SBIR Phase I: Creation of a virtual population of older, black patients with hypertension and comorbidities for improved treatment development

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is a tool that will streamline the clinical trial process, reducing time and money spent without sacrificing safety or an understanding of a treatment’s efficacy. Medical treatments do not work equally well on all patients. Finding a responsive population on which to perform a study directly impacts the likelihood of a new therapy’s success while minimizing risk to patients who would see little or no benefit. The tool will allow clinical trials to be run virtually as computer programs in software that simulates human physiology. This technology will be valuable at all stages of treatment development: suggesting new therapies, testing for adverse effect, planning clinical trials, interpreting trial results, and predicting results when a new therapy is introduced outside of the clinical trial. This project is focused initially on the development of a critical under-represented population: older black men with hypertension and associated diseases, including heart disease and diabetes. Simulations of specific populations will allow researchers and clinicians to anticipate issues, enabling more effective treatment plans. This Small Business Innovation Research (SBIR) Phase I project will develop a virtual population of 50-70 year old hypertensive African-American men with and without common cardiovascular comorbidities including congestive heart failure and type II diabetes. The virtual patients will be instantiations of a differential-algebraic model of human physiology, reflecting interactions between factors such as hormone concentrations, electrolytes, and more. The virtual population will be generated from clinical data collected from other studies and the approach can be used for other specific populations who may be historically under-represented in trials. This model can be used to estimate responses to clinical interventions (device and pharmaceutical therapies) to inform treatment development and further trials. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.