Anticancer Activity in Heterocyclic Organic Structures

Dr. Larbi EL Mchichi holds a PhD in Chemistry with a specialization in Theoretical Chemistry from Moulay Ismail University (Faculty of Sciences, Meknes). His doctoral research focused on the anticancer activity of heterocyclic organic molecules, using statistical and quantum methods, including QSAR modeling and molecular docking.In his research, Dr. EL Mchichi explores innovative computational approaches to the design of novel therapeutic agents, particularly anticancer compounds, utilizing techniques like 3D-QSAR, drug-likeness assessment, ADMET prediction, and molecular docking simulations.In addition to his research, he is a first-grade certified teacher of physics and chemistry at the secondary education level, where he strives to inspire his students with a passion for science and research.Dr. EL Mchichi has authored several peer-reviewed publications in international journals, including work on the design of pyrazole derivatives as anticancer agents and the discovery of a new isatin scaffold for BCR-ABL tyrosine kinase inhibitors.His research aims to contribute to the advancement of cutting-edge cancer therapies through the integration of computational methods and pharmaceutical science.

• Preface. iAknewlegment iiAuthor Biography. iiiTable of Contents iiiList of figure. viiiListe of Table. xGeneral introduction. 1Theoretical Part 5Chapter 01 : Cancers and Anticancer Drugs 6I..... Cancers 71. Definition. 72. Mechanisms of carcinogenesis 73. Characteristics of the cancer cell 8II.... Cancer drugs 91. Mechanisms of Action. 92. Classification of anticancer agents according to their mechanism of action. 102.1. Drugs with a direct action on DNA. 10a) Alkylating agents and derivatives 10b) Platinum salts 122.2. Drugs with an indirect action on DNA. 12a) Topoisomerase I and II inhibitors 122.3. Antimetabolite drugs. 142.4. Drugs targeting a specific receptor or mechanism in the tumor: Targeted therapy. 16a) Monoclonal antibodies 17b) Tyrosine kinase inhibitors (TKI) 20c) Serine/threonine kinase inhibitors (STKI) 212.5. Drugs targeting certain hormones: Hormone therapy. 24a) Hormone therapy for breast cancer. 25b) Hormone therapy for prostate cancer. 272.6. Drugs targeting the immune system: Immunotherapy 29III.. References 32Chapter 02: In Silico Methods Used in the Design of New Anticancer Agents 34I..... Introduction. 35II.... Discovery of New Drugs 351. Development of tailor-made molecules 352. Identification and validation of targets 363. Generation of Hits and Leads 364. Lead optimization. 375. Clinical trials and commercialization. 37III.. In Silico Screening Methods 381. Structure-based screening methods 392. Ligand-based screening methods « ligand-based ». 39IV.. ADMET Filtering. 401. Administration and absorption. 402. Lipinski’s rules 403. Lead-like selection criteria. 424. Distribution. 435. Metabolism and excretion 436. Toxicology. 44V.... Quantitative Structure-Activity Relationship (QSAR) Study. 441. Introduction. 442. History. 443. Definition. 454. Principle. 46VI. 2D-QSAR.. 461. QSAR Tools and Methodology. 471.1. Biological parameters. 471.2. Molecular descriptors. 48a) Types of descriptors 482. Descriptor selection and reduction. 533. Data modeling methods 53a) Principal Component Analysis (PCA) 54b) Multiple Linear Regression. 54c) Nonlinear regression. 55d) Artificial Neural Networks (ANN) 554. Model validation tools 57a) R² coefficient 58b) Fisher’s test 58c) Variance Inflation Factor (VIF) 59d) Internal validation or cross-validation. 59e) Mean Square Error (MSE) 60f) Randomization test 61g) External validation. 61h) Applicability domain. 625. Creation of training and test datasets 636. Global strategy for QSAR studies 63VII. 3D-QSAR.. 651. Comparative Molecular Field Analysis (CoMFA) 652. Comparative Molecular Similarity Index Analysis (CoMSIA) 663. Structure alignment 664. Calculation of molecular interaction fields in both CoMFA and CoMSIA.. 