Regulatory compliance updates
Guiding Principles of Good AI Practice in Drug Development
January 2026
Artificial Intelligence (AI) has the potential to transform the way drugs (medicines)1 are developed and
evaluated, ultimately improving health care. In this context, AI refers to system-level technologies
used to generate or analyze evidence across the drug product life cycle, including nonclinical, clinical,
post-marketing, and manufacturing phases. Drugs are authorized based on demonstrated quality, efficacy and safety, and when their benefits outweigh their risks. As new technologies emerge, including AI, it is essential that their use reinforces these requirements for the benefit and safety of patients.
The use of AI throughout the drug product life cycle has increased significantly in recent years. The
complex and dynamic processes involved in developing, deploying, using, and maintaining AI
technologies benefit from careful management throughout the drug product life cycle to ensure outputs
are accurate and reliable. Among other innovations, AI technologies are anticipated to support a multifaceted approach that promotes innovation, reduces time-to-market, strengthens regulatory excellence and pharmacovigilance, and decreases reliance on animal testing by improving the prediction of toxicity and efficacy in humans.
This document outlines a common set of ten guiding principles to inform, enhance, and promote the use of AI for generating evidence across all phases of the drug product life cycle.