Vol. 4 No. 1 (2024): Journal of Machine Learning in Pharmaceutical Research
Articles

Enhancing Pharmaceutical Manufacturing Facilities through Artificial Intelligence: A Comprehensive Review

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  • Vicrumnaug Vuppalapaty
Vicrumnaug Vuppalapaty
Technical Architect, CodeScience Inc. USA
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Published 22-03-2024

Keywords

  • Pharmaceutical manufacturing,
  • artificial intelligence,
  • AI,
  • efficiency,
  • quality control,
  • predictive maintenance,
  • process optimization,
  • drug discovery,
  • regulatory compliance,
  • innovation
    • ...More
    Less
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
V. Vuppalapaty, “Enhancing Pharmaceutical Manufacturing Facilities through Artificial Intelligence: A Comprehensive Review ”, Journal of Machine Learning in Pharmaceutical Research, vol. 4, no. 1, pp. 94–109, Mar. 2024, Accessed: Jan. 03, 2025. [Online]. Available: https://pharmapub.org/index.php/jmlpr/article/view/30

Abstract

The pharmaceutical manufacturing industry operates in a challenging environment, guided by continuous improvement, characterized by high quality standards and regulatory compliance. While the industry faces some fundamental obstacles like increasing competition, rising R&D (research and development) costs, and changing dynamics of business; AI, a disruptive yet efficient option- can drive the manufacturing processes by way of enhancing efficiency, quality, and safety all-around. This article provides a holistic view of the profound AI influence in pharmaceutical manufacturing with five major applications: predictive maintenance, process optimization, quality control, drug discovery, and regulatory compliance. Using R&D data and industry statistics, this review considers how AI applications, e.g., machine learning, robotics, and computer vision, can change existing processes and bring about innovations in the pharmaceutical field. Encounters with issues such as data quality, explainability, and regulatory compliance are ways through which AI can be overcome to also reveal all it is capable of. The results show that AI integration in pharmaceutical generation is capable of providing production efficiency and drug quality improvement and speed up the procedure of drug development and global patient' care on a professional level.

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References

  1. Always, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Abdulrahman Alshaya, Almohareb, S. N., Atheer Aldairem, Alrashed, M., Khalid Bin Saleh, Badreldin, H. A., Yami, A., Shmeylan Al Harbi, & Albekairy, A. M. (2023). Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Medical Education, 23(1). https://doi.org/10.1186/s12909-023-04698-z
  2. Ka, K., & Zhang, P. C. (2023). Advancing medical affairs capabilities and insight generation through machine learning techniques. Journal of Pharmaceutical Policy and Practice, 16(1). https://doi.org/10.1186/s40545-023-00670-w
  3. Kalyane, D., Sanap, G., Paul, D., Shenoy, S., Anup, N., Polaka, S., Tambe, V., & Tekade, R. K. (2020). Artificial intelligence in the pharmaceutical sector: current scene and prospect. The Future of Pharmaceutical Product Development and Research, 28(4), 73–107. https://doi.org/10.1016/b978-0-12-814455-8.00003-7
  4. Kassekert, R., Grabowski, N., Lorenz, D., Schaffer, C., Kempf, D., Roy, P., Kjoersvik, O., Saldana, G., & ElShal, S. (2022). Industry Perspective on Artificial Intelligence/Machine Learning in Pharmacovigilance. Drug Safety, 45(5), 439–448. https://doi.org/10.1007/s40264-022-01164-5
  5. Kolluri, S., Lin, J., Liu, R., Zhang, Y., & Zhang, W. (2022). Machine Learning and Artificial Intelligence in Pharmaceutical Research and Development: a Review. The AAPS Journal, 24(1). https://doi.org/10.1208/s12248-021-00644-3
  6. Kulkov, I. (2021). The role of artificial intelligence in business transformation: A case of pharmaceutical companies. Technology in Society, 66(24), 101629. sciencedirect. https://doi.org/10.1016/j.techsoc.2021.101629
  7. Lodhi, D. S., Verma, M., Golani, P., Pawar, A. S., & Nagdev, S. (2022). Impact Artificial Intelligence in the Pharmaceutical Industry on Working Culture: A Review. International Journal of Pharmaceutical Sciences and Nanotechnology, 15(1), 5771–5780. https://doi.org/10.37285/ijpsn.2022.15.1.5
  8. Lu, M., Yin, J., Zhu, Q., Lin, G., Mou, M., Liu, F., Pan, Z., You, N., Lian, X., Li, F., Zhang, H., Zheng, L., Zhang, W., Zhang, H., Shen, Z., Gu, Z., Li, H., & Zhu, F. (2023). Artificial Intelligence in Pharmaceutical Sciences. Engineering, 24(2). https://doi.org/10.1016/j.eng.2023.01.014
  9. Paul, D., Sanap, G., Shenoy, S., Kalyane, D., Kalia, K., & Tekade, R. K. (2020). Artificial intelligence in drug discovery and development. Drug Discovery Today, 26(1). ncbi. https://doi.org/10.1016/j.drudis.2020.10.010
  10. Pazhayattil, A. B., & Konyu-Fogel, G. (2023). An empirical study to accelerate machine learning and artificial intelligence adoption in pharmaceutical manufacturing organizations. Journal of Generic Medicines: The Business Journal for the Generic Medicines Sector, 24(2), 174113432211511. https://doi.org/10.1177/17411343221151109
  11. Sharma, D., Patel, P., & Shah, M. (2023). A comprehensive study on Industry 4.0 in the pharmaceutical industry for sustainable development. Environmental Science and Pollution Research, 30(2). https://doi.org/10.1007/s11356-023-26856-y
  12. Vijayan, R. S. K., Kihlberg, J., Cross, J. B., & Poongavanam, V. (2021). Enhancing preclinical drug discovery with artificial intelligence. Drug Discovery Today, 27(4). https://doi.org/10.1016/j.drudis.2021.11.023
  13. Visan, A. I., & Negut, I. (2024). Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery. Life, 14(2), 233. https://doi.org/10.3390/life14020233
  14. Vora, L. K., Gholap, A. D., Jetha, K., Thakur, R. R. S., Solanki, H. K., & Chavda, V. P. (2023). Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design. Pharmaceutics, 15(7), 1916–1916. https://doi.org/10.3390/pharmaceutics15071916
  15. Yadav, S., Singh, A., Singhal, R., & Yadav, J. P. (2024). Revolutionizing drug discovery: The impact of artificial intelligence on advancements in pharmacology and the pharmaceutical industry. Intelligent Pharmacy, 24(2). https://doi.org/10.1016/j.ipha.2024.02.009

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