBypassStudyGroup. NEnglJMed 1985, 313: 1191 – 1200. 285.PowersW,ClarkeW,GrubbR,VideenT,AdamsH,DerdeynC: ResultsoftheCarotidOcclusionSurgeryStudy (COSS) .In InternationalStrokeConference(COSS) .LosAngeles;2011. 286.Editorial: Inpursuitofsystems. Nature 2005,435: 1. 287. SystemsBiology .https://sysbio.med.harvard.edu/. 288.VidalM: Aunifyingviewof21stcenturysystemsbiology. FEBSLett 2009, 583: 3891 – 3894. 289.LosaGA: Thefractalgeometryoflife. RivBiol 2009, 102: 29 – 59. 290.BrennerS: Biologicalcomputation. NovartisFoundSymp 1998, 213: 106 – 111.discussion111 – 106. 291.NobleD: Fromgenestowholeorgans:connectingbiochemistrytophysiology. NovartisFoundSymp 2001, 239: 111 – 123.doi:discussion123 – 118,150 – 119. 292.HengHH: Theconflictbetweencomplexsystemsandreductionism. JAMA 2008, 300: 1580 – 1581. 293.GersteinHC,MillerME,ByingtonRP,GoffDCJr,BiggerJT,BuseJB,CushmanWC,GenuthS,Ismail-BeigiF,Grimm RHJr, etal : Effectsofintensiveglucoseloweringintype2diabetes. NEngJMed 2008, 358: 2545 – 2559. 294.BearHD: Earlierchemotherapyforbreastcancer:perhapstoolatebutstilluseful. AnnSurgOncol 2003, 10: 334 – 335. 295.SavageL: High-IntensityChemotherapyDoesNotImproveSurvivalinSmallCellLungCancer. JNatlCancer Inst 2008, 100: 519. 296.MittraI: Thedisconnectionbetweentumorresponseandsurvival. NatClinPractOncol 2007, 4: 203. 297.BatesS: Progresstowardspersonalizedmedicine. DrugDiscovToday 2010, 15: 115 – 120. 298.BhathenaA,SpearBB: Pharmacogenetics:improvingdruganddoseselection. CurrOpinPharmacol 2008, 8: 639 – 646. 299.BlairE: Predictivetestsandpersonalisedmedicine.In DrugDiscoveryWorld .;2009:27 – 31. 300.DolginE: Bigpharmamovesfrom'blockbusters'to'nichebusters'. NatMed 2010, 16: 837.GreekandRice TheoreticalBiologyandMedicalModelling 2012, 9 :40Page32of33 http://www.tbiomed.com/content/9/1/40

PAGE 33

301.FlahertyKT,PuzanovI,KimKB,RibasA,McArthurGA,SosmanJA,O'DwyerPJ,LeeRJ,GrippoJF,NolopK, ChapmanPB: Inhibitionofmutated,activatedBRAFinmetastaticmelanoma. NEnglJMed 2010, 363: 809 – 819. 302.FroehlichTE,EpsteinJN,NickTG,MelguizoCastroMS,SteinMA,BrinkmanWB,GrahamAJ,LangbergJM,KahnRS: PharmacogeneticPredictorsofMethylphenidateDose – responseinAttention-Deficit/HyperactivityDisorder. JAmAcadChildAdolescPsychiatry 2011, 50: 1129 – 1139.e1122. 303.HudsonKL: Genomics,HealthCare,andSociety. NEngJMed 2011, 365: 1033 – 1041. 304.HughesAR,SpreenWR,MostellerM,WarrenLL,LaiEH,BrothersCH,CoxC,NelsenAJ,HughesS,ThorbornDE, et al : Pharmacogeneticsofhypersensitivitytoabacavir:fromPGxhypothesistoconfirmationtoclinicalutility. PharmacogenomicsJ 2008, 8: 365 – 374. 305.WangD,GuoY,WrightonSA,CookeGE,SadeeW: IntronicpolymorphisminCYP3A4affectshepaticexpression andresponsetostatindrugs. PharmacogenomicsJ 2011, 11: 274 – 286. 306.MischEA,BerringtonWR,VaryJCJr,HawnTR: Leprosyandthehumangenome. MicrobiolMolBiolRev 2010, 74: 589 – 620. 307.HerndonCN,JenningsRG: Atwin-familystudyofsusceptibilitytopoliomyelitis. AmJHumGenet 1951, 3: 17 – 46. 308.LinTM,ChenCJ,WuMM,YangCS,ChenJS,LinCC,KwangTY,HsuST,LinSY,HsuLC: HepatitisBvirusmarkers inChinesetwins. AnticancerRes 1989, 9: 737 – 741. 309.AngstMS,LazzeroniLC,PhillipsNG,DroverDR,TingleM,RayA,SwanGE,ClarkJD: AversiveandReinforcing OpioidEffects:APharmacogenomicTwinStudy. Anesthesiology 2012, 117: 22 – 37.doi:10.1097/ ALN.1090b1013e31825a31822a31824e. 310.ChapmanSJ,HillAVS: Humangeneticsusceptibilitytoinfectiousdisease. NatRevGenet 2012, 13: 175 – 188. 311.CheungDS,WarmanML,MullikenJB: Hemangiomaintwins. AnnPlastSurg 1997, 38: 269 – 274. 312.CouzinJ: Cancerresearch.Probingtherootsofraceandcancer. Science2007, 315: 592 – 594. 313.GregorZ,JoffeL: SenilemacularchangesintheblackAfrican. BrJOphthalmol 1978, 62: 547 – 550. 314.HaimanCA,StramDO,WilkensLR,PikeMC,KolonelLN,HendersonBE,LeMarchandL: Ethnicandracial differencesinthesmoking-relatedriskoflungcancer. NEnglJMed 2006, 354: 333 – 342. 315.KalowW: Interethnicvariationofdrugmetabolism. TrendsPharmacolSci 1991, 12: 102 – 107. 316.KoppJB,NelsonGW,SampathK,JohnsonRC,GenoveseG,AnP,FriedmanD,BriggsW,DartR,KorbetS, etal : APOL1GeneticVariantsinFocalSegmentalGlomerulosclerosisandHIV-AssociatedNephropathy. Journalof theAmericanSocietyofNephrology 2011, 22 (11):2129 – 37. 317.SpielmanRS,BastoneLA,BurdickJT,MorleyM,EwensWJ,CheungVG: Commongeneticvariantsaccountfor differencesingeneexpressionamongethnicgroups. NatGenet 2007, 39: 226 – 231. 318.StamerUM,StuberF: Thepharmacogeneticsofanalgesia. ExpertOpinPharmacother 2007, 8: 2235 – 2245. 319.WilkeRA,DolanME: GeneticsandVariableDrugResponse. JAMA 2011, 306: 306 – 307. 320.CantoJG,RogersWJ,GoldbergRJ,PetersonED,WengerNK,VaccarinoV,KiefeCI,FrederickPD,SopkoG,Zheng Z-J: AssociationofAgeandSexWithMyocardialInfarctionSymptomPresentationandIn-HospitalMortality. JAMA 2012, 307: 813 – 822. 321.HoldenC: Sexandthesufferingbrain. Science 2005, 308: 1574. 322.KaiserJ: Genderinthepharmacy:doesitmatter? Science 2005, 308: 1572. 323.KleinS,HuberS: Sexdifferencesinsusceptibilitytoviralinfection .In Sexhormonesandimmunitytoinfection EditedbyKleinS,RobertsC.Berlin:Springer;2010:93 – 122. 324.SimonV: Wanted:womeninclinicaltrials. Science 2005, 308: 1517.325.WaldC,WuC: OfMiceandWomen:TheBiasinAnimalModels. Science 2010, 327: 1571 – 1572. 326.WillyardC: HIVgendercluesemerge. NatMed 2009, 15: 830. 327.ShahRR: Pharmacogeneticsindrugregulation:promise,potentialandpitfalls. PhilosTransRSocLondBBiolSci 2005, 360: 1617 – 1638. 328.RosesAD: Pharmacogeneticsandthepracticeofmedicine. Nature 2000, 405: 857 – 865. 329.YucesoyB,JohnsonVJ,FluhartyK,KashonML,SlavenJE,WilsonNW,WeissmanDN,BiaginiRE,GermolecDR, LusterMI: Influenceofcytokinegenevariationsonimmunizationtochildhoodvaccines. Vaccine 2009, 27: 6991 – 6997. 330.KingC: Personalisedvaccinescouldprotectallchildren. NewSci 2009,(2737):11. 331.PirmohamedM: Pharmacogenetics:past,presentandfuture. DrugDiscovToday 2011, 16: 852 – 861. 332. TheCaseforPersonalizedMedicine .http://www.personalizedmedicinecoalition.org/sites/default/files/files/ Case_for_PM_3rd_edition.pdf. 333.BurggrenWW,BemisWE: StudyingPhysiologicalEvolution:ParadigmsandPitfalls .In EvolutionaryInnovations EditedbyNiteckiMH.Chicago:UniversityofChicagoPress;1990:191 – 228.doi:10.1186/1742-4682-9-40 Citethisarticleas: GreekandRice: Animalmodelsandconservedprocesses. TheoreticalBiologyandMedical Modelling 2012 9 :40.GreekandRice TheoreticalBiologyandMedicalModelling 2012, 9 :40Page33of33 http://www.tbiomed.com/content/9/1/40


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Abstract
Background
The concept of conserved processes presents unique opportunities for using nonhuman animal models in biomedical research. However, the concept must be examined in the context that humans and nonhuman animals are evolved, complex, adaptive systems. Given that nonhuman animals are examples of living systems that are differently complex from humans, what does the existence of a conserved gene or process imply for inter-species extrapolation?
Methods
We surveyed the literature including philosophy of science, biological complexity, conserved processes, evolutionary biology, comparative medicine, anti-neoplastic agents, inhalational anesthetics, and drug development journals in order to determine the value of nonhuman animal models when studying conserved processes.
Results
Evolution through natural selection has employed components and processes both to produce the same outcomes among species but also to generate different functions and traits. Many genes and processes are conserved, but new combinations of these processes or different regulation of the genes involved in these processes have resulted in unique organisms. Further, there is a hierarchy of organization in complex living systems. At some levels, the components are simple systems that can be analyzed by mathematics or the physical sciences, while at other levels the system cannot be fully analyzed by reducing it to a physical system. The study of complex living systems must alternate between focusing on the parts and examining the intact whole organism while taking into account the connections between the two. Systems biology aims for this holism. We examined the actions of inhalational anesthetic agents and anti-neoplastic agents in order to address what the characteristics of complex living systems imply for inter-species extrapolation of traits and responses related to conserved processes.
Conclusion
We conclude that even the presence of conserved processes is insufficient for inter-species extrapolation when the trait or response being studied is located at higher levels of organization, is in a different module, or is influenced by other modules. However, when the examination of the conserved process occurs at the same level of organization or in the same module, and hence is subject to study solely by reductionism, then extrapolation is possible.
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ins Americans For Medical Advancement (www.AFMA-curedisease.org), 2251 Refugio Rd, Goleta, CA, 93117, USA
Department of Anesthesiology, University of Florida College of Medicine, PO Box 100254, Gainesville, FL, 32610-0254, USA
source Theoretical Biology and Medical Modelling
issn 1742-4682
pubdate 2012
volume 9
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fpage 40
url http://www.tbiomed.com/content/9/1/40
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cpyrt 2012collab Greek and Rice; licensee BioMed Central Ltd.note This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
kwdg
kwd Anesthesia
Animal models
Cancer
Complexity
Conserved processes
Systems biology
abs
sec
st
Abstract
Background
The concept of conserved processes presents unique opportunities for using nonhuman animal models in biomedical research. However, the concept must be examined in the context that humans and nonhuman animals are evolved, complex, adaptive systems. Given that nonhuman animals are examples of living systems that are it differently complex from humans, what does the existence of a conserved gene or process imply for inter-species extrapolation?
Methods
We surveyed the literature including philosophy of science, biological complexity, conserved processes, evolutionary biology, comparative medicine, anti-neoplastic agents, inhalational anesthetics, and drug development journals in order to determine the value of nonhuman animal models when studying conserved processes.
Results
Evolution through natural selection has employed components and processes both to produce the same outcomes among species but also to generate different functions and traits. Many genes and processes are conserved, but new combinations of these processes or different regulation of the genes involved in these processes have resulted in unique organisms. Further, there is a hierarchy of organization in complex living systems. At some levels, the components are simple systems that can be analyzed by mathematics or the physical sciences, while at other levels the system cannot be fully analyzed by reducing it to a physical system. The study of complex living systems must alternate between focusing on the parts and examining the intact whole organism while taking into account the connections between the two. Systems biology aims for this holism. We examined the actions of inhalational anesthetic agents and anti-neoplastic agents in order to address what the characteristics of complex living systems imply for inter-species extrapolation of traits and responses related to conserved processes.
Conclusion
We conclude that even the presence of conserved processes is insufficient for inter-species extrapolation when the trait or response being studied is located at higher levels of organization, is in a different module, or is influenced by other modules. However, when the examination of the conserved process occurs at the same level of organization or in the same module, and hence is subject to study solely by reductionism, then extrapolation is possible.
bdy
Background
Marc Kirschner and John Gerhart introduced the concept of facilitated variation and conserved core processes in their book, The Plausibility of Life
abbrgrp
abbr bid B1 1
, in order to explain how novelty arises in evolution. Motivated by advances in evolutionary and developmental biology (evo devo), these investigators proposed that conserved processes are ubiquitous in eukaryotes but pointed out that by using conserved processes differently, for example by differently regulating the genes that code for the processes, expressing the genes differently, varying the sequences or combination of genes or transcription factors, novelty can arise. Mutations in the genes that regulate the conserved processes can accomplish this novelty. Moreover, by adjusting the regulatory genes, the organism can evolve with fewer mutations than would be the case if a trait had to arise de novo or from mutations in structural genes. This has implications for using nonhuman animals (hereafter referred to simply as animals) as models for humans in biomedical research. One should expect to discover information regarding conserved processes in humans by studying animal models. We sought to determine whether limits exist on this method and if so what those limits are.
Methods
We surveyed the relevant literature including philosophy of science, biological complexity, conserved processes, evolutionary biology, comparative medicine, anti-neoplastic agents, inhalational anesthetics, and drug development journals in order to determine the appropriate role for animal models when studying conserved processes. Philosophy of science is relevant to our discussion as it includes the premises and assumptions on which research is then based. A study or method can be methodologically sound but if the premises are incorrect, then the study loses much if not all of its value. The drug development literature was searched because the final application of much research is targeted intervention via drugs hence that literature can inform regarding the success of a practice or modality. The literature concerning biological complexity and conserved processes was surveyed as it directly relates to the issue being explored. All of this must be placed into the context of evolutionary biology in order to better explain the findings. We chose inhalational anesthetics and anti-neoplastic agents as examples because of the well-known conserved nature of these agents.
