A virologic marker, the number of HIV RNA copies or viral load, is currently
used to evaluate antiretroviral (ARV) therapies in AIDS clinical trials. This
marker can be used to assess the ARV potency of therapies, but is easily
affected by drug exposures, drug resistance and other factors during the
long-term treatment evaluation process. HIV dynamic studies have significantly
contributed to the understanding of HIV pathogenesis and ARV treatment
strategies. However, the models of these studies are used to quantify
short-term HIV dynamics (< 1 month), and are not applicable to describe
long-term virological response to ARV treatment due to the difficulty of
establishing a relationship of antiviral response with multiple treatment
factors such as drug exposure and drug susceptibility during long-term
treatment. Long-term therapy with ARV agents in HIV-infected patients often
results in failure to suppress the viral load. Pharmacokinetics (PK), drug
resistance and imperfect adherence to prescribed antiviral drugs are important
factors explaining the resurgence of virus. To better understand the factors
responsible for the virological failure, this paper develops the
mechanism-based nonlinear differential equation models for characterizing
long-term viral dynamics with ARV therapy. The models directly incorporate drug
concentration, adherence and drug susceptibility into a function of treatment
efficacy and, hence, fully integrate virologic, PK, drug adherence and
resistance from an AIDS clinical trial into the analysis. A Bayesian nonlinear
mixed-effects modeling approach in conjunction with the rescaled version of
dynamic differential equations is investigated to estimate dynamic parameters
and make inference. In addition, the correlations of baseline factors with
estimated dynamic parameters are explored and some biologically meaningful
correlation results are presented. Further, the estimated dynamic parameters in
patients with virologic success were compared to those in patients with
virologic failure and significantly important findings were summarized. These
results suggest that viral dynamic parameters may play an important role in
understanding HIV pathogenesis, designing new treatment strategies for
long-term care of AIDS patients.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS192 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org