AI Services — Regulatory Readiness for AI in Pharma

Q: Do we still need regulatory AI documentation if our software isn't classified as a medical device?

Yes. The use context determines the compliance obligations. AI tools that influence regulatory content, clinical trial design, or safety monitoring are subject to authority scrutiny even if not classified as Software as a Medical Device (SaMD). The EU AI Act classifies many AI systems, including clinical decision support and quality control tools, as high-risk regardless of medical device status. Sponsors must assess compliance obligations based on context of use, not classification alone.

Q: If an external vendor developed our AI, who is responsible for compliance?

The provider (MAH) is responsible. According to the EMA Reflection Paper (2024) and FDA GMLP guidance, compliance responsibility lies with the marketing authorisation holder, not the tool vendor. Sponsors must verify fitness-for-purpose of the AI model in their use case, document human review and change control protocols, and maintain governance records demonstrating lifecycle responsibility.

Q: How do you stay current with regulatory AI developments?

Our regulatory intelligence team continuously monitors EMA, FDA, and EU Commission AI publications and stakeholder meetings, ICH guidance updates related to AI-supported trials, inspection trends, Scientific Advice feedback, and pilot program outcomes, as well as industry best-practice publications. We translate this into up-to-date checklists, document templates, and risk maps for our clients.

Q: Can you support multi-functional AI deployments (e.g., across Clinical, QA, and PV)?

Yes. We help clients build harmonized governance and lifecycle management for AI across clinical, quality, manufacturing, and PV domains, covering model development (selection, bias mitigation, data traceability, explainability), operation (SOPs, workflow documentation, version control), and oversight (human-in-the-loop review, audit trails, evidence packages for submissions). This holistic approach enables cross-functional consistency and streamlines authority interactions.

Q: Can you help us prepare AI content or justification for scientific advice or submissions?

Yes. We regularly support preparation of AI validation annexes, digital endpoint justification packages, bias and fairness documentation aligned with EU AI Act Annex IV, and human review and oversight documentation for Module 2/5 content.

Making AI in Pharma Regulatory-Ready — From Model to Submission

We empower pharma teams to document, justify, and defend the use of AI in regulatory-relevant settings across clinical development, CMC, regulatory, manufacturing, pharmacovigilance (PV), and more. Whether you're integrating AI into trial design or automating PV workflows, we support you to ensure your approach holds up under regulatory scrutiny.

AI Regulatory Readiness in Pharma

Why this matters: Regulatory expectations for AI, including guidance from the EMA, the FDA, and the EU AI Act are still evolving. Yet, they are united by common principles: the design choices and uses of AI systems must be tailored to the context of use, and potential risks must be mapped and addressed – with comprehensive validation spanning predictive accuracy, robustness, transparency, fairness, and more. While authorities are taking a cautious, case-by-case approach, sponsors must be able to demonstrate transparency, accuracy, robustness, cybersecurity, human oversight and data governance as well as additional requirements when AI influences clinical data, quality decisions, or submissions. Failure to comply risk review delays, additional questions, or inspection findings.
What we offer: We provide end-to-end support to align your AI use with current regulatory expectations from setting up explainability packages for traditional AI models and suites for AI agents, to drafting validation documentation, SOPs, human-in-the-loop review frameworks, and submission annexes.
How we help: Collaborating closely with your data science, regulatory and quality management teams, we anticipate authority concerns, prepare evidence, and plan interactions with regulators. Our role is to help you design and validate fit-for-purpose AI systems at their inception, to ultimately obtain defensible, transparent documentation - especially where standards are still emerging.
Who we work with: Our services support pharma, biotech, and digital health teams using AI in clinical, PV, CMC, manufacturing or regulatory workflows who require an expert partner to prepare customer-tailored strategies and inspection-ready documentation.

AI in Pharma: Built for Transparency, Validation, and Control

AI technologies are transforming pharmaceutical development, but regulatory authorities are watching closely. With evolving expectations such as EMA's Reflection Paper and the EU AI Act, pharma teams now face rising pressure to demonstrate transparency, control, and validation when using AI in regulated processes.

