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Modeling and Simulation of Systemic and Inhaled Corticosteroid Therapy

Permanent Link: http://ufdc.ufl.edu/UFE0022468/00001

Material Information

Title: Modeling and Simulation of Systemic and Inhaled Corticosteroid Therapy
Physical Description: 1 online resource (140 p.)
Language: english
Creator: Xu, Jian
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: chronopharmacokinetics, chronotherapy, corticosteroids, cortisol, dosing, growth, inhalation, modeling, pharmacodynamics, pharmacokinetics, prednisolone, simulation
Pharmacy -- Dissertations, Academic -- UF
Genre: Pharmaceutical Sciences thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Corticosteroids are widely used drugs for various diseases, such as inflammatory and immune diseases. Corticosteroids are not only used systemically, but also applied with other dosing routes, such as nasal, topical administration and inhalation. However, both short-term and chronic treatment of corticosteroids could result in various side effects, including growth retardation in pediatric patients, oropharyngeal candidiasis, skin atrophy, adrenal suppression, risk of cataracts, and osteoporosis. Current advance knowledge of pharmacokinetics and pharmacodynamics is prompting researchers to elucidate the risk and benefits of corticosteroids and develop new corticosteroids with better efficacy and fewer adverse effects using modeling and simulation approaches. Prednisolone, a systemic corticosteroid, has been used over decades. It shows nonlinear pharmacokinetics mainly due to the nonlinear protein binding. However, the complexity of pharmacokinetics of total prednisolone remained unsolved since several factors potentially account for this nonlinearity. The first specific aim of this work investigated this nonlinearity with a novel and integrated pharmacokinetic and pharmacodynamic approach. With the aid of modeling and simulation, it was found that the nonlinear total prednisolone pharmacokinetics can be adequately described by combining reversible metabolism, cortisol circadian, cortisol suppression, and recognized competitive nonlinear protein binding. Further investigation on prednisolone, which is the second specific aim, was performed with simulations. Because of the involvement of cortisol circadian variation, time-dependency was assessed for the pharmacokinetics and pharmacodynamics of total prednisolone. An interactive algorithm was developed based on the approach developed in the first objective. The predictability of this algorithm was evaluated. Several simulated scenarios were performed within this algorithm. The results suggested that the pharmacokinetics and pharmacodynamics of prednisolone are time-dependent and dose-dependent, and it is necessary to consider the application of chronotherapy to achieve better clinical outcomes with fewer side effects of prednisolone. Inhaled corticosteroids are the first-line therapy for asthma. Whereas, corticosteroid induced growth retardation in pediatrics remains an issue. It will be beneficial to predict this long term effect with some short term clinical studies. Therefore, growth velocity in children was evaluated with cumulative cortisol suppression in the third specific aim. This meta-analysis approach with available literature information identified a linear relationship between growth velocity and cortisol suppression. The results illustrated that cumulative cortisol suppression, which can be predicted with the pharmacokinetics and pharmacodynamics of corticosteroids, is an excellent predictor for the inhaled corticosteroid induced growth effect, a potential chronic adverse effect. Overall, this research work was performed with modeling and simulation approaches based on the pharmacokinetic and pharmacodynamic properties of corticosteroids. The work helped in quantitatively understanding corticosteroid mediated effects, and extending pharmacokinetic and pharmacodynamic approaches in clinical application for risk and benefit assessment.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Jian Xu.
Thesis: Thesis (Ph.D.)--University of Florida, 2008.
Local: Adviser: Derendorf, Hartmut C.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0022468:00001

Permanent Link: http://ufdc.ufl.edu/UFE0022468/00001

Material Information

Title: Modeling and Simulation of Systemic and Inhaled Corticosteroid Therapy
Physical Description: 1 online resource (140 p.)
Language: english
Creator: Xu, Jian
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: chronopharmacokinetics, chronotherapy, corticosteroids, cortisol, dosing, growth, inhalation, modeling, pharmacodynamics, pharmacokinetics, prednisolone, simulation
Pharmacy -- Dissertations, Academic -- UF
Genre: Pharmaceutical Sciences thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Corticosteroids are widely used drugs for various diseases, such as inflammatory and immune diseases. Corticosteroids are not only used systemically, but also applied with other dosing routes, such as nasal, topical administration and inhalation. However, both short-term and chronic treatment of corticosteroids could result in various side effects, including growth retardation in pediatric patients, oropharyngeal candidiasis, skin atrophy, adrenal suppression, risk of cataracts, and osteoporosis. Current advance knowledge of pharmacokinetics and pharmacodynamics is prompting researchers to elucidate the risk and benefits of corticosteroids and develop new corticosteroids with better efficacy and fewer adverse effects using modeling and simulation approaches. Prednisolone, a systemic corticosteroid, has been used over decades. It shows nonlinear pharmacokinetics mainly due to the nonlinear protein binding. However, the complexity of pharmacokinetics of total prednisolone remained unsolved since several factors potentially account for this nonlinearity. The first specific aim of this work investigated this nonlinearity with a novel and integrated pharmacokinetic and pharmacodynamic approach. With the aid of modeling and simulation, it was found that the nonlinear total prednisolone pharmacokinetics can be adequately described by combining reversible metabolism, cortisol circadian, cortisol suppression, and recognized competitive nonlinear protein binding. Further investigation on prednisolone, which is the second specific aim, was performed with simulations. Because of the involvement of cortisol circadian variation, time-dependency was assessed for the pharmacokinetics and pharmacodynamics of total prednisolone. An interactive algorithm was developed based on the approach developed in the first objective. The predictability of this algorithm was evaluated. Several simulated scenarios were performed within this algorithm. The results suggested that the pharmacokinetics and pharmacodynamics of prednisolone are time-dependent and dose-dependent, and it is necessary to consider the application of chronotherapy to achieve better clinical outcomes with fewer side effects of prednisolone. Inhaled corticosteroids are the first-line therapy for asthma. Whereas, corticosteroid induced growth retardation in pediatrics remains an issue. It will be beneficial to predict this long term effect with some short term clinical studies. Therefore, growth velocity in children was evaluated with cumulative cortisol suppression in the third specific aim. This meta-analysis approach with available literature information identified a linear relationship between growth velocity and cortisol suppression. The results illustrated that cumulative cortisol suppression, which can be predicted with the pharmacokinetics and pharmacodynamics of corticosteroids, is an excellent predictor for the inhaled corticosteroid induced growth effect, a potential chronic adverse effect. Overall, this research work was performed with modeling and simulation approaches based on the pharmacokinetic and pharmacodynamic properties of corticosteroids. The work helped in quantitatively understanding corticosteroid mediated effects, and extending pharmacokinetic and pharmacodynamic approaches in clinical application for risk and benefit assessment.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Jian Xu.
Thesis: Thesis (Ph.D.)--University of Florida, 2008.
Local: Adviser: Derendorf, Hartmut C.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0022468:00001


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PAGE 1

1 MODELING AND SIMULATION OF SYST EMIC AND INHALED CORTICOSTEROID THERAPY By JIAN XU A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2008

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2 2008 by Jian Xu

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3 To my parents and my wife

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4 ACKNOWLEDGMENTS First and forem ost, I would like to express my gratitude and great appreciation to my advisor, Dr. Hartmut Derendorf in the Department of Pharmaceutics, the University of Florida. It has been a great honor to be one of his graduate students. I am deeply indebted to Dr. Hartmut Derendorf, whose help, guidance, stimulating suggestions and encouragement helped me in all the time of research for and writing of my Ph.D project. My special thanks also go to the members of my supervisory committee, Dr. Guen ther Hochhaus, Dr. Jeffrey Hughes, and Dr. Scott K. Powers, who gave me their guidance and helpful advice during these three years in my Ph.D. pursuit, and offer me the possibility to complete this project. My extended special thanks go to the pos tdoctoral fellows from Dr. Derendorfs lab, including Dr. Vipul Kumar and Dr. Sreedharan N. Sabarinath, for their help, support, interest, discussion and valuable hints. I am also thankful for the t echnical and admi nistrative support provided by staff in the Department of pharmaceutics, including Mrs. Patricia J. Khan, Mr. Marty Rhoden and Ms. Robin KeirnanSanchez, throughout my Ph.D. program. My time at Gainesville, Florida, was made en joyable and enriched due to the support and friendships from my friends and my colleagues. I am grateful for time spent on my Ph.D. project, weekly exercises and social gatheri ngs, and for many other people and memories. I gratefully acknowledge the financial support that made my P h.D. work achievable. I was supported by the assistantship from the College of Pharmacy, the University of Florida. I was also funded by an educational grant from Pfizer. Lastly, and especially, I would like to give my special thanks to my parents, and my sweet, loving, supportive, encouraging wife, Ye Lu, whos e patient love, encouragement and faithful and unconditional support enabled me to complete my work.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ............................................................................................................... 4LIST OF TABLES ...........................................................................................................................7LIST OF FIGURES .........................................................................................................................8ABSTRACT ...................................................................................................................... .............10 CHAP TER 1 INTRODUCTION .................................................................................................................. 12Overview of Corticosteroids ...................................................................................................12Systemic Corticosteroids: Pr ednisolone and Prednisone ........................................................ 12Inhaled Corticosteroids ....................................................................................................... ....13Pharmacokinetic and Pharmacodynamic Appr oaches for Inhaled Corticosteroids ........ 15Effects on hypothalamic-pitu itary-adrenal axis ....................................................... 16Effects on growth .....................................................................................................19Effects on bone .........................................................................................................21Effects on immune system .......................................................................................22Other adverse effects ................................................................................................25Pharmacokinetic and Pharmacodynamic Properties of an Ideal Inhaled Corticosteroid ............................................................................................................... 25Pulmonary properties ............................................................................................... 26Systemic properties ..................................................................................................28Other properties ........................................................................................................31Conclusions .....................................................................................................................322 PHARMACOKINETIC/PHARMACODYNAMIC APPROACH TO PREDICT TOTAL PREDNISOLONE CONCENTR ATIONS IN HUM AN PLASMA ........................ 41Introduction .................................................................................................................. ...........41Materials and Methods ...........................................................................................................43Free Prednisone and Predni solone Pharmacokinetics .....................................................43Intravenous model for predni sone and prednisolone ...............................................44Oral model for prednisone and prednisolone ........................................................... 45Cortisol Circadian Rhythm and Cor tisol Suppression by Prednisolone ..........................46Prednisolone and Cortisol Protein Binding ..................................................................... 48Prediction of Total Prednisolone Concentration in Plasma ............................................. 49Predictability of the Modeling Approach ........................................................................ 50Results .....................................................................................................................................50Discussion and Conclusions ...................................................................................................53

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6 3 ASSESSMENT OF THE IMPACT OF DOSING TIME ON THE PHARM ACOKINETICS/PHARMACODYNAMICS OF PREDNISOLONE ..................... 63Introduction .................................................................................................................. ...........63Materials and Methods ...........................................................................................................65Pharmacokinetics and Pharmacodynamics of Prednisolone and Prednisone .................. 65Pharmacokinetics of prednisolone and prednisone .................................................. 65Pharmacodynamics of predni solone and prednisone ...............................................67Design of an Interactive Algorithm ................................................................................. 68Predictability of the Algorithm ........................................................................................ 69Simulations in the Algorithm .......................................................................................... 70Results .....................................................................................................................................71Predictability of the Algorithm ........................................................................................ 71Simulation of Intravenous Admi nistration of Prednisolone ............................................72Simulation of Oral Admini stration of Prednisone ........................................................... 72Discussion and Conclusions ...................................................................................................734 Cortisol Suppression as a surrogate mark er for inhaled CORTICOSTEROI D induced GROWTH RETARDATION in children ............................................................................... 86Introduction .................................................................................................................. ...........86Materials and Methods ...........................................................................................................88Literature Data .................................................................................................................88Cumulative Cortisol Suppression Data ........................................................................... 89Data Analysis ...................................................................................................................89Model Evaluation ............................................................................................................91Results .....................................................................................................................................92Characteristics of Dataset ................................................................................................92Model Development ........................................................................................................ 93Model Evaluation ............................................................................................................94Discussion and Conclusions ...................................................................................................945 CONCLUSIONS .................................................................................................................. 111 APPENDIX A PHARMACOKINETIC MODELI NG IN ADAPT II .......................................................114B INSTRUCTION FOR THE INTERACTI VE ALGORITHM IN EXCEL .......................117C LINEAR MIXED EFFECTS MODELING IN NONMEM .............................................. 118LIST OF REFERENCES .............................................................................................................119BIOGRAPHICAL SKETCH .......................................................................................................140

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7 LIST OF TABLES Table page 1-1 Structures of currently ava ilable inhaled corticosteroids ................................................... 391-2 Pharmacokinetic and pharmacodynamic pr operties of currently available inhaled corticosteroids ............................................................................................................... .....402-1 Final estimated pharmacokinetic paramete rs for free prednisolone (PNL) and free prednisone (PN) plasma concentration-time data .............................................................. 602-2 Oral pharmacokinetic parameter estimates for free prednisolone (PNL) and free prednisone (PN) plasma concentration-time data .............................................................. 612-3 Protein binding parameters for pred nisolone (PNL) and cortisol (COR) ..........................623-1 Comparison of simulated cumulative co rtisol suppression (CCS) results with literature reported values .................................................................................................... 854-1 Beclomethasone dipropiona te, ciclesonide and mometas one furoate pharmacokinetic and pharmacodynamic parameters ................................................................................... 1044-2 Growth studies for inhaled co rticosteroids from literature .............................................. 1054-3 Summary of model development ..................................................................................... 1084-4 Parameter estimates for the final model describing relationship of growth velocity with cumulative cortisol suppression % .......................................................................... 1094-5 Predicted change in growth velocity with the current inha led corticosteroid treatment .. 110

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8 LIST OF FIGURES Figure page 1-1 Structures of prednisone and prednisol one and interconversion between each other ....... 351-2 General structure for currently available inhaled corticosteroids ......................................361-3 Time-course of mean serum cortis ol concentrations within 24 hr ..................................... 371-4 Fate of inhaled corticosteroids ...........................................................................................382-1 Pharmacokinetic models of free pre dnisone (PN) and prednisolone (PNL) ...................... 562-2 Concentration-time profiles of total prednisone (PN) and free prednisolone (PNL) concentrations after intravenous PN or PNL administration .............................................572-3 Total prednisone (PN) and free predniso lone (PNL) concentration time-course after oral PN or PNL administration .......................................................................................... 582-4 Time-course of total prednisolone (PNL) concentrations in plasma ................................. 593-1 Pharmacokinetic and pharmacodynamic m odel of prednisone and prednisolone ............. 783-2 EXCEL interface for prediction of pharmacokinetics and pharmacodynamics of prednisolone and prednisone ..............................................................................................793-3 Time-course of total cortisol concentrations (panel A and panel B) and total lymphocyte percentages of the count at 8: 00AM (panel C and panel D) in plasma ......... 803-4 Single intravenous dose (2 to 100 mg) of prednisolone in contour plots .......................... 813-5 Contour plots after single intravenous administration of prednisolone with a dose range from 0.01 to 1 mg ..................................................................................................... 823-6 Single oral administratio n of prednisone (5 to 20 0 mg) in contour plots .......................... 833-7 Multiple oral doses of pr ednisone in contour plots ............................................................ 844-1 Plot of pooled growth velocity data from literature reports versus predicted cumulative cortisol suppression %..................................................................................... 984-2 Individual plots of growth velocity ve rsus cumulative cortisol suppression % at steady state .........................................................................................................................994-3 Plots of growth velocity versus cumu lative cortisol suppression % based on two methods ....................................................................................................................... .....1004-4 Diagnostic plots for fit of model to growth velocity ........................................................ 101

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9 4-5 Distribution of 25th, 50th, 75th, and 100th percentiles in the sim ulated datasets (histograms) with respective percentiles in the original dataset (dash lines) ...................1024-6 Pattern check plots of population prediction versus observed growth velocity from fit and three simulated datasets.............................................................................................103

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10 Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy MODELING AND SIMULATION OF SYST EMIC AND INHALED CORTICOSTEROID THERAPY By Jian Xu August 2008 Chair: Hartmut Derendorf Major: Pharmaceutical Sciences Corticosteroids are widely used drugs for va rious diseases, such as inflammatory and immune diseases. Corticosteroids are not only us ed systemically, but also applied with other dosing routes, such as nasal, topical administ ration and inhalation. However, both short-term and chronic treatment of corticosteroids could result in variou s side effects, including growth retardation in pediatric patients, oropharynge al candidiasis, skin at rophy, adrenal suppression, risk of cataracts, and osteoporosis. Curre nt advance knowledge of pharmacokinetics and pharmacodynamics is prompting researchers to elucid ate the risk and benef its of corticosteroids and develop new corticosteroids with better efficacy and fewer adverse effects using modeling and simulation approaches. Prednisolone, a systemic corticosteroid, has been used over decades. It shows nonlinear pharmacokinetics mainly due to the nonlinear protein binding. However, the complexity of pharmacokinetics of total prednisolone remain ed unsolved since several factors potentially account for this nonlinearity. The first specific aim of this work investigated this nonlinearity with a novel and integrated pharmacokinetic and pharmacodynamic approach. With the aid of modeling and simulation, it was found that the no nlinear total predniso lone pharmacokinetics can be adequately described by combining revers ible metabolism, cortisol circadian, cortisol

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11 suppression, and recognized competitive nonlinear protein binding. Further investigation on prednisolone, which is the second specific aim, was performed with simulations. Because of the involvement of cortisol circadian vari ation, time-dependency was assessed for the pharmacokinetics and pharmacodynamics of total pr ednisolone. An interactive algorithm was developed based on the approach de veloped in the first objective. The predictability of this algorithm was evaluated. Several simulated scenarios were performed within this algorithm. The results suggested that the pharmacokineti cs and pharmacodynamics of prednisolone are time-dependent and dose-dependent, and it is ne cessary to consider the application of chronotherapy to achieve better clinical outcomes with fewer si de effects of prednisolone. Inhaled corticosteroids are the first-line th erapy for asthma. Whereas, corticosteroid induced growth retardation in pediatrics remains an issue. It will be beneficial to predict this long term effect with some short term clinical studies. Therefore, growth velocity in children was evaluated with cumulative cortisol suppression in the third specific aim. This meta-analysis approach with available literat ure information identified a linear relationship between growth velocity and cortisol suppression. The results illustrated that cumulative cortisol suppression, which can be predicted with the pharmacokinetics and pharmacodynamics of corticosteroids, is an excellent predictor for the i nhaled corticosteroid induced grow th effect, a potential chronic adverse effect. Overall, this research work was performed with modeling and simulation approaches based on the pharmacokinetic and pharmacodynamic properties of corticosteroids. The work helped in quantitatively understanding corticosteroid mediat ed effects, and extending pharmacokinetic and pharmacodynamic approaches in clinical appl ication for risk and benefit assessment.

