A phase 1 trial of the MEK inhibitor selumetinib in combination with pembrolizumab for advanced or metastatic solid tumors
Bibliographic record
Abstract
MEK inhibitors have immunomodulatory activity and potential for synergistic activity when combined with PD-1 inhibitors. We evaluated selumetinib (inhibitor of MEK1/2) plus pembrolizumab (anti‒PD-1 antibody) in patients with advanced/metastatic solid tumors. In this phase 1b study, adults with previously treated advanced/metastatic solid tumors received pembrolizumab 200 mg intravenously every 3 weeks plus selumetinib on days 1‒14 per 3-week cycle (2 weeks on/1 week off); selumetinib dosing began at 50 mg orally twice daily with escalation in 25 mg increments for ≤ 35 cycles. Primary endpoints were dose-limiting toxicities (DLTs), adverse events (AEs), and treatment discontinuations due to AEs. Thirty-two patients were enrolled. Dose escalation was completed up to selumetinib 125 mg twice daily. The target DLT rate of 30% was not reached at any dose level. In the selumetinib 100 mg group, 2/11 patients (18.2%) experienced DLTs (n = 1 grade 3 diarrhea, n = 1 grade 3 fatigue). In the selumetinib 125 mg group, 3/14 (21.4%) experienced DLTs (n = 1 grade 2 retinal detachment, n = 1 grade 3 retinopathy, n = 1 grade 3 stomatitis). Dose-related changes in pharmacokinetic exposures were observed for selumetinib and N-desmethyl selumetinib up to 100 mg (saturation at 125 mg). Two patients achieved partial responses (1 each with selumetinib 75 mg and 125 mg) for an objective response rate of 6%. The study was stopped early because of insufficient efficacy. Although the target DLT rate was not reached at any dose level and no new safety signals were identified, selumetinib plus pembrolizumab had limited antitumor activity in this population. Trial registration: ClinicalTrials.gov , NCT03833427.
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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 | 0.000 | 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 | 0.000 |
| 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".