Guiding Principles of Good AI Practice in Drug Development
The European Medicines Agency (EMA) and the US Food and Drug Administration (FDA) have jointly established new AI principles in drug development to reduce regulatory divergence.
These are the ten principles selected to give broad guidance on AI use in evidence generation and monitoring across all phases of a medicine, from early research and clinical trials to manufacturing and safety monitoring:
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- Human-centric by design
- Risk-based approach
- Adherence to standards
- Clear context of use
- Multidisciplinary expertise
- Data governance and documentation
- Model design and development practices
- Risk-based performance assessment
- Life cycle management
- Clear, essential information
The principles are relevant for those developing medicines, as well as for marketing authorisation applicants and holders. They will underpin future AI guidance in the different jurisdictions and support enhanced international collaboration among regulators, organisations setting technical standards and other stakeholders.
The use of AI technologies across the medicines lifecycle has increased significantly in recent years. As emphasised in the European Commission’s Biotech Act proposal, AI holds great promise as a tool to accelerate the path from innovation to safe and effective medicines. The new pharmaceutical legislation accommodates the broader use of AI in the lifecycle of medicines in regulatory decision-making, and creates additional possibilities for testing innovative AI driven methods for medicines in a controlled environment.
To realise these benefits, AI needs to be expertly managed, including the mitigation of risks. As AI continues to evolve, a principles-based approach will help regulators, pharmaceutical companies and medicines developers harness the potential of these technologies while ensuring patient and animal safety and regulatory compliance.
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