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12 CHAPTER 1 INTRODUCTION Overview of Corticosteroids Corticosteroids exist endogenously and exogenously. Endogenous corticosteroids, such as cortisol, are im portant for the physiological processes, including immune response, stress response, and growth. Exogenous corticosteroid s, including systemic (e.g. prednisolone), inhaled (e.g. ciclesonide) and t opical corticosteroids (e.g. hydrocor tisone), are widely used for the various indications, such as asthma, rheumatoid arthritis, systemic lupus, eczema, and Crohns disease, due to their anti-inflamma tory and immunosuppressive effects [1]. However, both short-term and chronic exposure of corticosteroids coul d result in a variety of adverse effects, including gr owth retardation in pediatric pa tients, oropharyngeal candidiasis, skin atrophy, adrenal suppression, ri sk of cataracts, and osteoporosis [2,3]. In order to elucidate the risk and benefit of corticosteroid th erapy from a pharmacokinetic and pharmacodynamic point of view, modeling and simu lation approaches could be perf ormed to assess the risk-benefit of corticosteroids quantitatively [4]. Systemic Corticosteroids: Prednisolone and Prednisone Prednisolone, one of exogenous corticosteroids, has been used system ically over several decades. Total prednisolone concentrations in plasma showed nonlinear pharmacokinetics in several species [5-8]. It has b een suggested that nonlinear protein binding mainly contributes to the nonlinearity of tota l prednisolone pharmacokinetics [8-1 1]. Prednisolone is the active metabolite of prodrug, prednisone, and they under go interconversion (Figure 1-1). As a common complication of steroid thera py, prednisolone suppresses endo genous cortisol production and alters lymphocyte trafficking in plasma [11]. All these factors, including cortisol competitive protein binding with prednisolone cortisol circadian rhythm, a nd prednisolone-induced cortisol

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13 suppression, further complicate th e understanding of total prednisolone pharmacokinetics in plasma. Until now, no comprehensive model, inco rporating all these factors to explain total prednisolone nonlinear pharmacokinetics in human plasma, has been reported. Prednisolone mediated systemic effects ar e commonly evaluated with two biomarkers, cortisol and blood lymphocytes in plasma [11] Circadian patterns are observed in both biomarkers. In addition, the disease itself ma y show a circadian pattern. For example, in rheumatoid arthritis patients, better therapeutic ou tcomes have been reported when prednisolone was administered in the very early morning [12,13]. However, the time-dependency of the pharmacokinetics and pharmacodynamics of predniso lone has not been in tensively evaluated. Inhaled Corticosteroids Asthm a is a chronic inflammatory disease, which could be triggered by many factors, including allergens, cold air, and even some medications. The World Health Organization (WHO) has estimated that asthma affects 300 million people in the world, of which over 7 percent are in the United States [14,15]. Currently, asthma ca nnot be cured, but the symptoms can be controlled and quality of the life can be improved with certain medications, such as 2 agonists, theophylline, and steroi ds [1,16]. Inhaled corticosteroids have been the first-line treatment for inflammatory diseases, especia lly asthma, over decades [14]. Seven inhaled corticosteroids are available on the market (Figur e 1-2 and Table 1-1). They are triamcinolone acetonide (TAA), flunisolide (FLU), beclomethas one dipropionate (BDP), budesonide (BUD), fluticasone propionate (FP), mometasone furoat e (MF), and ciclesonide (CIC) [17]. CIC and BDP are prodrugs, and they are readily converted to the active metabolites in vivo as desisobutyryl-CIC (Des-CIC) and beclomethasone-17-monopropionate (17-BMP), respectively [18,19]. Recently, a new corticos teroid, fluticasone furoate ( FF), was approved in the United States as a nasal spray, which may be further de veloped as a new inhaled corticosteroid [20].

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14 Though inhaled corticosteroids have shown reas onable targeted efficacy, such as improved pulmonary function, induced local and systemic e ffects are still inevitable. Such complications include growth retardation in pediatric patients oropharyngeal candidiasis, skin atrophy, adrenal suppression, risk of cataract s, and osteoporosis [2]. Current pharmacokinetic and pharmacodynamic ev aluation involves analysis of the time course of drug effects with combining drug concentr ation at the site of act ion over time. With a pharmacokinetic and pharmacodynamic approach, i nhaled corticosteroid mediated effects, including anti-inflammatory activities and adverse e ffects, could be assessed quantitatively [21]. Modern advances in the pharmacokinetic and pharmacodynamic approach for inhaled corticosteroids are to find appropriate biomarkers for systemic effects of inhaled corticosteroids, such as endogenous cortisol levels in blood and urine (hypoth alamic-pituitary-adrenal axis effects), osteocalcin concentra tions (bone turnover), and lym phocyte counts in blood (immune suppression) [21-24]. The time course of some bi omarkers has been well characterized from the short-term clinical studies. Several mathemati cal models have been developed, and adequately described possible pharmacokinetics and pha rmacodynamics relationship after inhaled corticosteroid exposure [23,25-28]. Thus, with th ese mathematical models, the safety of longterm application of inhaled cortic osteroids could be extrapolated and/or predicted from clinical trial simulations; and best dosing regimen can be advised for optimal risk-benefit value of the currently available inhaled corticosteroids. Another strategy to minimize adverse effects of inhaled corticosteroids for the treatment of asthma is to design and develop a new and ideal inhaled corticosteroid based on the knowledge of those existing inhaled corticosteroids. Th is can be achieved by designing a new inhaled corticosteroid with optimal pharmacokinetic and pharmacodynamic properties. Such ideal

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15 inhaled corticosteroid should have high efficacy in the airways and few local and systemic adverse effects. Therefore, compared to the pharmacokinetic and pharmacodynamic properties of the currently available inhaled corticosteroids, this projected new inhaled corticosteroid will have a high systemic clearance, and a low or negligible oral bi oavailability, all of which may help to reduce side effects. Furthermore, high plasma protein binding will reduce systemic side effects. However, it is also possible that high plasma protein binding will result in reduced efficacy [29]. In order to achieve high pulmonary effects, this ideal inhaled corticosteroid will possess a long pulmonary residence time a nd a high pulmonary availability. From pharmacodynamic point of view, future design of an ideal inhaled corticos teroid can also be based on the regulation of genes after corticostero id binds to the glucocorticoid receptors (GR), including activation of anti-inflammatory genes, and repression of side effect and inflammatory genes [30]. Pharmacokinetic and Pharmacodynamic A pproaches for Inhaled Corticosteroids Pharm acokinetics describes the fate of drug in the body, which includes absorption, distribution, metabolism and ex cretion after drug administ ration; and pharmacodynamics assesses the relationship between drug concentrat ions and effects, incl uding both desirable and adverse pharmacological effects. An in tegrated pharmacokinetic and pharmacodynamic approach is to understand drug effects over ti me after drug administration, and quantitatively illustrate time course of drug effects [31]. System ic effects of inhaled corticosteroids, such as inhibition of cortisol release and growth suppres sion, have been successfully captured with some pharmacokinetic and pharmacodynamic appro aches [26,28,32]. Although some of these evaluations were based on short-te rm clinical studies, the impact of inhaled corticosteroids after chronic use could be derived with the aid of computer modeling and simulation approaches [4].

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16 Effects on hypothalamic-pituitary-adrenal axis Hypothalamic-pituitary-adrena l (HPA) axis involves seve ral endogenous horm ones, such as cortisol, and adrenocorticotropic hormone (ACT H). Cortisol is the end hormone in the HPA axis, and it is synthesized in adrenal cortex a nd released by ACTH stimulation [1]. In the presence of exogenous corticosteroids, such as inhaled corticosteroids, cortisol production is inhibited via a negative feedback mechanism in the HPA axis [2]. Several clinical tests are available for the function of HPA axis; and am ong them cortisol levels are commonly used, including basal serum or plasma cortisol concentration in the mo rning, urinary cortisol levels, and serum or plasma cortisol concentrations over a certain period time, such as 24 hr [2,22,33]. Morning serum or plasma cortisol method is simple, but dose-depende nt and insensitive, which could be due to the high variability among indi viduals. It is not li kely to detect adrenal insufficiency only based on this single morning plasma cortisol measurement [34-36]. In addition, it is not easy to compare and predict results of different clinical studies with various dosing regimens. Some studies reported no statis tically significant change of morning cortisol concentration after BDP, BUD, CIC, FP, or TAA treatment [37-63]; whereas considerable alteration of the morning cortisol levels was also observed in the asthmatic patients and healthy volunteers after administration of BDP, BUD, FP, FLU, or MF, particularly at high dose amounts [63-82]. Another way to identify inhaled corticosteroid mediated adrenal suppression is to monitor urinary cortisol levels, which ev aluates urinary free cortisol excr etion (adjusted for creatinine, nmol/ mol creatinine) over time (e.g. 24 hr) [ 73,83]. This method is noninvasive and reproducible. Recently, an Emax model with a pharmacokineti c and pharmacodynamic approach sufficiently illustrated the rela tionship between trough inhaled cort icosteroid concentrations and urinary free cortisol after FP or MF treatment [84].

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17 The most sensitive approach for the HPA axis function is to monitor serum or plasma cortisol levels over 24 hr to avoi d any artifacts due to the distinct circadian rhythm of cortisol release [22]. A typical 24-hr serum cortisol ba seline profile is shown in figure 1-3 as open symbols; and closed circles in figure 1-3 repres ent a serum cortisol profile after a single dose of FP treatment. Normally, the serum cortisol pr ofile is evaluated by the cumulative cortisol suppression over a period of time (e.g. 24 hr) (C SS), which is quantified via calculating the difference between area under ba seline or placebo curve (COR baselineAUC) and area under the treatment curve (COR treatmentAUC) (Equation 1-1) [26,85,86]. The trap ezoidal rule can be used to obtain the area under the curve. This endpoint (CCS) has been shown a reliable biomarker for systemic exposure of inha led corticoste roids [26]. %100 % COR baseline COR treatment COR baselineAUC AUC AUC CCS (1-1) However, cortisol samples are needed to access cumulative cortisol suppression in this approach. Therefore, if there are appropriate models to describe cortisol concentration-time profiles, prediction of cortisol suppression from the other clinical s cenarios can be achieved without cortisol samples. It is well known that serum or plasma cortisol concentrations undergo circadian rhythm, with the highest concentration in the morning, a nd lowest concentration in the evening. This diurnal variation of cortisol concentration is linked to the light and dark cycle, and mainly keeps system under homeostasis [87,88]. Exogenous corticosteroids, such as inhaled corticosteroids, suppress endogenous cortisol pr oduction, which subsequently affects cortisol concentration-time profile. Over last decade, several attempts were made to quantitatively describe serum or plasma cortisol profile s, including pharmacokinetic and pharmacodynamic approaches and statistic al approaches [25,27,89,90]. Among pharmacokinetic and

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18 pharmacodynamic approaches, cosinor functions, a linear ramp release model and even Fourier analysis have been applied for cortisol circad ian baseline [25,27]. Recently, a surge-based model was developed to capture cortisol circadian rhythm and a surge [90] With a statistical approach, Guo and his colleagues combined a smoothing spline function and a multiprocess dynamic linear model to describe cortisol circadian and pulsatile release, respectiv ely [89]. Although the complicated models, such as the model with a stat istical approach, effectiv ely described cortisol profiles, application of the model to predict cortisol s uppression can be limited due to model complexity and lack of pharmacokinetic in formation of exogenous corticosteroids. A simple linear ramp model via a pharmac okinetic and pharmacodynamic approach was developed and adequately described the serum or plasma cortisol baseline [27]. Briefly, the change of cortisol concentration ( dt dCCOR) at baseline is determined by endogenous cortisol release (RCOR) and first-order elimination (ke) (Equation 1-2), and the RCOR is determined by two straight lines (Equa tions 1-3 and 1-4). CORe COR CORCkR dt dC (1-2) ) ()( )24 (min max min min max maxttttt ttV R RCOR COR (1-3) ) ( )( )(max min min min max maxttt tt ttV R RCOR COR (1-4) where tmax and tmin are the time at maximum ( Rmax) and minimum cortisol release, respectively; and VCOR is the volume of distribution of cortisol, and t is the time after monitoring cortisol concentration.

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19 After exogenous corticosteroids, such as inhale d corticosteroids, are given, suppression of cortisol release can be char acterized with a sigmoidal Emax equation (Equation 1-5). CORe ICS ICS COR CORCk CIC CI R dt dC ) 1(50 max (1-5) where Imax is the maximum inhibition effect of inhaled corticosteroid; and IC50 is the inhaled corticosteroid plasma concentration to give half of the maximum effect; and CICS is the inhaled corticosteroid plasma concentration at time t The predictive power of this approach for CCS has been assessed with actual clinical studies of inhaled cortic osteroid treatment [21,26]. The pr ediction from this approach shows good accuracy and consistence. With simulation approaches, Meibohm and Krishnaswami found that the maximum CCS in plasma was obs erved when inhaled corticosteroids were administrated in the very early morning, and minimum seru m cortisol suppression occurred when inhaled corticosteroids were given in the late afternoon around 4 to 7 PM [26,85]. Such pharmacokinetic and pharmacodynamic approach pr ovides a valuable tool to optimize dosing regimens of current i nhaled corticosteroids. Effects on growth Endogenous corticosteroids, such as cortisol are essential to the normal growth under physiological condition [91,92]. However, the body cannot disti nguish endogenous and exogenous corticosteroids, which bind to the same type of glucocorticoid receptors (GR) to produce pharmacological effects. The excess or de ficiency of corticosteroids in the body has impacts on growth in children. Chronic exposur e of exogenous corticoste roids, such as BDP, BUD, FP and prednisone, could result in as much as 2.86 cm/yr reductio n in growth velocity (GV) in pediatric asthmatic patients [32]. However, corticosteroid mediated growth retardation, especially at low dose amounts, is not permanen t. After the puberty stage, the normal adult

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20 height can be regained with the catch-up growth [2,3]. Currently, the Food and Drug Administration (FDA) in the Unite d States requires implementati on of precautionary labeling on all inhaled corticosteroids. A draft guidance was also proposed for future studies on inhaled corticosteroid induced growth effects [93,94]. The growth inhibition reflects systemic effects of inhaled corticosteroids. Although specific studies to quantitatively describe inhaled corticostero id exposure and GV are limited, several meta-analyses have been performed to ad equately describe possible relationship between corticosteroid exposure and growth effect s [3,32,95]. A linear relationship between corticosteroid dose and growth effects was identified; and the results suggested that current use of inhaled corticosteroids is a ppropriate, which does not produ ce significant growth inhibition effects [95]. Daley-Yates and Ri chards revisited some of the st udies and added more data from other studies [32]. They developed a pharmac okinetic and pharmacodynamic approach to link corticosteroid exposure to GV. A nonlinear Emax model was used to depict this relationship, and corticosteroid exposure was expressed as cortisol equivalents (Equation 1-6). u ss u u ssAUC AUC AUCE EGV 50 max 0 (1-6) where GV is the change of the GV per year; and E0 is baseline or placebo GV; and Emax is the maximum of the change of the GV per year; andu ssAUC represents the area under the curve of unbound corticosteroid at steady-state; and uAUC50 is the unbound AUC to give half of the maximum of the change of the GV per year; and is the hill coefficient. This model nicely captured observed data and was also validated. It has been proposed that mechanism of cor ticosteroid mediated growth inhibition in children could involve several leve ls in the growth axis [3]. Corticosteroids can directly and

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21 indirectly influence growth hormone (GH) re lease, including suppres sion of pulsatile GH release, inhibition of growth hormone releasi ng hormone (GHRH) release, and stimulation of somatostatin (SRIF) secretion [3]. Moreove r, corticosteroids have impacts on the other hormones, such as insulin-like gr owth factor-1, and adrenal androge n [3]. Clinically, excessive exposure of corticosteroids can be controlled by avoiding high dosing regimens, or be corrected with GH treatment. However, the side effects of chronic exposure of corticosteroids on growth could be persistent as observed by the failure to achieve target height even with GH treatment during corticosteroid therapy [91, 92]. Therefore, it seems that GH could play a pivotal role on the inhaled corticosteroid mediat ed growth retardation, and it ca n be reasonable to develop a mechanism based pharmacokinetic and pharmac odynamic model by incorporating GH in order to review risks of inhaled corticosteroids on growth. Effects on bone Once inhaled corticosteroids reach the systemic circulation, they execute the same effects as the other corticosteroids. Corticosteroids influence bone turnover, including bone formation and bone resorption [2,91]. Such effects on bone could result in co mmon complications of inhaled corticosteroids, such as osteoporosis a nd fracture. It was repor ted that TAA influences bone metabolism more than BDP, followed by BUD and FP [2]. Chronic use of inhaled corticosteroids has shown the alteration of bone meta bolism markers, such as osteocalcin, in both adults and children. A pharmacodynamic model has been developed to characterize serum osteocalcin concentration-time prof ile after systemic administrati on of prednisolone [24]. The circadian rhythm of osteocalci n was described with a cosinar function, and the inhibition of osteocalcin release was modeled wi th a sigmoid equation. This model was able to sufficiently capture observed data after administration of 2.5 mg and 10 mg of prednisolone. However, in another study, after a single dose of inhale d BDP, statistically si gnificant suppression of

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22 osteocalcin was not observed after a dose of e ither 250 g or 1000 g even though inhibition of cortisol release was observed in both groups [96]. This may sugge st that serum osteocalcin is not a sensitive biomarker for inhaled cortic osteroid mediated systemic effects on bone. Another approach to monitor corticosteroid effects on bone is to measure bone mineral density (BMD), which can be achieved with eith er dual-energy x-ray absorptiometry (DEXA) or quantitative computer tomography [97]. Studies, performed in asthmatic patients, have given controversial results on BMD after inhaled corticosteroid treatment [2]. Either no effects or reduction of BMD has been reported. In childr en, the results of change of BMD have been attributed to the physiological changes during growth. Wong and his coworkers investigated the possible relationship between cumulative dose am ounts of inhaled cortic osteroids and BMD in adults; and they found that a negative relation be tween these two variable s after adjusted with age and gender [98]. This finding is consistent with some other studies: the increase of inhaled corticosteroid amount could lead to the decrease of BMD in certain bone areas, such as neck and spine [99]. Whereas, this approach may not be appropriate since only the area under inhaled corticosteroid concentration curve (AUC), not cumulative dose amounts of inhaled corticosteroids, represents the exact steroid exposure in body. Until now, no attempts have been made to link of inhaled corticosteroid mediated osteoporosis or fracture to the steroid exposure, and a quantitative approach, such as pharmacokinetic and pharmacodynamic approach, could be possibly conducted for the analysis and prediction of long term eff ects of inhaled corticosteroids on bone. Effects on immune system The anti-inflammatory effects of corticostero ids are due to their direct and indirect interactions at molecular and cellular levels [ 100]. After binding to the GR, corticosteroids can regulate some gene transcripti ons and increase or decrease e xpression of certain proteins,

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23 cytokines, and receptors, such as lipocrotin 1, IL -4, and NK1-receptor. The direct interactions of corticosteroids with some cells play another im portant role for the treatment of inflammatory diseases. Systemic available corticosteroids can keep T lymphocytes out of circulation by suppressing activation and proliferat ion, or stimulating cell apoptos is [101,102]. Several studies on inhaled corticosteroids have shown the redu ction of inflammatory cells in the airway, including T lymphocytes, and eosinophils [103]. W ith high systemic exposure to corticosteroids, especially after oral or intr avenous administration, lymphoc ytopenia could occur [23,104,105]. Several models have been developed to qua ntitatively describe lymphocytopenia after systemic administration of corticosteroid s [23,105,106]. Among them, a mechanism based pharmacokinetic and pharmacodynamic model was successfully developed to account for the circadian rhythm of total lymphoc ytes trafficking in blood and corticosteroid exposure [23,105]. The absolute corticosteroid exposure resulted from endogenous cort isol and exogenous corticosteroids, such as inhaled corticosteroid s, and relative receptor bi nding affinity. This pharmacokinetic and pharmacodynamic approach is briefly described as follows. Total lymphocyte counts in blood are converted as a per centage of the total ly mphocyte count at predose. Total lymphocyte trafficking was described by a first-order elimination (LYM outk) process, and a zero-order production (LYM ink) with inhibition effects from endogenous cortisol alone (at baseline or placebo, Equation 1-7) or absolute corticosteroid exposure (d uring the treatment of corticosteroids, Equation 1-8). baseline LYM out COR free COR LYM COR free COR LYM LYM in baselineNk CIC CI k dt dN ,50 max,1 (1-7)

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24 treatment LYM out Exogenous free Exogenous LYM COR LYM COR free COR LYM Exogenous free Exogenous LYM COR LYM COR free COR LYM LYM in treatmentNk CICICC IC CICICCI k dt dN ) ( ) ( 1,50 ,50 ,50 ,50 ,50 max, (1-8) where N is the percentage of the count at pre-dose at baseline or during drug treatment; COR LYMImax, is the maximum inhibition effect by cortisol; COR LYMIC,50is the free cortisol concentration to produce 50% of maximum inhibition; Exogenous LYMIC,50is the free exogenous cortic osteroid concentration to produce 50% of maximum inhibition which can be calculated from COR LYMIC,50 and relative receptor binding affinities of cortisol and exoge nous corticosteroid. In this model, it was assumed that the circadian rhythm of total lym phocytes was secondary to the cortisol in blood. Protein binding information of corticosteroids is essential to this model since only free corticosteroid concentration is pharmacologically relevant. Some corticosteroids, such as prednisolone, show nonlinear protei n binding, and the incorporation of this nonlinearity into this pharmacokinetic and pharmacodynamic approach is necessary. Recently, a pharmacokinetic and integrated pharmacodynamic approach has been pr oposed to account for the nonlinearity of total prednisolone pharmacokinetics, and adequately predicted total prednisolone after either prednisone or prednisol one administration [107]. The total lymphocyte trafficking pharmacoki netic and pharmacodynamic model has been used for the systemic administration of corticos teroids, and literature reports on the systemic immune effects after inhaled steroid exposure is limited. Recently, Cameron RG and his colleagues found that a high dose of inhaled BDP (1000 g) produced similar systemic effects to those after oral prednisone admi nistration (2.5 mg) in terms of inflammatory cell counts [108]. In another study, after inhalati on of FLU, the reduction of total lymphocyte counts in blood was reported [109]. As a good surrogate for systemic e ffects of corticosteroids, especially for inhaled

