EMA and FDA Publish 10 Common Principles for AI in Drug Development

EMA and FDA set 10 common principles for AI in drug development: what dev teams need to know

The EMA and FDA released a joint list of 10 principles for using AI across drug discovery, clinical trials, manufacturing, and safety monitoring. This framework will drive future guidance in the US and EU and inform international standards.

If you build or integrate AI for biopharma, this is your blueprint. It's about risk, documentation, human oversight, and systems that stand up in audits.

The 10 principles at a glance

• Human-centric by design and aligned with ethical values.
• Risk-based approach that considers context of use.
• Compliance with current standards across legal, ethical, technical, scientific, cybersecurity, and regulatory domains.
• Clear context of use defined up front.
• Multidisciplinary development with the right domain experts involved.
• Strict data governance and documentation, including privacy and protection of sensitive data.
• Best practices in model/system design and software engineering.
• Risk-based performance assessments before use, including human-AI interaction testing.
• Ongoing monitoring and periodic re-evaluation to ensure fitness for purpose.
• Plain-language communication for users and patients.

What this means for engineering and data teams

Think GxP-grade MLOps. Every model and pipeline step needs traceability, version control, and change management. If a regulator asks "why this model, on this data, at this time," your system should answer in one click.

"Context of use" isn't a slide. It's a spec. Define who uses the model, decisions it supports, data it sees, failure modes, and acceptable risk. Tie that to controls, alerts, and rollback paths.

Privacy and security aren't box-ticking. You'll need defensible PII/PHI handling, secure enclaves or VPC isolation, SBOMs for AI components, vendor risk assessments, and auditable access policies.

Build these capabilities into your stack

• Model governance: model cards, data sheets, lineage, immutable artifacts, signed releases, and a model registry.
• Validation and verification: protocol-driven evaluation, predefined acceptance criteria, bias checks, stress tests, and human factors testing.
• Monitoring: live drift detection, data quality checks, performance decay alerts, incident playbooks, and scheduled revalidation.
• Documentation: ALCOA+ compliant records, 21 CFR Part 11/Annex 11 alignment for electronic records and signatures, and clear SOPs.
• Security: least-privilege access, secrets management, model and dataset SBOMs, supply chain scanning, and verified training data sources.
• Plain language UX: clear explanations, risk disclosures, and user guidance in the interface-especially for clinician and patient touchpoints.

Where to apply it across the lifecycle

• Discovery: target ID, generative design, and prioritization models with traceable datasets and reproducible pipelines.
• Clinical: site selection, patient screening, eCOA signal processing, and protocol optimization with documented human oversight.
• Manufacturing: predictive maintenance, process control, and release testing with validated sensors and change control.
• Safety: signal detection, case intake, and triage with high-precision thresholds and audit-ready logs.

Market signal: AI deals keep stacking

Partnership activity is a tell. Iktos struck new deals with Servier and Pierre Fabre, Insilico Medicines teamed with Servier, GSK signed with Noetik and Helix, and AstraZeneca acquired Modella AI to bring foundation models and AI agents in-house. Translation: expect stricter vendor evaluations and integration standards.

Practical next steps for Q1

• Map every AI use case to a context of use doc with risk tiering and acceptance criteria.
• Stand up a GxP-aware MLOps toolchain: versioned data, reproducible training, model registry, signed artifacts, and approval gates.
• Create a validation playbook: datasets, metrics, adversarial tests, human-in-the-loop evaluation, and release checklists.
• Implement runtime monitoring with drift, data quality, and performance alerts tied to rollback procedures.
• Tighten data governance: lineage, retention, PII handling, and vendor controls with documented audits.
• Update UI copy and help content to meet the plain language requirement.

Bottom line: build for trust and audit from day one. It's faster than retrofitting later and puts your team on the right side of upcoming guidance.

If your team needs a focused skill path on MLOps, governance, and practical AI deployment, explore our AI courses by job.