Metronomic S-1 Chemotherapy and Vandetanib: An Efficacious and Nontoxic Treatment for Hepatocellular Carcinoma
Bibliographic record
Abstract
BACKGROUND: Metronomic chemotherapy involves frequent, regular administration of cytotoxic drugs at nontoxic doses, usually without prolonged breaks. We investigated the therapeutic efficacies of metronomic S-1, an oral 5-fluorouracil prodrug, and vandetanib, an epidermal growth factor receptor and vascular endothelial growth factor (VEGF) receptor tyrosine kinase inhibitor, in models of hepatocellular carcinoma (HCC). METHODS: We compared anti-HCC effects and toxicity in the six treatment groups: control (untreated), maximum tolerated dose (MTD) S-1, metronomic S-1, vandetanib, MTD S-1 with vandetanib, and metronomic S-1 with vandetanib. Tumor microvessel density (MVD) and tumor apoptosis were evaluated by immunohistochemistry. The expression of VEGF and thrombospondin-1, an endogenous inhibitor of angiogenesis, was analyzed by Western blot. RESULTS: Metronomic S-1 significantly inhibited tumor growth, which was enhanced by combination with vandetanib. With respect to toxicities, MTD S-1 caused severe body weight loss and myelosuppression, whereas metronomic S-1 did not cause any overt toxicities. Moreover, metronomic S-1 or metronomic S-1 with vandetanib prolonged survival, the latter treatment providing the greatest benefit. Metronomic S-1 and metronomic S-1 with vandetanib decreased MVDs and increased apoptosis in tumor tissues. The expression of VEGF in tumor tissues was upregulated by vandetanib and metronomic S-1 with vandetanib, whereas the expression of thrombospondin-1 was upregulated by metronomic S-1 and metronomic S-1 with vandetanib. CONCLUSION: Metronomic S-1 with an antiangiogenic agent seems to be an effective and safe therapeutic strategy for HCC.
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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) | 0.001 | 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 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".