臺灣博碩士論文加值系統

良性前列腺增生症是由過量的雄性激素二氫睪固酮作用於人體中的前列腺使其異常增生所導致。而雄性激素二氫睪固酮主要是由胞內酵素第二型5a還原酶將睪固酮催化而成。因此，抑制第二型5a還原酶成為治療良性前列腺增生症重要的策略。然而，現今的藥物多屬於固醇類結構，但會導致干擾人體荷爾蒙的副作用。為了要開發出一個有效且不具副作用的抗良性前列腺增生症藥物，本研究採用多種電腦輔助藥物設計模組包含了藥效基團、三維定量構效關係、虛擬篩選、分子嵌合和分子動態模擬來進行本次實驗。由配體為基礎建立了可做為化合物活性預測的藥效基團模型經由Guner-Henry (GH) 方法驗證後進行Maybrige和NCI資料庫的篩選，再經由分子嵌合和分子動態模擬篩選出比最有抑制活性的抑制劑 (Finasteride) 具有更高預測活性和與第二型5a還原酶有更好結合力的前導藥物。另外，在附錄研究中，我們以25個非固醇的第二型5a還原酶抑制劑來建立藥效基團和比較分子場分析的模型並成功篩選篩出了七個具高預測活性並有多樣化骨幹結構的化合物。而本實驗所挑選出來的化合物可應用於日後設計新型並更具活性的抗良性前列腺增生症用藥來達到臨床上的應用。

Benign prostatic hyperplasia (BPH) is caused by the augmented levels of androgen dihydrotestosterone (DHT) that is involved in the growth of prostate in human. 5a-Reductase type II (5aR2) is an intracellular enzyme that catalyzes the formation of DHT from testosterone; hence the inhibition of 5aR2 has emerged as one of the most promising strategies for the treatment of BPH. However, the steroidal structure of 5aR2 inhibitors may incur hormonal adverse effects. Until recently, an effective anti-BPH drug without side effects has not been discovered. Therefore, we applied many computational approaches that integrate ligand-based pharmacophore, 3D-QSAR, virtual screening, molecular docking and molecular dynamic (MD) simulations for searching more effective and less side effects 5aR2 inhibitors. The best pharmacophore model (Hypo1) was validated by Guner-Henry (GH) scoring method. This well validated Hypo1 was then used as a 3D-query in virtual screening to identify potential hits from Maybridge and National Cancer Institute (NCI) databases. Then these hits were subsequently filtered by molecular docking and MD simulations. After screening, one hit was identified as a potential lead based on high predicted inhibitory activity and binding affinity to 5aR2 in comparison to the most active inhibitor (Finasteride). In appendixes, pharmacophore and CoMFA model was performed on a set of 25 human nonsteroidal 5?R2 inhibitors and successfully identified 7 hit compounds with novel scaffolds were retrieved as leads. In summary, the results of this study can be applied to the design of new and more potent anti-BPH drugs for clinical purposes.

摘要 i
ABSTRACT iii
ACKNOWLEDGEMENTS v
CONTENTS vi
TABLE CONTENTS ix
FIGURE CONTENTS x
Chapter 1 GENERAL INTRODUCTION 1
Chapter 2 LITERATURE REVIEW 3
2.1 Human androgens 3
2.1.1 The introduction of testosterone (T) and dihydrotestosterone (DHT) 4
2.1.2 The relationship between human androgens and diseases 5
2.2 Benign prostatic hyperplasia (BPH) 8
2.2.1 Treatment of BPH 9
2.3 5?-reductase (5?R) 10
2.3.1 Previous studies of 5?R2 inhibitors 11
Chapter 3 MOLECULAR MODELING 14
3.1 Overview 14
3.2 Pharmacophore modeling 15
3.2.1 HypoGen pharmacophore 17
3.3 Virtual screening 17
3.4 Molecular docking 18
3.4.1 Docking programs 19
3.4.1.1 CDOCKER algorithm 19
3.4.1.2 DOCK algorithm 22
3.4.1.3 FlexX algorithm 22
3.4.1.4 GOLD algorithm 23
3.4.1.5 LibDock 24
3.5 Scoring function for docking 25
3.5.1 The types of scoring functions 26
3.5.2 Scoring functions 28
3.5.2.1 Piecewise Linear Potential (PLP) 29
3.5.2.2 PLP1 29
3.5.2.3 PLP2 30
3.5.2.4 Potential of Mean Force (PMF) 32
3.5.2.5 Jain scoring function 33
3.5.2.6 LigScore1 scoring function 33
3.5.2.7 LigScore2 scoring function 35
3.5.2.8 Ludi scoring function 36
3.6 CoMFA 37
3.6.1 Step of CoMFA modeling 38
3.7 Molecular dynamics simulations 40
3.7.1 Force Fields 41
3.7.1.1 CHARMm force field 42
3.7.1.2 The Parameters in the Force Field 42
3.7.1.3 Functional Form of the CHARMm Force Field 47
3.7.2 Minimization 49
3.7.3 Equilibration 52
3.7.4 Molecular Dynamics 53
Chapter 4 Discovery of novel 5?-reductase type II inhibitors by pharmacophore modeling, virtual screening,molecular docking andmolecular dynamics simulations 56
4.1 Abstract 56
4.2 Introduction 57
4.3 Materials and Methods 60
4.3.1 Data preparation 60
4.3.2 Pharmacophore models generation (HypoGen) 62
4.3.3 Validations of pharmacophore model 63
4.3.4 Virtual screening 63
4.3.5 Molecular docking 64
4.3.6 Molecular dynamics simulation 64
4.4 Results and discussion 65
4.4.1 Pharmacophore model studies 65
4.4.2 Pharmacophore model validations 69
4.4.3 Virtual screening 72
4.4.4 Molecular docking 74
4.4.5 MD simulations 78
4.5 Conclusions 82
4.6 References 83
Chapter 5 GENERAL CONCLUSIONS 89
Chapter 6 GENERAL REFERENCES 91
APPENDIXES 100

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