We help you prepare in advance and not when your submission is already at risk. From explainability and audit trails to submission-ready documentation, our team works with you to anticipate authority concerns and translate complex AI systems into regulator-ready deliverables. Get in touch to discuss your needs.

What we do:

Validation and performance files for AI-based patient selection or digital endpoints (Clinical)
Audit-ready SOPs and explainability packages for NLP or signal detection tools (PV)
Lifecycle and change control documentation for AI-enhanced QC or batch release (GMP/CMC)
SME-reviewed summaries and oversight records for AI-generated content in CTD modules (RA)
Pre-submission compliance checks for AI use across the development lifecycle (cross-functional)

Unsure if your AI design choices or documentation will hold up with regulators?
Contact our AI regulatory experts today.

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.

Why Regulatory Readiness for AI Is Non-Negotiable

AI is no longer seen as peripheral in pharmaceutical development. Authorities increasingly treat it as a regulated technology, subject to lifecycle oversight depending on its impact on patient safety and regulatory decision-making.

The EMA's Reflection Paper on Artificial Intelligence (Sept 2024) outlines a risk-based approach across the product lifecycle, requiring documentation, explainability, and human oversight for AI systems used in clinical, quality, and regulatory contexts¹.
The EU AI Act (Regulation EU 2021/0106), scheduled to apply from January 2024, classifies many AI applications in clinical trials, pharmacovigilance, and manufacturing as "high-risk" systems. While legal provisions are uniform, enforcement practices and interpretations may vary across Member States and use cases².
In the US, FDA guidance on Clinical Decision Support Software and the Digital Health Innovation Action Plan signal evolving oversight. Importantly, AI tools used in clinical decision support may qualify as Software as a Medical Device (SaMD) — subject to full regulatory controls — whereas AI used in regulatory document drafting or internal analysis may fall outside these classifications, depending on intended use and claims³.

While few public enforcement examples exist yet, industry-facing summaries from EFPIA and joint Deloitte/RAPS reports suggest growing concern over traceability, validation, and oversight of AI in GxP settings^4,5. (Note: These sources are primarily intended for regulatory and compliance professionals and may not be publicly accessible in full.)

The burden of proof lies with the sponsor - not with the software vendor. This principle is echoed across EMA, FDA, and other global health authorities. Sponsors must proactively show that any AI tool used in their regulatory workflows is fit for purpose, appropriately governed, and transparently documented.

Sponsors must demonstrate compliance across the AI lifecycle:

Model development: justify training datasets, conduct bias and fairness analysis consistent with ethical AI principles, apply version control for models, and document the scientific rationale for model selection (especially when "black-boxes" such as gradient boosted trees or deep neural networks).
Operational use: maintain GxP-compliant workflows, SOPs, and system access logs; monitor data distribution shifts, implement change controls and update tracking.
Output review: ensure all AI-assisted content is reviewed by qualified experts; document the review process, including annotations, acceptance status, and justification for use in submissions.

Ongoing model updates require structured change control, re-validation, and continuous traceability — particularly in GxP and authority-facing environments.

This isn't just about managing risk. Regulatory readiness is a strategic asset. While quantitative data on time savings is limited, early and comprehensive compliance preparation is widely recognized to reduce authority queries and post-submission clarifications, and position sponsors as credible and trustworthy. It creates clarity for reviewers and builds confidence across internal and external stakeholders.

As regulations and interpretations continue to evolve, sponsors should establish procedures for ongoing review and adaptation of their AI governance, validation, and documentation programs.

Typical Risk Scenarios We Help Prevent

Digital endpoints rejected or delayed due to lack of model validation and incomplete traceability documentation¹.
GMP inspection delays following the use of AI-driven quality decisions without documented workflows, change control logs, or role assignments⁴.
Authority questions during dossier review about the use of generative AI in Module 2 or clinical summaries — without a human-in-the-loop validation protocol or audit trail^1,3.
AI-enabled pharmacovigilance systems flagged for explainability gaps in NLP signal detection, including lack of drift monitoring and insufficient evidence of human oversight⁵.

