An in silico–in vitro pipeline for drug cardiotoxicity screening identifies ionic pro‐arrhythmia mechanisms

Abstract

Background and Purpose Before advancing to clinical trials, new drugs are screened for their pro‐arrhythmic potential using a method that is overly conservative and provides limited mechanistic insight. The shortcomings of this approach can lead to the mis‐classification of beneficial drugs as pro‐arrhythmic. Experimental Approach An in silico–in vitro pipeline was developed to circumvent these shortcomings. A computational human induced pluripotent stem cell‐derived cardiomyocyte (iPSC‐CM) model was used as part of a genetic algorithm to design experiments, specifically electrophysiological voltage clamp (VC) protocols, to identify which of several cardiac ion channels were blocked during in vitro drug studies. Such VC data, along with dynamically clamped action potentials (AP), were acquired from iPSC‐CMs before and after treatment with a control solution or a low‐ (verapamil), intermediate‐ (cisapride or quinine) or high‐risk (quinidine) drug. Key Results Significant AP prolongation (a pro‐arrhythmia marker) was seen in response to quinidine and quinine. The VC protocol identified block of IKr (a source of arrhythmias) by all strong IKr blockers, including cisapride, quinidine and quinine. The protocol also detected block of ICaL by verapamil and Ito by quinidine. Further demonstrating the power of the approach, the VC data uncovered a previously unidentified If block by quinine, which was confirmed with experiments using a HEK‐293 expression system and automated patch‐clamp. Conclusion and Implications We developed an in silico–in vitro pipeline that simultaneously identifies pro‐arrhythmia risk and mechanism of ion channel‐blocking drugs. The approach offers a new tool for evaluating cardiotoxicity during preclinical drug screening.

Creating cell-specific computational models of stem cell-derived cardiomyocytes using optical experiments

“…Currently, these data are still difficult to obtain from a large group of patients or cell preparations. More recent work has demonstrated the utility of genetic algorithms and the resulting recalibrated models in prediction of arrhythmic behaviors and drug mechanisms, but these also used complex voltage step protocols to calibrate model parameters [35]. These prior results motivated our search for protocols that could calibrate mathematical models using voltage- and calcium-sensitive fluorescent dye measurements, which are considerably easier to acquire than patch-clamp recordings.…”

Inhibitory Effects of Nobiletin on Voltage-Gated Na+ Channel in Rat Ventricular Myocytes Based on Electrophysiological Analysis and Molecular Docking Method

“…The SARS-CoV-2 infection is a high-risk-inducing factor for cardiovascular disease due to severe cytokine storms and systemic inflammatory responses [ 34 , 35 , 36 , 37 , 38 ]. Anti-arrhythmic drugs currently in clinical use, such as amiodarone, which has pulmonary toxicity, and quinidine, which has proven to show a pro-arrhythmia effect, may aggravate the damage to the cardiopulmonary circulatory system [ 39 , 40 , 41 , 42 ]. Therefore, the development of low-toxicity and multi-target cardiovascular drugs is also urgently needed [ 43 ].…”

Optimization of a Cardiomyocyte Model Illuminates Role of Increased INaLin Repolarization Reserve

“…A genetic algorithm is a method used to search for solutions through a process inspired by natural selection and has been used in the field to build models or extract desired parameters from data (Clark et al, 2022; Tomek et al, 2019; Bot et al, 2012; Groenendaal et al, 2015; Syed et al, 2005; Kherlopian et al, 2011). Through this technique, a population of individuals evolve towards an optimum in an iterative process.…”