Maraviroc treatment in non-R5-HIV-1-infected patients results in the selection of extreme CXCR4-using variants with limited effect on the total viral setpoint
Bibliographic record
Abstract
OBJECTIVES: Using deep sequencing methods, we intensively investigated the selective pressure of maraviroc on the viral population in four patients with dual/mixed HIV-1 experiencing treatment failure. METHODS: Patients received maraviroc add-on therapy (n = 4). Tropism was determined by Monogram's Trofile assay and/or 'deep' sequencing. Longitudinal 'deep' sequence analysis used triplicate HIV V3 RT-PCR on plasma samples. Sequences were interpreted using the geno2phenocoreceptor algorithm with a 3.5% false-positive rate (FPR) cut-off. RESULTS: Patients had a median viral load of 4.7 log10 HIV RNA copies/mL with a median of 24% chemokine (C-X-C motif) receptor 4 (CXCR4)-using virus at baseline. Following maraviroc exposure, the chemokine (C-C motif) receptor 5 (CCR5)-using virus (R5) plasma viral load decreased by at least 1 log10, and only non-R5 variants with extremely low FPR values predominated after 21 days. Virus with an FPR ≤1.8% accounted for more than 90% of the circulating virus, having expanded to occupy the 'space' left by the suppression of R5 variants. Population genetic estimates of viral fitness in the presence of maraviroc showed a steep rise around an FPR value of 2%. CONCLUSIONS: Longitudinal analysis of independent R5 and non-R5 HIV populations shows that maraviroc selects viruses with an extremely low FPR, implying that the antiviral activity of maraviroc may extend to a broader range of HIV variants than previously suspected.
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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 | 0.000 | 0.001 |
| 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".