Cytotoxic Effects of Sorafenib, Lapatinib, and Bevacizumab, Alone and in Combination, on Medullary Thyroid Carcinoma Cells
Bibliographic record
Abstract
Background: Medullary thyroid carcinoma is a rare neuroendocrine tumor with limited therapeutic options, as current kinase inhibitors are often associated with significant toxicity and drug resistance. This study aimed to explore novel treatment strategies by testing targeted agents alone and in combination. Methods: Human medullary thyroid carcinoma TT cells with RET mutations were treated with Sorafenib, Lapatinib, and Bevacizumab. Cell proliferation was monitored in real time using the xCELLigence system, and apoptosis was assessed by flow cytometry. Results: Sorafenib and Lapatinib each showed strong, dose-dependent cytotoxic effects, with Lapatinib demonstrating the greatest potency. Bevacizumab alone exhibited minimal cytotoxic activity, but when combined with Sorafenib or Lapatinib it significantly enhanced their effects, even at concentrations that were only partially effective individually. The Lapatinib–Bevacizumab combination produced the most potent inhibition of cell viability, comparable to high-dose monotherapy. Conclusions: These findings suggest that combining kinase inhibitors with Bevacizumab may enhance antitumor activity, allow the use of lower drug doses, and overcome resistance, representing a promising therapeutic strategy for medullary thyroid carcinoma that warrants further investigation in clinical settings.
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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) | 0.000 | 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) | 0.002 | 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 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".