- ホーム
- > 洋書
- > 英文書
- > Science / Mathematics
Full Description
This new volume in New Directions in Organic & Biological Chemistry, explores the development of cancer therapeutics, focusing on molecular chemistry and advanced drug design approaches. Written by experts in theoretical chemistry and molecular chemistry, they bridge the gap between theoretical chemistry, molecular biology, and drug development. In Part I, they focus on the fundamental properties of heterocyclic compounds and innovative methodologies being employed to enhance therapeutic potential. By exploring various classes of heterocyclic compounds and diverse anticancer mechanisms, this book provides a valuable resource for researchers, pharmaceutical scientists, and oncologists.
· Provides an overview of computational approaches used in drug discovery, including molecular docking, QSAR, and virtual screening.
· Focuses on the theoretical and practical aspects of these techniques, with applications across various therapeutic areas, including cancer.
· Addresses the challenges in translating scientific research into effective treatments, offering insights into overcoming common obstacles in the development process.
· These compounds represent a significant opportunity for the pharmaceutical industry to provide more effective and tailored cancer treatments, further driving market growth.
· The breadth of the market for heterocyclic anticancer agents is vast and continues to expand as scientific advancements uncover new therapeutic targets.
Contents
Preface. i
Aknewlegment ii
Author Biography. iii
Table of Contents iii
List of figure. viii
Liste of Table. x
General introduction. 1
Theoretical Part 5
Chapter 01 : Cancers and Anticancer Drugs 6
I..... Cancers 7
1. Definition. 7
2. Mechanisms of carcinogenesis 7
3. Characteristics of the cancer cell 8
II.... Cancer drugs 9
1. Mechanisms of Action. 9
2. Classification of anticancer agents according to their mechanism of action. 10
2.1. Drugs with a direct action on DNA. 10
a) Alkylating agents and derivatives 10
b) Platinum salts 12
2.2. Drugs with an indirect action on DNA. 12
a) Topoisomerase I and II inhibitors 12
2.3. Antimetabolite drugs. 14
2.4. Drugs targeting a specific receptor or mechanism in the tumor: Targeted therapy. 16
a) Monoclonal antibodies 17
b) Tyrosine kinase inhibitors (TKI) 20
c) Serine/threonine kinase inhibitors (STKI) 21
2.5. Drugs targeting certain hormones: Hormone therapy. 24
a) Hormone therapy for breast cancer. 25
b) Hormone therapy for prostate cancer. 27
2.6. Drugs targeting the immune system: Immunotherapy 29
III.. References 32
Chapter 02: In Silico Methods Used in the Design of New Anticancer Agents 34
I..... Introduction. 35
II.... Discovery of New Drugs 35
1. Development of tailor-made molecules 35
2. Identification and validation of targets 36
3. Generation of Hits and Leads 36
4. Lead optimization. 37
5. Clinical trials and commercialization. 37
III.. In Silico Screening Methods 38
1. Structure-based screening methods 39
2. Ligand-based screening methods « ligand-based ». 39
IV.. ADMET Filtering. 40
1. Administration and absorption. 40
2. Lipinski's rules 40
3. Lead-like selection criteria. 42
4. Distribution. 43
5. Metabolism and excretion 43
6. Toxicology. 44
V.... Quantitative Structure-Activity Relationship (QSAR) Study. 44
1. Introduction. 44
2. History. 44
3. Definition. 45
4. Principle. 46
VI. 2D-QSAR.. 46
1. QSAR Tools and Methodology. 47
1.1. Biological parameters. 47
1.2. Molecular descriptors. 48
a) Types of descriptors 48
2. Descriptor selection and reduction. 53
3. Data modeling methods 53
a) Principal Component Analysis (PCA) 54
b) Multiple Linear Regression. 54
c) Nonlinear regression. 55
d) Artificial Neural Networks (ANN) 55
4. Model validation tools 57
a) R² coefficient 58
b) Fisher's test 58
c) Variance Inflation Factor (VIF) 59
d) Internal validation or cross-validation. 59
e) Mean Square Error (MSE) 60
f) Randomization test 61
g) External validation. 61
h) Applicability domain. 62
5. Creation of training and test datasets 63
