| Titre : | Structure-activity approaches for prediction of chemical reactivity and pharmacological properties of some heterocyclic compounds |
| Auteurs : | Rachida Djebailli, Auteur ; Nadjib Melkemi, Directeur de thèse ; Samir Kenouche, 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. (114 p.) / ill. en coul., / 30 cm |
| Langues: | Anglais |
| Langues originales: | Anglais |
| Mots-clés: | Benzodiazepine, GABAA receptor, allosteric modulation, DFT, molecular docking, molecular dynamic, MEP, QSAR, PLS, ADME. |
| Résumé : |
Benzodiazepine drugs are widely prescribed to treat many psychiatric and neurologic disorders. As its pharmacological action is exerted in a sensitive area of the brain; ''the central nervous system'', it is crucial to provide detailed reports on the chemistry of benzodiazepines, model the mechanism of action that occurs with GABAA receptors, identify the overlap with other modulators, as well as explore the structural requirements that better potentiate the receptor response to benzodiazepines. This dissertation consists of two original studies that consider the new lines of research related to benzodiazepines, particularly the identification of three new TMD binding sites. The first study provides, on the one hand, an overview of the chemistry of six Benzodiazepine basic rings starting from structural characteristics, electronic properties, Global/local reactivities, up to intermolecular interactions with long-range nucleophilic/electrophilic reactants. This was achieved by combining a DFT investigation with a quantitative MEP analysis on the vdW surface. On the other hand, the performed molecular docking simulations allowed identifying the best binding modes, binding interactions, and binding affinities with residues, which helped to validate the quantitative MEP analysis predictions. The second study was conducted on a dataset of [3H]diazepam derivatives. First, molecular docking simulation was used to initially screen the dataset and select the best ligand/target complexes. Afterwise, the best-docked complexes were refined by performing molecular dynamics simulation for 1000 picoseconds. For both simulations, the binding modes, binding interactions, and binding affinities were thoroughly discussed and compared with each other and with outcomes collected from the literature. Additionally, the good pharmacokinetic properties (ADME prediction) as well as compliance with all druglikeness rules were checked via in silico tools for all the dataset compounds. Finally, a QSAR analysis was carried out using an improved version of PLS regression. Briefly, the dataset is randomly split into 10 000 training and test sets that involve, respectively, 80% and 20% of chemicals. Subsequently, 10 000 statistical simulations were conducted that; after excluding outlying observations, yielded 10 000 best training models following the Bayesian Information Criterion. Among these 10 000 best models, the best predictors with the highest probability of occurrence were selected. As a consequence, the derived PLS regression equation explains 63.2% of the variability in BDZ activity around its mean. Furthermore, Internal and external validation metrics assure the robustness and predictability of the developed model. The developed model was interpreted based on literature investigations and a combination of implemented approaches. |
| Sommaire : |
List of works .... i List of main abbreviations.... iii List of figures........................ iv List of Tables ....................... viii general introduction...............1 Chapter I Overview on Neurotransmission, GABA receptors, and Benzodiazepines 1 Introduction ....................10 2 Neuronal transmission in the brain...............11 3 Neurotransmitter receptors 13 3.1 G-protein-coupled receptors..13 3.2 Ligand-gated ion channel receptors ....14 3.2.1 Cys-loop family…….