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International Journal of Bioinformatics and Computational Biology Review Article

Pharma Tech: Leveraging Software for Drug Development & Clinical Research

  1. Vikash Yadav Department of Pharmacy, Prasad Institute of Technology, Jaunpur,
  2. Mohd. Wasiullah Department of Pharmacy, Prasad Institute of Technology, Jaunpur
  3. Piyush Yadav Department of Pharmacy, Prasad Institute of Technology, Jaunpur
  4. Rahul Nishad Department of Pharmacy, Prasad Institute of Technology, Jaunpur

Abstract

The pharmaceutical sector is progressively adopting software solutions to enhance the drug development process and optimize clinical research results. Drug development is a time-consuming, expensive, and intricate process that traditionally requires extensive laboratory research, preclinical testing, and several stages of clinical trials. Software tools are revolutionizing these stages by improving efficiency, minimizing errors, and speeding up timelines. During preclinical testing, predictive software tools are used to model toxicological effects and assess the safety profiles of drug candidates, reducing the risk of adverse outcomes in human trials. These tools contribute to making early stage testing more efficient and focused on viable candidates. In clinical research, software solutions play a critical role in trial design, patient recruitment, data collection, and monitoring. Clinical trial management systems (CTMS), electronic data capture (EDC) platforms, and data analytics tools facilitate real-time data collection and integration, simplifying the tracking of progress, identification of trends, and ensuring adherence to regulatory standards. The use of artificial intelligence (AI) and machine learning (ML) is becoming increasingly prevalent in clinical research, as these technologies allow for advanced data analysis and predictive modelling. AI and ML can uncover patterns in patient responses, predict drug outcomes, and optimize treatment plans, thus improving the precision of clinical trials. Software tools also support regulatory compliance, ensuring that clinical data is accurate, traceable, and consistent with health authority guidelines

Keywords

References (25)

