Digital CMC Centre for Excellence in Regulatory Science and Innovation

Enabling regulatory-ready digital transformation in pharmaceutical development and manufacturing

The challenge

Less than 15% of digital tools reach regulatory submissions.

We seek to change that through practical frameworks, shared workflows, and cross-sector collaboration.

Digital and AI-enabled tools are transforming pharmaceutical development and manufacturing. Yet adoption in regulated Chemistry, Manufacturing and Controls (CMC) activities remains uneven, held back not by technology, but by uncertainty around regulatory expectations, fragmented guidance, and capability gaps.

Outputs and Resources

The Digital CMC Regulatory Lifecycle (DCRL) Framework

A risk‑based structure that supports the management of digital predictive tools across the pharmaceutical CMC lifecycle, enabling confident, regulatory‑ready adoption.

The Digital CMC Regulatory Lifecycle (DCRL) Workflow

A guided sequence for the development, validation, and regulatory use of digital and AI-enabled tools across their full lifecycle.

Webinars and videos

Papers

Submitted

Digital and AI-Enabled Models in Pharmaceutical Development and Manufacturing: A Regulatory-Focused Industry Survey

A global industry survey of pharmaceutical companies exploring the use of computational models in CMC, focusing on barriers to adoption and levels of implementation in both regulated and non-regulated settings. The study includes knowledge-based, data-driven, and hybrid AI/ML-enabled models.

Coming soon

‍Digital Transformation in Pharmaceutical CMC: Enabling Regulatory-Ready Adoption of Computational Models

A white paper co-authored with industry and the MHRA outlining the rationale, philosophy, and proposed approach to accelerating the adoption of predictive digital tools in regulatory submissions and inspections. It sets out the case for digital tools in CMC and proposes a consistent framework for ensuring predictive models (including AI/ML) are regulatory-ready, bringing together current regulatory guidance, standards, and scientific best practice.

Exemplification of a workflow for credibility assessment on 4 predictive model types 

A case study–led paper co-authored with industry that details the DCRL workflow, demonstrating its application across four representative predictive model types to illustrate practical implementation and assessment of model credibility.

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Funded and supported by the MHRA and the Office for Life Sciences (OLS) managed by Innovate UK with delivery partner Medical Research Council (MRC) the as part of the “RS&IN Implementation Phase: Human Health CERSI” Innovate UK: Project no. 10139447

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