Pharmacodynamics of Antiretroviral Agents in HIV-1 Infected Patients: Using Viral Dynamic Models that Incorporate Drug Susceptibility and Adherence

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Abstract
We developed a novel HIV-1 dynamic model with consideration of pharmacokinetics, drug adherence and drug susceptibility to link plasma drug concentration to the long-term changes in HIV-1 RNA observation after initiation of therapy. A Bayesian approach is proposed to fit this model to clinical data from ACTG A5055, a study of two dosage regimens of indinavir (IDV) with ritonavir (RTV) in subjects failing their first protease inhibitor treatment. The HIV RNA testing was completed at days 0, 7, 14, 28, 56, 84, 112, 140, and 168. An intensive pharmacokinetic (PK) 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 were 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. As univariate predictors, none of the four PK parameters Ctrough, C12 hour, Cmax, and AUC was significantly related to virologic response (p>0.05). By including drug susceptibility (IC50), or IC50 and adherence measured by pill counts together, Ctrough, C12 hour, Cmax and AUC were each significantly correlated to long-term virologic response (p=0.0055,0.0002,0.0136,0.0002 with IC50 and adherence measured by pill counts considered). The IC50 and adherence measured by pill counts alone were not related to the virologic response. In predicting virologic response adherence measured by pill counts did not provide any additional information to PK parameters (p=0.064), to drug susceptibility IC50 (p=0.086), and to their combination (p=0.22). Simple regression approaches did not detect any significant pharmacodynamic (PD) relationships. Any single factor of PK, adherence measured by pill counts and drug susceptibility did not contribute to long-term virologic response. But their combinations in viral dynamic modeling significantly predicted virologic response. The HIV dynamic modeling can appropriately capture complicated nonlinear relationships and interactions among multiple covariates.