Predicting individual-specific cardiotoxicity responses induced by tyrosine kinase inhibitors.

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.

Transcriptomic profiling of human cardiac cells predicts protein kinase inhibitor-associated cardiotoxicity.

Kinase inhibitors (KIs) represent an important class of anti-cancer drugs. Although cardiotoxicity is a serious adverse event associated with several KIs, the reasons remain poorly understood, and its prediction remains challenging. We obtain transcriptional profiles of human heart-derived primary cardiomyocyte like cell lines treated with a panel of 26 FDA-approved KIs and classify their effects on subcellular pathways and processes. Individual cardiotoxicity patient reports for these KIs, obtained from the FDA Adverse Event Reporting System, are used to compute relative risk scores. These are then combined with the cell line-derived transcriptomic datasets through elastic net regression analysis to identify a gene signature that can predict risk of cardiotoxicity. We also identify relationships between cardiotoxicity risk and structural/binding profiles of individual KIs. We conclude that acute transcriptomic changes in cell-based assays combined with drug substructures are predictive of KI-induced cardiotoxicity risk, and that they can be informative for future drug discovery.

Mechanistic Systems Modeling to Improve Understanding and Prediction of Cardiotoxicity Caused by Targeted Cancer Therapeutics.

Tyrosine kinase inhibitors (TKIs) are highly potent cancer therapeutics that have been linked with serious cardiotoxicity, including left ventricular dysfunction, heart failure, and QT prolongation. TKI-induced cardiotoxicity is thought to result from interference with tyrosine kinase activity in cardiomyocytes, where these signaling pathways help to control critical processes such as survival signaling, energy homeostasis, and excitation-contraction coupling. However, mechanistic understanding is limited at present due to the complexities of tyrosine kinase signaling, and the wide range of targets inhibited by TKIs. Here, we review the use of TKIs in cancer and the cardiotoxicities that have been reported, discuss potential mechanisms underlying cardiotoxicity, and describe recent progress in achieving a more systematic understanding of cardiotoxicity via the use of mechanistic models. In particular, we argue that future advances are likely to be enabled by studies that combine large-scale experimental measurements with Quantitative Systems Pharmacology (QSP) models describing biological mechanisms and dynamics. As such approaches have proven extremely valuable for understanding and predicting other drug toxicities, it is likely that QSP modeling can be successfully applied to cardiotoxicity induced by TKIs. We conclude by discussing a potential strategy for integrating genome-wide expression measurements with models, illustrate initial advances in applying this approach to cardiotoxicity, and describe challenges that must be overcome to truly develop a mechanistic and systematic understanding of cardiotoxicity caused by TKIs.

microRNA Function Is Limited to Cytokine Control in the Acute Response to Virus Infection.

With the capacity to fine-tune protein expression via sequence-specific interactions, microRNAs (miRNAs) help regulate cell maintenance and differentiation. While some studies have also implicated miRNAs as regulators of the antiviral response, others have found that the RISC complex that facilitates miRNA-mediated silencing is rendered nonfunctional during cellular stress, including virus infection. To determine the global role of miRNAs in the cellular response to virus infection, we generated a vector that rapidly eliminates total cellular miRNA populations in terminally differentiated primary cultures. Loss of miRNAs has a negligible impact on both innate sensing of and immediate response to acute viral infection. In contrast, miRNA depletion specifically enhances cytokine expression, providing a posttranslational mechanism for immune cell activation during cellular stress. This work highlights the physiological role of miRNAs during the antiviral response and suggests their contribution is limited to chronic infections and the acute activation of the adaptive immune response.

In Vivo RNAi Screening Identifies MDA5 as a Significant Contributor to the Cellular Defense against Influenza A Virus.

Responding to an influenza A virus (IAV) infection demands an effective intrinsic cellular defense strategy to slow replication. To identify contributing host factors to this defense, we exploited the host microRNA pathway to perform an in vivo RNAi screen. To this end, IAV, lacking a functional NS1 antagonist, was engineered to encode individual siRNAs against antiviral host genes in an effort to rescue attenuation. This screening platform resulted in the enrichment of strains targeting virus-activated transcription factors, specific antiviral effectors, and intracellular pattern recognition receptors (PRRs). Interestingly, in addition to RIG-I, the PRR for IAV, a virus with the capacity to silence MDA5 also emerged as a dominant strain in wild-type, but not in MDA5-deficient mice. Transcriptional profiling of infected knockout cells confirmed RIG-I to be the primary PRR for IAV but implicated MDA5 as a significant contributor to the cellular defense against influenza A virus.