已入深夜,您辛苦了!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!祝你早点完成任务,早点休息,好梦!

Do we really understand how drug eluted from stents modulates arterial healing?

紫杉醇 药品 支架 生物医学工程 药物开发 细胞生长 医学 药理学 外科 化学 内科学 癌症 生物化学
作者
Alistair McQueen,Javier Escuer,Ankush Aggarwal,Simon Kennedy,Christopher McCormick,Keith G. Oldroyd,Sean McGinty
出处
期刊:International Journal of Pharmaceutics [Elsevier]
卷期号:601: 120575-120575 被引量:8
标识
DOI:10.1016/j.ijpharm.2021.120575
摘要

The advent of drug-eluting stents (DES) has revolutionised the treatment of coronary artery disease. These devices, coated with anti-proliferative drugs, are deployed into stenosed or occluded vessels, compressing the plaque to restore natural blood flow, whilst simultaneously combating the evolution of restenotic tissue. Since the development of the first stent, extensive research has investigated how further advancements in stent technology can improve patient outcome. Mathematical and computational modelling has featured heavily, with models focussing on structural mechanics, computational fluid dynamics, drug elution kinetics and subsequent binding within the arterial wall; often considered separately. Smooth Muscle Cell (SMC) proliferation and neointimal growth are key features of the healing process following stent deployment. However, models which depict the action of drug on these processes are lacking. In this article, we start by reviewing current models of cell growth, which predominantly emanate from cancer research, and available published data on SMC proliferation, before presenting a series of mathematical models of varying complexity to detail the action of drug on SMC growth in vitro. Our results highlight that, at least for Sodium Salicylate and Paclitaxel, the current state-of-the-art nonlinear saturable binding model is incapable of capturing the proliferative response of SMCs across a range of drug doses and exposure times. Our findings potentially have important implications on the interpretation of current computational models and their future use to optimise and control drug release from DES and drug-coated balloons.
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
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

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
刚刚
刚刚
田様应助yushe采纳,获得10
2秒前
情怀应助谨慎蜗牛采纳,获得10
2秒前
lvzhechen发布了新的文献求助10
4秒前
4秒前
BoBo发布了新的文献求助10
4秒前
GR发布了新的文献求助10
6秒前
10秒前
霸气的依瑶完成签到,获得积分10
10秒前
青瓜大薯完成签到 ,获得积分10
11秒前
11秒前
12秒前
12秒前
13秒前
13秒前
Brook1985完成签到,获得积分10
15秒前
无聊人完成签到,获得积分10
15秒前
16秒前
16秒前
飞翔的梦发布了新的文献求助20
17秒前
17秒前
时间如水发布了新的文献求助10
19秒前
21发布了新的文献求助10
19秒前
20秒前
20秒前
daytoy完成签到,获得积分10
20秒前
wentong完成签到,获得积分10
21秒前
hongxuezhi发布了新的文献求助10
23秒前
追寻麦片完成签到 ,获得积分10
23秒前
24秒前
大个应助年轻的星月采纳,获得10
24秒前
赵浩杰完成签到,获得积分10
25秒前
酷波er应助小不溜采纳,获得10
27秒前
活力迎梦发布了新的文献求助10
28秒前
科研通AI6应助发文章12138采纳,获得10
31秒前
32秒前
风为裳完成签到,获得积分10
32秒前
年轻的星月完成签到,获得积分10
33秒前
打打应助无解采纳,获得10
34秒前
高分求助中
(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
List of 1,091 Public Pension Profiles by Region 1001
Latent Class and Latent Transition Analysis: With Applications in the Social, Behavioral, and Health Sciences 500
On the application of advanced modeling tools to the SLB analysis in NuScale. Part I: TRACE/PARCS, TRACE/PANTHER and ATHLET/DYN3D 500
L-Arginine Encapsulated Mesoporous MCM-41 Nanoparticles: A Study on In Vitro Release as Well as Kinetics 500
Washback Research in Language Assessment:Fundamentals and Contexts 400
Haematolymphoid Tumours (Part A and Part B, WHO Classification of Tumours, 5th Edition, Volume 11) 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 物理化学 基因 遗传学 催化作用 冶金 量子力学 光电子学
热门帖子
关注 科研通微信公众号,转发送积分 5469690
求助须知:如何正确求助?哪些是违规求助? 4572675
关于积分的说明 14336868
捐赠科研通 4499634
什么是DOI,文献DOI怎么找? 2465126
邀请新用户注册赠送积分活动 1453693
关于科研通互助平台的介绍 1428209