Regional variations of rates and determinants of drug resistance mutations in people failing first-line therapy for HIV-1: a substudy from the D2EFT phase 3b/4 clinical trial.

Obeng, Billal Musah; Hutchinson, Jolie; Shaik, Ansari; Chetchotisakd, Ploenchan; Azwa, Iskandar; Wulan, Wahyu Nawang; Kumarasamy, Nagalingeswaran; Wolff, Marcelo et al. · Clin Infect Dis · 2026

cross_sectional · Level IV

Where this comes from

Abstract

D²EFT (Dolutegravir and Darunavir Evaluation in Adults Failing Therapy) was a phase 3b/4 randomized clinical trial designed to assess second-line treatment options systematically. This sub-study evaluated the distribution of drug resistance mutations (DRMs) before treatment randomisation in individuals failing first-line therapy. From a total of 826 participants across 14 countries, 727 sequences that covered the pr-rt-int (700), pr-rt (24), and rt (3) were analyzed for drug resistance. Sequences were submitted to the Stanford HIV drug resistance database to detect DRMs and assign subtypes. By adjusting for country and ART regimen, we assessed the association between DRMs and country and reported DRMs. Subtype C of HIV-1 accounted for most (59.3%) infections. There were extraordinarily high rates of high-level resistance to 3TC and FTC, both at 88.3%, and for EFV and NVP, at 92.8% and 96.8%, respectively. On average, nucleoside reverse transcriptase inhibitors (NRTI) mutations had the highest occurrence across countries with M184V/I detected in 86.2% of the samples, while the highest proportion of non-nucleoside reverse transcriptase inhibitors (NNRTI) mutations was K103N at 57.8%. K103N had an increased likelihood of occurrence in African and South American countries (p<0.05). Participants with prior exposure to a ZDV-containing regimen had an increased likelihood of T215F/Y mutations 6.80[2.59, 17.86], while those with NVP/RPV exposure had a decreased likelihood of K103N mutation 0.29[0.15, 0.56]. The regional specificity of mutations underscores the dynamic nature of HIV-1 drug resistance patterns and the importance of monitoring and understanding local mutation profiles.