| Titre : | Virtual screening applied in several series of bioactive heterocyclic molecules |
| Auteurs : | Nedjla Khelfa, Auteur ; Salah Belaidi, Directeur de thèse ; Majdi Hochlaf, Auteur |
| 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. (127 p.) / ill., couv. ill. en coul / 30 cm |
| Langues: | Anglais |
| Langues originales: | Anglais |
| Mots-clés: | Malaria, PfDHFR, Sym-triazine, 2D-QSAR(MLR/ANN), 3D-QSAR(PLS), Virtual Screening. |
| Résumé : |
Malaria is one of the most widespread and fearsome parasitic disease in the world. Plasmodium falciparum is the parasite that causes the most severe type of malaria, this species transmits in the human body and attack the important organ; which is the human liver cells, will then reenter the blood stream and begin infecting the red blood cells. The major enzymes in this parasite is Plasmodium falciparum dihydrofolatereductase (PfDHFR), it is responsible for the biosynthesis of essential amino acids and nucleotide bases. The emergence of resistance of this kind of parasite to antimalarial agents had led for several years, still represents a threat, which draws our attention for the adoption of new guidelines for the treatment of malaria cases of P. falciparum species. For this reason, in this thesis we focus light on the discovery and development of Sym-triazine derivatives, which have provided a class of antimalarial drugs highly effective against PfDHFR. In this context, it is necessary to focus on Virtual Screening computational approaches in the fields of target identification and lead discovery. The aim of our study was to apply this type of in silico methodology, with the aim of modelling and evaluating for screening bioactive molecules derived from Sym-triazine. Our work combined between ligand-based VS methods; we based on the QSARs methods Two-dimensional (2D-QSAR(MLR/ANN)) and a three-dimensional stereo (3DQSAR( PLS)) which contains effective biological properties. The both of methods coupled with a Virtual Screening examination, by using a technique similarity search. Subsequently, we confirm the powerful and robustness of developed QSAR models by using various statistical OECD principles for the validation; internal and external validation (for training and test set), Y-randomization, and exploit of applicability domain. And structure-based VS methods; we concretized on the molecular docking analysis to determine the best interactions of the most active compound or the reference ligand which form stable complexes with the PfDHFR enzyme. The final results of our study, these different in silico methods allowed us to identify 9 new derivatives of Sym-triazine from both studies, show excellent inhibitory concentration activities against resistant P. falciparum bearing the mutant enzymes, making them good candidates for further development as antimalarial drugs. |
| Sommaire : |
Acknowledgements List of works List of Abbreviations List of Figures List of Tables INTRODUCTION Chapter I : Background on Malaria Diseases & Their Inhibitors I.1. Introduction I.2. Malaria disease I.2.1. Epidemiology of malaria I.2.1.1. Epidemiological facts I.2.2. Pathogens and vectors agents I.2.2.1. The pathogen agent I.2.2.2. The vector agent I.2.3. The evolutionary cycle of Plasmodium I.2.3.1. In human body I.2.3.2. In female Anopheles I.2.4. Pathophysiology I.3. The PfDHFR therapeutic target I.3.1 Generality on folates I.3.2. Plasmodum falciparum protein dihydrofolate reductase (PfDHFR) I.4. Antimalarial drugs and resistance I.4.1. Pharmacological classes and mechanisms of action I.4.2. Focus on sym-triazine derivatives I.4.2.1. Physiological and biochemical role of amino-sym-triazine derivatives I.4.2.2. Diamino-sym-triazines as antimalarial drugs References Chapter II: Virtual Screening in Drug Design & Discovery. II.1. Introduction II.2. Virtual screening III.2.1. Ligand-based virtual screening II.2.1.1. Similarity-Based Virtual Screening II.2.1.2. Quantitative structure activity relationship II.2.1.2.1. Generale methodology of a QSAR study A. Biological data collection B. Molecular descriptors C. Development of statistical models D. Validation of QSAR model E. Applicability domain (AD) II.2.1.2.2. 2D and 3D QSAR analysis A. 2D QSAR B. 3D QSAR II.2.1.3. Ligand-based pharmacophore approaches II.2.1.3.1. Elucidation of the pharmacophore II.2.2. Structure-Based Virtuel Screening II.2.2.1. Molecular Docking II.2.2.1.1. Docking process a. Ligand-protein docking b. Protein-protein docking II.2.2.1.2. Search algorithms II.2.2.1.3. Score functions II.2.2.1.4. Analysis of results II.2.2.1.5. Different types of docking a- Rigid docking b- Semi-flexible docking c- Docking flexible II.2.2.2. Molecular dynamics II.2.2.2.1. General principle II.2.2.2.2. Key components of molecular dynamics II.2.2.2.3. Issues and Limitations of Molecular Dynamics References Realized Works Chapter III : In silico-Based Identification of new anti-PfDHFR drug candidates via 1,3,5-triazine derivatives. III.1. Introduction III.2. Materials and methods III.2.1. Computational details III.2.2. Dataset selection III.2.3. QSAR modeling studies III.2.3.1. Statistical analysis and model validation III.2.3.2. Applicability domain approach III.2.4. Drug likeness parameter and lipophilicity indices III.3. Results and discussion III.3.1. Validation method III.3.1.1. Equilibrium geometries of 1,3,5-triazine III.3.1.2. 3D molecular electrostatic potential surface maps (3D MESP) of 1,3,5-triazine III.3.2. Quantitative structure-activity relationship studies III.3.2.1. Multiple linear regression (MLR) III.3.2.2. Artificial Neural Networks (ANN) III.3.3. Design of Novel PfDHFR Inhibitors III.3.4. Drug likeness screening of 1,3,5-triazine derivatives III.3.4.1. ADME study of new designed compounds References Chapter IV : Combined 3D-QSAR, molecular docking, ADMET, and drug likeness scoring of novel Diaminodihydrotriazines as potential antimalarial agents. IV.1. Introduction IV.2. Methodologies IV.2.1. Dataset IV.2.2. 3D-QSAR, In silico pharmacokinetics, ADMET study and Molecular docking analysis IV.3. Results and discussion IV.3.1. Validation of the developed CoMSIA model IV.3.2. Contour Plot Analysis |
| Type de document : | Thése doctorat |
| En ligne : | http://thesis.univ-biskra.dz/id/eprint/6383 |
Disponibilité (1)
| Cote | Support | Localisation | Statut |
|---|---|---|---|
| TCH/108 | Théses de doctorat | bibliothèque sciences exactes | Consultable |
