Artificial intelligence in pharmaceutical manufacturing: Transforming sterile compounding and quality assurance
Keywords:
artificial intelligence, data integrity, pharmaceutical quality, predictive analytics, risk-based validation, sterile compoundingAbstract
Artificial intelligence (AI) applications in sterile compounding and 503B outsourcing facilities represent a transformative approach to enhancing quality, safety, and operational throughput in pharmaceutical manufacturing. This paper examines the current state of AI implementation in sterile compounding environments, focusing on key applications including AI-driven robotics for aseptic processing, real-time quality monitoring systems, predictive analytics, and regulatory intelligence platforms. However, implementation faces significant challenges related to data integrity, system validation, and regulatory compliance under current Good Manufacturing Practices (cGMP). The FDA's evolving regulatory framework, including the recent risk-based credibility assessment guidance, establishes structured approaches for AI system validation while emphasizing the importance of context-specific performance evaluation. Key data integrity challenges include ensuring accuracy, completeness, and consistency across multiplesystems, while maintaining comprehensive audit trails. This paper presents compliance-by-design strategies that embed regulatory requirements into AI system architecture from initial development phases, addressing critical areas such as traceability, accountability, and continuous performance monitoring. Successful AI implementation requires robust data governance frameworks, risk-based validation approaches, and integrated automation architectures that span compounding, release testing, and supply chain planning. Future opportunities include advances in explainable AI, integration with continuous manufacturing technologies, and collaborative development initiatives that will accelerate industry-wide adoption while ensuring compliance .
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References
Ajuzieogu, U. (2024). AI data quality and bias: Challenges, implications, and solutions in modern machine learning.
Aksu, B., Paradkar, A., de Matas, M., Özer, Ö., Güneri, T., & York, P. (2012). Quality by design approach: application of artificial intelligence techniques of tablets manufactured by direct compression. AAPS PharmSciTech, 13(4), 1138-1146. DOI: https://doi.org/10.1208/s12249-012-9836-x
Bhat, V. N., Bharati, S., Bothiraja, C., Sangshetti, J., & Gaikwad, V. (2025). A Review on Intervention of AI in Pharmaceutical Sector: Revolutionizing Drug Discovery and Manufacturing. Intelligent Pharmacy. https://doi.org/10.1016/j.ipha.2025.04.001 DOI: https://doi.org/10.1016/j.ipha.2025.04.001
Choudhury, A., & Asan, O. (2020). Role of artificial intelligence in patient safety outcomes: systematic literature review. JMIR medical informatics, 8(7), e18599. DOI: https://doi.org/10.2196/18599
Gabay, M. (2014). The drug quality and security act. Hospital Pharmacy, 49(7), 615-676. DOI: https://doi.org/10.1310/hpj4907-615
Garcia-Segura, L. A. (2024). The role of artificial intelligence in preventing corporate crime. Journal of Economic Criminology, 5, 100091. https://doi.org/10.1016/j.jeconc.2024.100091 DOI: https://doi.org/10.1016/j.jeconc.2024.100091
Gianturco, S. L., & Mattingly, A. N. (2021). Distinguishing between compounding facilities and the development of the 503B bulk drug substance list. Journal of the American Pharmacists Association, 61(1), e8-e11. https://doi.org/10.1016/j.japh.2020.06.024 DOI: https://doi.org/10.1016/j.japh.2020.06.024
Gianturco, S. L., Yoon, S., Yuen, M. V., & Mattingly, A. N. (2021). Outsourcing facilities and their place in the US drug supply chain. Journal of the American Pharmacists Association, 61(1), e99-e102. https://doi.org/10.1016/j.japh.2020.07.021 DOI: https://doi.org/10.1016/j.japh.2020.07.021
Grandinetti, C., Rivera, D. R., Pai-Scherf, L., Choe, A., Kluetz, P. G., Kraus, S., Innes, G. K., & Ayalew, K. (2025). Keeping the end in mind: Reviewing U.S. FDA inspections of submissions including real-world data. Therapeutic Innovation & Regulatory Science, 59(5), 956–962. DOI: https://doi.org/10.1007/s43441-025-00791-1
