A Phase I Trial of the MET/ALK/ROS1 Inhibitor Crizotinib Combined with the VEGF Inhibitor Pazopanib in Patients with Advanced Solid Malignancies
Bibliographic record
Abstract
BACKGROUND: Crizotinib inhibits ALK, MET and ROS1 tyrosine kinases but the development of resistance to monotherapy is an issue. The anti-angiogenic properties of pazopanib could overcome crizotinib drug resistance. Additionally, the anti-angiogenic properties of crizotinib could augment the clinical efficacy of pazopanib. METHODS: We evaluated the safety and responses in patients with advanced solid tumors treated with crizotinib and pazopanib. RESULTS: Eighty-two patients (median age 53 years, range 18-78 years) were enrolled. The median number of prior systemic therapies was 3 (range, 0-8). We were able to dose escalate to dose level 8 (crizotinib 250 mg twice daily and pazopanib 800 mg daily) with no MTD identified. Grade 3 or 4 toxicities were seen in 32% of patients with the highest prevalence being fatigue (n=9, 11%), diarrhea (n=6, 7%), vomiting (n=3, 4%), anemia (n=2, 2%) and ALT increased (n=2, 2%). Of the 82 patients, 61 (74%) had measurable disease by RECISTv1.1 and reached first restaging (6 weeks). Partial response (PR) was observed in 6/61 (10%) patients, and stable disease (SD) lasting ≥6 months was observed in 10/61 patients (16%) (total = 16/61 (26%) of patients with SD ≥6 months/PR). CONCLUSION: Dose level 6 (crizotinib 200 mg twice daily and pazopanib 600 mg daily) was the most tolerable dosing of the combination and can be used in future studies. We also observed moderate clinical activity in patients with advanced solid tumors that had received numerous prior therapies.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".