Predicting emergent Dolutegravir resistance in South Africa: A modelling study
Bibliographic record
Abstract
Summary Background In response to the rising prevalence of non-nucleoside reverse transcriptase inhibitors (NNRTIs) resistance, millions of people living with HIV (PWH) have switched to dolutegravir-based antiretroviral therapy (ART). Understanding the possible emergence of dolutegravir resistance is essential for health policy and planning. We developed a mathematical model to predict the trends of dolutegravir resistance in PWH in South Africa. Methods MARISA (Modelling Antiretroviral drug Resistance In South Africa) is a deterministic compartmental model consisting of four layers: (i) the cascade of care, (ii) disease progression, (iii) gender, and (iv) drug resistance. MARISA was calibrated to reproduce the HIV epidemic in South Africa. We assumed dolutegravir was introduced in 2020. We extended the model by including key resistance mutations observed in PWH experiencing virologic failure on dolutegravir-based ART (G118K, E138AKT, G140ACS, Q148HKNR, N155H, and R263K). Model outcomes were acquired (ADR) and transmitted drug resistance (TDR) to dolutegravir and NNRTIs stratified by duration on failing dolutegravir-based ART and under different counterfactual scenarios of switching to protease-inhibitor (PI)-based ART. Finding The model predicts that ADR will increase rapidly, from 18.5% (uncertainty range 12.5% to 25.4%) in 2023 to 46.2% (32.9% to 58.9%) in 2040. The prevalence of ADR in 2040 increased with the duration of virologic failure on dolutegravir-based ART: 18.0% (12.2% to 23.7%) for 6 months of failing ART compared to 54.8% (41.1% to 63.9%) for over 1.5 years. For TDR, the model predicts a slow but steady increase from 0.1% (0.1% to 0.2%) in 2023 to 8.8% (4.4% to 17.3%) in 2040. Transmitted NNRTI resistance will cease to increase but remain prevalent at 7.7% in 2040. Rapid resistance testing-informed switching to PI-based ART would substantially reduce both ADR and TDR. Interpretation The prevalence of dolutegravir ADR and TDR will likely increase, with the 10% threshold of TDR possibly reached by 2035, depending on monitoring and switching strategies. The increase will likely be greater in settings where resources for HIV-1 RNA monitoring and resistance testing or options for switching to alternative ART regimens are limited. Funding Swiss National Science Foundation, National Institutes of Health, UZH URPP Evolution in Action Research in context Evidence before this study Dolutegravir has demonstrated high efficacy, even in individuals with compromised backbone drugs. We searched Scopus on April 15 2024, using free text words dolutegravir and resistance. We did not identify any modelling studies attempting to predict dolutegravir resistance trends in the coming years. A recent collaborative analysis of predominantly European cohort studies involving 599 people living with HIV (PWH) who underwent genotypic resistance testing at the point of dolutegravir-based treatment failure showed that the risk of dolutegravir resistance increases significantly in the presence of Nucleoside Reverse Transcriptase Inhibitor (NRTI) resistance. This is particularly concerning in settings such as South Africa, where a high proportion of individuals already exhibit NRTI resistance. Indeed, recent surveys in South Africa already hint at rapidly increasing levels of acquired dolutegravir resistance. Added value of this study This study is the first to model the likely dynamics of dolutegravir resistance in South Africa. Covering the period 2020 to 2040, it extends a previous model of antiretroviral drug resistance evolution in South Africa to dolutegravir-based ART. The results indicate that while dolutegravir resistance is currently low, it will increase at the population level, and transmitted dolutegravir resistance may exceed 10% by around 2035, depending on the duration PWH spend on failing dolutegravir-based ART. Interventions such as switching to protease-inhibitor (PI)-based ART based on genotypic resistance tests could reduce or even curb the rise of dolutegravir resistance. Implications of all the available evidence Dolutegravir resistance may undermine the success of integrase strand transfer inhibitor (INSTI)-based ART in South Africa, where the guidelines limit drug resistance testing to PWH with repeated viral load measurements above 1,000 copies/mL and evidence of good adherence. Monitoring the evolution of dolutegravir resistance at the population level is crucial to inform future changes in guidelines on drug resistance testing and switching to PI-based ART.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".