RT Journal Article
T1 Improved darunavir genotypic mutation score predicting treatment response for patients infected with HIV-1 subtype B and non-subtype B receiving a salvage regimen.
A1 De Luca, Andrea
A1 Flandre, Philippe
A1 Dunn, David
A1 Zazzi, Maurizio
A1 Wensing, Annemarie
A1 Santoro, Maria Mercedes
A1 Günthard, Huldrych F
A1 Wittkop, Linda
A1 Kordossis, Theodoros
A1 Garcia, Federico
A1 Castagna, Antonella
A1 Cozzi-Lepri, Alessandro
A1 Churchill, Duncan
A1 De Wit, Stéphane
A1 Brockmeyer, Norbert H
A1 Imaz, Arkaitz
A1 Mussini, Cristina
A1 Obel, Niels
A1 Perno, Carlo Federico
A1 Roca, Bernardino
A1 Reiss, Peter
A1 Schülter, Eugen
A1 Torti, Carlo
A1 van Sighem, Ard
A1 Zangerle, Robert
A1 Descamps, Diane
A1 CHAIN and COHERE in EuroCoord, 
AB 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. 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). 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  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.
YR 2016
FD 2016-01-28
LK http://hdl.handle.net/10668/9793
UL http://hdl.handle.net/10668/9793
LA en
DS RISalud
RD Apr 10, 2025