| Titre : | Étude de la structure et des propriétés SAR/QSAR de quelques molécules à visée thérapeutique |
| Auteurs : | Mariem Ghamri, Auteur ; Dalal Harkati, Directeur de thèse |
| Type de document : | Thése doctorat |
| Editeur : | Biskra [Algérie] : Faculté des Sciences Exactes et des Sciences de la Nature et de la Vie, Université Mohamed Khider, 2022 |
| Format : | 1 vol. (161 p.) / couv. ill. en coul / 30 cm |
| Langues: | Anglais |
| Langues originales: | Anglais |
| Mots-clés: | Carbazole derivatives ; Topo II inhibition ; Molecular structure ; QSAR model. |
| Résumé : |
Recently, a series of carbazole derivatives containing chalcone analogues (CDCAs) were synthetized as potent anticancer agents and apoptosis inducers. These compounds target the inhibition of topoisomerase II and present cytotoxic activities. After comparison to experiment, we validated the use of B3LYP, a density functional theory-based approach, to describe the structure and molecular properties of the carbazole subunit and CDCAs compounds of interest. Then, we derived relationships between the chemical descriptors and activity of these carbazole derivatives using multi-parameter optimization and quantitative structure activity relationships (QSAR) approaches. For the QSAR studies, we used multiple linear regression and artificial neural network statistical modelling. Our predicted activities are in good agreement with the experimental ones. We found that the most important parameter influencing the activity of the considered compounds is the octanol-water partition coefficient, highlighting the importance of flexibility as a key molecular parameter to favor cell membrane crossing and enhance the action of these CDCAs against topoisomerase II. Our results provide useful guidelines for designing new oral active CDCAs medicaments for cytotoxic inhibition. |
| Sommaire : |
ACKNOWLEDGMENTS TABLE OF CONTENTS LIST OF FIGURES LIST OF TABLES General Introduction 2 Chapter I: Drug discovery: finding a lead I.Context: drug research 5 I.1 Research and Development (R&D) 6 I.1.1 Target identification and validation 6 I.1.1.1 Choosing a disease 6 I.1.1.1.1 General about cancer 6 I.1.1.1.1.1 Definition of cancer 7 I.1.1.1.2 Types of cancer 8 I.1.1.1.3 Distribution of cancer 9 I.1.1.1.4 Anticancer drugs 10 I.1.1.1.4.1 The various types of anticancer drugs and their mechanisms of action 10 I.1.1.1.4.2 Toxicity of anticancer drugs 12 I.1.1.1.5 Signs and symptoms 12 I.1.1.1.6 Treatment 13 I.1.1.1.6.1 Surgery 13 I.1.1.1.6.2 Radiotherapy 13 I.1.1.1.6.3 Medical treatment 13 I.1.1.1.6.4 Radiation therapy 13 I.1.1.1.6.5 Hormone therapy 13 I.1.1.1.6.6 Biological response modifier therapy 14Table of contents I.1.1.1.6.7 Immunotherapy 14 I.1.1.1.6.8 Bone marrow transplant 14 I.1.1.2 Choosing a drug target 14 I.1.1.2.1 Historical Overview 14 I.1.1.2.2 Drug targets 15 I.1.1.2.3 Discovering drug targets 16 I.1.1.3 Finding a lead compound 18 I.1.1.3.1 Generation of Hits and Leads 18 I.1.1.3.2 Screening on the targets 18 I.1.1.3.3 Phenotypical screening 19 I.1.1.4 Lead Optimization 19 I.1.1.4.1 Rationalize the selection 20 I.1.1.4.1.1 “Drug-like” compounds 20 I.1.1.4.1.2 "lead-like" compounds 21 I.1.2 Pre-clinical trials 22 I.1.3 Clinical tests 22 I.2. Limitations and failures 24 I.3. Medicinal Chemists Today 25 I.4 Conclusion 25 I.5 References 27 Chapter II Part I: Computer in medicinal chemistry II.1 Theoretical Background for Quantum Mechanical Calculations 35 II.1.1 Molecular mechanics 36 II.1.1.1 Introduction 36Table of contents II.1.1.1.1 Elongation energy 38 II.1.1.1.2. Bending energy 39 II.1.1.1.3. Torsional energy 39 II.1.1.1.4. Van der Waals