| Titre : | Investigation of cytotoxic properties of some heterocyclic derivatives by molecular modeling |
| Auteurs : | Ahlem Belkadi, Auteur ; Nadjib Melkemi, 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: | Français |
| Mots-clés: | k-means clustering, cytotoxic activity, statistical analysis, ADME, drug-likeness, Molecular docking, CHK1, Xanthones, MD simulation, ADME-TOX, MEP analysis, Prexasertib. |
| Résumé : |
Currently, many technologies have been adopted to boost the efficiency of drug development and overcome obstacles in the drug discovery pipeline. The application of these approaches spans a wide range, from bioactivity predictions, de novo compound synthesis, target identification to hit discovery, and lead optimization. This dissertation comprises two studies. First, we proposed an original approach based on statistical consideration dedicated to k-means clustering analysis in order to define a set of rules for structural features that would help in designing novel anti-cancer drug candidates. It has been applied successfully to classify 500 cytotoxic compounds with 21 molecular descriptors into distinct clusters. The percentage of molecules in each cluster is 50%, 24.88%, and 25.12% for cluster 1, cluster 2, and cluster 3, respectively. Each cluster groups a homogeneous class of molecules with respect to their molecular descriptors. Silhouette analysis, used as a cluster validation approach proves that the molecules are very well clustered, and there are no molecules placed in the wrong cluster. In silico screening of pharmacological properties ADME and evaluation of drug-likeness were performed for all molecules. The quantitative analysis of molecular electrostatic potential was performed to identify the nucleophilic and electrophilic sites in the representative molecule of each cluster. In addition, a molecular docking study was carried out to investigate the interactions of the paragon molecules with the active binding sites of six different targets. Our findings provide a guide to assist the chemist in selecting and testing only the potential molecules with good pharmacokinetic profiles to improve the clinical outcomes of drug therapies. Second, a simulation-based investigation was conducted to examine the CHK1 inhibitory activity of cytotoxic xanthone derivatives using a hierarchical workflow for molecular docking, MD simulation, ADME-TOX prediction, and MEP analysis. A molecular docking study was conducted for the forty-three xanthone derivatives along with standard Prexasertib into the selected CHK1 protein structures 7AKM and 7AKO. Furthermore, MD studies support molecular docking results and validate the stability of studied complexes in physiological conditions. Moreover, in silico ADME-TOX studies are used to predict the pharmacokinetic, pharmacodynamic, and toxicological properties of the selected eight xanthones and the standard Prexasertib. The quantitative analysis of electrostatic potential was performed for the lead compound L36 to identify the reactive sites and possible noncovalent interactions. Our study provides new unexplored insights into xanthones as CHK1 inhibitors and identified L36 as a potential drug candidate that could undergo further in vivo assays and optimization, laying a solid foundation for the development of CHK1 inhibitors and cancer drug discovery. To the best of our knowledge, this is the first time such a study was conducted for the xanthones with CHK1 by using a computational based approach. |
| Sommaire : |
