Digital CMC Regulatory Lifecycle (DCRL) Framework
The Digital CMC Regulatory Lifecycle (DCRL) Framework provides a structured, science-based approach to the development, validation, deployment, and lifecycle management of digital predictive tools in pharmaceutical CMC.
It enables industry, regulators, and academia to apply digital tools consistently and confidently while ensuring product quality, patient safety, and regulatory readiness.
What the Framework Supports
The DCRL framework provides guidance across:
Context of use definition
Risk-proportionate credibility assessment
Validation and uncertainty evaluation
Lifecycle management
Regulatory documentation readiness
Inspection and submission preparedness
It promotes predictable, proportionate adoption of digital and AI-enabled tools in regulated environments.
Key Principles
The framework is underpinned by core regulatory and scientific principles:
Risk-based implementation
Human accountability and oversight
Transparency and explainability
Robust data governance
Lifecycle management and control
Regulatory readiness and traceability
These principles align with international guidance including ICH, EMA, FDA, MHRA, and relevant standards and best practice.
Framework Overview
The Digital CMC Regulatory Lifecycle (DCRL) Framework is a practical, science-based approach developed by the Digital CMC CERSI to support the responsible use of digital predictive tools, including AI and machine learning, in pharmaceutical Chemistry, Manufacturing and Controls (CMC). Its purpose is to help innovators, manufacturers and regulators build shared confidence that digital tools are reliable, transparent, proportionate to risk, and suitable for their intended regulatory or manufacturing use.
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Figure 1: Informing Digital Predictive Tool Development in CMC
The framework supports the CERSI’s wider aim: to accelerate safe, sustainable and efficient medicines development while protecting product quality, patient safety and regulatory trust. It provides a common language and workflow for industry, academia and global regulators, reducing uncertainty around evidence expectations, model credibility, documentation and lifecycle oversight.
Figure 2: Digital CMC Regulatory Lifecycle (DCRL) Framework
The DCRL framework has three connected layers:
Top layer: Supporting Enablers[IH1]
The DCRL framework is supported by four enablers that facilitate practical adoption and shared understanding across stakeholders. These include 4 regulatory use cases to demonstrate real-world application, Virtual Expert for Regulatory Assistance (VERA), a collaborative Digital CMC Sandbox for static credibility assessment with an interactive element and evidence generation, and targeted, modular and interactive training platforms (e.g. SkillsFactory) to build capability.
Together, these enablers provide the consistent language, approach, templates, knowledge for regulators, industry and academia to translate principles into consistent, regulator-ready practice.
Middle Layer: Practical workflow[IH2]
The pathway guides digital tools through 4 levels of maturity readiness from early concept through development, validation, regulatory use and lifecycle management: concept and risk analysis; development and verification; validation and credibility assessment; and regulatory use and lifecycle management.
Ten detailed workflow stages help users define the question of interest, context of use, model type, model risk, data requirements, credibility evidence, regulatory documentation and post-deployment control.
Bottom layer: Foundational regulatory principles and evidence base
The bottom layer anchors the framework in five core principles adapted for Digital CMC which are applicable to knowledge driven, data-driven (incl. AI, and empirical) and hybrid models :
· Safety, security and robustness
· Transparency and explainability
· Fairness and avoidance of bias
· Accountability and governance
· Contestability, human oversight and redress.
Not all the principles are applicable to all model types, but by collating them and adapting them for a CMC context, they provide a valuable aide-memoir resource to ensure users consider all aspects to ensure their credibility assessments are regulatory-aligned. These principles are aligned to collated global regulatory guidance, standards and scientific literature, including ICH guidance, FDA, EMA and MHRA expectations, ASME standards, and peer-reviewed best practice (Figure 3).
Figure 3: Regulatory, Standards and Scientific Best Practice Basis for DCRL Framework
An interactive DCRL Framework has been developed to help the user consider the relevant principles that apply totheir computational models at various stages of the workflow and provides key regulatory and standards links
Embed interactive element into website here – Steve knows how to do it
https://www.cmac.ac.uk/cersi-home
Together, these layers make the DCRL framework a regulator-ready bridge between digital innovation and pharmaceutical quality assurance. It enables digital tools to be developed and assessed consistently, supports global harmonisation, and helps ensure that digital transformation in CMC delivers faster access to medicines without compromising quality, safety or public trust.
Digital CMC Regulatory Lifecycle (DCRL) Workflow
The Digital CMC CERSI has developed the Digital CMC Regulatory Lifecycle (DCRL) Workflow to support the regulator-ready development, deployment, and lifecycle management of digital predictive tools used in pharmaceutical CMC.
The workflow provides a structured, risk-proportionate approach that helps organisations move from early-stage digital tool development through to regulatory use and ongoing lifecycle oversight.
