Abstract CT058: Ripretinib (DCC-2618) pharmacokinetics (PK) in a Phase I study in patients with gastrointestinal stromal tumors (GIST) and other advanced malignancies: A retrospective evaluation of the PK effects of proton pump inhibitors (PPIs)
Bibliographic record
Abstract
Abstract Objectives: Ripretinib is a novel, oral kinase switch control inhibitor of KIT and PDGFRα. Encouraging clinical benefit from the phase 1 dose escalation and expansion study as measured by ORR, DCR and PFS in 2nd, 3rd, and >4th line GIST patients with a favorable tolerability profile at doses >100 mg/day has been previously reported (ESMO 2018, abstract #1603O). It has been reported that more than 40% of GIST patients use acid-reducing agents. PPIs are the most potent acid-reducing agents that may impair the absorption of kinase inhibitors 1,2. This retrospective analysis aims to explore whether ripretinib can be used regardless of concomitant PPI use. Methods: The analysis assessed the impact of PPIs on the plasma concentration of ripretinib using PK data from the expansion cohort at the recommended Phase 2 dose of 150 mg QD. Plasma concentrations of ripretinib and its active metabolite DP-5439 obtained from patients who used or did not use PPIs were compared on Cycle 1 Day 1 (C1D1, n=106) and Day 15 (C1D15, n=102). Patients using PPIs were defined as those who continuously took PPIs for at least 4 days prior to C1D1 or C1D15. Patients who did not use PPIs were defined as those who did not take PPIs or any other acid-reducing agents during the study. Results: PK profiles were consistent between patients using and not using PPIs (Table 1), indicating a low likelihood of a clinically significant drug interaction between PPIs and ripretinib. Conclusions: This retrospective PK analysis provides supporting evidence that restriction of co-administration of PPIs with ripretinib may not be necessary. A dedicated drug interaction study is planned to provide a definitive assessment. References: 1. Smelick et al, Mol. Pharmaceutics 2013, 10, 4055−4062 2. Budha et al, Clin Pharmacol Ther. 2012, 92(2):203-13 Table 1.PK Exposure of Ripretinib and DP-5439 in Patients Using or not Using PPIsPK ConcentrationsRipretinib in ng/mL [mean (CV%)]DP-5439 in ng/mL [mean (CV%)]Ripretinib + DP-5439 in ng/mL [mean (CV%)]Using PPIsNot using PPIsUsing PPIsNot using PPIsUsing PPIsNot using PPIsC1D1 6 hr566 (58%) n=24670 (53%) n=82302 (64%) n=23297 (59%) n=82862 (54%) n=24975 (48%) n=82C1D15 pre-dose364 (79%) n= 24344 (63%) n=78960 (80%) n=24889 (86%) n=781350 (72%) n=241260 (75%) n=78C1D15 6 hr834 (51%) n=24871 (47%) n=731170 (69%) n=241060 (67%) n=732040 (53%) n=241960 (49%) n=73Notes: Table 1 is based on data from patients without a history of gastrectomy. A separate analysis in patients with gastrectomy will be published after sufficient data is obtained. Citation Format: Filip Janku, Michael Heinrich, Ping Chi, Albiruni Abdul Razak, Margaret von Mehren, Michael Gordon, Kristen Ganjoo, Jonathan Trent, Robin L. Jones, Hans Gelderblom, Kelli Running, Jing Wang, Rodrigo Ruiz-Soto, Suzanne George. Ripretinib (DCC-2618) pharmacokinetics (PK) in a Phase I study in patients with gastrointestinal stromal tumors (GIST) and other advanced malignancies: A retrospective evaluation of the PK effects of proton pump inhibitors (PPIs) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr CT058.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".