Strategies to Enhance Epidermal Growth Factor Inhibition: Targeting the Mevalonate Pathway
Bibliographic record
Abstract
Mevalonate metabolites play an essential role in transducing epidermal growth factor (EGF) receptor (EGFR)-mediated signaling, as several of these metabolites are required for the function of this receptor and the components of its signaling cascades. Thus, the depletion of mevalonate metabolites may have a significant effect on EGFR function. Lovastatin is a specific and potent inhibitor of 3-hydroxy-3-methylglutaryl CoA reductase, the rate-limiting enzyme of the mevalonate pathway. Targeting 3-hydroxy-3-methylglutaryl CoA reductase using lovastatin induces a potent tumor-specific apoptotic response in a variety of tumor types at therapeutically achievable levels of this drug. The effects of lovastatin on EGFR function and the potential combination effects with EGFR tyrosine kinase inhibitors, such as gefitinib, were evaluated. Lovastatin treatment inhibited EGF-induced EGFR autophosphorylation and its downstream signaling cascades by 24 hours. Combining lovastatin and gefitinib showed enhanced inhibition and cooperative cytotoxicity in a variety of cell lines that included all eight squamous cell carcinomas, four non-small cell lung carcinoma, and four colon carcinoma cell lines tested. Isobologram analyses confirmed that this combination was synergistic, inducing a potent apoptotic response. A phase I study has shown the safety and potential clinical benefit of high-dose lovastatin in patients with recurrent squamous cell carcinoma. The use of lovastatin, which is metabolized by CYP3A4, is contraindicated with drugs, such as gefitinib and erlotinib, which are also metabolized by CYP3A4 due to greatly enhanced toxicity. Rosuvastatin, a relatively novel potent mevalonate pathway inhibitor that is not metabolized significantly by CYP3A4, is a more appropriate statin to combine with either erlotinib or gefitinib. The combination of erlotinib and rosuvastatin has been proposed for a phase I/II study in advanced non-small cell lung carcinoma.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".