Mathematical Modeling of Antiretroviral Therapy in Pursuit of HIV Prevention and Cure

San Diego State University Foundation, San Diego CA

Investigators

Abstract

There is currently no known cure or effective vaccine for human immunodeficiency virus (HIV) infection. In the absence of a vaccine, the most effective strategy for preventing HIV transmission is to implement the “treatment as prevention” approach, that is, antiretroviral medication to achieve undetectable viral load in patients, thereby preventing sexual transmission. For treatment and cure of HIV infection, one of the viable options is to begin therapy early in infection before the establishment of virus reservoirs such as latent infected CD4+ T cells and infected brain cells. This project develops and uses mathematical models of cell-virus interactions and computational methods to study the effectiveness of early antiretroviral therapy (ART), including a recently developed nanoparticle-based preventive therapy (NBPT), for treatment of HIV. The study will focus on HIV infections in three critical sites: vaginal mucosa, circulation, and brain. In addition to improving our current knowledge of HIV transmission dynamics, pathogenesis, and nanomedicine, this research will have a significant positive and practical impact on the development of therapies and vaccines for the control of HIV. This project aims to generate novel nonautonomous systems of differential equations to study the effects of ART pharmacodynamics on the spatiotemporal distribution of HIV virus in vaginal mucosa and on the persistence of HIV in the virus reservoirs. The project will utilize experimental data from HIV infected humans and SIV (Simian Immunodeficiency Virus) infected macaques, as well as data from experiments on effectiveness of ARTs and NBPTs, for the model validation and the parameter estimation. The developed models will be extensively analyzed using dynamical systems theory as well as statistical, numerical, and machine learning methods. Mathematical and computational challenges anticipated in this research will offer opportunities to develop innovative techniques that advance the field of applied non-autonomous complex differential equations as well as optimal control theory. This project will also broaden the application of differential equation modeling to a machine learning framework. The results, including optimal treatment protocols, are intended help to inform evidence-based guidelines for managing, preventing, and curing HIV infection via early ARTs and NBPTs, thereby enhancing the quality of life for HIV infected patients. In addition, this project will provide extensive interdisciplinary research training opportunities for undergraduate and graduate students. 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.