Monotherapy with either dolutegravir or raltegravir fails to durably suppress HIV viraemia in humanized mice
Bibliographic record
Abstract
Objectives: To compare the effectiveness of HIV integrase inhibitor monotherapy between raltegravir and dolutegravir as an approach to simplify therapy. Methods: We evaluated and compared the efficacy of 20 week monotherapy with dolutegravir or raltegravir in humanized mice (HSC-NSG) infected with HIVBaL. Plasma HIV RNA was measured by quantitative RT-PCR (limit of detection of 150 copies/45 μL of plasma) and drug levels by LC-MS/MS. Escape viruses were genotyped and analysed for replication capacity and drug susceptibility in tissue culture. Results: Drug-untreated control mice maintained constant viraemia throughout the study. Virus isolates from these mice were susceptible to both raltegravir (EC50 of <8 nM) and dolutegravir (EC50 of <1 nM). Mice treated with raltegravir or dolutegravir had plasma drug levels comparable to those in humans. Monotherapy with raltegravir initially suppressed HIV viraemia, but failed to maintain suppression in 4/4 mice. Viruses from raltegravir failing mice developed mutations G140G/S and Q148H/K, and were resistant to both raltegravir (EC50 values of >100 nM) and dolutegravir (EC50 values ranging from 8.8 to 13.3 nM). Monotherapy with dolutegravir suppressed viraemia in 5/5 of mice, but viraemia rebounded in one animal. The virus from this mouse had mutations E138K, G140S, Q148H, N155H and S230R, was highly resistant to both raltegravir (EC50 of >1000 nM) and dolutegravir (EC50 of 550 nM), and replicated to levels similar to those of control viruses in PBMCs. Conclusions: Monotherapy with either raltegravir or dolutegravir does not consistently maintain HIV suppression, suggesting that dual therapy may be required in simplification strategies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".