AI in Drug Development: Regulators Are Clear. Your Build Should Be Too.
"Regulators have been very clear in their positions and guidance on how to use AI in drug development more safely, effectively, and in line with their expectations." That's the signal. For IT and development teams, the next step is turning that clarity into system design, documentation, and repeatable practice.
The goal is simple: provable safety, traceability, and fitness for purpose. Here's how to translate guidance into code, pipelines, and reviews that pass scrutiny.
What "clear guidance" means for builders
- Intended use first: State the clinical or R&D decision your model supports, where it fits in the workflow, and who can use it.
- Data quality and lineage: Show source, transformations, filters, exclusions, and approvals. Make it reproducible end to end.
- Risk management: Identify failure modes, severity, likelihood, and controls. Revisit risks after each model or data change.
- Human oversight: Define review points, escalation paths, and who can override model output.
- Transparency: Provide rationale summaries, limitations, and known blind spots in language stakeholders can understand.
- Bias assessment: Track cohort coverage, missingness, and performance across subgroups that matter clinically.
- Privacy and security: Minimize PII, enforce access control, and log everything that matters.
- Change control: Version data, code, models, prompts, and configs. Tie each change to a ticket and a reviewer.
- Validation and monitoring: Validate before release, then monitor for drift, data shifts, and model decay with alerts.
Technical checklist for your stack
- Data pipelines: Immutable raw zone, curated zone with checks, and feature store with lineage. Block unapproved datasets.
- Reproducibility: Containerize training/inference, lock seeds, snapshot dependencies, and sign artifacts.
- Model registry: Store model binaries, metrics, datasets used, config, and approvals. No deploy without a reviewed record.
- Validation suite: Holdout, external validation where feasible, stress tests, ablations, and scenario tests aligned to risks.
- Explainability: Use fit-for-purpose methods (e.g., SHAP for tabular, attention or probe tests for transformers) with stability checks.
- Fairness metrics: Track performance by subgroup; define thresholds and remediation steps.
- Prompt and LLM control: Version prompts, retrieval sources, safety filters, and tool access. Log inputs/outputs with redaction.
- Security: Secrets management, least-privilege IAM, network isolation for sensitive workloads, and encryption in transit/at rest.
- Observability: Link model logs to business events. Keep audit trails that map a prediction to exact data, code, and parameters.
Process map aligned to GxP and clinical workflows
- Requirements: Intended use, acceptance criteria, and user roles.
- Risk assessment: Use a structured method (e.g., aligned with ICH Q9 principles) and document controls.
- SOPs: Data handling, training, validation, deployment, monitoring, and incident response.
- Training and access: Verify role-based training completion before granting permissions.
- Change control: Impact analysis, approvals, and rollback plans for data, code, and models.
- CAPA: Root-cause analysis and tracked remediation for incidents or metric breaches.
- Periodic review: Revalidate when indications, populations, or endpoints shift.
Artifacts reviewers expect
- Intended Use Statement and workflow map
- Data specification, lineage diagram, and quality report
- Validation Plan and Validation Report
- Risk Management File with mitigations and residual risk
- Model Card and Datasheet for datasets
- Monitoring Plan, alert thresholds, and on-call runbook
- Change Control Log with versions and approvals
Common pitfalls that get flagged
- Shadow datasets or features without lineage
- Great internal metrics but weak external validation
- Opaque model behavior with no guardrails or overrides
- Missing subgroup analysis or unclear inclusion criteria
- LLMs with unreviewed prompts or unlogged tool calls
- No plan for drift, data shifts, or model decay
Helpful references
Start with primary sources and map them to your system design and SOPs.
Team enablement
If your engineers and data scientists need a structured path to skills like MLOps, validation, and governance, explore focused role-based learning tracks here: Complete AI Training - Courses by Job.
Regulators have set the bar. Treat AI features like any regulated system: clear intent, tight data practices, measurable risk control, and evidence that your process works. If you can show it, you can ship it.
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