Antigen-induced stimulation of the immune system can generate heterogeneity in CD4؉ T cell division rates capable of explaining the temporal patterns seen in the decay of HIV-1 plasma RNA levels during highly active antiretroviral therapy. Posttreatment increases in peripheral CD4؉ T cell counts are consistent with a mathematical model in which host cell redistribution between lymph nodes and peripheral blood is a function of viral burden. Model fits to patient data suggest that, although therapy reduces HIV replication below replacement levels, substantial residual replication continues. This residual replication has important consequences for long-term therapy and the evolution of drug resistance and represents a challenge for future treatment strategies.T he advent of highly active antiretroviral therapy (HAART) has provided a wealth of information on the interaction between HIV and the human immune system and is continuing to stimulate the debate on the basic mechanisms of viral pathogenesis (1-3). Posttherapy patterns of viral load decline, and changes in CD4ϩ T cell population structure inform our knowledge of heterogeneity in within-host viral replication and immune system reconstitution. A central paradox remains, however. How does HIV cause immune system failure while infecting only a small overall proportion of CD4ϩ T cells? We address this question by placing the within-host dynamics of HIV replication within the context of an immune system that is heterogeneously structured in terms of the distribution of cell turnover rates by diverse and repeated antigenic stimulation. We use a model of antigen-driven T cell proliferation (4-8) to show that HIV infection can reduce the ability of a single antigen-specific activated CD4ϩ T cell subpopulation to help antigen clearance. The proliferation rate distribution of the entire CD4ϩ T cell population then emerges naturally from a consideration of the effect of constant immune system stimulation by multiple antigens. Inclusion of such population heterogeneity within a model of HIV replication in the lymph nodes is then shown to provide a conceptual framework capable of explaining the following key observations: a rapid multiphase decline in HIV RNA levels after treatment (9-15); a rapid initial rise in CD4ϩ T cells after treatment, followed by a phase of slower recovery (16-18); a low prevalence of HIV-infected CD4ϩ T cells in the peripheral blood and lymph nodes, as measured by HIV DNA levels (19-24); an increase in viral load after antigenic stimulation induced by vaccination (25-30); and faster CD4ϩ T cell replication after therapy than before treatment (31).One key point arising from this framework is that the CD4ϩ T cell population itself-not other cell classes-can provide the main reservoir (32-35) of long-lived infected cells, which have been shown by earlier models (36, 37) to be necessary to describe the observed multiphase posttreatment decline in HIV RNA levels. The hypothesis that long-lived CD4ϩ T cells are the dominant infected cell reservoir has ...

In the absence of widespread access to individualized laboratory monitoring, which forms an integral part of HIV patient management in resource-rich settings, the roll-out of highly active antiretroviral therapy (HAART) in resource-limited settings has adopted a public health approach based on standard HAART protocols and clinical/immunological definitions of therapy failure. The cost-effectiveness of HIV-1 viral load monitoring at the individual level in such settings has been debated, and questions remain over the long-term and population-level impact of managing HAART without it. Computational models that accurately predict virological response to HAART using baseline data including CD4 count, viral load and genotypic resistance profile, as developed by the Resistance Database Initiative, have significant potential as an aid to treatment selection and optimization. Recently developed models have shown good predictive performance without the need for genotypic data, with viral load emerging as by far the most important variable. This finding provides further, indirect support for the use of viral load monitoring for the long-term optimization of HAART in resource-limited settings.

We developed computational models that predict virological response to ART without a genotype with comparable accuracy to genotyping with rule-based interpretation. These models have the potential to help optimize antiretroviral therapy for patients in RLSs where genotyping is not generally available.

No abstract