Efpia on AI Across the Medicines Lifecycle: Governance, GxP and Regulatory Policy Insights

Key Takeaways

• AI governance across the medicines lifecycle requires attention to five critical stages: planning and design, data collection and processing, model development and validation, deployment and use, and ongoing monitoring and risk mitigation.
• Common governance practices among leading pharmaceutical companies include early multidisciplinary planning, data standardization to formats such as SDTM, prioritization of model transparency and explainability, training and change management, and proactive risk assessment even during pilot phases.
• Many AI uses may already be sufficiently governed under existing frameworks such as GCP, GMP, and GVP, with AI-specific controls added where needed, rather than requiring entirely new regulatory structures.
• EFPIA recommends regulators clarify AI-related exemptions, foster iterative industry-regulator dialogue, harmonize expectations across jurisdictions, and adopt dynamic guidance formats such as Q&A documents to support responsible innovation.

What This Report Covers

EFPIA's report presents preliminary case studies examining AI governance practices across four distinct medicines lifecycle applications: AI-assisted histopathology analysis in clinical research, synthetic data generation for clinical trials, AI-enabled quality oversight in pharmaceutical R&D, and AI-based adverse event screening in pharmacovigilance. For each case study, the report maps governance structures across the standardized AI lifecycle stages and connects those practices to regulatory and ethical considerations.

Why This Matters for Pharmaceutical Teams

For R&D, clinical development, manufacturing, regulatory affairs, and pharmacovigilance teams, the EFPIA report offers a practical framework for assessing whether AI tools are adequately documented, validated, explainable, and monitored within existing quality systems. The report emphasizes that AI adoption is not solely a technical issue but also a governance and compliance challenge requiring cross-functional alignment on data quality, model transparency, training, and continuous oversight.

Frequently Asked Questions

Does AI governance require entirely new regulatory frameworks, or can existing GxP frameworks accommodate AI?

According to EFPIA's preliminary findings, many AI applications may already fit within existing regulatory frameworks such as GCP, GMP, and GVP, with AI-specific controls and validations added where needed. However, additional regulatory clarity is needed on how AI should be governed throughout its lifecycle, particularly regarding the scope of AI-related exemptions and iterative engagement between industry and regulators.

What are the key governance practices companies should implement across the AI lifecycle?

EFPIA's case studies identified five critical governance practices: early multidisciplinary planning and clear documentation; data standardization and quality assurance; prioritization of model transparency and explainability over purely predictive performance; comprehensive training and change management to build internal trust; and proactive monitoring and risk assessment, even during pilot phases, to sustain accountability.

How can pharmaceutical organizations balance AI innovation with regulatory alignment?

EFPIA recommends that regulators and industry engage in ongoing iterative dialogue, with greater transparency and sharing of AI use cases and governance strategies. The report also recommends that regulators adopt dynamic guidance formats, such as Q&A or FAQ documents, that can be updated more quickly to reflect evolving best practices and reduce the time lag between innovation and regulatory clarity.

What policy actions does EFPIA recommend for regulators?

EFPIA recommends that the EMA, European Commission, and other regulators clarify the scope of AI-related exemptions; encourage iterative regulator-industry engagement on AI policy themes; support harmonized expectations across jurisdictions; consider regulatory sandboxes to explore AI use cases; and integrate AI oversight into existing regulatory frameworks such as endpoint qualification and real-world evidence processes.

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