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A virologic marker, the number of HIV RNA copies or viral load, is currentlyused to evaluate antiretroviral (ARV) therapies in AIDS clinical trials. Thismarker can be used to assess the ARV potency of therapies, but is easilyaffected by drug exposures, drug resistance and other factors during thelong-term treatment evaluation process. HIV dynamic studies have significantlycontributed to the understanding of HIV pathogenesis and ARV treatmentstrategies. However, the models of these studies are used to quantifyshort-term HIV dynamics ($<$ 1 month), and are not applicable to describelong-term virological response to ARV treatment due to the difficulty ofestablishing a relationship of antiviral response with multiple treatmentfactors such as drug exposure and drug susceptibility during long-termtreatment. Long-term therapy with ARV agents in HIV-infected patients oftenresults in failure to suppress the viral load. Pharmacokinetics (PK), drugresistance and imperfect adherence to prescribed antiviral drugs are importantfactors explaining the resurgence of virus. To better understand the factorsresponsible for the virological failure, this paper develops themechanism-based nonlinear differential equation models for characterizinglong-term viral dynamics with ARV therapy. The models directly incorporate drugconcentration, adherence and drug susceptibility into a function of treatmentefficacy and, hence, fully integrate virologic, PK, drug adherence andresistance from an AIDS clinical trial into the analysis. A Bayesian nonlinearmixed-effects modeling approach in conjunction with the rescaled version ofdynamic differential equations is investigated to estimate dynamic parametersand make inference. In addition, the correlations of baseline factors withestimated dynamic parameters are explored and some biologically meaningfulcorrelation results are presented. Further, the estimated dynamic parameters inpatients with virologic success were compared to those in patients withvirologic failure and significantly important findings were summarized. Theseresults suggest that viral dynamic parameters may play an important role inunderstanding HIV pathogenesis, designing new treatment strategies forlong-term care of AIDS patients.

Modeling long-term longitudinal HIV dynamics with application to an AIDS clinical study