The proposals should address all of the following activities:

• Establish a multidisciplinary approach for assessing the utility of mature computational models, as tools for supporting the optimal design of innovative clinical trials for small populations and as fit-for-purpose solutions for enabling the regulatory scientific advice and marketing authorisation assessment of orphan and/or paediatric medicines, including their pharmacovigilance follow-up.
• Calibrate and optimise mature computational models for enhancing their clinical performance, by using relevant sources of patient data (e.g. natural history and observational clinical studies, medical records, registries, pharmacovigilance and longitudinal studies etc.). The models should include a variety of modelling methods and in particular hybrid solutions linking quantitative mechanistic modelling with advanced statistical modelling (e.g. quantitative systems pharmacology, disease mechanistic models, physiology-based pharmacodynamic/pharmacokinetic models, Bayesian modelling, artificial intelligence algorithms etc.).
• Assess validated in-silico models for their capability to increase the statistical robustness, improve the risk/benefit assessment in small population clinical trials, and for their accuracy to predict and extrapolate the therapeutic and dose effects, taking into account the patient’s genotypes/phenotypes, disease characteristics/stage variables and/or clinical/surrogate endpoints for delivering robust evidence of safety and efficacy of the orphan and paediatric medicines under study. The assessment of the in-silico models should be demonstrated in use cases representing well-justified group(s) of rare and/or paediatric diseases with commonalities, such as shared molecular denominators/disease pathways within the same and/or across different medical areas, excluding cancer and infectious diseases.
• Benchmark of diverse computational models by showcasing their simulation performance in virtual patient cohorts and by demonstrating that the models’ synthetic data estimates match to actual clinical trial data. This should lead to an assessment of the performance and credibility of a model simulation in the context of their specific use for regulatory purposes. Benchmark studies should be performed in the use cases mentioned above. Availability of clinical trials data and other relevant data is an indispensable requirement that must be demonstrated at the proposal submission.
• Set-up the criteria for the performance and credibility assessment of any relevant computational models for small population clinical trials to progress on their regulatory qualification and acceptability. Further develop and disseminate standards for the design, performance assessment and reporting of modelling and simulation tools with an emphasis on those of high regulatory value for accelerating the clinical development of orphan and paediatric medicinal products.