Andrea De Luca1, Philippe Flandre2, David Dunn3, Maurizio Zazzi4, Annemarie Wensing5, Maria Mercedes Santoro6, Huldrych F Günthard7, Linda Wittkop8, Theodoros Kordossis9, Federico Garcia10, Antonella Castagna11, Alessandro Cozzi-Lepri12, Duncan Churchill13, Stéphane De Wit14, Norbert H Brockmeyer15, Arkaitz Imaz16, Cristina Mussini17, Niels Obel18, Carlo Federico Perno19, Bernardino Roca20, Peter Reiss21, Eugen Schülter22, Carlo Torti23, Ard van Sighem24, Robert Zangerle25, Diane Descamps26. 1. Division of Infectious Diseases, Siena University Hospital, Siena, Italy Department of Medical Biotechnologies, University of Siena, Siena, Italy andrea.deluca@unisi.it. 2. INSERM UMR-S 1136, Paris, France. 3. MRC Clinical Trials Unit at University College London, London, UK. 4. Department of Medical Biotechnologies, University of Siena, Siena, Italy. 5. University Medical Center Utrecht, Utrecht, The Netherlands. 6. University of Rome Tor Vergata, Rome, Italy. 7. Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Switzerland and Institute of Medical Virology, University of Zurich, Zurich, Switzerland. 8. Inserm U897, ISPED, Université de Bordeaux, CHU Bordeaux, France/Cohere in Eurocoord RCC, Bordeaux, France. 9. University of Athens, Athens, Greece. 10. Hospital San Cecilio, Granada, Spain. 11. San Raffaele Hospital, Milan, Italy. 12. University College London, London, UK. 13. Brighton and Sussex University Hospitals NHS Trust, Brighton, UK. 14. St Pierre University Hospital, Brussels, Belgium. 15. Department of Dermatology and Venereology, Center for Sexual Health and Medicine, Ruhr University Bochum, Bochum, Germany and Competence Network for HIV/AIDS, Ruhr University Bochum, Bochum, Germany. 16. Bellvitge University Hospital, Barcelona, Catalonia, Spain. 17. University of Modena and Reggio Emilia, Modena, Italy. 18. Copenhagen University Hospital, Copenhagen, Denmark. 19. INMI 'L. Spallanzani', Rome, Italy. 20. University of Valencia, Castellon, Spain. 21. Stichting HIV Monitoring, Amsterdam, The Netherlands, and Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands/Cohere in Eurocoord RCC, Copenhagen, Denmark. 22. University of Cologne, Cologne, Germany. 23. University Magna Graecia, Catanzaro, Italy. 24. Stichting HIV Monitoring, Amsterdam, The Netherlands. 25. Universitätsklinik für Dermatologie und Venerologie, Innsbruck, Austria. 26. AP-HP, Hôpital Bichat-Claude Bernard, Laboratoire de Virologie, IAME, UMR_1137, INSERM, Univ Paris Diderot, Sorbonne Paris Cité, Paris, France.
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.
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.
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