Techniques and Tools for in Silico Drug Design for The Development of Anticancer Drugs
Pharmaceutical Science-Pharmacoinformatics
DOI:
https://doi.org/10.22376/ijlpr.2023.13.5.P130-P148Keywords:
In Silico, Cancer, Drug design, Docking, Pharmacophore, Quantitative Structure-Activity Relationship, and Virtual screening.Abstract
This review focuses on different techniques used in the in-silico drug design, such as molecular modeling, molecular docking, pharmacophore mapping, QSAR, and more, and also highlights and looks at how these techniques are being used to create new potential anticancer drugs for their effective cancer treatments. Most of the article studies focus on In-silico approaches only but rarely on the In-silico approach used to develop anticancer drugs with effective targets. Cancer, which is caused by pathophysiological changes in the normal process of cell division, has become a serious disorder that kills a lot of people every year all over the world. Recently, more than 19.3 million (19,300,000) new instances of cancer were identified and reported; based on the available data, this will result in almost 10 million fatalities in 2020. The necessity and desire for powerful medications to treat various malignancies have been sparked by the persistently rising occurrences of cancer worldwide, resulting in millions of deaths each year. Developing new anticancer drugs is a high priority for researchers and medical professionals, and designing these anticancer drugs is challenging, expensive, and time-consuming. In-silico drug design, also known as computer-aided drug discovery/design (CADD) approaches, have been created to get around these restrictions and manage massive amounts of emerging data. It is possible to use computational tools to aid in the design of experiments and, more crucially, to clarify the links between structure and activity that underlie drug discovery and lead optimization techniques. To design effective new drugs, one should understand the molecular processes that cause cancer on the molecular level. In silico drug design is a powerful tool for understanding these molecular processes and developing new and effective anticancer drugs. Keywords: , , , , , Quantitative Structure-Activity Relationship, and Virtual screening.
References
Basith S, Cui M, Macalino SJY, Choi S. Expediting the design, discovery, and development of anticancer drugs using computational approaches. Curr Med Chem. 2017;24(42):4753-78. doi: 10.2174/0929867323666160902160535, PMID 27593958.
Sun J, Wei Q, Zhou Y, Wang J, Liu Q, Xu H. A systematic analysis of FDA-approved anticancer drugs. BMC Syst Biol. 2017 Oct 3;11;Suppl 5:87. doi: 10.1186/s12918-017-0464-7, PMID 28984210.
Pacheco C, Baião A, Ding T, Cui W, Sarmento B. Recent advances in long-acting drug delivery systems for an anticancer drug. Adv Drug Deliv Rev. 2023;194(Mar):114724. doi: 10.1016/j.addr.2023.114724, PMID 36746307.
Olgen S. Overview on anticancer drug design and development. Curr Med Chem. 2018;25(15):1704-19. doi: 10.2174/0929867325666171129215610, PMID 29189124.
Wang L, Song Y, Wang H, Zhang X, Wang M, He J, Li S, Zhang L, Li K, Cao L. Advances of Artificial Intelligence in Anti-Cancer Drug Design: A Review of the Past Decade. Pharmaceuticals. 2023 Feb 7;16(2):253.
Kumar B, Singh S, Skvortsova I, Kumar V. Promising targets in anti-cancer drug development: recent updates. Curr Med Chem. 2017;24(42):4729-52. doi: 10.2174/0929867324666170331123648, PMID 28393696.
Cui W, Aouidate A, Wang S, Yu Q, Li Y, Yuan S. Discovering anti-cancer drugs via computational methods. Frontiers in pharmacology. 2020 May 20;11:733.
Gagic Z, Ruzic D, Djokovic N, Djikic T, Nikolic K. In silico Methods for Design of Kinase Inhibitors as Anticancer Drugs. Front Chem. 2019;7:873. doi: 10.3389/fchem.2019.00873, PMID 31970149.
Raval K, Ganatra T. Basics, types and applications of molecular docking: a review. IJCAAP. 2022;7(1):12-6. doi: 10.18231/j.ijcaap.2022.003.
Wang X, Song K, Li L, Chen L. Structure-based drug design strategies and challenges. Curr Top Med Chem. 2018;18(12):998-1006. doi: 10.2174/1568026618666180813152921, PMID 30101712.
Ismail FMD, Nahar L, Sarker SD, Chapter 6. High-throughput screening of phytochemicals: application of computational methods. In: Sarker SD, Nahar L, editors. Computational. Phytochemistry Elsevier; 2018. p. 165-92.
Song CM, Lim SJ, Tong JC. Recent advances in computer-aided drug design. Briefings in bioinformatics. 2009 Sep 1;10(5):579-91.
