Emerging mutations at virological failure of HAART combinations containing tenofovir and lamivudine or emtricitabine
Bibliographic record
Abstract
OBJECTIVE: To compare the emergence of drug-resistant HIV variants at failure of lamivudine (3TC)/tenofovir (TDF)-containing or emtricitabine (FTC)/TDF-containing HAART as a consequence of the different 3TC and FTC intracellular half-lives. DESIGN: Retrospective evaluation of 859 patients selected from an Italian HIV resistance database (Antiretroviral Resistance Cohort Analysis). METHODS: Patients were selected for analysis if treated with a HAART whose nucleoside/nucleotide reverse transcriptase inhibitor backbone was either 3TC/TDF or FTC/TDF; if they experienced a virological failure after at least 6 months of plasma HIV-RNA undetectability; and if HIV genotypes before treatment and at failure were available. Univariate and multivariate logistic regression analyses were done to detect predictors of resistance mutations emerging at failure. RESULTS: Of 714 patients failing with 3TC/TDF and 145 with FTC/TDF, 35.8 and 21.1% were in Centers for Disease Control and Prevention stage C, and 8.8 and 15.2% were on first-line HAART, respectively. At multivariate analysis, the emergence of K70R (P = 0.002), M184V (P = 0.031), T215F (P = 0.020) and Y181C (P = 0.005) was significantly more common in 3TC-treated than in FTC-treated patients, with an odds ratio of 4, 1.56, 1.89 and 3.84, respectively. CONCLUSION: Despite their close structural similarity, 3TC and FTC are associated with a significantly different rate of drug resistance at treatment failure when combined with TDF in HAART regimens independently of the third drug used.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".