Bayesian Hierarchical Modeling of the HIV Evolutionary Response to Therapy

A major challenge for the treatment of human immunodeficiency virus (HIV) infection is the development of therapy-resistant strains. We present a statistical model that quantifies the evolution of HIV populations when exposed to particular therapies. A hierarchical Bayesian approach is used to estimate differences in rates of nucleotide changes between treatment- and control-group sequences. Each group's rates are allowed to vary spatially along the HIV genome. We employ a coalescent structure to address the sequence diversity within the treatment and control HIV populations. We evaluate the model in simulations and estimate HIV evolution in two different applications: a conventional drug therapy and an antisense gene therapy. In both studies, we detect evidence of evolutionary escape response in the HIV population. Supplementary materials for this article are available online.