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25 corticosteroids, total lymphocytes in blood should be further quantitatively explored with some pharmacokinetic and pharmacodynamic approaches. Other adverse effects Other inhaled corticosteroid mediated side effects include ocular complications, skin atrophy, and local effects in the oropharynx (e.g candidiasis and dysphonia) [2]. Although most of these clinical adverse effects cannot be quant itatively described with any pharmacokinetic and pharmacodynamic models at present, some effect s have shown good correlations with other side effects, such as relationship between skin atrophy and suppression on HPA axis function after inhaled corticosteroid administ ration [110]. This could provid e another way to understand skin atrophy quantitatively. Pharmacokinetic and Pharmacodynamic Properties of an Ideal Inhaled Corticosteroid From a pharmacokinetic and pharmacodynamic pe rspective, minimal adverse effects of inhaled corticosteroids can be achieved by not on ly optimizing dosing regmens of the currently available inhaled corticosteroid s, but also by designing a new a nd ideal inhaled corticosteroid with most favorable pharmacokinetic and pharmacodynamic properties. With the aid of exploration of the pharmacokine tic and pharmacodynamic properties of the available inhaled corticosteroids, an ideal inhaled corticosteroid can be projected. For this ideal inhaled corticosteroid, it is essential to fully understand the fate of inhaled corticosteroids, including pharmacokinetic a nd pharmacodynamic pathways, after administration [21,111]. The fate of inhaled co rticosteroids is graphically pres ented in the figure 1-4. Once corticosteroids are released from the inhaler, less than 60% of inhaled corticosteroids is delivered into the lung [17,112]. This portion of inhaled corticosteroids binds to the intracellular GR to mediate anti-inflammatory effects in the airways. Part of inhaled corticosteroids deposited in the lung could be eliminated by the mucociliary esca lator, and the remaining in the airways can

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26 eventually reach systemic circul ation. Majority of inhaled co rticosteroids, around 40% to 90%, is deposited in the oral cavity, where they can be swallowed or absorbed into the gastrointestinal (GI) tract (Please refer to Table 1-2). The locally impacted steroids could result in the local side effects in the oropharynx, such as candidiasis and dysphonia [2]. Therefore, to achieve high therapeutic effects and low adverse effects or best ratio of risk-ben efit, an ideal inhaled corticosteroid should possess the following optimal pharmacokinetic and pharmacodynamic properties: High therapeutic effects in the airway s: high pulmonary deposition and prolonged pulmonary residence time Low adverse effects on the oropharynx and system : low oral deposition, low or negligible oral bioavailability, inactive prodrug to minimi ze local side effects in the throat, and high systemic clearance These preferable properties could be acco mplished with several methods, including prodrug or softdrug design, modified formulatio n and devices, and optimized drug structure. Pulmonary properties Pulmonary deposition is crucial to the therapeu tic effects of inhaled corticosteroids in the lung especially when inhaled corticosteroid has nonnegligible oral bioavailability. The high pulmonary deposition results in more available st eroids in the airways a nd less deposited inhaled corticosteroids in the oral cavity. Several impor tant factors have impacts on the lung deposition, such as inhalation device, formulation, and par ticle size. Metered dos e inhaler (MDI) and dry powder inhaler (DPI) are two most widely used aerosol delivery systems for inhaled corticosteroids. With MDIs, the lung depositio n of steriods, including FLU, FP, BUD, MF, and BDP, ranges from 10% to 30% of the metered dos e, and the percentage of the metered dose of CIC can even reach around 50% (Table 1-2) However, pulmonary deposition could be compromised by dose amounts as to an inhaled steroi d with insignificant oral bioavialbility [21].

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27 Additional spacer could further improve the pul monary deposition of inhaled corticosteroids [113,114]. With the termination of the use of chlorofluorocarbon (CFC) as propellants in MDIs, hydrofluoroalkane (HFA), incl uding HFA-134a and HFA-227a, ha s been utilized as new propellants, which are environmentally friendly and do not damage the ozone layer [115]. HFA suspension formulation has shown similar l ung deposition to CFC based MDIs; and HFA solution aerosols provides improve d deposition of steroids in th e lung due to more effective delivery of steroids to the l ung and precise control of the dos e of steroids [116-118]. Some studies reported several fold in crease of the lung deposition after HFA MDIs when compared to the CFC MDIs [119,120]. DPIs, second commonly used system, have advantages over the MDIs because of the consistent delivery of the dose by DPIs and no usage of propellants. Similar pulmonary depositions have been observed with MDIs and DPIs [121,122] Nebulizer device is another system for inhaled steroids, which im proves the lung deposition of steroids and is frequently used in dilate. It was found that about 45% of the FLU dose wa s deposited in the lung with a nebulizer [123]. Compared with aerosol vehicles and devices, aerosol particle size is another critical factor to the pulmonary deposition of inhaled corticosteroids. If the particle size of steroids is reduced close or less than 2 m, Corticosteroids can be available in the smallest airways. The large size of steroids (large than 5 m) could result in low lung depos ition [17,115]. Thus the small particle size is desirable and helps corticosteroid s more efficiently penetrate into the peripheral regions of the lung. It was reported that HFABDP can achieve more than 60% lung deposition compared with less than 25% peripheral lundepos ition from CFC-BDP, which is due to the much smaller aerodynamic diameter (around 1 m) of the aerosol in HFA-BDP [117,124,125].

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28 Once inhaled corticosteroids are deposited in th e airways, they can bind to intracellular GR to produce anti-inflammatory effects, or be absorbed into systemic circulation, or be cleared by the mucociliary escalator. Pulmonary residen ce time of inhaled corticosteroids is another parameter to influence steroid medicated effect s, and prolonged pulmonary residence time of corticosteroids can increase the efficacy and potentially reduce the syst emic adverse effects [126,127]. Possible strategies to alter pulmonary residence time, such as formation of fatty acid conjugates and increase of lipophili city of corticosteroid s, have been attempted. BUD and active form of CIC (Des-CIC) have s hown esterification with fatty aci ds in the lung [128-130]. A recent in vitro study reported possible fatty acid conj ugations of the metabolites of BDP, including BMP and BOH, in the lung slices [131] The fatty acid esters formed from conjugation of inhaled corticosteroids are readily and gradua lly converted back to the free steroids. These conjugates serve as active steroid reservoir, which could be associated with prolonged antiinflammatory effects of some corticosteroids, su ch as CIC and BUD. This mechanism might be responsible for once daily dosing regimen for BUD and CIC. It has been found that the steric free hydroxyl group at the C-21 pos ition is essential to the fatty acid c onjugation of inhaled corticosteroids [132]. Therefore, future design of new inhaled corticosteroids can be based on the fatty acid conjugation mechanism to improve pulmonary residence time. Systemic properties Higher pulmonary deposition means less inhaled corticosteroids are deposited in the oral cavity. The portion of steroids deposited in the oropha rynx can cause local si de effects and can also be swallowed. These available corticosteroid s in the systemic circulation from GI tract and lung ultimately contribute to the systemic a dverse effects. Currently available inhaled corticosteroids experience heavy first-pass effect in the liver; and only a small percent (less than 10%) of corticosteroids gets into the systemic circulation by absorption from GI tract (Table 1-

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29 2). It was reported that the oral bioavailability of FP, MF, or CIC is even less than 1 % [133137]. Therefore, it is limited to further reduce or al bioavailability of ne w inhaled corticosteroids to achieve less systemic side effects. Obviously, a sufficiently high lung deposition of corticosteroids is desirable in order to obtain better local anti-inflammatory therapeuti c effects. However, with the high lung deposition, also more inhaled corticosteroids wi ll be absorbed into the systemic circulation through the airways. So ev en if the oral bioavailability of in haled corticosteroids is quite low, the overall systemic bioavailability of inhaled co rticosteroids can be high due to this pulmonary absorption. Prodrug is designed to minimize local and system ic side effects of inhaled corticosteroids assuming the conversion is only pres ent in the lung. Currently, onl y two inhaled corticosteroids, BDP and CIC, are prodrugs, and both of them are hydrolyzed to the active metabolites to exert their anti-inflammatory effects. Prodrug approach also reduces systemic availability of active steroids, and then decreases the pote ntial systemic side effects [132]. Inhaled corticosteroid mediated systemic effect s result from the total exposure of steroids in the system, which can be quantitatively de scribed by the area under the concentration-time curve (AUC) of corticosteroids. As shown in equation 1-9, AUC is determined by the amounts of steroids available in systemic circulation ( FDOSEICS ), and by clearance of corticosteroids ( CLICS), which describes the ability to remove e xogenous corticosteroids fr om system within a certain time period. ICS ICSCL FDOSE AUC (1-9) where DOSEICS is the given amount of i nhaled corticosteroids; and F represents the systemic availability of inhaled steroids.

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30 With the increase of CLICS, AUC and adverse effects of co rticosteroids ca n be reduced. Liver is the major organ for metabolism of drug. The blood flow of liver is approximately 90 L per hour. Therefore, if a drug is only cleared in liver, maximum cl earance of this drug can reach the same rate as liver blood flow. Clearance of currently available inhale d corticosteroids ranges from 60 L/h to 90 L/h (Table 1-2). It has been reported that clearances of the active metabolites of BDP (e.g. 17-BMP) and CIC (e .g. Des-CIC) are even greater than liver blood flow (120 L/hr and 228 L/hr, respectively, via assuming 100% conversion from prodrug to relevant active metabolite), which may suggest other extra-hepatic elimination pathways involved [18,135,138]. Since the clearance of current inhaled corticosteroids has b een maximized close to liver blood flow, other elimination pathways can be exploited to increase systemic clearance of inhaled corticosteroids. Soft steroid design can be an approach to serve this purpose. Soft steroids exert their effects and then they are e liminated with a controlled mechanism, such as specific metabolism pathway with certain non-he patic enzymes. Several steroid compounds, including loteprednol etabonate (LE), fluocortin-21 butyl es ter, tipredane, itrocinonide, butixocort propionate, and GW 215864, have be en designed and developed based on this approach [139,140]. These compounds were presum ably metabolized in the liver, or the blood or the lung. Although these compounds have shown reasonable efficacy in in vitro and/or animal studies, the development of most of these soft steroids has b een terminated due to the nonspecificity of the deactivation leading to low effi cacy in clinical studies compared to the current inhaled steroids [139,140]. Only LE is used widely to treat ocular inflammation [141-143]. Inhaled corticosteroids can bind to a couple of proteins in plasma, including albumin, and 1-acid glycoprotein. As per the free drug hypothe sis, only free, unbound drug is thought to be pharmacologically active. Limiting the circulati ng free corticosteroids c ould potentially reduce

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31 their systemic adverse effects as the bound corticos teroids are incapable of interacting with GR outside the lung. Currently available inhaled corticosteroids ha ve less than 20% of free fraction in plasma (Table 1-2). Less than 2% of free, unbound MF and CIC in plasma have been reported in several studies, which could partly explai n the less systemic side effects of these two steroids [144,145]. A small change of protein binding of corticosteroids in plasma could dramatically alter the free fraction, which accounts for the pharmacological or adverse effects in syst em. For example, in terms of a drug with 99% protein binding, one percent of decrease of protein binding could increase two fold of free fraction of drug in plasma, which could elevate systemic adverse effects. Therefore, a new inhaled corticosteroid with a high protein bindi ng is preferable for the few systemic adverse effects. However, it is possible that this hi gh protein binding could go along with reduced efficacy due to the high tissue binding [29]. Other properties Inhaled corticosteroid mediated pharmacologi cal and therapeutic effects result from the interaction between free corticoste roid and GR. In the airways, the receptor binding leads to the anti-inflammatory effects; howev er, outside the lungs, the binding or stimulating GR could cause adverse effects. The receptor binding affinity of steroids is commonly expressed as a relative receptor binding affinity (RRA) compared to de xamethasone binding affinity, which is assumed to be 100. The RRA values of current inhaled cortic osteroids are listed in table 1-2. It has been reported that MF has the highest RRA, around 3000. BDP and CIC have RRAs less than 100, but they are prodrugs, which are intentionally developed with lower receptor binding affinities. However, the active metabolites from these tw o steroids (17-BMP and Des-CIC) have shown much higher receptor affinities. Therefore, the prodrug approach can possibly reduce some adverse effects of inhaled corticos teroids, especially in terms of local side effects in the mouth

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32 and oropharynx, since the active meta bolites are produced only in pr esence of certain enzymes. For example, CIC is converted to Des-CIC in the airways by specific enzymes, such as carboxyesterases and cholinestera ses [146]. Swallowed CIC is inactive and less of converted Des-CIC is available in the or opharyneal region, which could account for the less local side effects of CIC [147]. Howeve r, difference in the receptor binding affinity of inhaled corticosteroids could be compensated by the dose adjustment. It has been reported that several genes are regulated by binding of corticosteorid s to GR, including activation of some antiinflammatory genes, repression of side-effect genes, and suppression of some inflammatory genes [30]. Thus, from the pharmacodynamic asp ect, the future design of an ideal inhaled corticosteroid can be rooted in the understanding of these corticosteroid mediated transactivation and transrepression. Conclusions Corticosteroids have been used clinically to treat anti-inflammatory diseases over years. Topical corticosteroids, such as inhaled cortic osteroids, are the most effective treatment for asthma. With the understanding of pharmacoki netic and pharmacodynamic properties of inhaled corticosteroids in the market, more preferable inhaled steroids, such as MF and CIC, with improved safety profiles have been developed. These corticosteroids have high efficacy and fewer side effects [111,148]. However, the presen ted side effects, including systemic and local effects, are still issues for chronic use of inhale d corticosteroids, especially at high doses and in special populations, such as pediatric patients. Two possible strategies are discussed here to help in achieving better risks and benefits value of inhaled corticosteroids. Firstly, current application of the available inhaled corticosteroids, such as dosing regimens, could be optimized with modeling and simulation approaches. Secondly, it is possi ble to develop an ideal inhale d corticosteroid with optimal

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33 pharmacokinetic and pharamcodynamic properties. With the knowledge of pharmacokinetics and appropriate biomarker(s) for the currently av ailable inhaled corticosteroids, best dosing regimen could be advised to minimize inhaled cort icosteroid mediated adverse effects. Although the pharamcokinetics of inhaled steroids have been well documented, clinically relevant biomarker(s) are still unrevealed. The exis ting biomarkers in the pharmacokinetic and pharmacodynamic approaches inadequately help in the prediction of chronic exposure of inhaled corticosteroids [22]. Changes in cortisol concentrations in blood or urine appear to be a sensitive biomarker for the sysmetic exposure of inhale d corticosteroids [22]. Several pharmacokinetic and pharmacodynamic models have been developed for plasma cortisol suppression undergoing exposure of inhaled corticostero id exposure [25,27,90]. With c linical trial simulations, these models can be used to forecast systemic exposure of inhaled corticosteroid s in terms of cortisol suppression. Other biomarkers, such as oste ocalcin concentrations for bone turnover, and lymphocyte counts in blood for immune suppression, have also been explored [24,105]. Further applications of these biomarkers are needed in order to predict adverse effects after long term treatment of inhaled corticosteroids, such as osteoporosis, fracture, and growth retardation in children. Some models have b een developed to link steroid expos ure or dose to the clinical measurements, such as growth velocity for gr owth inhibition effects of corticosteroids in pediatric patients. These models are useful, but possess some limitations as they are not mechanism based. With more information availa ble, such as GH levels during corticosteroid treatment, it is possible that mechanism ba sed pharmacokinetic and pharmacodynamic models could be developed for the prediction of corticosteroid induced adverse effects. By reviewing pharmacokinetic and pharmac odynamic properties of current inhaled corticosteroids, an ideal inhaled steroid with minimal adverse effects and optimal efficacy can be

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34 projected. Few side effects, or less systemic exposure of this new steroid, can be accomplished with low or negligible oral bioavailability, a nd high systemic clearance. On the other hand, optimal efficacy, or high pulmonary effects of this new steroid, can be obtained with long pulmonary residence time and high pulmonary availa bility. Apparently, such an ideal inhaled corticosteroid with these optimal properties can be developed with the knowledge of current inhaled corticosteroids and pharmaceutical techno logies, including the various aerosol vehicles and delivery systems, prodrug and soft drug de signs, and optimized drug structure with high lipophilicity and lipid co njugation capability.

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35 Figure 1-1. Structures of pre dnisone and prednisolone and inte rconversion between each other

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36 Figure 1-2. General structure for currently available inhaled corticosteroids (for individual structure of inhaled corticosteroid please refer to the Table 1-2.)

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37 Figure 1-3. Time-course of mean serum cortisol concentrations within 24 hr (open circles: baseline or placebo group; cl osed circles: single dose of 500 g fluticason propionate (FP)) (Data from reference [149])

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38 Figure 1-4. Fate of inhaled corticosteroids (shadowed area represents pharmacodynamics of inhaled corticosteroids, including therapeutic and adverse effects) (1: deposited in the lung; 2: eliminated by the mucociliary es calator; 3: absorbed from the lung; 4: systemic clearance; 5: swallowed, deposited in the oral cavity; 6: absorbed in the gastrointestinal (GI) tract; 7: eliminated by the first-pass effect) (adapted from reference [111])

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39 Table 1-1. Structures of currently available inhaled corticosteroids Drug name X1 X2 Y Z Triamcinolone acetonide F H Flunisolide H F Budesonide H H Fluticasone propionate F F Beclomethasone dipropionate Cl H Mometasone furoate Cl H Ciclesonide H H The positions of these groups (X1, X2, Y and Z) are given in Figure 1-2.

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40 Table 1-2. Pharmacokinetic and ph armacodynamic properties of currently available inhaled corticosteroids Drug name Delivery system a Pulmonary deposition Oral bioavailability Clearance (L/hr) Volume of distribution (L) Elimination half-life (hr) Protein binding Binding affinity b Triamcinolone acetonide [28,150-152] CFC 10% 23% 37 103 2.0 71% 233 HFA 26% Flunisolide [32,109,153-155] CFC 17%~40% 7~20% 58~65 61~96 1.6~1.8 80% 190 HFA 39% Budesonide [149,152,156-162] DPI 18~29% 11~13% 80~84 202~301 2.3~4.5 88% 935 Fluticasone propionate [133,136,162-168] CFC 12~13% <1% 66~93 318~859 6~8 90% 1800 HFA 26% DPI 15% Mometasone furoate [137,145,169-173] HFA 10~20% <1% 54 152 4~6 98~99 % 2900 DPI NA c Beclomethasone dipropionate [18,113,174-177] CFC 4%~10% 15~21% 150~230 20 0.1~0.5 87% 53 HFA 50%~60% DPI 19% Beclomethasone monopropionate [10,18,177-179] NA 5~36% 26~41% 120 d 424 d 2.7~7.4 NA 1022 Ciclesonide [134,135,138,180,181] HFA 52% <1% 152 207 0.4~0.6 ~99% 12 Desisobutyryl ciclesonide [134,135,138,180] NA NA <1% 228 d 897 d 3.5~6.0 ~99% 1200 a Delivery system: CFC represents metered dose inhaler (MDI) with chlorofluorocarbon (CFC) as th e propellants; HFA represents metered dose inhaler (MDI) with hydrof luoroalkane (HFA) as the propellants; DPI represents dry powder inhaler. b Receptor binding affinity is expressed relatively to dexameth asone binding affinity, which is assumed to be 100 (Reference [17,111,112]). c NA indicates not available. d Clearance and volume of distribution for 17BMP or Des-CIC are only valid if complete conversion from th eir respective prodrug occurs.