Results
Animal models
The use of models has a long history in science, which led philosopher of science Richard Braithwaite to warn that: “The price of employment of models is eternal vigilance”
B2 2
. In this section, we will explore what animal models are, how they can be used in scientific investigation, including biomedical research, and discuss classification schemes. In this article, we will address the use of predictive animal models in light of the concepts of complex systems, personalized medicine and pharmacogenomics, and evolutionary biology. We will then explore what this implies when using animal models to study conserved processes.Models are important for scientific pursuits and can take the form of abstract models, computational models, heuristic models, mathematical models, physical models such as scale models, iconic models, and idealized models. Models can also be divided on the basis of whether they are used to replicate a portion of the item being modeled or are used to test hypotheses or interpret aspects of a theory. Examples of historically important models include Watson and Crick’s physical model of DNA, Pauling’s model of chemical bonds, Bohr’s solar system model of the atom, and the billiard ball model of gases. More recent models include the computer model of the brain, mathematical models of disease spread, and Lorenz’s model of the atmosphere.Robert Hinde observed that models:indent 1 ● Should be different from the thing being modeled, because if it is not, the modeler might assume that all properties demonstrated by the model exist in the thing being modeled;● Are usually less complicated than the thing being modeled;● Are more readily available than the thing being modeled, and;● “pose questions, suggest relations, or can be manipulated in ways not possible with the original”
B3 3
.In light of the importance of models, some philosophers of science assert that the study of models per se has been neglected by the philosophy of science community. Frigg and Hartmann
B4 4
state: “What fills in the blank in ‘M represents T if and only if ____,’ where M is a model and T a target system?” Moreover, how one classifies models and what criteria must be fulfilled in order for M to be considered a specific type of model has arguably not been adequately addressed by the philosophy of science community. Yet another problem with the philosophy of models is the relationship between theory and model
4
. We maintain that this lack of scholarly attention to models has played a role in what we see as the confusion surrounding the use of animals as models.Animal models are physical models and can be further classified based on various features and uses. For example, they can be distinguished by the phylogenetic distance of the model species from humans. Animal models can also be classified based on fidelity—how well the model resembles humans—as well as based on validity—how well what you think you are measuring corresponds to what you really are measuring. Animal models can also be considered based on reliability—the precision and accuracy of the measurement
B5 5
. Hau explains that animal models can be categorized as spontaneous, induced, transgenic, negative and orphan. Hau states: “The majority of laboratory animal models are developed and used to study the cause, nature, and cure of human disorders” [
B6 6
p3]. This is important as Hau further states that animal models can be used to predict human responses: “A third important group of animal models is employed as predictive models. These models are used with the aim of discovering and quantifying the impact of a treatment, whether this is to cure a disease or to assess toxicity of a chemical compound. The appropriateness of any laboratory animal model will eventually be judged by its capacity to explain and predict the observed effects in the target species”
6
. Others agree that predicting human response is a common use for animal models
B7 7
B8 8
B9 9
B10 10
B11 11
B12 12
. For example, Heywood stated: “Animal studies fall into two main categories: predictive evaluations of new compounds and their incorporation into schemes designed to help lessen or clarify a recognised hazard”
B13 13
.Animals are utilized for numerous scientific purposes (see ]Table tblr tid T1 1) and one of the authors (Greek) has addressed these various uses in previous publications
B14 14
B15 15
B16 16
B17 17
B18 18
B19 19
B20 20
. One cannot have a meaningful discussion regarding the utility of animal models unless one specifies the category under discussion. For example, areas in which animal models have been successfully employed include the evaluation of a phenomenon that can be described by the physicochemical properties of the organism, the study of basic physiologic functions, and the study of other traits that can be described by the use of conversion factors based on the body surface area of the organism. In general, animal models can be successfully employed in categories 3–9 in Table 1. However, animal models have failed to be predictive modalities for human response to drugs and disease
13
14
15
16
18
B21 21
B22 22
B23 23
B24 24
B25 25
B26 26
B27 27
B28 28
B29 29
B30 30
B31 31
B32 32
B33 33
B34 34
B35 35
B36 36
B37 37
B38 38
B39 39
B40 40
B41 41
, depicted by categories 1 and 2 in Table 1. (The authors have addressed this failure in numerous publications and, because an exploration for this failure is not the purpose of the article, we refer the reader to those publications
14
15
16
17
18
19
20
23
even though we realize that some view this position as controversial
7
11
B42 42
B43 43
B44 44
.) This is not to say that a species can never be found in retrospect that mimics an outcome in humans. Such a species usually can be identified, however retrospective correlation is obviously not the same as prediction
B45 45
B46 46
B47 47
. Moreover, any process or modality claiming to be predictive can be evaluated by use of the binomial classification table and equations in Table T2 2 (as illustrated in Table T3 3
B48 48
). Such calculations are commonly used in science
B49 49
B50 50
B51 51
B52 52
B53 53
.
table
Table 1
caption
b Categories of animal use in science and research
16
tgroup align left cols 2
colspec colname c1 colnum colwidth 1*
c2
thead valign top
row rowsep
entry
tbody
1.
As predictive models for human disease
2.
As predictive models to evaluate human exposure safety in the context of pharmacology and toxicology (e.g., in drug testing)
3.
As sources of ‘spare parts’ (e.g., aortic valve replacements for humans)
4.
As bioreactors (e.g., as factories for the production of insulin, or monoclonal antibodies, or the fruits of genetic engineering)
5.
As sources of tissue in order to study basic physiological principles
6.
For dissection and study in education and medical training
7.
As heuristic devices to prompt new biological/biomedical hypotheses
8.
For the benefit of other nonhuman animals
9.
For the pursuit of scientific knowledge in and of itself
Table 2
Binary classification test
4
center
c3 3
c4
nameend namest
Gold standard
GS+
GS-
Test
T+
TP
FP
T-
FN
TN
tfoot
The binary classification test allows calculations for determining how well a test or practice compares with reality or the gold standard.
Sensitivity = TP/(TP + FN)
Specificity = TN/(FP + TN)
Positive Predictive Value = TP/(TP + FP)
Negative Predictive Value = TN/(FN + TN)
T- = Test negative
T + = Test positive
FP = False positive
TP = True positive
FN = False negative
TN = True negative
GS- = Gold standard negative
GS + = Gold standard positive
Table 3
Example of binary classification values
Gold standard (human)
GS+
GS-
Test
T+
22
26
T-
22
30
Binary classification values for cardiovascular toxicity test in monkeys from 25 compounds also tested in humans
48
. Note the values are approximately what would be expected from a coin toss.
Sensitivity = 22/(22 + 22) = 0.5
Specificity = 30/(26 + 30) = 0.54
Positive Predictive Value = 22/(22 + 26) = 0.46
Negative Predictive Value = 30/(22 + 30) = 0.58
When judging the predictive value of a modality, one is not using the term predict in the same sense as when describing how hypotheses generate predictions to be tested. The predictive value of a commonly used modality usually is known, or can be ascertained, for example the positive and negative predictive value of x-ray computed tomography (commonly referred to as a CT scan) for diagnosing pneumothorax (a rupture of, or interference in, the pleural membrane which allows air to enter the pleural space and thus interferes with breathing) approaches 1.0 (is accurate for diagnosing the condition in 100% of cases).Animal models as used in biomedical research, can also be categorized as causal analogical models (CAMs) or as heuristic or hypothetical analogical models (HAMs)
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. The use of animal models to predict human response to drugs and disease, in accordance with categories 1 and 2 in Table 1, would be an example of using animals as CAMs. Analogical models in general include the hydraulic model of economies and the computer model of the brain and can be further divided based on various criteria
4
. Causalism or causal determinism dates to Aristotle who stated: “what is called Wisdom is concerned with the primary causes and principles.” Causalism can be summarized succinctly, as “everything has a cause.” This notion of causation was the basis for animal models as can be appreciated by the writings of Claude Bernard
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, considered the father of animal modeling since the 19sup th century. Bernard’s thoughts on animal models are an extension of Aristotle via the determinism of Descartes and Newton
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. Causal determinism and the principle of uniformity led to the concept, still accepted by many animal modelers today, that the same cause would result in the same effect in qualitatively similar systems. This line of thinking was in keeping with the creationist thinking of 19th century French physiologists, including Bernard, who rejected Darwin’s Theory of Evolution
60
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. The notion of causal determinism and the principle of uniformity combined with the rejection of evolution led to the belief in the interchangeability of parts. Therefore, if one ascertained the function of the pancreas in a dog, he could directly extrapolate that knowledge to the function of the pancreas in humans, once scaling for size had been factored in
14
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. Unfortunately, this linear thinking persists as the baboon heart transplant to Baby Fae illustrates. The operation was performed by the creationist surgeon Leonard Bailey of Loma Linda University in 1984 [
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p162-3].We acknowledge that the concept of causation is problematic
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. Russell suggested it be abandoned in 1913
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and it is clearly more useful for linear systems than complex systems. While an exhaustive explanation and discussion of the controversies surrounding causation would occupy more space than is available for this article (see Bunge
61
for such an analysis) we should note that a more current explanation for causation is that of a “first order approximation.” Causation is usually discussed in the context of a chain of causes. Bunge summarizes current thinking: “neodeterminism asserts in this connection that causation is only one among several interrelated categories concurring in real processes”
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. This principle is appreciated even more fully in complex systems. Current thinking notwithstanding, the use of animal models assumes the Cartesian concept of causation in that a causal model assumes a deterministic causal relationship between variables. We will explore this thinking and show that even in the traditional context there are problems with using animal models to discover “causal” relationships. These problems are increased exponentially when placed in the context of complex systems.Based on the writings of LaFollette and Shanks [
58
p63], we suggest the following in order for a model to be considered a CAM. X (the model) and Y (the subject being modeled) share properties {a…e}. In X, these properties are associated with, and thought relevant to, state S1. S1 has not been observed directly in Y, but Y likely also has would exhibit S1 under the same conditions as X. This concept is illustrated in Table T4 4. LaFollette and Shanks
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state that, “there should be no causally-relevant disanalogies between the model and the thing being modeled.” Unfortunately, causally relevant disanalogies do exist among species and even within a species, which leads to different states or outcomes, as illustrated in Table 4. We again paraphrase LaFollette and Shanks [
58
p112] and suggest that two more conditions must be met for a model to qualify as a CAM: the shared properties {a,…,e} must have a causal relationship with state S1 and be the only causally relevant properties associated with S1. As Table 4 illustrates, the commonalities between the humans and chimpanzees are insufficient to qualify chimpanzees as CAMs for human response to HIV infection. (For more on animal models of HIV/AIDS see
14
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.) As we will show, animals and humans are evolved complex systems and as such exhibit the properties of robustness and redundancy; hence numerous “causes” can result in the same effect and the same perturbation can result in different outcomes. Because of this and other properties of complex systems, we should expect different species to exhibit different causal relationships.
Table 4
Causal analogical models
6
c5 5
c6
X, the model
Y, the system being modeled
Shared properties between X and Y
Perturbation to the model
Outcome in model
Outcome in system being modeled
Shared properties a e for humans and chimpanzee do not result in state S1 also being shared.
Animal system (for example, Pan troglodytes)
Human system
a. Genes. >90% of nucleotide sequences identical.
Exposure to HIV.
State S1. Mild illness of limited duration.
AIDS. State S1 is not shared despite the presence of shared, relevant properties.
b. Immune system. Many commonalities. Constructed on generally the same plan.
c. White blood cells present and function similarly.
d. Receptors on white blood cells also present and function similarly.
e. Shared intracellular components of white blood cells.
Correspondingly, Giere, Bickle, and Mauldin
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note that some question the use of causal models in the study of humans because humans are complex systems whereas casual models assume a deterministic system: an outcome in a simple system is fixed by the variables. The problems of determining causation are further explored by Bunge
61
in his neodeterminism explanation alluded to above and his analysis is highly relevant to this discussion. While we will attempt to contrast the traditional deterministic view of causality in light of complexity science, this article will not do justice the current thinking on causation and we refer the reader to Bunge
61
for a fuller explanation.Giere, Bickle, and Mauldin suggest a probabilistic relationship instead of a 100% causal relationship for the model: “C is a positive causal factor (probabilistic) for E in an individual, I, characterized by residual state, S, if in I the probability of E given C is greater than the probability of E given Not-C.” Likewise, LaFollette and Shanks raise the question as to whether animal models can be weak CAMs: “Begin with two systems Ssub 1 and S2. S1 has causal mechanisms {a,b,c,d,e}, S2 has mechanisms {a,b,c,x,y}. When we stimulate sub-system {a,b,c} of S1 with stimuli sf response rf regularly occurs. We can therefore infer that were we to stimulate sub-systems {a,b,c}of S2 with sf rf would probably occur” [
58
p141]. LaFollette and Shanks then explain that this outcome will be highly probable if and only if {a,b,c} are causally independent of {d,e} and {x,y}.Again we anticipate problems in using animal models as weak CAMs, even in the traditional deterministic-causation view, because, as we shall discuss, various properties of complex systems will likely give rise to difficulties in isolating subsystems, which would be required for an animal model to be a weak CAM. These problems have been referred to as causal/functional asymmetry and mandates caution in extrapolating data between species. Kirschner and Gerhart give an example of this:"The case of the octopus and the human camera eye has been looked into, and the lessons are clear. Underneath the gross anatomical similarities are many differences. The eye derives from different tissues by different developmental means. Although both structures use the same pigment (rhodopsin) for photoreception, and both send electrical signals to the brain, we now know that the intervening circuitry is completely different [
1
p240-01]."Independent evolution has also produced spindle neurons in species as diverse as humans and cetaceans. Spindle neurons connect parts of the brain involved in higher cognition and were thought to only occur in primates but have recently been discovered in cetaceans, such as humpback whales and fin whales, as well as elephants
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. Convergent evolution, the acquisition of the same trait in different lineages, is also important when considering the role of animal models.
