A parallel-arm phase I trial of the humanised anti-IGF-1R antibody dalotuzumab in combination with the AKT inhibitor MK-2206, the mTOR inhibitor ridaforolimus, or the NOTCH inhibitor MK-0752, in patients with advanced solid tumours
Bibliographic record
Abstract
BACKGROUND: Two strategies to interrogate the insulin growth factor 1 receptor (IGF-1R) pathway were investigated: vertical inhibition with dalotuzumab and MK-2206 or ridaforolimus to potentiate PI3K pathway targeting and horizontal cross-talk inhibition with dalotuzumab and MK-0752 to exert effects against cellular proliferation, angiogenesis, and stem cell propagation. METHODS: A phase I, multi-cohort dose escalation study was conducted in patients with advanced solid tumours. Patients received dalotuzumab (10 mg kg(-1)) and escalating doses of MK-2206 (90-200 mg) or escalating doses of dalotuzumab (7.5-10 mg kg(-1)) and MK-0752 (1800 mg) weekly. Upon maximum tolerated dose determination, patients with low-RAS signature, high-IGF1 expression ovarian cancer were randomised to dalotuzumab/MK-2206 versus dalotuzumab/ridaforolimus, whereas patients with high IGF1/low IGF2 expression colorectal cancer received dalotuzumab/MK-0752. RESULTS: A total of 47 patients were enrolled: 29 in part A (18 in the dalotuzumab/MK-2206 arm and 11 in the dalotuzumab/MK-0752 arm) and 18 in part B (6 in each arm). Dose-limiting toxicities (DLTs) for dalotuzumab/MK-2206 included grade 4 neutropenia and grade 3 serum sickness-like reaction, maculopapular rash, and gastrointestinal inflammation. For dalotuzumab/MK-0752, DLTs included grade 3 dehydration, rash, and diarrhoea. Seven patients remained on study for >4 cycles. CONCLUSIONS: Dalotuzumab/MK-2206 and dalotuzumab/MK-0752 combinations were tolerable. Further developments of prospectively validated predictive biomarkers to aid in patient selection for anti-IGF-1R therapies are needed.
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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) | 0.002 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".