| Titre : | Contribution to the Modeling of Biomolecules and Their Interactions: Inhibition of Enzymes Involved in Cancer Diseases by a New Class of Derivatives. |
| Auteurs : | Khadidja Saghiri, 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, 2024 |
| Format : | 1 vol. (117 p.) / ill., couv. ill. en coul / 30 cm |
| Langues: | Anglais |
| Langues originales: | Anglais |
| Mots-clés: | Breast cancer, 2-phenyl-1H-indole, Withangulatin A, allosteric site, molecular docking, molecular dynamic, MEP, QSAR, PLS, ADME |
| Résumé : |
This dissertation presents the results of two research studies aimed at improving our understanding of breast cancer treatment drugs and potential targets. The first study used PLS regression to create QSAR models for 54 analogs of 2-phenyl-1H-indole, known for their antiproliferative activity on MDA MB231 and MCF-7 cancer cell lines. The dataset was split into training and testing datasets 10,000 times, with 75% of the molecules used for training and the rest for external validation. The best models were selected based on the highest probability of occurrence according to the Bayesian information criterion. As a result, the PLS regression equation derived explains 6.79% and 63% of the variability in anticancer activity around its mean for model 1(MDA MB231), and model 2 (MCF-7), respectively. The leave-one-out cross-validation R2CV, the bootstrapping correlation coefficients R2boots, and the predicted R2pred indicated a high predictive power for both models. This study was accompanied by molecular docking/dynamics simulations, revealing that ligands L39, L40, and L48 fit into the pocket of estrogen-α receptor (PDB:1A52), while ligand L47 showed affinity with progesterone receptor (PDB:1A28). This affinity was confirmed by high negative score values and the establishment of several non-covalent interactions with the active site residues of both receptors. Furthermore, drug-likeness and ADME prediction analyses showed favorable absorption and oral bioavailability characteristics for ligands L39 and L48, suggesting their potential as precursor compounds for breast cancer drug development. The second study aims to identify the binding mechanism of Glutaminase C (GAC) as a potential target for triple-negative breast cancer (TNBC). Molecular docking was employed to explore the interaction of 26 Withangulatin A (WA) derivatives with the allosteric site of GAC. The molecular docking/dynamics simulation results revealed that compounds A5, A8, A13, and A18 show high affinity toward the allosteric pocket of the GAC (PDB:3UO9), as confirmed by the high negative score values. These compounds interact with the most important residues and suggest a similar binding mechanism to the native compound (BPTES) and the clinical trial drug (CB-839). The combination of MEP analysis and molecular docking/dynamics studies confirms the favorable reactive sites of these compounds. Finally, pharmacokinetics prediction showed that A8 and A13 present the best ADMET profile among the selected compounds. |
| Sommaire : |
List of figu.................................. iii List of tables .........................................vi List of main abbreviations .................................viii General introduction ............................... 1 References.................. 4 CHAPTERⅠ : Breast Cancer an 1. I....................................... 6 2. Signs and symptoms ................................... 7 3. Etiology and risk factors......................... 8 4. Hormone receptors........................................ 9 4.1. Estrogen receptors ................................................... 10 4.2. Progesterone receptors ....................................... 10 5. Histological types of breast cancer ................................ 10 5.1. Invasive (infiltrating breast cancer) ................ 11 5.2. Noninvasive (in situ breast cancer).......................... 11 6. Breast cancer molecular subgroups ........................... 12 6.1. Luminal A ................................ 13 6.2. Luminal B ......................................... 13 6.2.1. HER2- luminal type B . 6.2.2. Luminal B/HER2 +............................. 13 6.3. HER2- positive (non-luminal) .......................... 13 6.4. Basal-like (Triple-Negative)........................... 14 7. Development of breast cancer .............................. 14 8. Breast cancer treatment............................ 16 8.1. Endocrine (hormonal) therapy ................... 16 8.2. Chemotherapy................................ 