Effect of Vemurafenib on the Pharmacokinetics of a Single Dose of Tizanidine (a CYP1A2 Substrate) in Patients With <i>BRAF</i><sup>V600</sup> Mutation–Positive Malignancies
Bibliographic record
Abstract
Abstract This phase 1 open‐label, multicenter, 3‐period, fixed‐sequence study evaluated the effect of multiple doses of vemurafenib on the pharmacokinetics of 1 dose of tizanidine, a probe CYP1A2 substrate, in patients with BRAFV600 mutation–positive metastatic malignancy. Patients received 1 dose of tizanidine 2 mg on day 1 (period A), vemurafenib 960 mg twice daily on days 2‐21 (period B), and 1 dose of tizanidine 2 mg and vemurafenib 960 mg twice daily on day 22 (period C). Log‐transformed area under the concentration‐time curve (AUC) and maximum plasma concentration (Cmax) values for tizanidine in 16 patients were compared between periods A (tizanidine alone) and C (tizanidine plus vemurafenib) using an analysis of variance model. Multiple doses of vemurafenib increased plasma exposure of 1 dose of tizanidine, with geometric mean ratios (period C/period A) for Cmax, AUCinf, and AUClast of 2.15 (90%CI, 1.71‐2.71), 4.22 (90%CI, 3.37‐5.28), and 4.74 (90%CI, 3.55‐6.33), respectively; 90%CIs were all outside predefined limits for lack of drug‐drug interaction (0.82‐1.22). This study confirmed vemurafenib as a moderate inhibitor of CYP1A2 in vivo, with a statistically significant drug‐drug interaction with tizanidine. Caution should be exercised when dosing vemurafenib concurrently with CYP1A2 substrates.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".