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41 CHAPTER 2 PHARMACOKINETIC/PHARMACODYNAMIC APPROACH TO PREDICT TOTAL PREDNISOLONE CONCENT RATIONS IN HUMAN PLASMA Introduction Prednisolone and prednisone are two widely used systemic corticosteroids for the treatment of various inflammatory and immune diseases including arthritis, lupus, and asthma. Prednisolone and prednisone undergo in terconversion or reversible metabolism in vivo (Figure 11) [182]. Prednisone serves as both a prodr ug and metabolite of prednisolone. The dosedependent pharmacokinetics of prednisolone wa s observed in several studies within various species [5-8,183]. It was suggested that nonlinea r pharmacokinetics of prednisolone is mainly due to its nonlinear protein binding [8,9,11]. Prednisolone binds to both albumin and transcortin (or human corticosteroid binding globulin (CBG)) in plasma. Albumi n has a low affinity and high capacity to prednisolone, whereas transcortin ha s a relatively high affinity and low capacity to prednisolone. The binding to transcortin is saturable with the increase of prednisolone concentration within the therapeu tic dose range. The total protei n binding of prednisolone is around 95% when prednisolone concentration is approximately 200ng/ml. Once prednisolone concentrations approach and exceed the Kt (binding affinity constant) of transcortin, binding fraction decreases from 95% to 60-70% nonlinearly [8,184]. In addition to the nonlinear protein binding of prednisolone, endogenous cortisol can competitively bind to transcortin, which adds comp lexity in explaining the nonlinearity of total prednisolone in plasma [185-187] The circadian rhythm of endogenous cortisol in plasma further complicates the nonlinear pharmacokinetics of prednisolone. Thus, we proposed that total prednisolone pharmacokinetics could be mo deled with a nonlinear protein binding process by combining cortisol competitive binding and cortisol circadian rhythm.

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42 As with other corticosteroids, a major systemic side effect of prednisolone is the suppression of endogenous cortisol production by a negative feedback mechanism in the hypothalamic-pituitary-adrenal axis. This pharmac odynamic effect alters the cortisol circadian baseline pattern [11,188]. Therefore, cortisol circadian variation afte r the administration of prednisolone makes it difficult to predict total pr ednisolone concentrations in plasma. Several pharmacokinetic and pharmacodynamic models have been developed to describe the baseline cortisol circadian rhythm and cortisol rel ease suppression after exogenous corticosteroids exposure [27,189]. A comparison revealed that a linear release rate pharmacokinetic and pharmacodynamic model adequately described availabl e datasets [27]. This model was utilized in our approach to predict total cortisol c oncentrations in plasma after prednisolone administration. Until now, no comprehensive model, incorporati ng all the above factors to explain total prednisolone nonlinear pharmacokinetics in human plasma, has been reported. The purpose of this study was to find a simple approach to describe and predict total prednisolone pharmacokinetics after prednisolone or prednisone administration and to help in understanding nonlinearity of prednisolone pharmacokineti cs. This pharmacokinetic and pharmacodynamic approach was developed based on th e available literature information and was able to describe reversible metabolism, nonlinear competitive protein binding, cortisol circadian rhythm, and cortisol suppression. In the present approach, pharmacokinetic models for free prednisolone and free prednisone after intravenous and oral administrations were developed; and the final integrated pharmacokinetic and pharmacodynamic approach was used to predict total prednisolone

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43 concentrations in human plasma. The predictive power of this approach was evaluated by comparing the predicted total prednisolone plasma concentrations with literature reported values. Materials and Methods Free Prednisone and Prednisolone Pharmacokinetics Several literature pharmacoki netics datasets, incl uding intravenous bolus [11], short-time infusion [190], and oral administration were extracted via computer digitalization (Graph Digitizer, Version 2.0, Russia )[25,190,191]. All these published studies are based on the same drug products (Prednisolone: Hydeltrasol, Me rck Sharp & Dohme, West Point, PA; and Prednisone: Deltasone, Pharmacia-Upjohn Co., Kalamazoo, MI). Once the drug prednisolone sodium phosphate gets into the system, it is assumed that the activ e form of drug prednisolone is rapidly formed as observed from the prednisolone concentration-time profile in literature. All computer model fittings were conducted us ing ADAPTII (BMSR-USC, Los Angeles, CA) program with the maximum likelihood method ( ADAPTII codes in Appendix A) [192]. All datasets from intravenous (i.v.) and oral (p.o.) administrations were fitted simultaneously at molar basis. The variance model was defined as: 22)(Y Variance (2-1) where and Y represent variance model parameter and th e predicted value from model fitting. Criteria for goodness-of-fit of modeling were: m odel convergence, variances for parameter estimates, Akaike Information Criterion (AIC), Sc hwarz Criterion (SC), a nd visual inspections. Free prednisone concentrations were genera ted by a constant fraction (25%) of total prednisone plasma concentrations based on the linear protein binding fo r prednisone [9,193].

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44 The stepwise simulations to predict total pr ednisolone concentrations were conducted in Excel (Microsoft, Redmond, WA), and classica l 4th order Runge-Kutta method was performed to solve all the ordinary differential equations (ODEs). Intravenous model for prednisone and prednisolone After intravenous administration of either pred nisone or prednisolone, free prednisone and prednisolone concentrations were fitted with a linear two-compartment reversible metabolism model (Figure 2-1A), where k represents either the first-order distribution rate constant between compartments, or elimination rate constant (s ee Table 2-1 for parameter symbols). A single linear interconversion process is assumed between predniso lone and prednisone. The pharmacokinetics of prednisolone and prednisone is one-compartmental with elimination from each compartment. This model can be describe d by two differential equations (Equations 2-2and 2-3) which characterize the rate of amount change of each species prednisone and prednisolone. PN PNL PNAkkAk dt dA )(1210 21 (2-2) PNL PN PNLAkkAk dt dA )(2120 12 (2-3) PN PN Free PNV A C (2-4) PNL PNL Free PNLV A C (2-5) where APN and APNL are the amounts of prednisone and prednisolone and Free PNC and Free PNLC are the free concentrations of prednisone and prednisolo ne in each compartment, respectively. Four literature datasets with either total prednisone or free prednisolone plasma concentrations were selected for the final model fitting [11,190].

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45 Oral model for predniso ne and prednisolone After oral administration, prednisone or prednisolone was absorbed into system via a firstorder process from a depot comp artment; and then prednisone and prednisolone experienced instant first-pass conversion in liver. Once prednisone and prednisolone reach the systemic circulation, the linear two-compartment reversible metabolism model as described for i.v. administration was applied. The p.o. m odel is described in figure 2-1B, where FPN represents the bioavailability for prednisone (or FPNL when prednisolone is administrated), and fPN is the fraction of drug as prednisone form afte r conversion (or as prednisolone form, fPNL), and kaPN and kaPNL are the first-order absorption rate constants for prednisone and prednisolone. The change of amount of each species is determined by diffe rential equations 2-7 to 2-9 and 2-11 to 2-13 (see Tables 2-1 and 2-2 for parameter symbols). When prednisone is administered, PNPN aPNFDose A )0( (2-6) aPN aPN aPNAk dt dA (2-7) PN PNL aPN aPN PN PNAkkAkAkf dt dA )(1210 21 (2-8) PNL PN aPN aPN PN PNLAkkAkAkf dt dA )( )1(2120 12 (2-9) When prednisolone is administered, PNL PNL aPNLFDose A )0( (2-10) aPNL aPNL aPNLAk dt dA (2-11)

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46 PN PNL aPNL aPNL PNL PNAkkAkAkf dt dA )( )1(1210 21 (2-12) PNL PN aPNL aPNL PNL PNLAkkAkAkf dt dA )(2120 12 (2-13) where, AaPN(0) and AaPNL(0) are the amounts of prednisone and pr ednisolone to be absorbed in the depot compartment at time zero and DosePN and DosePNL are the doses of prednisone and prednisolone. Free prednisone a nd prednisolone concentrations ar e described as in equations 2-4 and 2-5. Five previously published datasets with either free prednisolone or to tal prednisone plasma concentrations were chosen for the final mode l development [25,190,191]. The final parameter estimations were conducted with free prednisone and free prednisolone via simultaneously fitting all the i.v. and p.o. data (9 datasets). Th e predicted free prednisone concentrations were converted back to total predni sone via the linear pr otein binding which were plotted in the figures 2-2 and 2-3. Cortisol Circadian Rhythm and Cort isol Suppression by Prednisolone A previously published linear release rate pharmacokinetic and pharmacodynamic model was used to describe endogenous cortisol circad ian rhythm and its suppression after prednisone or prednisolone administration [27]. Briefly, the change of total cortisol (Total CORC ) concentration in plasma at baseline is determined by endogenous co rtisol release and elimination, as described Total CORe COR Total CORCkR dt dC (2-14) where RCOR is the cortisol release rate (concentration per unit time), and ke is the first-order elimination rate constant. Two straight lines are used to describe RCOR. For the time between maximum cortisol release (tmax) and minimum cortisol release ( tmin), RCOR is determined as

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47 )( )24 (min min max maxtt ttV R RCOR d COR (2-15) where COR dV is the volume of distribution of cortisol, and Rmax is the maximum release rate (amount per unit time) at time tmax, and t is the time after starting cortisol concentration monitoring. For the time between tmin and tmax, RCOR is determined to be: )( )(min min max maxtt ttV R RCOR d COR (2-16) After prednisone or prednisolone admi nistration, free pharmacologically active prednisolone can suppress endogenous cortisol release, which is desc ribed by an indirect response model [11]. The resulting change of total cortisol concentration in plasma is then given by equation 2-17 Total CORe Free PNL Free PNL COR Total CORCk CIC CI R dt dC 50 max1 (2-17) where Imax is the maximum suppressive effect, whic h is equal to 1 with possibly complete cortisol release suppression, IC50 is the free prednisolone plasma c oncentration to give half of the maximum effect, and Free PNLC is free prednisolone plasma concentration, which can be calculated from pharmacokinetic models described in earli er section. All the parameters for cortisol linear release model with suppressive effect were obtained from literature. Rmax was estimated to be 2966 mg/hr and tmin and tmax were 16.2hr and 20.7hr [194]. ke was 0.64 hr and COR dV has been described as 33.7 L [27,194]. IC50 was set to 0.98 ng/mL [11].

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48 Prednisolone and Cort isol Protein Binding Prednisolone is known to show nonlinear protein binding and binds to both albumin and transcortin in plasma [8,9,11,185,186,193,195]. The binding process can be described by the sum of a Langmuir type (for transcortin) and a linear (for albumin) binding [9,186] Free PNL PNL aaa Free PNL PNL t Free PNL PNL ttt Bound PNLCKPN CK CKPN C 1 (2-18) where Nt and Na are equal to the total number of binding sites per protein molecule, transcortin and albumin, and are assumed to be unity. Pt and Pa are protein concentrati ons of transcortin and albumin in plasma. Although different Na has been reported in the literature, the total binding sites in the system will be consistent with the commonly reported value in the literature [11,185,186,195,196]. PNL tK and PNL aKare the affinity constants of prednisolone to transcortin and albumin. Bound PNLC is the prednisolone-protein complex concentration in plasma. Equation 2-18 only describes the nonlinea r, non-competitive protein binding of prednisolone to transcortin and albumin. However, in reality, endogenous cortisol and prednisolone compete for the binding sites on tran scortin, but not on albumin [185]. In order to account for this competition for prednisolone pr otein binding, equation 2-18 is modified, and is shown below Free PNL PNL aaa Free COR COR t Free PNL PNL t Free PNL PNL ttt Bound PNLCKPN CKCK CKPN C 1 (2-19) where COR tK represents the affinity constant of cortisol to transcortin and Free CORC is the free cortisol plasma concentration. For the cortisol protein binding, a simila r equation can be derived based on the above analogy

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49 Free COR COR aaa Free PNL PNL t Free COR COR t Free COR COR ttt Bound CORCKPN CKCK CKPN C 1 (2-20) where COR aK is the affinity constant of cortisol to albumin and Bound CORC is the cortisol-protein complex concentration in plasma. All parameters used in protein bindi ng for both prednisolone and cortisol are listed in table 2-3 [196,197]. Prediction of Total Prednisolo ne Concentration in Plasma Total prednisolone plasma concentra tion can be determined by equation 2-21 Free PNL Bound PNL Total PNLCCC (2-21) where Total PNLC is the total prednisolone concentration in plasma. Substitution of equation 2-19 into equation 2-21 yields Free PNL Free PNL PNL aaa Free COR COR t Free PNL PNL t Free PNL PNL ttt Total PNLCCKPN CKCK CKPN C 1 (2-22) where Free PNLC is the predicated free prednisolone con centration from pharmacokinetic models, but Free CORC is not readily available. As to cortisol total concentration, equati on 2-23 can be derived based on equation 2-20 Free COR Free COR COR aaa Free PNL PNL t Free COR COR t Free COR COR ttt Total CORCCKPN CKCK CKPN C 1 (2-23) where Total CORC is the total cortisol concentration in plasma, which can be predicted by the linear release rate pharmacokinetic and pharmacodynami c model after prednisone or prednisolone administration. Equation 2-23 can be rearranged to equation 2-24 0 )(2CCBCAFree COR Free COR (2-24)

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50 where ) 1(COR aaa COR tKPN KA ) 1() 1() (Free PNL PNL t COR aaa Total COR tt COR tCK KPN CPNKB ) 1(Free PNL PNL t Total CORCK CC, There are two roots for equation 2-24 A CABB CFree COR 2 42 (2-25) Under physiological condition, Total CORC is less than 200 ng/mL [27], then0Total COR ttCPN Thus, B is always greater than zero. Only the positiv e root is applicable as the free cortisol plasma concentration. The total prednisolone plasma concentrati on at each time point can be calculated by substituting the value of Free CORC obtained from equation 2-25 in to equation 2-22. With the stepwise approach, total prednisolone plasma concentration-time profiles can be generated. Predictability of the Modeling Approach The predictability of this proposed pharmacokinetic and pharmacodynamic approach for total prednisolone plasma concentrations was illustrated by comparing predicted values to the reported data in several clin ical studies [8,11,190,198,199]. The simulated pharmacokinetic profiles were plotted with the da ta extracted from literature. Results Wald and his coworkers performed a pha rmacokinetic and pharmacodynamic study of prednisolone in healthy male volunteers [11]. In this crossover study, each subject received intravenous doses of 16.4 and 49.2 mg of predni solone in two phases. The tri-exponential equations successfully described the time-course of free prednisolone plasma concentrations for these two doses without consid ering interconversion between pr ednisolone and prednisone. Pharmacokinetic data of free prednisolone for a typical subject at both dose amounts were

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51 extracted from this original report. Two other total prednisone mean pharmacokinetic data in a four-way, randomized crossover study were obtai ned and intravenous doses of either 10 mg prednisolone or 10 mg prednisone were given as 0.5-hour infusion in this study [190]. These four i.v. concentration-time pr ofiles are shown in figure 2-2. Chakraborty and his colleagues evaluated prednisolone pharmacokinetics with and without interleukin-10 with the oral admi nistration of prednisone 15 mg [25]. The mean observed total prednisone and free pred nisolone concentration-time data after dosing prednisone alone are shown in figure 2-3. Two other datasets, including mean observed total prednisone and free prednisolone concentrations afte r 20 mg oral administration of prednisone were obtained from a study by Meno-Tetang and her colleagues in healthy volunteers [191]. In bot h studies, either a monoor bi-exponential disposi tion with first-order absorp tion was used to describe pharmacokinetics of prednisone or prednisolone. In one oral prednisolone study, the total prednisone concentration-time profile was reported and no mode ling was conducted [190]. All extracted p.o. concentration-time profiles are shown in figure 2-3. Before model development, free prednisone concentrations were calculated from the reported total prednisone concentrations based on the linear protein binding. With the simultaneous model fitting of free prednisone and free prednisolone from all i.v. and p.o. studies, a two-compartment model with a linear intercon version between prednisone and prednisolone was developed based on the model fitting criteria. The model-predicted pharmacokinetic profiles with original reported da ta are shown in figures 2-2 and 23. Predicted free prednisone concentrations were converted back to the total prednisone concen trations which were plotted in figures 2-2 and 2-3. Model estimated parameters with %CV are listed in tables 2-1 and 2-2. A good agreement was found between th e reported data and the model-predicted data and most

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52 parameters had a relatively low %CV indicating that the model suitably predicts the experimental data. Four fundamental clearances, including elimination clearances of prednisone and prednisolone ( CLPN and CLPNL), interconversion clearance of prednisone to prednisolone ( CLPNPNL), and vice versa ( CLPNLPN) were also reported with %CV and are shown in table 2-1. Based on the pharmacokinetic models deve loped above for free prednisone and prednisolone and previously published lin ear release rate pharmacokinetic and pharmacodynamic model for cortisol circadian rhythm [27], total cortisol concentration-time profiles were generated with a stepwise simulation after exoge nous administration of either prednisone or prednisolone. Then, with the pr otein binding information as described in method section, free cortisol concen trations were calculated and ul timately total prednisolone concentration-time data were predicted. In order to assess the predic tive power of this new pharmacokinetic and pharmacodynamic approach, simulations for the total prednisolone in plasma were performed at various dose amounts with intravenous predniso lone or oral prednisone ad ministration, for which total prednisolone plasma concentrations were reporte d in actual clinical studies [8,11,190,198,199]. The predicted total prednisolone plasma concentr ations were compared with the actual measured concentrations from the clinical studies. The choice of simu lated dose amounts and routes was dictated by the availability of c linical studies reporting total pre dnisolone plasma concentrations. The simulated pharmacokinetic profiles and data re ported in literature are presented graphically in figure 2-4. The results showed good agreement between simulated and observed total prednisolone plasma concentrations. The re latively high values of Pearson Product-Moment Correlation Coefficients (PMCC) ( r >0.8) also indicated a good co rrelation between predicted and observed total prednisol one plasma concentrations.

