Improved darunavir genotypic mutation score predicting treatment response for patients infected with HIV-1 subtype B and non-subtype B receiving a salvage regimen.

OBJECTIVES: The objective of this study was to improve the prediction of the impact of HIV-1 protease mutations in different viral subtypes on virological response to darunavir. METHODS: Darunavir-containing treatment change episodes (TCEs) in patients previously failing PIs were selected from large European databases. HIV-1 subtype B-infected patients were used as the derivation dataset and HIV-1 non-B-infected patients were used as the validation dataset. The adjusted association of each mutation with week 8 HIV RNA change from baseline was analysed by linear regression. A prediction model was derived based on best subset least squares estimation with mutational weights corresponding to regression coefficients. Virological outcome prediction accuracy was compared with that from existing genotypic resistance interpretation systems (GISs) (ANRS 2013, Rega 9.1.0 and HIVdb 7.0). RESULTS: TCEs were selected from 681 subtype B-infected and 199 non-B-infected adults. Accompanying drugs were NRTIs in 87%, NNRTIs in 27% and raltegravir or maraviroc or enfuvirtide in 53%. The prediction model included weighted protease mutations, HIV RNA, CD4 and activity of accompanying drugs. The model's association with week 8 HIV RNA change in the subtype B (derivation) set was R(2) = 0.47 [average squared error (ASE) = 0.67, P < 10(-6)]; in the non-B (validation) set, ASE was 0.91. Accuracy investigated by means of area under the receiver operating characteristic curves with a binary response (above the threshold value of HIV RNA reduction) showed that our final model outperformed models with existing interpretation systems in both training and validation sets. CONCLUSIONS: A model with a new darunavir-weighted mutation score outperformed existing GISs in both B and non-B subtypes in predicting virological response to darunavir.

Prevalence of drug resistance mutations and non-B subtypes in newly diagnosed HIV-1 patients in Denmark.

The aim of this study was to monitor the prevalence of drug resistance mutations in newly diagnosed HIV-1 positive individuals in Denmark. In addition we assessed the prevalence of non-B subtypes based on phylogenetic analysis of the pol gene. Plasma samples from 104 newly diagnosed HIV-1 positive patients were obtained in the year 2000. The entire protease gene and 320 amino acids of the reverse transcriptase gene were genotyped. Sequences were obtained from 97 patients. No subjects displayed primary resistance mutations in the protease gene, whereas all carried 1 or more secondary mutations. Resistance mutations in the RT-gene associated with NRTI-resistance were found in 1 patient, who was infected with zidovudine resistant HIV-1 harbouring the M41L mutation in combination with T215S and L210S. The T215S mutation has been showed to be associated with reversion of zidovudine resistance. The T215S mutation was found in 1 additional patient. The subtype distribution was subtype B 59%, C 18%, A 8%, CRF02_AG 5%, CRF01_AE 4%, D 3% and G 2%. We found 2 patients (2%) with mutations associated with resistance in the RT-gene and none in the protease gene indicating a low prevalence of resistant HIV-1 in Denmark in the year 2000.