Concomitant Use of an Active Boosted Protease Inhibitor with Enfuvirtide in Treatment-Experienced, HIV-Infected Individuals: Recent Data and Consensus Recommendations
Bibliographic record
Abstract
Recent data from clinical trials investigating the efficacy of enfuvirtide, a fusion inhibitor, in treatment-experienced patients have revealed that the addition of enfuvirtide (ENF) to an active boosted protease inhibitor regimen doubles the rate of virological response. At week 48 of the TORO studies, 55% of patients previously naive to and receiving lopinavir/ritonavir (LPV/r) with ENF achieved a viral load of <400 copies/mL compared with 24% of patients treated with LPV/r alone. At week 24 of the RESIST studies, 70% of previously ENF-naive patients who took both ENF and tipranavir/ritonavir (TPV/r) achieved a >or=1 log10 reduction in viral load compared with 37% of such patients treated with TPV/r alone. Similarly, concomitant use of TMC114/ritonavir (TMC114/r) with ENF, compared with TMC114/r alone, increased the number of patients with <50 copies/mL from 46% to 64% in a combined 24-week analysis from the POWER trials. Data from these trials suggest that combining one agent from a new class with a new agent from a previously exposed class offers a greater chance of achieving full virological control than either type of agent alone. Undetectable viraemia should be the primary objective for treatment-experienced patients requiring a switch in therapy, and the present data support the combination of an active boosted protease inhibitor with an agent from a new class (e.g., ENF) for triple-class-experienced patients.
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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 | 0.035 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".