Validation of Artificial Intelligence Models in the Pharmaceutical Industry: Toward Rigorous and Responsible Compliance

Artificial intelligence is progressively establishing itself as a major driver of transformation in the pharmaceutical industry. From molecule discovery to pharmacovigilance, including the optimization of clinical trials and the control of industrial processes, AI models now contribute to decisions that can have a direct impact on patient safety and the quality of medicines. In such a highly regulated environment, their validation cannot be treated as a mere technical exercise: it is a strategic, scientific, ethical, and societal challenge.

The question is no longer whether AI will be used throughout the drug lifecycle, but how to ensure that it is used in a reliable, transparent, and responsible manner.

A Structuring Regulatory Framework: Annex 22 and the ISPE AI Guide

The prospect of integrating artificial intelligence into GxP environments has led authorities and professional bodies to formalize specific requirements.

Draft Annex 22 of the EU GMP, dedicated to artificial intelligence, marks a decisive milestone. It acknowledges that AI systems are not traditional software applications, but evolving systems whose performance may vary over time. Consequently, their validation cannot be limited to traditional qualification activities: it must be embedded within a dual lifecycle management approach (data and models).

Annex 22 requires a risk-based approach proportionate to the model’s context of use. An algorithm directly influencing a critical decision for batch release or the assessment of a pharmacovigilance signal will not require the same level of control as an internal planning support tool. A clear definition of the “context of use” therefore becomes the cornerstone of any validation strategy.

In parallel, the ISPE Guide on Artificial Intelligence provides an operational translation of these requirements. Aligned with the principles of GAMP 5 Second Edition, it structures validation around data governance, model robustness, change control, and post-deployment monitoring. It emphasizes the need for multidisciplinary expertise: data scientists, subject matter experts, quality assurance, IT, cybersecurity, and regulatory affairs must collaborate from the design phase onward.

The 10 EMA–FDA Principles: The Foundation of International Good Practice

In January 2026, the EMA and the FDA published a joint document entitled Guiding Principles of Good AI Practice in Drug Development. This text now serves as the international reference framework for governing the use of artificial intelligence throughout the medicinal product lifecycle. It is not merely a theoretical framework: these ten principles reflect how regulatory authorities expect industry to design, validate, and maintain AI systems.

The first principle states that AI must be “human-centric by design.” This means systems must be developed primarily to serve patient interests and uphold fundamental ethical values. The algorithm must remain a tool supporting responsible medical or industrial decision-making, under human oversight.

The second principle is based on a risk-based approach. Not all AI systems present the same level of criticality. Authorities therefore expect validation efforts to be proportionate to risk, with controls adapted to the system’s context of use.

The third principle emphasizes adherence to standards. AI systems must comply with existing legal, scientific, technical, and regulatory frameworks, including GxP requirements.

The fourth principle requires that each system have a clear “context of use.” The model’s exact role, scope of application, and the decisions it influences must be clearly defined and documented.

The fifth principle highlights the importance of multidisciplinary expertise. AI in pharmaceuticals cannot be developed solely by data scientists; it requires the integration of subject matter experts, quality professionals, regulatory specialists, and cybersecurity experts.

The sixth principle focuses on data governance and documentation. Authorities require full traceability of data provenance, processing steps, and analytical decisions. The protection of sensitive data, particularly patient data, must be ensured throughout the system’s lifecycle.

The seventh principle addresses model design and development practices. Systems must be built using data that are appropriate and relevant to their intended purpose.

The eighth principle introduces risk-based performance evaluation. It is not sufficient to assess the algorithm in isolation. Authorities expect evaluation of the complete system, including human–AI interaction. Metrics must be aligned with the context of use, and testing methodologies must be rigorously defined.

The ninth principle emphasizes lifecycle management. AI models may evolve in performance over time, particularly dynamic models. Authorities therefore expect continuous monitoring, proactive management of data drift, and periodic reassessment to ensure sustained performance at the validated level.

Finally, the tenth principle stresses the importance of clear and accessible communication. Essential information regarding context of use, performance, model limitations, underlying data, and level of explainability must be presented in language that is understandable to users—and, where relevant, to patients.

Taken together, these ten principles reposition artificial intelligence as an integrated component of the pharmaceutical quality system, subject to the same standards of scientific rigor, transparency, and accountability as any other element contributing to the safety and efficacy of medicinal products.

Explainability: A Condition for Trust and Regulatory Acceptability

In the pharmaceutical environment, predictive performance alone is not sufficient. A model may demonstrate excellent statistical indicators yet remain unacceptable from a regulatory standpoint if its decisions are not understandable.

Explainability therefore becomes a cornerstone of validation. It enables the documentation of influential variables, the identification of potential biases, and the justification of decisions to regulatory authorities. It also facilitates auditability and deviation management.

The objective is not necessarily to oversimplify complex architectures, but to make their behavior interpretable within the relevant business context. An AI system used to prioritize pharmacovigilance signals, for example, must be able to demonstrate the elements on which its assessment is based. This ability to explain outcomes is a key component of regulatory trust.

Data Governance and Protection: A Central Challenge

No AI validation effort in pharmaceuticals can be dissociated from data quality. Models learn from historical data; if those data are biased, incomplete, or poorly governed, the model will replicate those deficiencies.

Traceability of data sources, documentation of transformations, version control, and access management are fundamental requirements. In Europe, compliance with the GDPR additionally imposes strict anonymization or pseudonymization measures when handling clinical or patient data. In the United States, similar frameworks exist under HIPAA.

Data governance goes beyond legal compliance: it directly contributes to the scientific robustness of the model. A sound validation strategy requires demonstrating that data are fit for use, representative of real-world conditions, and protected against alteration or unauthorized use.

Toward Responsible Validation: Ethics and Environmental Footprint

Beyond strict regulatory requirements, AI model validation is now part of a broader reflection on corporate and societal responsibility.

The analysis of algorithmic bias and cross-population fairness has become an ethical imperative. A model used in clinical development must not disadvantage certain populations that are underrepresented in the training data. Human oversight remains essential to ensure that final decisions remain under appropriate control.

In addition, the energy footprint of AI systems is emerging as a significant concern. Training complex models can require substantial computational resources and generate considerable energy consumption. Responsible validation should therefore incorporate reflection on architectural optimization, rationalization of retraining cycles, and the selection of low-carbon infrastructure solutions.

Conclusion: From Performance to Trust

The validation of artificial intelligence models in the pharmaceutical sector now goes far beyond the simple demonstration of technical performance. It is embedded within a structured regulatory framework shaped by EU GMP Annex 22, the ISPE AI Guide, and the harmonized principles of the EMA and FDA.

The future of AI in pharmaceuticals will not depend solely on algorithmic sophistication. It will rely on organizations’ ability to demonstrate scientific reliability, data robustness, decision explainability, and ethical and environmental responsibility.

In a sector where trust determines the authorization and acceptance of innovation, AI model validation becomes a strategic pillar of digital transformation.

ADN Services

ADN supports you in the validation and control of your artificial intelligence systems within regulated environments.

We operate across the full range of use cases, including:

• Predictive or generative models (statistical models, machine learning, deep learning),

• Intelligent agents (decision-support assistants, conversational agents, etc.),

• AI functionalities embedded within clients’ business tools and software,

• AI components integrated into vendor solutions requiring independent and documented assessment.

  
  

Our objective: enable you to innovate with artificial intelligence while ensuring regulatory compliance, risk control, and sustained trust from health authorities.

For more information, you can find a detailed presentation of our offering here.

To get in touch or request further information: here.