Lu D-Y, Lu T-R. Anticancer drug development, challenge and dilemma. Nurse Care Open Access J. 2020;7(3):72-5. doi: 10.15406/ncoaj.2020.07.00222.
Dr. Da-Yong Lu, Dr. Bin Xu and Dr. Ting-Ren Lu. Anticancer drug development, pharmacology updating. EC pharmacology and toxicology SI.02., 2020: 01-6.
Surabhi SBK, Singh B. Computer-aided drug design: an overview. J Drug Deliv Ther. 2018;8(5):504-9. doi: 10.22270/jddt.v8i5.1894.
Ece A. Computer-aided drug design. BMC Chem. 2023;17(1):26. doi: 10.1186/s13065-023-00939-w, PMID 36964610.
Zajac M, Muszalska I, Jelinska A. New molecular targets of anticancer therapy - current status and perspectives. Curr Med Chem. 2016;23(37):4176-220. doi: 10.2174/0929867323666160814002150, PMID 27528054.
Kumar R, Lal N. PET in anti-cancer drug development and therapy. Recent Patents on Anti-Cancer Drug Discovery. 2007 Nov 1;2(3):259-63..
Kinch MS. An analysis of FDA-approved drugs for oncology. Drug discovery today. 2014 Dec 1;19(12):1831-5..
Jarada TN, Rokne JG, Alhajj R. A review of computational drug repositioning: strategies, approaches, opportunities, challenges, and directions. J Cheminform. 2020;12(1):46. doi: 10.1186/s13321-020-00450-7, PMID 33431024.
Oprea TI, Overington JP. Computational and practical aspects of drug repositioning. Assay Drug Dev Technol. 2015 Jul-Aug;13(6):299-306. doi: 10.1089/adt.2015.29011.tiodrrr, PMID 26241209.
Shaker B, Ahmad S, Lee J, Jung C, Na D. In silico methods and tools for drug discovery. Comput Biol Med. 2021 Oct;137:104851. doi: 10.1016/j.compbiomed.2021.104851, PMID 34520990.
Makrynitsa GI, Lykouras M, Spyroulias GA, Matsoukas M-T. In silico Drug Design. In: eLS. Chichester: John Wiley & Sons, Ltd; Aug 2018. doi: 10.1002/9780470015902.a0028112.
Lu W, Zhang R, Jiang H, Zhang H, Luo C. Computer-aided drug design in epigenetics. Front Chem. 2018;6:57. doi: 10.3389/fchem.2018.00057, PMID 29594101.
Muhammed MT, Aki-Yalcin E. Homology modeling in drug discovery: overview, current applications, and future perspectives. Chem Biol Drug Des. 2019;93(1):12-20. doi: 10.1111/CBD.13388, PMID 30187647.
Dar AM, Mir S. Molecular docking: approaches, types, applications, and basic challenges. J Anal Bioanal Tech. 2017;08(2):356. doi: 10.4172/2155-9872.1000356.
Zhou Y, Di B, Niu MM. Structure-based pharmacophore design and virtual screening for novel tubulin inhibitors with potential anticancer activity. Molecules. 2019 Sep 1;24(17):3181:1-10. doi: 10.3390/molecules24173181, PMID 31480625.
Meshram RJ, Baladhye VB, Gacche RN, Karale BK, Gaikar RB. Pharmacophore mapping approach for drug target identification: A chemical synthesis and in silico study on novel thiadiazole compounds. J Clin Diagn Res. 2017 May;11(5):KF01-8. doi: 10.7860/JCDR/2017/22761.9925, PMID 28658808.
Prada-Gracia D, Huerta-Yépez S, Moreno-Vargas LM. Application of computational methods for anticancer drug discovery, design, and optimization. Bol Med Hosp Infant Mex. 2016;73(6):411-23. doi: 10.1016/j.bmhimx.2016.10.006, PMID 29421286.
Kim E, Yang C. Application of quantitative structure-activity relationship models for virtual screening drug libraries: an overview of state-of-the-art methodologies and strategies. Drug Des Rev. 2020;6(2):75-88.
Thafar M, Raies AB, Albaradei S, Essack M, Bajic VB. Comparison study of computational prediction tools for drug-target binding affinities. Frontiers in chemistry. 2019 Nov 20;7:782..
Sharan S, Swaminathan PN. Exploring new avenues using pharmacophore mapping. Int J Pharm Sci Res. 2020;11(11):5270-9.
Yadav M, Eswari JS. Modern paradigm towards potential target identification for antiviral (SARS-nCoV-2) and anticancer lipopeptides: A pharmacophore-based approach. Avicenna J Med Biotechnol. 2022 Jan-Mar;14(1):70-8. doi: 10.18502/ajmb.v14i1.8172, PMID 35509362.
