A deep learning platform to assess drug proarrhythmia risk

Human induced pluripotent stem cell-cardiomyocytes for cardiotoxicity assessment: a comparative study of arrhythmiainducing drugs with multi-electrode array analysis

Reliable preclinical models for assessing drug-induced cardiotoxicity are essential to reduce the high rate of drug withdrawals during development. Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) have emerged as a promising platform for such assessments due to their expression of cardiacspecific ion channels and electrophysiological properties. In this study, we investigated the effects of eight arrhythmogenic drugs-E4031, nifedipine, mexiletine, JNJ303, flecainide, moxifloxacin, quinidine, and ranolazine-on hiPSC-CMs derived from both healthy individuals and a long QT syndrome (LQTS) patient using multielectrode array systems. The results demonstrated dose-dependent changes in field potential duration and arrhythmogenic risk, with LQTS-derived hiPSC-CMs showing increased sensitivity to hERG channel blockers such as E4031. Furthermore, the study highlights the potential of hiPSC-CMs to model disease-specific cardiac responses, providing insights into genetic predispositions and personalized drug responses. Despite challenges related to the immaturity of hiPSC-CMs, their ability to recapitulate human cardiac electrophysiology makes them a valuable tool for preclinical cardiotoxicity assessments. This study underscores the utility of integrating patientderived hiPSC-CMs with advanced analytical platforms, such as multi-electrode array systems, to evaluate drug-induced electrophysiological changes. These findings reinforce the role of hiPSC-CMs in drug development, facilitating safer and more efficient screening methods while supporting precision medicine applications.

Promising tools for future drug discovery and development in antiarrhythmic therapy

Arrhythmia refers to irregularities in the rate and rhythm of the heart, with symptoms spanning from mild palpitations to life-threatening arrhythmias and sudden cardiac death. The complex molecular nature of arrhythmias complicates the selection of appropriate treatment. Current therapies involve the use of antiarrhythmic drugs (class I-IV) with limited efficacy and dangerous side effects and implantable pacemakers and cardioverter-defibrillators with hardware-related complications and inappropriate shocks. The number of novel antiarrhythmic drugs in the development pipeline has decreased substantially during the last decade and underscores uncertainties regarding future developments in this field. Consequently, arrhythmia treatment poses significant challenges, prompting the need for alternative approaches. Remarkably, innovative drug discovery and development technologies show promise in helping advance antiarrhythmic therapies. In this article, we review unique characteristics and the transformative potential of emerging technologies that offer unprecedented opportunities for transitioning from traditional antiarrhythmics to next-generation therapies. We assess stem cell technology, emphasizing the utility of innovative cell profiling using multiomics, high-throughput screening, and advanced computational modeling in developing treatments tailored precisely to individual genetic and physiological profiles. We offer insights into gene therapy, peptide, and peptibody approaches for drug delivery. We finally discuss potential strengths and weaknesses of such techniques in reducing adverse effects and enhancing overall treatment outcomes, leading to more effective, specific, and safer therapies. Altogether, this comprehensive overview introduces innovative avenues for personalized rhythm therapy, with particular emphasis on drug discovery, aiming to advance the arrhythmia treatment landscape and the prevention of sudden cardiac death. SIGNIFICANCE STATEMENT: Arrhythmias and sudden cardiac death account for 15%-20% of deaths worldwide. However, current antiarrhythmic therapies are ineffective and have dangerous side effects. Here, we review the field of arrhythmia treatment underscoring the slow progress in advancing the cardiac rhythm therapy pipeline and the uncertainties regarding evolution of this field. We provide information on how emerging technological and experimental tools can help accelerate progress and address the limitations of antiarrhythmic drug discovery.

[Drug discovery using iPS cells and in silico model]

Human induced pluripotent stem cells derived cardiomyocytes (hiPSC-CMs) can recapitulate the properties of human cardiomyocyte and exhibit disease phenotypes in vitro, attributable to their healthy- or patient-specific genetic backgrounds. Therefore, hiPSC-CMs are a crucial tool for developing therapeutic agents for cardiovascular diseases, and regenerative medicine using hiPSC-CMs is expected to be an alternative therapy to heart transplantation. Moreover, the development of organoid models has been advanced to replicate the complex structure of heart tissue in vitro, thereby effectively facilitating drug discovery. On the other hand, current methods for advancing drug discovery using hiPSC-CMs face limitations, including the difficulty of quantifying characteristics such as cell structure and predicting the risk and efficacy of candidate drug in clinical practice. In the field of regenerative medicine, challenges include quality control and the verification of safety of transplanted cells in human. In silico model, including artificial intelligence (AI) and simulation, have been developed in the field of drug discovery using hiPSC-CMs. These advancements encompass phenotype scoring via AI and risk prediction through simulations. This review outlines the current status and challenges of drug discovery using hiPSC-CMs and in silico model, based on the published reports.

