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Navigating the Regulatory Landscape: Advances in Respiratory Modeling and Simulation

De Backer J, Sadafi H.

Respiratory Drug Delivery 2025. Volume 1, 2025: 76-81.

Abstract:

Recent advancements in computational modeling, imaging, and artificial intelligence have significantly transformed the landscape of respiratory drug development. Functional Respiratory Imaging (FRI) and Rapid Deposition Analysis (RDA) are two complementary in silico tools that provide detailed, patient-specific insights into pulmonary drug deposition. FRI combines low-dose high-resolution computed tomography (HRCT) with computational fluid dynamics (CFD) to simulate airflow and drug deposition, validated against SPECT/CT data. RDA, on the other hand, leverages machine learning and dimensional analysis to deliver near-instantaneous predictions of regional lung deposition, enabling efficient digital bioequivalence (BE) trials. These tools allow developers to optimize inhaler formulations and device performance while reducing reliance on costly and time-intensive clinical endpoint studies. Regulatory frameworks are evolving to embrace such modeling tools, notably through the US Food and Drug Administration’s (FDA) Model Master File (MMF) system, which enables developers to share validated simulation tools confidentially across multiple applications. RDA, for instance, not only predicts deposition with high accuracy but also helps determine optimal sample sizes and assess acceptable variability in in vitro test parameters, such as Impactor Stage Mass (ISM) ratios, for establishing BE. With growing regulatory endorsement and a proven scientific foundation, FRI and RDA represent a scalable, cost-effective approach to accelerate the development of inhaled generics and personalized therapies in respiratory medicine.