Analysis of Drug Effects on iPSC Cardiomyocytes with Machine Learning

Applications of artificial intelligence in regenerative dentistry: promoting stem cell therapy and the scaffold development

Tissue repair represents a critical concern within the domain of dentistry. On a daily basis, countless individuals seek dental clinic services due to inadequate dental care. Many of the treatments that patients receive have unfavorable side effects. The employment of innovative methodologies, including gene therapy, tissue engineering, and stem cell (SCs) applications for regenerative purposes, has garnered significant interest over the past years. In recent times, artificial intelligence, particularly neural networks, has emerged as a topic of considerable attention among many medical professionals. Artificial intelligence possesses the capability to analyze data patterns through learning algorithms. Research opportunities in the rapidly expanding field of health sciences have been made possible by the use of artificial intelligence (AI) technologies. Though its uses are not restricted to these situations, artificial intelligence (AI) has the potential to improve and accelerate many aspects of regenerative medicine research and development, especially when working with complicated patterns. This review article is to investigate how artificial intelligence might be used to enhance regenerative processes in dentistry by using scaffolds and stem cells, in light of the continuous advances in artificial intelligence in the fields of medicine and tissue regeneration. It highlights the difficulties that still exist in this developing sector and explores the possible uses of AI with a particular emphasis on dentistry practices.

Effect of laser photobiomodulation combined with hydroxyapatite nanoparticles on the osteogenic differentiation of mesenchymal stem cells using artificial intelligence: An in vitro study

AIM

To evaluate in vitro the effect of laser photobiomodulation (PBM) combined or not with 30-nm hydroxyapatite nanoparticles (HANp), on the osteogenic differentiation of human umbilical cord mesenchymal stem cells (hUC-MSCs) by morphometric analysis using artificial intelligence programs (TensorFlow and ArcGIS).

METHODS

UC-MSCs were isolated and cultured until 80% confluence was reached. The cells were then plated according to the following experimental groups: G1 -control (DMEM), G2 -BMP-2, G3 -BMP-7, G4 -PBM (660 nm, 10 mW, 2.5 J/cm2, spot size of 0.08 cm2), G5 -HANp, G6 -HANp + PBM, G7 -BMP-2 + PBM, and G8 -BMP-7 + PBM. The MTT assay was used to analyze cell viability at 24, 48 and 72 h. Osteogenic differentiation was assessed by Alizarin Red staining after 7, 14 and 21 days. For morphometric analysis, areas of osteogenic differentiation (pixel2) were delimited by machine learning using the TensorFlow and ArcGIS 10.8 programs.

RESULTS

The results of the MTT assay showed high rates of cell viability and proliferation in all groups when compared to control. Morphometric analysis revealed a greater area of osteogenic differentiation in G5 (HANp = 142709,33±36573,39) and G6 (HANp + PBM = 125452,00±24226,95) at all time points evaluated.

CONCLUSION

It is suggested that HANp, whether combined with PBM or not, may be a promising alternative to enhance the cellular viability and osteogenic differentiation of hUC-MSCs.

Enhanced drug classification using machine learning with multiplexed cardiac contractility assays

Cardiac screening of newly discovered drugs remains a longstanding challenge for the pharmaceutical industry. While therapeutic efficacy and cardiotoxicity are evaluated through preclinical biochemical and animal testing, 90 % of lead compounds fail to meet safety and efficacy benchmarks during human clinical trials. A preclinical model more representative of the human cardiac response is needed; heart tissue engineered from human pluripotent stem cell derived cardiomyocytes offers such a platform. In this study, three functionally distinct and independently validated engineered cardiac tissue assays are exposed to increasing concentrations of known compounds representing 5 classes of mechanistic action, creating a robust electrophysiology and contractility dataset. Combining results from six individual models, the resulting ensemble algorithm can classify the mechanistic action of unknown compounds with 86.2 % predictive accuracy. This outperforms single-assay models and offers a strategy to enhance future clinical trial success aligned with the recent FDA Modernization Act 2.0.

