亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

Cardiovascular disease models: A game changing paradigm in drug discovery and screening

心脏毒性 药物发现 药品 药物开发 医学 疾病 生物信息学 不利影响 重症监护医学 药理学 临床试验 体内 生物信息学 生物 内科学 毒性 生物技术 基因 生物化学
作者
Houman Savoji,Mohammad Hossein Mohammadi,Naimeh Rafatian,Masood Khaksar Toroghi,Erika Yan Wang,Yimu Zhao,Anastasia Korolj,Samad Ahadian,Milica Radisic
出处
期刊:Biomaterials [Elsevier BV]
卷期号:198: 3-26 被引量:200
标识
DOI:10.1016/j.biomaterials.2018.09.036
摘要

Cardiovascular disease is the leading cause of death worldwide. Although investment in drug discovery and development has been sky-rocketing, the number of approved drugs has been declining. Cardiovascular toxicity due to therapeutic drug use claims the highest incidence and severity of adverse drug reactions in late-stage clinical development. Therefore, to address this issue, new, additional, replacement and combinatorial approaches are needed to fill the gap in effective drug discovery and screening. The motivation for developing accurate, predictive models is twofold: first, to study and discover new treatments for cardiac pathologies which are leading in worldwide morbidity and mortality rates; and second, to screen for adverse drug reactions on the heart, a primary risk in drug development. In addition to in vivo animal models, in vitro and in silico models have been recently proposed to mimic the physiological conditions of heart and vasculature. Here, we describe current in vitro, in vivo, and in silico platforms for modelling healthy and pathological cardiac tissues and their advantages and disadvantages for drug screening and discovery applications. We review the pathophysiology and the underlying pathways of different cardiac diseases, as well as the new tools being developed to facilitate their study. We finally suggest a roadmap for employing these non-animal platforms in assessing drug cardiotoxicity and safety.
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
星悦完成签到,获得积分10
1秒前
理li发布了新的文献求助10
3秒前
3秒前
Copyright应助科研通管家采纳,获得10
6秒前
小马甲应助科研通管家采纳,获得10
7秒前
7秒前
浩然山河完成签到,获得积分10
9秒前
10秒前
喜悦的虔发布了新的文献求助10
17秒前
20秒前
21秒前
猛gan论文发布了新的文献求助10
22秒前
23秒前
23秒前
23秒前
24秒前
24秒前
25秒前
26秒前
jianzi927发布了新的文献求助10
27秒前
27秒前
27秒前
27秒前
jianzi927发布了新的文献求助10
27秒前
jianzi927发布了新的文献求助10
27秒前
27秒前
27秒前
28秒前
28秒前
28秒前
28秒前
29秒前
jianzi927发布了新的文献求助10
29秒前
29秒前
jianzi927发布了新的文献求助10
29秒前
jianzi927发布了新的文献求助10
29秒前
jianzi927发布了新的文献求助10
30秒前
30秒前
jianzi927发布了新的文献求助100
30秒前
jianzi927发布了新的文献求助10
31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
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
CLSI M07 2024 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 7247055
求助须知:如何正确求助?哪些是违规求助? 8870497
关于积分的说明 18711815
捐赠科研通 6924623
什么是DOI,文献DOI怎么找? 3197845
关于科研通互助平台的介绍 2373149
邀请新用户注册赠送积分活动 2172723