6. Global strategy for QSAR studies 63
VII. 3D-QSAR.. 65
1. Comparative Molecular Field Analysis (CoMFA) 65
2. Comparative Molecular Similarity Index Analysis (CoMSIA) 66
3. Structure alignment 66
4. Calculation of molecular interaction fields in both CoMFA and CoMSIA.. 66
5. Graphical visualization of models 68
6. Prediction and extrapolation. 69
VIII. Molecular Docking. 69
1. Definition. 69
2. Docking Approches 70
2.1. Rigid Docking. 70
2.2. flexible Docking. 71
3. Ligand-receptor interaction. 71
3.1. Ionic bonds. 71
3.2. Hydrogen bonds. 71
3.3. π-π interactions. 72
3.4. Cation-π interactions. 72
3.5. π interactions. 72
3.6. Van der Waals interactions. 73
3.7. Hydrophobic effect 73
4. Molecular docking tools 73
4.1. Preparation and selection of receptors. 73
4.2. Main docking software. 74
5. Evaluation of docking methods 75
5.1. Re-docking. 75
5.2. Root Mean Square Deviation (RMSD) 75
X. Conclusion. 78
XI. References 79
Experimental Part 84
Chapter 03 : Study of the anticancer activity of heterocyclic organic molecules using the 2D QSAR method.. 85
Application 1: QSAR Study of New Compounds Based on 1, 2, 4-triazole as Potential Anticancer Agents 86
I..... Introduction. 88
II.... Material and Methods 88
1. Experimental Data. 88
2. Calculation of molecular descriptors. 90
3. Statistical analysis 90
III.. Results and Discussion. 92
1. Principal Components Analysis (PCA) 92
2. Multiple linear regressions (MLR) 96
3. Multiple nonlinear regressions (MNLR) 96
4. External validation. 97
5. Artificial Neural networks (ANN) 98
IV.. Conclusion. 100
V.... References 101
Chapter 04 : Study of the anticancer activity of heterocyclic organic molecules using the 3D-QSAR and Molecular Docking methods 107
Application 2 :3D-QSAR Study of the Chalcone Derivatives as Anti-cancer Agents 108
I..... Introduction. 110
II.... Materials and Methods 111
1. Computer simulations 111
2. Data set 111
3. Molecular Modeling. 113
4. Molecular Alignment 113
5. CoMFA and CoMSIA studies 114
6. Partial least square analysis 114
7. Validation of the models 114
8. Y-randomization test 115
9. Model acceptability criteria. 115
10. Lipinski's Rule and ADMET Prediction. 115
III.. Results and Discussion. 116
1. CoMFA statistical results 116
2. CoMSIA Statistical Results 116
3. Analysis of CoMFA and CoMSIA contour maps 119
3.1. CoMFA contour map. 119
3.2. CoMSIA contour map. 121
4. Y-randomization test 123
5. Design for new chalcone as anticancer agents 124
6. Lipinski's Rule and ADMET Prediction. 126
IV.. Conclusion. 127
V.... References 130
Application 3: In Silico Design of Novel Pyrazole derivatives containing thiourea skeleton as anti-cancer agents using: 3D QSAR, Drug-Likeness studies, ADMET Prediction and Molecular Docking. 131
I..... Introduction. 133
II.... Material and Methods 134
1. Computer simulations 134
1.1. Data set 134
1.2. Molecular alignment 137
2. CoMFA and CoMSIA studies 138
3. Partial least square analysis 138
4. Validation of the models 139
5. Y-randomization test 139
6. Model acceptability criteria. 139
7. Drug Likeness and ADMET Prediction. 140
8. Molecular Docking Study. 140
III.. Results and discussion. 140
1. Molecular alignment 140
2. CoMFA statistical results 141
3. CoMSIA Statistical Results 141
4. Y‑randomization. 143
5. Contour analysis 144
5.1. CoMFA Contour map. 144
5.2. CoMSIA Contour maps 146
6. Design for new Pyrazole as anticancer agents 148
7. Drug-likeness studies 151
8. ADMET prediction. 153
9. Molecular docking study. 154
IV.. Conclusion. 156
V.... References 157
Application 4: Molecular Docking, Drug likeness Studies and ADMET prediction of Flavonoids as Platelet-Activating Factor (PAF) Receptor Binding. 161
I..... Introuduction. 163
II.... Material and Methods 165
1. Data collection. 165
1.1. Ligands 165
1.2. Receptor 165
2. Molecular Docking. 166
3. Docking validation protocol 167
4. Drug-likeness studies 167
5. ADMET prediction. 167
III.. Results and Discussion. 168
1. Molecular Docking. 168
2. Docking validation protocol 172
3. Drug-likeness studies 172
IV... Conclusion. 174
V.... References 174
General Conclusion. 175
