Guest Column | June 23, 2025

AI-Assisted QMS: Bridging ISO/IEC 42001:2023 And ICH Q10 For Pharmaceutical Excellence

By Ajaz S. Hussain, Ph.D., independent pharmaceutical regulatory science expert

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Maintaining consistent pharmaceutical quality and regulatory compliance is increasingly complex in today's rapidly evolving geopolitical and economic landscape. Furthermore, the U.S. FDA launched Elsa, a generative artificial intelligence (AI) tool designed to help employees work more collectively and efficiently, from scientific reviewers to investigators.1. How this might impact the “One Quality Voice”2 we all seek will depend on how we manage AI applications in the pharmaceutical quality system. Integrating AI into quality management systems (QMS) can be a transformative opportunity to enhance quality assurance, improve compliance, and enable proactive risk management. The recent release of ISO/IEC 42001:2023,3 which provides a structured approach for embedding AI within organizational management systems, presents a unique chance to harmonize technological advancements with established pharmaceutical quality frameworks, particularly the International Council for Harmonization’s (ICH) Q10.4 This article explores the benefits of integrating these two standards into a cohesive AI-assisted quality management system, AI QMS. It outlines practical steps for pharmaceutical professionals to leverage this approach effectively.

Why Integrate ISO/IEC 42001:2023 And ICH Q10?

ISO/IEC 42001:2023 provides a structured framework for embedding AI within organizational management systems, ensuring ethical use, operational transparency, and continuous improvement. Complementing this, ICH Q10 established a foundational Pharmaceutical Quality System (PQS) framework emphasizing robust process management, product life cycle oversight, and proactive continuous improvement. Integrating these frameworks can result in a practical, perhaps self-correcting, resilient AI QMS that overcomes persistent blind spots5 to remain current in a rapidly evolving regulatory landscape and drive operational excellence.

Key Terms Defined:

  • Self-correcting refers to an AI system's capability to detect, identify, analyze, report, and, perhaps shortly, prevent and rectify deviations without manual intervention, ensuring ongoing compliance and quality improvement.
  • Operational transparency means documenting AI processes, decisions, and outcomes in a manner accessible to internal and external stakeholders, ensuring accountability and trust.
  • Ethical governance requires establishing clearly defined rules, roles, and responsibilities to ensure AI is used responsibly, ethically, and transparently within an organization.

Overcoming Traditional Quality System Limitations

Pharmaceutical quality systems have historically relied on reactive, compliance-driven practices and are prone to errors of omission in the original or legacy regulatory application.6 While this reactive baseline has persisted for decades, it misses proactive opportunities to self-correct, identify risks based on underlying mechanisms, and continuously improve operations. AI-driven management can transform this paradigm, creating systems that continuously learn from internal data, adapt to evolving risks, and foster a proactive quality culture. These limitations compromise product quality and restrict the agility needed in modern regulatory environments, where a self-correcting AI QMS comes into play.

For instance, AI tools can detect subtle patterns signaling potential deviations, preempting quality issues long before they surface in routine inspections or product recalls. Such an automated pattern recognition capability would curtail human biases and make deviations difficult to ignore or hide and push the system from a reactive to a proactive stance, resonating strongly with the ethos of "One Quality Voice" advocated by the FDA Office of Pharmaceutical Quality (OPQ) but now in the context of Elsa and corporate AI QMS.

Establishing A Self-Correcting AI QMS

Integrating ISO/IEC 42001:2023 and ICH Q10 principles can promote the development of a self-correcting AI-based QMS, significantly improving quality management practices. AI algorithms can analyze extensive data sets that include manufacturing conditions, batch records, and quality outcomes. This analysis enables the early identification and correction of deviations, which helps reduce the likelihood of regulatory noncompliance and enhances therapeutic equivalence assurance. For example, AI systems can detect equipment drift and signal maintenance needs in continuous manufacturing environments before batch deviation occurs.

A notable feature of ISO/IEC 42001:2023 is its emphasis on transparency and accountability in AI processes. This aligns seamlessly with the risk management ethos central to ICH Q10. Together, these standards ensure that the AI system optimizes pharmaceutical processes transparently, ethically, and with traceable accountability.

Potential Challenges And Mitigation Strategies

Integrating AI into pharmaceutical quality management systems is not without challenges. Potential pitfalls include:

  • Algorithm Bias: AI algorithms may inadvertently perpetuate biases based on incomplete or unrepresentative data sets. Mitigation involves rigorous validation of data quality, regular audits, and ensuring diverse data representation.
  • Data Security and Integrity: Increased reliance on AI necessitates robust data security measures. Implementing stringent cybersecurity practices and regular integrity audits can mitigate potential data risks.