665. Graphical visualization of models 686. Prediction and extrapolation. 69VIII. Molecular Docking. 691. Definition. 692. Docking Approches 702.1. Rigid Docking. 702.2. flexible Docking. 713. Ligand-receptor interaction. 713.1. Ionic bonds. 713.2. Hydrogen bonds. 713.3. π-π interactions. 723.4. Cation-π interactions. 723.5. π interactions. 723.6. Van der Waals interactions. 733.7. Hydrophobic effect 734. Molecular docking tools 734.1. Preparation and selection of receptors. 734.2. Main docking software. 745. Evaluation of docking methods 755.1. Re-docking. 755.2. Root Mean Square Deviation (RMSD) 75X. Conclusion. 78XI. References 79Experimental Part 84Chapter 03 : Study of the anticancer activity of heterocyclic organic molecules using the 2D QSAR method.. 85Application 1: QSAR Study of New Compounds Based on 1, 2, 4-triazole as Potential Anticancer Agents 86I..... Introduction. 88II.... Material and Methods 881. Experimental Data. 882. Calculation of molecular descriptors. 903. Statistical analysis 90III.. Results and Discussion. 921. Principal Components Analysis (PCA) 922. Multiple linear regressions (MLR) 963. Multiple nonlinear regressions (MNLR) 964. External validation. 975. Artificial Neural networks (ANN) 98IV.. Conclusion. 100V.... References 101Chapter 04 : Study of the anticancer activity of heterocyclic organic molecules using the 3D-QSAR and Molecular Docking methods 107Application 2 :3D-QSAR Study of the Chalcone Derivatives as Anti-cancer Agents 108I..... Introduction. 110II.... Materials and Methods 1111. Computer simulations 1112. Data set 1113. Molecular Modeling. 1134. Molecular Alignment 1135. CoMFA and CoMSIA studies 1146. Partial least square analysis 1147. Validation of the models 1148. Y-randomization test 1159. Model acceptability criteria. 11510. Lipinski’s Rule and ADMET Prediction. 115III.. Results and Discussion. 1161. CoMFA statistical results 1162. CoMSIA Statistical Results 1163. Analysis of CoMFA and CoMSIA contour maps 1193.1. CoMFA contour map. 1193.2. CoMSIA contour map. 1214. Y-randomization test 1235. Design for new chalcone as anticancer agents 1246. Lipinski’s Rule and ADMET Prediction. 126IV.. Conclusion. 127V.... References 130Application 3: In Silico Design of Novel Pyrazole derivatives containing thiourea skeleton as anti-cancer agents using: 3D QSAR, Drug-Likeness studies, ADMET Prediction and Molecular Docking. 131I..... Introduction. 133II.... Material and Methods 1341. Computer simulations 1341.1. Data set 1341.2. Molecular alignment 1372. CoMFA and CoMSIA studies 1383. Partial least square analysis 1384. Validation of the models 1395. Y-randomization test 1396. Model acceptability criteria. 1397. Drug Likeness and ADMET Prediction. 1408. Molecular Docking Study. 140III.. Results and discussion. 1401. Molecular alignment 1402. CoMFA statistical results 1413. CoMSIA Statistical Results 1414. Y‑randomization. 1435. Contour analysis 1445.1. CoMFA Contour map. 1445.2. CoMSIA Contour maps 1466. Design for new Pyrazole as anticancer agents 1487. Drug-likeness studies 1518. ADMET prediction. 1539. Molecular docking study. 154IV.. Conclusion. 156V.... References 157Application 4: Molecular Docking, Drug likeness Studies and ADMET prediction of Flavonoids as Platelet-Activating Factor (PAF) Receptor Binding. 161I..... Introuduction. 163II.... Material and Methods 1651. Data collection. 1651.1. Ligands 1651.2. Receptor 1652. Molecular Docking. 1663. Docking validation protocol 1674. Drug-likeness studies 1675. ADMET prediction. 167III.. Results and Discussion. 1681. Molecular Docking. 1682. Docking validation protocol 1723. Drug-likeness studies 172IV... Conclusion. 174V.... References 174General Conclusion. 175