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.

Evaluating content requires a structured approach. Teams should use consistent methods to assess both human- and AI-generated content objectively.

This guide outlines key categories for technical writing in regulatory submissions for therapeutics. Each category includes example metrics and a scoring rubric to support consistency across reviewers. The metrics are semi-quantitative and explainable. While they may not apply to all content types, they provide a practical framework for evaluation.

Teams should apply the rubric to the smallest logical unit of writing, such as an individual study summary. Reviewing multiple examples helps improve consistency and accuracy.

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.