Huanbutta, K., Burapapadh, K., Kraisit, P., Sriamornsak, P., Ganokratanaa, T., Suwanpitak, K., & Sangnim, T. (2024). Artificial intelligence-driven pharmaceutical industry: A paradigm shift in drug discovery, formulation development, manufacturing, quality control, and post-market surveillance. European Journal of Pharmaceutical Sciences, 203, 106938. https://doi.org/10.1016/j.ejps.2024.106938 DOI: https://doi.org/10.1016/j.ejps.2024.106938
Jain, D. (2024). Artificial intelligence in quality control systems: A cross-industry analysis of applications, benefits, and implementation frameworks. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(6), 1321–1333. DOI: https://doi.org/10.32628/CSEIT241061162
Jiang, K., & Cao, X. (2011). Design and implementation of an audit trail in compliance with US regulations. Clinical Trials (London, England), 8(5), 624–633. DOI: https://doi.org/10.1177/1740774511413943
Kalokyri, V., Tachos, N. S., Kalantzopoulos, C. N., Sfakianakis, S., Kondylakis, H., Zaridis, D. I., ... & Tsiknakis, M. (2025). AI Model Passport: Data and System Traceability Framework for Transparent AI in Health. arXiv preprint arXiv:2506.22358. DOI: https://doi.org/10.1016/j.csbj.2025.09.041
Kodumuru, R., Sarkar, S., Parepally, V., & Chandarana, J. (2025). Artificial intelligence and internet of things integration in pharmaceutical manufacturing: A smart synergy. Pharmaceutics, 17(3), 290. DOI: https://doi.org/10.3390/pharmaceutics17030290
Kuthuru, A. (2025). Pharmaceutical research databases: Balancing AI innovation with regulatory compliance. Journal of Computer Science and Technology Studies, 7(4), 822–828. DOI: https://doi.org/10.32996/jcsts.2025.7.4.95
Mahmood, U., Shukla-Dave, A., Chan, H. P., Drukker, K., Samala, R. K., Chen, Q., ... & Hadjiiski, L. (2024). Artificial intelligence in medicine: mitigating risks and maximizing benefits via quality assurance, quality control, and acceptance testing. BJR| Artificial Intelligence, 1(1), ubae003. DOI: https://doi.org/10.1093/bjrai/ubae003
Mazhar, T., Talpur, D. B., Shloul, T. A., Ghadi, Y. Y., Haq, I., Ullah, I., ... & Hamam, H. (2023). Analysis of IoT security challenges and its solutions using artificial intelligence. Brain sciences, 13(4), 683. DOI: https://doi.org/10.3390/brainsci13040683
Myllyaho, L., Raatikainen, M., Männistö, T., Mikkonen, T., & Nurminen, J. K. (2021). Systematic literature review of validation methods for AI systems. arXiv preprint arXiv:2107.12190. DOI: https://doi.org/10.1016/j.jss.2021.111050
Naim, A., Muniasamy, A., Khan, M. I., & Khan, M. F. (2026). AI-driven smart manufacturing and automation in pharmaceutical production. In Applications of artificial intelligence in pharmaceuticals (pp. 157-194). IGI Global Scientific Publishing. DOI: https://doi.org/10.4018/979-8-3373-2043-4.ch006
Nene, L., Flepisi, B. T., Brand, S. J., Basson, C., & Balmith, M. (2024). Evolution of drug development and regulatory affairs: the demonstrated power of artificial intelligence. Clinical therapeutics, 46(8), e6-e14. https://doi.org/10.1016/j.clinthera.2024.05.012 DOI: https://doi.org/10.1016/j.clinthera.2024.05.012
Niazi, S. K. (2025). Regulatory Perspectives for AI/ML Implementation in Pharmaceutical GMP Environments. Pharmaceuticals, 18(6), 901. DOI: https://doi.org/10.3390/ph18060901
Okuyelu, O., & Adaji, O. (2024). AI-driven real-time quality monitoring and process optimization for enhanced manufacturing performance. Journal of Advances in Mathematics and Computer Science, 39(4), 81–89. DOI: https://doi.org/10.9734/jamcs/2024/v39i41883
Pahune, S., Akhtar, Z., Mandapati, V., & Siddique, K. (2025). The importance of AI data governance in large language models. In Preprints. DOI: https://doi.org/10.20944/preprints202504.0219.v1
Palumbo, F. B., Rosebush, L. H., & Zeta, L. M. (2016). Navigating through a complex and inconsistent regulatory framework: Section 503B of the federal food drug and cosmetic act Outsourcing Facilities engaged in clinical investigation. Therapeutic Innovation & Regulatory Science, 50(3), 270–278. DOI: https://doi.org/10.1177/2168479015618695