Villoutreix BO, Bismuth E, Corpet F. Molecular docking studies in drug discovery. Nat Rev Drug Discov. 2000;1(1):51-60.
Prabhakar A, Lee SK. Molecular docking techniques: fundamentals and recent advances in drug discovery. Asian J Pharm Clin Res. 2015;8(6):136-43.
Rane M, Pateriya A, Makwana H, Pradhan A. A comprehensive review on Molecular Docking Approach for Drug Discovery Process. Int J Res Technol. 2017;6(7, Jul):111-8.
Prieto-Martínez FD, Arciniega M, Medina-Franco JL. Molecular docking: current advances and challenges. TIP. Revista especializada en ciencias químico-biológicas. 2018;21.
Bell N. An overview of pharmacophore modeling and computer-aided drug design in lead identification for cancer therapy. World J Clin Oncol. 2011;2(2):33-40.
Varma DA, Singh M, Wakode S, Dinesh NE, Vinaik S, Asthana S, Tiwari M. Structure-based pharmacophore mapping and virtual screening of natural products to identify polypharmacological inhibitor against c-MET/EGFR/VEGFR-2. Journal of Biomolecular Structure and Dynamics. 2023 May 3;41(7):2956-70.
Agarwal S, Mishra M, Mishra VK, Vinod D, Verma E, Kashaw SK. In-silico pharmacophore mapping and docking studies of indole/benzoximidazole-5-carboximidine derivatives as anti-cancer agents. International journal of pharmaceutical sciences and research. 2016 Aug 1;7(8).
Khedkar SA, Malde AK, Coutinho EC, Srivastava S. Pharmacophore modeling in drug discovery and development: an overview. Med Chem. 2007 Mar;3(2):187-97. doi: 10.2174/157340607780059521, PMID 17348856.
Zeng X, Dong W, Gong H. QSAR modeling: A novel tool for drug discovery against cancers. Comp Struct Biotechnol J. 2015;13(1):85-90.
Subramani AK, Sivaperuman A, Natarajan R, Bhandare RR, Shaik AB. QSAR and molecular docking studies of pyrimidine-coumarin-triazole conjugates as prospective anti-breast cancer agents. Molecules. 2022 Mar 11;27(6):1845. doi: 10.3390/molecules27061845, PMID 35335208.
Perkins R, Fang H, Tong W, Welsh WJ. Quantitative structure-activity relationship methods: perspectives on drug discovery and toxicology. Environ Toxicol Chem. 2003 Aug;22(8):1666-79. doi: 10.1897/01-171, PMID 12924569.
Speck-Planche A, Kleandrova VV, Luan F, Cordeiro MN. Rational drug design for anti-cancer chemotherapy: multi-target QSAR models for the in silico discovery of anti-colorectal cancer agents. Bioorg Med Chem. 2012;20(15):4848-55. doi: 10.1016/j.bmc.2012.05.071, PMID 22750007.
Zhao L, Teng Q, Liu Y, Chen H, Chong W, Du F, Xiao K, Sang Y, Ma C, Cui J, Shang L. Machine learning-based identification of a novel prognosis-related long noncoding RNA signature for gastric cancer. Frontiers in Cell and Developmental Biology. 2022 Nov 11;10:1017767.
Storchi L. Computational tools used in modern medicinal chemistry for drug discovery. Curr Top Med Chem. 2018;18(4):441-68.
Gupta SP. Quantitative structure-activity relationship studies on anticancer drugs. Chemical reviews. 1994 Sep;94(6):1507-51.
Maruca A, Ambrosio FA, Lupia A, Romeo I, Rocca R, Moraca F, Talarico C, Bagetta D, Catalano R, Costa G, Artese A. Computer-based techniques for lead identification and optimization I: Basics. Physical Sciences Reviews. 2019 Jan 11;4(6):20180113.
Vaishnaw A. Multi-targeted anticancer drugs: Design strategies and recent development. International Journal of Green Pharmacy (IJGP). 2022;16(4).
Lu DY, Xu B, Lu TR. Anticancer Drug Development, Pharmacology Updating. EC Pharmacology and Toxicology SI.02., 2020: 1-6.
Lu DY, Xu B, Ding J. Antitumor effects of two bisdioxopiperazines against two experimental lung cancer models in vivo. BMC Pharmacol. 2004: 24;4:32. doi: 10.1186/1471-2210-4-32, PMID 15617579.
Belete TM. Recent updates on the development of deuterium-containing drugs for the treatment of cancer. Drug design, development and therapy. 2022 Jan 1:3465-72.
