Oscar Della Pasqua1, Anisa Khan1, Umberto Villani1
1Clinical Pharmacology & Therapeutics Group, University College London
Objectives: Regulatory agencies need a more efficient approval process for drugs targeting paediatric and rare diseases, where randomized clinical trials are often unfeasible. Adopting a question-centric approach and utilising advanced modelling and simulation (M&S) techniques [1], the ERAMET consortium aims to create a structured framework supporting regulatory bodies in evaluating alternative methods for evidence generation. Aligning with ICH M15 guidelines on Model-Informed Drug Development (MIDD) [2], ERAMET proposes a credibility matrix (CM) to systematically assess the implementation of M&S activities, their regulatory impact as well as patient risks associated with incorrect assumptions and data gaps [3]. This work illustrates the role of CMs in regulatory decision-making using a case study on chelation therapy for transfusion-dependent hemoglobinopathies. Accordingly, we here demonstrate the requirements for extrapolating efficacy and dose optimization of deferiprone (DFP) in paediatric sickle cell disease (SCD) patients. Methods: A CM was developed to address the following question: “What is the ferritin response to optimized iron chelation therapy in paediatric patients with sickle cell disease affected by transfusion-dependent iron overload? “. The question of interest guides the assessment of the implications of the used of in silico evidence. Key elements of the CM include: •Context of Use: A description of the model’s purpose and its outputs, how it addresses the question of interest, as well as of the data sources used for its development. •Model Influence: The weight of the model’s predictions in regulatory decision-making, taking into consideration the possible co-existence of other information contributing to final decisions. •Consequence of a Wrong Decision: Consequences arising from taking a wrong decision considering all the available information, evaluated in terms of safety/efficacy issues in patients. •Model Risk: The contribution of the model outcomes to a possible wrong decision and subsequent potential undesirable consequences. •Regulatory Impact: The extent to which the model outcomes contribute to regulatory decision-making, in relation to current regulatory standards without MIDD-generated evidence. •Answer to the Question of Interest: the multidisciplinary answer to the question of interest arising from the results of the model-based analysis. CMs further incorporate technical criteria to evaluate the suitability of the proposed M&S rationale. In this case study, a nonlinear mixed-effects approach, implemented in NONMEM 7.5, was used. Results: The CM was developed to address the key requirements outlined in the Methods section with respect to the chelation therapies case study: •Context of Use: The extrapolation of efficacy and dose rationale for the use of DFP in the target paediatric population is based on 1) a pharmacokinetic model describing the time course of deferiprone in plasma [4] and 2) pharmacokinetic-pharmacodynamic model describing the effect of chelation therapy on iron homeostasis and serum ferritin in adults [5]. •Model Influence: Rated as medium. The model provides essential evidence for dose optimization but does not replace the need for prospective efficacy data in the target population. •Consequence of a Wrong Decision: Rated as medium— suboptimal dosing could lead to inadequate chelation (iron overload and organ damage) or drug-related adverse effects (monitorable and reversible, e.g., neutropenia) •Model Risk: Rated as low, given the established nature of the model and the robustness of the available data. However, incorrect identification of covariates that are relevant for the SCD population (e.g., co-morbidities, co-mediations) could result in biased predictions. •Regulatory Impact: Rated as medium. Currently, there is need for further insight into the safety profile of iron chelators in SCD patients. The proposed model would exclude the need for comparator group or long term follow up, as safety data from chelation therapy in other transfusion-dependent haemoglobinopathies could be used in lieu of new data in a subgroup of patients. Conclusions: The presented framework ensures a rigorous assessment of a MIDD plan, highlighting its aim, limitations, and regulatory implications. Importantly, CMs foster dialogue among stakeholders—clinicians, regulators, and technical experts—to align perspectives and ensure comprehensive evaluations of in silico evidence.
1. Musuamba FT, Cheung SYA, Colin P, Davies EH, Barret JS, Pappalardo F, Chappell M, Dogne JM, Ceci A, Della Pasqua O, Rusten IS. Moving toward a question-centric approach for regulatory decision making in the context of drug assessment. Clin Pharmacol Ther. 2023;114(1):41-50. 2. ICH M15 guideline on general principles for model-informed drug development. (accessible at: https://database.ich.org/sites/default/files/ICH_M15_ConceptPaper _Final_2022_1102.pdf) 3. ERAMET – Ecosystem for RApid adoption of modelling and simulation METhods to address regulatory needs in the development of orphan and paediatric medicines. (accessible at: https://www.erametproject.eu/) 4. Bellanti F, Danhof M, Della Pasqua O. Population pharmacokinetics of deferiprone in healthy subjects. Br J Clin Pharmacol. 2014;78(6):1397-406. 5. Borella E, Oosterholt S, Magni P, Della Pasqua O. Characterisation of individual ferritin response in patients receiving chelation therapy. Br J Clin Pharmacol. 2022; 88(8):3683-3694.
Reference: PAGE 33 (2025) Abstr 11400 [www.page-meeting.org/?abstract=11400]
Poster: Drug/Disease Modelling - Other Topics