Drug Resistance Genotypes Predict Response to Amprenavir-Containing Regimens in Highly Drug-Experienced HIV-1-Infected Patients
Bibliographic record
Abstract
We have undertaken a study of virological responses to amprenavir-containing antiretroviral regimens, during the expanded access programme within the UK. Ninety-five HIV-1-infected patients were included for which virological and immunological follow-up was available for 75, and baseline drug resistance data available for 51. These were highly drug-experienced patients, having previously received a median of nine antiviral drugs, within all available classes. Eighty-eight percent of patients had a virological response to the new regimen, with a median maximal decline of 1.45 log10 copies/ml, and 34% of patients reached <400 copies/ml on treatment. Although 68% of patients with resistance data had protease inhibitor resistance mutations, only 10% patients had key amprenavir resistance mutations, and virological response was predicted by the number of active drugs utilized in the amprenavir-containing regimen, as determined by the baseline genotypic resistance test. Other independent predictors of viral load decline were a higher baseline viral load and fewer previous antiviral drugs. We conclude that amprenavir can contribute to antiviral efficacy in salvage regimens, and that resistance testing may help to optimize its use in this scenario. New formulations of amprenavir, together with boosted regimens, may enhance the activity in the presence of protease inhibitor-resistant virus.
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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 | 0.003 |
| 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 | 0.001 |
| 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".