The role of artificial intelligence in drug discovery: Accelerating the development of new medicines

https://doi.org/10.53730/ijhs.v2nS1.15018

Authors

  • Nawal Ganai M Bahari KSA, National Guard Health Affairs
  • Alhanouf Zaid Almutairi KSA, National Guard Health Affairs
  • Nawal Hlal Almutairi KSA, National Guard Health Affairs
  • Samar Saad Alotaibi KSA, National Guard Health Affairs
  • Faisal Nasser Alharbi KSA, National Guard Health Affairs
  • Manal Nasser Almasad KSA, National Guard Health Affairs

Keywords:

Drug Development, Pharmaceutical Industry, Clinical Trials, agricultural industry, Technology, Artificial Intelligence, Drug Discovery, Drug Administration, Drug Management

Abstract

Background _ Drug research and development (R&D) is a complex and lengthy process characterized by high costs, risks, and regulatory hurdles. It involves a multidisciplinary team of scientists, clinicians, and regulatory experts working collaboratively to bring innovative therapies to market. The process typically encompasses several stages, including target identification, drug discovery, preclinical and clinical testing, regulatory approval, and post-market surveillance. Aim of Work – This review aims to provide an overview of the drug development process, highlighting the critical role of technology and management in optimizing efficiency, reducing costs, and accelerating time-to-market. Methods – A comprehensive review of relevant literature was conducted to explore the various stages of drug development, challenges faced by the pharmaceutical industry, and the emerging role of technology in addressing these challenges. Results – Drug development is a high-stakes endeavor characterized by significant investment and uncertainty. Advancements in technology have transformed the drug discovery process, enabling researchers to identify potential drug targets more efficiently and screen vast chemical libraries rapidly. The integration of artificial intelligence and machine learning has accelerated drug design and optimization. Clinical trials, a crucial phase of drug development, have also benefited from technological advancements.

Downloads

Download data is not yet available.

References

Loscher W, Klitgaard H, Twyman RE, Schmidt D. New avenues for anti-epileptic drug discovery and development. Nat Rev Drug Discov. 2013;12:757–776.

Thabane L, Mbuagbaw L, Zhang S, Samaan Z, Marcucci M, Ye C, Thabane M, Giangregorio L, Dennis B, Kosa D, Debono VB. A tutorial on sensitivity analyses in clinical trials: the what, why, when and how. BMC medical research methodology. 2013 Dec;13:1-2.

Bassetto M, De Burghgraeve T, Delang L, Massarotti A, Coluccia A, Zonta N, Gatti V, Colombano G, Sorba G, Silvestri R, Tron GC. Computer-aided identification, design and synthesis of a novel series of compounds with selective antiviral activity against chikungunya virus. Antiviral research. 2013 Apr 1;98(1):12-8.

Kirchmair J, Göller AH, Lang D, Kunze J, Testa B, Wilson ID, Glen RC, Schneider G. Predicting drug metabolism: experiment and/or computation?. Nature reviews Drug discovery. 2015 Jun;14(6):387-404.

Sun W, Zheng W, Simeonov A. Drug discovery and development for rare genetic disorders. American Journal of Medical Genetics Part A. 2017 Sep;173(9):2307-22.

Huber W. A new strategy for improved secondary screening and lead optimization using high-resolution SPR characterization of compound-target interactions. J Mol Recognit. 2005;18:273–281.

Han YS, Penthala NR, Oliveira M, Mesplede T, Xu H, Quan Y, Crooks PA, Wainberg MA. Identification of resveratrol analogs as potent anti-dengue agents using a cell-based assay. J Med Virol. 2017;89:397–407.

Quan Y, Xu H, Han Y, Mesplede T, Wainberg MA. JAK-STAT signaling pathways and inhibitors affect reversion of envelope-mutated HIV-1. J Virol. 2017;91

Zhang L, Tan J, Han D, Zhu H. From machine learning to deep learning: progress in machine intelligence for rational drug discovery. Drug discovery today. 2017 Nov 1;22(11):1680-5.

Dhalluin C, Ross A, Huber W, Gerber P, Brugger D, Gsell B, Senn H. Structural, kinetic, and thermodynamic analysis of the binding of the 40 kDa PEG-interferon-alpha2a and its individual positional isomers to the extracellular domain of the receptor IFNAR2. Bioconjug Chem. 2005;16:518–527.

Ekins S. The next era: deep learning in pharmaceutical research. Pharmaceutical research. 2016 Nov;33(11):2594-603.

Wilson P, Aizenman YI. Value for money in malaria programming: issues and opportunities. Center for Global Development Working Paper. 2012 Apr 1(291).

Jaafarpour M, Hatefi M, Khani AL, Khajavikhan J. Comparative effect of cinnamon and Ibuprofen for treatment of primary dysmenorrhea: a randomized double-blind clinical trial. Journal of clinical and diagnostic research: JCDR. 2015 Apr;9(4):QC04.

Han Y, Mesplede T, Wainberg MA. Investigational HIV integrase inhibitors in phase I and phase II clinical trials. Expert Opin Investig Drugs. 2017;26:1207–1213.