17 8.3. Targeted therapy (anti-HER2).................... 18 8.4. Glutaminase as a potential therapeutic target for therapeutic intervention ......... 19 8.4.1. Allosteric inhibitors and inhibition strategies... References.. 24 CHAPTER II: Background on Computer-Aided- Drug Design (CADD) Methods 1. Brief overview of computer-aided drug design (CA........ 31 2. In silico virtual screenin............ 32 2.1. Ligand-based drug design............................. 33 2.1.1. Quantitative structure-activity relationships (QSAR).............. 33 2.1.1.1. Data collection, preparation, and curation .............35 2.1.1.2. Descriptor selection and generation ..................35 2.1.1.2.1.Types of descriptors.................. 35 2.1.1.2.2.Calculation of molecular descriptors .......... 36 2.1.1.3. Regression analysis and model development........ 37 2.1.1.3.1.Partial least squares regression (PLS).................. 37 2.1.1.4. Variable selection methods....................... 37 2.1.1.4.1.Stepwise regression methods.................. 38 2.1.1.4.1.1.Backward elimi 2.1.1.4.1.2.Forward selection.. 38 2.1.1.4.1.3.Stepwise selection.. 39 2.1.1.4.2.All possible subset selection ....... 39 2.1.1.4.3.Stopping rule and selection criteria ... 40 2.1.1.5. Outlier detection.... 41 2.1.1.5.1.Types of outliers 42 2.1.1.5.2.Studentized deleted residual and leverage values............... 43 2.1.1.6. Model validation methods....... 44 2.1.1.6.1.Internal validation. 45 2.1.1.6.2.External validation 49 2.2. Structure-based drug design (SBDD)..... 49 2.2.1.Molecular docking ..... 49 2.2.1.1. Types of molecular docking .. 50 2.2.1.2. Molecular docking basics 51 2.2.1.2.1.Searching algorithm. 52 2.2.1.2.2.Scoring functions (SFs) ... 53 2.2.1.3. Steps involved in molecular docking . 56 2.2.1.3.1.Protein preparation. 56 2.2.1.3.2.Binding site detection . 57 2.2.1.3.3.Ligand preparation 58 2.2.1.3.4.Molecular docking validation . 58 2.2.2.Molecular dynamic simulation (MD) 59 2.3. Quantitative molecular electrostatic potential analysis . 60 2.4. ADMET prediction...... 60 2.4.1. Oral administration 61 2.4.1.1. Absorption ... 61 2.4.1.2. Distribution...... 62 2.4.1.3. Metabolism 63 2.4.1.4. Excretion.......63 2.4.1.5. Toxicity... 63 2.4.2. Drug-likeness and rule-of-five .. 64 Bookmark not defined. Chapter III: QSAR Study, Molecular Docking/Dynamics Simulations and ADME Prediction of 2- Phenyl-1H-Indole Derivatives as Potential Breast Cancer Inhibitors 1. Introduction... 75 2. Materials and method .. 78 2.1. Biological data.78 2.2. QSAR modeling..... 81 2.2.1.Molecular descriptor calculation ... 81 2.2.2.Regression analysis.. 83 2.2.3.Molecular descriptors selection .. 83 2.2.4.Partial least square 84 2.2.5.Validation for QSAR models... 84 2.2.5.1.Internal validation.. 84 2.2.5.2.External validation. 84 2.3. Molecular docking protocol.. 84 2.3.1.Preparation of ligands 85 2.3.2.Selection and preparation of target ... 85 2.4. Molecular dynamics simulation (MD).... 86 2.5. ADME drug-likeness and pharmacokinetics 86 3. Results and discussion .87 3.1. QSAR modeling... 87 3.2. Molecular docking .94 3.2.1.Identification of the active site of ER and PR .. 94 3.2.2.Interaction between ligands and both receptors........ 94 3.3. MD simulation .... 99 3.4. ADME/Pharmacokinetics predictions . 103 4. Conclusions.. 105 References... 107 Chapter Ⅳ Molecular Docking/Dynamics Simulations, MEP analysis, and Pharmacokinetics prediction of some Withangulatin A derivatives as Allosteric Glutaminase C Inhibitors in Breast Cancer 1.Introduction.. 115 2.Materials and methods 116 2.1. Biological data.. 116 2.2. Molecular docking 118 2.2.1.Ligands preparation 118 2.2.2.Target selection and preparation . 119 2.3. Molecular dynamics simulation (MD). 120 2.4. Molecular electrostatic potential. 121 2.5. Pharmacokinetics (PK) and toxicity prediction ... 121 3. Results and discussion . 122 3.1. Molecular docking study.. 122 3.1.1.Binding site residues of the target.. 122 3.1.2.Receptor-compounds interactions .. 122 3.2. MD simulations.. 130 3.2.1.Protein-ligand interactions after MD simulations . 131 3.3. MEP analysis . 136 3.4. Pharmacokinetics predictions.................................. 139 4. Conclusion ... 142References.. 143 General conclusion 148 |
| Type de document : | Thése doctorat |
| En ligne : | http://thesis.univ-biskra.dz/id/eprint/6572 |
Disponibilité (1)
| Cote | Support | Localisation | Statut |
|---|---|---|---|
| TCH/116 | Théses de doctorat | bibliothèque sciences exactes | Consultable |
