A Phase I Study of Dinaciclib in Combination With MK‐2206 in Patients With Advanced Pancreatic Cancer
Bibliographic record
Abstract
The combination of drugs targeting Ral and PI3K/AKT signaling has antitumor efficacy in preclinical models of pancreatic cancer. We combined dinaciclib (small molecule cyclin dependent kinase inhibitor with MK‐2206 (Akt inhibitor) in patients with previously treated/metastatic pancreatic cancer. Patients were treated with dinaciclib (6–12 mg/m 2 i.v.) and MK‐2206 (60–135 mg p.o.) weekly. Tumor biopsies were performed to measure pAKT, pERK, and Ki67 at baseline and after one completed cycle (dose level 2 and beyond). Thirty‐nine patients participated in the study. The maximum tolerated doses were dinaciclib 9 mg/m 2 and MK‐2206 135 mg. Treatment‐related grade 3 and 4 toxicities included neutropenia, lymphopenia, anemia, hyperglycemia, hyponatremia, and leukopenia. No objectives responses were observed. Four patients (10%) had stable disease as their best response. At the recommended dose, median survival was 2.2 months. Survival rates at 6 and 12 months were 11% and 5%, respectively. There was a nonsignificant reduction in pAKT composite scores between pretreatment and post‐treatment biopsies (mean 0.76 vs. 0.63; P = 0.635). The combination of dinaciclib and MK‐2206 was a safe regimen in patients with metastatic pancreatic cancer, although without clinical benefit, possibly due to not attaining biologically effective doses. Given the strong preclinical evidence of Ral and AKT inhibition, further studies with better tolerated agents should be considered.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".