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53 Discussion and Conclusions The complexity of total prednisolone concen tration-time profiles in plasma can prove difficult in terms of modeling and predicting it s pharmacokinetics, due to its dose-dependency and reversible metabolism [5-8,182,183]. Although conventional pharmacokinetic models have been applied to describe tota l prednisolone plasma concentr ations via either mono or biexponential distribution, the result ing parameters, such as clearan ce, volume of distribution, and bioavailability differed from dose amounts and regimens, indicating nonlinearity of pharmacokinetics [8,11]. The potential causes an d possible explanations have been discussed extensively in the l iterature [8,9,11,200,201]. We pres ent a simple pha rmacokinetic and pharmacodynamic approach to predict total predniso lone plasma concentration, which offers a new way to describe and elucidate nonlinea r pharmacokinetics of total prednisolone by incorporating reversible metabo lism, protein binding, cortisol circadian, an d prednisolone mediated cortisol suppression. Moreover, the estimates of pharmacokinetic parameters were based on free drug concentrations which are esse ntially consistent with in various dose amounts and dosing regimens [8,11]. We developed both i.v. and p.o. pharmacokine tic models for free prednisone and free prednisolone, which well characterized the ex tracted literature data Free prednisone concentrations were generated via a linear pr otein binding fraction ( 75%). Although Boudinot and his coworkers reported a different binding fraction value [195], which was conducted in vitro the binding parameter chosen in our study was from a previously p ublished study based on in vivo plasma samples [186]. Th e observed and predicted pharm acokinetic data showed good agreement and the precision of parameter estimati on was good. The deviation in fitting different datasets could be attributed to the inter-occas ional variability, such as the variability among studies in terms of design and analytical variations, which was not readily modeled in our

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54 approach due to limited literature information. CLPNPNL for free drugs is about 2.5-fold greater than CLPNLPN for free drugs, suggesting prednisone is readily converted into prednisolone in vivo VPNL and CLPNL for free prednisolone reported here are similar to those found by Wald, Rose and their colleagues via conventional pharmacokinetic anal yses [8,11]. The estimated VPN larger than VPNL agrees with their different physical-chemi cal properties. The bioavailability for prednisone and prednisolone were 75% ([70%-81%] 95%C I) and 92% ([71%-110%] 95%CI) respectively, which are similar to the value reported in the lite rature [190,198]. The ratio of prednisone to prednisolone in depot compartment after prednis one administration differs from that after prednisolone administration, which indicates th at liver handles prednisone and prednisolone differently as Frey and his coworkers suggested [202]. The cortisol circadian rhythm and its suppression have been described by a linear release rate pharmacokinetic and pharmacodynamic model in literature [27]. With a stepwise approach in this linear pharmacokinetic a nd pharmacodynamic model, we were able to get total cortisol plasma concentration at each time point with a corresponding free prednisolone concentration. The eliminations of cortisol and prednisolone were handled differently in this pharmacokinetic and pharmacodynamic approach (total cortisol and free prednisolone were used), however, there is no need to obtain free cortisol information in order to fit the total cortisol profile since the effect of cortisol on the total pr ednisolone profile is minimal, es pecially at a high dose. Using previously reported parameters for the competitive protein binding between cortisol and prednisolone, a simulation was performed to predic t prednisolone binding with the various free cortisol concentration, and simu lated protein binding curves were in a good agreement with the reported literature [185,186]. Based on the validated protein binding inform ation, free cortisol plasma concentrations were then obtained via equation 2-25. Eventually, total prednisolone

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55 plasma concentrations were predicted base d on equation 2-22. The validity of this pharmacokinetic and pharmacodynamic approach to predict total prednisolone plasma concentrations was further confirmed by comparis on with results for total prednisolone plasma concentrations observed in seve ral actual clinical studies [8,1 1,190,198,199]. Regardless of the dose and administration routes, th e predicted data correlated w ith observed values very well, which suggests that the factors, including reversible metabolis m, protein binding, cortisol circadian and its suppression, accounted for the nonlinea r pharmacokinetics of total prednisolone. Although some aut hors pointed out that other possibl e saturable processes, such as the saturable conversion of prednisolone to pr ednisone [183], could at tribute to the nonlinear pharmacokinetics of total prednisolone, this issue was not considered during our model development due to limited data and over-p arameterization of the final model. In conclusion, an integrated pharmacokine tic and pharmacodynamic approach, which was able to incorporate several important factors, wa s developed to predict to tal prednisolone plasma concentrations and to demonstrat e the nonlinearity of total prednisolone pharmacokinetics. This approach also offers possible predictions of other species in the system, including free prednisolone, total prednisone, fr ee prednisone, total cortisol, and free cortisol in plasma, besides total prednisolone. With the readily available co rtisol concentrations from this approach, it is feasible to predict the cumulative cortisol suppres sion as one important surrogate marker for the systemic activity of prednisone and prednisolone. Although this pharmacokinetic and pharmacodynamic approach was based on the single dose regimen, it could be extended for the application in the multiple doses. Future work is n eeded to evaluate the benefits of this approach to the clinical application.

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56 Dose (PN/PNL) Aa(PN or PNL)F, fPNor fPNLk10 APNL(PNL) (VPNL) APN(PN) (VPN) k20k12k21kaka B:k10 APNL(PNL) (VPNL) APN(PN) (VPN) k20k12k21 Dose (PN) Dose (PNL)A: Dose (PN/PNL) Aa(PN or PNL)F, fPNor fPNLk10 APNL(PNL) (VPNL) APN(PN) (VPN) k20k12k21kaka B: Dose (PN/PNL) Aa(PN or PNL)F, fPNor fPNLk10 APNL(PNL) (VPNL) APN(PN) (VPN) k20k12k21kaka Dose (PN/PNL) Aa(PN or PNL)F, fPNor fPNLk10 APNL(PNL) (VPNL) APN(PN) (VPN) k20k12k21kaka B:k10 APNL(PNL) (VPNL) APN(PN) (VPN) k20k12k21 Dose (PN) Dose (PNL)A:k10 APNL(PNL) (VPNL) APN(PN) (VPN) k20k12k21 Dose (PN) Dose (PNL)k10 APNL(PNL) (VPNL) APN(PN) (VPN) k20k12k21 Dose (PN) Dose (PNL)A: Figure 2-1. Pharmacokinetic models of free prednisone (PN) and pr ednisolone (PNL) (A: intravenous administration. B: oral administration. The model structure is described in the text. Symbol s are defined in the Tables 2-1 and 2-2.)

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57 Figure 2-2. Concentration-time profiles of total prednisone (P N) and free prednisolone (PNL) concentrations after intr avenous PN or PNL admini stration (Symbols are data extracted from literature [11,190]. Solid (PNL) and dash (PN) lines are modelpredicted profiles.)

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58 Figure 2-3. Total prednisone (P N) and free prednisolone (PNL) concentration time-course after oral PN or PNL administration (Symbols are data extracted from literature [25,190,191]. Solid, dash and dotted lines are model-predicted profiles.)

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59 Figure 2-4. Time-course of total prednisolone (PNL) concentrati ons in plasma (A: intravenous prednisolone administration (, : ref. [8]; : ref. [11]; : ref. [199]); B: oral prednisone administration (: ref. [8]; : ref. [190]; : ref. [198]; : ref. [199]); Dash lines are simulated profiles vi a the pharmacokinetic and pharmacodynamic approach.)

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60 Table 2-1. Final estimated pharmacokinetic pa rameters for free prednisolone (PNL) and free prednisone (PN) plasma concentration-time data Parameters (units) Description Estimates (%CV) k10 (hr-1) First-order elimination rate constant for PN 0.25 (13.33) k20 (hr-1) First-order elimination rate constant for PNL 0.33 (13.97) k12 (hr-1) First-order conversion rate constant from PN to PNL 0.23 (17.59) k21 (hr-1) First-order conversion rate constant from PNL to PN 0.31 (14.23) VPN (L) Volume of PN 397.3 (12.56) VPNL (L) Volume of PNL 110.4 (7.38) CLPN (Lhr-1) Clearance of PN 101.0 (22.95) CLPNL (Lhr-1) Clearance of PNL 36.37 (13.05) CLPNPNL (Lhr-1) Conversion clearance from PN to PNL 90.64 (13.38) CLPNLPN (Lhr-1) Conversion clearance from PN to PNL 34.78 (13.00)

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61 Table 2-2. Oral pharmacokinetic parameter es timates for free prednisolone (PNL) and free prednisone (PN) plasma concentration-time data Parameters (units) Description Estimate (%CV) Oral PN Oral PNL kaPN (hr-1) First-order absorption rate c onstant for PN 1.08 (8.24) NA a kaPNL (hr-1) First-order absorption rate c onstant for PNL NA 0.42 (17.24) F Bioavailability 0.75 (3.76) 0.92 (11.61) fPN Fraction of PN entered into system 0.14 (17.83) 0.36 b fPNL Fraction of PNL entered into system 0.86 b 0.64 (38.48) a NA: not applicable b Calculation: fPN + fPNL = 1

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62 Table 2-3. Protein binding parameters for predniso lone (PNL) and cortisol (COR) Parameters (units) Description Values PNL c COR d Kt (M-1) Affinity constant to transcortin 2x107 3x107 Ka (M-1) Affinity constant to albumin 1.4x103 5x103 Pt ( M) Transcortin concentration in plasma 0.8 c Pa ( M) Albumin concentration in plasma 700 c c Reference [196] d Reference [197]

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63 CHAPTER 3 ASSESSMENT OF THE IMPACT OF DOSING TIME ON THE PHARM ACOKINETICS/PHARMACODYNAMICS OF PREDNISOLONE Introduction Time-dependency of biological and physiologica l processes, such as rhythms of blood pressure, enzyme expression, immune cell pr oduction, and endocrine functions, has been recognized in most mammalian syst ems. Based on this principle, chronotherapy was developed, and has been applied in several disease areas, including cancer, im mune diseases, and cardiovascular diseases [203-206]. Chronotherapy is implemented to achieve better clinical outcomes with fewer side effects by choosing an op timal dosing time. It is not surprising that drug pharmacokinetics and pharmacological eff ects are perhaps dependent of dosing time. Corticosteroids are widely used in the treatment of allergies, inflammation and autoimmune diseases, such as asthma, lupus, rh eumatoid arthritis (RA), and Crohns disease. Generally, corticosteroids are given in the morn ing due to the disease related process. For example, patients with RA experience more clinical symptoms during the early morning, which is believed to be associated w ith the peak level of interleukin 6 (IL-6) [12]. However, cells and cytokine levels in immune syst em show circadian variations, in cluding T lymphocytes [207], and IL-6 [12,208]. Corticosteroids could potentially alter the rhythmic va riation in the immune system. Therefore, it is important to understand the impact of dosing time on the pharmacological effects of corticos teroids. When prednisolone was administered in the very early morning hours, better therapeutic outcomes have been observed in RA patients [13]. When prednisolone was given in the af ternoon at 4 PM, duration of suppression of cortisol in plasma was short compared with 8 PM treatment [209 ]. Recently, Glass-marmor and colleagues found that patients with multiple sclerosis preferred night-time treatment of methylprednisolone, and higher clinical recovery was observed for night-time treatme nt [204]. In a simulation study,

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64 Krishnaswami and his coworkers found that dosing of inhaled steroids at around 3 to 4 AM gave the maximum 24-hr cumulative cortisol suppr ession (CCS) in plas ma, and minimum CCS occurred in the afternoon around 4 to 7 PM. Recently, the time-dependency of pharmacodynamics of inhaled corticosteroids wa s further evaluated vi a a population analysis approach, and a similar conclusion was drawn [ 210]. These results indi cated that clinical outcomes and side effects could be influenced by the time of administration of corticosteroids [85]. The exogenous corticosteroid, prednisolone, has been used systemically for several decades [182]. Total prednisolone in plasma showed nonlinear pharmacokinetics in several species, including humans [5-8,183,211]. Predniso lone, like other cortic osteroids, mediates several important pharmacological effects, including immune-suppression, and cortisol suppression. So far, however, the time -dependency of the pharmacokinetics and pharmacodynamics has not been assessed to help improve the clinical use of prednisolone. Recently, we developed a pharmacokinetic and pharmacodynamic approach to predict total prednisolone concentrations in human plasma, and this approach adequately explained the nonlinear prednisolone pharmacokinetics by combining nonlinear protein binding of prednisolone and other factors, including reversible metabol ism between prednisolone and prednisone, prednisolone-i nduced cortisol suppression, and cortis ol circadian rhythm [107]. In this section, we propose to use this phar macokinetic and pharmacodynamic approach to investigate time-dependency of prednisolone ph armacokinetics, and extend this pharmacokinetic and pharmacodynamic approach to evaluate the impact of dosing time on this pharmacodynamics. Two major biomarkers, cortis ol concentration and lymphocyte trafficking in plasma, are monitored for the systemic activit ies of prednisolone. Cortisol suppression was

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65 commonly modeled with the cortisol circadian a nd a suppression effect on cortisol release by exogenous corticosteroids [25,27]. Lymphocyte tr afficking was modeled w ith inhibition effects by both endogenous cortisol and exogenous cortic osteroids [105,106]. An interactive algorithm based on our pharmacokinetic and pharmacodynamic approach and other l iterature information was developed [105,107]. Several predictions were performed to assess the validity of this algorithm via comparing simulated results with literature reported data. In this algorithm, a series of simulations with various dosing time and dose amounts were conducted with either intravenous administration of pre dnisolone or oral administration of prednisone. The simulated results, including initial, maximum, and tr ough total prednisolone concentrations, CCS and altered lymphocyte trafficking, were evaluated for the time-depe ndency of the pharmacokinetics and pharmacodynamics of prednisolone. Materials and Methods Pharmacokinetics and Pharmacodynamics of Prednisolone and Prednisone Pharmacokinetic models for prednisolone and pr ednisone are described in the Chapter two, and pharmacodynamic models were based on the previous publis hed publications. Pharmacokinetics of prednisolone and prednisone A linear two-compartmental pharmacokinetic model, previously developed in this lab, was used to describe free predniso lone and free prednisone con centration-time pr ofiles [107]. Briefly, free prednisolone a nd free prednisone are one-compa rtmental with a first-order elimination process from each compartment. A simple linear interconversion is assumed between free prednisolone and free prednisone (Figure 3-1, sha dow area; and equations 3-1 and 3-2). PN free PNLPN PN PNL free PNPNL PN free PNC CLCLC CL dt dC V ) ( (3-1)

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66 PNL PNPNL PNL PN PNLPN PNL PNLfree free freeC CLCLC CL dt dC V ) ( (3-2) where PN freeC and PNL freeC are free concentrations of prednisone and prednisolone in each compartment; PNPNLCL and PNLPNCLare conversion clearances between free prednisolone and prednisone; PNLCL and PNCLare clearances of free pr ednisolone and prednisone; PNLV and PNVare volumes of distribution of free predniso lone and prednisone, respectively. When prednisolone or prednisone is given orally, their absorption pro cesses are assumed to be firstorder. Total prednisolone concentrations, influenced by competitive protein binding from cortisol, are obtained based on a combination of La ngmuir and linear type protein binding. Total prednisone concentrations are predicted with a linear type prot ein binding (Equations 3-3 and 34). PNL free PNL free PNL aaa COR free COR t PNL free PNL t PNL free PNL ttt PNL totalCCKPN CKCK CKPN C 1 (3-3) PN free PN free PN totalfCC (3-4) where PN totalC and PNL totalC are total concentrations of prednisone and prednisolone in plasma; tN, tP, and tK are total number of binding sites per transcor tin, plasma transcortin concentration, and transcortin affinity constant; aN, aP, and aK are total number of binding sites per albumin, plasma albumin concentration, and albumin affinity constant; PN freefis the free fraction of prednisone in plasma;COR freeCis free cortisol concentration in plasma, which is related to COR totalCas described in equation 3-5.

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67 COR free COR free COR aaa PNL free PNL t COR free COR t COR free COR ttt COR totalCCKPN CKCK CKPN C 1 (3-5) Pharmacodynamics of prednisolone and prednisone Cortisol circadian rhythm as well as cortis ol suppression by predniso lone was described by a linear release model with an inhibition effect (in Figure 1, right part, Equations 3-6 to 3-8) [107]. COR total COR e PNL free PNL PNL free PNL COR COR totalCk CIC CI R dt dC 50 max1 (3-6) ) ()( )24 (min max min min max maxttttt ttV R RCOR COR (3-7) ) ( )( )(max min min min max maxttt tt ttV R RCOR COR (3-8) where CORR, cortisol release rate, is described with two linear functions based on the time; and COR ekis the first-order elimination rate constant; PNLImax and PNLIC50 are the maximum inhibition effect by prednisolone and fr ee prednisolone concentrati on to produce 50% of maximum suppression; and maxRis the maximum cortisol release rate at time maxt. Cumulative cortisol suppression (CCS) within 24 hr is commonly used to assess prednisolone systemic activities. CCS is based on the difference between the area under cortisol baseline curve (COR baselineAUC) and the area under curve of plasma cortisol concentrations after prednisolone exposure (COR treatmentAUC) (Equation 3-9). The area under curve is calculated using the trapezoidal rule. %100 % COR baseline COR treatment COR baselineAUC AUC AUC CCS (3-9)

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68 The lymphocyte count was normalized as a percentage of the total lymphocyte count at 8:00 AM. Total lymphocyte trafficking was described by a first-order elimination (LYM outk) process, and a zer o-order production (LYM ink) with inhibition effects from both endogenous cortisol and exogenous predni solone (in Figure 1, right part, Equation 3-10) [105,106]. Nk CICICCIC CICICCI k dt dNLYM out PNL free PNL LYM COR LYM COR free COR LYM PNL free PNL LYM COR LYM COR free COR LYM LYM in ) ( ) ( 1,50 ,50 ,50 ,50 ,50 max, (3-10) where N is the percentage of the count at 8:00 AM; COR LYMImax, is the maximum inhibition effect by cortisol; COR LYMIC,50is free cortisol concentration to produce 50% of maximum inhibition; PNL LYMIC,50 is free prednisolone concentra tion to produce 50% of maximum i nhibition, which was calculated from COR LYMIC,50 and the receptor binding affinity of cortisol and prednisolone. Similar to CCS, altered total lymphocyte traffi cking within 24 hr is evaluated by difference between the area under the effect curv e (AUEC) without prednisolone (LYM baselineAUEC) and AUEC after prednisolone exposure (LYM treatmentAUEC) (Equation 3-11). The area under curve is calculated using the trapezoidal rule. %100 % LYM baseline LYM treatment LYM baselineAUEC AUEC AUEC AUEC (3-11) Design of an Interactive Algorithm Using the pharmacokinetic and pharmacodynami c models given above, an interactive algorithm was developed in Microsoft Excel. This algorithm was based on a deterministic approach without incorpor ating any variance component. All pharmacokinetic and pharmacodynamic parameters in this algorithm were obtained from literature [105,107]. Cortisol circadian rhythm and its suppression were solved in a similar manner as described by

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69 Krishnaswami and colleagues [85]. All the ot her ordinary differen tial equations (ODEs), including the pharmacokinetics of free prednisolone, free prednisone, and total lymphocyte trafficking, were solved by the classical 4th order Runge-Kutta method. Two Excel spreadsheets were developed for oral administration and in travenous administration, respectively. The spreadsheets were able to display pharmacoki netic and pharmacodynamic profiles in a real-time manner, and predict CCS% and AUEC% based on the input parameters: name of drug (prednisone or prednisolone), dose amount (mg) time of administration (24 hr clock scale), dosing regimen (single or multiple), and dose interv al (Tau, for multiple dos es). The interface of the spreadsheets is shown in figure 3-2. Predictability of the Algorithm The predictability of this algorithm was illustrated by comparing the simulated results to the literature data visually. Pharmacokinetic pred ictability of this algorithm was presented in our recently published paper [107]. In terms of pha rmacodynamic predictability, cortisol baseline and lymphocyte trafficking baseline were simulate d and plotted against da ta digitally extracted from several clinical studies [23,105,106,189,212-214]. If absolute counts of total lymphocytes are reported, data were convert ed to the percentage of the count at 8:00 AM. Cortisol suppression and inhibition of lym phocyte trafficking after predni sone or prednisolone exposure were simulated. Simulated cortisol and lymphocyt e profiles were plotted with the extracted data from literature [11,214-217]. Predicted CCS% wa s also compared with CCS% calculated from literature [189,191,211,213]. The choice of dose am ounts and routes in si mulation was dictated by clinical studies in literature.

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70 Simulations in the Algorithm Several simulations were performed to assess the impact of dosing time on the pharmacokinetics and pharmacodynamics of prednisol one in this algorithm. Scripts were created in Visual Basic Application (VBA) to facilitate simulations. Prednisolone is commonly administrated intr avenously at 2 to 100 mg dose range. First simulation was conducted within this range at a 2 mg step increase. Both endogenous cortisol and exogenous prednisolone inhi bit lymphocyte trafficking; in addition, exogenous prednisolone suppresses endogenous cortisol production; thus a second simulation scenario within a dose range of 0.01 to 1 mg was performed to unde rstand the net steroid effect on lymphocyte trafficking. Time of administra tion was chosen within a 24 hr ra nge at a 2 hr interval. During simulations, initial concentrations of total prednisolone (C(0)), CCS% and AUEC% were recorded. Contour plots were created with dose amounts, tim e of administration, and C(0), CCS%, or AUEC%. Prednisone is a prodrug of pr ednisolone. Prednisolone is the pharmacologically active form of prednisone in vivo. Clinically, the dose of prednisone ranges from 5 to 200 mg. Prednisone is given once a day, or every other day. Therefore, two simulation scenarios were proposed, including single oral, and multiple oral dosing with either every 24 hr, or every 48 hr interval. In both situations, time of administra tion was selected within 24hr range at a 2 hr interval, and dose amount was chosen based on therapeutic dose range of prednisone. For single oral dosing regimen, maximum concentrations of total prednisolone (Cmax), time at maximum concentrations (Tmax), CCS% and AUEC% were simulated. With multiple oral administrations, maximum concentrations (Cmax), trough concentrations of total prednisolone (Ctrough), CCS% and AUEC% at steady state were monitored and reported All results were plotted in contour graphs.

