Pharmacodynamic evaluation of pCDC2 as the target engagement biomarker to assess activity of MK-1775 a Wee1 tyrosine kinase inhibitor.
Bibliographic record
Abstract
e13598 Background: Wee1 kinase regulates the G2 checkpoint through phosphorylation of CDC2. MK-1775 is a first-in-class inhibitor of Wee1, and thereby reduces pCDC2 levels relative to CDC2. pCDC2therefore can be used as a target engagement (TE) biomarker to assess activity of MK-1775. This was investigated in a phase I first-in-man clinical trial of MK-1775. Methods: This is a multicenter, open-label, non-randomized phase I dose escalation study in patients with locally advanced or metastatic solid tumors. MK-1775 was administered orally in escalating doses as monotherapy, and either single dose or multi-dose in combination with chemotherapy including cisplatin, carboplatin and gemcitabine. Serial skin biopsies were performed at baseline and either 8, 24 or 48 hours following MK-1775 administration and analyzed by IHC for CDC2 and pCDC2. Based on preclinical efficacy experiments, TE was defined as a decrease of pCDC2 of at least 50% (or fold change > 0.50) from pre- to post-dose MK-1775. Results: To date a total of 176 patients have received at least one dose of MK-1775 either as monotherapy or in combination (single or multi-dose MK-1775) with chemotherapy at doses ranging from 25 mg to 1300 mg to define maximum tolerated dose. Dose dependent decreases in pCDC2 were observed in skin biopsies between pre-dose and post-dose treatment in all treatment groups. TE with monotherapy was achieved at 325 mg. TE with multi-dose MK-1775 in combination with cisplatin and carboplatin were achieved at 125 mg BID and 225 mg BID. Gemcitabinecombination treatment is on-going. In contrast, chemotherapy alone resulted in an increase of pCDC2. Conclusions: Based on preclinical data, in this first-in-man clinical trial of MK-1775, we were able to demonstrate TE required for maximal efficacy at tolerable doses of MK-1775 either as a single agent or in combination with chemotherapy. [Table: see text]
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".