Digital Tools in CMC Survey

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).

Aims of the study

  • 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

Key findings

    • 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

    • 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”

    • Improved process robustness and product quality

    • Increased efficiency and reduced development time

    • Cost savings and accelerated time to market

  • 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

  • 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

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.