Regenerative rehabilitation: a novel multidisciplinary field to maximize patient outcomes

Regenerative rehabilitation is a novel and rapidly developing multidisciplinary field that converges regenerative medicine and rehabilitation science, aiming to maximize the functions of disabled patients and their independence. While regenerative medicine provides state-of-the-art technologies that shed light on difficult-to-treated diseases, regenerative rehabilitation offers rehabilitation interventions to improve the positive effects of regenerative medicine. However, regenerative scientists and rehabilitation professionals focus on their aspects without enough exposure to advances in each other's field. This disconnect has impeded the development of this field. Therefore, this review first introduces cutting-edge technologies such as stem cell technology, tissue engineering, biomaterial science, gene editing, and computer sciences that promote the progress pace of regenerative medicine, followed by a summary of preclinical studies and examples of clinical investigations that integrate rehabilitative methodologies into regenerative medicine. Then, challenges in this field are discussed, and possible solutions are provided for future directions. We aim to provide a platform for regenerative and rehabilitative professionals and clinicians in other areas to better understand the progress of regenerative rehabilitation, thus contributing to the clinical translation and management of innovative and reliable therapies.

The Current State of Realistic Heart Models for Disease Modelling and Cardiotoxicity

One of the many unresolved obstacles in the field of cardiovascular research is an uncompromising in vitro cardiac model. While primary cell sources from animal models offer both advantages and disadvantages, efforts over the past half-century have aimed to reduce their use. Additionally, obtaining a sufficient quantity of human primary cardiomyocytes faces ethical and legal challenges. As the practically unlimited source of human cardiomyocytes from induced pluripotent stem cells (hiPSC-CM) is now mostly resolved, there are great efforts to improve their quality and applicability by overcoming their intrinsic limitations. The greatest bottleneck in the field is the in vitro ageing of hiPSC-CMs to reach a maturity status that closely resembles that of the adult heart, thereby allowing for more appropriate drug developmental procedures as there is a clear correlation between ageing and developing cardiovascular diseases. Here, we review the current state-of-the-art techniques in the most realistic heart models used in disease modelling and toxicity evaluations from hiPSC-CM maturation through heart-on-a-chip platforms and in silico models to the in vitro models of certain cardiovascular diseases.

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.

Integrated machine learning and multimodal data fusion for patho-phenotypic feature recognition in iPSC models of dilated cardiomyopathy

Integration of multiple data sources presents a challenge for accurate prediction of molecular patho-phenotypic features in automated analysis of data from human model systems. Here, we applied a machine learning-based data integration to distinguish patho-phenotypic features at the subcellular level for dilated cardiomyopathy (DCM). We employed a human induced pluripotent stem cell-derived cardiomyocyte (iPSC-CM) model of a DCM mutation in the sarcomere protein troponin T (TnT), TnT-R141W, compared to isogenic healthy (WT) control iPSC-CMs. We established a multimodal data fusion (MDF)-based analysis to integrate source datasets for Ca2+ transients, force measurements, and contractility recordings. Data were acquired for three additional layer types, single cells, cell monolayers, and 3D spheroid iPSC-CM models. For data analysis, numerical conversion as well as fusion of data from Ca2+ transients, force measurements, and contractility recordings, a non-negative blind deconvolution (NNBD)-based method was applied. Using an XGBoost algorithm, we found a high prediction accuracy for fused single cell, monolayer, and 3D spheroid iPSC-CM models (≥92 ± 0.08 %), as well as for fused Ca2+ transient, beating force, and contractility models (>96 ± 0.04 %). Integrating MDF and XGBoost provides a highly effective analysis tool for prediction of patho-phenotypic features in complex human disease models such as DCM iPSC-CMs.

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.

The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review

BACKGROUND

Stem cell research, particularly in the domain of induced pluripotent stem cell (iPSC) technology, has shown significant progress. The integration of artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), has played a pivotal role in refining iPSC classification, monitoring cell functionality, and conducting genetic analysis. These enhancements are broadening the applications of iPSC technology in disease modelling, drug screening, and regenerative medicine. This review aims to explore the role of AI in the advancement of iPSC research.

METHODS

In December 2023, data were collected from three electronic databases (PubMed, Web of Science, and Science Direct) to investigate the application of AI technology in iPSC processing.

RESULTS

This systematic scoping review encompassed 79 studies that met the inclusion criteria. The number of research studies in this area has increased over time, with the United States emerging as a leading contributor in this field. AI technologies have been diversely applied in iPSC technology, encompassing the classification of cell types, assessment of disease-specific phenotypes in iPSC-derived cells, and the facilitation of drug screening using iPSC. The precision of AI methodologies has improved significantly in recent years, creating a foundation for future advancements in iPSC-based technologies.

