GDPR & AI Compliance for Pharma: Drug Discovery & Clinical Trial AI

Drug Discovery & Target Identification

• Patient data for AI training: Using real-world patient data (EHR, genomic, biobank) to train AI models for drug target identification. Lawful basis: usually scientific research exemption (Art. 89) or consent.
• Anonymisation vs pseudonymisation: Truly anonymous data is outside GDPR scope. But genomic data and rare disease data are extremely difficult to anonymise — re-identification risk is high. Most pharma AI uses pseudonymised data (still in GDPR scope).
• International transfers: Drug discovery is global. Patient data from EU studies must be processed under GDPR even when analysis happens in US/Asia. Standard contractual clauses, transfer impact assessments required.
• Purpose limitation: Data collected for one clinical study cannot automatically be used for AI training on a different drug programme without compatible legal basis.

Clinical Trial AI

• Patient recruitment: AI screening EHR data to identify eligible trial participants — processing health data at scale
• Adaptive trial design: AI adjusting trial parameters (dosing, endpoints) based on interim data — requires robust governance
• Digital biomarkers: Wearable/sensor data from trial participants — continuous health monitoring with consent and data minimisation challenges
• Clinical Trial Regulation interaction: EU CTR (536/2014) has its own data protection provisions that interact with GDPR

Lawful Basis Options

• Consent (Art. 6(1)(a) + Art. 9(2)(a)): Specific consent for AI processing. Challenge: must be freely given, specific, and withdrawable without affecting the individual. Broad consent for "future research" is questioned by some DPAs.
• Scientific research (Art. 9(2)(j) + Art. 89): Processing of special category data for scientific research with appropriate safeguards. Most EU member states have national derogations.
• Public interest in public health (Art. 9(2)(i)): For pharmacovigilance and post-market surveillance — legally mandated activities.

1. Data governance platform: Centralised catalogue of all patient data assets used for AI, with lawful basis, retention periods, and access controls documented
2. Privacy-enhancing technologies: Federated learning, differential privacy, synthetic data generation — reduce GDPR exposure while enabling AI research
3. Transfer impact assessments: For every international data flow involving patient data — especially US transfers post-Schrems II
4. DPIA for each AI programme: GDPR Art. 35 — mandatory before processing health data at scale for AI
5. Data subject rights automation: Clinical trial participants can exercise access, rectification, and erasure rights — systems must support this