Disease progression modeling of the North Star Ambulatory Assessment for Duchenne Muscular Dystrophy

Author:

Hibma Jennifer E.1ORCID,Jayachandran Priya2ORCID,Neelakantan Srividya2,Harnisch Lutz O.3ORCID

Affiliation:

1. Global Product Development Pfizer Inc. La Jolla California USA

2. Global Product Development Pfizer Inc. Cambridge Massachusetts USA

3. Global Product Development Pfizer Inc. Sandwich, Kent UK

Abstract

AbstractDuchenne muscular dystrophy (DMD) is a rare genetic disorder caused by decreased or absent dystrophin gene leading to progressive muscle degeneration and weakness in young boys. Disease progression models for the North Star Ambulatory Assessment (NSAA), a functional measurement widely used to assess outcomes in clinical trials, were developed using a longitudinal population modeling approach. The relationship between NSAA total score over time, loss of ambulation, and potential covariates that may influence disease progression were evaluated. Data included individual participant observations from an internal placebo‐controlled phase II clinical trial and from the external natural history database for male patients with DMD obtained through the Cooperative International Neuromuscular Research Group (CINRG). A modified indirect response model for NSAA joined to a loss of ambulation (LOA) time‐to‐event model described the data well. Age was used as the independent variable because ambulatory function is known to vary with age. The NSAA and LOA models were linked using the dissipation rate constant parameter from the NSAA model by including the parameter as a covariate on the hazard equation for LOA. No covariates were identified. The model was then used as a simulation tool to explore various clinical trial design scenarios. This model contributes to the quantitative understanding of disease progression in DMD and may guide model‐informed drug development decisions for ongoing and future DMD clinical trials.

Funder

Pfizer

Publisher

Wiley

Subject

Pharmacology (medical),Modeling and Simulation

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