Pharma Tech: Leveraging software for drug development & clinical research

Notice

This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2025 | Volume :03 | Issue : 01 | Page : –
    By

    Vikash Yadav,

  • Mohd. Wasiullah,

  • Piyush Yadav,

  • Rahul Nishad,

  1. Assistant Professor, Department of Pharmacy, Prasad Institute of Technology, Jaunpur,, Uttar Pradesh, India
  2. Principal, Department of Pharmacy, Prasad Institute of Technology, Jaunpur, Uttar Pradesh, India
  3. Academic Head, Department of Pharmacy, Prasad Institute of Technology, Jaunpur, Uttar Pradesh, India
  4. Scholar, Department of Pharmacy, Prasad Institute of Technology, Jaunpur, Uttar Pradesh, India

Abstract

document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_abs_159351’);});Edit Abstract & Keyword

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: CTMS, Artificial Intelligence, Electronic Data Capture, Machine Learning

[This article belongs to International Journal of Bioinformatics and Computational Biology (ijbcb)]

How to cite this article:
Vikash Yadav, Mohd. Wasiullah, Piyush Yadav, Rahul Nishad. Pharma Tech: Leveraging software for drug development & clinical research. International Journal of Bioinformatics and Computational Biology. 2025; 03(01):-.
How to cite this URL:
Vikash Yadav, Mohd. Wasiullah, Piyush Yadav, Rahul Nishad. Pharma Tech: Leveraging software for drug development & clinical research. International Journal of Bioinformatics and Computational Biology. 2025; 03(01):-. Available from: https://journals.stmjournals.com/ijbcb/article=2025/view=0

document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_ref_159351’);});Edit

References

  1. Bellanti, Francesco, and Oscar Della Pasqua. “Modelling and simulation as research tools in paediatric drug development.” European journal of clinical pharmacology 67 (2011): 75-86.
  2. Mannan AB, Mubeen HA. Digitalisation and automation in pharmaceuticals from drug discovery to Drug Administration. International Journal of Pharmacy and Pharmaceutical Sciences. 2018;10(6):1.
  3. Patel JR, Joshi HV, Shah UA, Patel JK. A review on computational software tools for drug design and discovery. Indo Global Journal of Pharmaceutical Sciences. 2022 Jan 29;12:53-81.
  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 Dec 1;55(2):203-10.
  5. Sarkar RD. Pharma Software–A Complete Overview. International Journal of Science and Healthcare Research. 2023;8:166-77.
  6. Lincoln JE. Issues with medical device part 11 electronic records; electronic signatures. Journal of Validation Technology. 2012 Oct 1;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.
  8. Alsayed AO, Bilgrami AL, Foster WA. Improving software quality management: testing, review, inspection and walkthrough. International Journal of Latest Research in Science and Technology. 2017;6(1):1-2.
  9. Pulley JM, Shirey-Rice JK, Lavieri RR, Jerome RN, Zaleski NM, Aronoff DM, Bastarache L, Niu X, Holroyd KJ, Roden DM, Skaar EP. Accelerating precision drug development and drug repurposing by leveraging human genetics. Assay and drug development technologies. 2017 Apr 1;15(3):113-9.
  10. Izmailova ES, Maguire RP, McCarthy TJ, Müller ML, Murphy P, Stephenson D. Empowering drug development: Leveraging insights from imaging technologies to enable the advancement of digital health technologies. Clinical and translational science. 2023 Mar;16(3):383-97.
  11. Palakurti NR. Evolving Drug Discovery: Artificial Intelligence and Machine Learning’s Impact in Pharmaceutical Research. ESP Journal of Engineering & Technology Advancements. 2023;3(3):136-47.
  12. Dockendorf MF, Murthy G, Bateman KP, Kothare PA, Anderson M, Xie I, Sachs JR, Burlage R, Goldman A, Moyer M, Shah JK. Leveraging digital health technologies and outpatient sampling in clinical drug development: a phase I exploratory study. Clinical Pharmacology & Therapeutics. 2019 Jan;105(1):168-76.
  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 Jan 1;18(1):127-30.
  14. Maass KF, Barfield MD, Ito M, James CA, Kavetska O, Kozinn M, Kumar P, Lepak M, Leuthold LA, Li W, Mikhailov D. Leveraging patient‐centric sampling for clinical drug development and decentralized clinical trials: promise to reality. Clinical and Translational Science. 2022 Dec;15(12):2785-95.
  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. InArtificial intelligence in clinical practice 2024 Jan 1 (pp. 345-372). Academic Press.
  16. Chen B, Butte A. Leveraging big data to transform target selection and drug discovery. Clinical Pharmacology & Therapeutics. 2016 Mar;99(3):285-97.
  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 Aug 1;30(28):2187-205.
  18. Ravishankar N. IT Applications for Healthcare: Leverage Processes for High Quality. Improve Care Delivery with Technology. 2008 Sep:47.
  19. Orloff J, Douglas F, Pinheiro J, Levinson S, Branson M, Chaturvedi P, Ette E, Gallo P, Hirsch G, Mehta C, Patel N. The future of drug development: advancing clinical trial design. Nature reviews Drug discovery. 2009 Dec;8(12):949-57.
  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 Aug 15;82(16):1321-6.
  21. Mohs RC, Greig NH. Drug discovery and development: Role of basic biological research. Alzheimer’s & Dementia: Translational Research & Clinical Interventions. 2017 Nov 1;3(4):651-7.
  22. Eisenhauer EA, O’dwyer PJ, Christian M, Humphrey JS. Phase I clinical trial design in cancer drug development. Journal of Clinical Oncology. 2000 Feb 1;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 Jul 1;12(7):3049-62.
  24. Katara P. Role of bioinformatics and pharmacogenomics in drug discovery and development process. Network Modeling Analysis in Health Informatics and Bioinformatics. 2013 Dec;2:225-30.
  25. Mangialasche F, Solomon A, Winblad B, Mecocci P, Kivipelto M. Alzheimer’s disease: clinical trials and drug development. The Lancet Neurology. 2010 Jul 1;9(7):702-16.
Regular Issue Subscription Review Article
Volume 03
Issue 01
Received 14/12/2024
Accepted 07/01/2025
Published 07/02/2025
Total Visits: 34