IN SILICO DESIGN AND PRECLINICAL EVALUATION OF NOVEL COMPOUNDS AS POTENTIAL NEUROPROTECTIVE AND ANTI-INFLAMMATORY AGENTS: A COMPREHENSIVE REVIEW
Main Article Content
Keywords
In silico, drug design, neuroprotection, anti-inflammation, molecular docking, preclinical, neuroinflammation
Abstract
Neuroinflammation and neurodegeneration are characteristic of different disorders of the central nervous system, such as Alzheimer disease, Parkinson disease, and multiple sclerosis. The generation of new therapeutic agents to act on both neuroprotective and anti-inflammatory pathways has become an encouraging approach to the treatment of these debilitating conditions. This review has given an in-depth analysis of the present state of in silico drug design strategies and preclinical evaluation techniques used in the process of discovering dual-acting neuroprotective and anti-inflammatory molecules. We will talk about such methods of computations as molecular docking, pharmacophore modeling, QSAR analysis, and molecular dynamics simulations, as well as the preclinical evaluation plans that include in vitro and in vivo models. Recent discoveries in artificial intelligence and machine learning in drug discovery are also mentioned. Combination of computation and experiment methods has helped in speeding up the discovery of lead compounds and some of these have potentials of clinical translation.
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8. Pardridge WM. The blood-brain barrier: bottleneck in brain drug development. NeuroRx. 2005;2(1):3-14.
9. van de Waterbeemd H, Gifford E. ADMET in silico modelling: towards prediction paradise? Nat Rev Drug Discov. 2003;2(3):192-204.
10. Morris GM, Lim-Wilby M. Molecular docking. Methods Mol Biol. 2008;443:365-382.
11. Huang HJ, Yu HW, Chen CY, Hsu CH, Chen HY, Lee KJ, et al. Current developments of computer-aided drug design. J Taiwan Inst Chem Eng. 2010;41(6):623-635.
12. Cheng F, Li W, Zhou Y, Shen J, Wu Z, Liu G, et al. admetSAR: a comprehensive source and free tool for assessment of chemical ADMET properties. J Chem Inf Model. 2012;52(11):3099-3105.
13. Kitchen DB, Decornez H, Furr JR, Bajorath J. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov. 2004;3(11):935-949.
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18. Langer T, Hoffmann RD. Pharmacophores and pharmacophore searches. Weinheim: Wiley-VCH; 2006.
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21. Ekins S, Mestres J, Testa B. In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling. Br J Pharmacol. 2007;152(1):9-20.
22. Acharya C, Coop A, Polli JE, Mackerell AD Jr. Recent advances in ligand-based drug design: relevance and utility of the conformationally sampled pharmacophore approach. Curr Comput Aided Drug Des. 2011;7(1):10-22.
23. Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M, et al. QSAR modeling: where have you been? Where are you going to? J Med Chem. 2014;57(12):4977-5010.
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Nancy Macwan, Rahul Khannar, Dr. Pragnesh Patani, & Dr. Nishkruti R. Mehta. (2025). IN SILICO DESIGN AND PRECLINICAL EVALUATION OF NOVEL COMPOUNDS AS POTENTIAL NEUROPROTECTIVE AND ANTI-INFLAMMATORY AGENTS: A COMPREHENSIVE REVIEW. Journal of Population Therapeutics and Clinical Pharmacology, 32(9), 37-50. https://doi.org/10.53555/79ajg819
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Author Biographies
Nancy Macwan
Student, Khyati College of Pharmacy, Palodiya, Ahmedabad
Rahul Khannar
Assitant Professor, Department of Pharmacology, Khyati College of Pharmacy, Palodiya, Ahmedabad
Dr. Pragnesh Patani
Principal, Khyati College of Pharmacy, Palodiya, Ahmedabad
Dr. Nishkruti R. Mehta
HOD, Department of Pharmacology, Khyati College of Pharmacy, Palodiya, Ahmedabad
How to Cite
Nancy Macwan, Rahul Khannar, Dr. Pragnesh Patani, & Dr. Nishkruti R. Mehta. (2025). IN SILICO DESIGN AND PRECLINICAL EVALUATION OF NOVEL COMPOUNDS AS POTENTIAL NEUROPROTECTIVE AND ANTI-INFLAMMATORY AGENTS: A COMPREHENSIVE REVIEW. Journal of Population Therapeutics and Clinical Pharmacology, 32(9), 37-50. https://doi.org/10.53555/79ajg819