Time‐Dependent Interaction Between Lopinavir/Ritonavir and Fexofenadine
Bibliographic record
Abstract
This study investigated the effect of single-dose and steady-state lopinavir/ritonavir on the exposure to fexofenadine, as a measure of P-glycoprotein activity. Sixteen volunteers (8 women) received single-dose oral fexofenadine 120 mg alone, in combination with single-dose ritonavir 100 mg or lopinavir/ritonavir 400/100 mg (randomized 1:1, stratified by sex), and in combination with steady-state lopinavir/ritonavir 400/100 mg twice daily. Single-dose ritonavir and lopinavir/ritonavir increased the area under the fexofenadine plasma concentration-time curve from 0 to infinity (AUC(infinity)) by 2.2- and 4.0-fold, respectively (P < .02). Steady-state lopinavir/ritonavir increased the fexofenadine AUC(infinity) by 2.9-fold. No changes were observed in the fexofenadine elimination half-life (P > .12). The fexofenadine AUC(infinity) was increased by lopinavir/ritonavir, likely due to increased bioavailability secondary to P-glycoprotein inhibition. After repeated administration of lopinavir/ritonavir, the interaction was attenuated compared to the single-dose effect, although a net inhibitory effect was maintained. Time-dependent inhibition of P-glycoprotein by lopinavir/ritonavir should be considered when P-glycoprotein substrates are coadministered.
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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) | 0.001 | 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".