Residual phenotypic susceptibility to doravirine in multidrug-resistant HIV-1 from subjects enrolled in the PRESTIGIO Registry

Abstract

ObjectivesDoravirine shows a rather distinct resistance profile within the nonnucleoside reverse transcriptase inhibitor (NNRTI) class. This study aimed to evaluate the phenotypic susceptibility to doravirine, rilpivirine and etravirine in a panel of multidrug-resistant (MDR) HIV-1 isolates collected from people living with HIV (PLWH) enrolled in the PRESTIGIO Registry. MethodsRecombinant viruses expressing PLWH-derived protease, reverse transcriptase coding regions were generated from plasma samples at virological failure with documented resistance to protease inhibitors, nucleoside reverse transcriptase inhibitors, NNRTIs and integrase strand transfer inhibitors. In vitro susceptibility was assessed through a phenotypic assay measuring fold-change values with respect to the reference NL4-3 virus. Genotypic susceptibility was computed by the Stanford HIVdb algorithm 8.9-1. ResultsPlasma samples were collected from 22 PLWH: 20 (91%) were male, median age 55 years (IQR 50–58), time since HIV-1 diagnosis 27 years (23–31) and time on antiretroviral treatment 23 years (22–26). Median doravirine, etravirine and rilpivirine fold-change values were 9.8 (2.9–40.4), 42.9 (3.1–100.0) and 100.0 (17.9–100.0), respectively. According to the fold-change cut-offs, full susceptibility was observed in five (23%), four (18%) and one (5%) cases with doravirine, etravirine and rilpivirine, respectively. Irrespective of the presence of specific doravirine mutations, higher numbers of NNRTI mutations correlated with higher fold-change values for doravirine. By comparing the distribution of fold-change values with the Stanford HIVdb predicted susceptibility, a significant correlation was detected for doravirine and rilpivirine but not etravirine. ConclusionDespite extensive cross-resistance among NNRTIs, doravirine can be a valid option in a proportion of PLWH with MDR HIV-1. Doravirine activity appeared to be inferred with fair accuracy by the HIVdb algorithm.