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Satsuka A, Ribeiro AJS, Kawagishi H, Yanagida S, Hirata N, Yoshinaga T, Kurokawa J, Sugiyama A, Strauss DG, Kanda Y. Contractility assessment using aligned human iPSC-derived cardiomyocytes. J Pharmacol Toxicol Methods 2024; 128:107530. [PMID: 38917571 DOI: 10.1016/j.vascn.2024.107530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 05/17/2024] [Accepted: 06/05/2024] [Indexed: 06/27/2024]
Abstract
INTRODUCTION Cardiac safety assessment, such as lethal arrhythmias and contractility dysfunction, is critical during drug development. Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) have been shown to be useful in predicting drug-induced proarrhythmic risk through international validation studies. Although cardiac contractility is another key function, fit-for-purpose hiPSC-CMs in evaluating drug-induced contractile dysfunction remain poorly understood. In this study, we investigated whether alignment of hiPSC-CMs on nanopatterned culture plates can assess drug-induced contractile changes more efficiently than non-aligned monolayer culture. METHODS Aligned hiPSC-CMs were obtained by culturing on 96-well culture plates with a ridge-groove-ridge nanopattern on the bottom surface, while non-aligned hiPSC-CMs were cultured on regular 96-well plates. Next-generation sequencing and qPCR experiments were performed for gene expression analysis. Contractility of the hiPSC-CMs was assessed using an imaging-based motion analysis system. RESULTS When cultured on nanopatterned plates, hiPSC-CMs exhibited an aligned morphology and enhanced expression of genes encoding proteins that regulate contractility, including myosin heavy chain, calcium channel, and ryanodine receptor. Compared to cultures on regular plates, the aligned hiPSC-CMs also showed both enhanced contraction and relaxation velocity. In addition, the aligned hiPSC-CMs showed a more physiological response to positive and negative inotropic agents, such as isoproterenol and verapamil. DISCUSSION Taken together, the aligned hiPSC-CMs exhibited enhanced structural and functional properties, leading to an improved capacity for contractility assessment compared to the non-aligned cells. These findings suggest that the aligned hiPSC-CMs can be used to evaluate drug-induced cardiac contractile changes.
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Affiliation(s)
- Ayano Satsuka
- Division of Pharmacology, National Institute of Health Sciences, 3-25-26 Tonomachi, Kawasaki-ku, Kawasaki-shi, Kanagawa 210-9501, Japan
| | - Alexandre J S Ribeiro
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, Silver Spring, MD 20903, USA
| | - Hiroyuki Kawagishi
- Division of Pharmacology, National Institute of Health Sciences, 3-25-26 Tonomachi, Kawasaki-ku, Kawasaki-shi, Kanagawa 210-9501, Japan
| | - Shota Yanagida
- Division of Pharmacology, National Institute of Health Sciences, 3-25-26 Tonomachi, Kawasaki-ku, Kawasaki-shi, Kanagawa 210-9501, Japan
| | - Naoya Hirata
- Division of Pharmacology, National Institute of Health Sciences, 3-25-26 Tonomachi, Kawasaki-ku, Kawasaki-shi, Kanagawa 210-9501, Japan
| | - Takashi Yoshinaga
- Advanced Biosignal Safety Assessment, Eisai Co., Ltd, 5-1-3 Tokodai, Tsukuba, Ibaraki 300-2635, Japan
| | - Junko Kurokawa
- Department of Bio-Informational Pharmacology, School of Pharmaceutical Sciences, University of Shizuoka, 52-1 Yada, Suruga-ku, Shizuoka-shi, Shizuoka 422-8526, Japan
| | - Atsushi Sugiyama
- Department of Pharmacology, Faculty of Medicine, Toho University, 5-21-16 Omori-nishi, Ota-ku, Tokyo 143-8540, Japan
| | - David G Strauss
- Division of Applied Regulatory Science, Office of Translational Science, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD 20903, USA
| | - Yasunari Kanda
- Division of Pharmacology, National Institute of Health Sciences, 3-25-26 Tonomachi, Kawasaki-ku, Kawasaki-shi, Kanagawa 210-9501, Japan.