Evolved complex systems
Reductionism is a method of study that seeks to break a system down into its component parts, study each part individually, and then reach a conclusion about the system as a whole or at least the role of the individual part. Descartes introduced the concept and it has proven effective for ascertaining many facts about the material universe. Conversely, the clockwork universe of Descartes has not held up to scrutiny on all levels. Quantum mechanics, relativity, chaos, and complexity have revealed the stochastic nature of the supposedly clockwork, deterministic universe. Regrettably, while physicists recognized the limitations of reductionism, biologists were uncritically embracing it. Francis Crick extended reductionism to all aspects of biology when he stated: “The ultimate aim of the modern movement in biology is to explain all biology in terms of physics and chemistry”
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. Biological reductionism arguably reached its zenith in the Human Genome Project (HGP)
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and, ironically, the consequences of the HGP—that humans have a relatively small number of genes—have, in large part, been responsible for a re-examination of the role of reductionism in biology. This has been especially true for human pathophysiology where animals are used as models for humans.Systems can be categorized as simple or complex. The world of Newton and Descartes was largely confined to simple systems hence reductionism functioned well for discovery. At some levels, the components of a complex system can be simple systems and thus are subject to study by reductionism while at other levels these simple systems combine to make complex systems thus necessitating study of the intact whole. Mazzocchi points out that when reductionism takes a component out of its natural environment it has consequences for extrapolating the results back to the organism as a whole: “But this extrapolation is at best debatable and at worst misleading or even hazardous. The failure of many promising drug candidates in clinical research shows that it is not always possible to transfer results from mice or even primates to humans”
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.While evolution is defined as a change in allele frequency over time, complexity science can be defined as “the study of the behaviour of large collections of simple, interacting units, endowed with the potential to evolve with time”
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. Living organisms are complex systems that have highly variable evolutionary histories and as such are best modeled using nonlinear differential equations. The difficulty with this approach is that the values for many of the factors are unknown; hence solving the equation is impossible
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.Animals and humans are examples of living complex adaptive systems and as such exhibit the following properties
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:1. Complex systems are composed of many components. Some of these components may be simple systems, but many are complex systems. These components exist on many scales and interact extensively with each other. A complex system is a “system of systems.”2. The components can be grouped as modules. For example, the following could be considered as modules: the cell; the various processes in a cell; gene networks; gene-gene interactions; gene-protein interactions; protein-protein interactions; organs; and all the factors that influence the natural history of a disease. However, failure in one module does not necessarily spread demise to the system as a whole as redundancy and robustness (see #s 5 and 6 below) also exist and the various modules also communicate with each other.3. The different components of a complex system are linked to and affect one another in a synergistic manner. There is positive and negative feedback in a complex system
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.4. A complex system demonstrates hierarchal levels of organization
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. These levels range from the subatomic to the molecular to the whole individual to collections of individuals
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. Emergence (see # 13 below) occurs at each level; therefore, even a complete understanding of the lower level is insufficient for explaining the upper level. The various levels interact such that there is both upward causation and downward causation. In order to understand a particular level, one must alternate between looking at the components and looking at the whole while taking into account the connections between each
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. Moreover, the various levels may respond differently to the same perturbation.The various levels of organization are important when considering which responses to specific perturbations can be extrapolated among species. Living complex systems have numerous properties that can be studied without consideration of the fact that the whole, intact organism is a complex system. Some systems or components follow only the laws of physics, or even simple geometry, while others are best described by their physicochemical properties or just by chemistry. Some properties of complex systems can be described simply by math formulas. Growth, for example, can be described as geometrical in some cases and exponential in others. The surface area of a body increases by the square of the linear dimensions while the volume increases by the cube. This is a consequence of geometry and is important in physiology, in part, because heat loss is proportional to surface area while heat production is proportional to volume. Haldane stated: “Comparative anatomy is largely the story of the struggle to increase surface in proportion to volumes”
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. For example, chewing increases the surface area of food, the rate the small bowel absorbs nutrients and other chemicals depends in part on the surface area of the small bowel, and air sacs in the lungs rely on surface area for gas exchange, as do capillaries.Allometry is the study of the relationship of body size to shape. Examples of allometric laws include Kleiber’s law: q
0 ~ M
¾ where q
0 is metabolic rate and is proportional to M, body mass, raised to the ¾ power. The rate t, of breathing and heart contractions are proportional to M, body mass, raised to ¼ power: t ~ M
¼. Further, many physiological functions affect or depend on surface area.Levels of organization can also be described based on whether they are primarily chemical reactions and hence subject to analysis by chemistry. Reactions or perturbations that involve the denaturation of proteins should affect all systems, be they simple or complex, similarly because at this level of organization other factors do not come into play. Exactly what effects sulfuric acid would have in a person over an extended period of time are irrelevant as it denatures protein more or less immediately. Perhaps species differences would manifest if small amounts of H2SO4 were infused over long periods of time, but the immediate effects are the same across species because of the chemical properties of the acid.Animals can be successfully used for numerous purposes in science (see 3–9 in Table 1). One of the purposes for which animals can be successfully used is to evaluate phenomena that can be described by the physicochemical properties of the organism. The same applies to basic physiologic functions. There are physiological parameters that can be applied across species lines by the use of conversion factors based on the weight or surface area of the organism. There are also properties of organisms that can be anticipated by the physical or chemical properties of the substance acting on the organism. All of these are instances of successfully treating a complex system as if it were a simple system. Problems arise however, as the level of organization under study increases. Allometric scaling based on body surface area (BSA), for example, does not include differences that manifest at higher levels of organization for example in the elimination or metabolism of drugs. Different levels of organization can be acted on by single factors or many factors but perturbations of simple systems, or systems that can be described as simple on the level or organization being affected, should produce similar results.5. Complex systems are robust, meaning they have the capacity to resist change. This can be illustrated by the fact that knocking out a gene in one strain of mouse may produce negligible effects while being lethal to another strain. Gene pleiotropy is an additional example
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.6. Complex systems exhibit redundancy. For example, living systems exhibit redundancy of some genes and proteins
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.7. Complex systems are dynamic. They communicate with, and are acted on by, their environment.8. Complex systems exhibit self-organization, which allows adaptation to the environment
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. The intact cell is a prime example of this property.9. Complex systems are dependent on initial conditions. The well-known example of the butterfly flapping its wings and causing a weather catastrophe on the other side of the earth—the butterfly effect—is an example of dependence on initial conditions. An example in living complex systems would be that very small differences in genetic makeup between two systems could result in dramatic differences in response to the same perturbation. For example, monozygotic twins raised in the same environment may have different predispositions to diseases such as multiple sclerosis and schizophrenia
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. Additionally, the above-mentioned observation that knocking out a gene results in different outcomes in two stains of mice illustrates the concept that small differences in initial conditions—genetic makeup—can mean the difference between life and death
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.10. The initial conditions of a complex living system are determined, in part, by evolution. Various species have different evolutionary histories and thus are differently organized complex systems. Initial conditions can be different, despite the exact same genes, secondary to modifier genes, differences in regulation or expression of genes, epigenetics, and mutations among others factors. For example, small epigenetic changes probably account for the dissimilarities between monozygotic twins in terms of disease susceptibility
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.11. Perturbations to complex systems result in effects that are nonlinear
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. Large disturbances may result in no change to the system while minor perturbations may cause havoc
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. Efforts to describe complex systems in terms of linear cause and effect relationships are prone to failure
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. Extrapolating among complex systems is even more problematic because of nonlinearity, along with the other factors described.12. The whole of a complex system is greater than the sum of the parts; hence, some processes and or perturbations are not amenable to study by reductionism.13. Complex systems have emergent properties that cannot be predicted even in light of full knowledge of the component parts.Animal models have historically been utilized for the prediction of human responses to drugs and disease and this use has also been the justification for animal use in research in general
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. But because various levels of organization and different modules can be acted on by the same perturbation, in order to evaluate whether an animal model can be used as a predictive modality, one needs to understand the levels affected by the perturbation, what rules are being followed at those levels, and whether the system is simple or complex at the respective levels. Empirical evidence, explained and placed in context by theory developed from complexity science and evolutionary biology, suggests animal models cannot predict human responses to drugs and disease
14
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, despite the presence of shared physicochemical properties and conserved processes.
Conserved processes
Theodosius Dobzhansky famously stated: “Nothing in biology makes sense except in the light of evolution.” We want to examine the consequences that various characteristics of evolved complex systems, such as modules and different levels of organization, have on processes conserved by evolution in terms of determining the response of whole organisms to perturbations. Conserved processes and genes are the subject of much interest today
1
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. Kirschner and Gerhardt state: “all organisms are a mixture of conserved and nonconserved processes (said otherwise, or changing and unchanging processes)” [
1
p34-35]. Conserved processes are not reactions to the laws of physics or the determination of properties of an organism as they relate to chemistry or geometry. Nevertheless, conservation reaches across phyla and even kingdoms. Kirschner and Gerhardt have pointed out that processes conserved include those involved in cell function and organization, development, and metabolism and that these processes are similar in animals, yeast, and bacteria. They note that novelty has been the result of using the conserved processes in different ways rather than inventing completely new processes [
1
p34-35]. This has critical implications for what can be learned from interspecies study.Housekeeping genes in general perform the same function; make the same proteins, in mice, frogs or humans. The role of FOX transcription factors is conserved among species
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as is the role of Sarco(endo)plasmic reticulum (SER) Ca2+ ATPases (SERCA) pumps
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. Modules have also been conserved. The fin module of the modern fish for example, arose roughly 400 million years ago and has been conserved ever since [
1
p65].Conserved processes include core genes like those in the homeobox that are involved in the same developmental processes. Because these processes and genes are conserved among species, we could reasonably expect the same outcome from the same perturbation, regardless of the species containing these processes. But is this the case? In 1978 Lewis
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published his seminal work on the anterior-posterior layout of Drosophila. This was followed in 1984 by the discovery of the homeobox by McGinnis et al.
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. The field of evo devo developed in large part from this work. In the last decade, enormous strides have been made as a result of research in evo devo and the various genome projects. The results of such research have revealed an enormous genetic similarity among mammals. At the level of the genes centrally involved in development, e.g., the homeobox genes, bilaterians are virtually identical. The homeobox class of genes
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are conserved across species lines, functioning in early cellular organization and anterior-posterior body plan layout
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. There are important differences however. For example, there are nine Hox genes in flies but thirty-nine in mammals. Pertinently, we understand how modifications (gene duplications, deletions, changes in the regulatory processes and so forth) to these conserved processes have resulted in the evolution of different body types and indeed different species
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.MicroRNA (miRNA) has been found in essentially all species from Caenorhabditis elegans to humans and plays a large role in gene regulation
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. Apparently, over 50% of miRNAs are conserved across species lines in vertebrates
145
. An important consideration for drug development, however, is the fact that even though miRNA is conserved, up to 50% of miRNAs differs among vertebrates. This is important when considering the use of animals as predictive human models. Furthermore, miRNA expression levels change when tissues deteriorate from a healthy state to a diseased state
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. Thus the exact role of miRNA may differ intra-individually depending on age and disease. Hence, we see both inter-species and intra-individual differences with respect to this conserved process.It is well known that humans and nonhuman primates respond differently to infections. For example, untreated humans usually progress to AIDS when infected with HIV, are susceptible to malaria (except those with sickle cell anemia), have different reactions to hepatitis B and C than nonhuman primates and, appear more susceptible to many cancers and Alzheimer’s disease
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. Barreiro et al.
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studied gene expression levels in monocytes from humans, chimpanzees, and rhesus macaques and found that all three species demonstrated “the universal Toll-like receptor response” when stimulated with lipopolysaccharide (LPS). However they also discovered that only 58% of genes identified in the Toll-like receptor response “showed a conserved regulatory response to stimulation with LPS,” and only 31% of those genes demonstrated the same conserved regulatory response when exposed to viruses or bacteria. Barreiro et al. also discovered that 335 genes in humans are unique among the species in responding to LPS, with 273 genes responding only in chimpanzees, and 393 only in rhesus macaques
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. Even in conserved processes, there are going to be significant differences that influence the outcomes from disease perturbations. Significant differences in the details of conserved processes (also illustrated by Figure figr fid F1 1
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) mean that there are differences in the initial conditions of the complex system and this has major implications for inter-species extrapolation.
fig Figure 1Variation in sialic acid (Sia)-recognizing Ig-superfamily lectins among primates“Expression of CD33rSiglecs on human and great ape lymphocytestext
Variation in sialic acid (Sia)-recognizing Ig-superfamily lectins among primates. “Expression of CD33rSiglecs on human and great ape lymphocytes. (A) Percentage of positive lymphocytes for each Siglec Ab (staining above negative controls) for 16 chimpanzees, 5 bonobos, and 3 gorillas are shown, as well as data for 8 humans (the latter were tested on one or more occasions). Examples of flow cytometry histograms of human (B) and chimpanzee (C) lymphocytes using Abs recognizing Siglec-3, Siglec-5, Siglec-7, and Siglec-9 (y axis: normalized cell numbers expressed as percent of maximum cell number detected). In later samples examined, low levels of Siglec-11 staining (<5% positive) were occasionally detected on lymphocytes in both great apes and humans (data not shown)” 156.
graphic file 1742-4682-9-40-1 The implications of the various properties of complex systems also become apparent when scientists study processes such as preimplantation embryonic development (PED). PED is thought to be highly conserved among species which led Xie et al.
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to study gene expression profiles in embryos from humans, mice, and cows. They found that: “40.2% orthologous gene triplets exhibited different expression patterns among these species.” Differences in expression profiles have implications for drug and disease response.The Cdc14 gene was discovered in the yeast Saccharomyces cerevisiae and is classified as a dual-specificity phosphatase. It has since been found in many organisms including humans. Human Cdc14B fulfills the role, in yeast, of the yeast gene Cdc14. Because the yeast gene plays a role in regulating late mitosis, it was assumed the gene would have the same role in mammals. In actuality, neither Cdc14A nor Cdc14B are necessary for cell-cycle progression in humans
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. Thus, we have a conserved gene but not a conserved function.Pyrin proteins have been found to be ubiquitous in mammals. Pyrin-only protein 2 (POP2) was found in humans and thought to be important in inflammatory diseases. Atianand et al.
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studied mice but did not find POP2. They then discovered that POP2 was not in rodents or many other mammals but was present in chimpanzees (Pan troglodytes) and rhesus macaques (Macaca mulatta). Moreover, the chimpanzee POP2 was identical to humans POP2 at both the DNA and protein levels but the macaque POP2 was not.Conserved processes act, are affected by, or interact at multiple levels of organization. As Cairns-Smith points out, proteins, catalysts, nucleic acids, membranes, and lipids are interlocked and all are dependent on the others for their production. Cairns-Smith summarizes by stating: “Subsystems are highly interlocked The inter-locking is tight and critical. At the centre everything depends on everything” [
81
p39]. The same perturbation may result in different effects or outcomes for different levels of organization in the same intact system. This further complicates our ability to predict outcomes between two intact living complex systems. Thus it appears that a perturbation of complex system S1 containing conserved process P1 resulting in outcome O1 will not necessarily result in O1 in the very similar complex system S2 that also has P1.We will now examine in more detail the response of organisms to inhalational anesthetics and anti-neoplastic agents in order to illustrate what can and cannot be extrapolated between species knowing that species are acted on and affected by the fundamental principles of geometry, chemistry, and physics as well as shared conserved processes.
