A non-linear mixed effect dynamic model incorporating prior exposure and adherence to treatment to describe long-term therapy outcome in HIV-patients.

Long term therapy with antiretroviral agents in HIV-infected patients often result in failure to suppress the virus load. Imperfect adherence to prescribed antiviral drugs is an important factor explaining the resurgence of virus. A better understanding of the factors responsible for the virological failure is important for the development of new treatment strategies. Many complex non-linear models have been developed to describe and simulate the dynamics of HIV-1 virus. Those complicated viral dynamic models have not been used in clinical trials to estimate HIV dynamics parameters, due to their complexity, until the recent development of simplification and approximation techniques. The estimation of the parameters associated with the dynamics from real data has been mostly limited to linearized models that can only explain the decay (suppression) of the virus following antiviral treatment. Moreover, no complete characterization of typical clinical data in terms of inter-subject variability and identification of important covariates effecting HIV-1 dynamics has been attempted. The objective of our paper was to develop a hierarchical non-linear mixed effect model characterizing inter-subject variability in the long-term response to treatment of HIV-1 RNA, and show how the model can be used to quantify the effect of important covariates, such as physiological variables, adherence to treatment or previous exposure to treatment, on the dynamics of HIV-1 RNA. As an example we report the analysis of AIDS clinical trial data from AACTG 398, which shows that patients with previous exposure to treatment show faster death rates for HIV-1, and that higher adherence to treatment is associated with lower reproductive ratio.