Pulsatile Administration of the Epidermal Growth Factor Receptor Inhibitor Gefitinib Is Significantly More Effective than Continuous Dosing for Sensitizing Tumors to Paclitaxel
Bibliographic record
Abstract
PURPOSE: Gefitinib is an inhibitor of the epidermal growth factor receptor (EGFR) tyrosine kinase. Continuous inhibition of EGFR signaling is thought necessary for optimal inhibition of tumor cell proliferation. We hypothesized that continuous gefitinib may antagonize the effects of cytotoxics that inhibit tumor cells in other phases of the cell cycle. Furthermore, we hypothesized that intermittent dosing would allow for dose escalation and greater inhibition of EGFR-dependent antiapoptotic pathways. EXPERIMENTAL DESIGN: To test these assertions, we compared combinations of paclitaxel and gefitinib using either intermittent or continuous dosing schedules in mice. RESULTS: We found that when used in combination with paclitaxel, pulsatile gefitinib was significantly superior to continuous dosing. When gefitinib was administered for one or two consecutive days before paclitaxel, much higher doses could be given safely. Two days of gefitinib treatment before paclitaxel was most effective, causing significantly greater mean tumor regression and a higher percentage of complete responses than other schedules. CONCLUSIONS: The results suggest that the dose and schedule of an EGFR inhibitor required to effectively inhibit proliferation may differ from that required to stimulate apoptosis or to induce other effects. The former may require continuous EGFR inhibition to maintain cell cycle arrest, whereas sensitization to apoptosis may be optimally induced by profound but temporary inhibition of survival pathways. Our data suggest that the effects of receptor inhibition vary as a function of dose and schedule and that continuous administration of tyrosine kinase inhibitors may not be the best schedule with which to combine such agents with taxanes.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".