Conserved processes in anesthesia
General anesthesia by means of inhalational anesthetics (IAs) provides us with an excellent opportunity to examine where the effects of conserved processes can and cannot be extrapolated between species. We expect to see various effects at different levels of organization and in different modules. We also anticipate effects on emergent properties. Because IAs act on the system as a whole, we expect to see effects that cannot be predicted from reductionism. This has implications for what can be expected in terms of predicting human response by studying a different species or perhaps even a different individual. Therefore, both the primary effect of the anesthetic agent as well as the side effects may vary.Broadly speaking, general anesthesia in humans and animals is defined by amnesia, controlled insensitivity and consciousness, and immobility. It has been observed that most, if not all, extant vertebrate species exhibit an anesthetic-like response to a wide variety of chemicals that seemingly have little in common. This has been termed the universal response. Multiple mechanisms for the universal response have been postulated and this is an area of intense current research
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. There seems to be general agreement that ligand gated ion channel (LGIC) protein receptors are involved as well as possible effects on the cellular membrane. Regardless of the exact details, the conservation of mechanisms can be seen in that inhalational anesthetics (IAs) have observable effects on motor or motility responses in vertebrates and invertebrates
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, tactile plants
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and ciliated protists
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. (We note that this is probably an example of an exaptation, specifically a spandrel, rather than an adaptation
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.) Interestingly, effects have even been observed in S. cerevisiae (Baker’s yeast)
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, suggesting that crucial aspects of the universal response go beyond metazoans to include Eukaryotes. Moreover, IAs have been shown to have effects on membrane composition in prokaryote species
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e.g., A. laidlawii
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, Bacillus halodurans
175
and E. coli
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and the single-celled eukaryote tetrahymena
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(a ciliated protozoan). The universal response appears to date far back in evolutionary time and strongly suggests that the mechanism has been conserved among species.However, there are differences in outcomes with respect to IAs. Humphrey et al.
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studied genes in Caenorhabditis elegans and Drosophila melanogaster in order to assess the function of genes thought involved in the response to IAs. They found that a gene in C elegans, unc-79, and a gene in Drosophila, narrow abdomen (na), were related to each other and play a conserved role in response to anesthetics. However, mutations in each gene produced unique changes in sensitivity to IAs. The sensitivity to halothane, an IA, was increased but the sensitivity to enflurane, a different IA, was unchanged or perhaps even lowered. This is perplexing because one would have expected the two inhalational agents to be affected in a similar fashion by the mutation. The gene unc-79 appears to be a post-transcriptional regulator of na, thus the genes operate in the same pathway. Interestingly, both genes are also associated with similar phenotypes regarding locomotion: “fainting” in C. elegans and “hesitant walking” in Drosophila.Stimulation of the conserved processes controlling the universal response results in clinically significant variability among humans, even though the minimum alveolar concentration (MAC) for IAs for most species is approximately the same. MAC is the most often used metric to assess IA potency. However, the concept of MAC implies variability. MAC50, simply called MAC in anesthesiology, is the minimum alveolar concentration necessary to suppress movement in response to painful stimuli in 50% of subjects
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. MAC is significantly variable among humans depending on a number of factors including age and sex. Why is this the case?Sonner et al. reported, “one hundred forty-six statistically significant differences among the 15 strains [of mice] were found for the three inhaled anesthetics (isoflurane, desflurane, and halothane)”
164
. They concluded that multiple genes must be involved in anesthetic potency. Wang et al. developed two strains of mice that manifested different sensitivities to isoflurane
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. MAC is an example of a phenomenon controlled by quantitative trait loci
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, which may explain in part why, while one can obtain a rough approximation of MAC by studying other species, there will still be clinically significant differences.IAs also function at different levels of organization and on modules in addition to the one involved in the universal response. The side effects of the same chemical that produce an effect on the conserved receptors or other processes vary greatly from species to species and in some cases, even from person to person. A good example is the case of isoflurane and coronary steal. In the 1980s, there was heated controversy regarding the administration of the inhalation anesthetic isoflurane to patients with heart disease. The controversy centered on research using canines that indicated that the drug caused myocardial ischemia during certain situations in patients with coronary disease. The phenomenon appeared to result from isoflurane causing dilation of the normal coronary arteries, and thus blood being shunted away from the occluded coronary arteries; the arteries and tissues that most needed it. This was called coronary steal. Further, this situation was worsened by a decrease in blood pressure; a condition that often occurs during general anesthesia with IAs. This supposed danger, based almost entirely on studies in canines, was seized on by many in the anesthesiology community as dogma
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.This was an interesting reaction from clinicians for two reasons. First, experiments with other species had failed to demonstrate coronary steal
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and second, anesthesiologists had not noticed ischemic changes associated with isoflurane despite much use of the agent. The situation was also troublesome because isoflurane was a needed addition to an anesthesiologist’s armamentarium when initially approved for clinical practice. Six years after its introduction, it was the most frequently used IA, in part because of the favorable properties of the drug
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. Further studies continued to demonstrate varying effects intra- and inter-species
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. Ultimately, studies began to appear that suggested isoflurane was in fact cardio-protective. The mechanism for this protection was called “preconditioning and involves the opening of adenosine triphosphate-dependent potassium channels”
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. Isoflurane went from being contraindicated in patients with coronary artery disease to being the drug of choice in such patients. Studies from animals, specifically dogs, figured heavily in forming both, mutually exclusive, conclusions.Just as with the homeobox, miRNAs, and the response to inflammation, there are differences among species in how the conserved process known as the universal response to anesthesia manifests. Clinically, these differences are significant and limit the amount of information that can be extrapolated between species even when the underlying process is conserved. Inhalation anesthetics are also a good example of why, when the level of organization or module being examined changes, extrapolation breaks down. The same chemical that induces general anesthesia in a dog will probably result in the same effect in humans but the dose may vary in a clinically significant fashion and the side effects will most likely vary, as the conserved process does not dictate the side effects. Differences in outcomes from perturbations like the ones we have seen above have been explained by evolution-based species-specific differences, for example background genes, mutations, expression levels, and modifier genes
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.
Anti-neoplastic drugs acting on mitosis
As discussed, a relationship exists between BSA and many physiological parameters
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. For example, Reagan-Shaw, Nihal, and Ahmad state: “BSA correlates well across several mammalian species with several parameters of biology, including oxygen utilization, caloric expenditure, basal metabolism, blood volume, circulating plasma proteins, and renal function”
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. Dosing algorithms for first-in-man (FIM) trials are based on the assumption that there is a one-to-one dose scale between humans and animals when BSA is taken into account
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. The first study suggesting a relationship between dose and body surface area was performed by Pinkel in 1958
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involving anti-neoplastic agents, drugs where the effects and side effects are largely the same—cell death. Subsequently, Freireich et al.,
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studied 18 anti-neoplastic drugs in six animal species and concluded that the maximum tolerated dose (MTD) for humans was 1/12 of the dose in mice that resulted in the death of 10% of the mice (LD10). They also noted that the MTD was 1/7 of the LD10 in rats. These were also the ratios for converting from a mg/kg dose to a dose based on BSA. Fifty anti-neoplastic drugs were then studied using this formula and all were reportedly introduced into human trials without incident
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. The standard for FIM doses then became the 1/10th the LD10 for mice. Actually Freireich recommended a starting dose of 1/3rd the LD10 not 1/10th but that changed over time. The 1/3rd recommendation was found to be too large for FIM and was changed to 1/10th
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. More studies appeared to confirm the 1/10th value
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.The above makes a prima facie case that animal models can predict a starting dose for humans in clinical trials for anti-neoplastics. Further substantiating this is the fact that anti-neoplastics are not always metabolized by the liver
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, thus possibly eliminating a complex system from consideration. Cell division by mitosis is arguably the most conserved process one can find in biology and the traditional drugs for treating cancer act by interfering with mitosis. (Newer drugs act on targeted pathways as opposed to the cell cycle.) Anti-neoplastics kill the cells that are dividing most rapidly—the cancer cells. However, hair cells, cells in the bone marrow, and cells in the gut also divide at a similar rate such that anti-neoplastics can affect them. Thus, in part, the effects and side effects of anti-neoplastics are the same—cell death. The problem with traditional anti-neoplastics is that they do not discriminate adequately.Anti-neoplastic drugs are unique in medicine in that: 1) they are nonspecific; 2) long term toxicities are anticipated and accepted because the patient frequently does not have any other viable options; 3) the effects and side effects of the drugs are the same—cell death; and 4) they act on a universally conserved process—mitosis. This is why body surface area appears to be so effective for calculating FIM dose. Whereas, when one is examining effects and side effects of drugs based on interactions at the level of organization where complexity is relevant, for example metabolism
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, there are simply too many other factors to allow for the expectation of one-to-one correlations. Species-specific differences create perturbations in the complex system thus the differences among species outweigh the similarities
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.However, in the final analysis even the FIM dose of the anti-neoplastic agents cannot be reliably ascertained based on BSA. Most anti-neoplastics are effective only at doses near the maximum tolerated dose and the drugs are given in an escalating fashion during clinical trials. “Patients treated at the lower end of the dose escalation strategy are unlikely to receive even a potentially therapeutic dose since most cytotoxic drugs are only active at or near the MTD”
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. Differences among species in dose response for anti-neoplastics are due in part to differences in pharmacokinetics
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, which cannot be accounted for based on BSA. Brennan et al. state that: “While proper determination of drug doses can be complicated within the same species, it can be an incredible challenge and burden between species”
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. Brennan et al. continue by pointing out that metabolism and clearance differ among species and that “…the liver, kidneys and hematopoietic system between species may have significant differences in their sensitivity to chemotherapeutic agents. None of these factors are taken into account with the use of the species-specific dose calculations”
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. They recommend area under the curve (AUC) for calculating FIM dose but then concede: “However, there are numerous examples in which the species-specific conversion dose varies significantly from the AUC guided dose and/or far exceeds the animal’s maximum tolerated dose.” They then list examples from pediatrics where the recommended and actual doses differ significantly
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.Horstmann et al.
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reviewed 460 Phase I National Cancer Institute trials involving 11,935 adults that occurred between 1991 and 2002. Approximately 25% of the trials were FIM trials. Horstmann et al. found that serious nonfatal effects occurred in 15% of the patients undergoing single chemotherapy, with 58 deaths that were probably treatment-related
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. Concern has also been expressed that animal models have derailed anti-neoplastics that would have been successful in humans
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.FIM dose based on animal models is ineffective for predicting dose for other drug classes as well-TGN1412 being a recent notable example
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. An unnamed clinician, speaking of toxicity trials for new drugs in general in humans, was quoted in Science, stating, “If you were to look in [a big company’s] files for testing small-molecule drugs you’d find hundreds of deaths”
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. Chapman reinforced this stating: “. but other incidents of harm [besides TGN1412], even death, to participants in Phase I trials, some then known and other unpublicized, had taken place”
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. It is also important to note that the 1/3rd or 1/10th safety factor is fabricated. Perlstein et al. state: “Due to uncertainty in translating animal model findings to humans, particularly for unprecedented mechanisms, a wide dose range (1000-fold) is expected to cover the entire exposure–response curve”
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. Extrapolating from species to species should not require fudge factors if the process is truly science-based. In Phase I trials, where FIM or first in human (FIH) occurs, scientists want to characterize the drug’s PK properties and safety margins
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. Wexler and Bertelsen summarize the situation when they state: “Although allometric scaling techniques continue to provide poor predictive estimates for human pharmacokinetic parameters, FIH starting doses are selected with substantial safety factors applied to human equivalent dose, often in excess of regulatory guidelines. Approaches that could enhance the predictive nature of a compound’s disposition and adaptive nature of FIH studies could provide a tremendous benefit for drug development”
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. FIM for all classes of drug could be easily accomplished using microdosing
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with the first dose of one nanogram
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and increasing subsequent doses to the desired endpoint.Finally, one must recall that 95%
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of anti-neoplastic agents fail in clinical trials. Oncology drugs fail more frequently in clinical trials than most other categories
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and a higher percentage of anti-neoplastic drugs fail in Phase III trials than drugs from any other category
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. Reasons for the attrition include the fact that most of the effects and side effects, even of the anti-neoplastic agents, when placed into the context of a complex system, are not predicted from animal studies. Interfering in mitosis is a universal phenomenon but the degree and success of that interference varies. The FIM dose estimation is apparently successful because the level of organization in question is very basic and conserved and because the dose is lowered even further by fudge factors. Picking a starting dose based on the most toxic substances in nature
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would be more scientific. The apparent success also breaks down because the types of cancers in humans differ from those in animals, the genetic background of humans varies from that in animals, and because the reality of a complex system—the interactions of all the other systems (for example how the drugs are eventually metabolized and eliminated and how those metabolites interact with other systems and so on)—eventually appear. These are the problems that cannot be solved by animal models and are why the attrition rate is 95%. Weinberg stated: “it’s been well known for more than a decade, maybe two decades, that many of these preclinical human cancer models have very little predictive power in terms of how actual human beings—actual human tumors inside patients—will respond preclinical models of human cancer, in large part, stink hundreds of millions of dollars are being wasted every year by drug companies using these [animal] models”
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. Others have also pointed out the inadequacy of animal models of cancer, including genetically modified animal models
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.
Conserved processes in light of systems biology
As the level of organization in a complex system increases, we expect to see an increase in the number of emergent properties as well as more overall interactions. A gene or process that has been conserved will interact with the intact whole organism yielding new processes and states. Perturbations of these conserved genes or processes will thus likely result in new states not seen in other organisms that share the conserved processes; perhaps not even in organisms of the same lineage (clade) or species.The lack of appreciation for the differences between levels of organization and other properties of complex systems is apparent in the following from Kardong [
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p2], writing in his textbook of comparative vertebrate anatomy: “For example, by testing a few vertebrate muscles, we may demonstrate that they produce a force of 15 N (newtons) per square centimeter of muscle fiber cross section. Rather than testing all vertebrate muscles, a time-consuming process, we usually assume that other muscles of similar cross section produce a similar force (other things being equal). The discovery of force production in some muscles is extrapolated to others. In medicine, the comparative effects of drugs on rabbits or mice are extrapolated to tentative use in humans.” At the level of organization where one studies the force generated by muscle fibers, no doubt inter-species extrapolation is useful, but that is an entirely different level from where drug actions occur.Indeed the successes from using animal models have been examples of perturbations occurring at subsystems that can be described as simple systems and or outcomes or characteristics that apply on the gross level of examination. For example, the Germ Theory of Disease applies to humans and animals. The immune system reacts to foreign entities in a manner that is grossly similar across species lines. The details of immunity are clinically very different, for example HIV infection leads to AIDS in humans but not chimpanzees
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. Nevertheless, grossly, inflammation, white blood cells, and antibodies are identifying characteristics of the immune system in the phylum Chordata. Likewise, while the heart functions to circulate the blood in mammals, the diseases various mammalian hearts are subject to differ considerably
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. The failures of animal models have occurred when attempting to extrapolate data from higher levels of organization, levels where complexity is an important component in the system or subsystem under consideration. For example, a drug that has passed animal tests and is in Phase I human clinical trials has only an 8% chance of making it to market
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. Over 1,000 drugs have been shown to improve outcomes in cerebral ischemia in animal models but none, save aspirin and thrombolysis, which were not animal-based discoveries, have been successful in humans
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. The animal model for polio, monkeys, revealed a pathophysiology that was very different from that of humans
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. Extracranial-intracranial bypass for inoperable carotid artery disease was successful in animals but results in net harm for humans
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.Most diseases are multifactorial hence it should come as no surprise that conserved processes play a small, although at times important role, in major diseases like heart disease, cancer and stroke. The field of systems biology was formed in part in an attempt to place the parts of molecular biology and genetics in the larger context of the human system; the system that actually responds to drugs and disease. An editorial in Nature asks: “What is the difference between a live cat and a dead one? One scientific answer is 'systems biology'. A dead cat is a collection of its component parts. A live cat is the emergent behaviour of the system incorporating those parts”
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. According to the Department of Systems Biology at Harvard Medical School: “Systems biology is the study of systems of biological components, which may be molecules, cells, organisms or entire species. Living systems are dynamic and complex, and their behavior may be hard to predict from the properties of individual parts”
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. Systems biology
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takes a top-down approach as opposed to reductionism, which evaluates organisms from the bottom-up. Systems biology is concerned more with networks than individual components, although both are studied. It also recognizes the importance of emergent phenomena. (See Figure F2 2
79
). Such top-down approaches are used by the fields commonly referred to as “Omics,” for example: interactomics, metabolomics, proteomics, transcriptomics, and even fractalomics
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.
