Synergistic effects of methylnaltrexone with 5-fluorouracil and bevacizumab on inhibition of vascular endothelial growth factor–induced angiogenesis
Bibliographic record
Abstract
Many patients with cancer receive combinations of drug treatments that include 5-fluorouracil (5-FU) and bevacizumab. Therapeutic doses of 5-FU are often associated with unwanted side effects, and bevacizumab is costly. Therefore, we explored potential agents that can reduce the therapeutic concentration of these drugs. Our data indicate that methylnaltrexone (MNTX), a peripheral antagonist of the mu-opioid receptor, exerts a synergistic effect with 5-FU and bevacizumab on inhibition of vascular endothelial growth factor (VEGF)-induced human pulmonary microvascular endothelial cell (EC) proliferation and migration, two key components in cancer-associated angiogenesis. MNTX inhibited EC proliferation with an IC(50) of approximately 100 nmol/L. Adding 100 nmol/L MNTX to EC shifted the IC(50) of 5-FU from approximately 5 micromol/L to approximately 7 nmol/L. Further, adding 50 ng/mL MNTX shifted the IC(50) of bevacizumab on inhibition of EC migration from approximately 25 to approximately 6 ng/mL. These synergistic effects were not observed with naltrexone, a tertiary mu-opioid receptor antagonist. On a mechanistic level, we observed that treatment of human EC with MNTX, but not naltrexone, increased receptor protein tyrosine phosphatase mu activity, which was independent of mu-opioid receptor expression. Silencing receptor protein tyrosine phosphatase mu expression (small interfering RNA) in human EC inhibited both synergy between MNTX and bevacizumab or 5-FU and increased VEGF-induced tyrosine phosphorylation of Src and p190 RhoGAP with enhanced activation of Akt and the actin cytoskeletal regulatory protein, RhoA, whereas silencing Src, Akt, or RhoA blocked VEGF-induced angiogenic events. Therefore, addition of MNTX could potentially lower the therapeutic doses of 5-FU and bevacizumab, which could improve index.
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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) | 0.000 | 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".