Virological success despite archived INSTI drug resistance mutations on INSTI-based antiretroviral regimens
Bibliographic record
Abstract
OBJECTIVES: The clinical relevance of archived drug resistance mutations (DRMs) detected by HIV-1 proviral genotypic resistance testing (GRT) in virologically suppressed (VS) people with HIV (PWH) remains unclear, especially since these mutations may reside in defective proviruses. We assessed the impact of archived full-class integrase strand transfer inhibitor (INSTI) DRMs in a real-life setting. DESIGN: This was a retrospective study conducted in Paris, France, evaluating archived INSTI DRMs in VS PWH receiving INSTI-based regimens. METHODS: We included VS PWH receiving INSTI-based regimens with archived full-class INSTI DRMs. We assessed mutational load and inferred proviral defectiveness based on the presence of stop codons or G-to-A hypermutations. RESULTS: Among 883 PWH with INSTI proviral GRT since March 2022, 30 INSTI DRMs were identified in 26 VS PWH on INSTI-based regimens (69% male, median age 53). Median total HIV-1 DNA was 2.36 log 10 copies/10 6 leukocytes [interquartile range (IQR): 2.24-2.65, n = 17], and median mutational load was 1.94 log 10 copies/10 6 leukocytes (IQR: 1.34-2.39). Mutational load did not differ significantly between presumed defective and intact proviruses ( P = 0.786). DRMs were found in presumed defective proviruses in 18/26 (69%) individuals. No virologic failure occurred on INSTI-based therapy during a median follow-up of 202 days (IQR: 105-366). CONCLUSION: In this study, virological control was not compromised by archived INSTI DRMs in VS PWH on INSTI-based therapy. Prospective studies evaluating INSTI-based regimen switching despite these archived DRMs, using advanced sequencing methods to better link DRMs to proviral genome integrity, are needed to accurately assess their impact and refine clinical practices.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".