Figure 2Reductionism versus systems biology
Reductionism versus systems biology.
1742-4682-9-40-2 Nobel laureate Sydney Brenner, in 1998, emphasized that the interactions of components was important in understanding an organism
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. Only by studying proteins and processes in the context of their systems can we expect to understand what happens to the intact organisms as a result of these processes and genes. Further, evolution uses old pathways and processes in different ways to create novelty
1
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. Everything is context dependent. Noble stresses that in order to predict how drugs will act, one must understand “how a protein behaves in context” at higher levels of organization
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.Heng
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, writing in JAMA states that, because of reductionism, biological scientists have sought individual components in a disease process so they could intervene. A linear cause and effect relationship was assumed to exist. Heng cites diabetes intervention in an attempt to control blood glucose and cancer therapies as examples. He points out that while this has worked well in many cases, very tight control of blood glucose was recently found to increase the risk of death
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. Along the same lines, chemotherapies for cancer have decreased the size of the tumors but at the expense of an increase in frequency of secondary tumors and a very adversely affected lifestyle. Furthermore, most chemotherapy does not prolong life or result in a longer, high quality life
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. Instead of focusing on small modules or components of a system, complexity theory mandates that biomedical science look at the system as a whole.Closely related to systems biology are the concepts of personalized medicine and pharmacogenomics
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. It has long been appreciated that humans respond differently to drugs and have different susceptibilities to disease. Based on studies of twins, there appears to be a genetic component to susceptibility to leprosy, poliomyelitis and hepatitis B, as well as response to opioids
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. Other infectious diseases that appear to have a genetic component to susceptibility include HIV, Hepatitis C, malaria, dengue, meningococcal disease, variant Creutzfeldt–Jakob disease and perhaps tuberculosis among others
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. Differences in drug and disease response are manifest among ethnic groups
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and sexes
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. Even monozygotic twins manifest differences in response to such perturbations
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. Rashmi R Shah, previous Senior Clinical Assessor, Medicines and Healthcare products Regulatory Agency, London stated in 2005: “During the clinical use of a drug at present, a prescribing physician has no means of predicting the response of an individual patient to a given drug. Invariably, some patients fail to respond beneficially as expected whereas others experience adverse drug reactions (ADRs)”
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.Similarly, Allen Roses, then-worldwide vice-president of genetics at GlaxoSmithKline (GSK), said fewer than half of the patients prescribed some of the most expensive drugs derived any benefit from them: “The vast majority of drugs more than 90% only work in 30 or 50% of the people.” Most drugs had an efficacy rate of 50% or lower
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. Because of differences in genes, like SNPs, all children may not currently be protected by the same vaccine
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. It is estimated that “between 5 and 20 per cent of people vaccinated against hepatitis B, and between 2 and 10 per cent of those vaccinated against measles, will not be protected if they ever encounter these viruses”
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. In the future such children may be able to receive a personalized shot. Currently, numerous drugs have been linked to genetic mutations and alleles. See Table T5 5
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and Table T6 6
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. The number of personalized medicine products has increased from 13 in 2006 to 72 as of 2012
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.
Table 5
Examples of drugs with genetic information in thier labels
Drug
Sponsor
Indication
Gene or genotype
Effect of genotype
Clinical directive on label
*All drug labels were accessed through Drugs @FDA at www.accessdata.fda.gov/scripts/cder/drugsatfda. HIV-1 denotes human immunodeficiency virus type 1, TPMT thiopurine methyltransferase, UGT1A1 UDP glucuronosyltransferanse 1 family polypeptide A1, and VKORC1 vitamins K epoxide reductase complex subunit 1
Abacavir (Ziagen)
GlaxoSmithKline
HIV-1
HLA-B*5701
Hypersensitivity
Black-box warning. "Prior to initiating therapy with abacavir, screening for the HLA-B*5701 allele is recommended." "Your doctor can determine with a blood test if you have this gene variation."
Azathioprine (Imuran)
Prometheus
Renal allograft transplantation, rheumatoid
TPT*2TPT*3Aand TPMT*3C
Severe myeloxicity
"TPT genotyping or phenotyping can help identify patients who are at an increased risk for developing Imuran toxicity." "Phenotyping and genotyping methods are commercially available."
Carbamazepine (Tegretol)
Novartis
Epilepsy, trigeminal neuralgia
HLA-B*1502
Stevens-Johnson syndrome or toxic epidermal necrolysis
Black-box warning: "Patients with ancestry in genetically at-risk populations should be screened for the presence of HLA-B*1502 prior to initiating treatment with Tegretol. Patients testing positive for the allele should not be treated with Tegretol." "For genetically at-risk patients, high-resolution HLA-B*1502 typing is recommended."
Cetuximab (Erbitux)
Imclone
Metastatic colorectal cancer
KRAS mutations
Efficacy
"Retrospective subset analyses of metastatic or advanced colerectal cancer trials have not shown a treatment benefit for Erbitux in patients whose tumors had KRAS mutations in codon 12 or 13. Use of Erbitux is not recommended for the treatment of colorectal cancer with mutations."
Clopidogrel (Plavix)
Bristol-Myer Squibb
Anticoagulation
CYP2C19*2*3
Efficacy
"Tests are available to identify a patient’s CYP2C19 genotype; these tests can be used as an aid in determining therapeutic strategy. Consider alternative treatment or treatment strategies in patienrs identified as CYP2C19 poor metabolizer."
Irinotecan (Camptosar)
Pfizer
Metastatic colorectal cancer
UGT1A1*28
Diarrhea neutropenia
"A reduction in the starting dose by at least one level of Camptosar should be consider for patients knows to be homozygous for the UGT1A1*28 allele. "A laboratory test is available to determine the UGT1A1 status of patients."
Pantumumab (Vectibix)
Amgen
Metastatic colorectal cancer
KRAS mutations
Efficacy
"Retrospective subset analyses of metastatic colorectal cancer trials have not shown a treatment benefit for Vectibix in patients whose tumors had KRAS mutations in codon 12 or 13. Use of Vectibix is not recommended for the treatment of colorectal cancer with these mutations."
Transtuzumab (Herceptin)
Genetech
HER2-positive breastcancer
HER2 expression
Efficacy
"Detection of HER2 protein overexpression is necessary for selection of patients appropriate for Herceptin therapy because these are the only patients studied and for whom benefit has shown." "Several FDA-approved commercial assays are available to aid in the selection of breast cancer and metastatic cancer patients for Herptin therapy."
Wafarin (Coumadin)
Bristol-Myer Squibb
Venous thrombosis
CYP2C9*2*3 and VKORC1 variants
Bleeding complications
Includes the following table: Range of Expected Therapeutic Warfarin Doses Based on CYP2CP and VKORC1 Genotypes.
Table 6
The most significant genetic predictors of drug response
Organ or system involved
Associated gene/allele
Drug/drug response phenotype
Blood
Red blood cells
G6PD
Primaquine and others
Neutrophils
TMPT*2
Azathioprine/6MP-induced neutropenia
UGT1A1*28
Irintotecan-induced neutropenia
Plates
CYP2C19*2
Stent thrombusis
Coagulation
CY2C9*2, *3, VKORC1
Warfarin dose-requirement
Brain and peripheral nervous system
CNS depression
CYP2D6*N
Codeine-related sedation and respiratory depression
Anaesthesia
Butyrylcholinesterase
Prolonged apnoea
Peripheral nerves
NAT-2
Isoniazid-induced peripheral neuropathy
Drug hypersesitivity
HLA-B*5701
Abacavir hypersensitivity
HLA-B*1502
Carbamazepine-induced Steve Johnson syndrome (in some Asian groups )
HLA-A*3101
Carbamazepine-induced hypersensitivity in Causians and Japanese
HLA-B*5801
Allopurinol-induced serious cutaneous reactions
Drug-induced liver injury
HLA-B*5701
Flucloxacillin
HLA-DR81*1501-DQ81*0602
Co-amoxiclav
HLA-DR81*1501-DQ81*0602
Lumiracoxib
HLA-BR81*07-DOA1*02
Ximelagatran
HLA-DQA1*0201
Lapatinib
Infection
HIV-1 infection
CCRS
Maraviroc efficacy
Hepatitis C infection
IL288
Interferon-alpha efficacy
Malignancy
Breast cancer
CYP2D^
Response to tamoxifen
Chronic myeloid leukaemia
BCR-ABL
Imatinib and other tyrosine kinase inhibitors
Colon cancer
KRAS
Cetuximab efficacy
GI stromal tumours
c-kit
Imatinib efficacy
Lung cancer
EGFR
Gefinib efficacy
EML4-ALK
Crizotinib efficacy
Malignant melanoma
BRAF V600E
Vemurafenib efficacy
When animals were being used as models in the 19th century, many of the scientists who were using them had not accepted evolution and believed that animal parts were interchangeable with their human counterparts
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. Given that we now understand that intra-human variation results in such markedly different responses to drugs and disease, attempting to predict human response from animal models, even for perturbations acting on conserved processes, seems unwarranted. Yet, despite the implications of personalized medicine
22
, some scientists continue to commit the fallacy described by Burggren and Bemis: “Yet the use of ‘cockroach as insect,’ ‘frog as amphibian,’ or ‘the turtle as reptile’ persists, in spite of clear evidence of the dangers of this approach. Not surprisingly, this type of comparative physiology has neither contributed much to evolutionary theories nor drawn upon them to formulate and test hypotheses in evolutionary physiology” [
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p206]. Comparative research will yield a nice comparison of the trait or process among species or phyla. However, one simply cannot assume that the outcome from a specific perturbation in, say the cockroach, will be seen in insects in general and this concept becomes even more important when relying on animal models for medical interventions in humans.
Conclusion
A perturbation of living complex system S1 containing conserved process P1 resulting in outcome O1 will not result in O1 in the very similar living complex system S2 that also has P1 often enough to qualify S1 as a predictive modality for S2 when the trait or response being studied is located at higher levels of organization, is in a different module, or is influenced by other modules. However, when the examination of the conserved process occurs at the same or lower level of organization or in the same module, and hence is subject to study solely by reductionism, then extrapolation is possible. We believe this is a valuable principle.Our current understanding of evo devo, evolution in general, complexity science, and genetics allows us to generalize regarding trans-species extrapolation, even when conserved processes are involved. Shanks and Greek:"Living complex systems belonging to different species, largely as a result of the operation of evolutionary mechanisms over long periods of time, manifest different responses to the same stimuli due to: (1) differences with respect to genes present; (2) differences with respect to mutations in the same gene (where one species has an ortholog of a gene found in another); (3) differences with respect to proteins and protein activity; (4) differences with respect to gene regulation; (5) differences in gene expression; (6) differences in protein-protein interactions; (7) differences in genetic networks; (8) differences with respect to organismal organization (humans and rats may be intact systems, but may be differently intact); (9) differences in environmental exposures; and last but not least; (10) differences with respect to evolutionary histories. These are some of the important reasons why members of one species often respond differently to drugs and toxins, and experience different diseases. Immense empirical evidence supports this position (
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p358)."The failures of animal models as a predictive modality for human response to disease and drugs, even when such perturbations are acting on conserved processes, can be explained in the context of evolved complex systems. One does not need to study every such perturbation in every species in order to conclude that the animal model will not be a predictive modality for humans when perturbations occur at higher levels of organization or involve different modules or affect the system as a whole. This is not to deny that animal models, as characterized by 3–9 in Table 1, have contributed and will continue to contribute to scientific advancements.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
The authors contributed equally to this paper.