energy 40 II.1.1.1.5. Electrostatic energy 41 II.1.1.2 Force Fields 42 II.1.1.3 Limitations of Molecular Mechanics 43 II.1.2 Quantum Mechanics 44 II.1.2.1 Introduction 44 II.1.2.2 HF and DFT Methods 45 II.1.2.3 Semi-empirical methods 46 II.1.2.4 Limitations of Quantum Mechanics 47 II.2 Choice of method 49 II.3 Minima search method 50 II.3.1 Introduction 50 II.3.2 Minimization algorithms II.3.2.1 The "steepest descent" method II.3.2.2. The conjugate gradient method 52 II.3.2.3. The Newton-Raphson method 52 II.3.2.4. The simulated annealing method 53 II.4 Molecular Modeling 53 II.4.1 Elements of Computational Chemistry 53 II.4.1.1 Drawing chemical structures 53 II.4.1.2 Three-dimensional structures 55 II.4.1.3 Molecular Structure Databases 56Table of contents II.4.1.4 Energy minimization 58 II.4.1.5 Molecular dimensions 59 II.4.2 Molecular properties 60 II.4.2.1 Geometric Parameters 61 II.4.2.1.1 Bond length 61 II.4.2.1.2 Valence angle 61 II.4.2.1.3 Dihedral Angle (Torsion angle) 62 II.4.3 Electronic Parameters 63 II.4.3.1 Atomic charges 63 II.4.3.2 Molecular electrostatic potentials 63 II.4.3.3 Molecular orbitals 65 II.4.3.4 Fukui functions 66 II.5. Studies of vibrational properties 67 II.5.1 Introduction 67 II.5.2 Theoretical aspects of infrared vibration spectroscopy 67 II.5.3 Principle of infrared spectroscopy 68 II.5.3.1 Electromagnetic radiation 68 II.5.3.2 Infrared 70 II.5.3.3 Infrared absorption 70 II.5.4 Theoretical aspects 72 II.5.4.1 Internal vibration modes 72 II.5.4.2 Classification of vibration modes 73 II.5.4.3 Factors that influence vibration frequencies 74 II.5.5 Application 75 II.6 Conclusion 75Table of contents Part II: Computer-Assisted Drug Design II.1 Predictive Quantitative Structure–Activity Relationship Modeling 78 II.2. Principle of QSPR / QSAR methods 79 II.3 Key Quantitative Structure–Activity Relationship Concepts 80 II.3.1 Molecular Descriptors 82 II.3.3 Quantitative Structure–Activity Relationship Modeling Approaches 83 II.3.3.1 General Classification 83 II.3.3.2. General methodology of a QSPR / QSAR study 84 II.3.3.3 Transforming the Bioactivities 85 II.3.3.4 Determination of the Best Set of Descriptors Approaches 85 II.4 Building Predictive Quantitative Structure–Activity Relationship Models: The Approaches to Model Validation 86 II.4.1 Data analysis methods 86 II.4.1.1 Neural networks 86 II.4.1.2 Multiple linear regression 88 II.4.1.2.1 Randomization test 90 II.4.2 Interpretation and validation of a QSPR / QSAR model 91 II.4.2.1 The Importance of Validation 91 II.4.2.1.1 Internal Validation 92 II.4.2.1.1.1 Least Squares Fit 92 II.4.2.1.1.2 Fit of the Model 93 II.4.2.1.2 External validation 94 II.5 Application of QSAR 95 II.6 References 96Table of contents Chapter III: Results and Discussion III.1 Introduction 104 III.2 Structure, spectroscopy and electronic properties of carbazole and its derivatives 108 III.2.1 Carbazole structure and properties 109 III.3 Carbazole derivatives structures 120 III.4 CDCAs derivatives physicochemical properties, descriptors and drug-likeness scoring 123 III.5 QSAR modelling studies 129 III.5.1 Internal validation 129 III.5.2 External validation 130 III.5.3 Y-randomization 130 III.6 Multiple linear regression (MLR) 131 III.7 Artificial Neural Networks (ANN) 133 General Conclusion 137 APPENDIX 140 REFERENCES 160 ABSTRACT |
| Type de document : | Thése doctorat |
| En ligne : | http://thesis.univ-biskra.dz/5671/1/Thesis%20doctorat%20final.pdf |
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| Cote | Support | Localisation | Statut |
|---|---|---|---|
| TCH/90 | Théses de doctorat | bibliothèque sciences exactes | Consultable |
