A Phase I Expansion Cohort Study Evaluating the Safety and Efficacy of the CHK1 Inhibitor LY2880070 with Low-dose Gemcitabine in Patients with Metastatic Pancreatic Adenocarcinoma
Bibliographic record
Abstract
PURPOSE: Combining gemcitabine with CHK1 inhibition has shown promise in preclinical models of pancreatic ductal adenocarcinoma (PDAC). Here, we report the findings from a phase I expansion cohort study (NCT02632448) investigating low-dose gemcitabine combined with the CHK1 inhibitor LY2880070 in patients with previously treated advanced PDAC. PATIENTS AND METHODS: Patients with metastatic PDAC were treated with gemcitabine intravenously at 100 mg/m2 on days 1, 8, and 15, and LY2880070 50 mg orally twice daily on days 2-6, 9-13, and 16-20 of each 21-day cycle. Pretreatment tumor biopsies were obtained from each patient for correlative studies and generation of organoid cultures for drug sensitivity testing and biomarker analyses. RESULTS: Eleven patients with PDAC were enrolled in the expansion cohort between August 27, 2020 and July 30, 2021. Four patients (36%) experienced drug-related grade 3 adverse events. No objective radiologic responses were observed, and all patients discontinued the trial by 3.2 months. In contrast to the lack of efficacy observed in patients, organoid cultures derived from biopsies procured from two patients demonstrated strong sensitivity to the gemcitabine/LY2880070 combination and showed treatment-induced upregulation of replication stress and DNA damage biomarkers, including pKAP1, pRPA32, and γH2AX, as well as induction of replication fork instability. CONCLUSIONS: No evidence of clinical activity was observed for combined low-dose gemcitabine and LY2880070 in this treatment-refractory PDAC cohort. However, the gemcitabine/LY2880070 combination showed in vitro efficacy, suggesting that drug sensitivity for this combination in organoid cultures may not predict clinical benefit in patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".