Ine Skottheim Rusten1, Flora Musuamba Tshinanu2, Mike Chappell3, S. Y. Amy Cheung4, Alicia Gibson5, Jeff Barrett5
1Systems Resource Lab AS, 2Université de Namur ASBL, 3University of Warwick, 4Certara, 5Aridhia Bioinformatics
Objectives ERAMET (Ecosystem for RApid adoption of modelling and simulation METhods) is a funded (HORIZON-HLTH-2023-IND-06-04) initiative to address regulatory needs in the development of orphan and paediatric medicines. A cornerstone of the grant is the creation and operationalization of 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. While the infrastructure is in place the objectives for this year’s efforts are to promote individual workspace owners to participate in collaborative research activities and begin to develop an ecosystem which shares questions, methods and data in a secure environment. Most importantly, the ongoing research activities in the project on data and method quality standards and the meta-data needed for operationalizing the question-centric approach in rare and peadiatric drug development will be iteratively tested within the environment. Data on regulatory decision-making informed by a MIDD-based approach is being collected and will be utilized in the environment to link key questions underlying the regulatory evidence requirements to the credibility of the methods used to answer the questions. The credibility assessment includs aspects such as critical decisions to assumptions, and MIDD assets including data, code and models. Methods The DRE enables stakeholder communities’ access to data and tools relevant for their domain. Analytical tools, including artificial intelligence (AI) and machine learning(ML)-driven platforms and computational tools for assessing the methods and data credibility developed within the project will be iteratively refined on exemplars and will be applied to the project use-cases within the ERAMET DRE portal page. This will 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 focused on the implementation of open-science tools into the workspace environment with R studio, nlmixr, nlmixr2 and PK Sim / MOBI as well as commercial MIDD solutions including Phoenix , Simcyp, Monolix and Nonmem and also individual packages from CRAN and github. So far, researchers from WP2 (The University of Namur), WP3 (The University of Warwick and Systems Resource Lab AS), and WP5 (Certara) have been successfully onboarded to their Workspaces and exploring the environment for collaboration on their respective research projects. The next step, scheduled for completion is to train these teams on how to collaborate effectively across secure Workspaces and prepare the environment for sharing openly available tools and custom AI algorithms and credibility tools from the University of Warwick, University of Namur and Systems Resource Lab AS. Conclusion 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 (WP4-6) 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.
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. 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. 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.
Reference: PAGE 33 (2025) Abstr 11654 [www.page-meeting.org/?abstract=11654]
Poster: Methodology - Other topics