Predicted dolutegravir resistance in people living with HIV in South Africa during 2020–35: a modelling study
Bibliographic record
Abstract
BACKGROUND: In response to increasing resistance to non-nucleoside reverse transcriptase inhibitors, millions of people living with HIV have switched to dolutegravir-based antiretroviral therapy, so understanding the possible emergence of dolutegravir resistance is essential. We aimed to predict how dolutegravir resistance in South Africa will change over time. METHODS: For this modelling study, we used the Modelling Antiretroviral Drug Resistance in South Africa (MARISA) model, a deterministic compartmental model calibrated to reproduce the HIV-1 epidemic in South Africa from 2005 to 2035 using data from the International Epidemiology Databases to Evaluate AIDS collaboration and the literature. Key parameters for modelling dolutegravir-resistance evolution were acquisition rates of dolutegravir-resistance mutations, reversion rates of dolutegravir-resistance mutations, the effect of resistance to nucleoside reverse transcriptase inhibitors on dolutegravir-resistance acquisition, the effect of dolutegravir resistance on dolutegravir-treatment efficacy, the probability of transmitting dolutegravir drug-resistance mutations compared with the probability of transmitting wild-type HIV, and the proportion of people with virologic failure on dolutegravir-based antiretroviral therapy with detectable drug levels. Model outcomes were estimated transmitted dolutegravir resistance and estimated acquired dolutegravir resistance. FINDINGS: We estimated a substantial increase in the number of individuals on dolutegravir-based antiretroviral therapy after its introduction in 2020, increasing from 0 to approximately 7 million people (7·08-7·15) living with HIV on dolutegravir in 2035. We estimated the proportion of people living with HIV with viral suppression (ie, viral load <1000 copies per mL) on dolutegravir-based antiretroviral therapy to be 93% (uncertainty range 92·2-94·3) in 2035. We estimated that acquired dolutegravir resistance in people living with HIV on failing dolutegravir-based antiretroviral therapy would increase rapidly, from 18·5% (uncertainty range 12·5-25·4) in 2023 to 41·7% (29·0-54·0) in 2035. For transmitted dolutegravir resistance, we estimated an increase from 0·1% (0·0-0·2) in 2023 to 5·0% (1·9-11·9) in 2035. We estimated that resistance-mitigation strategies involving rapid switching to protease-inhibitor-based antiretroviral therapy could effectively reduce the increase in acquired dolutegravir resistance and slow the increase in transmitted dolutegravir resistance. INTERPRETATION: Although dolutegravir-based antiretroviral therapy maintains high virological suppression, acquired and transmitted dolutegravir resistance are likely to increase. This increase will likely be greater in settings where HIV RNA monitoring, genotypic-resistance testing, and options to switch antiretroviral therapy regimens are scarce. FUNDING: US National Institutes of Health National Institute of Allergy and Infectious Diseases, Swiss National Science Foundation, and University of Zurich Research Priority Program Evolution in Action.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".