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AI in Pharma: Why Regulation is No Longer Optional

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  Published in Science and Technology on Tuesday, December 2nd 2025 at 11:17 GMT by Forbes

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Annex 22: Why AI Regulation in Pharma Manufacturing Matters More Than Ever
(Summary of the Forbes Tech Council article published 2 Dec 2025)

1. The Growing Role of AI in Pharma Production

The pharmaceutical industry has always been a data‑rich environment, but recent breakthroughs in machine learning (ML) and artificial intelligence (AI) are accelerating the transformation of every stage of drug development and manufacture. From real‑time analytics on cell‑culture bioreactors to predictive maintenance of downstream equipment, AI promises faster, more cost‑effective, and safer production pipelines.

Yet with opportunity comes risk. The article stresses that AI systems in pharma are not simply tools; they can become integral to critical quality control decisions, supply‑chain optimization, and even regulatory submissions. As a result, the question is no longer “Can we use AI?” but “How do we ensure AI behaves responsibly, transparently, and safely in a regulated environment?”

2. Why Regulation Is Essential

2.1 Data Integrity and Traceability

Pharma regulations (FDA 21 CFR 11, EMA 2024 Good Manufacturing Practice guidelines) require unbroken, auditable data trails. AI models that ingest data from disparate sources—lab instruments, ERP systems, IoT sensors—must preserve the provenance, timestamping, and integrity of that data. Without regulation, companies risk non‑compliance, product recalls, and costly fines.

2.2 Patient Safety and Product Efficacy

AI decisions can directly affect batch release criteria or dosage formulation. The article cites a 2024 EMA study where an AI‑driven batch‑release algorithm missed a subtle trend that, if undetected, could have led to sub‑potent drug lots. This highlights the need for validation, continuous monitoring, and rollback protocols before fully deploying AI in the critical path.

2.3 Intellectual Property and Commercial Competitiveness

Pharma firms invest billions in proprietary process knowledge. AI models that inadvertently reveal trade secrets through data leakage or model inversion attacks pose a strategic risk. Regulation can codify best practices for data protection, model explainability, and access controls.

2.4 Cross‑Regulatory Harmonization

The industry operates globally. The article notes divergent AI guidance from the U.S. FDA (Draft Guidance for AI/ML‑Based Software as a Medical Device, 2024), the European Medicines Agency (EMA AI/ML‑based guidance, 2023), and Japan’s Pharmaceuticals and Medical Devices Agency (PMDA, 2025). Annex 22 is designed to bridge these gaps by providing a unified framework that can be adopted or adapted by national agencies.

3. What Is Annex 22?

Annex 22 is a framework developed by the International Society for Pharmaceutical Engineering (ISPE) in collaboration with the U.S. FDA, EMA, and other key stakeholders. It is part of the ISPE’s Digital Transformation initiative and is modeled after the ISO 21448 “Safety of Highly Automated Driving” and ISO/IEC 20233 “AI Risk Management for Digital Health.”

Key attributes of Annex 22:

1. Risk‑Based Approach – classify AI systems by impact level (high‑impact, moderate‑impact, low‑impact).
2. Lifecycle Governance – prescribes stages from concept, development, validation, deployment, monitoring, to decommissioning.
3. Validation & Verification (V&V) Protocols – includes requirements for data quality, model performance metrics (e.g., ROC, precision‑recall), and robustness testing against adversarial inputs.
4. Explainability & Transparency – mandates that stakeholders can interpret key decision points, particularly for critical quality attributes (CQAs).
5. Human‑in‑the‑Loop (HITL) Controls – defines thresholds where human oversight is mandatory (e.g., batch release, anomaly detection).
6. Auditability & Documentation – all training data, hyperparameters, versioning, and change‑control records must be captured in an auditable system.
7. Ethical and Societal Considerations – addresses data privacy (GDPR, HIPAA), bias mitigation, and environmental impact of AI training workloads.

Annex 22 is not a regulatory mandate itself but a guideline that regulators are increasingly referencing. The article highlights that the FDA’s Regulatory Review Guide for AI/ML‑Based Software (2024) now cites Annex 22 as a recommended standard for validation of AI systems used in GMP environments.

4. Implementation Roadmap (From the Article)

1. Establish an AI Governance Committee
   - Include regulatory affairs, quality, IT, data science, and production operations.
   - Adopt Annex 22 roles and responsibilities matrix.

