Angiogenesis, Inflammation & Therapeutics | Online ISSN  2207-872X
REVIEWS   (Open Access)

Transformative Role of Artificial Intelligence in the Pharmaceutical Sector

Balisa Mosisa Ejeta*1, Malay K Das2*, Sanjoy Das3, Fetene Fufa Bekere4, Dubom Tayeng5

+ Author Affiliations

Journal of Angiotherapy 8(9) 1-7 https://doi.org/10.25163/angiotherapy.899933

Submitted: 12 September 2024  Revised: 12 September 2024  Published: 12 September 2024 

Abstract

Background: The pharmaceutical sector is a critical component of healthcare, driving innovation in drug discovery, development, and delivery. With the increasing integration of artificial intelligence (AI), digital health technologies, and biotechnology, the industry is transforming rapidly. This review examines the key areas of the pharmaceutical industry and highlights the growing impact of AI in enhancing various processes, from drug discovery to clinical trials. To explore the applications of AI in drug discovery, development, manufacturing, clinical trials, personalized medicine, and regulatory compliance. This review also addresses the challenges, such as data privacy and interoperability, that accompany the adoption of AI in the pharmaceutical sector. Methods: A comprehensive review of existing literature and case studies on the application of AI in pharmaceutical research and operations was conducted. Key areas of focus include AI's role in predictive analytics, target identification, manufacturing, supply chain management, clinical trial optimization, and pharmacovigilance. Results: AI significantly enhances drug discovery by improving target identification, predictive modeling, and high-throughput screening. It optimizes manufacturing through real-time quality control and process automation. In clinical trials, AI facilitates patient recruitment and adaptive trial designs, while in personalized medicine, it enables biomarker discovery and treatment optimization. AI also supports regulatory compliance through automated monitoring and risk assessment. Conclusion: AI is transforming the pharmaceutical sector, making processes more efficient, precise, and tailored to individual patients. However, challenges such as data privacy, ethical considerations, and interoperability must be addressed to fully harness AI's potential. Standardization and collaboration will be essential in driving the next phase of innovation in pharmaceutical development and healthcare delivery.

Keywords: Artificial Intelligence, Drug Discovery, Biopharmaceuticals, Clinical Trials, Regulatory Compliance

References

Aliper, A., Plis, S., Artemov, A., Ulloa, A., Mamoshina, P., & Zhavoronkov, A. (2016). Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data. Molecular Pharmaceutics, 13(7), 2524–2530.

Anastas, P. T., & Warner, J. C. (1998). Green chemistry: Theory and practice. Oxford University Press.

Angermueller, C., Pärnamaa, T., Parts, L., & Stegle, O. (2016). Deep learning for computational biology. Molecular Systems Biology, 12(7), 878.

Aramaki, E., Miura, Y., Tonoike, M., & Ohkuma, T. (2010). Twitter catches the flu: detecting influenza epidemics using Twitter. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 1568–1576).

Barabási, A. L., & Oltvai, Z. N. (2004). Network biology: understanding the cell's functional organization. Nature Reviews Genetics, 5(2), 101–113.

Baum, A., & Akbari, O. (2020). Targets for SARS-CoV-2 mRNA vaccination. New England Journal of Medicine, 383(17), 1673-1675.

Beauchamp, T. L., & Childress, J. F. (2019). Principles of Biomedical Ethics. Oxford University Press.

Berger, M. L., Sox, H., Willke, R. J., Brixner, D. L., Eichler, H. G., Goettsch, W., ... & Watkins, J. B. (2017). Good practices for real-world data studies of treatment and/or comparative effectiveness: Recommendations from the joint ISPOR-ISPE special task force on real-world evidence in health care decision making. Pharmacoepidemiology and Drug Safety, 26(9), 1033-1039.

Bryant, J., Fisher, L., & Gent, M. (1984). The process of adjudication by committee in the clinical trial of symptomatic versus asymptomatic patients. Controlled Clinical Trials, 5(2), 99–110.

Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018). The rise of deep learning in drug discovery. Drug Discovery Today, 23(6), 1241–1250.

Chen, M., Hao, Y., Hwang, K., Wang, L., & Cho, C. (2015). Machine-to-machine communications: Architectures, standards, and applications. KSII Transactions on Internet and Information Systems, 9(6), 2236–2263.

Chesbrough, H. W. (2003). Open innovation: The new imperative for creating and profiting from technology. Harvard Business Press.

Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., ... & Xie, W. (2018). Opportunities and obstacles for deep learning in biology and medicine. Journal of The Royal Society Interface, 15(141), 20170387.

Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., et al. (2018). Opportunities and obstacles for deep learning in biology and medicine. Journal of The Royal Society Interface, 15(141), 20170387.

Cohen, A. M., Hersh, W. R., Dubay, C., & Spackman, K. (2005). Using co-occurrence network structure to extract synonymous gene and protein names from MEDLINE abstracts. BMC Bioinformatics, 6(1), 1–14.

Collins, F. S., & Varmus, H. (2015). A new initiative on precision medicine. New England Journal of Medicine, 372(9), 793-795.

Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.

Coyle, J. J., Bardi, E. J., & Langley, C. J. (2003). The management of business logistics: A supply chain perspective. South-Western Cengage Learning.

Dai, Y., & Devarajan, K. (2017). Learnings from a decade of virtual screening in the pharmaceutical industry. Expert Opinion on Drug Discovery, 12(6), 511–522.

Dehghan, A., & Casas, J. P. (2019). Personalised medicine and population health: Breast and ovarian cancer. The EPMA Journal, 10(3), 239–253.

DiMasi, J. A., Grabowski, H. G., & Hansen, R. W. (2016). Innovation in the pharmaceutical industry: New estimates of R&D costs. Journal of Health Economics, 47, 20-33.

Ding, C., & Peng, H. (2005). Minimum redundancy feature selection from microarray gene expression data. Journal of Bioinformatics and Computational Biology, 3(2), 185–205.

Doshi-Velez, F., & Perlis, R. H. (2014). Discovering personal topics in documents: An efficient algorithm and its evaluation. Journal of Machine Learning Research, 15, 751–773.

Downing, N. S., Shah, N. D., Aminawung, J. A., Pease, A. M., Zeitoun, J. D., Krumholz, H. M., & Ross, J. S. (2017). Postmarket safety events among novel therapeutics approved by the US Food and Drug Administration between 2001 and 2010. JAMA, 317(18), 1854–1863.

Durrant, J. D., & McCammon, J. A. (2011). NNScore: A neural-network-based scoring function for the characterization of protein-ligand complexes. Journal of Chemical Information and Modeling, 51(11), 2528–2545.

Ekins, S., & Clark, A. M. (2018). Ligand-based target prediction for small molecules with artificial intelligence. Journal of Chemical Information and Modeling, 58(1), 138–149.

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118.

FDA. (2020). Generic drugs. https://www.fda.gov/drugs/generic-drugs

FDA. (2022). Current good manufacturing practice (CGMP) regulations. https://www.fda.gov/drugs/pharmaceutical-quality-resources/current-good-manufacturing-practice-cgmp-regulations

Fernández-Alemán, J. L., Señor, I. C., Lozoya, P. Á. O., & Toval, A. (2013). Security and privacy in electronic health records: a systematic literature review. Journal of Biomedical Informatics, 46(3), 541–562.

Garg, A., & Deshmukh, S. G. (2006). A review of literature and an empirical study of supply chain integration: The Indian perspective. International Journal of Physical Distribution & Logistics Management, 36(9), 757–775.

Gawehn, E., Hiss, J. A., & Schneider, G. (2016). Deep learning in drug discovery. Molecular Informatics, 35(1), 3–14.

Grimmer, J., & Stewart, B. M. (2013). Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political Analysis, 21(3), 267–297.

Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157–1182.

Hamburg, M. A., & Collins, F. S. (2010). The path to personalized medicine. New England Journal of Medicine, 363(4), 301-304.

Harpaz, R., DuMouchel, W., Shah, N. H., Madigan, D., Ryan, P., & Friedman, C. (2012). Novel data-mining methodologies for adverse drug event discovery and analysis. Clinical Pharmacology & Therapeutics, 91(6), 1010–1021.

High, K. A., & Roncarolo, M. G. (2019). Gene therapy. New England Journal of Medicine, 381(5), 455-464.

Holmes, J. H., Elliott, T. E., Brown, J. S., Raebel, M. A., Davidson, A., & Nelson, A. F. (2012). Clinical research data warehouse governance for distributed research networks in the USA: A systematic review of the literature. Journal of the American Medical Informatics Association, 19(2), 149–159.

Iansiti, M., & Lakhani, K. R. (2017). The truth about blockchain. Harvard Business Review, 95(1), 118–127.

ICH. (1996). Guideline for good clinical practice E6(R2). https://ichgcp.net/

ICH. (2022). Regulatory affairs. https://www.ich.org/page/regulatory-affairs

Kadam, R. U., Jadhav, A. R., & Gambhire, M. N. (2014). Quality by design: A holistic approach. International Journal of Research in Pharmacy and Chemistry, 4(3), 614–625.

Kaitin, K. I., & DiMasi, J. A. (2011). Pharmaceutical innovation in the 21st century: New drug approvals in the first decade, 2000–2009. Clinical Pharmacology & Therapeutics, 89(2), 183-188.

Katsila, T., Patrinos, G. P., & Kardamakis, D. (2017). Whole genome sequencing in pharmacogenomics. Frontiers in Pharmacology, 8, 6.

Khan, M. U., Shah, S. A. A., Ahmad, F., & Akram, M. (2017). A comprehensive review of marketing strategies in the pharmaceutical industry. Health Marketing Quarterly, 34(2), 134-146.

Khozin, S., Blumenthal, G. M., & Pazdur, R. (2020). Real-world evidence and clinical trials. New England Journal of Medicine, 382(10), 958-962.

Kierkegaard, P. (2015). Vulnerability, trust, and patient–doctor contracts. Journal of Medicine and Philosophy, 40(3), 244–264.

Kleiner, A., & Talwalkar, A. (2015). A scalable bootstrap for massive data. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 77(2), 233–269.

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.

Lenselink, E. B., Ten Dijke, N., Bongers, B., Papadatos, G., Van Vlijmen, H. W. T., & Kowalczyk, W. (2017). Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set. Journal of Cheminformatics, 9(1), 45.

Lippi, M., & Frasconi, P. (2010). A survey of web information extraction systems. Data & Knowledge Engineering, 69(3), 259–267.

Lopes, F. M., de Souza, T. P., & de Jesus, V. C. (2017). A systematic review of the use of social media for the dissemination of gray literature. Journal of the Medical Library Association, 105(2), 131–140.

Mak, T. W., Saunders, M. E., & Jett, B. D. (Eds.). (2016). Primer to the immune response (2nd ed.). Academic Press.

Mullard, A. (2021). 2020 FDA drug approvals. Nature Reviews Drug Discovery, 20(2), 85-90.

Munos, B. (2009). Lessons from 60 years of pharmaceutical innovation. Nature Reviews Drug Discovery, 8(12), 959-968.

Musen, M. A., Bean, C. A., Cheung, K. H., Dumontier, M., Durante, K. A., Gevaert, O., ... & Zheng, J. (2015). The center for expanded data annotation and retrieval. Journal of the American Medical Informatics Association, 22(6), 1148–1152.

Névéol, A., Dalianis, H., Velupillai, S., Savova, G., & Zweigenbaum, P. (2014). Clinical natural language processing in languages other than English: opportunities and challenges. Journal of Biomedical Semantics, 5(1), 1–11.

Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future—big data, machine learning, and clinical medicine. New England Journal of Medicine, 375(13), 1216–1219.

Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future—big data, machine learning, and clinical medicine. New England Journal of Medicine, 375(13), 1216–1219.

Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453.

Osheroff, J., Teich, J., Levick, D., Saldana, L., Velasco, F., Sittig, D., & Rogers, K. (2007). Improving outcomes with clinical decision support: An implementer’s guide. HIMSS.

PhRMA. (2022). About us. https://www.phrma.org/about

Pocock, S. J., & Simon, R. (1975). Sequential treatment assignment with balancing for prognostic factors in the controlled clinical trial. Biometrics, 31(1), 103–115.

Prosperi, M., Min, J. S., Bian, J., & Modave, F. (2019). Big data hurdles in precision medicine and precision public health. BMC Medical Informatics and Decision Making, 19(1), 1–9.

Riquelme, J. C., & González, A. (2018). A review of machine learning for the prognosis of machinery health. Mechanical Systems and Signal Processing, 104, 799–834.

Ross, J., Tu, S., Carini, S., Sim, I., & the Analysis and Design of Informatics. (2010). Analysis of eligibility criteria complexity in clinical trials. AMIA Summits on Translational Science Proceedings, 2010, 46–50.

Sarker, A., Chandrashekar, P., Magge, A., Cai, H., Klein, A., Gonzalez, G., & Scotch, M. (2015). Discovering cohorts of pregnant women from social media for safety surveillance and analysis. Journal of Medical Internet Research, 17(10), e237.

Sarkis, J. (2003). A strategic decision framework for green supply chain management. Journal of Cleaner Production, 11(4), 397–409.

Shanmugam, S., Muthukumar, S., & Palanisamy, P. (2019). An intelligent system for quality control of pharmaceutical tablet using UV spectroscopy and machine learning. Journal of Pharmaceutical Analysis, 9(6), 451–457.

Sheldon, R. A. (2014). Green and sustainable manufacture of chemicals from biomass: State of the art. Green Chemistry, 16(3), 950-963.

Sherif, M. M., Hassen, D. I., & Salem, A. B. (2019). An artificial intelligence technique for predicting the possible risk of medical disease based on electronic health records. Health Information Science and Systems, 7(1), 1–10.

Steinhubl, S. R., Muse, E. D., & Topol, E. J. (2015). Can mobile health technologies transform health care? JAMA, 314(12), 1235–1236.

Steinhubl, S. R., Muse, E. D., & Topol, E. J. (2015). The emerging field of mobile health. Science Translational Medicine, 7(283), 283rv3.

Tang, Y., Davison, M., & Ekel, P. (2006). Data preparation for data mining. Applied Artificial Intelligence, 17(3), 375–381.

Topol, E. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Hachette UK.

Van der Maaten, L., & Hinton, G. (2008). Visualizing high-dimensional data using t-SNE. Journal of Machine Learning Research, 9(Nov), 2579–2605.

Vayena, E., & Blasimme, A. (2017). Biomedical big data: new models of control over access, use and governance. Journal of Bioethical Inquiry, 14(4), 501–513.

Wagholikar, K. B., MacLaughlin, K. L., Laje, P., & Sabb, F. W. (2014). Machine learning-based prediction of clinical trial enrollment. Journal of Biomedical Informatics, 52, 296–307.

Wang, D., & Li, P. (2015). Machinery fault diagnosis and signal processing—a review. Artificial Intelligence Review, 43(1), 83–119.

WHO. (2019). Good distribution practices for pharmaceutical products. https://www.who.int/medicines/areas/quality_safety/quality_assurance

Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? In Advances in Neural Information Processing Systems (pp. 3320–3328).

Zhang, X., Zhang, Y., Liu, Y., Hou, W., & Xu, J. (2018). Regulatory compliance in cloud service provisioning: A survey. IEEE Transactions on Cloud Computing, 6(2), 315–326.

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