Cross clinical-experimental-computational qualification of in silico drug trials on human cardiac purkinje cells for proarrhythmia risk prediction

In silico predictions of drug-induced changes in human cardiac contractility align with experimental recordings

Drug-induced changes in cardiac contractility (inotropy) can lead to cardiotoxicity, a major cause of discontinuation in drug development. Preclinical approaches to assess cardiac inotropy are imperfect, with in vitro assays limited to stem cell-derived or adult human primary cardiomyocytes. Human mechanistic in silico modelling and simulations are already successfully applied for proarrhythmia prediction, contributing to cardiac safety assessment strategies in early drug development. In this study, we investigated their ability to predict drug-induced effects on cardiac inotropy. We considered a validation set of 28 neutral/negative inotropic and 13 positive inotropic reference compounds and simulated their effects on cell contractility via ion channel inhibition and perturbation of nine biomechanical modelling parameters, respectively. For each compound, a wide range of drug concentrations was simulated in an experimentally calibrated control population of 323 human ventricular in silico cells. Simulated biomarkers indicating drug-induced inotropic effects were compared with in vitro preclinical data from the literature. Computer simulations predicted drug-induced inotropic changes observed in vitro for 25 neutral/negative inotropes and 10 positive inotropes. Predictions of negative inotropic changes were quantitatively in agreement for 86% of tested drugs. Active tension peak was identified as the biomarker with highest predictive potential. This study describes the validation and application of an in silico cardiac electromechanical model for drug safety evaluation, combining ion channel inhibition data and information on potential inotropic mechanisms to predict inotropic changes. Furthermore, a route for its integration as part of a preclinical drug safety assessment strategy is outlined.

In vivo safety evaluation and tracing of arginylglycylaspartic acid-engineered phage nanofiber in murine model

The engineered phage YSY184, mimicking the extracellular matrix nanofiber, effectively promotes stem cell differentiation and angiogenesis. This study evaluated its safety in a mouse model, monitoring weight, immunogenicity, spleen immune responses, and macrophage infiltration. Rapid clearance of YSY184 was observed, with peak tissue presence within three hours, significantly reduced by 24 hours, and negligible after one month. No adverse physiological or pathological effects were detected post-administration, affirming YSY184's safety and underscore its potential for therapeutic use, warranting further clinical exploration.

In silico evaluation of cell therapy in acute versus chronic infarction: role of automaticity, heterogeneity and Purkinje in human

Human-based modelling and simulation offer an ideal testbed for novel medical therapies to guide experimental and clinical studies. Myocardial infarction (MI) is a common cause of heart failure and mortality, for which novel therapies are urgently needed. Although cell therapy offers promise, electrophysiological heterogeneity raises pro-arrhythmic safety concerns, where underlying complex spatio-temporal dynamics cannot be investigated experimentally. Here, after demonstrating credibility of the modelling and simulation framework, we investigate cell therapy in acute versus chronic MI and the role of cell heterogeneity, scar size and the Purkinje system. Simulations agreed with experimental and clinical recordings from ionic to ECG dynamics in acute and chronic infarction. Following cell delivery, spontaneous beats were facilitated by heterogeneity in cell populations, chronic MI due to tissue depolarisation and slow sinus rhythm. Subsequent re-entrant arrhythmias occurred, in some instances with Purkinje involvement and their susceptibility was enhanced by impaired Purkinje-myocardium coupling, large scars and acute infarction. We conclude that homogeneity in injected ventricular-like cell populations minimises their spontaneous beating, which is enhanced by chronic MI, whereas a healthy Purkinje-myocardium coupling is key to prevent subsequent re-entrant arrhythmias, particularly for large scars.

Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation

The complexity of cardiac electrophysiology, involving dynamic changes in numerous components across multiple spatial (from ion channel to organ) and temporal (from milliseconds to days) scales, makes an intuitive or empirical analysis of cardiac arrhythmogenesis challenging. Multiscale mechanistic computational models of cardiac electrophysiology provide precise control over individual parameters, and their reproducibility enables a thorough assessment of arrhythmia mechanisms. This review provides a comprehensive analysis of models of cardiac electrophysiology and arrhythmias, from the single cell to the organ level, and how they can be leveraged to better understand rhythm disorders in cardiac disease and to improve heart patient care. Key issues related to model development based on experimental data are discussed, and major families of human cardiomyocyte models and their applications are highlighted. An overview of organ-level computational modeling of cardiac electrophysiology and its clinical applications in personalized arrhythmia risk assessment and patient-specific therapy of atrial and ventricular arrhythmias is provided. The advancements presented here highlight how patient-specific computational models of the heart reconstructed from patient data have achieved success in predicting risk of sudden cardiac death and guiding optimal treatments of heart rhythm disorders. Finally, an outlook toward potential future advances, including the combination of mechanistic modeling and machine learning/artificial intelligence, is provided. As the field of cardiology is embarking on a journey toward precision medicine, personalized modeling of the heart is expected to become a key technology to guide pharmaceutical therapy, deployment of devices, and surgical interventions.