Pharmacokinetic (PK) drug interaction studies of cabozantinib: Effect of CYP3A inducer rifampin and inhibitor ketoconazole on cabozantinib plasma PK and effect of cabozantinib on CYP2C8 probe substrate rosiglitazone plasma PK
Bibliographic record
Abstract
Cabozantinib is a small-molecule tyrosine kinase inhibitor that has been approved for the treatment of patients with progressive, metastatic medullary thyroid cancer. In vitro data indicate that (1) cytochrome P450 (CYP) 3A4 is the primary CYP isoenzyme involved in the metabolism of cabozantinib, and (2) CYP2C8 is the CYP isoenzyme most potently inhibited by cabozantinib with potential for in vivo inhibition at clinically relevant plasma exposures. Pharmacokinetic (PK) drug-drug interactions (DDIs) were evaluated clinically between cabozantinib and (1) a CYP3A inducer (rifampin) in healthy volunteers, (2) a CYP3A inhibitor (ketoconazole) in healthy volunteers, and (3) a CYP2C8 substrate (rosiglitazone) in patients with solid tumors. Compared with cabozantinib given alone, coadministration with rifampin resulted in a 4.3-fold higher plasma clearance (CL/F) of cabozantinib and a 77% decrease in cabozantinib plasma AUC0-inf , whereas coadministration with ketoconazole decreased cabozantinib CL/F by 29% and increased cabozantinib AUC0-inf by 38%. Chronic coadministration with cabozantinib resulted in no significant effect on rosiglitazone plasma Cmax , AUC0-24 , or AUC0-inf . In summary, chronic use of strong CYP3A inducers and inhibitors should be avoided when cabozantinib is administered, and cabozantinib at clinically relevant exposures is not anticipated to markedly affect the PK of concomitant medications via CYP enzyme inhibition.
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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 | 0.001 |
| 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 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".