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Au Yeung VPW, Obrezanova O, Zhou J, Yang H, Bowen TJ, Ivanov D, Saffadi I, Carter AS, Subramanian V, Dillmann I, Hall A, Corrigan A, Viant MR, Pointon A. Computational approaches identify a transcriptomic fingerprint of drug-induced structural cardiotoxicity. Cell Biol Toxicol 2024; 40:50. [PMID: 38940987 PMCID: PMC11213733 DOI: 10.1007/s10565-024-09880-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 05/15/2024] [Indexed: 06/29/2024]
Abstract
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.
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Affiliation(s)
- Victoria P W Au Yeung
- Safety Sciences, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Cambridge, UK.
- Phenomics, Data Sciences & Quantitative Biology, R&D AstraZeneca, Cambridge, UK.
| | - Olga Obrezanova
- Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Jiarui Zhou
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, UK
| | - Hongbin Yang
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Tara J Bowen
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, UK
| | - Delyan Ivanov
- High-Throughput Screening, R&D, AstraZeneca, Alderley Park, UK
| | - Izzy Saffadi
- Safety Sciences, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Alfie S Carter
- Safety Sciences, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Vigneshwari Subramanian
- Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Inken Dillmann
- Disease Molecular Profiling, Discovery Biology, R&D AstraZeneca, Gothenburg, Sweden
| | - Andrew Hall
- Safety Sciences, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Adam Corrigan
- Phenomics, Data Sciences & Quantitative Biology, R&D AstraZeneca, Cambridge, UK
| | - Mark R Viant
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, UK
- Phenome Centre Birmingham, University of Birmingham, Edgbaston, Birmingham, UK
| | - Amy Pointon
- Safety Sciences, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Cambridge, UK
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Vo QD, Saito Y, Ida T, Nakamura K, Yuasa S. The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review. PLoS One 2024; 19:e0302537. [PMID: 38771829 PMCID: PMC11108174 DOI: 10.1371/journal.pone.0302537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 04/09/2024] [Indexed: 05/23/2024] Open
Abstract
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.
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Affiliation(s)
- Quan Duy Vo
- Faculty of Medicine, Department of Cardiovascular Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan
- Faculty of Medicine, Nguyen Tat Thanh University, Ho Chi Minh City, Viet Nam
| | - Yukihiro Saito
- Department of Cardiovascular Medicine, Okayama University Hospital, Okayama, Japan
| | - Toshihiro Ida
- Faculty of Medicine, Department of Cardiovascular Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan
| | - Kazufumi Nakamura
- Faculty of Medicine, Department of Cardiovascular Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan
| | - Shinsuke Yuasa
- Faculty of Medicine, Department of Cardiovascular Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan
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Raniga K, Nasir A, Vo NTN, Vaidyanathan R, Dickerson S, Hilcove S, Mosqueira D, Mirams GR, Clements P, Hicks R, Pointon A, Stebbeds W, Francis J, Denning C. Strengthening cardiac therapy pipelines using human pluripotent stem cell-derived cardiomyocytes. Cell Stem Cell 2024; 31:292-311. [PMID: 38366587 DOI: 10.1016/j.stem.2024.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 11/27/2023] [Accepted: 01/19/2024] [Indexed: 02/18/2024]
Abstract
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.
