Kavita Raniga , William Stebbeds , Arun Shivalingam , Michelle Pemberton , Chris Denning
{"title":"设计用于高通量筛选的多细胞心脏模型自动开发实验","authors":"Kavita Raniga , William Stebbeds , Arun Shivalingam , Michelle Pemberton , Chris Denning","doi":"10.1016/j.slasd.2023.10.006","DOIUrl":null,"url":null,"abstract":"<div><p>Cardiovascular toxicity remains a major cause of drug attrition in early drug development, clinical trials, and post-market surveillance. <em>In vitro</em> assessment of cardiovascular liabilities often relies on single cell type-based model systems coupled with functional assays, like calcium flux and multielectrode arrays. Although these models offer high-throughput capabilities and demonstrate good predictivity for functional cardiotoxicities, they fail to consider the vital contribution of non-myocyte cells, thus limiting the potential for integrated risk assessment. Complex 3D hPSC-derived multicellular cardiac model systems have been growing in popularity; however, many of these models are limited to low-throughput with lengthy development timelines and high costs, which hampers their suitability to drug discovery.</p><p>To optimize the development of an <em>in vitro</em> multicellular model system containing human-induced pluripotent stem-cell derived cardiomyocytes, endothelial cells and cardiac fibroblasts, we employed the Synthace platform, which enables scientists to express complex experimental intent in a simple format (e.g. Design of Experiments) and to translate this to automation protocols using no-code. Utilizing this approach, we systematically screened the impact of multiple cell culture parameters, including the co-culture of three cell types, on cardiac contractility, with minimal hands-on time. Our platform accelerates the assay development process, providing users with an efficient means to explore and optimize the experimental space for the development of multicellular models. This is particularly valuable in scenarios involving variable biological responses and limited understanding of underling mechanisms. Moreover, users can make better use of resources, streamline their workflows, and drive data-driven decision-making throughout the assay development journey.</p></div>","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S247255522300076X/pdfft?md5=929999b07186c9c55f421bdaa5efed64&pid=1-s2.0-S247255522300076X-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Design of experiments for the automated development of a multicellular cardiac model for high-throughput screening\",\"authors\":\"Kavita Raniga , William Stebbeds , Arun Shivalingam , Michelle Pemberton , Chris Denning\",\"doi\":\"10.1016/j.slasd.2023.10.006\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Cardiovascular toxicity remains a major cause of drug attrition in early drug development, clinical trials, and post-market surveillance. <em>In vitro</em> assessment of cardiovascular liabilities often relies on single cell type-based model systems coupled with functional assays, like calcium flux and multielectrode arrays. Although these models offer high-throughput capabilities and demonstrate good predictivity for functional cardiotoxicities, they fail to consider the vital contribution of non-myocyte cells, thus limiting the potential for integrated risk assessment. Complex 3D hPSC-derived multicellular cardiac model systems have been growing in popularity; however, many of these models are limited to low-throughput with lengthy development timelines and high costs, which hampers their suitability to drug discovery.</p><p>To optimize the development of an <em>in vitro</em> multicellular model system containing human-induced pluripotent stem-cell derived cardiomyocytes, endothelial cells and cardiac fibroblasts, we employed the Synthace platform, which enables scientists to express complex experimental intent in a simple format (e.g. Design of Experiments) and to translate this to automation protocols using no-code. Utilizing this approach, we systematically screened the impact of multiple cell culture parameters, including the co-culture of three cell types, on cardiac contractility, with minimal hands-on time. Our platform accelerates the assay development process, providing users with an efficient means to explore and optimize the experimental space for the development of multicellular models. This is particularly valuable in scenarios involving variable biological responses and limited understanding of underling mechanisms. Moreover, users can make better use of resources, streamline their workflows, and drive data-driven decision-making throughout the assay development journey.</p></div>\",\"PeriodicalId\":2,\"journal\":{\"name\":\"ACS Applied Bio Materials\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2023-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S247255522300076X/pdfft?md5=929999b07186c9c55f421bdaa5efed64&pid=1-s2.0-S247255522300076X-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Bio Materials\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S247255522300076X\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, BIOMATERIALS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S247255522300076X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
Design of experiments for the automated development of a multicellular cardiac model for high-throughput screening
Cardiovascular toxicity remains a major cause of drug attrition in early drug development, clinical trials, and post-market surveillance. In vitro assessment of cardiovascular liabilities often relies on single cell type-based model systems coupled with functional assays, like calcium flux and multielectrode arrays. Although these models offer high-throughput capabilities and demonstrate good predictivity for functional cardiotoxicities, they fail to consider the vital contribution of non-myocyte cells, thus limiting the potential for integrated risk assessment. Complex 3D hPSC-derived multicellular cardiac model systems have been growing in popularity; however, many of these models are limited to low-throughput with lengthy development timelines and high costs, which hampers their suitability to drug discovery.
To optimize the development of an in vitro multicellular model system containing human-induced pluripotent stem-cell derived cardiomyocytes, endothelial cells and cardiac fibroblasts, we employed the Synthace platform, which enables scientists to express complex experimental intent in a simple format (e.g. Design of Experiments) and to translate this to automation protocols using no-code. Utilizing this approach, we systematically screened the impact of multiple cell culture parameters, including the co-culture of three cell types, on cardiac contractility, with minimal hands-on time. Our platform accelerates the assay development process, providing users with an efficient means to explore and optimize the experimental space for the development of multicellular models. This is particularly valuable in scenarios involving variable biological responses and limited understanding of underling mechanisms. Moreover, users can make better use of resources, streamline their workflows, and drive data-driven decision-making throughout the assay development journey.