Pharmacokinetic interaction between mefloquine and ritonavir in healthy volunteers
Bibliographic record
Abstract
AIMS: To evaluate the pharmacokinetic interaction between ritonavir and mefloquine. METHODS: Healthy volunteers participated in two separate, nonfasted, three-treatment, three-period, longitudinal pharmacokinetic studies. Study 1 (12 completed): ritonavir 200 mg twice daily for 7 days, 7 day washout, mefloquine 250 mg once daily for 3 days then once weekly for 4 weeks, ritonavir restarted for 7 days simultaneously with the last mefloquine dose. Study 2 (11 completed): ritonavir 200 mg single dose, mefloquine 250 mg once daily for 3 days then once weekly for 2 weeks, ritonavir single dose repeated 2 days after the last mefloquine dose. Erythromycin breath test (ERMBT) was administered with and without drug treatments in study 2. RESULTS: Study 1: Ritonavir caused less than 7% changes with high precision (90% CIs: -12% to 11%) in overall plasma exposure (AUC(0,168 h)) and peak concentration (Cmax) of mefloquine, its two enantiomers, and carboxylic acid metabolite, and in the metabolite/mefloquine and enantiomeric AUC ratios. Mefloquine significantly decreased steady-state ritonavir plasma AUC(0,12 h) by 31%, Cmax by 36%, and predose levels by 43%, and did not affect ritonavir binding to plasma proteins. Study 2: Mefloquine did not alter single-dose ritonavir pharmacokinetics. Less than 8% changes in AUC and Cmax were observed with high variability (90%CIs: -26% to 45%). Mefloquine had no effect on the ERMBT whereas ritonavir decreased activity by 98%. CONCLUSIONS: Ritonavir minimally affected mefloquine pharmacokinetics despite strong inhibition of CYP3A4 activity from a single 200 mg dose. Mefloquine had variable effects on ritonavir pharmacokinetics that were not explained by hepatic CYP3A4 activity or ritonavir protein binding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| 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 teacher head, 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".