Risk- Based Validation of Software, Automation and Artificial intelligence in Pharmaceuticals

Abstract

ABSTRACT: Artificial intelligence (AI) and machine learning (ML) are transforming the pharmaceutical value chain, yet their adaptive, data-driven behaviour challenges traditional validation frameworks designed for deterministic software. This narrative review synthesizes current global guidance—including GAMP 5 (Second Edition), ALCOA++, ICH Q8–Q11, the U.S. FDA’s Software as a Medical Device (SaMD) AI/ML Action Plan and the 2025 Draft Guidance on Predetermined Change Control Plans for AI/ML-enabled Devices, the European Union AI Act, and ISO/IEC 25010, 27001, and 42001—into a unified, risk-based blueprint encompassing three technology classes: conventional software, automation platforms, and AI/ML models. Differences in user requirement specification, qualification (IQ/OQ/PQ), change management, and lifecycle oversight are mapped, and four emerging pain points—model drift, bias, explainability, and cybersecurity—are highlighted. Building upon the classical V-model, a four-phase roadmap is proposed that integrates deterministic validation discipline with agile, data-centric controls and continuous performance monitoring. Adoption of this blueprint can shorten validation cycles, enhance regulatory compliance, and expedite the delivery of safer and more reliable medicines.

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