Available clinical markers of treatment outcome integrated in mathematical models to guide therapy in HIV infection.
Because treatment failure in many HIV-infected persons may be due to multiple causes, including resistance to antiretroviral agents, it is important to better tailor drug therapy to individual patients. This improvement requires the prediction of treatment outcome from baseline immunological or virological factors, and from results of resistance tests. Here, we review briefly the available clinical factors that have an impact on therapy outcome, and discuss the role of a predictive modelling approach integrating these factors proposed in a previous work. Mathematical and statistical models could become essential tools to address questions that are difficult to study clinically and experimentally, thereby guiding decisions in the choice of individualized drug regimens.
Vergu
Elisabeta
E
INSERM U436, Mathematical and Statistical Modelling in Biology and Medicine, CHU Pitié-Salpêtrière, Paris, France. eve@biomath.jussieu.fr
Mallet
Alain
A
Golmard
Jean-Louis
JL
D019380:Anti-HIV Agents / Q000627:therapeutic use*; D015415:Biomarkers; D018791:CD4 Lymphocyte Count; D018414:CD8-Positive T-Lymphocytes; D024882:Drug Resistance, Viral; D015658:HIV Infections / Q000188:drug therapy*; D006801:Humans; D015233:Models, Statistical; D011237:Predictive Value of Tests; D016896:Treatment Outcome
D019380:Anti-HIV Agents; D015415:Biomarkers