EMERGING NEUROPROTECTIVE AND ANTI-INFLAMMATORY AGENTS: A REVIEW OF IN SILICO DESIGN AND PRECLINICAL DEVELOPMENT
Main Article Content
Keywords
In silico drug design, neuroprotection, neuroinflammation, molecular docking, QSAR, preclinical evaluation
Abstract
Background: Neuroinflammation is an important pathophysiological factor in the etiology of multiple neurodegenerative conditions such as Alzheimer, Parkinson, and multiple sclerosis. Anti-inflammatory properties of novel neuroprotective agents are promising in the context of their development. In silico drug design has proven as a cost effective and time saving approach to finding potential lead compounds.
Objective: The aim of the review is to critically analyze the current position of in silico methods to design neuroprotective compounds with anti-inflammatory properties followed by preclinical validation.
Methods: PubMed, Scopus, and Web of Science databases were searched to identify the literature published in 2018-2024. The key words were in silico drug design, neuroprotection, anti-inflammatory, molecular docking and QSAR.
Results: Recent computational drug design progress has enabled the identification of new scaffolds against neuroinflammation. Machine learning methods, molecular docking experiments, and QSAR simulation have played a significant role in forecasting neuroprotective potential of compounds. There are a number of favourable candidates which have demonstrated high activity in preclinical models.
Conclusion: Convincing potentials in the design of suitable neuroprotective drugs have been evident in in silico drug design methods. To enable successful translation to clinical applications, it is necessary to integrate various methods of computation with strong preclinical validation.
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3. Cummings J, Lee G, Ritter A, Sabbagh M, Zhong K. Alzheimer's disease drug development pipeline: 2019. Alzheimers Dement (N Y). 2019;5:272-293.
4. Glass CK, Saijo K, Winner B, Marchetto MC, Gage FH. Mechanisms underlying inflammation in neurodegeneration. Cell. 2010;140(6):918-934.
5. Block ML, Zecca L, Hong JS. Microglia-mediated neurotoxicity: uncovering the molecular mechanisms. Nat Rev Neurosci. 2007;8(1):57-69.
6. DiMasi JA, Grabowski HG, Hansen RW. Innovation in the pharmaceutical industry: new estimates of R&D costs. J Health Econ. 2016;47:20-33.
7. Mouchlis VD, Afantitis A, Serra A, et al. Advances in de novo drug design: from conventional to machine learning methods. Int J Mol Sci. 2021;22(4):1676.
8. Śledź P, Caflisch A. Protein structure-based drug design: from docking to molecular dynamics. Curr Opin Struct Biol. 2018;48:93-102.
9. DiSabato DJ, Quan N, Godbout JP. Neuroinflammation: the devil is in the details. J Neurochem. 2016;139 Suppl 2:136-153.
10. Li Q, Barres BA. Microglia and macrophages in brain homeostasis and disease. Nat Rev Immunol. 2018;18(4):225-242.
11. Hansen DV, Hanson JE, Sheng M. Microglia in Alzheimer's disease. J Cell Biol. 2018;217(2):459-472.
12. Kawai T, Akira S. The role of pattern-recognition receptors in innate immunity: update on Toll-like receptors. Nat Immunol. 2010;11(5):373-384.
13. Liu T, Zhang L, Joo D, Sun SC. NF-κB signaling in inflammation. Signal Transduct Target Ther. 2017;2:17023.
14. Consilvio C, Vincent AM, Feldman EL. Neuroinflammation, COX-2, and ALS--a dual role? Exp Neurol. 2004;187(1):1-10.
15. Choi SH, Aid S, Bosetti F. The distinct roles of cyclooxygenase-1 and -2 in neuroinflammation: implications for translational research. Trends Pharmacol Sci. 2009;30(4):174-181.
16. Brown GC. Nitric oxide and neuronal death. Nitric Oxide. 2010;23(3):153-165.
17. Calabrese V, Mancuso C, Calvani M, et al. Nitric oxide in the central nervous system: neuroprotection versus neurotoxicity. Nat Rev Neurosci. 2007;8(10):766-775.
18. Mattson MP, Camandola S. NF-κB in neuronal plasticity and neurodegenerative disorders. J Clin Invest. 2001;107(3):247-254.
19. Kaltschmidt B, Kaltschmidt C. NF-κB in the nervous system. Cold Spring Harb Perspect Biol. 2009;1(3):a001271.
20. Heneka MT, Kummer MP, Stutz A, et al. NLRP3 is activated in Alzheimer's disease and contributes to pathology in APP/PS1 mice. Nature. 2013;493(7434):674-678.
21. Swanson KV, Deng M, Ting JP. The NLRP3 inflammasome: molecular activation and regulation to therapeutics. Nat Rev Immunol. 2019;19(8):477-489.
22. Anderson AC. The process of structure-based drug design. Chem Biol. 2003;10(9):787-797.
23. Meng XY, Zhang HX, Mezei M, Cui M. Molecular docking: a powerful approach for structure-based drug discovery. Curr Comput Aided Drug Des. 2011;7(2):146-157.
24. Pinzi L, Rastelli G. Molecular docking: shifting paradigms in drug discovery. Int J Mol Sci. 2019;20(18):4331.
25. Ferreira LG, Dos Santos RN, Oliva G, Andricopulo AD. Molecular docking and structure-based drug design strategies. Molecules. 2015;20(7):13384-13421.
26. Güner OF. Pharmacophore perception, development, and use in drug design. La Jolla, CA: International University Line; 2000.
27. Yang SY. Pharmacophore modeling and applications in drug discovery: challenges and recent advances. Drug Discov Today. 2010;15(11-12):444-450.
