How We Help You Prepare for Authority-Ready AI Use

Our support spans the full development lifecycle from early-stage models to submission-critical systems. Below, we outline what regulators will expect from your AI use in each area and how we help you meet those expectations.

Preclinical
AI is increasingly used in early-stage pharmaceutical R&D to generate insights into target selection, toxicology predictions, or dose estimations. If such outputs are used to inform CTA/IND submissions, regulators will expect traceability, justification, and alignment with GLP expectations and OECD guidance on computerised systems. Risk increases further when AI model outputs directly replace or supplement traditional preclinical evidence in the regulatory file.

• Prepare traceable documentation packages for AI-generated insights included in CTA/IND submissions, including intended use, decision relevance, and regulatory impact. Ensures that decision-impacting preclinical outputs meet regulatory demands, so that AI-derived evidence is accepted rather than challenged or delayed during review.
• Maintain robust version control and audit trails for training datasets and model architectures, especially for models that are retrained, refined, or incrementally improved over time.
• Document scientific rationale for model design and selection, including input parameters, statistical performance, and comparability to traditional preclinical methods.
• Justify bias mitigation methods using transparent metrics (e.g., demographic distribution, false-negative rate across subgroups) and include a clear explanation of ethical safeguards when using animal or human-derived data.
• Support early engagement with regulatory authorities — including preparation for scientific advice — where AI-generated evidence is being considered.

Clinical Development
AI applications in clinical trials — from digital endpoints and predictive tools to AI-supported patient monitoring — can materially influence trial conduct, outcomes, and data integrity. When these systems affect clinical decision-making, subject safety, or primary/secondary endpoints, they must meet both regulatory and ethical expectations. In some cases (e.g., digital biomarkers, wearables, closed-loop systems), the AI system itself may fall under medical device regulations. Authorities expect traceability, risk justification, and human oversight throughout.

• Document initial validation plans and ongoing monitoring procedures for AI tools influencing clinical trial execution or data interpretation — including periodic model performance reviews and change control.
• Prepare medical device classification justifications and regulatory roadmaps when AI tools meet the definition of Software as a Medical Device (SaMD) — especially in the context of digital endpoints or patient monitoring.
• Support GDPR-compliant data protection impact assessments (DPIA) and provide technical documentation demonstrating appropriate de-identification, access controls, and data minimization to support privacy and appropriate data use per GDPR and GCP.
• Trace and assess the representativeness of training and validation datasets across demographics and relevant clinical subgroups to address bias and support GCP-aligned subject protection.
• Prepare briefing materials and structured documentation for Scientific Advice or other early engagement with EMA or national authorities when AI influences study endpoints, eligibility criteria, or adaptive trial decisions.

GMP / CMC (Chemistry, Manufacturing & Controls)
AI tools are increasingly integrated into GMP-adjacent activities — from predictive maintenance and quality prediction to model-informed batch release. When such systems are used in GMP settings or influence product quality decisions, regulators expect them to be validated according to recognized frameworks such as GAMP5®. Additional scrutiny applies to any “adaptive” or retrained model, especially if it cannot be fully explained. Inspection readiness hinges on transparent documentation, version control, and full data lineage from source to decision.

• Apply GAMP5® principles for the computerized system lifecycle of AI systems in GMP use, including supplier assessment, URS, IQ/OQ/PQ documentation, and configuration management to establish clear regulatory alignment for AI tools in GMP use.
• Define and implement protocol-driven re-validation procedures for AI models that evolve over time or are periodically retrained — including thresholds for re-qualification.
• Justify and document the use of “black box” AI models in critical decision-making contexts using a structured risk management plan aligned with ICH Q9, Annex 11 and Annex 22 expectations.
• Provide an audit-readiness checklist tailored for AI use in GMP, covering authority expectations on documentation, access control, electronic records, explainability, and process ownership.
• Produce data lineage documentation that traces every input and transformation, supporting transparent oversight of all AI-driven QC or batch release outcomes.

Regulatory Affairs (RA)
AI tools are increasingly used to support drafting of regulatory dossiers — including summaries (e.g., Module 2.3), clinical overviews, risk management plans, or even Q&A responses. While the efficiency gains are real, regulators now expect clear oversight and traceability, especially where AI-generated content is submitted as part of the official CTD. The EMA’s AI reflection paper outlines the importance of human supervision, version control, and transparency. Additionally, digital record-keeping requirements (e.g., FDA 21 CFR Part 11, EMA Annex 11) apply where AI outputs are stored and reused.