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71 Results Predictability of the Algorithm Recently a new pharmacokinetic and pharmacodynamic approach was published to predict total prednisolone in plasma which adequately predicts literature pharm acokinetic data [107]. We extended this approach and combined other literature information to predict pharmacodynamics of prednisolone, including cortisol suppression, and inhibition of lymphocytes trafficking in plasma (Figure 3-1) [105,106]. We developed an algorithm (Figure 3-2) to predict pharmacokinetic and pharmac odynamic after prednisolone or prednisone administration. Appendix B describes how to use this algorithm in Excel spreadsheet. In figure 3-3A and 3-3C, simulated cortisol and total lymphocyte trafficking baselines from the algorithm are plotted with observed data in li terature [23,105,106,189,212-214]. In figure 3-3B and 3-3D, cortisol suppression and inhibition of total lympho cyte trafficking profiles are compared with the data reported in actual clin ical studies with prednisolone or prednisone exposure [11,214-217]. These results elucidated the validity of the pharmacodynamic prediction of this algorithm by visual inspection. The ratios of the area under the cortisol c oncentration curve (AUC SR) within 24 hr of prednisone or prednisolone e xposure have been reported in several studies [189,191,211,213]. This parameter can be readily converted to CCS% (SRAUC CCS 1 %). Thus, several simulations were implemented to further illustrate the predictive power of this algorithm. Two studies with oral administrati on of prednisone were reported by Chakraborty, Meno-Tetang and their coworkers [189,191]. The co rtisol suppression simulated usi ng this algorithm is close to the reported data. Results are shown in table 31. Results were also extracted from two other

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72 clinical studies with intravenous administration of predni solone [211,213], and the differences between the predicted CCS% and CCS% in lite rature are less than 10% (Table 3-1). Simulation of Intravenous Admi nistration of Prednisolone Figure 3-4 shows the results from the first simulation. Initial total prednisolone concentrations (C(0)) are presented in figure 3-4A. CCS% and AUEC% were generated and plotted in figure 3-4B and 3-4C, respectively, w ith the oscillation pattern of CCS% being the most pronounced. When prednisolone is administrated around 6:00AM, C(0) is lowest. However, the lowest CCS% and the highest AUEC% occur when prednisolone is given between 9:00AM to 3:00PM, and the maximum CCS% a nd the minimum AUEC% are observed when the drug is given in the early morning between 2: 00 AM and 4:00AM within various dose amounts. In figure 3-5, results from the second si mulation with small dose amounts (0.01 1 mg) are plotted. This simulation shows th at when the dose amount is small, C(0) is time-dependent, and fluctuation of C(0) is obvious. Compared to the results in the first simulation, the lowest CCS% and the highest AUEC% are observed when the drug is given in the late afternoon, around 6:00PM. In addition, the pos sible stimulation of the lymphoc yte trafficking, as indicated by the negative values of AUEC%, was identified within this dose range. The nadir of the effect of lymphocyte trafficking takes place when a dose (about 0.3 mg) is administrated at approximately 4:00 AM. Simulation of Oral Admini stration of Prednisone Figure 3-6 shows the simulation results after a single oral dosing of prednisone. The effects of dosing time of prednisone on C(0), CCS%, and AUEC% seem to be similar to those in figure 3-4, except for the larger dose range than that in the previous simulation. Timedependency for the time of maximum to tal prednisolone concentrations (Tmax) was also evaluated (data not shown he re), and it was found that Tmax is around 1.3 hour after dosing, and

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73 no differences or fluctuations of Tmax are observed among various dosing amounts and dosing time. In figure 3-7B, trough concentration (Ctrough) in a multiple dosing regimen (once a day) is apparently time-dependent, and the minimum Ctrough is observed when prednisone is taken at around 6:00AM. The patterns of Cmax, CCS%, and AUEC% in figure 3-7A, 3-7C, and 3-7D, are comparable to those after a single oral dosing in figure 3-6. Oral administ ration of prednisone every other day (48hr dosing interv al) was also simulated, and si milar results are obtained (plot not shown here). Discussion and Conclusions Chronological biology and physiology, such as circadian patterns of major endocrine systems, lymphatic systems, and blood pressu re, are the foundation of chronotherapy [203]. Chronotherapy has been applied for the treatmen t of certain diseases, including hypertension, coronary heart disease, inflammation, and cance r [203-206]. This strate gy has provided another method to improve the efficacy and safety or the ri sk-benefit values in the clinic. Ciclosporin and tacrolimus are two commonly used immunos uppressive drugs for the patients after organ transplantation. Several studies have found that pharmacokinetics of ciclosporin and tacrolimus are time-dependent. Compared to exposure afte r administration in the evening, higher exposure after morning dosing of ciclosporin or tacrolimus was observed, which has b een attributed to the change of clearance [206]. Statins, incl uding simvastatin, lovast atin, pravastatin and rosuvastatin, which are popular drugs for loweri ng low-density lipoprotein cholesterol (LDL-C), have shown time-dependence of the effects from se veral clinical studies [218]. All results have suggested that better loweri ng of LDL-C concentrations could be obtained after evening administration of statins compared to the morning dosing. The time-dependence of statin mediated lowering of LDL-C levels could result fr om the relatively short half life of statins (less

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74 than 5 hours), as well as increased cholesterol production in the very early morning (circadian burst of cholesterol synthesis) [218]. Recently, Glass-marmor and his colleauges found that night-time treatment of methylprednisolone was preferred by patients with multiple sclerosis although morning administration is generally recommended for corticostero id treatment [204]. Thus, an understanding of time-dependency in the system, including pharmacokinetics and pharmacodynamics, is essential for chronotherapy. Prednisolone has been extensively researched, and the involvement of circadian variation of cortisol release and non linear protein binding may lead to the time-dependency of the pharmacokinetics and pharmacodynamics of predniso lone. In this study, we integrated our pharmacokinetic and pharmacodynamic approach with other literature inform ation to create an algorithm to predict both pharmacokinetics and pharmacodynamics, including cortisol suppression and inhibition of lymphocyte tra fficking after prednisolone or prednisone administration [105-107]. The validity of the pharmacokinetics and pharmacodynamics prediction was elucidated by visual inspection and comparison between simulated results and reported data in literature. These re sults ensured further simulation steps. In simulations of both single and multiple dosing regimens, maximum total prednisolone concentrations were monitored (Figures 3-4A 3-5A, 3-6A, and 3-7A). As expected, the maximum total prednisolone concentrations s howed time-dependency because of the circadian rhythm of cortisol release, resulting in the oscillation of nonlin ear protein binding of prednisolone; however, when the dose amount of e xogenous corticosteroids increases, the impact of dosing time on total prednisolone concentrat ions diminishes due to the suppression of endogenous cortisol release, and the contribution of competitive nonlinear protein binding to the total prednisolone concentrations is reduced. In figure 3-7B, when oral do se is given once a day,

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75 Ctrough concentrations are influenced by the time of administration of prednisone. This effect is due to the low concentration of prednisolone and less inhibition of cortisol release, which makes the impacts of cortisol circadian rhyt hm and competitive protein binding on Ctrough significant. Interestingly, when Tmax was assessed after a single oral ad ministration, dosing amounts and time of administration did not show considerable effects on Tmax, which suggests that the absorption process of prednisone or prednisolone is neither dosenor timedependent This result is also consistent with our pharmacokinetic model for fr ee prednisolone and prednisone with first-order absorption processes [107]. When cumulative pharmacodynamic effects within 24 hr, including CCS% and AUEC%, were plotted with the dosing amounts and time of administration as shown in figures 3-4B, 3-4C, 3-5B, 3-5C, 3-6B, 3-6C, 3-7C, and 3-7D, bot h CCS% and AUEC% exhibit doseand timedependency, and the oscillation pa ttern in CCS% plots is more pronounced than that in AUEC% plots. As described in equati on 3-6, cortisol suppression is only dependent on the amount of exogenous corticosteroids present in vivo. However, alteration of lymphocyte trafficking in plasma is controlled by both endogenous cortis ol and exogenous cortic osteroids as shown in equation 3-10 [105,106]. Thus, the absolute active corticosteroid level in the system could be more, equal, or even less than baseline endoge nous cortisol concentra tion since the body cannot distinguish between endogenous and exogenous co rticosteroids. Therefore, the effect on lymphocyte trafficking may be inhibition or stimulation as indicated by either positive or negative values of AUEC%. Figure 3-5C illust rates the stimulation effect on lymphocyte trafficking when a small dose of pr ednisolone is given. However, this stimulation effect may not be evident in the clinic because of the low dose level. The simulated results also confirmed that the administration of corticosteroids in the morn ing is reasonable as indicated by the minimum

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76 cortisol suppression and maximum inhibition of total lymphocyte trafficking observed in this study. Furthermore, it is also of great importance to consider the circadian patterns of the respective disease states. In RA patients, the clinical symp toms not only show a circadian rhythm pattern, but are almost orchestrated with the release of some cy tokines and hormones in blood, including IL-6 and cortisol [219]. A lthough there is a delay be tween the levels of symptoms (e.g. stiffness, and pain) and serum co ncentrations of the cyto kines or hormones, the worst disease-related symptom is also coupled to the highest levels of some cytokines or hormones in the morning [219]. For the treatm ent of RA, it could be shown that timing of prednisolone administration might be critical in determining its effect on the diurnal rheumatoid inflammatory process. A total of 26 patients with rheumatoid arthritis were randomly divided into two equal groups and allocated to low doses of prednisolone at either 2:00 AM or 7:30 AM. Because of the diurnal variation in disease activity in RA, asse ssments of the two study groups were performed at 7:30 AM both at the start of the study (day 1) and after four doses of prednisolone (day 5). Administration of low doses of prednisolone (5 or 7.5 mg daily) at 2:00 AM had favorable effects on the duration of morni ng stiffness, joint pain, several disease indices and morning serum concentrations of IL-6. The other study group showed minor but significant effects on morning stiffness and circulating concen trations of IL-6 [13]. Hence, administration of low doses of corticosteroids with a rather shor t biological half life seems to improve acute RA symptoms if it precedes the peri od of circadian flare in inflam matory activity, as defined by enhanced IL-6 synthesis. This data suggests th at there is a circadian rhythm of the disease pattern with the optimum relief of symptoms when prednisolone is administ ered in the very early morning hours. In a recent study, Buttgerei t and coworkers applied a modified-release

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77 prednisone in RA patients, and a similar conclu sion was drawn [220]. Furthermore, the results from this study indicated that with a modified formulation in a timed release manner, the issue of patient compliance could be compromised and a small dose amount could be applied for better risk-benefit value. Although the pharmacokinetic and pharmacodynami c simulations conducted in our study are not based on a stochastic approach and dis ease pattern, the approach is predictable and adequate. To our knowledge, this study is the first one to show the time-dependency of the pharmacokinetics and pharmacodynamics of predniso lone. Our results suggest that the current clinical use of prednisolone c ould be improved with a chronothera py approach to find an optimal time of administration with higher efficacy and lower side effects. We developed an interactive algorithm to predict the pharmacokinetics and pharmacodynamics of prednisolone after predniso lone or prednisone ad ministration. This algorithm was evaluated visually fo r the predictability. Several simulations were performed to assess the impact of dosing time on the pharmacokinetics and pharmacodynamics of prednisolone. The results revealed ti me-dependency of the pharmacokinetics and pharmacodynamics of prednisolone, and suggested that it is necessary to c onsider the application of chronotherapy in order to ach ieve better clinical outcomes with fewer side effects of prednisolone. This pharmacokinetic and pharm acodynamic simulation approach could provide a valuable tool to evaluate and predict dosing time effect, and direct clinical use of prednisolone.

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78 Figure 3-1. Pharmacokinetic and pharmacodynamic model of prednisone and prednisolone (One-way arrows with dash lines represent the protein binding. One-way arrows with square dotted lines represent pharmacokinetics and pharmacodynamics interactions. The big two-way arrow indicates the comp etitive protein binding between cortisol and prednisolone. Model structure and symbols are described in the text.)

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79 Figure 3-2. EXCEL interface for prediction of pharmacokine tics and pharmacodynamics of prednisolone and prednisone

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80 Figure 3-3. Time-course of total cortisol co ncentrations (panel A and panel B) and total lymphocyte percentages of the count at 8:00A M (panel C and panel D) in plasma (A: cortisol baseline ( : ref [23], : ref [212], : ref [189], : ref [213], : ref [106].); B: cortisol suppression ( (16.4 mg IV PNL), (49.2 mg IV PNL): ref [11], (29.7 mg IV PNL): ref [215].); C: total lymphocyte per centage baseline ( : ref [105], : ref [23], : ref [214].); D: inhibition of total lymphocyte trafficking ( (37 mg PO PNL): ref [216], (20 mg PO PN), and (17.5 mg PO PN): ref [214], (40 mg PO PNL), (20 mg PO PNL), (20 mg PO PNL): ref [217].). Lines are simulated profiles using the algorithm. PO and IV represent oral and intravenous administrations. PNL and PN are abbreviations of prednisolone and prednisone.)

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81 Figure 3-4. Single intravenous dose (2 to 100 mg) of prednisolone in contour plots (A: initial total prednisolone concentrations (C(0)); B: cumulative cortisol suppression (CCS%); C: alteration of total lym phocyte trafficking (AUEC%))

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82 Figure 3-5. Contour plots after single intravenous administrati on of prednisolone with a dose range from 0.01 to 1 mg (A: initial total prednisolone concentrations (C(0)); B: cumulative cortisol suppression (CCS%); C: alteration of total lymphocyte trafficking (AUEC%))

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83 Figure 3-6. Single oral administration of prednisone (5 to 200 mg) in contour plots (A: maximum total prednisolone concentrations (Cmax); B: cumulative cortisol suppression (CCS%); C: alte ration of total lymphocyte trafficking (AUEC%))

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84 Figure 3-7. Multiple oral doses of prednisone in contour plots (daily dose range from 5 to 200 mg) (A: maximum total prednisolone concentrations (Cmax); B: trough total prednisolone concentrations (Ctrough); C: cumulative cortisol suppression (CCS%); D: alteration of total lymphocyte trafficking (AUEC%) at steady state)

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85 Table 3-1. Comparison of simulated cumula tive cortisol suppression (CCS) results with literature reported values Drug Dosing regimen Dose amount (mg) Reported CCS% a Simulated CCS% Difference % Prednisone Oral, single 15 b 51.00 54.15 3.15 20 c 64.00 56.73 -7.27 Prednisolone Intravenous, single 14.8 d 62.71 54.46 -8.25 52.5 e 70.00 65.88 -4.12 a Reported CCS% is calculated based on the ar ea under curve of suppr ession ratios (AUCSR) within 24hr from literature (mean values). b Reference [189] c Reference [191] d Reference [213] e Reference [211]

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86 CHAPTER 4 CORTISOL SUPPRESSION AS A SURROGATE MARKER FOR INHALED CORTIC OSTEROID INDUCED GROWTH RETARDATION IN CHILDREN Introduction Several hundred million people are affected by asthma in the world [14,15]. Asthma is a chronic inflammatory disorder of the airways, which is treated with some medications, mainly steroids and 2agonists [1,16]. Inhaled corticosteroid s have revolutionized the treatment of asthma, and been used as the first-line therapy for the asthma over decades. Seven inhaled corticosteroids, including beclomethasone dipropionate (BDP), budesoni de (BUD), ciclesonide (CIC), flunisolide (FLU), fl uticasone propionate (FP), mo metasone furoate (MF), and triamcinolone acetonide (TAA), are currently availa ble on the market [17]. The use of inhaled corticosteroids improves the quality of patie nts life by reducing asthma exacerbations and hospitalizations, but steroid thera py, especially at high doses, coul d result in local and systemic adverse effects through the same glucocor ticoid receptor-based mechanism [2]. In pediatric patients, potential growth re tardation remains a concern during chronic exposure of inhaled corticosteroids [3]. It has been reported that long-term treatment of inhaled steroids, such as BDP, could cause as much as 1.8 centimeters reduction in height per year in asthmatic children [32]. In 1998, after a meta-analysis of availabl e growth studies in children, the Food and Drug Administration (FDA) in the Un ited States has required implementation of precautionary labeling on all inhaled corticosteroid s [93]. However, it is hard to study steroid mediated growth effect because of the comple xity of growth mechan ism and long latency to detect [2,3]. It is well known that growth is regulated by hormones and other factors, such as growth hormone, diet, and diseas e conditions [3]. Growth is commonly evaluated by growth velocity (GV). Stadiometry and knemometry are the two methods to assess long-term and shortterm linear growth, respectively. GV is nor mally expressed as centimeters per year for

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87 stadiometry, and millimeters per week for knemometry. It takes normally weeks or months to conduct growth studies to evaluate linear growth. Several quantitative attempts have been made to understand any potential link of GV with dose or exposure of inhaled co rticosteroids [32,95]. Baraniuk and Murray reported a linear relationship between GV and dose of topical corticosteroids based on published growth studies [95]. Subsequently, a sigmoid model was developed to describe the relationship betw een GV and corticosteroid exposure with a pharmacokinetics and pharmacodynamics approach [32] However, it is stil l incomplete to find appropriate biomarkers, which could allow clinicians or practitioners to predict change of GV, a long-term adverse effect induced by corticosteroid s, in accordance with some short-term studies. Furthermore, these biomarkers could serve as sa fety markers and help in comparing difference among various inhaled corticosteroids and dosing regimens. It is well established that systemic exposure of exogenous corticostero ids, such as inhaled steroids, correlates with changes in endogenous cortisol conc entrations [2,22,33]. When corticosteroid is administrated, cortisol pr oduction is inhibited via a negative feedback mechanism in hypothalamic-pituitary-a drenal (HPA) axis. Clinically cortisol concentrations are monitored in various biological fluids, incl uding blood and urine [ 33]. For exogenous corticosteroid exposure and function of HPA axis, one of the sensitive methods is to calculate cumulative plasma cortisol suppression (CCS%, re lative to baseline or placebo) over a period of time (e.g. 24 hours) [22]. However, due to the ci rcadian variation of plasma cortisol and the difference in potency of inhaled corticosteroids, quantitatively describing plasma cortisol time course is complex. Over last 10 years, severa l mathematical models have been built up for plasma cortisol concentration time profiles [25,27,89,90]. Based on a pharmacokinetic and pharmacodynamic approach, Krishnaswami and cowork ers developed an inte ractive algorithm to

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88 adequately predict CCS% after administration of inhaled corticosteroids [85]. This algorithm was able to incorporate all pharmacokineti c and pharmacodynamic properties of inhaled corticosteroids to facilitate di rect comparison of systemic eff ects among various corticosteroids. Recently, dose dependency of CCS% was observed across several clinical studies of FP; in addition, it has been shown that GV decreases wi th the increase of dose of FP [149,162,221,222]. The purpose of this study was to examine the po ssible link of GV with CC S% and to develop a model to describe this relationship. For this study, GV was obtained from literature reports, and CCS% was calculated as per the published algorithm, in which the choice of dosing regimens was dictated by the availability of clinical studies reporting GV. The final model was evaluated with posterior predictive check and pattern check approaches [223] [224]. Materials and Methods Literature Data The published literature was reviewed for gr owth studies in pedi atrics with chronic administration of inhaled corticosteroids. Severa l searches for articles published before March 2008 were conducted in the National Library of Medicines PubMed database in EndNote (Version X1, Thomson Scientific, San Francisco, CA). The search terms were limited to: corticosteroid(s), growth, pediatric(s), children, stadiometry, knemometry, and names of seven inhaled corticosteroids. Each article in the EndNote database was examined, and only growth studies for inhaled steroids we re included. GV was acquired and expressed as centimeters per year. Dosing regimen and formulation inform ation were also recorded. Studies without inhalation device information and studies only reporting relative GV to placebo, or run-in period, or active control, or baseline were excluded from the final dataset.