Pantanowitz, L., Hanna, M., Pantanowitz, J., Lennerz, J., Henricks, W. H., Shen, P., ... & Rashidi, H. H. (2024). Regulatory aspects of artificial intelligence and machine learning. Modern Pathology, 37(12), 100609. https://doi.org/10.1016/j.modpat.2024.100609 DOI: https://doi.org/10.1016/j.modpat.2024.100609
Pasas-Farmer, S., & Jain, R. (2025). From discovery to delivery: Governance of AI in the pharmaceutical industry. Green Analytical Chemistry, 13, 100268. https://doi.org/10.1016/j.greeac.2025.100268 DOI: https://doi.org/10.1016/j.greeac.2025.100268
Patil, D. (2024). Artificial intelligence-driven predictive maintenance in manufacturing: enhancing operational efficiency, minimizing downtime, and optimizing resource utilization. Minimizing Downtime, And Optimizing Resource Utilization (December 11, 2024). DOI: https://doi.org/10.2139/ssrn.5057406
Pereira, A., Nakka, G., & Gupta, S. (2025). Beyond Ethylene Oxide (EtO): A comprehensive review of sustainable sterilization technologies for medical devices. Biomedical and Therapeutics Letters, 12(2), 1161. DOI: https://doi.org/10.62110/sciencein.btl.2025.v12.1161
Prifti, K., Morley, J., Novelli, C., & Floridi, L. (2024). Regulation by design: Features, practices, limitations, and governance implications. Minds and Machines, 34(2). DOI: https://doi.org/10.1007/s11023-024-09675-z
Rajesh, M. V., & Elumalai, K. (2025). The transformative power of artificial intelligence in pharmaceutical manufacturing: Enhancing efficiency, product quality, and safety. Journal of Holistic Integrative Pharmacy, 6(2), 125-135. https://doi.org/10.1016/j.jhip.2025.03.007 DOI: https://doi.org/10.1016/j.jhip.2025.03.007
Registered Outsourcing Facilities. (2025, December 5). U.S. Food and Drug Administration; FDA. https://www.fda.gov/drugs/human-drug-compounding/registered-outsourcing-facilities
Schwabe, D., Becker, K., Seyferth, M., Klaß, A., & Schaeffter, T. (2024). The METRIC-framework for assessing data quality for trustworthy AI in medicine: a systematic review. NPJ digital medicine, 7(1), 203. DOI: https://doi.org/10.1038/s41746-024-01196-4
Sembiring, M. H., & Novagusda, F. N. (2023). Enhancing data security resilience in AI-Driven Digital Transformation: Exploring industry challenges and solutions through ALCOA+ principles. Acta Informatica Medica: AIM: Journal of the Society for Medical Informatics of Bosnia & Herzegovina: Casopis Drustva Za Medicinsku Informatiku BiH, 32(1), 65–70. DOI: https://doi.org/10.5455/aim.2024.32.65-70
Singh, R., Paxton, M., & Auclair, J. (2025). Regulating the AI-enabled ecosystem for human therapeutics. Communications Medicine, 5(1), 181. DOI: https://doi.org/10.1038/s43856-025-00910-x
Syed, N., Anwar, A., Baig, Z., & Zeadally, S. (2025). Artificial Intelligence as a service (AIaaS) for cloud, fog and the edge: State-of-the-art practices. ACM Computing Surveys, 57(8), 1–36. DOI: https://doi.org/10.1145/3712016
Tanzini, A., Ruggeri, M., Bianchi, E., Valentino, C., Vigani, B., Ferrari, F., Rossi, S., Giberti, H., & Sandri, G. (2023). Robotics and aseptic processing in view of regulatory requirements. Pharmaceutics, 15(6), 1581. DOI: https://doi.org/10.3390/pharmaceutics15061581
Tsopra, R., Fernandez, X., Luchinat, C., Alberghina, L., Lehrach, H., Vanoni, M., Dreher, F., Sezerman, O. U., Cuggia, M., de Tayrac, M., Miklasevics, E., Itu, L. M., Geanta, M., Ogilvie, L., Godey, F., Boldisor, C. N., Campillo-Gimenez, B., Cioroboiu, C., Ciusdel, C. F., … Burgun, A. (2021). A framework for validating AI in precision medicine: considerations from the European ITFoC consortium. BMC Medical Informatics and Decision Making, 21(1), 274. DOI: https://doi.org/10.1186/s12911-021-01634-3
Wah, J. N. K. (2025). Revolutionizing surgery: AI and robotics for precision, risk reduction, and innovation. Journal of Robotic Surgery, 19(1), 47. DOI: https://doi.org/10.1007/s11701-024-02205-0
Yang, W., Fu, R., Amin, M. B., & Kang, B. (2025). The impact of modern AI in metadata management. Human-Centric Intelligent Systems, 5(3), 323–350. DOI: https://doi.org/10.1007/s44230-025-00106-5
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