Xiao Z, Morris‐Natschke SL, Lee KH. Strategies for the optimization of natural leads to anticancer drugs or drug candidates. Medicinal research reviews. 2016 Jan;36(1):32-91..
Fu RG, Sun Y, Sheng WB, Liao DF. Designing multi-targeted agents: an emerging anticancer drug discovery paradigm. Eur J Med Chem. 2017 Aug 18;136:195-211. doi: 10.1016/j.ejmech.2017.05.016, PMID 28494256.
Talevi A. Multi-target pharmacology: possibilities and limitations of the skeleton key approach from a medicinal chemist perspective. Front Pharmacol. 2015;6205:1-7.
Pradhan T, Gupta O, Singh G, Monga V. Aurora kinase inhibitors as potential anticancer agents: Recent advances. European Journal of Medicinal Chemistry. 2021 Oct 5;221:113495..
Shim JS, Liu JO. Recent advances in drug repositioning for the discovery of new anticancer drugs. Int J Biol Sci. 2014;10(7):654-63. doi: 10.7150/ijbs.9224, PMID 25013375.
Tian W, Chen C, Lei X, Zhao J, Liang J. CASTp 3.0: computed atlas of surface topography of proteins. Nucleic Acids Res. Jul 2 2018;46(W1):W363-7. doi: 10.1093/nar/gky473, PMID 29860391.
Laurie ATR, Jackson RM. Q-SiteFinder: an energy-based method for the prediction of protein–ligand binding sites. Bioinformatics. 2005;21(9, May):1908-16. doi: 10.1093/bioinformatics/bti315, PMID 15701681.
Qin S, Zhou HX. meta-PPISP: a meta web server for protein-protein interaction site prediction. Bioinformatics. 2007;23(24):3386-7. doi: 10.1093/bioinformatics/btm434, PMID 17895276.
McGreig JE, Uri H, Antczak M, Sternberg MJE, Michaelis M, Wass MN. 3DLigandSite: structure-based prediction of protein-ligand binding sites. Nucleic Acids Res. 2022 Apr 12;50(W1):W13-20. doi: 10.1093/nar/gkac250, PMID 35412635.
van Hasselt JG, van Eijkelenburg NK, Beijnen JH, Schellens JH, Huitema AD. Optimizing drug development of anti‐cancer drugs in children using modelling and simulation. British Journal of Clinical Pharmacology. 2013 Jul;76(1):30-47..
Bajorath J, editor. Chemoinformatics: concepts, methods, and tools for drug discovery. Springer Science & Business Media; 2008 Feb 4..
Sim EH, Yang IA, Wood-Baker R, Bowman RV, Fong KM. Gefitinib for advanced non-small cell lung cancer. Cochrane Database Syst Rev. 2018 Jan 16;1(1):CD006847. doi: 10.1002/14651858.CD006847.pub2, PMID 29336009.
Chelli SM, Gupta P, Belliraj SK. An in silico design of bioavailability for kinase inhibitors evaluating the mechanistic rationale in the CYP metabolism of erlotinib. J Mol Model. 2019 Feb 14;25(3):65. doi: 10.1007/s00894-018-3917-z, PMID 30762124.
Liu T-P, Hong YH, Yang PM. In silico and in vitro identification of inhibitory activities of sorafenib on histone deacetylases in hepatocellular carcinoma cells. Oncotarget. 2017 Sep 16;8(49):86168-80. doi: 10.18632/oncotarget.21030, PMID 29156785.
Medina PJ, Goodin S. Lapatinib: a dual inhibitor of human epidermal growth factor receptor tyrosine kinases. Clin Ther. 2008 Aug;30(8):1426-47. doi: 10.1016/j.clinthera.2008.08.008, PMID 18803986.
Hoy SM. Abiraterone acetate: a review of its use in patients with metastatic castration-resistant prostate cancer. Drugs. 2013 Dec;73(18):2077-91. doi: 10.1007/s40265-013-0150-z, PMID 24271422.
Noha SM, Atanasov AG, Schuster D, Markt P, Fakhrudin N, Heiss EH, Schrammel O, Rollinger JM, Stuppner H, Dirsch VM, Wolber G. Discovery of a novel IKK-β inhibitor by ligand-based virtual screening techniques. Bioorganic & medicinal chemistry letters. 2011 Jan 1;21(1):577-83.
Published
How to Cite
Issue
Section
Copyright (c) 2023 K. Masilamani, B. Senthilnathan, M. Manoyogambiga, R. Gowri, M. Vigneshwar, R. Sathiyasundar, K. Rajaganapathy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.