2. Conduct a Maturity Assessment
   - Map existing AI projects to Annex 22 risk categories.
   - Identify gaps in data infrastructure, model documentation, or audit trails.

3. Develop a Standard Operating Procedure (SOP) for AI Lifecycle
   - SOP must reference Annex 22’s “Lifecycle Governance” section.
   - Include change‑control, versioning, and rollback procedures.

4. Validate with a Pilot Project
   - Use a non‑critical, high‑impact AI use‑case (e.g., predictive maintenance on a mixer) to test validation protocols.
   - Document performance metrics and audit evidence in a Validation Master File.

5. Scale & Integrate
   - Use the validated pilot as a template for other AI projects.
   - Ensure interoperability with existing MES (Manufacturing Execution Systems) and LIMS (Laboratory Information Management Systems).

6. Continuous Monitoring & Periodic Re‑validation
   - Implement real‑time dashboards for key model KPIs.
   - Schedule annual or event‑driven re‑validation cycles.

7. Training & Change Management
   - Provide cross‑functional training on Annex 22 concepts.
   - Encourage a culture of “data stewardship” and “model stewardship”.

5. Real‑World Case Studies

The article cites a 2025 GSK white paper that quantified a 22% reduction in overall manufacturing cost after incorporating Annex 22‑aligned AI controls.

6. Regulatory Perspective: “What Regulators Are Saying”

• FDA: The Draft Guidance on AI/ML‑Based Software now references Annex 22 as a “preferred” framework for validating AI in manufacturing. The FDA also announced a 2026 workshop on AI regulatory readiness for pharma, encouraging the adoption of Annex 22.
• EMA: The European Commission’s Digital Strategy for Medicines (2025) earmarked Annex 22 as a key compliance tool for the upcoming EU AI Act provisions that apply to AI used in life‑science manufacturing.
• PMDA (Japan): PMDA’s 2025 AI/ML guidance includes Annex 22’s risk‑based approach as a benchmark for “high‑impact” AI solutions.

These endorsements underscore that while Annex 22 itself isn’t law, it has become a de‑facto standard that regulators look for during audits and inspections.

7. The Economic & Competitive Edge

The article argues that companies that adopt Annex 22 early will enjoy:

• Reduced Inspection Time: Inspectors can verify compliance faster when a standardized Annex 22 evidence package is available.
• Lowered Development Risk: Early alignment with risk categories prevents costly mid‑stream redesigns.
• Market Credibility: Investors and patients increasingly demand transparency; a robust AI governance framework signals commitment to safety and ethics.

A 2024 Deloitte study cited in the article shows that pharma firms that incorporated AI governance frameworks saw a 9% increase in pipeline efficiency and a 4% improvement in customer satisfaction scores.

8. Looking Ahead: AI Regulation in the Next Decade

The article concludes by framing Annex 22 as a “living document.” As AI technologies evolve—think of federated learning, edge AI, or generative models—the framework will need to iterate. The ISPE has already set up a Digital Health Advisory Board to update Annex 22 annually, ensuring it remains aligned with emerging global AI regulations (e.g., the EU AI Act, U.S. AI Bill of Rights).

Pharma leaders are urged to view Annex 22 not as a bureaucratic hurdle but as a strategic investment that will position them at the forefront of the digital transformation wave.

9. Key Takeaways

1. AI in pharma is no longer optional; it is reshaping production and regulatory landscapes.
2. Annex 22 offers a comprehensive, risk‑based, lifecycle framework that aligns with FDA, EMA, and other regulatory expectations.
3. Early adoption accelerates compliance, reduces risk, and enhances competitiveness.
4. Ongoing collaboration between regulators and industry is essential to keep the framework relevant.

By weaving together technical rigor, regulatory alignment, and practical implementation guidance, Annex 22 is set to become the cornerstone of AI governance in pharmaceutical manufacturing.

References & Further Reading (for deeper context)

• FDA Draft Guidance on AI/ML‑Based Software (2024) – https://www.fda.gov/medical-devices/digital-health-center-excellence/draft-guidance-ai-ml-based-software
• EMA AI/ML Guidance (2023) – https://www.ema.europa.eu/en/documents/technical-guideline/ai-ml-guidance
• ISPE Digital Transformation Initiative – https://ispe.org/digital-transformation
• Deloitte Pharma AI Study 2024 – https://www2.deloitte.com/us/en/pages/life-sciences-and-health-care/articles/ai-in-pharma.html

(Note: URLs provided for illustrative purposes and may not point to the exact resources cited in the Forbes article.)