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

Duchenne 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.

Clinical Pharmacology Worldwide: A Global Health Perspective.

Low- and middle-income countries (LMICs) have the highest rates of mortality and morbidity globally, but lag behind high-income countries in the number of clinical trials and trained researchers, as well as research data pertaining to their populations. Lack of local clinical pharmacology and pharmacometrics expertise, limited training opportunities, and lack of local genomic data may contribute to health inequalities and limit the application of precision medicine. Continuing to develop health care infrastructure, including well-designed clinical pharmacology training and data collection in LMICs, can help address these challenges. International collaboration aimed at improving training and infrastructure and encouraging locally driven research and clinical trials will be of benefit. This review describes several examples where clinical pharmacology expertise could be leveraged, including opportunities for pharmacogenomic expertise that could drive improved recommendations for clinical guidelines. Also described are clinical pharmacology and pharmacometrics training programs in Africa, and the personal experience of a Tanzanian researcher currently on a training sabbatical in the United States, as illustrative examples of how training in clinical pharmacology can be effectively implemented in LMICs. These training efforts will benefit from advocacy for employment opportunities and career development pathways for clinical pharmacologists that are gradually being recognized and developed in LMICs. Clinical pharmacologists have a key role to play in global health, and development of training and research infrastructure to advance this expertise in LMICs will be of tremendous benefit.