Acknowledgments ii List of works .vii List of abbreviations ix List of figuresxii List of tables..xvi General Introduction....1 CHAPTERⅠ : Biological background I.1 What is cancer? 6 I.2 Healthy cells vs. cancer cells 8 I.3 Cell cycle phases. 9 I.4 Cell death mechanisms 11 I.5 Classification of chemotherapeutic drugs. 12 I.6 Mechanism of action of anticancer drugs 14 I.7 Drug discovery and development 16 I.7.1 Preclinical trials 17 I.7.1.1 In vitro assays 17 I.7.1.2 In vivo assays 19 I.7.2 Clinical trials 19 I.7.2.1 Stage Ⅰ 19 I.7.2.2 Stage Ⅱ 19 I.7.2.3 Stage Ⅲ 20 I.7.2.4 Stage Ⅳ 20 I.8 Serine/threonine protein kinase ............... 20 I.8.1 Checkpoint kinase 1. 21 I.8.2 CHK1 inhibition in cancer therapy .. 22 I.9 Xanthones 23 I.10 References25 CHAPTER Ⅱ : Computational methods for drug discovery and development II.1 Clustering analysis 35 II.2 Type of clustering algorithm 35 II.3 Principal component analysis 36 II.4 Hierarchical cluster analysis . 37 II.5 K-means clustering . 38 II.6 Choosing the number of clusters . 39 II.6.1 The elbow method 39 II.6.2 The silhouette analysis 39 II.7 Similarity and dissimilarity metrics 40 II.8 Hopkins statistic for validating cluster tendency 41 II.9 Molecular Electrostatic Potential. 42 II.10 ADME-TOX prediction. 42 II.10.1 The gastrointestinal absorption . 43 II.10.2 The volume of distribution. 43 II.10.3 The blood-brain barrier permeability 44 II.10.4 Permeability glycoprotein substrates and inhibitors 44 II.10.5 Cytochrome P450 substrates and inhibitors 44 II.10.6 Clearance 45 II.10.7 AMES toxicity 45 II.10.8 Hepatotoxicity46II.10.9 HERG inhibition 46 II.11 Molecular docking 46 II.11.1 Types of molecular docking 47 II.11.1.1 Rigid docking . 47 II.11.1.2 Semi-flexible docking 48 II.11.1.3 Flexible-flexible docking 48 II.11.2 Molecular docking procedure 48 II.11.2.1 Target preparation 48 II.11.2.2 Ligand preparation. 49 II.11.2.3 Active site detection . 49 II.11.2.4 Docking 49 II.11.2.5 Evaluating Docking Results 49 II.11.3 Docking theory. 50 II.11.3.1 Search algorithm . 51 II.11.3.2 Scoring Functions 52 II.12 Molecular dynamics 54 II.12.1 MD simulation applications 54 II.13 References 56 CHAPTER Ⅲ: K-means clustering analysis, ADME/Pharmacokinetic Prediction, MEP and Molecular docking studies of potential cytotoxic agents III.1 Introduction . 69 III.2 Materials and methods 70 III.2.1 Biological database 70 III.2.2 Molecular descriptors generation 70 III.2.3 ADME and drug-likeness prediction 70 III.2.4 Molecular electrostatic potential 70 III.2.5 Molecular docking study.71 III.2.5.1 Compounds preparation 71 III.2.5.2 Targets preparation. 71 III.2.6 Data clustering 71 III.2.7 Clusters characterization by descriptors and molecules III.3 Results and discussion 73 III.3.1 Clusters analysis. 73 III.3.2 ADME properties and drug-likeness evaluation.. 77 III.3.3 Quantitative MEP analysis of paragon molecules 80 III.3.4 Molecular docking simulation . 82 III.4 Conclusion . 87 III.5 References . 88 CHAPTER Ⅳ: Molecular Docking/Dynamic simulations, MEP, ADME-TOX based analysis of xanthone derivatives as CHK1 inhibitors IV.1 Introduction 102 IV.2 Materials and methods 103 IV.2.1 Biological database 103 IV.2.2 Molecular docking study 106 IV.2.2.1 Targets preparation 106 IV.2.2.2 Compounds preparation . 108 IV.2.3 Molecular dynamics simulation 109 IV.2.4 ADME-TOX and drug-likeness prediction.... 111 IV.2.5 Molecular electrostatic potential 112 IV.3 Results and discussion . 112 IV.3.1 Molecular docking 112 IV.3.2 MD simulation 120 IV.3.3 In silico analysis of ADME-TOX properties 127 IV.3.4 Quantitative MEP analysis of L36.. 132 IV.4 Conclusion . 135 IV.5 References . 136 General Conclusion .140 Appendix A.142 Appendix B .151 |
| Type de document : | Thése doctorat |
| En ligne : | http://thesis.univ-biskra.dz/6068/1/BELKADI%20AHLEM%20PHD%20DISSERTATION.pdf |
Disponibilité (1)
| Cote | Support | Localisation | Statut |
|---|---|---|---|
| TCH/97 | Théses de doctorat | bibliothèque sciences exactes | Consultable |
