Digital CMC Regulatory Lifecycle Workflow
Overview
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, the workflow supports 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.
It is designed to improve consistency, transparency and predictability in the use of digital and AI-enabled tools across regulated pharmaceutical environments, while supporting alignment with emerging expectations relating to model credibility, explainability, data governance and human oversight.
What the Workflow supports
The workflow supports the structured development, assessment and lifecycle management of digital predictive tools used across pharmaceutical development, manufacturing and regulatory decision-making.
This includes:
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
Together, these activities support the consistent and proportionate assessment of digital predictive tools throughout their lifecycle.
The 10 stages of the DCRL Workflow
The workflow is structured around 10 connected stages that guide the development, assessment and regulatory readiness of digital predictive tools in CMC.
-
Clearly identify the scientific or operational question the digital tool is intended to address.
-
Specify how and where the tool will be used (e.g. development, manufacturing, regulatory submission) and its impact on decision-making.
-
Determine the most appropriate modelling approach (e.g. mechanistic, data-driven, hybrid) based on the question and available data.
-
Assess the potential impact of model errors on product quality, patient safety and regulatory decisions.
-
Define the data strategy, including sources, quality requirements, and experimental or operational data generation.
Need to add more detail for this section.
-
Develop the model and begin assessing its scientific validity and performance against the intended use.
-
Evaluate whether the model is sufficiently reliable and robust for its defined context of use.
-
Understand how model uncertainty could propagate into manufacturing control or regulatory decisions.
-
Prepare clear, transparent documentation to support regulatory submission, inspection, or internal governance.
-
Maintain and monitor the model over time, including updates, revalidation, and ongoing performance oversight.
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.
Supporting innovation in digital CMC
For the Digital CMC CERSI, the DCRL workflow is an important mechanism for bridging innovation and regulation, enabling digital and AI-enabled tools to be adopted in a way that is consistent, risk-informed and globally aligned.
The workflow helps ensure that advances in digital CMC translate into real-world benefits, including faster development, more resilient supply chains and continued assurance of safe, high-quality medicines.