Lack of Effect of Efavirenz on the Pharmacokinetics of Tipranavir-Ritonavir in Healthy Volunteers
Bibliographic record
Abstract
Previously it has been shown that tipranavir-ritonavir (TPV/r) does not affect efavirenz (EFV) plasma concentrations. This study investigates the effect of steady-state EFV on steady-state TPV/r pharmacokinetics. This was a single-center, open-label, multiple-dose study of healthy adult female and male volunteers. TPV/r 500/200 mg twice a day (BID) was given with food for 24 days. After dosing with TPV/r for 10 days, EFV 600 mg once a day was added to the regimen. Intensive pharmacokinetic (PK) sampling was done on days 10 and 24. Validated bioanalytical high-pressure liquid chromatography-tandem mass spectrometry methods were used to determine plasma tipranavir (TPV), ritonavir (RTV), and EFV concentrations. Thirty-four subjects were entered into the study, and 16 subjects completed it. The geometric mean ratios (90% confidence intervals) for TPV and RTV area under the curves, C(max)s, and C(min)s comparing TPV/r alone and in combination with EFV were 0.97 (0.87 to 1.09), 0.92 (0.81 to 1.03), and 1.19 (0.93 to 1.54) for TPV and 1.03 (0.78 to 1.38), 0.92 (0.65 to 1.30), and 1.04 (0.72 to 1.48) for RTV. Frequently observed adverse events were diarrhea, headache, dizziness, abnormal dreams, and rash. EFV had no effect on the steady-state PK of TPV or RTV, with the exception of a 19% increase in the TPV C(min), which is not clinically relevant. TPV/r can be safely coadministered with EFV and without the need for a dose adjustment.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".