Recommendations on modelling and simulations in the new EMA Regulatory Science Strategy to 2025

Recommendations

Supporting actions

1. Develop understanding of, and regulatory response to, nanotechnology and new materials in pharmaceuticals

Share knowledge and harmonise regulatory practices by generating guidance on PK/PD (including modelling) requirements and long-term efficacy and safety.

2. Leverage non-clinical models and 3Rs principles;

• Stimulate developers to use novel pre-clinical models, including those adhering to the 3Rs;
• Re-focus the role of the Joint 3Rs working group (J3R WG) to support qualification of new alternative 3R-compliant methods/models including in silico and novel in vitro assays.

3. Develop the regulatory framework for emerging clinical data generation

• Develop methodology to incorporate clinical care data sources in regulatory decision-making;
• Modernise the GCP regulatory oversight to enable decentralised models of clinical trials coupled with direct digital data accrual.

4. Invest in special populations initiatives

• Develop a strategic initiative in maternal-foetal health with other regulators and international stakeholders, and by this initiative,à include considerations regarding PK/PD (pharmacokinetic/pharmacodynamic)modelling, epigenetics, reproductive toxicity studies, clinical trial design as well as post-authorisation follow-up methods.

5. Optimise capabilities in modelling, simulation and extrapolation

Please find in blue the actions that have been added compared to the draft RSS published  in December 2018

• Enhance modelling and simulation and extrapolation use across the product lifecycle;
• Develop guidance and standards on the use of AI in modelling and simulation for regulatory submissions;
• Deploy advances in RWD, modelling, simulation and extrapolation to benefit special populations particularly neglected patient populations;
• Promote the development and international harmonisation of methods and standards via a multi-stakeholder platform;
• Increase capability and redesign the operations of relevant working parties to ensure wider knowledge exchange:
• Invest in Centres of Excellence in regulatory science at an EU level, to work with regulatory agencies to provide training and research on modelling & simulation tools;
• Enhance collaboration with external partners/consortia with expertise in modelling and simulation, and EU funded or co-founded projects e.g. IMI, Horizon 2020;
• Investigate possibilities for conducting modelling and simulation analyses to address key regulatory questions as part of product specific assessment or development of guidelines and policies;
• Consider working with stakeholders to foster data sharing through developing data standards and platforms for data exchange.

6. Exploit digital technology and artificial intelligence in decision making

• Create a Health Data Science and AI forum to engage with a diverse set of stakeholders and engaging in efforts for achieving greater global alignment with other regulators (e.g. FDA);

• Review of the utility of using electronic health records for detecting drug safety issues (including drug interactions);
• Mapping of good examples of use of RWD in different phases of drug development to develop guidance on such use

7.  Develop network competence and specialist collaborations to engage with big data

Establish a Methodologies Working Party that encompasses biostatistics, modelling and simulation, extrapolation, pharmacokinetics, real world data, epidemiology and advanced analytics.

8. Promote global cooperation to anticipate and address supply problems

Support international harmonisation of regulatory science standards for complex generic medicines addressing bioequivalence, waivers and modelling.

9. Develop network-led partnerships with academic/research centres to undertake research in strategic areas of regulatory science

Identify, in consultation with research institutions, academia and other relevant stakeholders, fundamental research and associated training/ education topics in strategic areas of regulatory science relevant to patients (such as PROs, omics-based diagnostics, epigenetics, drugdevice combinations, modelling and simulation, Big Data, and artificial intelligence