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.

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
哈利波特大完成签到,获得积分10
刚刚
刚刚
蓝天发布了新的文献求助10
1秒前
33发布了新的文献求助10
1秒前
2秒前
guoym发布了新的文献求助10
2秒前
椰汁驳回了英姑应助
2秒前
2秒前
Lawenced完成签到,获得积分10
3秒前
3秒前
321完成签到,获得积分10
4秒前
4秒前
tong发布了新的文献求助30
4秒前
5秒前
Ruadong完成签到,获得积分10
5秒前
6秒前
6秒前
6秒前
Stone发布了新的文献求助10
6秒前
852应助33采纳,获得10
6秒前
Nobody完成签到,获得积分10
7秒前
DreamSeker8发布了新的文献求助10
8秒前
8秒前
8秒前
Owen_Hu_11完成签到,获得积分10
9秒前
小兵大大怪完成签到,获得积分10
9秒前
苦苦发布了新的文献求助10
9秒前
MANI完成签到,获得积分20
9秒前
Ruadong发布了新的文献求助10
10秒前
10秒前
爆米花应助盛夏采纳,获得10
10秒前
mmmmm发布了新的文献求助10
11秒前
Jerrie完成签到,获得积分10
11秒前
echo1993完成签到 ,获得积分10
11秒前
xue发布了新的文献求助10
11秒前
勤恳的曼凡完成签到 ,获得积分10
11秒前
11秒前
Hello应助Auoror采纳,获得10
11秒前
爱学习完成签到 ,获得积分10
11秒前
11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
Basic And Clinical Science Course 2025-2026 3000
Encyclopedia of Agriculture and Food Systems Third Edition 2000
人脑智能与人工智能 1000
花の香りの秘密―遺伝子情報から機能性まで 800
Principles of Plasma Discharges and Materials Processing, 3rd Edition 400
Pharmacology for Chemists: Drug Discovery in Context 400
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5608436
求助须知:如何正确求助?哪些是违规求助? 4693073
关于积分的说明 14876620
捐赠科研通 4717595
什么是DOI,文献DOI怎么找? 2544222
邀请新用户注册赠送积分活动 1509305
关于科研通互助平台的介绍 1472836