Clinical trials in-a-dish for cardiovascular medicine

Cardiovascular diseases persist as a global health challenge that requires methodological innovation for effective drug development. Conventional pipelines relying on animal models suffer from high failure rates due to significant interspecies variation between humans and animal models. In response, the recently enacted Food and Drug Administration Modernization Act 2.0 encourages alternative approaches including induced pluripotent stem cells (iPSCs). Human iPSCs provide a patient-specific, precise, and screenable platform for drug testing, paving the way for cardiovascular precision medicine. This review discusses milestones in iPSC differentiation and their applications from disease modelling to drug discovery in cardiovascular medicine. It then explores challenges and emerging opportunities for the implementation of 'clinical trials in-a-dish'. Concluding, this review proposes a framework for future clinical trial design with strategic incorporations of iPSC technology, microphysiological systems, clinical pan-omics, and artificial intelligence to improve success rates and advance cardiovascular healthcare.

Tackling the challenges of new approach methods for predicting drug effects from model systems

The passage of the FDA Modernization Act 2.0 in 2022 has provided additional impetus to develop new approach methods for predicting the effects of drug candidates in humans from models such as microphysiological systems based on human-derived induced pluripotent stem cells. Here, we highlight progress in the field and strategies to address various challenges, including the application of artificial intelligence tools.

Using induced pluripotent stem cells for drug discovery in arrhythmias

INTRODUCTION

Arrhythmias are disturbances in the normal rhythm of the heart and account for significant cardiovascular morbidity and mortality worldwide. Historically, preclinical research has been anchored in animal models, though physiological differences between these models and humans have limited their clinical translation. The discovery of human induced pluripotent stem cells (iPSC) and subsequent differentiation into cardiomyocyte has led to the development of new in vitro models of arrhythmias with the hope of a new pathway for both exploration of pathogenic variants and novel therapeutic discovery.

AREAS COVERED

The authors describe the latest two-dimensional in vitro models of arrhythmias, several examples of the use of these models in drug development, and the role of gene editing when modeling diseases. They conclude by discussing the use of three-dimensional models in the study of arrythmias and the integration of computational technologies and machine learning with experimental technologies.

EXPERT OPINION

Human iPSC-derived cardiomyocytes models have significant potential to augment disease modeling, drug discovery, and toxicity studies in preclinical development. While there is initial success with modeling arrhythmias, the field is still in its nascency and requires advances in maturation, cellular diversity, and readouts to emulate arrhythmias more accurately.

Design optimization of geometrically confined cardiac organoids enabled by machine learning techniques

Stem cell organoids are powerful models for studying organ development, disease modeling, drug screening, and regenerative medicine applications. The convergence of organoid technology, tissue engineering, and artificial intelligence (AI) could potentially enhance our understanding of the design principles for organoid engineering. In this study, we utilized micropatterning techniques to create a designer library of 230 cardiac organoids with 7 geometric designs. We employed manifold learning techniques to analyze single organoid heterogeneity based on 10 physiological parameters. We clustered and refined the cardiac organoids based on their functional similarity using unsupervised machine learning approaches, thus elucidating unique functionalities associated with geometric designs. We also highlighted the critical role of calcium transient rising time in distinguishing organoids based on geometric patterns and clustering results. This integration of organoid engineering and machine learning enhances our understanding of structure-function relationships in cardiac organoids, paving the way for more controlled and optimized organoid design.

