AI-driven batch release in pharma

In an industry where precision and compliance are non-negotiable, artificial intelligence (AI) is opening the door to a new era of intelligent batch release. But introducing AI into Good Manufacturing Practice (GMP)-regulated processes isn’t about replacing humans — it’s about augmenting decisions, reducing risk and accelerating safe product delivery.

So how do you harness AI in batch release while meeting GxP, FDA, EMA and data protection expectations? Let’s break it down.

AI in a GxP world: What must be true

AI used in batch release must be treated as a GxP-critical system. That means following the same rules as any other computerised system under:

EU GMP Annex 11 & 15
21 CFR Part 11
ICH Q8–Q10
GAMP 5
EMA’s AI Reflection Paper (2023)
FDA’s CSA Guidance (2022 Draft)

AI doesn’t get a free pass — it must be validated, explainable, secure and auditable.

How to validate AI: The GAMP 5 way

Define scope, risk and compliance boundaries.

Document:

Data sources and quality
Model architecture
Bias mitigation and performance metrics

3.1. Design qualification (DQ): Is the AI system properly designed to address the needs and requirements for GMP batch release? → System architecture and components reviewed.

DQ: AI model architecture (layers, logic) documented.
DQ: Interfaces with MES, LIMS, ERP or QMS systems validated.

3.2. Installation qualification (IQ): Is the AI system installed correctly? → Infrastructure and software dependencies installed and documented.

IQ: AI model and runtime environment versions logged.
IQ: Security, access control and user roles validated.

3.3. Operational qualification (OQ): Does it function as intended? → AI functions tested with valid and invalid inputs.

OQ: Performance metrics validated (e.g. precision, recall, AUC).
OQ: System behaviour under failure scenarios tested.

3.4. Performance qualification (PQ): Can it reliably operate in real-world production? → Test performance in real or simulated production conditions.

PQ: Results reviewed by SMEs/QPs for consistency with expectations.
PQ: Model output compared to manual review or benchmark.

Use tools like SHAP or LIME to ensure QPs and auditors can understand how the model reached its conclusion.

Continuously track performance and revalidate after changes such as retraining or software updates.

Documentation is your best friend

Keep detailed, inspection-ready records:

Validation reports and risk assessments
Training data, model versions and changes
SOPs and training logs
QP decisions and overrides

No documentation = no compliance.

Security and vendor qualification for AI tools

If you’re using tools like OpenAI or Azure OpenAI in batch release workflows, you must qualify the vendor like any GxP supplier:

Request SOC 2, ISO 27001 and security whitepapers
Perform a risk-based supplier audit
Confirm data handling practices (DPA, GDPR compliance)

Secure system architecture is key:

API access controls
Encrypted communication
Isolated environments (e.g. containers, VPCs)
Strict logging and prompt traceability

AI risk? Yes. But identified and manageable.

Here are the top risks — and how to mitigate them:

Risk	Control strategy
Data privacy (GDPR)	Input redaction, vendor DPA, data minimisation
Black-box models	Input redaction, vendor DPA, data minimisation
Model drift	Continuous monitoring, performance alerts
Prompt injection	Guardrails, input sanitisation, prompt whitelisting
Unauthorised access	RBAC, 2FA, API key rotation
Regulatory non-compliance	GxP validation, documentation, audit readiness