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89 Cumulative Cortisol Suppression Data Cumulative cortisol suppression (CCS%, expresse d as percentage, relative to the baseline or placebo) was predicted with in 24 hours at steady state with the published algorithm [85]. In this published algorithm, pharmacokinetic and ph armacodynamic parameters for four inhaled corticosteroids, including BUD, FP, FLU, and TAA, have been integrated. Parameters for the other three steroids, including BD P, CIC and MF, were obtained fr om literature and listed in Table 4-1. Since BDP and CIC are prodrugs, a 100% conversion rate was assumed from prodrugs to their relevant active metabolites, beclomethasone-17-monopropionate (17-BMP), and desisobutyryl-ciclesonide (Des-CIC) [18,19 ]. The CCS% was calculated according to the pharmacokinetic and pharmacodynamic properties of inhaled corticosteroids and study design, such as dosing regimen and inhala tion device, which were reported in the actual clinical studies. During placebo, or run-in period, or active control, or baseline CCS% was assumed to be zero. Data Analysis Data analysis was performed with consolidat ed GV (as dependent variable) and CCS% (as independent variable). To allow prediction of inter-individual variability, the identification number (ID) in the dataset was created per each study design. A dedicated ID was given to the study without placebo, or run-in period, or active control, or base line. If the study was a parallel design with respective non-steroid control group or placebo group, or baseline, each arm was assigned an ID. Covariates incl uded in the analysis were names of inhaled corticosteroids and dosing regimens as categorical variables. Graphical exploration of data set was performed, and a possi ble linear relationship was observed once the dataset was grouped by the methods of growth assessment, either stadiometry or knemometry. Consequently, a lin ear mixed effects model was proposed.

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90 The model was developed in NONMEM (Version VI, Level 1.0, GloboMax LLC., Hanover, MD) (installed by NMQual 6.22 (Metrum Institute, Augusta, ME)) with Compaq Visual Fortran (Version 6.6C3, Hewlett-Pack ard Development Company L.P., Huston, TX). Execution of NONMEM analysis was conducted with Wings (Version 6.13, Holford NHG, Auckland, New Zealand) for NONMEM. The model was parameterized in terms of slope and intercept. The data were modeled with NONMEM subroutine PRED, which utilizes user defined equations. The correction factor was applied to account for the differences in the intercept between stadiometry and knemometry. The data were analyzed with first-order conditional estimation (FOCE) with INTERACTION. Initial estimates for slope and intercept were obtained using a linear regression in Excel (Version 2007, Mi crosoft Corp., Redmond, WA) Inter-individual variability (IIV) for intercept was modeled using an additive error model as follows (Equation 4-1) ),0(~ where, 2 1 1111Ni i i (4-1) where 1i is the true intercept as predicted by the model for the ith individual; 1 is the typical population value of intercept; 1i represents the difference between the ith individual intercept value and the population value; and 1i are independent, normally di stributed random variables with a mean of zero and variance of 1 2. IIV for slope was not estimated because of statistical insignificance. Residual variability (RV or intra-individual variability) was modeled using an additive error model described by equation 4-2 ),0(~ where, %2 ij 21 N CCS GVijij iiij (4-2) where GVij is the jth observed GV for the ith individual; 2i is the true slope as predicted by the model for the ith individual; CCS%ij is the jth calculated CCS% with the algorithm for the ith

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91 individual; ij is the difference between the jth observed GV value and predicted GV value for the ith individual; and ij are independent, identically distribut ed random variables with a mean of zero and variance of 2. The final composite model is described by equation 4-3 ][%1 21ijiij ijCCS GV (4-3) where 2 is the typical popul ation value of slope. Individual parameter estimates were acquired with POSTHOC feature in NONMEM. Parameter-covariate relationships were explored graphically in S-plus (Version 8.0, Insightful Corp, Seattle, WA). Neither of them was significant. The model was assessed using statistical and graphical approaches. The coefficient of variation (CV) for each parameter estimate was calculated based on the results from COVARI ANCE feature in NONMEM (NONMEM code is in Appendix C.). Model Evaluation The final model was assessed for its validity and misspecification [223,224]. Simulation was performed with a random seed in NONMEM using the final model with its parameter estimates, inter-individual variability in paramete rs, and residual error. GV was simulated at CCS% values in the original dataset, and total 500 datasets were generated. Percentiles, including 25th, 50th, 75th, and 100th percentile, were obtained in both the simulated datasets and original dataset. A simplified posterior predictive check (PPC) was performed. Predictive P-value (P) was calculated with an em pirical distribution function approach. The original GV (GVobs) and simulated GV (GVsim) were compared based on the test statistic. Equations 4-4, 4-5 and 4-6 show the calculation of P value.

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92 N i obs sim iGVTGVTI N P1))()(( 1 1 (4-4) )5.01(1 PPP (4-5) )5.01(11 PPP (4-6) where N is the number of total simulated datasets (i.e. N=500); I() is the function of counter; and T() is the test static (i.e. percentile). Distributi on of 25th, 50th, 75th, or 100th percentile in the simulated datasets was further plotted against respective percentile in the observed data. This step of model evaluation was executed in S-plus. Model misspecification was checked with diagnos tic plots. Three simulated datasets were randomly selected from 500 simulated datasets. The diagnostic plots of observed GV versus population predictions were created from these th ree simulated datasets. The patterns of these three diagnostic plots were visua lly compared to the diagnostic plot from fitting with the original dataset. Results Characteristics of Dataset A total of 33 published growth studies with inhaled corticosteroids were collected for the model development [67,225-254]. GV in each study was assessed with either stadiometry (21 studies) or knemometry (12 studies). The duration of studies varied from 1 week to 4.3 years. At least one growth study for each inhaled ster oid was identified from literature. Table 4-2 shows the study details, includi ng names of inhaled corticostero ids, dosing regimens, annual GV, method, and reference number. A total of 43 IDs were created based on study design. CCS% calculated with the published algorithm facilitate d the comparison of inhaled corticosteroids

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93 across studies. GV ranges from 0.3 to 8.6 centimet ers per year while CCS% is in the range of 0 and 31% (Figure 4-1). Model Development Inspection of individual profiles revealed an obvious linear relationship of GV with CCS% (Figure 4-2). This potential linear relationship was further co nfirmed when data was plotted based on the methods (MHD), either stadiometry or knemo metry (Figure 4-3); however, two distinct intercepts for two MHDs were observed in figure 4-3. Therefore, a correction factor (3) was applied to account for the differences in th e population intercepts be tween these two MHDs (Equation 4-7) Knemometry MHD yStadiometr MHDi i, ) ,1 where, 33 3 1311 (4-7) In the final model, the diagnostic plots (Fig ure 4-4) (weighted residuals versus CCS% and weighted residuals versus popul ation predicted GVs) do not s how any distinct trend, and weighted residuals of GVs randomly spread ar ound zero. The upper panels in figure 4-4 illustrate that population predicted GVs adequate ly matched the original GVs, and individual Bayesian predicted GVs with the original GVs were symmetrically distri buted around the line of unity. IIV for the slope was tested; and it was no sta tistically significant. Additional IIV on the intercepts based on MHD made a significant dr op of objective function value and improvement of goodness-of-fit plots (Table 4-3). In the covariate analysis, na mes of inhaled corticosteroids and dosing regimens were explored According to the visual insp ection, the impact of tested covariates was not observed, and further test was not conducted.

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94 Final parameter estimates describing the rela tionship between GV and CCS% are listed in table 4-4. Table 4-5 shows the prediction of changes in GV with available inhaled corticosteroids on the market. Model Evaluation The diagnostic plots (Figure 44) indicate a reasonable model was developed for this dataset. Further model evaluations were performe d using a simulation approach with the aid of a simplified PPC and a pattern check. In figure 45, the histogram of 25th, 50th, 75th, or 100th percentile show a normal distribut ion; and the observed GVs (dash lines) lie close to the middle of histogram. The P values in figure 4-5 varied from 0.25 to 0.32, which is close to 0.5 (value in the ideal situation). In figure 4-6, a similar pattern among the diagnostic plots from three simulated datasets and modeling dataset is observed. There is very good agreement between the predictions from the original dataset and simulate d datasets as indicated by both figures 4-5 and 4-6. Discussion and Conclusions As an effective treatment for asthma, inhaled corticosteroids have been used for over decades. However, the corticosteroid induced local and systemic complications, such as oropharyngeal candidiasis, adrenal suppression and growth retardat ion in pediatrics, are still concerns [2,3]. Although inhaled corticosteroid mediated growth suppressi on in children is not permanent and catch-up growth was observed in seve ral studies, this adve rse effect remains an issue for asthmatic children [2,3]. It has b een reported that the difference of GV between treatment of inhaled steroids and placebo, or run-in period, or baseline could reach approximate 2 centimeters per year [32,247]. In order to assess the risk-benefit of steroid therapy, especially in terms of growth retardation in asthmatic pe diatrics, height or lowe r leg length is commonly measured by either stadiometry or knemom etry method. Then, the ultimate inhaled

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95 corticosteroid induced growth effect can be eval uated by GV, which is calculated from a growth study over a relatively long time period. The grow th study is time-consuming and costly and the mechanism of growth is highly complicated. With the advance of pharmacokinetic and pharmacodynamic knowledge of corticosteroids, a couple of mathematical models, including a linear relation and a sigmoid model, were constr ucted to link growth effect to dose or exposure of corticosteroids [32,247]. Th ese attempts sufficiently described possible relations between growth effect via corticosteroid s and dose or exposure of cortic osteroids via meta-analyses. However, there are still some limitations on these approaches, such as lack of combining formulation, or bioavailability or dosing regimen information, and inability to account for interindividual variability, which is critical for high variable GV data from literature. Cortisol, an endogenous corticosteroid, is essential to the normal growth under physiological condition. In addition, cortisol levels represent the f unction of HPA axis, and through a negative feedback mechanism, cortisol release is inhibited w ith the presence of exogenous corticosteroids, such as inhaled corticosteroids [2,22, 33]. Despite the difference of pharmacokinetic and pharmacodynamic propertie s among various inhaled corticosteroids, cortisol is considered as a common currency [ 22]. Cortisol circadia n rhythm and suppression in plasma has been well described with pharmacokinetic and pharmacodynamic approaches [25,27,90]. Cumulative cortisol suppression in plasma over a period time (e.g. 24hr) has been used to compare the effects of exogenous corticosteroids with pharmacokinetic and pharmacodynamic approach in an interactive algorithm. The comparison of cortisol suppression among various inhaled steroids has been accomplished, and this algorithm can be further used to optimize dosing regimens [85]. It seems that co rtisol suppression in plasma can be utilized to predict growth effect since cortisol not only invo lves growth, but also re presents the exposure of

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96 exogenous corticosteroids. It has been found th at cortisol suppression was dose-dependent with the treatment of FP across several studies; moreover, additional studies of FP indicated that GV was also dose-dependent [149,162,221,222]. Thus it is reasonable to link GV to CCS% at steady-state. In the present study, the relationship between GV and CCS% was explored and adequately described using a linear mixed effects model. This linear relationship was disclosed with exploratory graphical analyses. This mixed effects modeling appro ach was able to explain both IIV and RV. The final parameter estimates show ed reasonable precisions. GV decreases with the increase of CCS% as indicated by a negative value of slope. However, the IIV for slope, which is the rate of change of GV with CCS%, was not able to be estimated from this dataset. Two distinct intercepts were gr aphically identified based on two methods, either stadiometry or knemometry. Different IIV for each intercept was also estimated in the final model. Two intercepts and two IIVs fo r the intercepts could be explaine d by the difference of the method for the growth assessment and the accuracy of the methods. Stadiometry and knemometry measure the whole body height and the lower leg length, re spectively. The estimat ed intercepts in the model corresponded to the GVs of the whole body or the lower leg during placebo, or run-in period, or active control, or ba seline. In the final model, the difference in the two typical population intercepts was described by a correcti on factor. The estimated population correction factor is consistent with th e results from a growth study by Wo lthers and Heuck [246]. GV from stadiometry is expected to show approximately three fold higher than that from knemometry when a same CCS% is observed. None of the tested covariates, including names of inhaled steroids and dosing regimens, influenced the re lationship of GV with CC S% since only three drugs, BDP, BUD and FP, were intensively studied and majority of stud ies was conducted with a

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97 twice a day regimen. Other covariates, such as gender, age, and durat ion of study, could be tested if sufficient information is available in literature; however, this sort of information is study-specific in general [255]. During model evaluation, both simplified PPC and pattern check demonstrated a relatively good agreement between si mulated datasets and original dataset, which suggests that the developed model is stable [223,224]. In conclusion, a meta-analysis was conducted for the inhaled corticosteroid mediated growth retardation, and a linear mixed effects m odel was developed to su fficiently describe the relationship of GV with CCS%. This validated model indicated that CCS% is an excellent predictor of GV in subjects undergoing inhaled cort icosteroid exposure, and long-term effects by corticosteroids could be foreseen with some short-term studies.

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98 Figure 4-1. Plot of pooled growth velocity data from literature reports versus predicted cumulative cortisol suppression %

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99 Figure 4-2. Individual plots of growth velocity versus cu mulative cortisol suppression % at steady state (Each panel represents one ID.)

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100 Figure 4-3. Plots of growth velocity versus cumulative co rtisol suppression % based on two methods (left panel: knemometry met hod; right panel: stadiometry method)

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101 Figure 4-4. Diagnostic plots for fit of model to growth velocity (upper left: population prediction versus observed growth velocity; upper ri ght; empirical bayesian prediction versus observed growth velocity; lower left: weight ed residuals versus population prediction; lower right: weighted residuals versus cumulative cortisol suppression %)

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102 Figure 4-5. Distributi on of 25th, 50th, 75th, and 100th per centiles in the simulated datasets (histograms) with respective percentiles in the original dataset (dash lines) (P value represents predictive P-value.)

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103 Figure 4-6. Pattern check plots of population pr ediction versus observed growth velocity from fit and three simulated datasets (ITER: iteration number in NONMEM)

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104 Table 4-1. Beclomethasone di propionate, ciclesonide and mome tasone furoate pharmacokinetic and pharmacodynamic parameters Parameter (units) a Beclomethasone dipropionate b Ciclesonide d Mometasone furoate f CL (L/hr) 120 396 39.6 ka (1/hr) 1.67 4.42 0.7 Vdss (L) 424 1190 94.5 / 152 Plasma free fraction (%) 13% 1% 1% Oral bioavailability (%) 21% 1% 0.1% Pulmonary deposition (%) 5.5%/18.9%/56% (CFC/DPI/HFA c) 52% (HFA) 13.90% (DPI) EC50(free) (ng/mL) 0.0721 0.0808 e 0.036 e a CL: clearance; ka: first order absorption rate constant; Vdss: volume of distribution at steadystate; EC50(free): unbound corticosteroid concentration to produ ce 50% of maximum effect (active metabolites for ciclesonide and beclomethasone dipropionate) b Reference [10,18,113,174-179] c CFC: chlorofluorocarbon; DPI: drypowder inhaler; HFA: hydrofluoroalkane d Reference [134,135,138,144,180,181] e EC50(free) is in terms of the re spective active metabolite for ci clesonide or beclomethasone dipropionate f Pharmacokinetic parameters were estimated in WinNonlin (Version 5.2, Pharsight Corp. Mountain View, CA) based on Reference [169]. Second volume of distribution (152 L) was from the product information. Other para meters are from Reference [137,145,170-172,256]

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105 Table 4-2. Growth studies for inhaled corticosteroids from literature Drug name (formulation) g Dosing regimen h Growth velocity (cm/year) Method Reference BDP (CFC) 84 g QID 4.4 Stadiometry [227] Non-steroid 6.0 BDP (DPI) 200 g BID 3.96 Stadiometry [233] Placebo 5.04 BDP (DPI) 200 BID 4.53 Stadiometry [234] Non-steroid 5.87 BDP (DPI) 200 g BID 4.33 Stadiometry [235] 400 g BID 3.47 Non-steroid 4.91 BDP (HFA) 50 g BID 5.67 Stadiometry [241] 100 g BID 4.87 150 g BID 4.79 200 g BID 4.45 BDP (CFC) 100 g BID 6.13 200 g BID 5.58 300 g BID 5.82 400 g BID 5.05 BDP (CFC) 200 g BID 4.74 Stadiometry [247] Placebo 5.48 Non-steroid 5.57 BDP (HFA) 100 g BID 0.47 Knemometry [250] BDP (CFC) 100 g BID 0.52 200 g BID 0.42 Run-in 2.24 BUD (DPI) 100 g BID 0.57 Knemometry [225] 200 g BID 0.88 400 g BID 1.87 Runin/Washout 3.30 BUD (DPI) 100 g BID 1.40 Knemometry [229] 200 g BID 0.94 Placebo 1.77 BUD (CFC) 400 g BID 0.94 Knemometry [231] Placebo 2.65 BUD (DPI) 100 g BID 2.03 Knemometry [238] 200 g BID 1.30 BUD (DPI) 500 g QD 6.55 Stadiometry [239] 500 g BID 5.68 500 g QD 6.96

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106 Table 4-2. Continued Drug name (formulation) g Dosing regimen h Growth velocity (cm/year) Method Reference BUD (DPI) 200 g BID 5.28 Stadiometry [236] Placebo 5.53 Non-steroid 5.51 BUD (DPI) 100 g BID 1.82 Stadiometry [226] 200 g BID 1.72 400 g BID 0.68 Placebo 2.25 BUD (DPI) 200 g BID 1.14 Knemometry [242] 200 g BID 1.30 BUD (DPI) 200 g BID 5.08 Stadiometry [244] 200 g BID 4.34 BUD (DPI) 412 g QD 5.50 Stadiometry [237] Run-in 6.10 BUD (DPI) 200 g BID 1.40 Knemometry [243] Placebo 2.81 BUD (DPI) 200 g BID 1.72 Knemometry [246] Control 2.39 200 g BID 4.80 Stadiometry Control 6.20 BUD (DPI) 200 g BID 1.87 Knemometry [253] Non-steroid 2.21 CIC (HFA) 40 g QD 5.73 Stadiometry [254] 160 g QD 5.60 Placebo 5.75 MF (DPI) 100 g QD 3.64 Knemometry [257] 100 g BID 3.47 BUD (DPI) 100 g BID 2.83 Placebo 4.61 FP (DPI) 50 g BID 6.00 Stadiometry [232] Non-steroid 6.50 FP (CFC) 100 g BID 8.40 Stadiometry [67] Non-steroid 8.64 FP (DPI) 100 g BID 5.67 Stadiometry [245] 100 g BID 5.81 FP (CFC) 44 g BID 7.76 Stadiometry [249] 88 g BID 7.76 Placebo 8.02 FP (CFC) 44 g BID 6.60 Stadiometry [248] Placebo 7.30 FP (DPI) 500 g BID 3.96 Stadiometry [251] Placebo 5.49

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107 Table 4-2. Continued Drug name (formulation) g Dosing regimen h Growth velocity (cm/year) Method Reference FLU (HFA) 170 g BID 5.80 Stadiometry [240] 170 g BID 6.20 BDP (CFC) 168 g BID 5.10 Placebo 6.20 FP (CFC) 100 g BID 1.77 Knemometry [228] BDP (CFC) 200 g BID 0.47 400 g BID 0.31 Washout 3.67 FP (DPI) 100 g BID 1.98 Knemometry [230] 200 g BID 1.92 BUD (DPI) 100 g BID 1.35 200 g BID 1.56 Placebo 2.26 FP (DPI) 100 g BID 5.50 Stadiometry [252] Run-in 5.90 BUD (DPI) 200 g BID 4.60 Run-in 6.00 TAA (CFC) 600 g QD 5.30 Stadiometry [258] Placebo 6.10 Control 5.90 g BDP: beclomethasone dipropion ate; BUD: budesonide; CIC: cicl esonide; FLU: flunisolide; FP: fluticasone propionate; MF: mometasone furoate; TAA: triamcinolone acetonide; CFC, HFA and DPI are defined in Table 4-1. h QD: once a day; BID: twice a day; QID: four times a day

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108 Table 4-3. Summary of model development Run Description i Objective function value Comments 001 IIV on intercept ( 1+1 j ) 63.328 002 IIV on Slope ( 2+2 j ) 95.842 003 IIV on intercept and slope ( 1+1 j and 2+2 j ) 63.338 IIV on slope is not significant. 004 Two IIVs on intercept 1 3+1j, where 3=1 1 3+3 j where 3= 3 59.291 Better goodness-of-fit plots and drop of objective function value i IIV: inter-individual variability; : typical population value; : additive error. Details are given in the methods section.

