There are limited real-world mutational and virological outcomes data of treatment-experienced persons diagnosed with HIV-1 subtype C (HIV-1 C) who are failing Integrase Strand Transfer Inhibitor-based regimens. Requisition forms sent for HIV-1 genotypic resistance testing (GRT) between May 2015 and September 2019 were reviewed and participants experiencing virologic failure while on dolutegravir (DTG) or raltegravir (RAL) cART at sampling recruited. Sanger sequencing of the HIV-1 Pol gene was performed from residual plasma samples and drug resistance mutational (DRM) analysis performed using the Stanford University HIV drug resistance database. 40 HIV-1C integrase sequences were generated from 34 individuals, 24 of whom were on DTG cART, three on RAL cART and seven on an unknown (DTG or RAL)-anchored cART at time of GRT. 11/34 (32%) individuals had DRMs to DTG and other integrase inhibitors. 7/11 (64%) patients had exposure to a RAL-based cART at the time of sampling. Out of the 11 individuals with DRMs, one (9%) had 2-class, 6 (55%) had 3-class, and 4 (36%) had 4-class multidrug-resistant HIV-1C. 7/11 individuals (64%) are currently virologically suppressed. Of the four individuals not virologically suppressed, three had extensive DRMs involving 4-classes of ARV drugs and one individual has demised. Resistance to DTG occurs more often in patients exposed to RAL cART. Individuals with 4-class DRMs plus integrase T97 and E157Q mutations appear to have worse outcomes. There is a need for frequent VL monitoring and GRT amongst treatment-experienced HIV-1C diagnosed individuals.

Purpose: Monitoring HIV-1 drug resistance mutations (DRM) in treated patients on combination antiretroviral therapy (cART) with a detectable HIV-1 viral load (VL) is important for the selection of appropriate cART. Currently, there is limited data on HIV DRM at low-level viremia (LLV) (VL 401– 999 copies/mL) due to the use of a threshold of VL ≥ 1000 copies/mL for HIV DRM testing. We here assess the performance of an in-house HIV drug resistance genotyping assay using plasma for the detection of DRM at LLV. Methods: We used a total of 96 HIV plasma samples from the population-based Botswana Combination Prevention Project (BCPP). The samples were stratified by VL groups: 50 samples had LLV, defined as 401– 999 copies/mL, and 46 had ≥ 1000 copies/mL. HIV pol (PR and RT) region was amplified and sequenced using an in-house genotyping assay with BigDye sequencing chemistry. Known HIV DRMs were identified using the Stanford HIV Drug Resistance Database. Genotyping success rate between the two groups was estimated and compared using the comparison of proportions test. Results: The overall genotyping success rate was 79% (76/96). For VL groups, the genotyping success was 72% (36/50) at LLV and 87% (40/46) at VL ≥ 1000 copies/mL. Among generated sequences, the overall prevalence of individuals with at least 1 major or intermediate-associated DRM was 24% (18/76). The proportions of NNRTI-, NRTI- and PI-associated resistance mutations were 28%, 24%, and 0%, respectively. The most predominant mutations detected were K103N (18%) and M184V (12%) in NNRTI- and NRTI-associated mutations, respectively. The prevalence of DRM was 17% (6/36) at LLV and 30% (12/40) at VL ≥ 1000 copies/mL. Conclusion: The in-house HIV genotyping assay successfully genotyped 72% of LLV samples and was able to detect 17% of DRM amongst them. Our results highlight the possibility and clinical significance of genotyping HIV among individuals with LLV.

Background: Individuals living with human immunodeficiency virus (HIV) who experience virological failure (VF) after combination antiretroviral therapy (cART) initiation may have had low-frequency drug resistance mutations (DRMs) at cART initiation. There are no data on low-frequency DRMs among cART-naïve HIV-positive individuals in Botswana. Methods: We evaluated the prevalence of low-frequency DRMs among cART-naïve individuals previously sequenced using Sanger sequencing. The generated pol amplicons were sequenced by next-generation sequencing. Results: We observed low-frequency DRMs (detected at 20%. K65R was the most common low-frequency DRM detected in 8 individuals. Eighty-two of the 103 individuals had follow-up viral load data while on cART. Twenty-seven of the 82 individuals harbored low-frequency DRMs. Only 12 of 82 individuals experienced VF. The following low-frequency DRMs were observed in four individuals experiencing VF: K65R, K103N, V108I, and Y188C. No statistically significant difference was observed in the prevalence of low-frequency DRMs between individuals experiencing VF (4/12) and those not experiencing VF (23/70) (P = .97). However, individuals with non-nucleoside reverse transcriptase inhibitors-associated low-frequency DRMs were 2.68 times more likely to experience VF (odds ratio, 2.68; 95% confidential interval, 0.4–13.9) compared with those without (P = .22). Conclusion: Next-generation sequencing was able to detect low-frequency DRMs in this cohort in Botswana, but these DRMs did not contribute significantly to VF.

