Clinical assessment of momelotinib drug–drug interactions via <scp>CYP3A</scp> metabolism and transporters
Bibliographic record
Abstract
Abstract Momelotinib—approved for treatment of myelofibrosis in adults with anemia—and its major active metabolite, M21, were assessed as drug–drug interaction (DDI) victims with a strong cytochrome P450 (CYP) 3A4 inhibitor (multiple‐dose ritonavir), an organic anion transporting polypeptide (OATP) 1B1/1B3 inhibitor (single‐dose rifampin), and a strong CYP3A4 inducer (multiple‐dose rifampin). Momelotinib DDI perpetrator potential (multiple‐dose) was evaluated with CYP3A4 and breast cancer resistance protein (BCRP) substrates (midazolam and rosuvastatin, respectively). DDI was assessed from changes in maximum plasma concentration ( C max ), area under the concentration–time curve (AUC), time to reach C max , and half‐life. The increase in momelotinib (23% C max , 14% AUC) or M21 (30% C max , 24% AUC) exposure with ritonavir coadministration was not clinically relevant. A moderate increase in momelotinib (40% C max , 57% AUC) and minimal change in M21 was observed with single‐dose rifampin. A moderate decrease in momelotinib (29% C max , 46% AUC) and increase in M21 (31% C max , 15% AUC) were observed with multiple‐dose rifampin compared with single‐dose rifampin. Due to potentially counteracting effects of OATP1B1/1B3 inhibition and CYP3A4 induction, multiple‐dose rifampin did not significantly change momelotinib pharmacokinetics compared with momelotinib alone ( C max no change, 15% AUC decrease). Momelotinib did not alter the pharmacokinetics of midazolam (8% C max , 16% AUC decreases) or 1′‐hydroxymidazolam (14% C max , 16% AUC decreases) but increased rosuvastatin C max by 220% and AUC by 170%. Safety findings were mild in this short‐term study in healthy volunteers. This analysis suggests that momelotinib interactions with OATP1B1/1B3 inhibitors and BCRP substrates may warrant monitoring for adverse reactions or dose adjustments.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".