| Titre : | Contribution to drug design through computational studies of several series of bioactive heterocyclic molecules |
| Auteurs : | Imane Almi, Auteur ; Salah Belaidi, 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, 2021 |
| Format : | 1 vol. (101p.) / 30 cm |
| Langues: | Anglais |
| Langues originales: | Anglais |
| Résumé : |
Detoxification enzymes play an important role in cleaning the body from the toxins. These ones represent a hindrance to some drugs to fulfill their tasks, especially active compounds likeanti-cancer drugs. These latter are considered to have a high degree of toxicity in the body, whichmakes them targeted by the previous enzymes. We refer in this study to GSTp1-1, which is included in the detoxification enzymes class II targeted by NBD derivatives. It has dual defense feature, namely: inhibition GSTp1-1 and prevent the formation of each of the following complexes JNK1-GSTp1-1 and theTRAF2-GSTp1-1, that causes prolonged stopping of the cell cycle and facilitates apoptosis of damaged cells. This is what made us in this study shed light on the modeling similar compounds, and to achieve this goal, we applied a set of methods adopted in the modeling of active materials of high biological quality. Among them, the QSAR Two-dimensional (2D-QSAR) coupled with a virtual examination, by using a technique similarity search. In addition, we concretized a three - dimensional stereo (3D-QSAR) which contains effective biological properties (Pharmacophore). This application resulted to determine a quantity of compounds bearing the same previously identified characteristics. Therefore, we put limits, selectivity features extracted from specialized references, to reduce and identify biologically the best. We make sure of the validity and safety ofextracted models mentioned above by using several ways, namely: LOO-CV, external test set validation, fisher randomization, and cost analysis. As a final result of this research, we identified 28 new derivatives of NBD From both studies, at different inhibitory concentrations, micromolar unit (µM); the value of the half-maximal inhibitory concentration of a compound is 6.531 µM. |
| Sommaire : |
Acknowledgements ii List of works iii List of Abbreviations iv List of Figures v List of Tables ix I. Introduction 1 I.1. Contributions 3 I. 2. Organization of the dissertation 3 I.3. References5 II. Background on cancer disease and drugs discovery 7 II.1. Life cycle of a drug 7 II.1.1. Target identification and Validation 9 II.1.2. Hit identification 10 II.1.3. Lead generator and optimization 10 II.1.4. Preclinical studies 11 II.2. Cancer 12II.2.1. Cancer, a major health issue 12 II.2.2. Pathogenesis of cancer 15 II.2.2.1. Outside Body Factors (Environmental Factors) 16 II.2.2.2.Inside Body Factors 16 II.2.3. Treatment of cancer 16 II.2.3.1Glutathione-S-Transferase (GST) 17 II.2.3.2. GST P1-1 19 II.2.3.3. GST P1-1 physiological function (Role in cancer diseases) 19 II.2.3.4.GSTπ inhibitors 20 II. 3. References23 III. Computer-Aided Drug Design and Discovery 27 III.1. Generality 27 Part I: Ligand-based drug design 30 III.2. Ligand-based drug design (LBDD) 31 III.2.1. QSAR analysis 31 III.2.1.1. Object of QSAR study 32 III.2.1.2. Steps involved in QSAR study: 33 III.2.1.2.1. Data collection and selection of training set 33 III.2.1.2.2. Molecular Descriptors used in QSAR 34 III.2.1.2.3. Variable selection methods 35 III.2.1.2.4. Development of QSAR model 36 III.2.1.2.4.1. Linear regression: 36 a. Multiple linear regression (MLR) 36 b. Partial least squares regression (PLS) 36 III.2.1.2.4.2. Non-linear regression 37 III.2.1.2.5. Validation of QSAR model 39 III.2.1.2.6. Applicability domain (AD) 42 III.2.2. Chemical similarity analysis 43 III.2.3. Ligand_ Based Pharmacophore 44Part II: Structure-Based Drug Design 47 III.3. Structure-based drug design (SBDD) 48 III. 3. 1. Molecular Docking 48 III.3.1.1. Theory of docking 49 III.3.1.2. Search algorithm 50 III.3.1.3. Scoring 52 III.3.1.4. Molecular docking types 55 III.3.2. Generality on molecular dynamic 55 III. 4. References58 IV. Contributions and result 62 Part 1. QSAR investigations and Ligand-based virtual screening on a series of nitrobenzoxadiazole derivatives targeting human glutathione-Stransferases 63 VI. 1. 1. Introduction 64 VI. 1. 2. Methodologies 72 VI. 1. 2. 1. Equilibrium structure optimizations 72 VI. 1. 2. 2. Molecular descriptors generation 73 VI. 1. 2. 3. Model development 74 VI. 1. 2. 4. Virtual screening 75 VI. 1. 3. Results and discussion 75 VI. 1. 3. 1. Equilibrium structure of the nitrobenzoxadiazole derivatives 75 VI. 1. 3. 2. Quantitative structure activity relationships (QSAR) study 76 VI. 1. 3. 3. Applicability domain of the model 80 VI. 1. 3. 4. Importance of descriptors within different QSAR models 81 VI. 1. 3 .5. Virtual Screening Application 82 Part 2. Combined 3D-QSAR based Virtual Screening and Molecular Docking study of cytotoxic agents targeting human glutathione-s transferases 85 VI. 2. 1. Introduction 86VI. 2. 2. Data collection and preparation 86 VI. 2. 3. Results and discussion 87 VI. 2 .3 .1. Generation of pharmacophore models: 87 VI. 2. 3. 2. Validation of the pharmacophore model 89 VI. 2. 3. 2. 1. Cost analysis 90 VI. 2. 3. 2. 2. Test set analysis 90 VI. 2. 3. 2. 3. Fischer Randomization Method 91 VI. 2. 3. 3. Virtual screening 91 VI. 2. 3. 4. Docking 92 VI. 3. References 94 V. Conclusion 99 Appendix 101 |
| Type de document : | Thése doctorat |
| En ligne : | http://thesis.univ-biskra.dz/5437/1/Thesis_ALMI_Imane.pdf |
Disponibilité (1)
| Cote | Support | Localisation | Statut |
|---|---|---|---|
| TCH/81 | Théses de doctorat | bibliothèque sciences exactes | Consultable |
