BackgroundInfection with drug-resistant human immunodeficiency virus type 1 (HIV-1) can impair the response to combination therapy. Widespread transm

Therapeutic success of anti-HIV therapies is limited by the development of drug resistant viruses. These genetic variants display complex mutational patterns in their pol gene, which codes for protease and reverse transcriptase, the molecular targets of current antiretroviral therapy. Genotypic resistance testing depends on the ability to interpret such sequence data, whereas phenotypic resistance testing directly measures relative in vitro susceptibility to a drug. From a set of 650 matched genotype-phenotype pairs we construct regression models for the prediction of phenotypic drug resistance from genotypes. Since the range of resistance factors varies considerably between different drugs, two scoring functions are derived from different sets of predicted phenotypes. Firstly, we compare predicted values to those of samples derived from 178 treatment-naive patients and report the relative deviance. Secondly, estimation of the probability density of 2000 predicted phenotypes gives rise to an intrinsic definition of a susceptible and a resistant subpopulation. Thus, for a predicted phenotype, we calculate the probability of membership in the resistant subpopulation. Both scores provide standardized measures of resistance that can be calculated from the genotype and are comparable between drugs. The geno2pheno system makes these genotype interpretations available via the Internet (http://www.genafor.org/).

Viral tropism is the ability of viruses to enter and infect specific host cells and is based on the ability of viruses to bind to receptors on those cells. Testing for HIV tropism is recommended before prescribing a chemokine receptor blocker. In most European countries, HIV tropism is identified with tropism phenotype testing. New data support genotype analysis of the HIV third hypervariable loop (V3) for the identification of tropism. The European Consensus Group on clinical management of tropism testing was established to make recommendations to clinicians and clinical virologists. The panel recommends HIV-tropism testing for the following groups: drug-naive patients in whom toxic effects are anticipated or for whom few treatment options are available; patients who have poor tolerability to or toxic effects from current treatment or who have CNS pathology; and patients for whom therapy has failed and a change in treatment is considered. In general, an enhanced sensitivity Trofile assay and V3 population genotyping are the recommended methods. Genotypic methods are anticipated to be used more frequently in the clinical setting because of their greater accessibility, lower cost, and faster turnaround time than other methods. For the interpretation of V3 loop genotyping, clinically validated systems should be used when possible. Laboratories doing HIV tropism tests should have adequate quality assurance measures. Similarly, close collaboration between HIV clinicians and virologists is needed to ensure adequate diagnostic and treatment decisions.

The SPREAD Programme investigated prospectively the time trend from September 2002 through December 2005 of transmitted drug resistance (TDR) among 2

Drug resistance testing has been shown to be beneficial for clinical management of HIV type 1 infected patients. Whereas phenotypic assays directly measure drug resistance, the commonly used genotypic assays provide only indirect evidence of drug resistance, the major challenge being the interpretation of the sequence information. We analyzed the significance of sequence variations in the protease and reverse transcriptase genes for drug resistance and derived models that predict phenotypic resistance from genotypes. For 14 antiretroviral drugs, both genotypic and phenotypic resistance data from 471 clinical isolates were analyzed with a machine learning approach. Information profiles were obtained that quantify the statistical significance of each sequence position for drug resistance. For the different drugs, patterns of varying complexity were observed, including between one and nine sequence positions with substantial information content. Based on these information profiles, decision tree classifiers were generated to identify genotypic patterns characteristic of resistance or susceptibility to the different drugs. We obtained concise and easily interpretable models to predict drug resistance from sequence information. The prediction quality of the models was assessed in leave-one-out experiments in terms of the prediction error. We found prediction errors of 9.6–15.5% for all drugs except for zalcitabine, didanosine, and stavudine, with prediction errors between 25.4% and 32.0%. A prediction service is freely available athttp://cartan.gmd.de/geno2pheno.html.

Understanding HIV-1 subtype distribution and epidemiology can assist preventive measures and clinical decisions. Sequence variation may affect antiviral drug resistance development, disease progression, evolutionary rates and transmission routes.