28. Cherkasov A, Muratov EN, Fourches D, et al. QSAR modeling: where have you been? Where are you going to? J Med Chem. 2014;57(12):4977-5010.
29. Hansch C, Leo A. Exploring QSAR: fundamentals and applications in chemistry and biology. Washington, DC: American Chemical Society; 1995.
30. Ghasemi JB, Safavi-Sohi R, Barbosa EG. 3D-QSAR study of anti-inflammatory activity of dihydropyrimidines by CoMFA and CoMSIA. Med Chem Res. 2012;21:2788-2797.
31. Willett P. Similarity searching using 2D structural fingerprints. Methods Mol Biol. 2011;672:133-158.
32. Maggiora G, Vogt M, Stumpfe D, Bajorath J. Molecular similarity in medicinal chemistry. J Med Chem. 2014;57(8):3186-3204.
33. Chen H, Engkvist O, Wang Y, Olivecrona M, Blaschke T. The rise of deep learning in drug discovery. Drug Discov Today. 2018;23(6):1241-1250.
34. Wu Z, Ramsundar B, Feinberg EN, et al. MoleculeNet: a benchmark for molecular machine learning. Chem Sci. 2018;9(2):513-530.
35. Schenone M, Dančík V, Wagner BK, Clemons PA. Target identification and mechanism of action in chemical biology and drug discovery. Nat Chem Biol. 2013;9(4):232-240.
36. Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol. 2008;4(11):682-690.
37. Barabási AL, Gulbahce N, Loscalzo J. Network medicine: a network-based approach to human disease. Nat Rev Genet. 2011;12(1):56-68.
38. Yildirim MA, Goh KI, Cusick ME, Barabási AL, Vidal M. Drug-target network. Nat Biotechnol. 2007;25(10):1119-1126.
39. Khatri P, Sirota M, Butte AJ. Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput Biol. 2012;8(2):e1002375.
40. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27-30.
41. Keller TH, Pichota A, Yin Z. A practical view of 'druggability'. Curr Opin Chem Biol. 2006;10(4):357-361.
42. Le Guilloux V, Schmidtke P, Tuffery P. Fpocket: an open source platform for ligand pocket detection. BMC Bioinformatics. 2009;10:168.
43. Hajduk PJ, Huth JR, Fesik SW. Druggability indices for protein targets derived from NMR-based screening data. J Med Chem. 2005;48(7):2518-2525.
44. Hopkins AL, Groom CR. The druggable genome. Nat Rev Drug Discov. 2002;1(9):727-730.
45. Blobaum AL, Marnett LJ. Structural and functional basis of cyclooxygenase inhibition. J Med Chem. 2007;50(7):1425-1441.
46. Kalgutkar AS, Crews BC, Rowlinson SW, et al. Biochemically based design of cyclooxygenase-2 (COX-2) inhibitors: facile conversion of nonsteroidal antiinflammatory drugs to potent and highly selective COX-2 inhibitors. Proc Natl Acad Sci U S A. 2000;97(2):925-930.
47. Chen L, Wang Y, Zhang Q, et al. Design and synthesis of novel benzothiazole derivatives as selective COX-2 inhibitors for neuroprotection. J Med Chem. 2020;63(15):8471-8485.
48. Singh B, Kumar A, Malik SK, et al. Synthesis and evaluation of benzothiazole analogs as COX-2 selective inhibitors for the treatment of inflammation and pain. Eur J Med Chem. 2021;209:112907.
49. Swanson KV, Deng M, Ting JP. The NLRP3 inflammasome: molecular activation and regulation to therapeutics. Nat Rev Immunol. 2019;19(8):477-489.
50. Zahid A, Li B, Kombe AJK, Jin T, Tao J. Pharmacological inhibitors of the NLRP3 inflammasome. Front Immunol. 2019;10:2538.
51. Kumar S, Sharma B, Mehra V, et al. In silico identification and experimental validation of natural product inhibitors of NLRP3 inflammasome. J Biomol Struct Dyn. 2021;39(14):5235-5248.
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Sep 30, 2025
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Mahesh Suthar, Sonika Maheshwari, Dr. Nishkruti R. Mehta, & Dr. Pragnesh Patani. (2025). EMERGING NEUROPROTECTIVE AND ANTI-INFLAMMATORY AGENTS: A REVIEW OF IN SILICO DESIGN AND PRECLINICAL DEVELOPMENT. Journal of Population Therapeutics and Clinical Pharmacology, 32(8), 1258-1270. https://doi.org/10.53555/qt33dn73
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Author Biographies
Mahesh Suthar
Student, Khyati College of Pharmacy, Palodiya, Ahmedabad
Sonika Maheshwari
Assistant Professor, Department of Pharmacology, Khyati College of Pharmacy, Palodiya, Ahmedabad
Dr. Nishkruti R. Mehta
HOD, Department of Pharmacology, Khyati College of Pharmacy, Plodiya, Ahmedabad
Dr. Pragnesh Patani
Principal, Khyati College of Pharmacy, Palodiya, Ahmedabad
How to Cite
Mahesh Suthar, Sonika Maheshwari, Dr. Nishkruti R. Mehta, & Dr. Pragnesh Patani. (2025). EMERGING NEUROPROTECTIVE AND ANTI-INFLAMMATORY AGENTS: A REVIEW OF IN SILICO DESIGN AND PRECLINICAL DEVELOPMENT. Journal of Population Therapeutics and Clinical Pharmacology, 32(8), 1258-1270. https://doi.org/10.53555/qt33dn73