Batch Certification for New Manufacturing Technologies: The Qualified Person Role in a Digital Environment

The role of the qualified person (QP) has always been fundamental to pharmaceutical manufacturing in Europe, ensuring that each batch of medicinal product is certified before release to market (1). Traditionally, this responsibility has centered on the review of documentation and the verification of laboratory test results, providing assurance that a product complies with good manufacturing practice (GMP) and meets specifications. However, the pharmaceutical sector is undergoing profound transformation. Advanced technologies—ranging from continuous biomanufacturing and artificial intelligence to 3D printing, bioinformatics, and point-of-care production—are changing how medicines are designed, manufactured, and delivered. These innovations promise improved efficiency, tailored therapies, and faster time-to-market, yet they also challenge the established paradigms of validation, quality assurance, and regulatory oversight. For QPs, this evolution requires a redefinition of their role, particularly in an increasingly digital and data-driven environment.

How has the rise of advanced manufacturing technologies impacted pharmaceutical quality?

The pharmaceutical industry has historically relied on batch manufacturing supported by rigorous but static testing (2). The emergence of advanced platforms introduces fundamentally different dynamics. Continuous manufacturing, for example, enables uninterrupted processes that improve productivity and reduce variability but also generate continuous data streams that require constant oversight. Artificial intelligence (AI) and machine learning (ML) accelerate protein engineering and process optimization, yet their reliance on complex, evolving algorithms creates new validation challenges (3). Similarly, 3D printing and bioprinting offer the possibility of personalized medicines and complex dosage forms, but raise difficult questions about reproducibility, scalability, and sterilization (4).

Gene editing, nanotechnology, and tissue engineering further complicate the regulatory picture by introducing products with unprecedented complexity. Bioinformatics, meanwhile, generates vast datasets in genomics, proteomics, and metabolomics, often using tools originally developed for research rather than GMP compliance (5,6). All these developments create opportunities for innovation but also strain the traditional structures of batch certification.

Data as the new product

One of the most profound shifts brought by Industry 4.0 is the centrality of data. It is increasingly the case that producing a batch of medicine takes less time than analyzing it (7,8). Modern manufacturing platforms, from process analytical technology (PAT) to electronic lab notebooks and laboratory information management systems, generate massive and continuous data flows. For QPs, these data are no longer merely supplementary but forms the backbone of certification decisions.

The challenge lies in ensuring that these data are valid, reproducible, and trustworthy. Many advanced analyses depend on external laboratories, which may not be fully GMP qualified. Algorithms that drive decision-making require robust validation, yet their complexity often makes them opaque to traditional quality systems (9). Bioinformatics tools, though indispensable in managing biological datasets, were rarely designed with GMP integration in mind, creating issues of interoperability and traceability. Bioinformatics tools emerged from academic research, where flexibility, speed, and innovation are prioritized over regulated robustness. Because they lack built-in audit trails, standardization, controlled versioning, and metadata tracking, they do not naturally fit into GMP workflows. As a result, interoperability between systems is poor, and traceability of data—essential for GMP compliance—is difficult or impossible without significant adaptation (10,11). The QP must therefore expand their oversight from physical samples and documents to complex digital ecosystems. Certification increasingly depends on data integrity, system validation, and the capacity to interpret results generated by AI/ML models.

From quality by design to certification by design

The traditional paradigm of batch certification has always been retrospective: testing the end product and verifying compliance (12). Industry 4.0 calls for a different approach. Certification can no longer be confined to the end of the process but must be embedded within the design and execution of manufacturing itself (13). This shift has been described as “Certification by Design.”

Certification by design builds on the principles of quality by design (QbD), which emphasize understanding processes and designing them to ensure consistent quality (12). In practice, this means adopting risk-based control strategies that integrate directly into the pharmaceutical quality system. The pharmaceutical quality system is a framework of policies, processes, and procedures that ensures pharmaceutical products are consistently made to meet quality standards throughout their entire lifecycle (14).

Real-time monitoring through PAT, digital twins, and predictive models allows deviations to be detected and corrected within the validated operating space, reducing reliance on end-product testing. Data-driven decision-making becomes central, with predictive and even prescriptive analytics providing insights not only into what may happen but also into how to respond.

Importantly, certification by design also requires early and ongoing dialogue with regulators. Agencies are adapting to digital-first approaches but often require extensive evidence of control and reproducibility (15,16). By involving regulators early, companies can align expectations and reduce uncertainty when introducing novel manufacturing technologies. For the QP, this model transforms batch certification from a final checkpoint into a continuous, proactive process that is inseparable from development and production.

How have regulations influenced certification by design?

The regulatory environment reflects this evolution. The European Union’s GMP Annex 16 (1) places significant emphasis on quality risk management, recognizing that the QP’s responsibility extends beyond simple compliance to assessing risk throughout the manufacturing chain. Annex 17 further opens the door to real-time release, explicitly allowing information collected during production to replace some aspects of end-product testing (17). These frameworks signal a recognition by regulators that advanced manufacturing requires flexible, data-driven oversight.

Nevertheless, global harmonization remains a challenge. Expectations differ across jurisdictions, and the pace of regulatory adaptation is uneven. QPs must therefore navigate a complex landscape where approaches that are acceptable in one region may not be fully recognized in another.

What is the expanding role of the qualified person?

In this transformed landscape, the responsibilities of the QP expand significantly. Traditional expertise in GMP and analytical methods remains essential, but it is no longer sufficient. QPs must now engage with digital tools, from PAT systems and automation platforms to AI-driven analytics. They must understand bioinformatics and software validation, ensuring that data integrity and reproducibility are maintained across platforms.

