Heart-on-a-chip platforms and biosensor integration for disease modeling and phenotypic drug screening

精密医学 药物开发 诱导多能干细胞 临床试验 疾病 医学 计算机科学 杠杆(统计) 药物发现 药品 风险分析(工程) 计算生物学 生物信息学 药理学 生物 病理 人工智能 胚胎干细胞 基因 生物化学
作者
Joseph Criscione,Zahra Rezaei,Carol M. Hernandez Cantu,Sean Murphy,Su Ryon Shin,Deok‐Ho Kim
出处
期刊:Biosensors and Bioelectronics [Elsevier BV]
卷期号:220: 114840-114840 被引量:53
标识
DOI:10.1016/j.bios.2022.114840
摘要

Heart disease is the leading cause of death worldwide and imposes a significant burden on healthcare systems globally. A major hurdle to the development of more effective therapeutics is the reliance on animal models that fail to faithfully recapitulate human pathophysiology. The predictivity of in vitro models that lack the complexity of in vivo tissue remain poor as well. To combat these issues, researchers are developing organ-on-a-chip models of the heart that leverage the use of human induced pluripotent stem cell-derived cardiomyocytes in combination with novel platforms engineered to better recapitulate tissue- and organ-level physiology. The integration of novel biosensors into these platforms is also a critical step in the development of these models, as they allow for increased throughput, real-time and longitudinal phenotypic assessment, and improved efficiency during preclinical disease modeling and drug screening studies. These platforms hold great promise for both improving our understanding of heart disease as well as for screening potential therapeutics based on clinically relevant endpoints with better predictivity of clinical outcomes. In this review, we describe state-of-the-art heart-on-a-chip platforms, the integration of novel biosensors into these models for real-time and continual monitoring of tissue-level physiology, as well as their use for modeling heart disease and drug screening applications. We also discuss future perspectives and further advances required to enable clinical trials-on-a-chip and next-generation precision medicine platforms.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
黄丽发布了新的文献求助50
刚刚
小小康康完成签到,获得积分10
2秒前
3秒前
3秒前
细腻老太发布了新的文献求助10
4秒前
6秒前
6秒前
赘婿应助执笔客采纳,获得10
6秒前
李健应助111采纳,获得10
7秒前
7秒前
jjyuan完成签到 ,获得积分20
8秒前
顾矜应助自然芙采纳,获得10
9秒前
9秒前
唐唐完成签到,获得积分10
10秒前
10秒前
10秒前
11秒前
onion发布了新的文献求助10
11秒前
共享精神应助笨笨雨灵采纳,获得25
11秒前
12秒前
12秒前
13秒前
SCI的李发布了新的文献求助10
13秒前
14秒前
Maria发布了新的文献求助10
15秒前
谨慎映冬发布了新的文献求助10
15秒前
梅代匕花发布了新的文献求助10
16秒前
如意的醉蓝发布了新的文献求助100
16秒前
科研通AI6.3应助釉小皮采纳,获得10
17秒前
7_发布了新的文献求助10
17秒前
17秒前
18秒前
20秒前
风清扬发布了新的文献求助10
20秒前
科目三应助谨慎映冬采纳,获得10
21秒前
AlinaLee完成签到,获得积分10
21秒前
NguyenPhuong18完成签到,获得积分10
23秒前
思源应助sunhang526采纳,获得10
23秒前
lindabrly完成签到,获得积分10
24秒前
myl666发布了新的文献求助10
24秒前
高分求助中
Principles of Economics, 11th Edition 10000
University Physics with Modern Physics, 16th edition 10000
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Environmental Leverage in Times of Climate Crisis: Product Standards, Carbon Border Measures and Preferential Trade Agreements 1000
Matrix Methods in Data Mining and Pattern Recognition 510
Social Skills Improvement System-Rating Scales--Chinese Version 500
Dynamische Polarisation von H-1 und B-11 in (CH-3)-3NBH-3 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7221256
求助须知:如何正确求助?哪些是违规求助? 8850938
关于积分的说明 18677404
捐赠科研通 6879429
什么是DOI,文献DOI怎么找? 3186971
关于科研通互助平台的介绍 2350858
邀请新用户注册赠送积分活动 2161174