3.2 Digital Tools in CMC survey

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

Authors: Graham Cook, Ian Houson, Chantal Mustoe, Andrew Maloney, Walkiria Schlindwein and Daniel Markl

Overview[IH3.1][IH3.2]

This study presents findings from a cross-sector industry survey exploring the adoption, use, and regulatory challenges of digital and AI-enabled tools in pharmaceutical Chemistry, Manufacturing and Controls (CMC). Conducted through the Digital CMC CERSI, it provides a data-driven view of current practice and identifies priorities for advancing regulatory science.

[IH4.1]

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• Aims

The study aimed to:

• Assess current adoption of digital and AI-enabled tools across pharmaceutical development and manufacturing

• Understand how tools are used (internal decision-making vs regulatory submissions)

• Identify key barriers to wider implementation, particularly in regulated environments

• Capture industry perspectives on regulatory expectations and future support needs

• Inform regulatory science priorities to enable responsible and scalable adoption

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• Key Findings

• 1. Widespread Use in Development and Manufacturing

• Digital tools are commonly used across CMC activities, particularly in process development, monitoring, and control

• AI-enabled tools are increasingly adopted, though at lower levels than traditional digital approaches

• Adoption is higher in small molecule and drug product applications

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• 2. Significant Gap in Regulatory Use

• Fewer than 15% of tools are included in regulatory submissions

• Most tools are used internally to support decision-making, rather than as formal regulatory evidence

➡️ This highlights a critical “adoption-to-regulation gap”

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• 3. Key Drivers for Adoption

• Improved process robustness and product quality

• Increased efficiency and reduced development time

• Cost savings and accelerated time to market

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• 4. Major Barriers to Wider Adoption

The main challenges are not technical, but systemic:

• Uncertainty in regulatory expectations, especially for AI

• Skills and expertise gaps in digital and data science

• Organisational and cultural barriers to change

• Lack of clear frameworks for validation, governance, and lifecycle management

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• 5. Strong Demand for Regulatory Enablement

Respondents highlighted the need for:

• Clear, harmonised international guidance

• Regulatory training and upskilling

• Case studies and practical workflows

• Cross-sector collaboration between industry, academia, and regulators

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Implications for Regulatory Science

The findings show that while digital transformation in CMC is advancing, regulatory implementation is lagging. This creates a need for: Risk-based, proportionate frameworks, clear definitions of context of use, Robust approaches to model credibility and lifecycle management

These are central to the mission of the Digital CMC CERSI in enabling regulator-ready adoption of digital and AI tools.