• Require full annotation and version control for all AI-generated content used in submissions, including timestamps, editor identification, acceptance history, and differentiation between original AI output and final submitted text.
• Define a submission declaration strategy, outlining how and where AI use is disclosed within the CTD — particularly in high-impact modules or sections subject to direct regulator review.
• Implement a human-in-the-loop review protocol, ensuring expert validation of accuracy, alignment with regulatory guidance, and consistency with the overall submission narrative.
• Conduct plagiarism and proprietary content screening on AI-generated outputs to prevent accidental inclusion of non-public, third-party material.
• Maintain digital records of AI system configurations, model versioning, and review workflows in compliance with Annex 11 and 21 CFR Part 11 requirements.

Pharmacovigilance (PV)
AI is now used in PV workflows for tasks like signal detection, ICSRs triage, and automation of medical coding - often via NLP models. This introduces regulatory scrutiny from both EMA and FDA regarding reliability, oversight, and transparency. Systems must comply with EMA GVP Module IX, FDA PV guidance, and general expectations for computerized systems (e.g., Annex 11). Continuous performance monitoring, model explainability, and concept drift detection are key to avoiding inspection findings, especially in high-volume or real-time PV environments.

• Ensure PV-related AI systems meet GVP Module IX (EU) and FDA guidance requirements, including documentation of intended use, validation, and oversight responsibilities.
• Include negative control and stress-test analyses to demonstrate robustness in signal detection and avoid overfitting to specific ADR patterns.
• Implement structured model monitoring and retraining plans, with thresholds for performance degradation and documented change history.
• Develop procedures to monitor NLP models for concept drift, shifts in medical terminology, or changes in spontaneous reporting language over time.
• Provide a SOP for audit trail management, ensuring traceability of AI-assisted decisions — especially for models updated continuously or via online learning.

Cross-Functional / Compliance Infrastructure
AI systems used in regulated pharmaceutical processes cannot be treated as isolated tools — they require a documented, organization-wide compliance infrastructure. This includes aligning digital systems with GxP expectations, staying ahead of evolving regulatory frameworks (e.g., EMA AI reflection paper, EU AI Act, FDA AI/ML guidance), and ensuring teams across functions understand how to implement and defend AI-based processes in front of authorities.

• Implement structured regulatory horizon scanning procedures to ensure policies, templates, and validation approaches are continuously updated with EMA, FDA, and EU AI Act changes.
• Define escalation pathways and deviation management protocols for situations where an authority raises concerns related to AI system performance, validation, or documentation.
• Require cross-functional AI/ML literacy training, documented in SOPs or training logs, to ensure clinical, regulatory, CMC, QM and PV teams can apply and defend AI use appropriately.
• Recommend independent audit or third-party review cycles to periodically assess the robustness of your AI validation and compliance framework.
• Establish standardized due diligence procedures for onboarding AI vendors or tools — covering software validation status, data access controls, audit history, and regulatory track record.

Designed for Authority-Facing Pharma Teams Using AI

This service is built for pharma, biotech, and digital health companies whose use of AI is part of regulated development, manufacturing activities. It’s not about building AI tools but it’s about using them in ways that regulators will review, question, or inspect.

We work directly with Regulatory Affairs, Clinical Operations, PV, CMC/Quality, and Digital Innovation teams when AI is used in trial protocols, submissions, or GxP-governed systems where regulatory defensibility is a must.

These examples illustrate how we can help translate AI into regulator-ready, defensible outcomes without slowing development timelines.

Use cases we support:

• Clinical Operations: Using AI to stratify patients or define digital endpoints in interventional trials.
• Regulatory Affairs: Reviewing and validating AI-generated content in Module 2 or 5 of the CTD.
• Quality and Manufacturing: Applying ML models for real-time quality control or batch release decisions.
• Pharmacovigilance: Integrating NLP tools to classify adverse events or detect safety signals.
• CMC Teams: Leveraging predictive models for process parameters or shelf-life estimations.
• R&D and RA: Including in-silico toxicology or trial simulations in scientific advice or CTA dossiers.
• Regulatory Compliance: Preparing for inspection when AI use is not yet fully documented in SOPs or QMS.

A Partner for Compliance-Ready AI
Our teams combine regulatory, clinical, quality management and technical expertise to help you deploy AI with confidence. Whether you’re piloting a new tool or preparing for submission or inspection, we provide the structure and documentation needed to stay compliant — and ahead of regulator expectations.