ExpectedOutcome:
This topic aims at supporting activities that are enabling or contributing to one or several expected impacts of destination 6 “Maintaining an innovative, sustainable and globally competitive health industry”. To that end, proposals under this topic should aim to deliver results that are directed, tailored towards and contributing to all of the following expected outcomes:
- Developers and regulators have access to robust modelling and simulation tools to accelerate the effective development of orphan and/or paediatric medicinal products.
- Clinical researchers, developers and regulators use accurate computational models to improve the statistical robustness in clinical trials intended for small populations and guide cost-effective clinical trial designs.
- Clinical researchers and regulators have access to accurate in-silico tools for assessing the actionable use of real-world data and for successfully estimating the risk-benefit effects in clinical trials for small populations.
- Regulators develop guidance for the use of validated computational models to support a robust extrapolation framework and facilitate the safety and efficacy assessment in the process of regulatory appraisal of orphan and/or paediatric medicinal products.
Scope:
In its "Regulatory Science Strategy to 2025", the European Medicines Agency included specific recommendations to optimise the capabilities of modelling and simulation in the medicines development process and in particular to benefit special populations and neglected patient populations.
Orphan drug development faces numerous challenges, including low disease prevalence, patient population heterogeneity and strong presence of paediatric patient populations. Consequently, clinical trials for orphan and/or paediatric medicines are often smaller than traditional large-scale randomised ones and they require the development of efficient trial designs relevant to small.
Model-based approaches are significantly advantageous in small populations, as extrapolation tools for rationalising and increasing the statistical robustness in clinical trial designs and pharmacometric studies.
The topic will support research and innovation activities focusing on the development of diverse modelling and simulation methods, as tools for addressing some of the regulatory needs in the clinical development cycle of new orphan and paediatric medicinal products. The topic is not intended to implement new preclinical/clinical studies but to use the existing knowledge/data for assessing and optimising the performance of mature in-silico models in the regulatory context with the goal of improving the clinical trial designs for small populations. Availability of the relevant data to address the requirements of the topic is an indispensable condition that must be demonstrated at the proposal submission.
Proposals should involve national healthcare product regulatory bodies and the European Medicines Agency (EMA) in order to catalyse an effective collaboration between the researchers and the regulators. The active involvement of patient representatives is required in all phases of the research and innovation activities. Furthermore, SME(s) participation is encouraged with the aim to strengthen their scientific and technological basis.
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.
The proposals should adhere to the FAIR data[1] principles, adopt data quality standards, data integration operating procedures and GDPR-compliant data sharing/access good practices developed by the European research infrastructures, where relevant. Proposals are invited to consider adopting recommendations for in-silico models construction and validation[2]. Data-intensive proposals, particularly those using data from patient registries, should take stock of the tools and services provided by the European Platform on Rare Disease Registration (EU RD Platform). For example, retrospective registry data are expected to be made accessible via EU RD platform, if reasonably feasible.
All projects funded under this topic are strongly encouraged to participate in networking and joint activities. These networking and joint activities could, for example, involve the participation in joint workshops, the exchange of knowledge, the development and adoption of best practices, or joint communication activities. This could also involve networking and joint activities with projects funded under other clusters and pillars of Horizon Europe, or other EU programmes. Therefore, proposals are expected to include a budget for the attendance to regular joint meetings and may consider covering the costs of any other potential joint activities without the prerequisite to detail concrete joint activities at this stage. The details of these joint activities will be defined during the grant agreement preparation phase. In this regard, the Commission may take on the role of facilitator for networking and exchanges, including with relevant stakeholders.
Applicants envisaging to include clinical studies should provide details of their clinical studies in the dedicated annex using the template provided in the submission system. See definition of clinical studies in the introduction to this work programme part.
[1]See definition of FAIR data in the introduction to this work programme part.
[2]ISO-paper under development “Recommendations and requirements for predictive computational models in personalized medicine research — Part 1: Guidelines for constructing, verifying and validating models”.
