Developing Reporting Guidelines for Studies of HIV Drug Resistance Prevalence: Protocol of a Mixed-methods Study (Preprint)

BACKGROUND HIV drug resistance is a global health problem which limits the effectiveness of antiretroviral therapy (ART). Adequate surveillance of HIV drug resistance is challenged by heterogenous and inadequate data reporting, which compromises the accuracy, interpretation, and usability of prevalence estimates. Previous research has found that the quality of reporting in studies of HIV drug resistance prevalence is low, and thus better guidance is needed to ensure complete and uniform reporting. OBJECTIVE This paper aims to develop reporting guidelines for studies of HIV drug resistance by achieving consensus among experts on what items should be reported in these studies. METHODS We will conduct a sequential explanatory mixed methods study among authors and users of studies of HIV drug resistance. The two-phase design will include a cross-sectional electronic survey (quantitative phase) followed by a focus group discussion (qualitative phase). Survey participants will rate the essentiality of various reporting items, which will be analyzed in a validity ratio to determine the items that will be retained for further evaluation. Retained items will form a list of potential reporting items that will be reviewed in a focus group discussion informed by grounded theory to produce a finalized set of reporting items. RESULTS This study received ethics approval from the Hamilton Integrated Research Ethics Board (project number #11558) on November 11, 2020. As of March 2021, 46 participants provided informed consent and completed the electronic survey. In October 2021 nine of these participants participated in virtual focus group discussions. CONCLUSIONS This study will provide a reporting checklist for studies of HIV drug resistance by achieving consensus among experts on what items should be reported in these studies. The results of this work will be refined and elaborated on by a writing committee of HIV drug resistance experts and external reviewers to develop finalized reporting guidelines.

Factors Associated with HIV Drug Resistance in Dar es Salaam, Tanzania: Analysis of a Complex Adaptive System

HIV drug resistance (HIVDR) is a complex problem with multiple interconnected and context dependent causes. Although the factors influencing HIVDR are known and well-studied, HIVDR remains a threat to the effectiveness of antiretroviral therapy. To understand the complexity of HIVDR, a comprehensive, systems approach is needed. Therefore, a local systems map was developed integrating all reported factors influencing HIVDR in the Dar es Salaam Urban Cohort Study area in Tanzania. The map was designed based on semi-structured interviews and workshops with people living with HIV and local actors who encounter people living with HIV during their daily activities. We visualized the feedback loops driving HIVDR, compared the local map with a systems map for Sub-Saharan Africa, previously constructed from interviews with international HIVDR experts, and suggest potential interventions to prevent HIVDR. We found several interconnected balancing and reinforcing feedback loops related to poverty, stigmatization, status disclosure, self-esteem, knowledge about HIVDR and healthcare system workload, among others, and identified three potential leverage points. Insights from this local systems map were complementary to the insights from the Sub-Saharan systems map showing that both viewpoints are needed to fully understand the system. This study provides a strong baseline for quantitative modelling, and for the identification of context-dependent, complexity-informed leverage points.

Comparing mutational pathways to lopinavir resistance in HIV-1 subtypes B versus C

Although combination antiretroviral therapies seem to be effective at controlling HIV-1 infections regardless of the viral subtype, there is increasing evidence for subtype-specific drug resistance mutations. The order and rates at which resistance mutations accumulate in different subtypes also remain poorly understood. Most of this knowledge is derived from studies of subtype B genotypes, despite not being the most abundant subtype worldwide. Here, we present a methodology for the comparison of mutational networks in different HIV-1 subtypes, based on Hidden Conjunctive Bayesian Networks (H-CBN), a probabilistic model for inferring mutational networks from cross-sectional genotype data. We introduce a Monte Carlo sampling scheme for learning H-CBN models for a larger number of resistance mutations and develop a statistical test to assess differences in the inferred mutational networks between two groups. We apply this method to infer the temporal progression of mutations conferring resistance to the protease inhibitor lopinavir in a large cross-sectional cohort of HIV-1 subtype C genotypes from South Africa, as well as to a data set of subtype B genotypes obtained from the Stanford HIV Drug Resistance Database and the Swiss HIV Cohort Study. We find strong support for different initial mutational events in the protease, namely at residue 46 in subtype B and at residue 82 in subtype C. The inferred mutational networks for subtype B versus C are significantly different sharing only five constraints on the order of accumulating mutations with mutation at residue 54 as the parental event. The results also suggest that mutations can accumulate along various alternative paths within subtypes, as opposed to a unique total temporal ordering. Beyond HIV drug resistance, the statistical methodology is applicable more generally for the comparison of inferred mutational networks between any two groups.

