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Updated: May 26, 2026

Pre-clinical Evaluation of Tyrosine Kinase Inhibitors for Treatment of Acute Leukemia
Published on: September 18, 2013
Mechanisms, Clinical Phenotype and Potential Risk Prediction for Cardiovascular Toxicity Induced by Tyrosine Kinase
Hui Zheng1, Chentong Huang1, Zhanna Zhang1
1Department of Hematology, Dongyang Hospital Affiliated to WenZhou Medical University, Jinhua, Zhejiang, China.
Tyrosine kinase inhibitors (TKIs) improve chronic myeloid leukemia (CML) outcomes but can cause cardiovascular adverse events (CV-AEs). This review explains TKI-induced CV-AE mechanisms and proposes risk reduction strategies.
Area of Science:
- Cardiology
- Hematology
- Pharmacology
Background:
- Tyrosine kinase inhibitors (TKIs) are essential for chronic myeloid leukemia (CML) treatment.
- Long-term TKI use, especially newer generations, is linked to cardiovascular adverse events (CV-AEs).
- CV-AEs impact patient quality of life and treatment adherence.
Purpose of the Study:
- To systematically review mechanisms of TKI-induced cardiovascular toxicity.
- To propose the "Metabolism-Endothelium-Thrombosis Axis" as a unifying framework for TKI-related CV-AEs.
- To highlight the importance of risk prediction and cardioprotective strategies.
Main Methods:
- Narrative review of existing literature.
- Systematic elucidation of pathophysiological pathways.
- Integration of molecular, cellular, and clinical data.
Main Results:
- TKI-induced CV-AEs involve a complex cascade including endothelial dysfunction, metabolic changes, oxidative stress, inflammation, and platelet activation.
- The "Metabolism-Endothelium-Thrombosis Axis" integrates these mechanisms.
- Risk prediction models show value in identifying patients prone to CV-AEs, especially arterial occlusive events (AOEs).
Conclusions:
- Understanding the "Metabolism-Endothelium-Thrombosis Axis" is key to managing TKI-related cardiovascular risks.
- Identifying high-risk patients and implementing cardioprotective strategies are crucial.
- Further research is needed to refine TKI-specific toxicity understanding and risk prediction models.
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