CONCLUSIONS

Our review offers insights into the role of AI in regenerative and personalized medicine, highlighting both challenges and opportunities. Although still in its early stages, AI technologies show significant promise in advancing our understanding of disease progression and development, paving the way for future clinical applications.

Role of Machine Learning and Artificial Intelligence in Arrhythmias and Electrophysiology

Machine learning (ML), a subset of artificial intelligence (AI) centered on machines learning from extensive datasets, stands at the forefront of a technological revolution shaping various facets of society. Cardiovascular medicine has emerged as a key domain for ML applications, with considerable efforts to integrate these innovations into routine clinical practice. Within cardiac electrophysiology, ML applications, especially in the automated interpretation of electrocardiograms, have garnered substantial attention in existing literature. However, less recognized are the diverse applications of ML in cardiac electrophysiology and arrhythmias, spanning basic science research on arrhythmia mechanisms, both experimental and computational, as well as contributions to enhanced techniques for mapping cardiac electrical function and translational research related to arrhythmia management. This comprehensive review delves into various ML applications within the scope of this journal, organized into 3 parts. The first section provides a fundamental understanding of general ML principles and methodologies, serving as a foundational resource for readers interested in exploring ML applications in arrhythmia research. The second part offers an in-depth review of studies in arrhythmia and electrophysiology that leverage ML methodologies, showcasing the broad potential of ML approaches. Each subject is thoroughly outlined, accompanied by a review of notable ML research advancements. Finally, the review delves into the primary challenges and future perspectives surrounding ML-driven cardiac electrophysiology and arrhythmias research.

Stem cell therapy for heart failure in the clinics: new perspectives in the era of precision medicine and artificial intelligence

Stem/progenitor cells have been widely evaluated as a promising therapeutic option for heart failure (HF). Numerous clinical trials with stem/progenitor cell-based therapy (SCT) for HF have demonstrated encouraging results, but not without limitations or discrepancies. Recent technological advancements in multiomics, bioinformatics, precision medicine, artificial intelligence (AI), and machine learning (ML) provide new approaches and insights for stem cell research and therapeutic development. Integration of these new technologies into stem/progenitor cell therapy for HF may help address: 1) the technical challenges to obtain reliable and high-quality therapeutic precursor cells, 2) the discrepancies between preclinical and clinical studies, and 3) the personalized selection of optimal therapeutic cell types/populations for individual patients in the context of precision medicine. This review summarizes the current status of SCT for HF in clinics and provides new perspectives on the development of computation-aided SCT in the era of precision medicine and AI/ML.

Prediction of inotropic effect based on calcium transients in human iPSC-derived cardiomyocytes and machine learning

Functional changes to cardiomyocytes are undesirable during drug discovery and identifying the inotropic effects of compounds is hence necessary to decrease the risk of cardiovascular adverse effects in the clinic. Recently, approaches leveraging calcium transients in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) have been developed to detect contractility changes, induced by a variety of mechanisms early during drug discovery projects. Although these approaches have been able to provide some predictive ability, we hypothesised that using additional waveform parameters could offer improved insights, as well as predictivity. In this study, we derived 25 parameters from each calcium transient waveform and developed a modified Random Forest method to predict the inotropic effects of the compounds. In total annotated data for 48 compounds were available for modelling, out of which 31 were inotropes. The results show that the Random Forest model with a modified purity criterion performed slightly better than an unmodified algorithm in terms of the Area Under the Curve, giving values of 0.84 vs 0.81 in a cross-validation, and outperformed the ToxCast Pipeline model, for which the highest value was 0.76 when using the best-performing parameter, PW10. Our study hence demonstrates that more advanced parameters derived from waveforms, in combination with additional machine learning methods, provide improved predictivity of cardiovascular risk associated with inotropic effects.

Emerging role of artificial intelligence in cardiac electrophysiology

Artificial intelligence (AI) and machine learning (ML) have significantly impacted the field of cardiovascular medicine, especially cardiac electrophysiology (EP), on multiple fronts. The goal of this review is to familiarize readers with the field of AI and ML and their emerging role in EP. The current review is divided into 3 sections. In the first section, we discuss the definitions and basics of AI, ML, and big data. In the second section, we discuss their application to EP in the context of detection, prediction, and management of arrhythmias. Finally, we discuss the regulatory issues, challenges, and future directions of AI in EP.