Application of artificial intelligence in the diagnosis and treatment of cardiac arrhythmia

The rapid growth in computational power, sensor technology, and wearable devices has provided a solid foundation for all aspects of cardiac arrhythmia care. Artificial intelligence (AI) has been instrumental in bringing about significant changes in the prevention, risk assessment, diagnosis, and treatment of arrhythmia. This review examines the current state of AI in the diagnosis and treatment of atrial fibrillation, supraventricular arrhythmia, ventricular arrhythmia, hereditary channelopathies, and cardiac pacing. Furthermore, ChatGPT, which has gained attention recently, is addressed in this paper along with its potential applications in the field of arrhythmia. Additionally, the accuracy of arrhythmia diagnosis can be improved by identifying electrode misplacement or erroneous swapping of electrode position using AI. Remote monitoring has expanded greatly due to the emergence of contactless monitoring technology as wearable devices continue to develop and flourish. Parallel advances in AI computing power, ChatGPT, availability of large data sets, and more have greatly expanded applications in arrhythmia diagnosis, risk assessment, and treatment. More precise algorithms based on big data, personalized risk assessment, telemedicine and mobile health, smart hardware and wearables, and the exploration of rare or complex types of arrhythmia are the future direction.

Strengthening cardiac therapy pipelines using human pluripotent stem cell-derived cardiomyocytes

Advances in hiPSC isolation and reprogramming and hPSC-CM differentiation have prompted their therapeutic application and utilization for evaluating potential cardiovascular safety liabilities. In this perspective, we showcase key efforts toward the large-scale production of hiPSC-CMs, implementation of hiPSC-CMs in industry settings, and recent clinical applications of this technology. The key observations are a need for traceable gender and ethnically diverse hiPSC lines, approaches to reduce cost of scale-up, accessible clinical trial datasets, and transparent guidelines surrounding the safety and efficacy of hiPSC-based therapies.

AI-organoid integrated systems for biomedical studies and applications

In this review, we explore the growing role of artificial intelligence (AI) in advancing the biomedical applications of human pluripotent stem cell (hPSC)-derived organoids. Stem cell-derived organoids, these miniature organ replicas, have become essential tools for disease modeling, drug discovery, and regenerative medicine. However, analyzing the vast and intricate datasets generated from these organoids can be inefficient and error-prone. AI techniques offer a promising solution to efficiently extract insights and make predictions from diverse data types generated from microscopy images, transcriptomics, metabolomics, and proteomics. This review offers a brief overview of organoid characterization and fundamental concepts in AI while focusing on a comprehensive exploration of AI applications in organoid-based disease modeling and drug evaluation. It provides insights into the future possibilities of AI in enhancing the quality control of organoid fabrication, label-free organoid recognition, and three-dimensional image reconstruction of complex organoid structures. This review presents the challenges and potential solutions in AI-organoid integration, focusing on the establishment of reliable AI model decision-making processes and the standardization of organoid research.

Understanding genomic medicine for thoracic aortic disease through the lens of induced pluripotent stem cells

Thoracic aortic disease (TAD) is often silent until a life-threatening complication occurs. However, genetic information can inform both identification and treatment at an early stage. Indeed, a diagnosis is important for personalised surveillance and intervention plans, as well as cascade screening of family members. Currently, only 20% of heritable TAD patients have a causative mutation identified and, consequently, further advances in genetic coverage are required to define the remaining molecular landscape. The rapid expansion of next generation sequencing technologies is providing a huge resource of genetic data, but a critical issue remains in functionally validating these findings. Induced pluripotent stem cells (iPSCs) are patient-derived, reprogrammed cell lines which allow mechanistic insights, complex modelling of genetic disease and a platform to study aortic genetic variants. This review will address the need for iPSCs as a frontline diagnostic tool to evaluate variants identified by genomic discovery studies and explore their evolving role in biological insight through to drug discovery.

Harnessing human genetics and stem cells for precision cardiovascular medicine

Human induced pluripotent stem cell (iPSC) platforms are valuable for biomedical and pharmaceutical research by providing tissue-specific human cells that retain patients' genetic integrity and display disease phenotypes in a dish. Looking forward, combining iPSC phenotyping platforms with genomic and screening technologies will continue to pave new directions for precision medicine, including genetic prediction, visualization, and treatment of heart disease. This review summarizes the recent use of iPSC technology to unpack the influence of genetic variants in cardiovascular pathology. We focus on various state-of-the-art genomic tools for cardiovascular therapies-including the expansion of genetic toolkits for molecular interrogation, in vitro population studies, and function-based drug screening-and their current applications in patient- and genome-edited iPSC platforms that are heralding new avenues for cardiovascular research.