Data Integrity in the Pharma Space

Jaenisch, Muraru, and Pandey discuss how regulators will audit pharma against its own AI policies and ways QA and regulatory teams can build defensible, inspection-ready AI practices.

Impurity deficiencies in submissions are remarkably consistent. Subathra Ramamoorthy, Veranova, explains how to catch them before agency filing.

USP’s Sheehan and Liu talk at AAPS PharmSci 360 abou getting novel excipients past the monograph hurdle through early engagement with FDA.

CDMOs announced expansions, exhibitors showcased automated equipment, and supply chain strategy discussions were a focus at CPHI Milan 2026.

Mitigating nitrosamine risk requires assessing each product individually and chasing zero nitrite in pharmaceutical excipients is often impractical and costly.

The business case for regulated AI turns on the cost of producing an outcome that the quality system can accept.

In October, FDA reorganized rare disease development around a fit-for-purpose framework and a new pathway for individualized gene editing. Regulators in Europe and the UK set out long-term plans for clinical trials, AI, and moving away from animal testing.

Clinical, manufacturing, safety, and commercial teams all lean on shared data. They need to agree on who publishes each data product, who can use it, and what evidence goes with it.