The DCRL workflow supports:
Definition of the question of interest and context of use
Risk assessment and credibility planning
Data collection and model development
Verification, validation, and uncertainty assessment
Documentation for regulatory submission and inspection
Lifecycle monitoring and governance
The workflow is designed to improve consistency, transparency, and predictability in the use of digital and AI-enabled tools across regulated pharmaceutical environments. It also helps organisations align with emerging regulatory expectations relating to model credibility, explainability, data governance, and human oversight.
Enablers Supporting Adoption
The Digital CMC CERSI is also developing enabling infrastructure and capability-building activities to support practical adoption.
Virtual Expert for Regulatory Assistance (VERA)
An AI-enabled support platform designed to assist with interpretation of regulatory expectations and framework implementation.
Case Studies and Demonstrators
Practical examples demonstrating regulator-ready implementation pathways for digital predictive tools and workflows.
Digital CMC Sandbox
A collaborative environment for testing, evaluation, and demonstration of digital regulatory science approaches.
SkillsFactory and Training
Training programmes and digital capability development resources designed to support workforce readiness and digital literacy across pharmaceutical CMC.
Why This Matters
Digital transformation has the potential to fundamentally improve how medicines are developed, manufactured, controlled, and supplied.
The responsible adoption of digital predictive tools could enable:
Faster patient access to medicines
More robust and resilient manufacturing
Improved sustainability
Better process understanding
Reduced development costs and timelines
Stronger supply chain agility
However, achieving these benefits requires trusted, transparent, and regulator-ready approaches that maintain patient safety and product quality.
The Digital CMC CERSI exists to help bridge the gap between innovation and implementation.
Supporting the Future of Pharmaceutical Manufacturing
The Digital CMC CERSI works collaboratively with:
Industry
Regulators
Academia
Technology providers
Standards organisations
International partners
Together, we are helping shape the future of digitally enabled pharmaceutical development and manufacturing.
The Digital CMC Regulatory Lifecycle (DCRL) workflow provides a structured, end-to-end pathway for developing, assessing and deploying digital predictive tools in pharmaceutical CMC. Developed by the Digital CMC CERSI, it directly supports the CERSI mission to enable innovation in regulatory science while maintaining product quality, patient safety and global regulatory confidence.
The workflow translates high-level regulatory principles into a practical, stepwise approach that can be applied consistently by industry, regulators and researchers across the product lifecycle.
Figure 4: Digital CMC Regulatory Lifecycle (DCRL) Workflow
The 10 Stages of the DCRL Workflow
1. Define the Question of Interest (QoI)
Clearly identify the scientific or operational question the digital tool is intended to address.
2. Define the Context of Use (CoU)
Specify how and where the tool will be used (e.g. development, manufacturing, regulatory submission) and its impact on decision-making.
3. Select Model Type
Determine the most appropriate modelling approach (e.g. mechanistic, data-driven, hybrid) based on the question and available data.
4. Conduct Risk Assessment (Model Risk)
Assess the potential impact of model errors on product quality, patient safety and regulatory decisions.
5. Plan Data Collection
Define the data strategy, including sources, quality requirements, and experimental or operational data generation.[IH7.1]
6. Model Development and Initial Credibility Assessment
Develop the model and begin assessing its scientific validity and performance against the intended use.
7. Review Credibility and Assess Fitness for Use
Evaluate whether the model is sufficiently reliable and robust for its defined context of use.
8. Assess Risk of Uncertainty in Decision-Making
Understand how model uncertainty could propagate into manufacturing control or regulatory decisions.
9. Document for Regulatory Use
Prepare clear, transparent documentation to support regulatory submission, inspection, or internal governance.
10. Lifecycle Management
Maintain and monitor the model over time, including updates, revalidation, and ongoing performance oversight.
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5 Evidence Base and Sources
The DCRL workflow is grounded in a broad and evolving evidence base. It integrates:
• Global regulatory guidance (e.g. ICH quality guidelines, FDA, EMA, MHRA)
• International standards (e.g. ASME, ISO and related model credibility frameworks)
• Scientific literature and peer-reviewed best practice
• Industry white papers and cross-sector experience
By consolidating these sources into a single, coherent workflow, the DCRL provides a harmonised and practical approach that reduces ambiguity, aligns expectations across regions, and supports consistent regulatory decision-making.
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For CERSI, the DCRL workflow is a key mechanism to bridge innovation and regulation—enabling digital and AI tools to be adopted in a way that is predictable, proportionate, and globally aligned. It helps ensure that advances in digital CMC translate into real-world benefits: faster development, more resilient supply chains, and continued assurance of safe, high-quality medicines.