As artificial intelligence becomes more embedded in drug development, regulatory expectations for AI are rapidly evolving.

In January 2026, the FDA and EMA released Guiding Principles of Good AI Practice in Drug Development, establishing a shared framework for responsible AI use across the therapeutic and regulatory lifecycle Good AI Practice and the Regula….

This commentary examines what Good AI Practice (GxP for AI) means for regulatory teams managing submission strategy, documentation workflows, and health authority interactions.

Inside the piece, we explore:

• FDA and EMA expectations for AI governance in drug development
• Human oversight and risk-based application in regulatory environments
• AI alignment with eCTD structure and submission lifecycle management
• Governance, traceability, and transparency across IND, NDA, and BLA milestones

• For regulatory leaders, AI is not simply about efficiency. It must operate within the same rigor, structure, and lifecycle continuity as the submissions it supports.

Download the full commentary to understand how Good AI Practice applies to regulatory operations and submission lifecycle management.

When evaluating content—whether produced by humans or AI—it is crucial to adopt a systematic and structured approach to ensure an objective and consistent assessment.

We have identified several categories that encompass the key aspects of technical writing for regulatory submissions related to therapeutics.Within each category we provide example metrics and a scoring rubric to promote consistency across reviewers. While these example metrics are not exhaustive and may not apply directly to all types of content(e.g., CMC), they are intended to be semi-quantitative and explainable.This rubric is most effective when applied to the smallest logical “unit”of writing, such as an individual study summary, and measured for numerous examples.

Artificial Intelligence (AI) is transforming industries, and drug development is no exception. The regulatory lifecycle, a critical part of therapeutic drug development, offers numerous opportunities for AI to streamline processes, reduce errors, and accelerate time to market.How do you effectively incorporate AI into this complex lifecycle? It requires a thoughtful approach and a clear framework to ensure success, which we present here as five key elements.