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Affiliation(s)
- Kavita Raniga
- The Biodiscovery Institute, University of Nottingham, Nottingham NG7 2RD, UK; Pathology, Non-Clinical Safety, GlaxoSmithKline R&D, Stevenage SG1 2NY, UK.
| | - Aishah Nasir
- The Biodiscovery Institute, University of Nottingham, Nottingham NG7 2RD, UK
| | - Nguyen T N Vo
- The Biodiscovery Institute, University of Nottingham, Nottingham NG7 2RD, UK
| | | | | | | | - Diogo Mosqueira
- The Biodiscovery Institute, University of Nottingham, Nottingham NG7 2RD, UK
| | - Gary R Mirams
- Centre for Mathematical Medicine & Biology, School of Mathematical Sciences, University of Nottingham, Nottingham NG7 2RD, UK
| | - Peter Clements
- Pathology, Non-Clinical Safety, GlaxoSmithKline R&D, Stevenage SG1 2NY, UK
| | - Ryan Hicks
- BioPharmaceuticals R&D Cell Therapy Department, Research and Early Development, Cardiovascular, Renal, and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden; School of Cardiovascular and Metabolic Medicine & Sciences, King's College London, London WC2R 2LS, UK
| | - Amy Pointon
- Safety Sciences, Clinical Pharmacology and Safety Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge CB2 0AA, UK
| | | | - Jo Francis
- Mechanstic Biology and Profiling, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge CB2 0AA, UK
| | - Chris Denning
- The Biodiscovery Institute, University of Nottingham, Nottingham NG7 2RD, UK.
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Application of Machine Learning for Cardiovascular Disease Risk Prediction. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2023. [DOI: 10.1155/2023/9418666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
Abstract
Cardiovascular diseases (CVDs) are a common cause of heart failure globally. The need to explore possible ways to tackle the disease necessitated this study. The study designed a machine learning model for cardiovascular disease risk prediction in accordance with a dataset that contains 11 features which may be used to forecast the disease. The dataset from Kaggle on cardiovascular disease includes approximately 70,000 patient records that were used to determine the outcome. Compared to the UCI dataset, the Kaggle dataset has many more training and validation records. Models created using neural networks, random forests, Bayesian networks, C5.0, and QUEST were compared for this dataset. On training and testing data sets, the results acquired a high accuracy (99.1 percent), which is significantly superior to previous methods. Ahead-of-time detection and diagnosis of cardiac disease, as well as better treatment outcomes, are strong possibilities for the suggested prediction model. Additionally, it may help patients better manage their illness or life forms in order to increase their chances of recovery/survival. The result showed greater accuracy and promising signs that machine-learning algorithms can indeed assist in early identification of the disease and improvement of the treatment outcome.
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Yang H, Obrezanova O, Pointon A, Stebbeds W, Francis J, Beattie KA, Clements P, Harvey JS, Smith GF, Bender A. Prediction of inotropic effect based on calcium transients in human iPSC-derived cardiomyocytes and machine learning. Toxicol Appl Pharmacol 2023; 459:116342. [PMID: 36502871 DOI: 10.1016/j.taap.2022.116342] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 11/23/2022] [Accepted: 12/05/2022] [Indexed: 12/13/2022]
Abstract
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.
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Affiliation(s)
- Hongbin Yang
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, UK
| | - Olga Obrezanova
- Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Amy Pointon
- Functional and Mechanistic Safety, Clinical Pharmacology & Safety Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Will Stebbeds
- Screening Profiling and Mechanistic Biology, Medicinal Science and Technology, GlaxoSmithKline, Stevenage, UK
| | - Jo Francis
- Mechanistic & Structural Biology, AstraZeneca, Cambridge, UK
| | - Kylie A Beattie
- Target and Systems Safety, Non-Clinical Safety, In Vivo/In Vitro Translation, GlaxoSmithKline, Ware, UK
| | - Peter Clements
- Pathology UK, Non-Clinical Safety, In Vivo/In Vitro Translation, GlaxoSmithKline, Ware, UK
| | - James S Harvey
- Target and Systems Safety, Non-Clinical Safety, In Vivo/In Vitro Translation, GlaxoSmithKline, Ware, UK
| | - Graham F Smith
- Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, UK.
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