…………………………………………………………16 4 γ-aminobutyric acid receptors ........................................16 4.1 Neurotransmitter γ-aminobutyric acid ...........................17 4.1.1 GABA metabolism or GABA shunt ..................18 4.2 Type A γ-aminobutyric acid receptor................20 4.2.1 Overall architecture...20 4.2.2 Subunits...21 4.2.2.1 Regional distribution in CNS ...................23 4.2.3 Structural classification..26 4.2.3.1 Homo-Oligomeric GABAA receptors ...........26 4.2.3.2 Composed of two different subunits .........26 4.2.3.3 Composed of three or more different subunit subtypes ..27 4.2.4 Modulation.......27 4.2.5 Orthosteric GABA binding sites in isoform ...28 5 Benzodiazepines .............30 5.1 Chemistry ...............30 5.2 Pharmacology.........30 5.3 1,4-dinitrogenetad BDZ .......32 5.3.1 Stereochemistry..32 5.3.2 Structure-Activity relationship...................33 5.4 BDZ allosteric binding sites................33 5.4.1 ECD binding site..33 5.4.2 TMD binding sites ...........34 5.5 Pharmacokinetics .34 5.5.1 Absorption.......35 5.5.2 Distribution .......35 5.5.3 Metabolism .....35 5.5.4 Elimination.....35 Bibliography ..36 Chapter II Overview on computational methods: Conceptual-DFT, MEP analysis, QSAR analysis, and Molecular docking/Dynamic simulations 1 Introduction ...44 2 Conceptual-DFT ...45 0.0 Global reactivity descriptors ...........46 2.2 Local reactivity descriptors .......................47 3 Quantitative molecular electrostatic potential analysis ....49 4 Quantitative structure–activity relationship..................51 4.1 Recent QSAR approaches ...................51 4.1.1 Fragment-Based 2D-QSAR (FB-QSAR)...........52 4.1.2 Multiple Field 3D-QSAR (MF-3D-QSAR) ...........52 4.1.3 Amino Acid-Based Peptide or Protein Prediction (AABPP)......52 4.2 General workflow of QSAR Studies........52 4.2.1 Data preparation..53 4.2.1.1 Datasets curation ...............................53 4.2.1.2 Molecular descriptors: selection and generation......................5 4.2.1.2.1 Types of molecular descriptors.........................56 4.2.1.2.2 Selection of relevant descriptors.........56 4.2.2 Data analysis/model development ..........57 4.2.2.1 Statistical methods...........57 4.2.2.1.1 Partial least square analysis ........................58 4.2.2.2 Variable selection methods ............58 4.2.2.2.1 Best subset ...59 4.2.2.2.2 Stepwise methods ..................59 4.2.2.2.2.1 Forward selection................................59 4.2.2.2.2.2 Backward elimination ..........................60 4.2.2.2.2.3 Stepwise selection .......................60 4.2.2.2.2.4 Stopping rules....................61 4.2.2.2.3 Choosing the optimal model.........62 4.2.2.2.3.1 Cp, AIC, BIC, and ......63 4.2.2.3 Check for Outliers ......63 4.2.2.3.1 Types of outliers ..65 4.2.3 Model validation .65 4.2.3.1 Internal validation ...........66 4.2.3.1.1 and 66 4.2.3.1.2 F-statistics.66 4.2.3.2 External validation .............67 1.0.3.0.0 , , and ..........67 5 Molecular docking........68 5.1 General protocol...69 5.1.1 Ligand preparation .........70 5.1.2 Target preparation.70 5.1.3 Binding site detection ..............71 4.0.1 Docking validation71 5.2 Types of molecular docking............71 5.2.1 Rigid docking..71 4.0.0 Semi-flexible docking..71 5.2.3 Flexible-flexible docking.....................72 5.2.3.1 Single target conformation ......................73 5.2.3.1.1 Soft docking.73 5.2.3.1.2 Side-chain flexibility ...........................73 4.0.3.0 Multiple protein conformations..........73 5.2.3.2.1 Combined target grid (average grid) ...73 5.2.3.2.2 United description of the target .........74 5.2.3.2.3 Individual conformations...............74 5.3 Search algorithms......74 5.3.1 Ligand sampling..75 5.3.1.1 Systematic algorithms ..........75 5.3.1.2 Stochastic algorithms ........76 5.3.1.3 Deterministic algorithms ......77 5.4 Scoring Functions (SFs) ....77 5.4.1 Physics-based SFs.78 5.4.2 Empirical SFs...79 4.1.3 Knowledge-based SFs.......79 5.4.4 Consensus scoring.81 5.4.5 Machine-learning-based SFs.....81 6 Molecular dynamics (MDs)81 6.1 MD simulation before/during the docking process .........82 7 Combined molecular docking/ MD simulations.......82 Bibliography ....84 Chapter III Combined Conceptual-DFT, Quantitative MEP Analysis, and Molecular Docking Study of Benzodiazepine Analogs 1 Introduction .94 2 Material and methods ....95 2.1 Statistical analysis .95 2.2 Computational details.95 2.3 Molecular docking protocol .96 3 Results and discussion ...97 3.0 Atomic charges...97 3.2 Geometry and electronic properties ..............99 3.3 Global reactivity...101 3.4 Local reactivity and quantitative MEP analysis..........101 3.5 Molecular docking simulation.....107 4 Conclusion.......113 Bibliography 114 |
| Type de document : | Thése doctorat |
| En ligne : | http://thesis.univ-biskra.dz/5987/1/DJEBAILI%20RACHIDA_PHD%20DISSERTATION.pdf |
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