This shift requires QPs to acquire new skills, often in fields far removed from traditional pharmaceutical training. Knowledge of data science, software engineering, and systems integration becomes critical. At the same time, QPs must remain attuned to the unique risks of biological products, where variability and complexity make standardization difficult. Ensuring sterility, product consistency, and patient safety in these contexts is more challenging than ever.

Upskilling and reskilling are therefore essential. QPs cannot remain confined to historical practices but must evolve into professionals capable of bridging science, technology, and regulation. This transformation does not diminish their role but rather elevates it, making the QP a central figure in enabling innovation, throughout product lifecycle while safeguarding quality.

What are some of the challenges?

Despite the promise of advanced manufacturing, the practical challenges for QPs are significant. Variability remains a persistent problem, particularly with living organisms and complex biological systems. Defining acceptance criteria in such contexts can be difficult, and reproducibility may be limited. External laboratories, essential for advanced testing, often operate under standards that do not fully align with GMP, raising questions about reliability (18,19).

Algorithm validation is another major hurdle. Machine learning models are dynamic, adapting as they encounter new data. Demonstrating their consistency and reliability to regulators is an ongoing challenge. Moreover, integrating bioinformatics tools into GMP workflows requires bridging a gap between research-grade software and regulatory compliance.

Finally, QPs must contend with regulatory uncertainty. While there is recognition of the need for flexible approaches, global harmonization remains incomplete, and the risk of divergent expectations is high. QPs are thus required not only to certify quality but also to act as interpreters of evolving regulatory landscapes.

What are some opportunities for leadership?

However, within these challenges lie opportunities. By embracing digital tools and certification by design, QPs can help reduce time-to-market and support the development of personalized medicines. Their involvement early in process design can ensure that quality considerations are embedded from the outset, enabling innovation without compromising compliance. As the guardians of batch release, QPs are uniquely positioned to advocate for robust data integrity, reproducibility, and regulatory engagement.

In the long term, the role of the QP may become even more strategic. Rather than being primarily associated with compliance, QPs can emerge as leaders of digital transformation within quality systems. Their cross-disciplinary knowledge, bridging pharmaceutical science, data analytics, and regulatory expertise, will make them indispensable to organizations navigating Industry 4.0.

Outlook: toward agile, data-driven certification

The future of batch certification lies in agility and data integration. Real-time release, informed by validated models and continuous monitoring, will become increasingly common. Agile, risk-based lifecycle management will allow QPs to move from reactive verification to proactive assurance, shifting quality from a retrospective checkpoint to a dynamic, continuous process.

Certification by design provides a blueprint for this future. By embedding quality into digital systems and process architectures, organizations can ensure that innovation translates into safe and effective products. For QPs, the challenge is significant, but so is the opportunity. They are not only certifying medicines but also certifying the processes, data, and technologies that make them possible.

Conclusion

As new manufacturing technologies reshape the pharmaceutical industry, the QP stands at a crossroads. The traditional model of batch certification, based on document review and end-product testing, is no longer sufficient in an era dominated by continuous manufacturing, AI, and data-driven decision-making. QPs must evolve into digitally fluent professionals capable of navigating complex ecosystems of data and algorithms, ensuring that innovation does not compromise safety or quality.

The path forward requires upskilling, cross-disciplinary engagement, and close collaboration with regulators. But it also offers QPs a chance to redefine their role as leaders of quality in the digital age. By embracing certification by design and the principles of Industry 4.0, QPs can continue to uphold their central mission: ensuring that every medicine reaching patients is safe, effective, and of the highest quality.

References

1. EMA. Annex 16: Certification by a Qualified Person and Batch Release. EudraLex Volume 4, European Medicines Agency, 2019.
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3. ISPE. Industry 4.0 and Pharma 4.0™ Guidance Documents. International Society for Pharmaceutical Engineering, 2021.
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6. EMA. Reflection Paper on Pharmacogenomic Samples, Testing, and Data Handling.
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12. PharmaAccess. New Paradigms for Validation for Industry 4.0. Aug. 30, 2021. https://www.pharmaaccess.net/new-paradigms-for-validation-for-industry4-0/
13. Sanford, M. Validation 4.0 for the Pharmaceutical Industry. ISPE. iSpeak Blog. July 7, 2025. https://ispe.org/pharmaceutical-engineering/ispeak/validation-40-pharmaceutical-industry
14. EMA. Chapter 1 Pharmaceutical Quality System, EudraLex Volume 4, 2013.
15. FDA. Discussion Paper: Artificial Intelligence in Drug Manufacturing.
16. ISPE. Good Practice Guide: Validation 4.0. (ISPE, June 2025).
17. EMA. Annex 17: Real Time Release Testing and Parametric Release. EudraLex Volume 4, European Medicines Agency, 2012
18. MHRA. GxP Data Integrity Guidance and Definitions. 2018
19. ISPE. Good Practice Guide: Quality Oversight in Outsourced GMP Operations. (ISPE, 2020).

About the author

Miguel Fagundes is a Qualified Person eligible and experienced in QA/Operations with 15 years leading pharmaceutical manufacturing, quality assurance, and regulatory compliance across global markets. Proven track record in building state-of-the-art facilities, and implementing quality management systems, overseeing GMP operations and quality, leading tech transfer from R&D to commercial scale. Certified Lean Six Sigma Black Belt with deep knowledge of EMA/FDA regulations and a continuous improvement mindset.