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Title Page

Contents

List of Abbreviations 6

List of Symbols 8

I. Introduction 18

1. Drug Discovery Process 19

2. Prediction of Human Oral Bioavailability 25

2.1 Intestinal Permeability as a Predictor of the Human Fraction Absorbed 31

2.2. Hepatic Microsomal Stability as a Predictor of the Human Fraction Escaping Clearance by the Liver 47

3. Prediction of BBB Penetration 49

II. Aims 59

III. Experimental 61

1. Materials 61

2. Measurement of Immobilized Artificial Membrane Partition Coefticients 62

2.1. HPLC Conuitiolls 62

2 2. Data Analysis 62

3. Measurement of Hepatic Microsomal Intrinsic Clearances 68

3.1. HPLC Conditions 68

3.2. Data Analysis 68

4. Determination of Oral Bioavailability 73

5. Blood-Brain Barrier 73

5.1. HPLC Conditions 73

5.2. Method Development 74

5.3. Validation 75

IV. Results and Discussion 77

1. Effect of Molecular Size on lAM Partition Coeflicients 77

2. Effect of Changes in pH and Composition of Mobile Phase on Predictability 88

3. Drug Absorption from the Stomach 97

4. Comparison of IAMPCDD kIAM/MW2.5 with log P, PSA, Caco-2 Cell Permeability and PAMPA Permeability(이미지참조) 100

5. Prediction of Human Clearance 109

6. Prediction of Human Bioavailability 120

7. Prediction of BloodBrain Barrier Penetration 127

7.1. Effect of Molecular Size on lAM Partition Coefficients 127

7.2. Comparison of IAMPCDD Capacity Factors with log P, PSA, and PAMPA Permeability 139

7.3. Method Validation 146

V. Conelusions 149

VI. References 153

Abstract 175

List of Tables

Table I. Log D value s and their implication for drug development 35

Taole 2. Lipid compos ition (% w/w) of biological membrance 44

Table 3. Chemical structures and pharmacology effects of drugs for which the human fraction absorbed (Fa) have been determined 79

Table 4. Values for the retention time (Rt). capacity factor (kIAM) and kIAM/MWa as a power function of n at 20% acetonitrile (pH 5.5) 84

Table 5. Values for kiAM/MW25determined at different pH on IAM column 91

Tablc 6. Bestfit sigmoid equations obtained at different plI and composition of the mobile phase 92

Table 7. Values for kIAM/MW2.5 determined at different composition of the mobile phase on lAM column 94

Tahlc 8. Values for the human fract ion absorbed (Fa) , molccubr weight (MW), physicochemical paramers (pKa, Clog P and PSA) and membrane permeability (kIAMIMW2_5, Papp and P,) of 40 structurally unrelated drugs 106

Table 9. HPLC conditions used for the analysis of 16 drugs for in vilro disappearance halflife measurement from human liver microsomal incubation 112

Table 10. Percentage (%) remaining during incubation with human li\er microsomes 113

Table 11. Values for in vitro disappearance halflife (t,d. in vitro hepatic microsomal intrinsic clearance (CL"".,,') . ill vi\'{) hepatic clearance (CLh), hepatic extraction ratio (ERh) , and prcsystemic metabol ism of 16 structurally unrelated drugs 118

Table 12. Prediction of human bioavailability froIll calculated the human fraction absorbed (Fa) and the fraction esca ping the tlrstpass hepatic metabolism effect (Fh) 123

Table 13. Oral bioavailability classified into the high, medium. low categories based on predicted and experimental oral bioavailability va lues for 16 structurally unrelated drugs 125

Table 14. Chemical structures and pharmacological effects of drugs for which predictions of bloodbrain barrier (13I3B) permeation ha ve been determined 129

Table 15. Values for the capacity factor (kfA,lf) determined at C! iftcr~nt pll and composition of the mobile phase 132

Table 16. Classification for the BBB penetration in method development using [AMPCD 0 chromatographyt 138

Table 17. Values for the human CNS penetration, physicochemical parameters (pK", Clog P and PSA) and membrane permeabilitv (PAMPA and k/ ./ ,IMW·') of 23 structurally unrelated drugs 142