Strategic Steps For Effective Integration

For pharmaceutical professionals looking to capitalize on this integration, a structured approach is essential:

  • Cultivate Organizational Readiness and Cultural Change: Integrating AI into quality systems demands more than just technological upgrades. It also requires a profound cultural shift within the organization, including accounting for legacy errors of omission. Investing in comprehensive training, fostering open communication, and encouraging critical thinking will enable staff to transition from compliance-focused to proactive, risk-aware approaches, empowering them as true agents of quality improvement.
  • Establish Ethical AI Governance: Develop an ethical governance framework aligned with ISO/IEC 42001:2023 principles. This framework should delineate clear roles and responsibilities, transparency requirements, and protocols for maintaining AI accountability, which is crucial for regulatory compliance.
  • Data Integrity and Continuous Learning: Prioritize robust data management systems that align with ALCOA+ principles (Attributable, Legible, Contemporaneous, Original, Accurate — plus Complete, Consistent, Enduring, and Available). Ensure AI algorithms continuously learn and adapt based on validated high-quality data sets.
  • Gap Analysis and Cross-Standard Harmonization: Begin with a comprehensive gap analysis to identify overlaps and differences between ISO/IEC 42001:2023 and ICH Q10. This analysis should pinpoint opportunities for integration, especially in risk management, continuous improvement, and data integrity domains.
  • Proactive Risk Management: Implement AI-driven predictive analytics to identify and manage quality risks proactively. This includes real-time monitoring systems that flag deviations and initiate immediate corrective and preventive actions (CAPA).

Conclusion: A Call To Action

Integrating ISO/IEC 42001:2023 with ICH Q10 to establish a self-correcting AI QMS presents a compelling pathway forward for pharmaceutical quality assurance. It bridges technological innovation with regulatory robustness, enhancing public trust and ensuring continuous therapeutic equivalence. Adopting AI QMS strategically positions organizations to realize long-term advantages, including heightened quality assurance through early detection and mitigation of risks, streamlined regulatory alignment ensuring smoother compliance processes, and enhanced competitive advantage by demonstrating a forward-thinking, adaptive, and robust approach to quality management. Pharmaceutical professionals would benefit from proactively exploring this integrated approach, transforming regulatory compliance from a routine obligation into a strategic asset and setting new benchmarks for pharmaceutical excellence in the AI era. This transformative integration underscores a critical industrywide shift from merely "checking the box" on compliance to fostering a genuinely resilient, adaptive, and continuously improving pharmaceutical quality culture. As AI technologies mature and regulatory expectations evolve, organizations that embrace this integration early will help shape, rather than react to, the future of pharmaceutical quality.

References

  1. FDA NEWS RELEASE (2025): FDA Launches Agency-Wide AI Tool to Optimize Performance for the American People. https://www.fda.gov/news-events/press-announcements/fda-launches-agency-wide-ai-tool-optimize-performance-american-people
  2. FDA. (2013). One Quality Voice: Office of Pharmaceutical Quality. U.S. Food and Drug Administration.
  3. ISO/IEC. (2023). ISO/IEC 42001:2023: Artificial intelligence — Management system. International Organization for Standardization
  4. ICH. (2008). ICH Q10: Pharmaceutical Quality System. International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use.
  5. Hussain, A.S (2025). Addressing Blind Spots In Assuring Therapeutic Equivalence. Pharmaceutical Online Guest Column | January 14, 2025. https://www.pharmaceuticalonline.com/doc/addressing-blind-spots-in-assuring-therapeutic-equivalence-0001
  6. Hussain, A.S., Gurvich, V.J. & Morris, K. (2019). Pharmaceutical “New Prior Knowledge”: Twenty-First Century Assurance of Therapeutic Equivalence. AAPS PharmSciTech 20, 140 (2019). https://doi.org/10.1208/s12249-019-1347-6

About The Author:

Ajaz S. Hussain, Ph.D., is a pharmaceutical quality and regulatory science expert with decades of experience across academia, industry, and government. As a former deputy director of the FDA's Office of Pharmaceutical Science, he was pivotal in pioneering initiatives like the process analytical technology (PAT) framework and the Pharmaceutical Quality for the 21st Century Initiative. Hussain also served as president of the National Institute for Pharmaceutical Technology and Education (NIPTE), advancing pharmaceutical technology and regulatory science. Now an independent consultant, he empowers organizations to integrate design thinking with a systems approach to advance innovative technologies that hold high potential for enhancing patient care and operational excellence.