Improving Hypertension Treatment in African Americans Using Computational Modeling and Predictive Analytics

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ABSTRACT As compared to whites, African Americans (AA) develop hypertension (HTN) at an earlier age, have a greater frequency and severity of HTN, poorer control of blood pressure (BP), and have twice the mortality rate from HTN. For 47 years our department has been developing computer simulations of integrative physiology for research purposes. The current model, HumMod, is comprised of 14 organ systems, and includes neural, endocrine, circulatory, and renal physiology. We have created tools that generate and analyze large cohorts of computer-generated (virtual) patients. With these techniques HumMod has been used for hypothesis generation and for understanding underlying physiological mechanisms that are not able to be determined in either whole animal or human experiments. This proposed work will use these tools and this mathematical model of human physiology to develop a realistic AA virtual population for studying antihypertensive therapies that have well-known (diuretic or salt reduction), variable (angiotensin converting enzyme, or ACE inhibition), or unclear (renal denervation (RDX), and baroreflex activation therapy (BAT) therapeutic efficacies in AA. Published data from our laboratory show that our model is robust and can realistically simulate salt sensitivity, multiple types of HTN, and device-based antihypertensive therapy. As shown in our preliminary data, we have successfully created a virtual population that was similar to the clinical data (AA population with resistant HTN) in 5-dimensions (blood pressure, heart rate, glomerular filtration rate, cardiac output, and peripheral resistance) and have conducted in silico trials for new device-based therapy currently being evaluated for the treatment of resistant HTN—namely RDX, BAT, and arteriovenous fistula. Based on these preliminary data, we hypothesize that these techniques will allow us to investigate the physiological mechanisms responsible for the variation in response to therapy in a wide range of AA patient types and predict the likelihood of success for a particular treatment. Aim 1 of the proposal will test the hypothesis that a virtual AA population with resistant HTN can be successfully calibrated and validated. Aim 2 of the proposal will test the hypothesis that in silico trials using the calibrated populations from the first Aim can be used for testing and predicting mechanisms of nonresponse to device-based antihypertensive therapies. Aim 3 will test the hypothesis that our predictive analytic techniques can be used to identify mechanisms and proxy markers of therapeutic resistance in hypertensive AA. These proposed studies have clinical relevance because they address a leading cause of morbidity and mortality as well as potential mechanisms of therapeutic resistance in an underserved and understudied minority. Furthermore, these applications and the potential insights gleaned from our physiological model and predictive analytic tools may have broad implications for BP control in other resistant hypertensive populations.