Drug Treatment Effect Model Based on MODWT and Hawkes Self-Exciting Point Process

In precision medicine, especially in the pharmacodynamic area, the lack of an adequate long-term drug effect monitoring model leads to a quite low robustness to the instant drug treatment. Modelling the effect of drug based on the monitoring variables is essential to measure the drug benefit and its side effect preciously. In order to model the complex drug behavior in the context of time series, a sin function is selected to describe the basic trend of heart rate variable that is medically monitored. A Hawkes self-exciting point process model is chosen to describe the effect caused by multiple and sequential drug usage at different time points. The model considers the time lag between the drug given time and the drug effect during the whole drug emission period. A cumulative Gamma distribution is employed to describe the time lag effect. Simulation results demonstrate the established model effectively when describing the baseline trend and the drug effect with low noise levels, where the maximal overlap discrete wavelet transformation is utilized for the information decomposition in the frequency zone. The real data of the variables heart rate and drug liquemin from a medical database is analyzed. Instead of the original time series, scale variable s4 is selected according to the Granger cointegration test. The results show that the model accurately characterizes the cumulative drug effect with the Pearson correlation test value as 0.22, which is more significant for the value under 0.1. In the future, the model can be extended to more complicated scenarios through taking into account multiple monitoring variables and different kinds of drugs.

Integrating nonlinear analysis and machine learning for human induced pluripotent stem cell-based drug cardiotoxicity testing

Utilizing recent advances in human induced pluripotent stem cell (hiPSC) technology, nonlinear analysis and machine learning we can create novel tools to evaluate drug-induced cardiotoxicity on human cardiomyocytes. With cardiovascular disease remaining the leading cause of death globally it has become imperative to create effective and modern tools to test the efficacy and toxicity of drugs to combat heart disease. The calcium transient signals recorded from hiPSC-derived cardiomyocytes (hiPSC-CMs) are highly complex and dynamic with great degrees of response characteristics to various drug treatments. However, traditional linear methods often fail to capture the subtle variation in these signals generated by hiPSC-CMs. In this work, we integrated nonlinear analysis, dimensionality reduction techniques and machine learning algorithms for better classifying the contractile signals from hiPSC-CMs in response to different drug exposure. By utilizing extracted parameters from a commercially available high-throughput testing platform, we were able to distinguish the groups with drug treatment from baseline controls, determine the drug exposure relative to IC50 values, and classify the drugs by its unique cardiac responses. By incorporating nonlinear parameters computed by phase space reconstruction, we were able to improve our machine learning algorithm's ability to predict cardiotoxic levels and drug classifications. We also visualized the effects of drug treatment and dosages with dimensionality reduction techniques, t-distributed stochastic neighbor embedding (t-SNE). We have shown that integration of nonlinear analysis and artificial intelligence has proven to be a powerful tool for analyzing cardiotoxicity and classifying toxic compounds through their mechanistic action.

Bioengineering Strategies to Create 3D Cardiac Constructs from Human Induced Pluripotent Stem Cells

Human induced pluripotent stem cells (hiPSCs) can be used to generate various cell types in the human body. Hence, hiPSC-derived cardiomyocytes (hiPSC-CMs) represent a significant cell source for disease modeling, drug testing, and regenerative medicine. The immaturity of hiPSC-CMs in two-dimensional (2D) culture limit their applications. Cardiac tissue engineering provides a new promise for both basic and clinical research. Advanced bioengineered cardiac in vitro models can create contractile structures that serve as exquisite in vitro heart microtissues for drug testing and disease modeling, thereby promoting the identification of better treatments for cardiovascular disorders. In this review, we will introduce recent advances of bioengineering technologies to produce in vitro cardiac tissues derived from hiPSCs.

Exploring the Current Trends of Artificial Intelligence in Stem Cell Therapy: A Systematic Review

