Impact of Tesamorelin, a Growth Hormone‐Releasing Factor (GRF) Analogue, on the Pharmacokinetics of Simvastatin and Ritonavir in Healthy Volunteers
Bibliographic record
Abstract
The potential impact of tesamorelin on CYP3A activity was investigated by examining its effect on the pharmacokinetics of simvastatin and ritonavir. In two randomized, two-way crossover studies, subjects were administered 2 mg tesamorelin on Days 1-7 with 80 mg simvastatin or 100 mg ritonavir co-administered on Day 6 (Treatment A), and a single dose of simvastatin or ritonavir alone on Day 6 (Treatment B). Pharmacokinetic samples were collected on Day 6 to measure simvastatin, ritonavir and tesamorelin plasma concentrations. For simvastatin, A/B ratios of least squares geometric means and corresponding 90% confidence intervals (CIs) for AUC0-t , AUC0-inf and Cmax were contained within the usual no effect range of 80-125%. For ritonavir, ratios and 90% CIs for AUCs were within this acceptance range, but the lower CI for Cmax was 74.8%, suggesting a decreased rate of exposure. However, since the A/B ratios for AUCs and Cmax parameters were approximately 90%, these were minor decreases and no dose adjustment of ritonavir is required in the presence of tesamorelin. These studies showed that the impact of tesamorelin on CYP3A activity appears to be minimal, if any. Either medication may be co-administered with tesamorelin in patients without changing their original dosing regimen.
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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 | 0.002 |
| 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 | 0.001 | 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".