Constructing the ERAMET Digital Research Environment (DRE)
Jeff Barrett1, Flora Musuamba Tshinanu2, Ine Skottheim Rusten3, Scott Russell1, Kara Lasater1, Alicia Gibson1, David Sibbald1
1Aridhia Bioinformatics; 2Université de Namur ASBL; 3Systems Resource Lab
Objectives:
ERAMET (Ecosystem for RApid adoption of modelling and simulation METhods) is a recently funded (HORIZON-HLTH-2023-IND-06-04) initiative to address regulatory needs in the development of orphan and paediatric medicines. This is an EU-funded project aimed at rethinking the assessment and development of orphan and paediatric medicines with a focus on integrating modelling and simulation methods alongside real-world data. The overall objective of ERAMET is to provide and implement a framework for development and establishing the credibility (assessment) of mature modelling and simulation to address regulatory needs in the development and assessment of orphan and paediatric medicines. A key component of work package 7 of the grant is to enable the pediatric rare disease stakeholder community broad access to data and tools particularly in the ecosystem that includes academic researchers, drug developers, regulators, patients, providers and technical partners that focus on solutions to accelerate rare disease treatments. A cornerstone of this work package is a digital research environment (DRE) that provides secure and customized access to global data sources made available to this community via a variety of mechanisms and a portal to ongoing trials across the rare disease space.
Methods:
The ERAMET project relies on 3 pillars of the ecosystem: 1) a repository connecting questions, data and methods, 2) development and validation of high quality standards for data and methods and 3) an AI-based approach for automation of data collection and credibility assessment. All aspects of the grant activities including data ingestion, project management, metadata cataloguing and tool development will be managed through the creation of the ERAMET DRE. The DRE will enable stakeholder communities in three different scientific domains (ataxia, transfusion-dependent haemoglobinopathies and drug induced cardiovascular toxicity) broad access to data and tools relevant for their domain. Analytical tools, including AI-driven platforms and computational tools for assessing the methods and data credibility will be developed, refined and applied to the three use-cases to foster improved ability in terms of both basal research on disease characterization and the development of health interventions and supportive methods such as diagnostic methods, risk detection and monitoring tools.
Results:
Early progress on the ERAMET DRE has been focused on the implementation of open-science tools into the workspace environment with nlmixr, nlmixr2 and PK Sim / MOBI already operational. Virtual machine deployment of CERTARA commercial MIDD solutions including Simcyp and Phoenix are in progress and custom AI algorithms from EMA and the University of Warwick are being tested by platform developers for workspace qualification against simulated data. A demonstration of the workspace environment will be made available at the meeting. Data use agreements from the 3 scientific domains are in various stages with the expectation that ingestion can occur prior to the end of the year. Code assessing data quality and credibility linked to models and tools developed are being integrated into the workspace environment presently and will be used to test the contributed data upon ingestion. The DRE will be utilized prospectively for targeted pediatric extrapolation use cases (Work package 5) in subsequent years.
Conclusions:
The key concept behind the ERAMET project is to implement a question centric approach to orphan and paediatric drug development and assessment. ERAMET’s vision is to deliver an approach and related platform applied to 5 use-cases (WPs 4,5,6) that will be brought to the EMA for ITF briefing meetings, SA/PA, QAs/QOs. This is expected to pave the road for successful marketing authorisation applications (MAA) and effective pharmacovigilance follow-up.
References:
[1] Barrett JS. The Precompetitive Space for drug or vaccine development: What does it look like now and what could it look like in the future? The Journal of Pediatric Pharmacology and Therapeutics (2023) 28 (5): 465–472.
[2] Musuamba Tshinanu F, Cheung SYA, Colin P, Davies EH, Barrett JS, Pappalardo F, Chappell M, Dogne JM, Ceci A, Della Pasqua O, Rusten IS. Moving towards a Question-centric approach for regulatory decision-making in the context of drug assessment. Clin Pharmacol Ther. 2023 Jan 27. doi: 10.1002/cpt.2856. Epub ahead of print. PMID: 36708100.
[3] Barrett JS., Betourne A, Walls R, Borens A, Roddy W, Lasater K, Russell S. The future of Rare Disease Drug development: the Rare Disease Cures Accelerator Data Analytics Platform. J Pharmacokinet Pharmacodyn. 2023 May 2. doi: 10.1007/s10928-023-09859-7. PMID: 37131052.
[4] Barrett JS, Eradat Oskoui S, Russell S, Borens A. (2023), Digital Research Environment (DRE)-enabled Artificial Intelligence (AI) to facilitate early stage drug development. Front. Pharmacol. 14:1115356. doi: 10.3389/fphar.2023.1115356.