Hanna NH, Kaiser R, Sullivan RN, Aren OR, Ahn MJ, Tiangco B, Voccia I, Pawel JV, Kovcin V, Agulnik J, Gaschler-Markefski B, Barrueco J, Sikken P, Schloss C, Kim JH LUME-Lung 2 Study group. Nintedanib plus pemetrexed versus placebo plus pemetrexed in patients with relapsed or refractory, advanced non-small cell lung cancer (LUME-Lung 2): a randomized, double-blind, phase III trial. Lung Cancer. 2016;102:65–73.

Zhang X, Zhang Y, Ye X, Guo X, Zhang T, He J. Overview of phase IV clinical trials for postmarket drug safety surveillance: a status report from the clinical trials. gov registry. BMJ Open. 2016;6:e010643.

Siah CJ, Yatim J. Efficacy of a total occlusive ionic silver-containing dressing combination in decreasing risk of surgical site infection: an RCT. journal of wound care. 2011 Dec;20(12):561-8.

Jin QY, Lian GY, Huang TK. Research on technological risk management of new drug. China Pharmaceuticals. 2006;15:14–15.

Yang ND, Zhang YL. On the establishment and function of enterprise risk manager (organization) in enterprises. Aeronautical Science and Technology. 1999;4:17–18.

Zhao YP, Lu F, Zi MJ, et al. Interpretation of “standards for quality management of drug clinical trials” Chin J New Drug. 2015;15:1747–1749

Shen WJ, Zhang KL, Wei J, Chen FJ. Reflection on the management of pre-clinical research laboratories in China. Chinese Pharmacy. 2011;12:1168–1170.

Wang CJ, Liu YP, Xu W, et al. Management of study drug in clinical trials. Chinese Journal of Clinical Pharmacology. 2016;9:858–860.

Shao Y. Expectation and history of scientific development on the drug clinical trials in China. Chin J Clin Pharmacol. 2008;2:180–186.

Zhang TX, Lu MY, Zhang CX, Chen WM. Key points of investigational product management in clinical trials. Chinese New Medicine and Clinical Journal. 2014;7:489–491.

Zhou Z, Jia JB, et al. Project management in the process of new drug clinical trials. Chinese Medical Biotechnology. 2007;4:314–316.

Allison G, Cain YT, Cooney C, Garcia T, Bizjak TG, Holte O, Jagota N, Komas B, Korakianiti E, Kourti D, Madurawe R, Morefield E, Montgomery F, Nasr M, Randolph W, Robert JL, Rudd D, Zezza D. Regulatory and quality considerations for continuous manufacturing. May 20-21, 2014 continuous manufacturing symposium. J Pharm Sci. 2015;104:803–812.

Yi YL. The role of HIS in the comprehensive management of the hospital. Journal of Medical Informatics. 2008;29:16–17.

Yadav MS, Shrivastav PS. Significance of ‘post bioanalysis phase’ in bioanalysis: is GMP creeping into GLP? Bioanalysis. 2015;7:15–20.

Liu L, Wen WS, et al. Phase IV clinical trial management system research and discussion. Chinese New Medicine Journal. 2010;17:1503–1507. [Google Scholar]

30. Yan M, Wu H, Li S. Phase IV clinical trial of new drug. Chinese Pharmacy. 2000;4:221–223.

Wang JN, Wu SB, Pan RH. Standardized management of phrase IV clinical trials. Acta Universitatis Medicinalis Nanjing (Social Science) 2016;16:506–514.

Xu YL, Ling Y, et al. Discussion on the key point of new drug research and development in China. Pharmaceutical and Clinical Research. 2011;19:78–80

Bright RA, Nelson PC. Automated support for pharmacovigilance: a proposed system. Pharmacoepidemiology Drug Safety. 2002;11:121–125.

Lavecchia A, Di Giovanni C. Virtual screening strategies in drug discovery: a critical review. Current medicinal chemistry. 2013 Aug 1;20(23):2839-60.

Smalley E. AI-powered drug discovery captures pharma interest. Nature Biotechnology. 2017 Jul 1;35(7):604-6.

Wright GD. Opportunities for natural products in 21 st century antibiotic discovery. Natural product reports. 2017;34(7):694-701.

Kim RS, Goossens N, Hoshida Y. Use of big data in drug development for precision medicine. Expert review of precision medicine and drug development. 2016 May 3;1(3):245-53.

Yan X, Ding P, et al. Big data in drugs design, Science China Press. 2015;60:558–565.

Tang Z, Ou X. Computer network planning and design of remote consultation system in hospital. Guangxi Medicine. 2008;9:1401–1402. [Google Scholar]

Published

15-01-2018

How to Cite

Bahari, N. G. M., Almutairi, A. Z., Almutairi, N. H., Alotaibi, S. S., Alharbi, F. N., & Almasad, M. N. (2018). The role of artificial intelligence in drug discovery: Accelerating the development of new medicines. International Journal of Health Sciences, 2(S1), 64–78. https://doi.org/10.53730/ijhs.v2nS1.15018

Issue

Section

Peer Review Articles

Most read articles by the same author(s)