The concept of healing in medicine has been taking a new form where scientists and researchers are in pursuance of regenerative medicine. Until now, doctors have "reacted" to disease by treating the symptoms; however, modern medicine is transforming toward regeneration rather than reactive treatment, which is where stem cell therapy comes into the play-the concept of replacing damaged cells with brand new cells that perform the same function better. Stem cell treatment is currently being used to treat autoimmune, inflammatory, neurological, orthopedic, and traumatic disorders, with various research being undertaken for a wide range of diseases. It could also be the answer to anti-aging and a disease-free state. Despite the benefits, numerous errors could prevail in treating patients with stem cells. With the advancement of technology and research in the modern period, medicine is beginning to turn to artificial intelligence (AI) to address the complicated errors that could occur in regenerative medicine. For successful treatment, one must achieve precision and accuracy when analyzing healthy and productive stem cells that possess all the properties of a native cell. This review intends to discuss and study the application of AI in stem cell therapy and how it influences how medicine is practiced, thus creating a path to a regenerative future with negligible adverse effects. The following databases were used for a literature search: PubMed, Google Scholar, PubMed Central, and Institute of Electrical and Electronics Engineers (IEEE) Xplore. After a thorough analysis, studies were chosen, keeping in mind the inclusion and exclusion criteria set by the authors of this review, which comprised reports published within the last six years in the English language. The authors also made sure to include studies that sufficed the quality of each report assessed using appropriate quality appraisal tools, after which eight reports were found to be eligible and were included in this review. This research mainly revolves around machine learning, deep neural networks (DNN), and other subclasses of AI encompassed in these categories. While there are concerns and limitations in implementing various mediums of AI in stem cell therapy, the analysis of the eligible studies concluded that artificial intelligence provides significant benefits to the global healthcare ecosystem in numerous ways, such as determining the viability, functionality, biosafety, and bioefficacy of stem cells, as well as appropriate patient selection. Applying AI to this novelty brings out the precision, accuracy, and a revolution in regenerative medicine. In addition, stem cell therapy is not currently FDA approved (except for the blood-forming stem cells) and, to date, is considered experimental with no clear outline of risks and benefits. Given this limitation, studies are conducted regularly around the world in hopes for a concrete conclusion where technological advances such as AI could help in shaping the future of regenerative medicine.

Moving Towards Induced Pluripotent Stem Cell-based Therapies with Artificial Intelligence and Machine Learning

The advent of induced pluripotent stem cell (iPSC) technology, which allows to transform one cell type into another, holds the promise to produce therapeutic cells and organs on demand. Realization of this objective is contingent on the ability to demonstrate quality and safety of the cellular product for its intended use. Bottlenecks and backlogs to the clinical use of iPSCs have been fully outlined and a need has emerged for safer and standardized protocols to trigger cell reprogramming and functional differentiation. Amidst great challenges, in particular associated with lengthy culture time and laborious cell characterization, a demand for faster and more accurate methods for the validation of cell identity and function at different stages of the iPSC manufacturing process has risen. Artificial intelligence-based methods are proving helpful for these complex tasks and might revolutionize the way iPSCs are managed to create surrogate cells and organs. Here, we briefly review recent progress in artificial intelligence approaches for evaluation of iPSCs and their derivatives in experimental studies.

Recent progress of iPSC technology in cardiac diseases

It has been nearly 15 years since the discovery of human-induced pluripotent stem cells (iPSCs). During this time, differentiation methods to targeted cells have dramatically improved, and many types of cells in the human body can be currently generated at high efficiency. In the cardiovascular field, the ability to generate human cardiomyocytes in vitro with the same genetic background as patients has provided a great opportunity to investigate human cardiovascular diseases at the cellular level to clarify the molecular mechanisms underlying the diseases and discover potential therapeutics. Additionally, iPSC-derived cardiomyocytes have provided a powerful platform to study drug-induced cardiotoxicity and identify patients at high risk for the cardiotoxicity; thus, accelerating personalized precision medicine. Moreover, iPSC-derived cardiomyocytes can be sources for cardiac cell therapy. Here, we review these achievements and discuss potential improvements for the future application of iPSC technology in cardiovascular diseases.

On computational classification of genetic cardiac diseases applying iPSC cardiomyocytes

BACKGROUND

Cardiomyocytes differentiated from human induced pluripotent stem cells (iPSC-CMs) can be used to study genetic cardiac diseases. In patients these diseases are manifested e.g. with impaired contractility and fatal cardiac arrhythmias, and both of these can be due to abnormal calcium transients in cardiomyocytes. Here we classify different genetic cardiac diseases using Ca²⁺ transient data and different machine learning algorithms.

METHODS

By studying calcium cycling of disease-specific iPSC-CMs and by using calcium transients measured from these cells it is possible to classify diseases from each other and also from healthy controls by applying machine learning computation on the basis of peak attributes detected from calcium transient signals.

RESULTS

In the current research we extend our previous study having Ca-transient data from four different genetic diseases by adding data from two additional diseases (dilated cardiomyopathy and long QT Syndrome 2). We also study, in the light of the current data, possible differences and relations when machine learning modelling and classification accuracies were computed by using either leave-one-out test or 10-fold cross-validation.