Footnotes

EMA, Reflection Paper on the Use of Artificial Intelligence (AI) in the Medicinal Product Lifecycle, Sept 2024.
European Parliament, Artificial Intelligence Act (Regulation EU 2021/0106) — final text adopted; phased application begins January 2024.
FDA, Clinical Decision Support Software Guidance (2022); Digital Health Innovation Action Plan (2017).
EFPIA, Digital Health and AI in Medicines Development: Industry Position (2024).
Deloitte & RAPS, AI in Regulatory Compliance: Emerging Challenges and Audit Trends (2024, member-access summary).

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.

Frequently Asked Questions

This section addresses common questions from regulatory, clinical, CMC, and quality professionals working with AI-enabled processes or documentation. Responses cite relevant guidance and reflect Regenold's direct experience supporting regulatory-facing use cases across the AI lifecycle.

When should we start thinking about AI compliance and how do you support scientific advice and early-stage planning?

You should address regulatory compliance as soon as AI is introduced into any process that informs clinical development, trial design, or submissions. Both the European Medicines Agency (EMA) and U.S. Food and Drug Administration (FDA) emphasize early, proactive engagement.

EMA, Reflection Paper on the Use of Artificial Intelligence (AI) in the Medicinal Product Lifecycle (EMA/657921/2023, September 2024) states that sponsors should "anticipate regulatory expectations and define responsibilities throughout the AI system's lifecycle."
FDA, Good Machine Learning Practice (GMLP) for Medical Device Development (FDA, October 2021) urges integration of oversight and documentation from initial design.

Our role:

Support scientific advice and early-phase strategy, including for in-silico modeling, synthetic data, or digital biomarker proposals.
Develop documentation frameworks to ensure explainability, traceability, and justification are regulator-ready from day one.
Collaborate with technical experts (in-house or your teams) to align internal validation with regulatory needs.

Example: We are currently supporting a biotech firm approaching the EMA to justify the use of AI-simulated clinical data, developing evidence frameworks and validation narratives to strengthen regulatory acceptance.

Do we still need regulatory AI documentation if our software isn't classified as a medical device?

Yes. The use context determines the compliance obligations of an AI tool that influence regulatory content, clinical trial design, or safety monitoring. They are subject to authority scrutiny even if they are not classified as Software as a Medical Device (SaMD).

The EU Artificial Intelligence Act (Regulation (EU) 2024/1010) classifies many AI systems, including clinical decision support and quality control tools, as "high-risk" regardless of medical device status.
FDA's Digital Health Policy Navigator distinguishes between regulated (device) and non-regulated (support) functions but applies Good Machine Learning Practices (GMLP) expectations across both.

Sponsors must assess compliance obligations based on context of use, not classification alone. We ensure AI systems used in these settings meet traceability, oversight, and change control requirements under GxP regulations.

Can you help remediate AI documentation gaps before or after an inspection?

Both. We help clients with pre- and post-inspection remediation, including:

Audit trail improvements, metadata tagging, validation protocol drafting
Root cause analysis, Corrective and Preventive Actions (CAPA), SOP development
Harmonizing governance frameworks across functions (clinical, CMC, quality, PV) to enable consistent oversight and streamline compliance discussions

Outcome:
Our structured support improves inspection readiness, reduces regulatory risk, and builds sustainable AI compliance aligned with GxP and industry and authority best practices.

If an external vendor developed our AI, who is responsible for compliance?

The provider is responsible. According to the EMA Reflection Paper (2024) and FDA GMLP guidance, compliance responsibility lies with the marketing authorisation holder (MAH), not the tool vendor

"The MAH should demonstrate oversight and full understanding of the AI system's performance, risks, and limitations, regardless of the developer." (EMA/657921/2023, §3.3)

Sponsors must:

Verify fitness-for-purpose of the AI model in your use case
Document human review and change control protocols
Maintain governance records demonstrating lifecycle responsibility

Our team helps establish this oversight layer, ensuring your use of vendor-developed (or in-house) solutions meets regulatory expectations and stands up to inspection - even when vendors supply pre-packaged validation documentation.

How do you stay current with regulatory AI developments?

