PDA 2025: Data Governance and AI's Impact on Drug Manufacturing

The bio/pharmaceutical industry is on the cusp of a profound transformation, driven by an exponential surge in data generation and the sophisticated capabilities of artificial intelligence. With approximately 400 exabytes of data, equivalent to 18,000 trillion books, produced globally each day—much of it unstructured—the challenge and opportunity for leveraging this information are immense, said Toni Manzano, PhD, co-founder and chief scientific officer, Aizon, at the PDA Regulatory Conference 2025 occurring Sept. 8–10 in Washington, DC.

During his Monday presentation on “Data Governance to Ensure Regulatory Compliance, Data Protection, and Future Readiness for Advanced Use Cases,” Manzano pointed out that this data deluge, combined with the widespread availability of computing power and data storage, is fueling an artificial intelligence (AI) renaissance that promises to redefine drug discovery, development, and manufacturing. AI is proving to be a scientifically validated tool, even playing a crucial role in Nobel Prize-winning research in chemistry and physics in 2024, demonstrating its capacity to solve complex problems and model intricate biological processes (1). Understanding and strategically implementing data-driven AI is no longer optional but an imperative for enhancing safety, quality, and efficiency, ultimately benefiting the patient.

How can bio/pharma operations maximize data value?

At its core, AI is a mathematical tool, Manzano explained in his talk. However, AI’s efficacy hinges entirely on the quality and management of data. Data in the pharmaceutical context extend far beyond traditional numbers and lists, encompassing diverse unstructured images, sounds, and continuous values collected from various systems.

Regulators are keenly aware of this, frequently referencing data and metadata in AI guidelines, emphasizing their critical role in transforming raw information into actionable knowledge. This focus is not without reason, Manzano said, emphasizing that more than 25% of warning letters issued by FDA since 2019 have cited data accuracy issues, a complex problem that continues to challenge the industry. This concern is further underscored by an article recently published by the Pharmaceutical Technology® Group highlighting common FDA 483 findings and strategies to prevent regulatory non-compliance, often rooted in data management deficiencies (2). For bio/pharmaceutical companies, ensuring robust data integrity and compliance is paramount, as regulatory actions can significantly impede progress and public trust.

The journey to effective AI implementation is heavily weighted toward data preparation, consuming an estimated 80% of an AI project's time. This rigorous preparation ensures that data are findable, accessible, interoperable, and reusable, known as the FAIR principles, which are essential for quality AI outcomes. As more data is integrated, the potential for knowledge extraction and sophisticated AI models increases, yet this also adds layers of complexity to data and model management, Manzano said in his talk.

What AI-driven innovations are the industry seeing in bio/pharmaceutical manufacturing and development?

The practical applications of AI across the bio/pharmaceutical lifecycle are profound. Consider the management of human plasma, a highly variable raw material for which donations cannot be refused and establishing full product genealogy is exceptionally difficult, Manzano highlighted. AI can be applied here to integrate batch record information with data from other systems, such as programmable logic controllers and online controls, which transforms these diverse data into knowledge that can guarantee expected yields. Through AI, crucial process variables, such as pH in plasma fractionation, can be identified and adjusted to achieve target outcomes. This capability moves beyond trial-and-error, enabling predictive models that recommend optimal process parameters based on raw material characteristics and desired yields (3,4).

Beyond manufacturing, AI is also advancing quality control in novel therapies. For instance, the Parenteral Drug Association, in collaboration with the European Medicines Agency, is utilizing AI for quality control in advanced therapy medicinal products, allowing for the prediction of batch success an hour in advance (5). This collaboration provides critical lead time for decision making without replacing human labor or traditional lab testing, enhancing efficiency and patient safety for those awaiting these crucial treatments, Manzano said.

"Everyone in this room, we are working for pharma, but we are [also] working for patients, so we have to [always] have in mind that everything we do is because there is a patient waiting,” he emphasized in his talk.

The industry's embrace of digitalization is progressing, Manzano concluded, pointing to a recent survey indicating an average maturity score of 3.5 out of 5. That is a notable increase from 2.6 in 2019. While challenges such as cybersecurity compliance and budget constraints remain top concerns for the industry, the perceived benefits are clear, Manzano asserts: respondents anticipate significant improvements in process efficiency and quality through digitalization, especially in areas such as production, research, and quality control. AI, when applied with scientific rigor and supported by high-quality, well-managed data, offers a significant opportunity to navigate the inherent complexities and variability of bio/pharmaceutical operations, ensuring that the ultimate focus remains on the patient.

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References

1. Longhi, S.; Ventura, S.; Macedo-Ribeiro, S.; et al. When Artificial Intelligence Meets Protein Research [Version 1; Peer Review: 2 Approved, 1 Approved with Reservations]. Open Res Europe 2025, 5, 185. DOI: 10.12688/openreseurope.20628.1
2. Falvey, et al. New Year, Same Challenges: Pharma’s Ongoing Struggle with FDA 483s. BioPharm International®/Pharmaceutical Technology®/Pharmaceutical Technology® Europe Quality and Regulations 2025 eBook August 2025.
3. IDBS. Enhancing Process Performance with AI in Bioprocessing. Blog Post. May 22, 2025. https://www.idbs.com/knowledge-base/enhancing-process-performance-with-ai-in-bioprocessing/
4. Cheng, Y.; Bi,X.; Xu, Y.; et al. Artificial Intelligence Technologies in Bioprocess: Opportunities and Challenges. Bioresour. Technol. 2023, 369, 128451. DOI: 10.1016/j.biortech.2022.128451
5. Manzano, T. PDA Europe and EMA Unite. PDA Letter. May 27, 2025. https://www.pda.org/pda-letter-portal/home/full-article/pda-europe-and-ema-unite