CONCLUSIONS

Despite more complex classification tasks compared to our earlier research and having more different genetic cardiac diseases in the analysis, it is still possible to attain good disease classification results. As excepted, leave-one-out test and 10-fold cross-validation achieved virtually equal results.

Recent trends in stem cell-based therapies and applications of artificial intelligence in regenerative medicine

Stem cells are undifferentiated cells that can self-renew and differentiate into diverse types of mature and functional cells while maintaining their original identity. This profound potential of stem cells has been thoroughly investigated for its significance in regenerative medicine and has laid the foundation for cell-based therapies. Regenerative medicine is rapidly progressing in healthcare with the prospect of repair and restoration of specific organs or tissue injuries or chronic disease conditions where the body’s regenerative process is not sufficient to heal. In this review, the recent advances in stem cell-based therapies in regenerative medicine are discussed, emphasizing mesenchymal stem cell-based therapies as these cells have been extensively studied for clinical use. Recent applications of artificial intelligence algorithms in stem cell-based therapies, their limitation, and future prospects are highlighted.

Machine Learning Techniques to Classify Healthy and Diseased Cardiomyocytes by Contractility Profile

Cardiomyocytes derived from human induced pluripotent stem (iPS) cells enable the study of cardiac physiology and the developmental testing of new therapeutic drugs in a human setting. In parallel, machine learning methods are being applied to biomedical science in unprecedented ways. Machine learning has been used to distinguish healthy from diseased cardiomyocytes using calcium (Ca²⁺) transient signals. Most Ca²⁺ transient signals are obtained via terminal assays that do not permit longitudinal studies, although some recently developed options can circumvent these concerns. Here, we describe the use of machine learning to identify healthy and diseased cardiomyocytes according to their contractility profiles, which are derived from brightfield videos. This noncontact, label-free approach allows for the continued cultivation of cells after they have been evaluated for use in other assays and can be readily extended to organs-on-chip. To demonstrate utility, we assessed contractility profiles of cardiomyocytes obtained from patients with Timothy Syndrome (TS), a long QT disease which can lead to fatal arrhythmias, and from healthy individuals. The videos were processed and classified using machine learning methods and their performance was evaluated according to several parameters. The trained algorithms were able to distinguish the TS cardiomyocytes from healthy controls and classify two different healthy controls. The proposed computational machine learning evaluation of human iPS cell-derived cardiomyocytes' contractility profiles has the potential to identify other genetic proarrhythmic events, screen therapeutic agents for inducing or suppressing long QT events, and predict drug-target interactions. The same approach could be readily extended to the evaluation of engineered cardiac tissues within single-tissue and multi-tissue organs-on-chip.

Efficacy of RyR2 inhibitor EL20 in induced pluripotent stem cell-derived cardiomyocytes from a patient with catecholaminergic polymorphic ventricular tachycardia

Catecholaminergic polymorphic ventricular tachycardia (CPVT) is an inherited cardiac arrhythmia syndrome that often leads to sudden cardiac death. The most common form of CPVT is caused by autosomal-dominant variants in the cardiac ryanodine receptor type-2 (RYR2) gene. Mutations in RYR2 promote calcium (Ca2+ ) leak from the sarcoplasmic reticulum (SR), triggering lethal arrhythmias. Recently, it was demonstrated that tetracaine derivative EL20 specifically inhibits mutant RyR2, normalizes Ca2+ handling and suppresses arrhythmias in a CPVT mouse model. The objective of this study was to determine whether EL20 normalizes SR Ca2+ handling and arrhythmic events in induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) from a CPVT patient. Blood samples from a child carrying RyR2 variant RyR2 variant Arg-176-Glu (R176Q) and a mutation-negative relative were reprogrammed into iPSCs using a Sendai virus system. iPSC-CMs were derived using the StemdiffTM kit. Confocal Ca2+ imaging was used to quantify RyR2 activity in the absence and presence of EL20. iPSC-CMs harbouring the R176Q variant demonstrated spontaneous SR Ca2+ release events, whereas administration of EL20 diminished these abnormal events at low nanomolar concentrations (IC50  = 82 nM). Importantly, treatment with EL20 did not have any adverse effects on systolic Ca2+ handling in control iPSC-CMs. Our results show for the first time that tetracaine derivative EL20 normalized SR Ca2+ handling and suppresses arrhythmogenic activity in iPSC-CMs derived from a CPVT patient. Hence, this study confirms that this RyR2-inhibitor represents a promising therapeutic candidate for treatment of CPVT.

