Aim Poor drug-adherence is an important factor explaining the resurgence of HIV-1 virus. Complex non-linear models have been developed to describe the population dynamics of HIV virus, but they are not used in clinical trials due to their complexity. Linearized models have been applied to real data. However, they can only explain the decay of the virus following antiviral treatment. The objective of our project is to develop a population non-linear mixed effect model characterizing the long-term dynamics of viral load in clinical data, and to quantify the effect of adherence in the dynamic of HIV virus. Methods The basic model incorporates physiologically meaningful variables (free virus, total T-cells, and latent T-cells), uses standard rescaling techniques to guarantee identifiability of its parameters given measurements of the free virus (viral load), and takes into account intra-subject variability. Drug-adherence is incorporated on the basic reproductive ratio of the virus (RR) as follows: RR=λ+γ*Aj(t), where λ is the RR for non compliers (Aj(t)=0) and λ+γ, γ≤ 0, is the RR for perfect compliers (Aj(t)=1). The model is applied on real AIDS clinical data. Results We show that adherence affects the RR. Perfect adherence decreases RR by 3% resulting in an important reduction of RNA levels. Conclusions The model may be used to draw biologically relevant interpretations from repeated HIV-1 RNA measurements and quantify the relative effect of drug-adherence on viral response in HIV-infected patients. Clinical Pharmacology & Therapeutics (2005) 77, P90–P90; doi: 10.1016/j.clpt.2004.12.238