Helene Haguet1, Prof Jean-Michel Dogné1,2, Prof Flora Musuamba1,2
1Department of pharmacy, University of Namur, 2Federal Agency for Medicines and Health Products (FAMHP)
Introduction Since 2003, biologics have been used to treat moderate-to-severe asthma, revolutionizing the field by targeting the mediators and processes that drive inflammation rather than targeting symptoms and the underlying inflammation. As compared to small molecules, modeling and simulation methods have been less extensively used, and important barriers still exist for their full adoption, including a lack of standardized methodology for integrating evidence from modeling studies into treatment guidelines.[2] Evidence generated by alternative or more innovative methods by regulatory authorities can be challenging due to a lack of systematic and integrated approach to assess and establish the acceptability of these methods. The ICH M15 as well as the ERAMET project (Horizon Europe grant 101137141) aim to define the framework for assessment of model informed drug development (MIDD) evidence to inform decision-making.[1] The ERAMET project aims to build an ecosystem including a repository of key regulatory questions linking the questions with the data and the methods used to answer them. Objectives We present here a repository of key regulatory questions answered in centralized procedures and evaluate the place of modeling and simulation methods in marketing authorization process for biologics approved for the treatment of moderate-to-severe asthma from 2014 to 2024. Benchmarking of modeling and simulation methods versus traditional methods is performed by assessing their quality using the table for assessment of MIDD evidence, a communication tool between applicant and regulatory authorities that has been set up in the ICH M15 guideline.[1] Methods We carried out a search for marketing authorization applications (MAAs) available on electronic common technical documents (eCTD) for the 5 approved biologics in the treatment of asthma from 2014 to 2024, namely benralizumab, dupilumab, mepolizumab, reslizumab and tezepelumab. We reviewed the eCTD summaries (quality, primary pharmacodynamic, toxicology, clinical efficacy, clinical safety and clinical pharmacology) for these treatments and translated the information into essential regulatory questions. These questions were classified and organized based on the level of the questions (i.e. cellular, molecular, tissular, individual or population) and their granularity. A graphical representation of the questions and a table listing data and methods used to answer these questions in the MAAs was built. Statistics of the place of modeling and simulation methods in these MAAs were computed. To benchmark modeling and simulation methods with traditional methods, credibility activities were performed by applying the credibility framework of the ICH M15 guidelines to questions answered by modeling and simulation methods. Results A total of 235 questions were extracted from the MAAs. 75 questions were related to efficacy, 94 to safety, 36 to pharmacokinetic (PK), 8 to indication, 13 to dose and 9 to formulation. The proportion of questions answered oscillated between 39% and 52% for each drug. Modeling and simulation methods represent 12% of the methods used to answer questions but vary between 11% to 16% for each drug. These methods are predominantly used to address PK questions, with 51% of these questions answered using these methods. Additionally, modeling and simulation are mainly used to answer efficacy questions at a population level (13%). Finally, modeling and simulation tools answer questions related to the dose, formulation and safety less frequently. Modeling and simulation approaches used are pop PK/PD models, models that predict PK in human from nonclinical data, popPK and ER models. In addition, we identified 26 questions where modeling and simulation methods could be used but have not been proposed by the applicant. Credibility matrices were fulfilled for questions answered by modeling and simulation methods. We selected an exposure-response model as an example. This model informs about the clinical efficacy (i.e. exacerbation rate) of a biologic in adolescent (aged 12 to 17 years old). This model has a medium influence on regulatory decision-making as the projection of the exacerbation rate from this model was complementary of data from two phase 3 clinical trials performed in adults and adolescents and because of the similarity of the pathophysiology between adults and adolescents. The consequence of wrong decisions is rated as high considering the disease severity as a wrong decision could lead to unregulated asthma. For the models with medium or high regulatory impact, credibility assessment reveals various levels of maturity and therefore the regulatory acceptance is also variable. Conclusions We locate the positioning of modeling and simulation and benchmark it for biologic used to treat moderate-to-severe asthma approved from 2014 to 2024. For the models with medium or high regulatory impact, credibility assessment reveals various levels of maturity and therefore the regulatory acceptance is also variable. This repository will be completed by searching pediatric investigation plans to assess the place of modeling and simulation in the context of pediatric drug development.
[1] International Council for Harmonisation. (2024). ICH M15 Guideline on general principles for model-informed drug development. [2] Sriprasart, T. et al. (2023) ‘The role of modeling studies in asthma management and clinical decision-making: a Delphi survey of physician knowledge and perceptions’, Journal of Asthma, 60(9), pp. 1687–1701. doi: 10.1080/02770903.2023.2180748.
Reference: PAGE 33 (2025) Abstr 11462 [www.page-meeting.org/?abstract=11462]
Poster: Methodology - Other topics