Table 18. In \';11'0 eNS penetration and in r;\'() brainlopl;lSma drug concentration ratio for 7 Skku compounds used for the method validation 147

List of Figures

Fig. I. The drug disco\"CI"y and development proccss. NeE: Nc\V ChemicalEntity; IND: Investigational New Drug; ND": New Drug Application 24

Fig. 2 Schematic represe ntation of the process of drug absorptiPIl fo li o\\ ingora l adm ini stration of drugs 30

Fig. 3. A filterimmobilized artificial membrane is llsed to model the cell membrane for passive transport of drug molecule 45

Fig. 4. Structure of phase of the lAM column packing materials commonly lIsed in drug discovery 46

Fig. 5. lliological barrier for eNS compounds 56

Fig. 6. Blood·brain barrier (BBB). Some nonpola r molecules sllch as glucose are transported out of the blood 57

Fig. 7. I3BB cell membranes (A) have less area for drug perlllc:"tion than othe r cell membranes (B) 58

Fig. 8. Fluid membrane and lAM drug part itionin g measu rements. Fluid membrane bilayer can be modeled by lAM column 67

Fig. 9. Representative chromatograms used for the calculation of klA .l/. Of 40 drugs tested, sample chromatograms for clonidine, naproxen. verupamil , indomethacin and progesterone are shown 83

Fig. 10. Relationship between the fraction of dose absorbed in humans (Fu) viii and k l.l.lI or kulI /1'viW: (A) log human F,,% "S. I ()~ kUIi . (1.\ ) I () ~ hUlll an F,,% vs . log (k/J.I/ IMW) 85

Fig. 11. Relationship between the fraction of dose " hslJrbed in hUlllans ( /'J and klA.'l;tvIW" as a po\\cr function of 11: (AJ po\\,〈:r function c, I; (lll po \\ e r functi on =2; (el power function =2.5 ; (D) p"m::r (un ction ,c~ . 6') 86

Fig. 12. Improvement of the correlation coenient (r) between the fraction o f close absorbed humans (Fa) and k/.H/IMWn as a power functi on of 11 87

Fig. 13. Relationship between the fraction o f dose absorbed in humans (Fa) 'lilt! k/AM IMW25 at different pH of the mobile phase: (Al pI! 25, 20% acetonitrile; (8) pH 5.5, 20% acetonitrile; (e) pH 7.0, 20% acetoni triic 93

Fig. 14. Rel ationship between the fraction of dose absorbed in hUlllans (Fa) and klAMIMW25 at different composition of the mobile phase: (A) pll 5.5. 5% acetonitrile; (B) pH 5.5, 10% acetonitrile; (e) pll S.D, 200 ";,aceton i tri Ie 95

Fig. 15. Effect of composition of the mobile phase on the permeability corrected for molecular size (kIAMIMW2 ;) 96

Fig. 16. Effect of pH on the permeability corrected for mo lecular size (k/1l1 IMW2 5) for groups of (A) acidic and (8) bas ic drugs 99

Fig. 17. Correlation between Caco2 permeability and IAMPCl)D G'pacity betors: (A) Caco2 vs. k₁AM/ MW2 5; (8) Caco2 vs k₁AM.(이미지참조) 104

Fig. 18. Correlation between log P and JAMPCDD capeKity fact"rs (A) log ['vs. kl.' II./MW25: (B) log P vs. k₁AM. 105

Fig. 19. Correlation between polar surface area (PS!\) anu JAMPCDD capacityfactors: (A) PSA vs. klW iMW25; (B) PSA \s. klill 107

Fig. 20. Correlation between PAMPA permeability and li\tvll'CDI) capacit)factors: (A) PAMPA vs. kl.4MIMW25; (B) PAMPA vs. kull 108

Fig. 21. Percentage (%) remaining during human microsomal incubation as a function of time determined for acetaminophen, atenolol, cefuroxime, and chlorpromazine 114

Fig. 22. Percentage (%) remaining during human microsomal incubation as a function of time determined for c1onidine, cromolyn, dexamethasone nd enalapril 115

Fig. 23. Percentage (%) remaining during human microsomal incubation as a function of time determined for etoposide, imipramine, nadolol and pra zos in 116

