Recommendations on data sharing in HIV drug resistance research.

• Human immunodeficiency virus (HIV) drug resistance has implications for antiretroviral treatment strategies and for containing the HIV pandemic because the development of HIV drug resistance leads to the requirement for antiretroviral drugs that may be less effective, less well-tolerated, and more expensive than those used in first-line regimens. • HIV drug resistance studies are designed to determine which HIV mutations are selected by antiretroviral drugs and, in turn, how these mutations affect antiretroviral drug susceptibility and response to future antiretroviral treatment regimens. • Such studies collectively form a vital knowledge base essential for monitoring global HIV drug resistance trends, interpreting HIV genotypic tests, and updating HIV treatment guidelines. • Although HIV drug resistance data are collected in many studies, such data are often not publicly shared, prompting the need to recommend best practices to encourage and standardize HIV drug resistance data sharing. • In contrast to other viruses, sharing HIV sequences from phylogenetic studies of transmission dynamics requires additional precautions as HIV transmission is criminalized in many countries and regions. • Our recommendations are designed to ensure that the data that contribute to HIV drug resistance knowledge will be available without undue hardship to those publishing HIV drug resistance studies and without risk to people living with HIV.

Next generation sequencing based in-house HIV genotyping method: validation report.

BACKGROUND: HIV genotyping has had a significant impact on the care and treatment of HIV/AIDS. At a clinical level, the test guides physicians on the choice of treatment regimens. At the surveillance level, it informs policy on consolidated treatment guidelines and microbial resistance control strategies. Until recently, the conventional test has utilized the Sanger sequencing (SS) method. Unlike Next Generation Sequencing (NGS), SS is limited by low data throughput and the inability of detecting low abundant drug-resistant variants. NGS can improve sensitivity and quantitatively identify low-abundance variants; in addition, it has the potential to improve efficiency as well as lowering costs when samples are batched. Despite the NGS benefits, its utilization in clinical drug resistance profiling is faced with mixed reactions. These are largely based on a lack of a consensus regarding the quality control strategy. Nonetheless, transitional views suggest validating the method against the gold-standard SS. Therefore, we present a validation report of an NGS-based in-house HIV genotyping method against the SS method in Uganda. RESULTS: Since there were no established proficiency test panels for NGS-based HIV genotyping, 15 clinical plasma samples for routine care were utilized. The use of clinical samples allowed for accuracy and precision studies. The workflow involved four main steps; viral RNA extraction, targeted amplicon generation, amplicon sequencing and data analysis. Accuracy of 98% with an average percentage error of 3% was reported for the NGS based assay against the SS platform demonstrating similar performance. The coefficient of variation (CV) findings for both the inter-run and inter-personnel precision showed no variability (CV ≤ 0%) at the relative abundance of ≥ 20%. For both inter-run and inter-personnel, a variation that affected the precision was observed at 1% frequency. Overall, for all the frequencies, CV registered a small range of (0-2%). CONCLUSION: The NGS-based in-house HIV genotyping method fulfilled the minimum requirements that support its utilization for drug resistance profiling in a clinical setting of a low-income country. For more inclusive quality control studies, well-characterized wet panels need to be established.

Comparison of HIV drug resistance profiles across HIV-1 subtypes A and D for patients receiving a tenofovir-based and zidovudine-based first line regimens in Uganda.

BACKGROUND: Resistance to antiretroviral drugs is a major challenge among Human Immunodeficiency Virus (HIV) positive patients receiving antiretroviral therapy (ART). Mutations that arise as a result of this are diverse across the various drugs, drug classes, drug regimens and subtypes. In Uganda, there is a paucity of information on how these mutations differ among the different drug regimens and the predominant HIV-1 subtypes. The purpose of this study was to determine mutation profile differences between first-line drug regimens: TDF/3TC/EFV and AZT/3TC/EFV and HIV-1 subtypes: A and D in Uganda. The study also investigated the potential usage of rilpivirine, doravirine and etravirine in patients who failed treatment on efavirenz. METHODS: A retrospective study was conducted on 182 archived plasma samples obtained from patients who were experiencing virological failure between 2006 and 2017 at five Joint Clinical Research Center (JCRC) sites in Uganda. Sanger sequencing of the Reverse Transcriptase (RT) gene from codons 1-300 was done. Mutation scores were generated using the Stanford University HIV Drug Resistance Database. A Chi-square test was used to determine the association between drug resistance mutations (DRMs) and drug regimens or HIV-1 subtypes. RESULTS: The prevalence of DRMs was 84.6% among patients failing a first-line efavirenz (EFV)-based regimen. The most prevalent Nucleoside Reverse Transcriptase Inhibitor (NRTI) mutations were M184V/I (67.3%), K219/Q/E (22.6%) and K65R (21.1%). While K103N (50.8%) and G190A/S/E/G (29.1%) were the most prevalent Non-Nucleoside Reverse Transcriptase Inhibitor (NNTRI) mutations. As expected, discriminatory DRMs such as K65R, L74I, and Y115F were noted in Tenofovir (TDF) containing regimens while the Thymidine Analogue Mutations (TAMs) L210W and T215 mutations were in Zidovudine (AZT)-based regimens. No significant difference (p = 0.336) was found for overall DRMs between HIV-1 subtypes A and D. Among the patients who had resistance to EFV, 37 (23.6%) were susceptible to newer NNRTIs such as Rilpivirine and Etravirine. CONCLUSION: Accumulation of DRMs between AZT/3TC/EFV and TDF/3TC/EFV is comparable but individual mutations that confer resistance to particular drugs should be considered at virological failure. Having either HIV-1 subtype A or D is not associated with the acquisition of DRMs, therefore HIV diversity should not determine the choice of treatment. Rilpivirine, etravirine and doravirine had minimal benefits for patients who failed on efavirenz.