Assessing drug effect from distributional data: A population approach with application to Duchenne Muscular Dystrophy treatment. (September 2019) - Lavezzi, S.M.; Rocchetti, M.; Bettica, P.; Petrini, S.; De Nicolao, G.
Highlights: Quantitative endpoints, e.g. muscle fiber size, are crucial to objectively assess drug efficacy in Duchenne Muscular Dystrophy (DMD) patients. Assessment of efficacy can be based on the comparison of pre- and post-treatment empirical distributions of fibers cross-sectional areas (CSAs), measures obtained via muscle biopsies (distributional data). Fibers CSAs data from a recent clinical study assessing the therapeutic effects of Givinostat are analyzed via a novel hierarchical statistical approach, considering both the patient and the population level. At the patient level, the distribution of log-transformed CSAs is represented as a two-component Gaussian mixture; drug effect is described as a multiplicative factor on fiber size. At the population level, inter-patient variability is characterized. The proposed approach was validated via muscle composition data, by comparing the observed post-treatment percentage areas of total muscular fiber, fibrotic, necrotic and fatty tissues with the ones derived from the model. This approach completely characterizes fiber CSA distributions and quantifies drug effect on muscle fiber size. It might be generalized to other contexts (other drugs, other diseases), provided that the endpoint is expressed in terms of distributional data. Abstract: Background and objective: In Duchenne Muscular Dystrophy (DMD) treatment, muscle fiber size can be considered as an indicator of muscle health and function. In particular, the statistical distribution of fibers cross-sectional areas (CSAs) has been used as quantitative efficacy endpoint. For each patient, assessment of treatment effect relies on the comparison of pre- and post-treatment biopsies. Since biopsies provide "distributional data", i.e. empirical distributions of fibers CSA, the comparison must be carried out between the empirical pre- and post-treatment distributions. Methods: Here, distributional fiber CSA data are analyzed by means of a hierarchical statistical model based on the population approach, considering both the single patient and the population level. Results: The proposed method was used to assess the histological clinical effects of Givinostat, a compound under study for DMD treatment. At the single patient level, a two-component Gaussian mixture adequately represents pre- and post-treatment distributions of log-transformed CSAs; drug effect is described via a dose-dependent multiplicative increase of muscle fiber size. The single patient model was also validated via muscle composition data. At the patient population level, typical model parameters and inter-patient variabilities were obtained. Conclusions: The proposed methodological approach completely characterizes fiber CSA distributions and quantifies drug effect on muscle fiber size, both at the single patient and at the patient population level. This approach might be applied also in other contexts, where outcomes measured in terms of distributional data are to be assessed.