Fig. 24. Percentage (%) remaining during human microsomal incubation as a function of time determined for progesterone, propranolol, ranitidine and verapamil 117

Fig. 25. Correlation between the presystemic metabolism and hepatic extraction ratio (ERh)(이미지참조) 119

Fig. 26. Correlation between the predicted and the experimental oral bioavailability val ties 124

Fig. 27. Graph ical oral bioavailability of drugs in human estimated from their respective liver permeability and intestinal absorption 126

Fig: 28. Corre latio n of \!\MPCOO capaci ty fa ctors beteen pH 5.5 and pH 7.0 133

Fig. 29. Improveme nt of differe ntiation between CNS+ and eNS drugs as a function o f klAM/MWn with a power function of nat S'Vo acetonitrile (pi I 5.5) of the mobile phase: (A) power function ~ I , (13) powcrfunction=2; (e) power function~3; (D) power functiolFI; (E) power function= 5 134

Fig. 30. Improvement of differentiation between eNS+ and eNS drugs as a function o f klA" lMWn with a power function o f n at 10% acetonitrile (pH 5.5) of the mobile phase: (A) power function =1; (8) power function=2; (e) power function=3; (D) power function =4; (E) power function= 5 135

Fig. 31. Improvement of differentiation betwc.:n eNS+ and eNS d rugs as a function of kIA~I/MWn with a power fu nct ion of n at 20% ace tonitril e (pH 5. 5) of the mobile phase: (A) power fun ct i o n ~ l; (8) power function=2; (C) power function=3; (D) power Itlllctiun=4; (E) power function= 5 136

Fig. 32. Improvement of differentiation between eNS+ and eNS drugs as a !tlnction of klAMIMWn with a power function of n ,It 20% acetonitril e(pH 7.0) of the mobile phase: (!\) power func ti o n= I , (8) power function=2: (C) [lower function=J: (D) power function ~'f: (E) pOIVe r functi on= 5 137

Fig, 33. Corre lation between log P and IAMPCDD capacity factor (k₁AM /MW¹) (A) pH 5.5; (B) pH 7.0.(이미지참조) 143

Fig. 34. Correlation between polar surface area and IAMPCDD capacity factor (k₁AM/ MW⁴) (A) pH 5.5; (B) pH 7.0(이미지참조) 144

Fig. 35. Correlation between PAMPA and IAMPCDD capacity betor(k₁AM/ MW⁴) (A) pH 5.5 ; (B) pH 7.0 145

Fig. 36. Correlation between in vivo braintoplasma drug concentration ratios and in vitro CNS penetration(k₁AM/ MW⁴) 148

초록보기

신약개발 단계에서 경구 흡수 및 혈액-뇌 관문의 약물 투과율 평가는 in vitro immobilized artificial membrane(IAM) permeability를 측정함으로써 신속한 약물 검색이 가능하고 소요비용을 절감할 수 있다. 본 연구에서는 신속한 IAM permeability를 측ㄱ정하는 방법을 개발하기 위해 다양한 이동상의 pH, 조정에서 chromatographic capacity factor를 측정하여 분자 부피로 보정함으로써 예측방법을 최적화 하였다. 아울러 human liver microsomes를 이용하여 약물의 in vitro 대사 반감기를 측정하고 scaling factor와 간 혈류 속도를 사용하여 in vivo intrinsic hepatic clearance를 결정하였다. 이와 같은 결과로부터 얻은 F_(a)와 in vivo clearance을 이용하여 in vitro 경구 흡수율을 평가하였다. 또한, 혈액-뇌 관문을 이행하는 CNS투과 예측을 IAMPC chromatography에 의해 평가하였다. 23종의 약물을 평가하여 혈액-뇌 관문 투과율을 신속하게 예측할 수 있는 in vitro 예측 방법을 개발하고 7종의 PDE4 inhibitor 합성 화합물을 이용하여 in vivo결과와 비교함으로써 개발된 방법에 대한 검증을 시행하였다. 본 연구에서 개발된 경구 흡수 및 뇌 투과도에 대한 예측법은 in vitro 초기 신약개발단계에서 drug candidate의 고속 약물 검색 기법으로 활용될 수 있다.

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