A predictive in vitro risk assessment platform for pro-arrhythmic toxicity using human 3D cardiac microtissues

Cardiotoxicity of pharmaceutical drugs, industrial chemicals, and environmental toxicants can be severe, even life threatening, which necessitates a thorough evaluation of the human response to chemical compounds. Predicting risks for arrhythmia and sudden cardiac death accurately is critical for defining safety profiles. Currently available approaches have limitations including a focus on single select ion channels, the use of non-human species in vitro and in vivo, and limited direct physiological translation. We have advanced the robustness and reproducibility of in vitro platforms for assessing pro-arrhythmic cardiotoxicity using human induced pluripotent stem cell-derived cardiomyocytes and human cardiac fibroblasts in 3-dimensional microtissues. Using automated algorithms and statistical analyses of eight comprehensive evaluation metrics of cardiac action potentials, we demonstrate that tissue-engineered human cardiac microtissues respond appropriately to physiological stimuli and effectively differentiate between high-risk and low-risk compounds exhibiting blockade of the hERG channel (E4031 and ranolazine, respectively). Further, we show that the environmental endocrine disrupting chemical bisphenol-A (BPA) causes acute and sensitive disruption of human action potentials in the nanomolar range. Thus, this novel human 3D in vitro pro-arrhythmic risk assessment platform addresses critical needs in cardiotoxicity testing for both environmental and pharmaceutical compounds and can be leveraged to establish safe human exposure levels.

Clinical Trial in a Dish: Using Patient-Derived Induced Pluripotent Stem Cells to Identify Risks of Drug-Induced Cardiotoxicity

Drug-induced cardiotoxicity is a significant clinical issue, with many drugs in the market being labeled with warnings on cardiovascular adverse effects. Treatments are often prematurely halted when cardiotoxicity is observed, which limits their therapeutic potential. Moreover, cardiotoxicity is a major reason for abandonment during drug development, reducing available treatment options for diseases and creating a significant financial burden and disincentive for drug developers. Thus, it is important to minimize the cardiotoxic effects of medications that are in use or in development. To this end, identifying patients at a higher risk of developing cardiovascular adverse effects for the drug of interest may be an effective strategy. The discovery of human induced pluripotent stem cells has enabled researchers to generate relevant cell types that retain a patient's own genome and examine patient-specific disease mechanisms, paving the way for precision medicine. Combined with the rapid development of pharmacogenomic analysis, the ability of induced pluripotent stem cell-derivatives to recapitulate patient-specific drug responses provides a powerful platform to identify subsets of patients who are particularly vulnerable to drug-induced cardiotoxicity. In this review, we will discuss the current use of patient-specific induced pluripotent stem cells in identifying populations who are at risk to drug-induced cardiotoxicity and their potential applications in future precision medicine practice. Graphic Abstract: A graphic abstract is available for this article.

Machine Learning in Arrhythmia and Electrophysiology

Machine learning (ML), a branch of artificial intelligence, where machines learn from big data, is at the crest of a technological wave of change sweeping society. Cardiovascular medicine is at the forefront of many ML applications, and there is a significant effort to bring them into mainstream clinical practice. In the field of cardiac electrophysiology, ML applications have also seen a rapid growth and popularity, particularly the use of ML in the automatic interpretation of ECGs, which has been extensively covered in the literature. Much lesser known are the other aspects of ML application in cardiac electrophysiology and arrhythmias, such as those in basic science research on arrhythmia mechanisms, both experimental and computational; in the development of better techniques for mapping of cardiac electrical function; and in translational research related to arrhythmia management. In the current review, we examine comprehensively such ML applications as they match the scope of this journal. The current review is organized in 3 parts. The first provides an overview of general ML principles and methodologies that will afford readers of the necessary information on the subject, serving as the foundation for inviting further ML applications in arrhythmia research. The basic information we provide can serve as a guide on how one might design and conduct an ML study. The second part is a review of arrhythmia and electrophysiology studies in which ML has been utilized, highlighting the broad potential of ML approaches. For each subject, we outline comprehensively the general topics, while reviewing some of the research advances utilizing ML under the subject. Finally, we discuss the main challenges and the perspectives for ML-driven cardiac electrophysiology and arrhythmia research.