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109 Table 4-4. Parameter estimates for the final m odel describing relationship of growth velocity with cumulative cortisol suppression % Parameter (units) Definition Estimate (% CV j )IIV k (cm/year) (% CV) 1 (cm/year) Intercept 6.11 (2.96) 0.92 (40.73) 2 (cm/year/CCS%) Slope -0.06 (12.73) NA l 3 Correction factor between stadiometry and knemometry 0.36 (8.76) 0.49 m (76.05) Residual variability (cm/year) Additive error 0.59 (29.7) NA l j % CV: coefficient of variation, calculated as standard error divided by parameter estimate and expressed as percentage k IIV: inter-individual variabili ty as an additive error l NA: not applicable m Estimate of IIV for knemometry method

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110 Table 4-5. Predicted change in growth velocity with the current inhaled corticosteroid treatment Drug Name n Device o Age of patients o Dosing regimen o Estimated CCS % p Predicated GV (cm/year) q BDP HFA 5-11 years 40 g BID 2.80% 0.17 80 g BID 5.40% 0.32 BUD DPI 6-17 years 180 g BID 10.70% 0.64 360 g BID 18.00% 1.08 Nebulizer1-8 years 500 g QD 12.50% 0.75 250 g BID 10.30% 0.62 1000 g QD 19.10% 1.15 500 g BID 17.40% 1.04 CIC HFA >12 years 80 g QD 0.07% 0.004 160 g QD 0.15% 0.009 FLU MDI 6-15 years 500 g BID 26.90% 1.61 FP DPI 4-11 years 50 g BID 6.30% 0.38 100 g BID 11.70% 0.70 MF DPI 4-11 years 110 g QD 3.10% 0.19 TAA MDI 6-12 years 75 g TID 10.30% 0.62 150 g BID 11.70% 0.70 300 g BID 20.20% 1.21 150 g QID 16.00% 0.96 300 g TID 30.70% 1.84 n As defined in table 4-2 o From product inserts; HFA a nd DPI as defined in table 4-2, MDI represents metered dose inhaler; QD, BID and QID as defined in table 4-2, TID is three times a day. p CCS%: cumulative cortisol s uppression within 24 hr at steady state; estimated with the published algorithm q GV: change in growth velocity compared to the placebo, or run-in period, or active control, or baseline; predicted with populati on estimates in the final model

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111 CHAPTER 5 CONCLUSIONS Prednisolone and prednisone are two widely us ed corticosteroids for various inflammatory and immune diseases. Prednisolone is the active form of prednisone in vivo [182]. Total prednisolone in plasma exhibi ts nonlinear pharmacokine tics mainly due to its nonlinear protein binding [8,9,11]. Other factors such as reve rsible metabolism (or interconversion between prednisolone and prednisone), competitive protein binding from endogenous cortisol, cortisol circadian rhythm, and predniso lone mediated cortisol suppr ession complicate prednisolone pharmacokinetics. The first spec ific objective of the study was ai med to develop a new approach to describe the nonlinear pharmacokinetics of tota l prednisolone and predict total prednisolone concentrations in plasma. Based on literature datasets, a linear two-compartment pharmacokinetic model was developed to adequately describe the reversible metabolism between free prednisone and prednisolone. Cortisol and predni solone protein bindi ng were described via the sum of a Langmuir and linear type binding [9,186]. The endogenous cortisol circadian rhythm and cortisol suppression during prednisone or prednisolone exposure were described with a previously reported linear release rate pha rmacokinetic and pharmacodynamic model [27]. By combining the pharmacokinetic models for free pre dnisone and prednisolone the linear release rate model for cortisol suppression, and co mpetitive protein binding between cortisol and prednisolone, we were able to predict total predni solone concentrations in plasma. The predicted total prednisolone concentrations in plasma were in good agreemen t with the lite rature reported data. Thus, this novel and integrated pharm acokinetic and pharmacodynamic approach shows that the combination of nonlinear protein bindi ng, cortisol circadian rhythm, and cortisol suppression could account for the non linearity of total prednisolone. In addition, it also allows a

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112 valid prediction of total prednisolone in plas ma after either predni sone or prednisolone administration. Prednisolone mediated systemic effects or pharmacodynamics are commonly evaluated with two biomarkers, cortisol and blood lymphocyt es in plasma [25,27]. Circadian patterns are observed in both biomarkers [105,106]. Furthermor e, the disease itself may show a circadian pattern. For example, in rheumatoid arthritis patients, better therapeutic outcomes have been reported when prednisolone was administered in the very early morning [12]. The second specific aim of this study is to evaluate the impact of dosing time on the pharmacokinetics and pharmacodynamics of prednisolone with a simulati on approach using an in teractive algorithm. A series of simulations were performed with either intravenous or oral administration of prednisolone or prednisone. The results showed that the initial or maximum concentration and trough concentration of total pred nisolone were lower when the drug was administered in the early morning around 6 AM. Oscillation pattern s were observed in cumulative cortisol suppression and alteration of total lymphocyte trafficking in blood. When the corticosteroid was given in the morning within the therapeutic do se range or around 6 PM for a small dose amount (less than 1 mg), the minimum cumulative cortisol suppression and maximum effect on lymphocytes were observed. These results indicated that the pharmacokinetics and pharmacodynamics of prednisolone are time-depe ndent and dose-dependent, and suggested that it is necessary to consider the application of ch ronotherapy to achieve better clinical outcomes with fewer side effects of prednisolone, a nd a pharmacokinetic and pharmacodynamic simulation approach could provide a valuable tool to evaluate and predict time-dependency in this system. Exposure of Inhaled corticostero ids in pediatrics results in adrenal suppression and growth inhibition [3]. The third object ive of the research was to assess relationship of inhaled

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113 corticosteroid mediated growth retardation with cortisol suppression in asthmatic children. A meta-analysis approach was performed with a to tal of 33 published articles. Growth velocity data were obtained from literature for evalua tion of growth. Cumula tive cortisol suppression within 24 hours was calculated at steady state with a published al gorithm from this lab [85]. Consolidated growth velocity and cumulative cortisol suppression data were employed for model development. A linear mixed effects model was developed to adequately describe the relationship between growth veloci ty and cumulative cortisol suppres sion. The impact of tested covariates was not observed. Population estimate of the rate of change in growth velocity was 0.06 centimeters/year per percentage of cumulati ve cortisol suppression (12.7%, coefficient of variation) for both stadiometry and knemometry methods. However, growth velocity from stadiometry is expected to show approximately three fold higher than that from knemometry when the same cumulative cortisol suppression was presented. The final model was evaluated with posterior predictive check and pattern ch eck approaches. The results from this study elucidate cumulative cor tisol suppression as an excellent pr edictor of inhale d corticosteroid mediated growth retardation in asthmatic children. In conclusion, with the modeling and si mulation approach, pharmacokinetics and pharmacodynamics of prednisolone have been exploited; adverse effects of inhaled corticosteroids, in terms of cor tisol suppression, inhibition of ly mphocyte trafficking, and growth retardation, have been investig ated. These results provide additional information to understand the properties of corticosteroids and optimize future application of corticosteroids.

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114 APPENDIX A PHARMACOKINETIC MODELING IN ADAPT II Subroutine DIFFEQ(T,X,XP) Implicit None Include 'globals.inc' Include 'model.inc' Real*8 T,X(MaxNDE),XP(MaxNDE) C DOSING: 2,4,9,12,15 C 1,3,5,7,10,13,16 Prednisone/2,4,6,8,11,14,17 Prednisolone XP(1)=-(P(1)+P(2))*X(1)+P(4)*X(2) XP(2)=-(P(3)+P(4))*X(2)+P(2)*X(1) XP(3)=-(P(1)+P(2))*X(3)+P(4)*X(4) XP(4)=-(P(3)+P(4))*X(4)+P(2)*X(3) XP(5)=-(P(1)+P(2))*X(5)+P(4)*X(6) XP(6)=-(P(3)+P(4))*X(6)+P(2)*X(5)+R(1) XP(7)=-(P(1)+P(2))*X(7)+P(4)*X(8)+R(2) XP(8)=-(P(3)+P(4))*X(8)+P(2)*X(7) XP(9)=-P(7)*X(9) XP(10)=-(P(1)+P(2))*X(10)+P(4)*X(11)+P(11)*P(7)*X(9) XP(11)=-(P(3)+P(4))*X(11)+P(2)*X(10)+(1-P(11))*P(7)*X(9) XP(12)=-P(7)*X(12) XP(13)=-(P(1)+P(2))*X(13)+P(4)*X(14)+P(11)*P(7)*X(12) XP(14)=-(P(3)+P(4))*X(14)+P(2)*X(13)+(1-P(11))*P(7)*X(12) XP(15)=-P(9)*X(15) XP(16)=-(P(1)+P(2))*X(16)+P(4)*X(17)+P(12)*P(9)*X(15) XP(17)=-(P(3)+P(4))*X(17)+P(2)*X(16)+(1-P(12))*P(9)*X(15) Return End Subroutine AMAT(A) Implicit None Include 'globals.inc' Include 'model.inc' Integer I,J Real*8 A(MaxNDE,MaxNDE) DO I=1,Ndeqs Do J=1,Ndeqs A(I,J)=0.0D0 End Do End Do Return End Subroutine OUTPUT(Y,T,X) Implicit None Include 'globals.inc' Include 'model.inc' Real*8 Y(MaxNOE),T,X(MaxNDE)

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115 C 1,2: PNL->PNL; 3: PNL->PN; 4: PN->PN <--IV PO --> 5,6: PN->PN; 7,8:PN->PNL Y(1) = X(2)/P(6) Y(2) = X(4)/P(6) Y(3) = X(5)/P(5) Y(4) = X(7)/P(5) Y(5) = X(10)/P(5)*P(8) Y(6) = X(13)/P(5)*P(8) Y(7) = X(11)/P(6)*P(8) Y(8) = X(14)/P(6)*P(8) Y(9) = X(16)/P(5)*P(10) Return End Subroutine SYMBOL Implicit None Include 'globals.inc' Include 'model.inc' NDEqs = 17 Enter # of Diff. Eqs. NSParam = 12 Enter # of System Parameters. NVparam = 9 Enter # of Variance Parameters. NSecPar = 4 Enter # of Secondary Parameters. NSecOut = 0 Enter # of Secondary Outputs (not used). Ieqsol = 1 Model t ype: 1 DIFFEQ, 2 AMAT, 3 OUTPUT only. Descr = 'all data.for: 2CM INTERCONVERSION (2cm)' Psym(1) = 'k10' Psym(2) = 'k12' Psym(3) = 'k20' Psym(4) = 'k21' Psym(5) = 'V1' Psym(6) = 'V2' Psym(7) = 'kapn' Psym(8) = 'Fpn' Psym(9) = 'kapnl' Psym(10) = 'Fpnl' Psym(11) = 'frpn->pn' Psym(12) = 'frpnl->lpn' PVsym(1) = 'S1' PVsym(2) = 'S2' PVsym(3) = 'S3' PVsym(4) = 'S4' PVsym(5) = 'S5' PVsym(6) = 'S6' PVsym(7) = 'S7' PVsym(8) = 'S8' PVsym(9) = 'S9' PSsym(1) = 'CL10' PSsym(2) = 'CL12'

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116 PSsym(3) = 'CL20' PSsym(4) = 'CL21' Return End Subroutine VARMOD(V,T,X,Y) Implicit None Include 'globals.inc' Include 'model.inc' Real*8 V(MaxNOE),T,X(MaxNDE),Y(MaxNOE) V(1) = (PV(1)*Y(1))**2 V(2) = (PV(2)*Y(2))**2 V(3) = (PV(3)*Y(3))**2 V(4) = (PV(4)*Y(4))**2 V(5) = (PV(5)*Y(5))**2 V(6) = (PV(6)*Y(6))**2 V(7) = (PV(7)*Y(7))**2 V(8) = (PV(8)*Y(8))**2 V(9) = (PV(9)*Y(9))**2 Return End Subroutine PRIOR(Pmean,Pcov,ICmean,ICcov) Implicit None Include 'globals.inc' Include 'model.inc' Integer I,J Real*8 Pmean(MaxNSP+MaxNDE), ICmean(MaxNDE) Real*8 Pcov(MaxNSP+MaxNDE,M axNSP+MaxNDE), ICcov(MaxNDE,MaxNDE) Return End Subroutine SPARAM(PS,P,IC) Implicit None Include 'globals.inc' Real*8 PS(MaxNSECP), P(MaxNSP+MaxNDE), IC(MaxNDE) PS(1) = P(1)*P(5) PS(2) = P(2)*P(5) PS(3) = P(3)*P(6) PS(4) = P(4)*P(6) Return End

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117 APPENDIX B INSTRUCTION FOR THE INTERACTIVE ALGORITHM IN EXCEL Two speadsheets were developed in Excel 2003, and both files are hosted online. Intravenous_Admin for intravenous administration of prednisone or prednisolone ( http://www.cop.ufl.edu/safezone/p at/pc/Derendorf/TheAAPSJ-D-08-000162008/SUPPLEMENTS/ 1-Intravenous-Admin.xls ) Oral_Admin for oral administration of prednisone or prednisolone. ( http://www.cop.ufl.edu/safezone/p at/pc/Derendorf/TheAAPSJ-D-08-000162008/SUPPLEMENTS/ 2-Oral-Admin.xls ) Interface of the interactive al gorithm is shown in figure 3-2. Input Section Drug Name: pull-down menu (Prednisone or Prednisolone) Dose (mg): dose amount (single dose) or each dose amount (multiple doses) Time of Admin: time of dosing (24hr scale (0 to 24), not clock scal e) (e.g. 8.5 for 8:30 AM, 20.5 for 8:30 PM) Dosing Regimen: pull-down menu (Single or Multiple) Tau: dosing interval for multiple doses ( only apply for the multiple dose regimen) Output Section CCS% (24hr): cumulative cortisol suppressi on within 24 hr comp ared with baseline AUEC% (24hr): cumulative alteration of to tal lymphocyte trafficking within 24 hr compared with baseline Pharmacokinetic Profiles: free and total prednisone, free and total prednisolone concentration-time profiles Pharmacodynamic Profiles: plasma cortisol c oncentration and altera tion of plasma total lymphocyte trafficking (%) time profiles at baseline and after treatment of prednisone or prednisolone

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118 APPENDIX C LINEAR MIXED EFFECTS MODELING IN NONMEM $PROB FINAL MODE L $DATA ..NM.CSV IGNORE=C $INPUT C ID CCS DV DGV MHD DRG REG DDS CS $PRED TSLP=THETA(1) ;TYPICAL POPULATION SLOPE TINT=THETA(2) ;TYPICAL POPULATION INTERCEPT IF (MHD.EQ.1) THEN TINT=THETA(2) ; TYPICAL POPULA TION INTERCEPT (STADIOMETRY) ETAB=ETA(2) END IF IF (MHD.EQ.2) THEN TINT=THETA(2)*THETA(3) ; TYPICAL PO PULATION INTERCEPT (KNEMOMETRY) ETAB=ETA(3) END IF A=TSLP+ETA(1) ;INDIVIDUAL SLOPE B=TINT+ETAB ;INDIVIDUAL INTERCEPT GV=A*CCS+B ;INDIVIDUAL GROWTH VELOCITY IPRE=GV IRES=DV-IPRE IWRE=IRES/GV Y=GV+EPS(1) $THETA ;INITIALS -0.1 ;SLOPE (0,0.1) ;INTERCEPT (STADIOMETRY) (0,0.3) ;PROPORTION (KNEMOMETRY) $OMEGA 0 FIXED ;IIV FOR SLOPE 0.1 ;IIV FOR INTER CEPT (STADIOMETRY) 0.1 ;IIV FOR INTER CEPT (KNEMOMETRY) $SIGMA 0.1 $ESTIMATION SIG=3 MAX=5000 PRINT=5 METHOD=1 INTER NOABORT POSTHOC $COVARIANCE $TABLE ID MHD DRG REG DDS A B CCS IPRE IRES IWRE NOPRINT FILE=FINAL.FIT

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122 40. Dal Negro R, Micheletto C, Tognella S, Mauroner L, Burti E, Turco P, Pomari C, Cantini L: Effect of inhaled beclomethasone dipropionate and budesonide dry powder on pulmonary function and serum eosinophil cationic protein in adult asthmatics. J Investig Allergol Clin Immunol 1999;9:241-247. 41. De Benedictis FM, Del Giudice MM, Vetrella M, Tressanti F, Tronci A, Testi R, Dasic G: Nebulized fluticasone propionate vs. budesonide as adjunctive treatm ent in children with asthma exacerbation. J Asthma 2005;42:331-336. 42. Fabbri L, Burge PS, Croonenborgh L, War lies F, Weeke B, Ciaccia A, Parker C: Comparison of fluticasone propionate with be clomethasone dipropion ate in moderate to severe asthma treated for one year. In ternational Study Group. Thorax 1993;48:817-823. 43. Girard JP, Vonlnthen MC, Heimlich EM: Therapeutic index of steroi d aerosols in asthma. A single-blind comparative trial of beclom ethasone dipropionate vs dexamethasone isonicotinate. Acta Allergol 1975;30:363-374. 44. Gordon AC, McDonald CF, Thomson SA, Fr ame MH, Pottage A, Crompton GK: Dose of inhaled budesonide required to produce clinical suppression of plasma cortisol. Eur J Respir Dis 1987;71:10-14. 45. Grzelewska-Rzymowska I, Malo lepszy J, de Molina M, Sladek K, Zarkovice J, Siergiejko Z: Equivalent asthma control and systemic safety of inhaled budesonide delivered via HFA-134a or CFC propellant in a broad range of doses. Respir Med 2003;97 Suppl D:S1019. 46. Jenkins C, Kolarikova R, Kuna P, Caillaud D, Sanchis J, Popp W, Pettersson E: Efficacy and safety of high-dose budesonide/formotero l (Symbicort) compared with budesonide administered either concomitantly with formot erol or alone in pati ents with persistent symptomatic asthma. Respirology 2006;11:276-286. 47. Karakoc F, Karadag B, Kut A, Ersu R, Bak ac S, Cebeci D, Dagli E: A comparison of the efficacy and safety of a half dose of fl uticasone propionate w ith beclamethasone dipropionate and budesonide in child hood asthma. J Asthma 2001;38:229-237. 48. Katz RM, Rachelefsky GS, Siegel SC, Spector SL, Rohr AS: Twice-daily beclomethasone dipropionate in the treatment of childhood asthma. J Asthma 1986;23:1-7. 49. Kaur C, Bansal SK, Chhabra SK: Study on se rum and urinary cortisol levels of asthmatic patients after treatment with high dose i nhaled beclomethasone dipropionate or budesonide. Indian J Chest Di s Allied Sci 2005;47:89-95. 50. Kriz RJ, Chmelik F, doPico G, Reed CE : A one-year trial of triamcinolone acetonide aerosol in severe steroid-depe ndent asthma. Chest 1977;72:36-44. 51. La Grutta S, Nicolini G, Capristo C, Bellodi SC, Rossi GA: Once daily nebulized beclomethasone is effective in maintaini ng pulmonary function and improving symptoms in asthmatic children. Monaldi Arch Chest Dis 2007;67:30-38.

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140 BIOGRAPHICAL SKETCH Jian Xu was born in 1977, in Jiangsu, P. R. China. In 1999, Jian received his B.E. (Bachelo r of Engineering) degree in biotech nical pharmaceutics from the China Pharmaceutical University, Jiangsu, P. R. China. In 2000, Jian joined the pharmaceutical sciences program in the School of Pharmacy and Pharmaceutical Scien ces at the State University of New York at Buffalo (Buffalo, NY, U. S. A.), and obtaine d his M.S. (Master of Science) degree in pharmaceutical sciences in August 2003. Afterw ard, Jian was employed as a pharmacometrics fellow at Cognigen Corporation (Buffalo, NY, U.S.A. ). Since May 2004, Jian has been at Vertex Pharmaceutical Incorporation (Cambridge, MA, U. S. A.), as a senior research associate. In August 2005, Jian entered graduate program in the Department of Pharmaceutics, College of Pharmacy, at the University of Florida (Gainesville, FL, U. S. A.), under the supervision of Dr. Hartmut Derendorf to work on his Doctor of Philo sophy (Ph.D.). Jian obtained his Ph.D. degree in pharmaceutical sciences in August 2008.