Predicting individual-specific cardiotoxicity responses induced by tyrosine kinase inhibitors

Area of Science:

• Cardiology
• Pharmacology
• Computational Biology

Background:

• Tyrosine kinase inhibitors (TKIs) are vital cancer therapies but can induce cardiotoxicity.
• The precise mechanisms of TKI-induced cardiotoxicity are not fully understood.
• Understanding these mechanisms is crucial for patient safety and drug development.

Purpose of the Study:

• To investigate the mechanisms of TKI-induced cardiotoxicity.
• To integrate transcriptomics, mathematical modeling, and physiological assays.
• To predict individual-specific risks of adverse cardiovascular events from TKIs.

Main Methods:

• Utilized induced pluripotent stem cell-derived cardiac myocytes (iPSC-CMs) from healthy donors.
• Treated iPSC-CMs with 26 FDA-approved TKIs and analyzed gene expression via mRNA-seq.
• Integrated transcriptomic data into a mechanistic mathematical model of cardiac electrophysiology and contraction.

Main Results:

• Model predictions of physiological outcomes in iPSC-CMs were experimentally validated with 81% accuracy.
• Simulations accurately predicted differential arrhythmia susceptibility in TKI-treated cells under hypokalemia.
• Identified ion channel expression differences as key to explaining variable TKI cardiotoxicity responses.

Conclusions:

• Transcriptional mechanisms underlying TKI cardiotoxicity have been identified.
• A novel approach combining transcriptomics and mechanistic modeling predicts individual-specific cardiotoxicity risk.
• This integrated approach offers a powerful tool for assessing drug safety and personalized medicine.