Kinase inhibitors for cancer alter metabolism, blood glucose, and insulin
Bibliographic record
Abstract
Small molecule kinase inhibitors (SMKIs) are a class of therapeutic drugs that target protein kinases in diseases such as cancer. SMKIs are often designed to inhibit kinases involved in cell proliferation, but these drugs alter cell metabolism and the endocrine control of organismal metabolism. SMKI treatment in diabetic cancer patients reveals that certain SMKIs improve blood glucose levels and can mitigate insulin dependence or diabetic medication requirements in both type 1 diabetes (T1D) and type 2 diabetes (T2D). Certain SMKIs can preserve functional β-cell mass and increase insulin secretion or insulin sensitivity. It is not yet clear why different SMKIs can have opposing effects on insulin and blood glucose. Understanding the therapeutic effects of these drugs in T1D and T2D is complicated by overlapping off-target effects of SMKIs. The potency of inhibition of the intended protein kinase and inhibition of multiple off-target kinases may underpin conflicting reports of how certain SMKIs alter blood glucose and insulin. We summarize the effects of SMKIs on the intended and off-target kinases that can alter blood glucose and insulin, including c-Abl, c-Kit, EGFR, and VEGF. Inhibition of PDGFRβ consistently lowers blood glucose in T1D and T2D. The effects of SMKIs on the kinases that regulate immune pathways, such as BTK and RIPKs, mediate many of the diverse effects of these drugs on metabolism. We highlight that inhibition of RIPK2 by SMKIs is a central node in metabolism that influences key metabolic pathways including lipolysis, blood glucose control, insulin secretion, and insulin resistance.
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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) | 0.002 | 0.001 |
| 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) | 0.000 | 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".