HIV Drug Susceptibility and Treatment Response to Mega-Haart Regimen in Patients from the Frankfurt HIV Cohort

Analysis of the Impact of Antiretroviral Drug Changes on Survival of Patients with Advanced-Stage AIDS with Multidrug-Resistant HIV Infection

Objectives:

This article aims to elucidate the relationship between antiretroviral (ARV) medication changes and all-cause mortality using a total of 368 patients recruited from the United States (78%), United Kingdom (11%), and Canada (11%).

Methods:

Data sources included demographic characteristics, ARV treatment history and modifications, and clinical biomarker data from the completed OPTions In Management with Antiretrovirals clinical trial. Descriptive analysis and graphical trajectory representation of ARV drug modifications and biomarker changes were undertaken. Three hypotheses aimed at assessing the impact of ARV modification parameters on clinical outcomes were tested. Kaplan-Meier survival techniques as well as Cox proportional hazard regression models were employed.

Results:

Results from the analyses suggest that (1) switching therapy strategy from an intensified ARV regimen to a less intense one or vice versa, (2) having a moderate number (up to 2) of ARV drug changes per 6 months, and (3) changes based on clinical/HIV-related reasons or nonclinical reasons compared to ARV drug regimen changes due to clinical non-HIV reasons improved survival.

Conclusion:

Modifications in the ARV regimens of HIV-infected patients with multidrug resistance are associated with improved survival.

Diversity and complexity of HIV-1 drug resistance: a bioinformatics approach to predicting phenotype from genotype

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 at http://cartan.gmd.de/geno2pheno.html.

Laboratory Guidelines for the Practical Use of HIV Drug Resistance Tests in Patient Follow-Up

HIV drug resistance is one of the major limitations in the successful treatment of HIV-infected patients using currently available antiretroviral combination therapies. When appropriate, drug susceptibility profiles should be taken into consideration in the choice of a specific combination therapy. Guidelines recommending resistance testing in certain circumstances have been issued. Many clinicians have access to resistance testing and will increasingly use these results in their treatment decisions. In this document, we comment on the different methods available, and the relevant issues relating to the clinical application of these tests. Specifically, the following recommendations can be made: (i) genotypic and phenotypic HIV-1 drug resistance analyses can yield complementary information for the clinician. However, insufficient information currently exists as to which approach is preferable in any particular clinical setting; (ii) when HIV-1 drug resistance testing is required, it is recommended that testing be performed on plasma samples obtained before starting, stopping or changing therapy, on samples that have a viral load above the detection limit of the resistance test; (iii) the panel recommends that genotypic and phenotypic HIV-1 drug resistance testing for clinical purposes be performed in a certified laboratory under strict quality control and quality assurance standards; and (iv) the panel recommends that resistance testing laboratories provide clinicians with resistance reports that include a list of drug-related resistance mutations (genotype) and/or a list of drug-related fold resistance values (phenotype), with interpretations of each by an experienced virologist. The interpretation of genotypic and phenotypic analysis is a complex and developing science, and in order to understand HIV-1 drug resistance reports, communication between the requesting clinician and the expert that interpreted the resistance report is recommended.