Our regulatory intelligence team continuously monitors:

EMA, FDA, and EU Commission AI publications and stakeholder meetings
ICH guidance (e.g., E6(R3), E8(R1)) updates related to AI-supported trials
Inspection trends, Scientific Advice feedback, and pilot program outcomes
Industry best-practice publications such as EFPIA's Position Paper on AI in Medicines Regulation (2024) and Deloitte-RAPS' AI Readiness in Regulatory Affairs Survey (2023)

We translate this into up-to-date checklists, document templates, and risk maps for our clients, ensuring alignment with the latest expectations.

How is this different from standard regulatory or QA support?

We focus exclusively on AI in authority-facing pharma workflows - not just general regulatory affairs - by delivering:

Fit-for-purpose validation annexes for submissions or scientific dialogue
SOPs for AI-assisted documentation generation and oversight
PV, CMC and manufacturing workflow integration with explainable AI, audit trails, and model lifecycle records per [GAMP5®]
Active collaboration with specialist technical experts on model interpretability, bias and risk assessment, and regulator-facing explainability

Our difference: We bridge the gap between robust technical practices and transparent, inspector-ready regulatory documentation.

Can you support multi-functional AI deployments (e.g., across Clinical, QA, and PV)?

We help clients build harmonized governance and lifecycle management for AI across clinical, quality, manufacturing, and PV domains:

Development: Model selection, bias mitigation, data traceability, and explainability
Operation: SOPs, workflow documentation, version control
Oversight: Human-in-the-loop review, audit trails, evidence packages for submissions

This holistic approach enables cross-functional consistency and streamlines authority interactions.

Do you validate the AI model itself, or just its regulatory use?

While we do not perform direct technical testing or algorithmic performance validation - that responsibility lies with your internal data science team or external vendor - we bring substantial in-house technical expertise that elevates the regulatory validation process.

Our ML and AI experts specialize in:

Interpretable Machine Learning and eXplainable AI (XAI), ensuring transparent, regulator-friendly reasoning paths aligned with EMA's and other authority expectations.
Designing validation frameworks that balance technical rigor (e.g., bias mitigation, drift monitoring, version control) with regulatory defensibility.
Generating synthetic data for privacy-preserving model development.

Leveraging this expertise, we:

Develop regulator-ready documentation covering AI explainability, bias/fairness assessments, governance, and model versioning.
Design or review validation plans calibrated for regulatory agencies, especially for borderline Software as a Medical Device (SaMD) and "AI-in-support-of" systems used in clinical, pharmacovigilance, or manufacturing settings.
Author and review technical annexes and submission packages that clearly communicate AI methodologies and results to regulatory and HTA bodies.
Deliver tailored training and enablement for internal teams and clients on AI explainability and auditability to support inspection readiness.

Essentially, we bridge the gap between sophisticated technical validation and practical regulatory compliance, helping ensure your AI use meets scientific standards and withstands the scrutiny of regulators - even in complex or evolving AI use cases.

Can you help us prepare AI content or justification for scientific advice or submissions?

Yes. We regularly support preparation of:

AI validation annexes
Digital endpoint justification packages
Bias/fairness documentation aligned with EU AI Act Annex IV
Human review and oversight documentation for Module 2/5 content

We are currently supporting a mid-sized biotech in the neurodegeneration space with preparations for EMA Scientific Advice related to the use of AI-simulated clinical data. Our assistance includes developing structured documentation and validation strategies to facilitate early regulatory alignment and mitigate submission risks.

Where can we learn more about regulatory expectations for AI in pharma?

We recommend:

EMA Reflection Paper on AI (EMA/657921/2023)
EU AI Act (Regulation (EU) 2024/1010) – EUR-Lex
FDA GMLP (2021) – FDA.gov Guidance Portal
EFPIA AI in Regulation Whitepaper – efpia.eu/publications
RAPS & Deloitte "AI Readiness in Regulatory Affairs" Survey (2023)

We also provide clients with tailored briefings and annotated summaries upon request, including relevance filters for their therapeutic area, system type, and submission status.

Keep Your Drug-Device Combination Products Variation-Ready

EU Submission Readiness for Global Pharma

Making AI in Pharma Regulatory-Ready From Model to Submission