Authors’ information
Ray Greek, MD has been on faculty in the Department of Anesthesiology at the University of Wisconsin-Madison and at Thomas Jefferson University in Philadelphia. He is currently president of the not-for-profit Americans For Medical Advancement (http://www.AFMA-curedisease.org).Mark Rice, MD is currently on faculty at the University of Florida (UF). He is chief of the liver transplant division at UF Department of Anesthesiology, has seven US patents, and reviews for several major journals.
bm
ack
Acknowledgements
None.
refgrp KirschnerMWGerhartJCThe Plausibility of Lifepublisher New Haven: Yale University Press2006BraithwaiteRBScientific explanation: a study of the function of theory, probability and law in scienceCambridge: Cambridge University Press1953Animal-Human ComparisonsHindeRThe Oxford Companion to the MindOxford: Oxford University Presseditor Gregory RL198725lpage 27Scientific ModelsFriggRHartmannSThe Philosophy of Science: An Encyclopedia Volume 2 N-ZNew York: RoutledgeSarkar S, Pfeifer J2012740749Animal Model Research. The Apples and Oranges QuandryShapiroKATLA200432405409HauJAnimal ModelsBoca Rotan: CRC PressHau J, Hoosier GK Jredition 2series
Handbook of Laboratory Animal Science Second Edition Animal Models
200319PrefaceGadSAnimal Models in ToxicologyBoca Rotan: CRC PressGad S2007118Longer Tests on Lab Animals Urged for Potential Carcinogenshttp://www.cspinet.org/new/200811172.html.The limits of two-year bioassay exposure regimens for identifying chemical carcinogensHuffJJacobsonMFDavisDLEnviron Health Perspect200811614391442Genomically humanized mice: technologies and promisesDevoyABunton-StasyshynRKATybulewiczVLJSmithAJHFisherEMCNat Rev Genet2012131420Alzheimer's therapy: a BACE in the hand?VassarRNat Med201117932933THS CEO criticized for links to animal testinghttp://m.torontosun.com/2011/09/23/ths-ceo-criticized-for-links-to-animal-testing?noimage.Clinical Toxicity--Could it have been predicted? Post-marketing experienceHeywoodRAnimal Toxicity Studies: Their Relevance for ManLancaster: QuayLumley CE, Walker S19905767ShanksNGreekRAnimal Models in Light of EvolutionBoca Raton: Brown Walker2009Is the use of sentient animals in basic research justifiable?GreekRGreekJPhilos Ethics Humanit Med2010514GreekRShanksNFAQs About the Use of Animals in Science: A handbook for the scientifically perplexedLanham: University Press of America2009Experimental use of nonhuman primates is not a simple problemShanksNGreekRNat Med200814807808Are animal models predictive for humans?ShanksNGreekRGreekJPhilos Ethics Humanit Med200942Animals and Medicine: Do Animal Experiments Predict Human Response?ShanksNGreekRNobisNGreekJSkeptic2007134451Letter. Dogs, Genes and DrugsGreekRAm Sci2008964An analysis of the Bateson Review of research using nonhuman primatesGreekRHansenLAMenacheAMedicolegal Bioethics20111322Animal models in an age of personalized medicineGreekRMenacheARiceMJPersonalized Med201294764The History and Implications of Testing Thalidomide on AnimalsGreekRShanksNRiceMJThe Journal of Philosophy, Science & Law201111http://www6.miami.edu/ethics/jpsl/archives/all/TestingThalidomide.html.Reengineering Translational Science: The Time Is RightCollinsFSSci Transl Med2011390cm17Predictive in vivo animal models and translation to clinical trialsCookNJodrellDITuvesonDADrug Discov Today201217253260Healthy animals and animal models of human disease(s) in safety assessment of human pharmaceuticals, including therapeutic antibodiesDixitRBoelsterliUDrug Discov Today200712336342In Vitro Biomimetic Model of the Human Immune System for Predictive Vaccine AssessmentsDrakeDRsuf IIISinghINguyenMNKachurinAWittmanVParkhillRKachurinaOMoserJMBurdinNMoreauMetal Disruptive Sci Technol201212840FDA Issues Advice to Make Earliest Stages Of Clinical Drug Development More Efficienthttp://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/2006/ucm108576.htm.Drug safety tests and subsequent clinical experienceFletcherAPJ R Soc Med197871693696Modern biomedical research: an internally self-consistent universe with little contact with medical reality?HorrobinDFNat Rev Drug Discov20032151154Can the pharmaceutical industry reduce attrition rates?KolaILandisJNat Rev Drug Discov20043711715Clinical toxicity: could it have been predicted? Premarketing experienceLumleyCAnimal Toxicity Studies: Their Relevance for ManQuayLumley C, Walker S19904956This Issuecnm M.EModels that better mimic human cancer
Nat Biotechnol
2010viiRemoving obstacles in neuroscience drug discovery: the future path for animal modelsMarkouAChiamuleraCGeyerMATricklebankMStecklerTNeuropsychopharmacol Offic Publ Am Coll Neuropsychopharmacol20093474891,026 experimental treatments in acute strokeO'CollinsVEMacleodMRDonnanGAHorkyLLvan der WorpBHHowellsDWAnn Neurol200659467477Promoting Convergence in Biomedical ScienceSharpPALangerRScience2011333527The absolute oral bioavailability of selected drugsSietsemaWKInt J Clin Pharmacol Ther Toxicol198927179211What can be learned from case studies? The company approachSuterKAnimal Toxicity Studies: Their Relevance for ManLancaster: QuayLumley C, Walker S19907178Are animal models as good as we think?WallRJShaniMTheriogenology20086929Detection of systemic hypersensitivity to drugs using standard guinea pig assaysWeaverJLStatenDSwannJArmstrongGBatesMHastingsKLToxicology2003193203217Building a better mouseZielinskaEScientist2010243438The use of nonhuman animals in biomedical researchRingachDLAm J Med Sci2011342305313RudczynskiABLetter to the editor. New Haven Register2011Available at http://www.nhregister.com/articles/2011/03/25/opinion/doc4d8bb9186a82b265857273.txt.Mouse models of brain tumors and their applications in preclinical trialsFomchenkoEIHollandECClin Cancer Res20061252885297Predictability of Conventional Animal Toxicity TestsLitchfieldJTJrAnn N Y Acad Sci1965123268272Regulatory agencies, drugs, and the pregnant patientLasagnaLDrug use in pregnancySydney: ADIS Health. Science PressStern L1984Species similarities and differences in pharmacokineticsLinJHDrug Metab Dispos19952310081021Toxicology of environmental agents: a blend of applied and basic researchDixonRLEnviron Health Perspect19722103116Clinical value of serum CA19-9 levels in evaluating resectability of pancreatic carcinomaZhangSWangY-MSunC-DLuYWuL-QWorld J Gastroenterol20081437503753Prehospital Termination of Resuscitation in Cases of Refractory Out-of-Hospital Cardiac ArrestSassonCHeggAJMacyMParkAKellermannAMcNallyBJAMA200830014321438Psychopathy and recidivism among female inmatesSalekinRTRogersRUstadKLSewellKWLaw Hum Behav199822109128Using verbal autopsy to assess the prevalence of HIV infection among deaths in the ART period in rural Uganda: a prospective cohort study, 2006–2008MayanjaBNBaisleyKNalweyisoNKibengoFMMugishaJOPaalLVMaherDKaleebuPPopulation Health Metrics201193610.1186/1478-7954-9-36Predictive values at risk of falling in physically active and no active elderly with Berg Balance ScaleSantosGSouzaAVirtuosoJTavaresGMazoGRev Bras Fisioter20111595101Committee on Models for Biomedical Research Board on Basic BiologyCommittee on Models for Biomedical Research. Board on Basic Biology. Commission on Life Science. National Research Council. Models for Biomedical Research: A New PerspectiveWashington, DC: National Academy Press1985From bedside to bench and back again: research issues in animal models of human diseaseTkacsNCThompsonHJBiol Res Nurs200687888Basic Issues in the Use of Animals in Health ResearchOvermierJBCarrollMEAnimal Research and Human HealthWashington DC: American Psychological AssociationCarroll ME, Overmier JB20015Two Models of Models in Biomedical ResearchLaFolletteHShanksNPhil Q199545141160LaFolletteHShanksNBrute Science: Dilemmas of animal experimentationLondon and New York: Routledge1996Theories, Models, and Equations in Systems BiologySchaffnerKFSystems Biology: Philosophical FoundationsNetherlands: ElsevierBoogerd F, Bruggeman FJ, Hofmeyr J-HS, Westerhoff HV2007145162BernardCAn Introduction to the Study of Experimental MedicineNew York: Dover1957BungeMCausality And Modern ScienceNew York: Dover31979Vivisection and the Emergence of Experimental Medicine in Nineteenth Century FranceElliotPVivisection in Historical PerspectiveNew York: Croom HelmRupke N19874877Animal Experimentation: The Legacy of Claude BernardLaFolletteHShanksNInt Stud Philos Sci19948195210Principles of ToxicologyKlaassenCDEatonDLCasarett and Doull's ToxicologyNew York: McGraw-HillAmdur MO, Doull J, Klaassen C41993MilnerRDarwin's Universe: Evolution from A to ZBerkeley: University of California Press2009Causality in Complex SystemsWagnerABiol Philos19991483101On the Notion of Cause. Proceedings of the Aristotelian SocietyRussellBNew Ser191313126Animal Models and the Development of an HIV VaccineGreekRJ AIDS Clin Res2012S8001GiereRNBickleJMauldinRFUnderstanding Scientific ReasonoingToronto: Thomson Wadsworth52006Random Samples. Well-Wired WhalesHoldenCScience20063141363Structure of the cerebral cortex of the humpback whale, Megaptera novaeangliae (Cetacea, Mysticeti, Balaenopteridae)HofPRVan der GuchtEAnat Rec (Hoboken)2007290131Von Economo neurons in the elephant brainHakeemAYSherwoodCCBonarCJButtiCHofPRAllmanJMAnat Rec (Hoboken)2009292242248CrickFOf Molecules and ManSeattle: University of Washington Press1966The sequence of the human genomeVenterJCAdamsMDMyersEWLiPWMuralRJSuttonGGSmithHOYandellMEvansCAHoltRAScience200129113041351A physical map of the human genomeMcPhersonJDMarraMHillierLWaterstonRHChinwallaAWallisJSekhonMWylieKMardisERWilsonRKNature2001409934941Complexity in biology. Exceeding the limits of reductionism and determinism using complexity theoryMazzocchiFEMBO Rep200891014Modelling biological complexity: a physical scientist's perspectiveCoveneyPVFowlerPWJ R Soc Interface20052267280CoveneyPVHighfieldRRFrontiers of complexityLondon: Faber and Faber1996The limits of reductionism in medicine: could systems biology offer an alternative?AhnACTewariMPoonCSPhillipsRSPLoS Med20063e208Biological networksAlmEArkinAPCurr Opin Struct Biol200313193202Cairns-SmithAGSeven Clues to the Origin of Life: A Scientific Detective StoryCambridge: Cambridge University Press1986Reverse engineering of biological complexityCseteMEDoyleJCScience200229516641669GoodwinBHow the Leopard Changed Its Spots: The Evolution of ComplexityPrinceton: Princeton University Press2001Regulatory mechanisms of gene expression: complexity with elements of deterministic chaosJuraJWegrzynPKojAActa Biochim Pol200653110KauffmanSAhe Origins of Order: Self-Organization and Selection in EvolutionOxford University Press1993Computational systems biologyKitanoHNature2002420206210Systems biology: a brief overviewKitanoHScience200229516621664Definitions, Measures, and Models of Robustness in Gene Regulatory Network. Report of research work for CSSS05http://www.santafe.edu/education/csss/csss05/papers/monte_et_al._cssssf05.pdf.MorowitzHJThe Emergence of Everything: How the World Became ComplexOxford: Oxford University Press2002The Concept of Integrative Levels and BiologyNovikoffABScience1945101209215Engineering complex systemsOttinoJMNature2004427399SoleRGoodwinBSigns of Life: How Complexity Pervades Biology
Basic Books
2002Reductionism and complexity in molecular biology. Scientists now have the tools to unravel biological complexity and overcome the limitations of reductionismVan RegenmortelMEMBO Rep2004510161020Biological complexity emerges from the ashes of genetic reductionismvan RegenmortelMJ Mol Recognit200417145148Van RegenmortelMHHullDLPromises and Limits of Reductionism in the Biomedical Sciences (Catalysts for Fine Chemical Synthesis)West Sussex: Wiley2002The bigger pictureVicsekTNature2002418131WoodgerJHBiological PrinciplesNew York: Humanities Press1967The state of innovation in drug developmentKolaIClin Pharmacol Ther200883227230How emergence arisesde HaanJEcol Complex20063293301Multi-scale computational modelling in biology and physiologySouthernJPitt-FrancisJWhiteleyJStokeleyDKobashiHNobesRKadookaYGavaghanDProg Biophys Mol Biol2008966089MorinEIntroduction á la Pensée ComplexeParis: ESF1990HaldaneJBSOn Being the Right SizeNew York: Harper's1926MorangeMThe misunderstood geneCambridge: Harvard University Press2001Theoretical BiologyKauffmanSEpigenetic and Evolutionary Order from Complex SystemsEdinburgh: Edinburgh University PressGoodwin B, Saunders P1990Self-organization, complexity and chaos: the new biology for medicineCoffeyDSNat Med19984882885The concept of self-organization in cellular architectureMisteliTJ Cell Biol2001155181185Phenotypically concordant and discordant monozygotic twins display different DNA copy-number-variation profilesBruderCEPiotrowskiAGijsbersAAAnderssonREricksonSde StahlTDMenzelUSandgrenJvon TellDPoplawskiAAm J Hum Genet200882763771Epigenetic differences arise during the lifetime of monozygotic twinsFragaMFBallestarEPazMFRoperoSSetienFBallestarMLHeine-SunerDCigudosaJCUriosteMBenitezJProc Natl Acad Sci USA20051021060410609Changes in the pattern of DNA methylation associate with twin discordance in systemic lupus erythematosusJavierreBMFernandezAFRichterJAl-ShahrourFMartin-SuberoJIRodriguez-UbrevaJBerdascoMFragaMFO'HanlonTPRiderLGGenome Res201020170179Remodeling rodent models to mimic human type 1 diabetesvon HerrathMNepomGTEur J Immunol20093920492054Surviving a knockout blowPearsonHNature200241589A successful form for reductionismMorangeMBiochem2001233739Disease-associated epigenetic changes in monozygotic twins discordant for schizophrenia and bipolar disorderDempsterELPidsleyRSchalkwykLCOwensSGeorgiadesAKaneFKalidindiSPicchioniMKravaritiEToulopoulouTHum Mol Genet20112047864796Changes in the pattern of DNA methylation associate with twin discordance in systemic lupus erythematosusJavierreBMFernandezAFRichterJAl-ShahrourFMartin-SuberoJIRodriguez-UbrevaJBerdascoMFragaMFO'HanlonTPRiderLGGenome Res201020170179Ontogenetic De Novo Copy Number Variations (CNVs) as a Source of Genetic Individuality: Studies on Two Families with MZD Twins for SchizophreniaMaitiSKumarKHBGCastellaniCAO'ReillyRSinghSMPLoS One20116e17125Phenotypic differences in genetically