We evaluated subsequent virologic outcomes in individuals experiencing low-level virem ia (LLV) on dolutegravir (DTG)-based first-line antiretroviral therapy (ART) in Botswana. We used a national dataset from 50,742 adults who initiated on DTG-based first-line ART from June 2016–December 2022. Individuals with at least two viral load (VL) measurements post three months on DTG-based first-line ART were evaluated for first and subsequent episodes of LLV (VL:51–999 copies/mL). LLV was sub-categorized as low-LLV (51–200 copies/mL), medium-LLV (201–400 copies/mL) and high-LLV (401–999 copies/mL). The study outcome was virologic failure (VF) (VL ≥ 1000 copies/mL): virologic non-suppression defined as single-VF and confirmed-VF defined as two-consecutive VF measurements after an initial VL < 1000 copies/mL. Cox regression analysis identified predictive factors of subsequent VF. The prevalence of LLV was only statistically different at timepoints >6–12 (2.8%) and >12–24 (3.9%) (p-value < 0.01). LLV was strongly associated with both virologic non-suppression (adjusted hazards ratio [aHR] = 2.6; 95% CI: 2.2–3.3, p-value ≤ 0.001) and confirmed VF (aHR = 2.5; 95% CI: 2.4–2.7, p-value ≤ 0.001) compared to initially virally suppressed PLWH. High-LLV (HR = 3.3; 95% CI: 2.9–3.6) and persistent-LLV (HR = 6.6; 95% CI: 4.9–8.9) were associated with an increased hazard for virologic non-suppression than low-LLV and a single-LLV episode, respectively. In a national cohort of PLWH on DTG-based first-line ART, LLV > 400 copies/mL and persistent-LLV had a stronger association with VF. Frequent VL testing and adherence support are warranted for individuals with VL > 50 copies/mL.

Background:HIV-1 drug resistance poses a major threat to the success of antiretroviral therapy. The high costs of available HIV drug resistance assays prohibit their routine usage in resource-limited settings. Pan-degenerate amplification and adaptation (PANDAA), a focused genotyping approach based on quantitative PCR (qPCR), promises a fast and cost-effective way to detect HIV drug resistance mutations (HIVDRMs). Given the high cost of current genotyping methods, we sought to use PANDAA for screening key HIVDRMs in antiretroviral-naïve individuals at codons 103, 106 and 184 of the HIV-1 reverse transcriptase gene. Mutations selected at these positions have been shown to be the most common driver mutations in treatment failure. Methods:A total of 103 samples from antiretroviral-naïve individuals previously genotyped by Sanger population sequencing were used to assess and verify the performance of PANDAA. PANDAA samples were run on the ABI 7500 Sequence Detection System to genotype the K103N, V106M and M184V HIVDRMs. In addition, the cost per sample and reaction times were compared. Results:Sanger population sequencing and PANDAA detected K103N mutation in three (2.9%) out of 103 participants. There was no evidence of baseline V106M and M184V mutations observed in our study. To genotype the six HIVDRMs it costs approximately 40 USD using PANDAA, while the reagents cost per test for Sanger population sequencing is approximately 100 USD per sample. PANDAA was performed quicker compared to Sanger sequencing, 2 hours for PANDAA versus 15 hours for Sanger sequencing. Conclusion:The performance of PANDAA and Sanger population sequencing demonstrated complete concordance. PANDAA could improve patient management by providing quick and relatively cheap access to drug-resistance information.

It remains unknown whether the C-C motif chemokine receptor type 5 (CCR5) coreceptor is still the predominant coreceptor used by Human Immunodeficiency Virus-1 (HIV-1) in Botswana, where the HIV-1 subtype C predominates. We sought to determine HIV-1C tropism in Botswana using genotypic tools, taking into account the effect of antiretroviral treatment (ART) and virologic suppression. HIV-1 gp120 V3 loop sequences from 5602 participants were analyzed for viral tropism using three coreceptor use predicting algorithms/tools: Geno2pheno, HIV-1C Web Position-Specific Score Matrices (WebPSSM) and the 11/25 charge rule. We then compared the demographic and clinical characteristics of people living with HIV (PLWH) harboring R5- versus X4-tropic viruses using χ² and Wilcoxon rank sum tests for categorical and continuous data analysis, respectively. The three tools congruently predicted 64% of viruses as either R5-tropic or X4-tropic. Geno2pheno and the 11/25 charge rule had the highest concordance at 89%. We observed a significant difference in ART status between participants harboring X4- versus R5-tropic viruses. X4-tropic viruses were more frequent among PLWH receiving ART (χ² test, p = 0.03). CCR5 is the predominant coreceptor used by HIV-1C strains circulating in Botswana, underlining the strong potential for CCR5 inhibitor use, even in PLWH with drug resistance. We suggest that the tools for coreceptor prediction should be used in combination.