The pharmaceutical industry is in full digital transformation, And artificial intelligence (AI) has ceased to be a promise of the future to become an increasingly accessible tool. However, its application in regulated environments, such as the validation of automated systems, requires more than efficiency: requires control, traceability and compliance.

In this article, we explore how to integrate AI in the validation processes according to GXP, under a risk -based approach, aligned with guides such as Gamp 5 (second edition), Ich Q9, 21 Cfr Part 11 and Annex 11 of EMA.

What role can AI play in validation processes?

Automated systems validation activities (such as SCADA, PLC, BMS, EMS, LIMS or month) require weather, funds and thorough documentation. This is where AI can make a clear difference, provided that it is integrated with a controlled approach and within the quality system.

Fields of application

• More efficient risk assessment. IA models can analyze historical validation and help identify modules or functionalities of greater risk. This allows prioritizing tests more objectively.
• Technical documentation generation. Models of generative AI can support the writing of USA, Test scripts and traceability matrices, reducing errors and gaining time in repetitive tasks.
• Analysis of results and trends. AI can detect hidden patterns in deviations, alarms or audits, and suggest preventive actions that strengthen continuous validation.
• Automated documentary review. The use of language processing algorithms (NLP) allows us to review specifications and validate the fulfillment of 21 CFR Part 11 or ANNEX 11, providing technical coherence and traceability.

A risk -based approach ... also for AI

The use of AI must follow the basic principle of any GXP system: controlled risk, documented benefit:

• Does AI affect GXP data? Then it must be validated as part of the computerized system.
• How do decisions? Its logic must be traceable, auditable and justified.
• Does human review require? The AI ​​must attend, but not replace the qualified professional.

These considerations are aligned with the recommendations of Gamp 5 (second edition, 2022), which explicitly addresses the use of complex algorithms and emerging validation technologies.

What do regulatory agencies say?

The use of emerging technologies such as artificial intelligence in regulated environments is no stranger to health authorities. Although There are still no exclusive specific guides on AI In the pharmaceutical industry, agencies have already begun to establish General principles for use, always under the premise of control, traceability and risk -based justification.

FDA (United States)

The FDA has issued several documents that, although initially oriented to the scope of medical devices and software as a product (SAMD), establish criteria that perfectly apply to the use of AI in validation of GXP systems:

• AI/ML-Based Software as a Medical Device (2021): Introduces the concept of “adaptive life cycle”, highlighting the importance of maintaining traceability, continuous validation and control of versions in algorithms that learn over time.
• 21 CFR Part 11: Any tool or system that manages GXP data must guarantee integrity, authentication, traceability and human review, principles that also apply to systems with AI components.

The FDA emphasizes that the use of predictive or generative algorithms does not eliminate the manufacturer's responsibility to maintain total control over its processes and records.

EMA (European Medicines Agency)

The EMA has not yet published a guide dedicated to AI, but has made it clear in multiple forums and documents that any technology that influences GXP decisions must be under a robust quality management system:

• Annex 11 (Computerised Systems): Apply to all systems that process regulated data. Although it does not mention, it establishes that any automated functionality must be validated, justified and under control.
• Reflection Paper on Use of AI in Drug Development (2023): The EMA recognizes the potential of the AI, but requires total transparency on how the model works, its training data, its impact on decision making and its control.

One of the key messages of the document: "Clinical, scientific or regulatory decisions cannot be delegated to non -explainable algorithms."

MHRA (United Kingdom)

MHRA has been one of the most active agencies in data integrity and systems control issues:

• GxP Data Integrity Guidance and Definitions (2018): Define the integrity requirements for any system that manages regulated information, and underlines the need to conserve evidence of all automated changes and decisions.
• In recent forums, MHRA has warned that the use of I must follow the same principles as any validated computerized system, even if only used to support documentation or review.

WHO (World Health Organization)

WHO published in 2021 a specific guide, Ethics & Governance of Artificial Intelligence for Health, where proposes principles For ethical and controlled use of AI In health environments, including algorithmic traceability, explainability of results, permanent human supervision and biased prevention.

Although not specific for pharmaceutical validation, this guide feels bases on the type of control and transparency expected when intelligent systems are used in critical processes.

In summary, Artificial intelligence is a great ally in the validation of automated systems, provided that it is integrated into a GXP approach. It is not about replacing processes, but about strengthening them, automating repetitive tasks, improving risk detection and helping to build more robust documentation.

In an increasingly regulated, competitive and technological environment, take advantage of the potential of AI can make a difference ... but always under control.

In IDI we help implement advanced validation technologies without compromising compliance. If you are evaluating how to integrate into your validation processes or need specialized support in CSV, compliance or data integrity, we are ready to help you. Contact us or follow us in RRSS to update with articles like this.