Computational approaches identify a transcriptomic fingerprint of drug-induced structural cardiotoxicity.

Structural cardiotoxicity (SCT) presents a high-impact risk that is poorly tolerated in drug discovery unless significant benefit is anticipated. Therefore, we aimed to improve the mechanistic understanding of SCT. First, we combined machine learning methods with a modified calcium transient assay in human-induced pluripotent stem cell-derived cardiomyocytes to identify nine parameters that could predict SCT. Next, we applied transcriptomic profiling to human cardiac microtissues exposed to structural and non-structural cardiotoxins. Fifty-two genes expressed across the three main cell types in the heart (cardiomyocytes, endothelial cells, and fibroblasts) were prioritised in differential expression and network clustering analyses and could be linked to known mechanisms of SCT. This transcriptomic fingerprint may prove useful for generating strategies to mitigate SCT risk in early drug discovery.

CardioMotion: identification of functional and structural cardiotoxic liabilities in small molecules through brightfield kinetic imaging.

Cardiovascular toxicity is an important cause of drug failures in the later stages of drug development, early clinical safety assessment, and even postmarket withdrawals. Early-stage in vitro assessment of potential cardiovascular liabilities in the pharmaceutical industry involves assessment of interactions with cardiac ion channels, as well as induced pluripotent stem cell-derived cardiomyocyte-based functional assays, such as calcium flux and multielectrode-array assays. These methods are appropriate for the identification of acute functional cardiotoxicity but structural cardiotoxicity, which manifests effects after chronic exposure, is often only captured in vivo. CardioMotion is a novel, label-free, high throughput, in vitro assay and analysis pipeline which records and assesses the spontaneous beating of cardiomyocytes and identifies compounds which impact beating. This is achieved through the acquisition of brightfield images at a high framerate, combined with an optical flow-based python analysis pipeline which transforms the images into waveform data which are then parameterized. Validation of this assay with a large dataset showed that cardioactive compounds with diverse known direct functional and structural mechanisms-of-action on cardiomyocytes are identified (sensitivity = 72.9%), importantly, known structural cardiotoxins also disrupt cardiomyocyte beating (sensitivity = 86%) in this method. Furthermore, the CardioMotion method presents a high specificity of 82.5%.