Bioinformatics-assisted anti-HIV therapy

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Abstract
Highly active antiretroviral therapy (HAART) has had a major impact on the clinical management of HIV-1 infection. However, the emergence of resistant variants requires that follow-up drug regimens be optimized to maximum therapeutic effect. This article focuses on bioinformatics approaches that can be used to support anti-HIV therapy. Highly active antiretroviral therapy (HAART), in which three or more drugs are given in combination, has substantially improved the clinical management of HIV-1 infection. Still, the emergence of drug-resistant variants eventually leads to therapy failure in most patients. In such a scenario, the high diversity of resistance-associated mutational patterns complicates the choice of an optimal follow-up regimen. To support physicians in this task, a range of bioinformatics tools for predicting drug resistance or response to combination therapy from the viral genotype have been developed. With several free and commercial software services available, computational advice is rapidly gaining acceptance as an important element of rational decision-making in the treatment of HIV infection.