Background:  Establishing a relationship between drug exposure and long-term virologic response has been challenging. Major impediments include inconsistent definitions of long-term response, undefined confounding factors, nonlinear relationships, and interactions among all factors and antiviral responses.

Methods:  We developed a novel long-term HIV-1 dynamic model with consideration of pharmacokinetics, drug adherence, and drug susceptibility to link plasma drug concentration to complete HIV-1 RNA trajectory. A Bayesian approach was used to fit this model to clinical data from ACTG A5055, a study of 2 dosage regimens of indinavir (IDV) with ritonavir (RTV) in subjects failing their first protease inhibitor (PI) treatment. HIV RNA testing was completed at days 0, 7, 14, 28, 56, 84, 112, 140, and 168. An intensive pharmacokinetic evaluation was performed on day 14 and multiple trough concentrations were subsequently collected. Pill counts were used to monitor adherence. IC₅₀ for IDV and RTV was determined at baseline and at virologic failure. Viral dynamic model fitting residuals were used to assess the significance of covariate effects on long-term virologic response.

Results:  As univariate predictors, none of the 4 pharmacokinetic K parameters C_trough, C_12h, C_max, and AUC_0-12h was significantly related to virologic response (p > 0.05). By including drug susceptibility (IC₅₀), or IC₅₀ and adherence together, C_trough, C_12h, C_max, and AUC_0-12h were each significantly correlated to long-term virologic response (p = 0.0055, 0.0002, 0.0136, 0.0002 with IC₅₀ and adherence considered). IC₅₀ and adherence alone were not related to the virologic response. Adherence did not provide any additional information to pharmacokinetic parameters (p = 0.064), to drug susceptibility IC₅₀ (p = 0.086), and to their combination (p = 0.22) in predicting virologic response. Simple regression approaches did not detect any significant PD relationships.

Conclusions:  Any single factor of pharmacokinetics, adherence, and drug susceptibility did not contribute to long-term virologic response. But their combinations in viral dynamic modeling significantly predicted virologic response. HIV dynamic modeling can appropriately capture the complicated nonlinear relationships and interactions among multiple covariates.

Keywords: Pharmacodynamics; Viral dynamics modeling; prediction of antiviral response