Human Immunodeficiency Virus (HIV) Drug Resistance: A Global Narrative Review

Background: Antiretroviral Therapy (ART) has significantly improved Human Immunodeficiency Virus (HIV) patients’ survival rates. However, the emergence of HIV Drug Resistance (HIVDR) has markedly reduced the effectiveness of Antiretroviral Therapy (ART). Aim: This narrative review was conducted to review published studies on HIV drug resistance and its consequences. Materials and methods: A literature search for this narrative review was carried out and the following databases were used PubMed, Google Scholar, and The Lancet. The cited articles were published from 1999 to 2021. The keywords used in the search of literature included ‘Antiretroviral therapy’, ‘resistance’, and ‘Human Immunodeficiency Virus drug resistance’, ‘HIV’, ‘HIV drug resistance’, ‘HIV vaccines’, and the Boolean word ‘AND’. Results: There is a high prevalence of HIV drug resistance globally that has been associated with some factors such as older age, non-adherence to treatment, long treatment duration, lower cell count and high viral load. HIV drug resistance may lead to treatment failure, prolongation of the time required to achieve viral suppression and leads to increased mortality. Increasing access to viral load monitoring can help mitigate HIV drug resistance. Conclusion: HIV drug resistance is a global threat to public health and has been associated with increased morbidity and mortality. Therefore, there is a need for more research to be carried out and various strategies like the use of antiretrovirals with a high genetic barrier to resistance need to be put in place to prevent further spread resistance. HIVDR must be monitored frequently taking into consideration the geographic variability. There is an urgent need for the development of anti-HIV vaccines that will help to prevent further transmission and spread of HIV.

Maternal HIV drug resistance is associated with vertical transmission and is prevalent in infected infants

Background We aimed to assess if maternal HIV drug resistance is associated with an increased risk of HIV vertical transmission and to describe the dynamics of drug resistance in HIV-infected infants. Methods A case-control study of PROMISE study participants. “Cases” were mother-infant pairs with HIV vertical transmission during pregnancy or breastfeeding and “controls” were mother-infant pairs without transmission matched 1:3 by delivery date and clinical site. Genotypic HIV drug resistance analyses were performed on mothers’ and their infants’ plasma at or near the time of infant HIV diagnosis. Longitudinal analysis of genotypic resistance was assessed in available specimens from infants, from diagnosis and beyond, including ART initiation and last study visits. Results Our analyses included 85 cases and 255 matched controls. Maternal HIV drug resistance, adjusted for plasma HIV RNA load at infant HIV diagnosis, enrollment CD4 count, and antepartum regimens, was not associated with in utero/peripartum HIV transmission. In contrast, both maternal plasma HIV RNA load and HIV drug resistance were independent risk factors associated with vertical transmission during breastfeeding. Furthermore, HIV drug resistance was selected across infected infants during infancy. Conclusions Maternal HIV drug resistance and maternal viral load were independent risk factors for vertical transmission during breastfeeding, suggesting that nevirapine alone may be insufficient infant prophylaxis against drug-resistant variants in maternal breast milk. These findings support efforts to achieve suppression of HIV replication during pregnancy and suggest that breastfeeding infants may benefit from prophylaxis with a greater barrier to drug resistance than nevirapine alone.

Prediction of HIV drug resistance based on the 3D protein structure: Proposal of molecular field mapping

A method for predicting HIV drug resistance by using genotypes would greatly assist in selecting appropriate combinations of antiviral drugs. Models reported previously have had two major problems: lack of information on the 3D protein structure and processing of incomplete sequencing data in the modeling procedure. We propose obtaining the 3D structural information of viral proteins by using homology modeling and molecular field mapping, instead of just their primary amino acid sequences. The molecular field potential parameters reflect the physicochemical characteristics associated with the 3D structure of the proteins. We also introduce the Bayesian conditional mutual information theory to estimate the probabilities of occurrence of all possible protein candidates from an incomplete sequencing sample. This approach allows for the effective use of uncertain information for the modeling process. We applied these data analysis techniques to the HIV-1 protease inhibitor dataset and developed drug resistance prediction models with reasonable performance.

HIV-1 drug resistance profiling using amino acid sequence space cartography

Motivation: Human immunodeficiency virus (HIV) drug resistance is a global healthcare issue. The emergence of drug resistance demands treatment adaptation. Computational methods predicting the drug resistance profile from genomic data of HIV isolates are advantageous for monitoring drug resistance in patients. Yet, the currently existing computational methods for drug resistance prediction are either not suitable for complex mutational patterns in emerging HIV strains or lack interpretability of prediction results which is of paramount importance in clinical practice. Hence, to overcome these limitations, new approaches for the HIV drug resistance prediction combining high accuracy and interpretability are required. Results: In this work, a new methodology for the analysis of protein sequence data based on the application of generative topographic mapping was developed and applied for HIV drug resistance profiling. It allowed achieving high accuracy of resistance predictions and intuitive interpretation of prediction results. The developed approach was successfully applied for the prediction of HIV re-sistance towards protease, reverse-transcriptase and integrase inhibitors and in-depth analysis of HIV resistance-inducing mutation patterns. Hence, it can serve as an efficient and interpretable tool to suggest optimal treatment regimens. Availability: https://github.com/karinapikalyova/ISIDASeq