title: Modeling the short-, middle- and long-term viral load responses for comparing estimated dynamic parameters.
abstract: A virologic marker, the number of HIV RNA copies or viral load, is currently used to evaluate antiviral therapies in AIDS clinical trials. This marker can be used to assess the antiviral potency of therapies, but is easily affected by drug exposures, drug resistance and other factors during the long-term treatment evaluation process. The study of HIV dynamics is one of the most important development in recent AIDS research for understanding the pathogenesis of HIV-1 infection and antiviral treatment strategies. Although many HIV dynamic models have been proposed by AIDS researchers in the last decade, they have only been used to quantify short-term viral dynamics and do not correctly describe long-term virologic responses to antiretroviral treatment. In other words, these simple viral dynamic models can only be used to fit short-term viral load data for estimating dynamic parameters. In this paper, a mechanism-based differential equation models is introduced for characterizing the long-term viral dynamics with antiretroviral therapy. We applied this model to fit different segments of the viral load trajectory data from a simulation experiment and an AIDS clinical trial study, and found that the estimates of dynamic parameters from our modeling approach are very consistent. We may conclude that our model can not only characterize long-term viral dynamics, but can also quantify short- and middle-term viral dynamics. It suggests that if there are enough data in the early stage of the treatment, the results from our modeling based on short-term information can be used to capture the performance of long-term care with HIV-1 infected patients.
authors.affiliation: Department of Epidemiology and Biostatistics, College of Public Health, MDC 56, University of South Florida, Tampa, FL 33612, USA. yhuang@health.usf.edu
authors.forename: Yangxin
authors.initials: Y
authors.lastname: Huang
mesh_terms: D000163:Acquired Immunodeficiency Syndrome / Q000188:drug therapy* / Q000821:virology; D000465:Algorithms; D019380:Anti-HIV Agents / Q000627:therapeutic use*; D001499:Bayes Theorem; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data; D003198:Computer Simulation; D018628:Gene Dosage; D015497:HIV-1 / Q000187:drug effects* / Q000235:genetics; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D012367:RNA, Viral / Q000032:analysis*
chemical_list: D019380:Anti-HIV Agents; D012367:RNA, Viral