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

TitleImproved darunavir genotypic mutation score predicting treatment response for patients infected with HIV-1 subtype B and non-subtype B receiving a salvage regimen.
Publication TypeJournal Article
Year of Publication2016
AuthorsDe Luca A, Flandre P, Dunn D, Zazzi M, Wensing A, Santoro M, Günthard H, Wittkop L, Kordossis T, García F, Castagna A, Cozzi-Lepri A, Churchill D, De Wit S, Brockmeyer N, Imaz A, Mussini C, Obel N, Perno C, Roca B, Reiss P, Schülter E, Torti C, van Sighem A, Zangerle R and Descamps D
Corporate AuthorsCHAIN and COHERE in EuroCoord
JournalJ Antimicrob Chemother
Volume71
Issue5
Pagination1352-60
Date Published2016 May
ISSN1460-2091
KeywordsAdolescent, Adult, Aged, Aged, 80 and over, Anti-HIV Agents, Darunavir, Drug Resistance, Viral, Europe, Female, Genotyping Techniques, HIV Infections, HIV Protease, HIV-1, Humans, Male, Microbial Sensitivity Tests, Middle Aged, Mutation, Prognosis, Treatment Outcome, Young Adult
Abstract

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.

DOI10.1093/jac/dkv465
Alternate JournalJ. Antimicrob. Chemother.
PubMed ID26825119
Grant ListMC_UU_12023/15 / / Medical Research Council / United Kingdom
MR/M004236/1 / / Medical Research Council / United Kingdom