Predicting individual-specific cardiotoxicity responses induced by tyrosine kinase inhibitors

Abstract

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.

Informing Hazard Identification and Risk Characterization of Environmental Chemicals by Combining Transcriptomic and Functional Data from Human-Induced Pluripotent Stem-Cell-Derived Cardiomyocytes

“…83−85 In addition, some studies integrated transcriptomics with disease-associated genetic variants and mechanistic models to generate experimentally testable hypotheses of adverse event risk. 27,86 Collectively, these studies demonstrate that transcriptional profiles in human cardiomyocytes can be used to derive mechanism-based drug signatures, information that could be useful to predict the cardiotoxic potential of existing and new chemicals. Our study provides additional information in terms of the number and diversity of chemicals.…”

“…Transcriptomic effects of various drugs on iPSC-derived cardiomyocytes have been explored by several groups. ,,,− These studies identified molecular mechanisms underlying adverse effects of both individual cardiotoxic compounds and entire drug classes. − In addition, some studies integrated transcriptomics with disease-associated genetic variants and mechanistic models to generate experimentally testable hypotheses of adverse event risk. , Collectively, these studies demonstrate that transcriptional profiles in human cardiomyocytes can be used to derive mechanism-based drug signatures, information that could be useful to predict the cardiotoxic potential of existing and new chemicals. Our study provides additional information in terms of the number and diversity of chemicals.…”

Neural network emulation of the human ventricular cardiomyocyte action potential for more efficient computations in pharmacological studies

“…Again, this expands the input space and might be unsuitable for solving the inverse problem when only AP data are used. Moreover, drugs that are applied over a longer period of time can also cause modifications of the maximum conductances through changes in gene expression ( Shim et al, 2023 ). This requires attention to avoid misinterpretations of found blocking or enhancement effects, for example by estimating the control maximum conductances again after a washout procedure.…”

From transcriptomics to digital twins of organ function

“…Although most models that capture biochemical SCPs use a pathways framework, biophysical models can also capture changes in gene expression to predict responses to perturbation. In a recent study using cardiomyocytes differentiated from healthy human subjects, gene expression changes induced by tyrosine kinase inhibitor drugs that are effective cancer therapeutics was used to develop computational models that predict arrhythmogenic responses to cancer drug therapy in individuals ( Shim et al, 2023 ). Changes in levels of gene expression of different channel proteins by drugs were scaled and incorporated as changes in level of channel proteins into a multicompartment ODE model of cardiomyocyte action potential and contractility.…”