identical organisms: the epigenetic perspectiveWongAHGottesmanIIPetronisAHum Mol Genet20051411118The evolution of molecular biologyKellenbergerEEMBO Rep20045546549Animal experiments under fire for poor designGilesJNature2006444981Editorial: A slippery slopeNature2009462699Animal models in biomedical research: some epistemological worriesLaFolletteHShanksNPubl Aff Q19937113130Olfaction: diverse species, conserved principlesAcheBWYoungJMNeuron200548417430Unlocking the power of cross-species genomic analyses: identification of evolutionarily conserved breast cancer networks and validation of preclinical modelsBennettCNGreenJEBreast Cancer Res200810213The Obg subfamily of bacterial GTP-binding proteins: essential proteins of largely unknown functions that are evolutionarily conserved from bacteria to humansCzyzAWegrzynGActa Biochim Pol2005523543Acidocalcisomes conserved from bacteria to manDocampoRde SouzaWMirandaKRohloffPMorenoSNNat Rev Microbiol20053251261Insulin resistance is an evolutionarily conserved physiological mechanism at the cellular level for protection against increased oxidative stressErolABioessays200729811818Evolutionarily conserved structural and functional roles of the FYVE domainHayakawaAHayesSLeonardDLambrightDCorveraSBiochem Soc Symp20077495105[Conserved telomeric-end structures among fission yeast and humans]MiyoshiTIshikawaFTanpakushitsu Kakusan Koso20085318501857Conserved developmental processes and the formation of evolutionary novelties: examples from butterfly wingsSaenkoSVFrenchVBrakefieldPMBeldadePPhilos Trans R Soc Lond B Biol Sci200836315491555Structure and function of the PB1 domain, a protein interaction module conserved in animals, fungi, amoebas, and plantsSumimotoHKamakuraSItoTSci STKE20074012007:re6.Teneurins: a conserved family of transmembrane proteins involved in intercellular signaling during developmentTuckerRPChiquet-EhrismannRDev Biol2006290237245Conserved functions of the pRB and E2F familiesvan den HeuvelSDysonNJNat Rev Mol Cell Biol20089713724YAP, TAZ, and Yorkie: a conserved family of signal-responsive transcriptional coregulators in animal development and human diseaseWangKDegernyCXuMYangXJBiochem Cell Biol2009877791The Theory of Facilitated VariationGerhartJKirschnerMthe Light of EvolutionWashington DC: National Acdemy of SciencesAvise JC, Ayala FJ
Adaptation and Complex Design
20074564FOXO animal models reveal a variety of diverse roles for FOXO transcription factorsArdenKCOncogene20082723452350SERCA pumps and human diseasesHovnanianASubcell Biochem200745337363A gene complex controlling segmentation in DrosophilaLewisEBNature1978276565570Molecular cloning and chromosome mapping of a mouse DNA sequence homologous to homeotic genes of DrosophilaMcGinnisWHartCPGehringWJRuddleFHCell198438675680Shaping animal body plans in development and evolution by modulation of Hox expression patternsGellonGMcGinnisWBioessays199820116125The zootype and the phylotypic stageSlackJMHollandPWGrahamCFNature1993361490492Hox cluster duplications and the opportunity for evolutionary noveltiesWagnerGPAmemiyaCRuddleFProc Natl Acad Sci USA20031001460314606Zebrafish hox clusters and vertebrate genome evolutionAmoresAForceAYanYLJolyLAmemiyaCFritzAHoRKLangelandJPrinceVWangYLScience199828217111714Hox, ParaHox, ProtoHox: facts and guessesGarcia-FernandezJHeredity200594145152The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14LeeRCFeinbaumRLAmbrosVCell199375843854An abundant class of tiny RNAs with probable regulatory roles in Caenorhabditis elegansLauNCLimLPWeinsteinEGBartelDPScience2001294858862New microRNAs from mouse and humanLagos-QuintanaMRauhutRMeyerJBorkhardtATuschlTRNA20039175179MicroRNA signatures in human cancersCalinGACroceCMNat Rev Cancer20066857866Changes in microRNA profile and effects of miR-320 in insulin-resistant 3T3-L1 adipocytesLingHYOuHSFengSDZhangXYTuoQHChenLXZhuBYGaoZPTangCKYinWDClin Exp Pharmacol Physiol200910.1111/j.1440-1681.2009.05207.xMicroRNA expression profiles classify human cancersLuJGetzGMiskaEAAlvarez-SaavedraELambJPeckDSweet-CorderoAEbertBLMakRHFerrandoAANature2005435834838Animal MicroRNAs confer robustness to gene expression and have a significant impact on 3'UTR evolutionStarkABrenneckeJBushatiNRussellRBCohenSMCell200512311331146Pharmacogenomics genes show varying perceptibility to microRNA regulationRukovJLVintherJShomronNPharmacogenet Genomics201121251262MicroRNAs as a molecular basis for mental retardation, Alzheimer's and prion diseasesProvostPBrain Res201013385866MicroRNA-21 in cardiovascular diseaseChengYZhangCJ Cardiovasc Transl Res20103251255Comparing the human and chimpanzee genomes: searching for needles in a haystackVarkiAAltheideTKGenome Res20051517461758Functional Comparison of Innate Immune Signaling Pathways in PrimatesBarreiroLBMarioniJCBlekhmanRStephensMGiladYPLoS Genet20106e1001249A chimpanzee genome project is a biomedical imperativeVarkiAGenome Res20001010651070Loss of Siglec expression on T lymphocytes during human evolutionNguyenDHHurtado-ZiolaNGagneuxPVarkiAProc Natl Acad Sci USA200610377657770Rewirable gene regulatory networks in the preimplantation embryonic development of three mammalian speciesXieDChenCCPtaszekLMXiaoSCaoXFangFNgHHLewinHACowanCZhongSGenome Res201020804815Cdc14: a highly conserved family of phosphatases with non-conserved functions?MocciaroASchiebelEJ Cell Sci201012328672876Recent evolution of the NF-kappaB and inflammasome regulating protein POP2 in primatesAtianandMKFuchsTHartonJABMC Evol Biol20111156Why can all of biology be anesthetized?EckenhoffRGAnesth Analg2008107859861Meyer and Overton revisitedLynchC3rdAnesth Analg2008107864867Genetics and the evolution of the anesthetic responseSedenskyMMMorganPGAnesth Analg2008107855858A hypothesis on the origin and evolution of the response to inhaled anestheticsSonnerJMAnesth Analg2008107849854Naturally occurring variability in anesthetic potency among inbred mouse strainsSonnerJMGongDEgerEI2ndAnesth Analg200091720726Sensitivity to anesthesia by pregnenolone appears late in evolutionOlverADeamerDMolecular and Cellular Mechanisms of Alcohol and AnestheticsNew York: Annals of the New York Academy of SciencesRubin E, Miller K, Roth S1991561565C. elegans and volatile anestheticsMorganPGKayserEBSedenskyMMWormBook2007111http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=18050492.Behavioral effects of volatile anesthetics in Caenorhabditis elegansCrowderCMShebesterLDSchedlTAnesthesiology199685901912Strain differences in minimum anesthetic concentrations in Drosophila melanogasterGamoSOgakiMNakashima-TanakaEAnesthesiology198154289293Inhalational and local anesthetics reduce tactile and thermal responses in mimosa pudicaMilneABeamishTCan J Anaesth199946287289The effect of inhalational anaesthetics on the swimming velocity of Tetrahymena pyriformisNunnJFSturrockJEWillsEJRichmondJEMcPhersonCKJ Cell Sci197415537554Exaptation — a missing term in the science of formGouldSJVrbaESPaleobiology19828415The exaptive excellence of spandrels as a term and prototypeGouldSJProc Natl Acad Sci USA1997941075010755The spandrels of San Marco and the Panglossian paradigm: a critique of the adaptationist programmeGouldSJLewontinRCProc R Soc Lond B Biol Sci1979205581598Molecular genetic analysis of volatile-anesthetic actionKeilRLWolfeDReinerTPetersonCJRileyJLMol Cell Biol19961634463453Isoflurane Inhibits NaChBac, a Prokaryotic Voltage-Gated Sodium ChannelOuyangWJihT-YZhangT-TCorreaAMHemmingsHCJrJ Pharmacol Exp Ther200732210761083Metabolic changes of membrane lipid composition in Acholeplasma laidlawii by hydrocarbons, alcohols, and detergents: arguments for effects on lipid packingWieslanderARilforsLLindblomGBiochemistry19862575117517Chronic exposure to inhaled anesthetics increases cholesterol content in Acholeplasma laidlawiiKoblinDDWangHHBiochim Biophys Acta1981649717725Adaptation of membrane lipids to alcoholsIngramLOJ Bacteriol1976125670678Membrane changes during growth of Tetrahymena in the presence of ethanolNandini-KishoreSGMattoxSMMartinCEThompsonGAJrBiochim Biophys Acta1979551315327Membrane fluidizing effects of the general anesthetic methoxyflurane elicit an acclimation response in TetrahymenaNandini-KishoreSGKitajimaYThompsonGAJrBiochim Biophys Acta1977471157161A putative cation channel and its novel regulator: cross-species conservation of effects on general anesthesiaHumphreyJAHammingKSThackerCMScottRLSedenskyMMSnutchTPMorganPGNashHACurr Biol: CB200717624629Minimum alveolar anesthetic concentration: a standard of anesthetic potencyEgerEI2ndSaidmanLJBrandstaterBAnesthesiology196526756763Selective breeding of mice strains with different sensitivity to isofluraneWangQZhengYLuJChenLWangJZhouJXChin Med J (Engl)201012313151319Mouse chromosome 7 harbors a quantitative trait locus for isoflurane minimum alveolar concentrationCascioMXingYGongDPopovichJEgerEI2ndSenSPeltzGSonnerJMAnesth Analg2007105381385Isoflurane induces coronary steal in a canine model of chronic coronary occlusionBuffingtonCWRomsonJLLevineADuttlingerNCHuangAHAnesthesiology198766280292Is isoflurane dangerous for the patient with coronary artery disease?BeckerLCAnesthesiology198766259261Systemic distribution of blood flow in swine while awake and during 1.0 and 1.5 MAC isoflurane anesthesia with or without 50% nitrous oxideLundeenGManoharMParksCAnesth Analg198362499512Regional distribution of brain and myocardial perfusion in swine while awake and during 1.0 and 1.5 MAC isoflurane anaesthesia produced without or with 50% nitrous oxideManoharMParksCCardiovasc Res198418344353Isoflurane anesthesia and myocardial ischemia: comparative risk versus sufentanil anesthesia in patients undergoing coronary artery bypass graft surgery. The SPI (Study of Perioperative Ischemia) Research GroupLeungJMGoehnerPO'KellyBFHollenbergMPinedaNCasonBAManganoDTAnesthesiology199174838847Practical treatment recommendations for the safe use of anaestheticsSearJWDrugs1992435468Isoflurane and coronary heart diseaseAgnewNMPennefatherSHRussellGNAnaesthesia200257338347Modifier genes and oligogenic diseaseAgarwalSMoorchungNJ Nippon Med Sch200572326334Genotype to Phenotype: A Complex ProblemDowellRDRyanOJansenACheungDAgarwalaSDanfordTBernsteinDARolfePAHeislerLEChinBScience2010328469Editorial: Deconstructing Genetic Contributions to Autoimmunity in Mouse ModelsPLoS Biol20042e220Genetic screening for signal transduction in the era of network biologyFriedmanAPerrimonNCell2007128225231Genetic background is an important determinant of metastatic potentialHunterKWelchDRLiuETNat Genet2003342324author reply 25.Comparison of differentially expressed genes in T lymphocytes between human autoimmune disease and murine models of autoimmune diseaseLiuZMaasKAuneTMClin Immunol2004112225230Genetic modifiers of beta-thalassemiaTheinSLHaematologica200590649660A Genome-Wide Study of DNA Methylation Patterns and Gene Expression Levels in Multiple Human and Chimpanzee TissuesPaiAABellJTMarioniJCPritchardJKGiladYPLoS Genet20117e1001316Genetic analysis of genome-wide variation in human gene expressionMorleyMMolonyCMWeberTMDevlinJLEwensKGSpielmanRSCheungVGNature2004430743747Genetic structure of human populationsRosenbergNAPritchardJKWeberJLCannHMKiddKKZhivotovskyLAFeldmanMWScience200229823812385Gene-expression variation within and among human populationsStoreyJDMadeoyJStroutJLWurfelMRonaldJAkeyJMAm J Hum Genet200780502509Evaluation of genetic variation contributing to differences in gene expression between populationsZhangWDuanSKistnerEOBleibelWKHuangRSClarkTAChenTXSchweitzerACBlumeJECoxNJDolanMEAm J Hum Genet200882631640The contributions of normal variation and genetic background to mammalian gene expressionPritchardCCoilDHawleySHsuLNelsonPSGenome Biol20067R26Evolution of gene expression in the Drosophila melanogaster subgroupRifkinSAKimJWhiteKPNat Genet200333138144Regional and strain-specific gene expression mapping in the adult mouse brainSandbergRYasudaRPankratzDGCarterTADel RioJAWodickaLMayfordMLockhartDJBarlowCProc Natl Acad Sci USA2000971103811043Differential profiles of genes expressed in neonatal brain of 129X1/SvJ and C57BL/6 J mice: A database to aid in analyzing DNA microarrays using nonisogenic gene-targeted miceSuzukiYNakayamaMDNA Res200310263275Evolutionary and biomedical insights from the rhesus macaque genomeGibbsRARogersJKatzeMGBumgarnerRWeinstockGMMardisERRemingtonKAStrausbergRLVenterJCWilsonRKScience2007316222234Defining the role of pharmacology in the emerging world of translational researchEnnaSJWilliamsMAdv Pharmacol200957130The use of body surface area as a criterion of drug dosage in cancer chemotherapyPinkelDCancer Res195818853856Dose translation from animal to human studies revisitedReagan-ShawSNihalMAhmadNFASEB J Offic Publ Fed Am Soc Exp Biol200822659661Learning lessons from drugs that have recently entered the marketTeagueSJDrug Discov Today200916398411Quantitative comparison of toxicity of anticancer agents in mouse, rat, hamster, dog, monkey, and manFreireichEJGehanEARallDPSchmidtLHSkipperHECanc Chemother Rep196650219244Murine Models to Evaluate Novel and Conventional Therapeutic Strategies for CancerTalmadgeJESinghRKFidlerIJRazAAm J Pathol2007170793804Revisions of general guidelines for the preclinical toxicology of new cytotoxic anticancer agents in Europe. The Cancer Research Campaign (CRC) Phase I/II Clinical Trials Committee and the European Organization for Research and Treatment of Cancer (EORTC) New Drug Development OfficeBurtlesSSNewellDRHenrarREConnorsTAEur J Cancer199531A408410Quantitative prediction of drug toxicity in humans from toxicology in small and large animalsGoldsmithMASlavikMCarterSKCancer Res19753513541364Phase I clinical studies with cytotoxic drugs: pharmacokinetic and pharmacodynamic considerationsNewellDRBr J Cancer199061189191Quantitative prediction of human cancer risk from rodent carcinogenic potencies: a closer look at the epidemiological evidence for some chemicals not definitively carcinogenic in humansGoodmanGWilsonRRegul Toxicol Pharmacol: RTP199114118146The allometric approach for interspecies scaling of pharmacokinetics and toxicity of anti-cancer drugsPaxtonJWClin Exp Pharmacol