Resistance to HIV integrase inhibitors
Bibliographic record
Abstract
PURPOSE OF REVIEW: HIV integrase inhibitors are potent antiretroviral drugs that efficiently decrease viral load in patients. Emergence of resistance mutations against this new class of drugs represents a threat to their long-term efficacy. The purpose of this review is to provide new information about the most recent mutations identified and other mutations that confer resistance to several integrase inhibitors. RECENT FINDINGS: New resistance mutations, such as G118R, R263K and S153Y, have been recently identified through in-vitro selection studies with second-generation integrase strand-transfer inhibitors (INSTIs). These add to the three main resistance pathways involving mutations at positions Y143, N155 and Q148. Structural modeling, biochemical analyses and deep sequencing are methods that currently help in the understanding of the mechanisms of resistance conferred by these mutations. Although these new resistance mutations appear to confer only low levels of cross-resistance to second-generation drugs, the Q148 pathway with numerous secondary mutations has the potential to significantly decrease susceptibility to all drugs of the INSTI family. SUMMARY: Recent mutations selected in vitro with second-generation INSTIs suggest the existence of low levels of cross-resistance between these drugs and first-generation compounds. In clinical practice, the emergence of mutations at position Q148 should be monitored whenever possible. More datasets are needed to assess the long-term efficacy of second-generation INSTIs in patients failing older INSTIs such as raltegravir and elvitegravir.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".