IGF1/insulin receptor kinase inhibition by BMS-536924 is better tolerated than alloxan-induced hypoinsulinemia and more effective than metformin in the treatment of experimental insulin-responsive breast cancer
Bibliographic record
Abstract
Epidemiologic and experimental evidence suggest that a subset of breast cancer is insulin responsive, but it is unclear whether safe and effective therapies that target the insulin receptor (IR), which is homologous to oncogenes of the tyrosine kinase class, can be developed. We demonstrate that both pharmacologic inhibition of IR family tyrosine kinase activity and insulin deficiency have anti-neoplastic activity in a model of insulin-responsive breast cancer. Unexpectedly, in contrast to insulin deficiency, pharmacologic IR family inhibition does not lead to significant hyperglycemia and is well tolerated. We show that pharmacokinetic factors explain the tolerability of receptor inhibition relative to insulin deficiency, as the small molecule receptor kinase inhibitor BMS-536924 does not accumulate in muscle at levels sufficient to block insulin-stimulated glucose uptake. Metformin, which lowers insulin levels only in settings of hyperinsulinemia, had minimal activity in this normoinsulinemic model. These findings highlight the importance of tissue-specific drug accumulation as a determinant of efficacy and toxicity of tyrosine kinase inhibitors and suggest that therapeutic targeting of the IR family for cancer treatment is practical.
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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) | 0.001 | 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 | 0.001 |
| 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".