Physiol199522851854Exaggerated carcinogenicity of chemicalsAbelsonPHScience19922561609Interspecies comparison of in vivo caffeine pharmacokinetics in man, monkey, rabbit, rat, and mouseBonatiMLatiniRTognoniGYoungJFGarattiniSDrug Metab Rev19841513551383Problems and opportunities in toxicity testing arising from species differences in xenobiotic metabolismCaldwellJToxicol Lett199264651659Species variations in the metabolism of phenolCapelIDFrenchMRMillburnPSmithRLWilliamsRTBiochem J197212725P26PThe fate of (14C)phenol in various speciesCapelIDFrenchMRMillburnPSmithRLWilliamsRTXenobiotica; Fate Foreign Compounds Biol Syst197222534The use of the dog in toxicity tests on pharmaceutical compoundsParkinsonCGrassoPHum Exp Toxicol19931299109Efficacy of tamoxifen based on cytochrome P450 2D6, CYP2C19 and SULT1A1 genotype in the Italian Tamoxifen Prevention TrialSerranoDLazzeroniMZambonCFMacisDMaisonneuvePJohanssonHGuerrieri-GonzagaAPlebaniMBassoDGjerdeJPharmacogenomics J201111100107Drug metabolism in non-human primatesSmithRLCaldwellJDrug metabolism from microbe to manLondon: Taylor & FrancisParke DV, Smith RL1977331356Furosemide induced hepatotoxicityWalkerRMMcElligottTFJ Pathol1981135301314An end to the search for new drugs?WeatherallMNature1982296387390Potential roles for preclinical pharmacology in phase I clinical trialsCollinsJMZaharkoDSDedrickRLChabnerBACancer Treat Rep1986707380Stereoselectivity of iododoxorubicin reduction in various animal species and humansStrolin BenedettiMFraierDPianezzolaECastelliMGDostertPGianniLXenobiotica; Fate Foreign Compounds Biol Syst199323115121Activity and toxicity of 4'-iodo-4'-deoxydoxorubicin in patients with advanced breast cancerGianniLCapriGGrecoMVillaniFBrambillaCLuiniACrippaFBonadonnaGAnn Oncol19912719725The war on cancer: have we won the battle but lost the war?BrennanRFedericoSDyerMAOncotarget201017783Risks and benefits of phase 1 oncology trials, 1991 through 2002HorstmannEMcCabeMSGrochowLYamamotoSRubinsteinLBuddTShoemakerDEmanuelEJGradyCN Eng J Med2005352895904Addressing the Ethical Challenges of First-in-Human TrialsChapmanARJ Clin Res Bioeth20112113LeafCWhy we are losing the war on cancerFortune20047792First-in-human trial participants: not a vulnerable population, but vulnerable nonethelessDresserRJ Law Med Ethics2009373850YoungMPrediction v AttritionDrug Discovery World2008912Cancer Models: Systems for identifying new drugs are often faultyGuraTScience199727810411042Developing drug prototypes: pharmacology replaces safety and tolerability?CohenAFNat Rev Drug Discov20109856865The safety and side effects of monoclonal antibodiesHanselTTKropshoferHSingerTMitchellJAGeorgeAJTNat Rev Drug Discov20109325338Gene therapy on trialMarshallEScience2000288951957Evaluation of agile designs in first-in-human (FIH) trials–a simulation studyPerlsteinIBologneseJAKrishnaRWagnerJAAAPS J200911653663How first-time-in-human studies are being performed: a survey of phase I dose-escalation trials in healthy volunteers published between 1995 and 2004BuoenCBjerrumOJThomsenMSJ Clin Pharmacol20054511231136A Brief Survey of First-in-Human StudiesWexlerDBertelsenKMJ Clin Pharmacol201151988993Big physics, small doses: the use of AMS and PET in human microdosing of development drugsLappinGGarnerRCNat Rev Drug Discov20032233240The utility of microdosing over the past 5 yearsLappinGGarnerRCExpert Opin Drug Metab Toxicol2008414991506Use of microdosing to predict pharmacokinetics at the therapeutic dose: experience with 5 drugsLappinGKuhnzWJochemsenRKneerJChaudharyAOosterhuisBDrijfhoutWJRowlandMGarnerRCClin Pharmacol Ther200680203215Bacterial toxins: a table of lethal amountsGillDMMicrobiol Rev1982468694National Institute of Occupational Safety and HealthRegistry of Toxic Effects of Chemical Substances (R-TECS)Cincinnati: National Institute of Occupational Safety and Health1996Foundation review: Improved preclinical safety assessment using micro-BAL devices: the potential impact on human discovery and drug attritionGiriSBaderADrug Discov Today201116382397WadeNNew Treatment for Cancer Shows Promise in TestingNew York: Times2009June 29, 2009.Economics of new oncology drug developmentDiMasiJAGrabowskiHGJ Clin Oncol: Offic J Am Soc Clin Oncol200725209216Trends in risks associated with new drug development: success rates for investigational drugsDiMasiJAFeldmanLSecklerAWilsonAClin Pharmacol Ther201087272277Can the pharmaceutical industry reduce attrition rates?KolaILandisJNat Rev Drug Discov20043711715Maximizing mouse cancer modelsFreseKKTuvesonDANat Rev Cancer20077645658Human tumor xenografts as predictive preclinical models for anticancer drug activity in humans: better than commonly perceived-but they can be improvedKerbelRSCancer Biol Ther20032S134139Assessing therapeutic responses in Kras mutant cancers using genetically engineered mouse modelsSinghMLimaAMolinaRHamiltonPClermontACDevasthaliVThompsonJDChengJHReslanHBHoCCKNat Biotechnol201028585593Integrating pharmacology and in vivo cancer models in preclinical and clinical drug developmentPetersonJKHoughtonPJEur J Cancer200440837844Raising the bar for cancer therapy modelsFranciaGKerbelRSNat Biotech201028561562Relationships between drug activity in NCI preclinical in vitro and in vivo models and early clinical trialsJohnsonJIDeckerSZaharevitzDRubinsteinLVVendittiJMSchepartzSKalyandrugSChristianMArbuckSHollingsheadMSausvilleEABr J Cancer20018414241431KardongKVVertebrates. Comparative Anatomy, Function, Evolution. International EditionSingapore: McGraw-Hill62012Long-term observations of human immunodeficiency virus-infected chimpanzeesJohnsonBKStoneGAGodecMSAsherDMGajdusekDCGibbsCJJrAIDS Res Hum Retroviruses19939375378The chimpanzee and other non-human-primate models in HIV-1 vaccine researchNathBMSchumannKEBoyerJDTrends Microbiol20008426431Animal models for HIV AIDS: a comparative reviewStumpDSVandeWoudeSComp Med2007573343Effects of α- and β-adrenergic agonists, phosphodiesterase inhibitors and adenosine on isolated human heart muscle preparationsSchmitzWScholzHErdmannETrends Pharmacol Sci19878447450Lipid changes in the plasma lipoproteins of baboons given an atherogenic diet. 3. A comparison between lipid changes in the plasma of the baboon and chimpanzee given atherogenic diets and those in human plasma lipoproteins of type II hyperlipoproteinaemiaHowardANBlatonVVandammeDVan LandschootNPeetersHAtherosclerosis197216257272Release of leukotrienes from porcine and human blood vessels by immunological and nonimmunological stimuliPiperPJAntoniwJWStantonAWAnn N Y Acad Sci1988524133141GrossDRAnimal Models in Cardiovascular ResearchThe Hague: Martinus Nijhoff1985When the party's overWadmanMNature200744513PetersJVan_SlykeDQuantitative Clinical ChemistryBaltimore: Williams & WilkinsSecond
Interpretations
1948Effects of dietary fibers on nonfasting plasma lipoprotein and apolipoprotein levels in ratsNishinaPMSchneemanBOFreedlandRAJ Nutr1991121431437Innovation or Stagnation? Challenge and Opportunity on the Critical Path to New Medical Productshttp://www.nipte.org/docs/Critical_Path.pdf.Preclinical studies of human disease: Time to take methodological quality seriouslyvan der WorpHBMacleodMRJournal of molecular and cellular cardiology201151444950The failure of neuronal protective agents versus the success of thrombolysis in the treatment of ischemic stroke. The predictive value of animal modelsJonasSAiyagariVVieiraDFigueroaMAnn N Y Acad Sci2001939257267Translational semantics and infrastructure: another search for the emperor’s new clothes?MullaneKWilliamsMDrug Discov Today201217459468Use of animal models has not contributed to development of acute stroke therapies: proKasteMStroke20053623232324The Poliomyelitis Story; a scientific hegiraHorstmannDYale J Biol Med1985587990OshinskyDMPolio: An American StoryOxford: Oxford University Press2005PaulJRA History of PoliomyelitisNew Haven: Yale University Press1971Testimony before the subcommittee on Hospitals and Health Care, Committee on Veterans Affair’s, House of Representatives, April 26, 1984 serial no. 98–48SabinABook Testimony before the subcommittee on Hospitals and Health Care, Committee on Veterans Affair’s, House of Representatives, April 26, 1984 serial no. 98–48 (Editor ed.^eds.)Washington DC1984The Challenges of Intracranial Revascularization for Stroke PreventionBroderickJPN Eng J Med201136510541055Stenting versus aggressive medical therapy for intracranial arterial stenosisChimowitzMILynnMJDerdeynCPTuranTNFiorellaDLaneBFJanisLSLutsepHLBarnwellSLWatersMFN Eng J Med20113659931003Failure of extracranial-intracranial arterial bypass to reduce the risk of ischemic stroke. Results of an international randomized trial. The EC/IC Bypass Study GroupThe EC/IC Bypass Study GroupN Engl J Med198531311911200Results of the Carotid Occlusion Surgery Study (COSS)PowersWClarkeWGrubbRVideenTAdamsHDerdeynCInternational Stroke Conference (COSS)Los Angeles2011In pursuit of systemsEditorialNature20054351Systems Biologyhttps://sysbio.med.harvard.edu/.A unifying view of 21st century systems biologyVidalMFEBS Lett200958338913894The fractal geometry of lifeLosaGARiv Biol20091022959Biological computationBrennerSNovartis Found Symp1998213106111discussion 111–106.From genes to whole organs: connecting biochemistry to physiologyNobleDNovartis Found Symp2001239111123discussion 123–118, 150–119The conflict between complex systems and reductionismHengHHJAMA200830015801581Effects of intensive glucose lowering in type 2 diabetesGersteinHCMillerMEByingtonRPGoffDCJrBiggerJTBuseJBCushmanWCGenuthSIsmail-BeigiFGrimmRHJrN Eng J Med200835825452559Earlier chemotherapy for breast cancer: perhaps too late but still usefulBearHDAnn Surg Oncol200310334335High-Intensity Chemotherapy Does Not Improve Survival in Small Cell Lung CancerSavageLJ Natl Cancer Inst2008100519The disconnection between tumor response and survivalMittraINat Clin Pract Oncol20074203Progress towards personalized medicineBatesSDrug Discov Today201015115120Pharmacogenetics: improving drug and dose selectionBhathenaASpearBBCurr Opin Pharmacol20088639646Predictive tests and personalised medicineBlairEDrug Discovery World20092731Big pharma moves from 'blockbusters' to 'niche busters'DolginENat Med201016837Inhibition of mutated, activated BRAF in metastatic melanomaFlahertyKTPuzanovIKimKBRibasAMcArthurGASosmanJAO'DwyerPJLeeRJGrippoJFNolopKChapmanPBN Engl J Med2010363809819Pharmacogenetic Predictors of Methylphenidate Dose–response in Attention-Deficit/Hyperactivity DisorderFroehlichTEEpsteinJNNickTGMelguizo CastroMSSteinMABrinkmanWBGrahamAJLangbergJMKahnRSJ Am Acad Child Adolesc Psychiatry20115011291139e1122.Genomics, Health Care, and SocietyHudsonKLN Eng J Med201136510331041Pharmacogenetics of hypersensitivity to abacavir: from PGx hypothesis to confirmation to clinical utilityHughesARSpreenWRMostellerMWarrenLLLaiEHBrothersCHCoxCNelsenAJHughesSThorbornDEPharmacogenomics J20088365374Intronic polymorphism in CYP3A4 affects hepatic expression and response to statin drugsWangDGuoYWrightonSACookeGESadeeWPharmacogenomics J201111274286Leprosy and the human genomeMischEABerringtonWRVaryJCJrHawnTRMicrobiol Mol Biol Rev201074589620A twin-family study of susceptibility to poliomyelitisHerndonCNJenningsRGAm J Hum Genet195131746Hepatitis B virus markers in Chinese twinsLinTMChenCJWuMMYangCSChenJSLinCCKwangTYHsuSTLinSYHsuLCAnticancer Res19899737741Aversive and Reinforcing Opioid Effects: A Pharmacogenomic Twin StudyAngstMSLazzeroniLCPhillipsNGDroverDRTingleMRayASwanGEClarkJDAnesthesiology2012117223710.1097/ALN.1090b1013e31825a31822a31824eHuman genetic susceptibility to infectious diseaseChapmanSJHillAVSNat Rev Genet201213175188Hemangioma in twinsCheungDSWarmanMLMullikenJBAnn Plast Surg199738269274Cancer research. Probing the roots of race and cancerCouzinJScience2007315592594Senile macular changes in the black AfricanGregorZJoffeLBr J Ophthalmol197862547550Ethnic and racial differences in the smoking-related risk of lung cancerHaimanCAStramDOWilkensLRPikeMCKolonelLNHendersonBELe MarchandLN Engl J Med2006354333342Interethnic variation of drug metabolismKalowWTrends Pharmacol Sci199112102107APOL1 Genetic Variants in Focal Segmental Glomerulosclerosis and HIV-Associated NephropathyKoppJBNelsonGWSampathKJohnsonRCGenoveseGAnPFriedmanDBriggsWDartRKorbetSJournal of the American Society of Nephrology20112211212937Common genetic variants account for differences in gene expression among ethnic groupsSpielmanRSBastoneLABurdickJTMorleyMEwensWJCheungVGNat Genet200739226231The pharmacogenetics of analgesiaStamerUMStuberFExpert Opin Pharmacother2007822352245Genetics and Variable Drug ResponseWilkeRADolanMEJAMA2011306306307Association of Age and Sex With Myocardial Infarction Symptom Presentation and In-Hospital MortalityCantoJGRogersWJGoldbergRJPetersonEDWengerNKVaccarinoVKiefeCIFrederickPDSopkoGZhengZ-JJAMA2012307813822Sex and the suffering brainHoldenCScience20053081574Gender in the pharmacy: does it matter?KaiserJScience20053081572Sex differences in susceptibility to viral infectionKleinSHuberSSex hormones and immunity to infectionBerlin: SpringerKlein S, Roberts C201093122Wanted: women in clinical trialsSimonVScience20053081517Of Mice and Women: The Bias in Animal ModelsWaldCWuCScience201032715711572HIV gender clues emergeWillyardCNat Med200915830Pharmacogenetics in drug regulation: promise, potential and pitfallsShahRRPhilos Trans R Soc Lond B Biol Sci200536016171638Pharmacogenetics and the practice of medicineRosesADNature2000405857865Influence of cytokine gene variations on immunization to childhood vaccinesYucesoyBJohnsonVJFluhartyKKashonMLSlavenJEWilsonNWWeissmanDNBiaginiREGermolecDRLusterMIVaccine20092769916997Personalised vaccines could protect all childrenKingCNew Sci2009273711Pharmacogenetics: past, present and futurePirmohamedMDrug Discov Today201116852861The Case for Personalized Medicinehttp://www.personalizedmedicinecoalition.org/sites/default/files/files/Case_for_PM_3rd_edition.pdf.Studying Physiological Evolution: Paradigms and PitfallsBurggrenWWBemisWEEvolutionary InnovationsChicago: University of Chicago PressNitecki MH1990191228


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