Chronic Treatment with Multi-Kinase Inhibitors Causes Differential Toxicities on Skeletal and Cardiac Muscles
Bibliographic record
Abstract
Despite recent progress, chemotherapy remains the preferred treatment for cancer. We have shown a link between anticancer drugs and the development of cachexia, i.e., body wasting accompanied by muscle loss. The multi-kinase inhibitors (MKIs) regorafenib and sorafenib, used as second-line treatment for solid tumors, are frequently accompanied by several side effects, including loss of muscle mass and strength. In the present study we aimed to investigate the molecular mechanisms associated with the occurrence of muscle toxicities in in vivo conditions. Hence, we treated 8-week old healthy CD2F1 male mice with MKIs for up to six weeks and observed decreased skeletal and cardiac muscle mass, consistent with muscle weakness. Modulation of ERK1/2 and GSK3β, as well as increased expression of markers of autophagy, previously associated with muscle atrophy conditions, were shown in skeletal muscle upon treatment with either drug. MKIs also promoted cardiac abnormalities consistent with reduced left ventricular mass, internal diameter, posterior wall thickness and stroke volume, despite unchanged overall function. Notably, different signaling pathways were affected in the heart, including reduced expression of mitochondrial proteins, and elevated AKT, GSK3β, mTOR, MEK1/2 and ERK1/2 phosphorylation. Combined, our data demonstrate detrimental effects on skeletal and cardiac muscle in association with chronic administration of MKIs, although different mechanisms would seem to contribute to the cachectic phenotype in the two tissues.
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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 | 0.001 |
| 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".