  1. Bellanti F, Della Pasqua O. Modelling and simulation as research tools in paediatric drug development. European Journal of Clinical Pharmacology. 2011;67(S1):75-86. doi:10.1007/s00228-010-0974-3
  2. Mannan A, Mubeen H. DIGITALISATION AND AUTOMATION IN PHARMACEUTICALS FROM DRUG DISCOVERY TO DRUG ADMINISTRATION. International Journal of Pharmacy and Pharmaceutical Sciences. 2018;10(6):1. doi:10.22159/ijpps.2018v10i6.24757
  3. Patel JR, Joshi HV, A. Shah U, K. Patel J. A Review on Computational Software Tools for Drug Design and Discovery. Indo Global Journal of Pharmaceutical Sciences. 2022;12:53-81. doi:10.35652/igjps.2022.12006
  4. Jamkhande PG, Ghante MH, Ajgunde BR. Software based approaches for drug designing and development: A systematic review on commonly used software and its applications. Bulletin of Faculty of Pharmacy, Cairo University. 2017;55(2):203-210. doi:10.1016/j.bfopcu.2017.10.001
  5. Das Sarkar R. Pharma Software - A Complete Overview. International Journal of Science and Healthcare Research. 2023;8(2):166-177. doi:10.52403/ijshr.20230220
  6. Lincoln JE. Issues with medical device part 11 electronic records; electronic signatures. J Validation Technol. 2012;18(4):15.
  7. Upadhyay P. The Role of Verification and Validation in System Development Life Cycle. IOSR Journal of Computer Engineering. 2012;5(1):17-20. doi:10.9790/0661-0511720
  8. Alsayed AO, Bilgrami AL, Foster WA. Improving software quality management: Testing, review, inspection and walkthrough. Int J Latest Res Sci Technol. 2017;6(1):2278–5299. doi:10.29111/ijlrst.
  9. Pulley JM, Shirey-Rice JK, Lavieri RR, Jerome RN, Zaleski NM, Aronoff DM, et al. Accelerating Precision Drug Development and Drug Repurposing by Leveraging Human Genetics. ASSAY and Drug Development Technologies. 2017;15(3):113-119. doi:10.1089/adt.2016.772
  10. Izmailova ES, Maguire RP, McCarthy TJ, Müller MLTM, Murphy P, Stephenson D. Empowering drug development: Leveraging insights from imaging technologies to enable the advancement of digital health technologies. Clinical and Translational Science. 2022;16(3):383-397. doi:10.1111/cts.13461
  11. Palakurti NR. Evolving Drug Discovery: Artificial intelligence and machine learning’s impact in pharmaceutical research. ESP J Eng Technol Adv. 2023;3(3):136–147.
  12. Dockendorf MF, Murthy G, Bateman KP, Kothare PA, Anderson M, Xie I, et al. Leveraging Digital Health Technologies and Outpatient Sampling in Clinical Drug Development: A Phase I Exploratory Study. Clinical Pharmacology & Therapeutics. 2018;105(1):168-176. doi:10.1002/cpt.1142
  13. Berger ML, Lipset C, Gutteridge A, Axelsen K, Subedi P, Madigan D. Optimizing the Leveraging of Real-World Data to Improve the Development and Use of Medicines. Value in Health. 2015;18(1):127-130. doi:10.1016/j.jval.2014.10.009
  14. Maass KF, Barfield MD, Ito M, James CA, Kavetska O, Kozinn M, et al. Leveraging patient‐centric sampling for clinical drug development and decentralized clinical trials: Promise to reality. Clinical and Translational Science. 2022;15(12):2785-2795. doi:10.1111/cts.13411
  15. Harrer S, Menard J, Rivers M, Green DV, Karpiak J, Jeliazkov JR, Shapovalov MV, del Alamo D, Sternke MC. Artificial intelligence drives the digital transformation of pharma. In: Artificial intelligence in clinical practice. Academic Press; 2024. pp. 345–372.
  16. Chen B, Butte A. Leveraging big data to transform target selection and drug discovery. Clinical Pharmacology & Therapeutics. 2016;99(3):285-297. doi:10.1002/cpt.318
  17. Arora P, Behera M, Saraf SA, Shukla R. Leveraging Artificial Intelligence for Synergies in Drug Discovery: From Computers to Clinics. Current Pharmaceutical Design. 2024;30(28):2187-2205. doi:10.2174/0113816128308066240529121148
  18. Ravishankar N. IT applications for healthcare: leverage processes for high quality. Improve Care Deliv Technol. 2008:47.
  19. Orloff J, Douglas F, Pinheiro J, Levinson S, Branson M, Chaturvedi P, et al. The future of drug development: advancing clinical trial design. Nature Reviews Drug Discovery. 2009;8(12):949-957. doi:10.1038/nrd3025
  20. Collins JM, Grieshaber CK, Chabner BA. Pharmacologically Guided Phase I Clinical Trials Based Upon Preclinical Drug Development. JNCI Journal of the National Cancer Institute. 1990;82(16):1321-1326. doi:10.1093/jnci/82.16.1321
  21. Mohs RC, Greig NH. Drug discovery and development: Role of basic biological research. Alzheimer's & Dementia: Translational Research & Clinical Interventions. 2017;3(4):651-657. doi:10.1016/j.trci.2017.10.005
  22. Eisenhauer EA, O’Dwyer PJ, Christian M, Humphrey JS. Phase I Clinical Trial Design in Cancer Drug Development. Journal of Clinical Oncology. 2000;18(3):684-684. doi:10.1200/jco.2000.18.3.684
  23. Sun D, Gao W, Hu H, Zhou S. Why 90% of clinical drug development fails and how to improve it? Acta Pharmaceutica Sinica B. 2022;12(7):3049-3062. doi:10.1016/j.apsb.2022.02.002
  24. Katara P. Role of bioinformatics and pharmacogenomics in drug discovery and development process. Network Modeling Analysis in Health Informatics and Bioinformatics. 2013;2(4):225-230. doi:10.1007/s13721-013-0039-5
  25. Mangialasche F, Solomon A, Winblad B, Mecocci P, Kivipelto M. Alzheimer's disease: clinical trials and drug development. The Lancet Neurology. 2010;9(7):702-716. doi:10.1016/s1474-4422(10)70119-8