Extended Disease Control with Unconventional Cabozantinib Dose Increase in Metastatic Renal Cell Carcinoma
Bibliographic record
Abstract
BACKGROUND: Cabozantinib is among the most potent tyrosine kinase inhibitors (TKIs) FDA-approved for metastatic renal cell carcinoma (mRCC). Effective treatments after progression on cabozantinib salvage therapy are limited. Dose escalation for other TKIs has been shown to afford added disease control. OBJECTIVE: We sought to evaluate whether dose escalation of cabozantinib (Cabometyx®) from conventional doses in select patients with limited treatment options offered additional disease control. We asked how cabozantinib dose increases may affect circulating drug levels. METHODS: We identified patients with mRCC at the University of Texas Southwestern Medical Center who were treated with cabozantinib dose escalation to 80 mg after progressing on conventional cabozantinib 60 mg. We then queried leading kidney cancer investigators across the world to identify additional patients. Finally, we reviewed pharmacokinetic (PK) data to assess how higher doses impacted circulating levels by comparison to other formulations (Cometriq® capsules). RESULTS: We report six patients treated at two different institutions with cabozantinib-responsive disease and good tolerability, where cabozantinib was dose escalated (typically to 80 mg, but as high as 120 mg) after progression on 60 mg, a strategy that resulted in added disease control (median duration, 14 months; 95% Confidence Interval [CI]: 8 –Not Estimable[NE]). Four patients (66.7%) had disease control lasting at least 1 year. No grade III/IV adverse events were identified in this small, select, cohort. A comparison of PK data to FDA-approved cabozantinib 140 mg capsules suggests that cabozantinib 80 mg tablets results in comparable exposures. CONCLUSIONS: mRCC patients with cabozantinib responsive disease and reasonable tolerability may benefit from dose escalation at progression.
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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) | 0.003 | 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".