UNDERSTANDING DRUG-RESISTANCE FOR MONOTHERAPY TREATMENT OF HIV-INFECTION

The purpose of this study was to investigate strategies in the monothe rapy treatment of HIV infection in the presence of drug-resistant (mut ant) strains. A mathematical system is developed to model resistance i n HIV chemotherapy. It includes the key players in the immune response to HIV infection: virus and both uninfected CD4(+) and infected CD4() T-cell populations. We model the latent and progressive stages of th e disease, and then introduce monotherapy treatment. The model is a sy stem of differential equations describing the interaction of two disti nct classes of HIV-drug-sensitive (wild type) and drug-resistant (muta nt)-with lymphocytes in the peripheral blood. We then introduce chemot herapy effects. In the absence of treatment, the model produces the th ree types of qualitative clinical behavior-an uninfected steady state, an infected steady state (latency), and progression to AIDS. Simulati on of treatment is provided for monotherapy, during the progression to AIDS state, in the consideration of resistance effects. Treatment ben efit is based on an increase or retention in CD4(+) T-cell counts toge ther with a low viral titer. We explore the following treatment approa ches: an antiviral drug which reduces viral infectivity that is admini stered early-when the CD4(+) T-cell count is greater than or equal to 300/mm(3), and late-when the CD4(+) T-cell count is less than 300/mm(3 ). We compare all results with data. When treatment is initiated durin g the progression to AIDS state, treatment prevents T-cell collapse, b ut gradually loses effectiveness due to drug resistance. We hypothesiz e that it is the careful balance of mutant and wild-type HIV strains w hich provides the greatest prolonged benefit from treatment. This is b est achieved when treatment is initiated when the CD4(+) T-cell counts are greater than 250/mm(3), but less than 400/mm(3) in this model (i. e. not too early, not too late). These results are supported by clinic al data. The work is novel in that it is the first model to accurately simulate data before, during and after monotherapy treatment. Our mod el also provides insight into recent clinical results, as well as sugg ests plausible guidelines for clinical testing in the monotherapy of H IV infection. (C) 1997 Society for Mathematical Biology.