引言：酪氨酸激酶抑制剂（TKIs）是高效的抗癌药物，但许多TKIs与各种形式的心毒性有关。导致这些药物不良反应的机制尚不清楚。我们通过整合几种互补的方法，包括全面的转录组学、机械化数学建模和培养的人心肌细胞的生理测定，研究了TKI诱导的心毒性机制。方法：由两个健康受体中获得的诱导多能干细胞（iPSCs）分化成心肌细胞（iPSC-CMs），并用26种FDA批准的TKIs对细胞进行处理。使用mRNA-seq定量测定药物诱导的基因表达变化，将基因表达变化整合到电生理学和收缩的机械化数学模型中，并使用模拟结果来预测生理结果。结果：iPSC-CMs中行动电位、细胞内钙离子和收缩的实验记录表明，模拟预测是准确的，在两个细胞系中81%的模拟预测被实验确认。令人惊讶的是，对TKI处理的iPSC-CMs如何应对额外的心律失常刺激，即低钾血症的模拟预测，预测药物对心律失常易感性的影响在细胞系之间存在显着差异，并且这些预测在实验中得到了确认。计算分析揭示出细胞系之间特定离子通道的上调或下调差异可以解释TKI处理的细胞如何对低钾血症产生不同的反应。讨论：总体而言，本研究确定了TKIs引起心毒性的转录机制，并演示了一种将转录组学与机械化数学模型相结合的新方法，以生成可实验测试的个体化不良事件风险预测。版权 © 2023 Shim、Xiong、Dhanan、Dariolli、Azeloglu、Hu、Jayaraman、Schaniel、Birtwistle、Iyengar、Dubois和Sobie。

Introduction: Tyrosine kinase inhibitor drugs (TKIs) are highly effective cancer drugs, yet many TKIs are associated with various forms of cardiotoxicity. The mechanisms underlying these drug-induced adverse events remain poorly understood. We studied mechanisms of TKI-induced cardiotoxicity by integrating several complementary approaches, including comprehensive transcriptomics, mechanistic mathematical modeling, and physiological assays in cultured human cardiac myocytes. Methods: Induced pluripotent stem cells (iPSCs) from two healthy donors were differentiated into cardiac myocytes (iPSC-CMs), and cells were treated with a panel of 26 FDA-approved TKIs. Drug-induced changes in gene expression were quantified using mRNA-seq, changes in gene expression were integrated into a mechanistic mathematical model of electrophysiology and contraction, and simulation results were used to predict physiological outcomes. Results: Experimental recordings of action potentials, intracellular calcium, and contraction in iPSC-CMs demonstrated that modeling predictions were accurate, with 81% of modeling predictions across the two cell lines confirmed experimentally. Surprisingly, simulations of how TKI-treated iPSC-CMs would respond to an additional arrhythmogenic insult, namely, hypokalemia, predicted dramatic differences between cell lines in how drugs affected arrhythmia susceptibility, and these predictions were confirmed experimentally. Computational analysis revealed that differences between cell lines in the upregulation or downregulation of particular ion channels could explain how TKI-treated cells responded differently to hypokalemia. Discussion: Overall, the study identifies transcriptional mechanisms underlying cardiotoxicity caused by TKIs, and illustrates a novel approach for integrating transcriptomics with mechanistic mathematical models to generate experimentally testable, individual-specific predictions of adverse event risk.Copyright © 2023 Shim, Xiong, Dhanan, Dariolli, Azeloglu, Hu, Jayaraman, Schaniel, Birtwistle, Iyengar, Dubois and Sobie.