Population dynamics and evolution of drug resistance

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DESCRIPTION (Provided by the applicant): I propose to conduct mathematical and in vitro studies of the dynamics and evolution of drug resistance in HIV-1. The proposal includes theoretical and experimental projects that are united by the use of mechanistic models that account for important features of the HIV-1 viral life cycle. The first goal of the proposal is to use mathematical models to evaluate strategies for minimizing selection for drug resistant virus in vivo. Using these models, I hope to generate theoretical predictions for the optimal time of treatment in primary infection patients and in patients undergoing structured treatment interruptions. The second major goal will be to improve mathematical models for the dynamics of interactions between HIV-1 and target cells in vitro. These models will be distinguished from previous efforts in that they will include explicit estimates for viral life history parameters and will be subjected to rigorous testing in vitro. These models will then be used to predict the results of competition between drug resistant and drug sensitive strains in vitro, test predictions of in vitro analogs of the models presented in part 1 and quantify life-history traits that underlie differences in the replicative capacity of viral isolates in vitro. I also hope to address the question of viral populations exposed to drug selection pressures for long periods of time converge on the same adaptive peak. I will also address the question of whether we can we relate reductions in viral fitness to changes in specific kinetic parameters and whether mathematical modeling of viral dynamics can explain why resistance in vitro does not always predict resistance in vivo. Answers to these questions may lead to improved treatment strategies for patients who continue to take antiretroviral drugs following the evolution of drug resistance.