Evaluation of the human prediction of clearance from hepatocyte and microsome intrinsic clearance for 52 drug compounds

微粒体 体内 肝细胞 国际的 化学 微粒体 体外 生物化学 生物 生物技术 计算机科学 操作系统
作者
Anna-Karin Sohlenius-Sternbeck,Lovisa Afzelius,Peteris Prūsis,Jan Neelissen,Janet Hoogstraate,Jenny Johansson,E. Floby,Å. Bengtsson,Olof Gissberg,John Sternbeck,Carl Petersson
出处
期刊:Xenobiotica [Taylor & Francis]
卷期号:40 (9): 637-649 被引量:128
标识
DOI:10.3109/00498254.2010.500407
摘要

We compare three different approaches to scale clearance (CL) from human hepatocyte and microsome CL(int) (intrinsic CL) for 52 drug compounds. By using the well-stirred model with protein binding included only 11% and 30% of the compounds were predicted within 2-fold and the average absolute fold errors (AAFE) for the predictions were 5.9 and 4.1 for hepatocytes and microsomes, respectively. When predictions were performed without protein binding, 59% of the compounds were predicted within 2-fold using either hepatocytes or microsomes and the AAFE was 2.2 and 2.3, respectively. For hepatocytes and microsomes there were significant correlations (P = 8.7 x 10(-13), R(2) = 0.72; P = 2.8 x 10(-9), R(2) = 0.61) between predicted CL(int in vivo) (obtained from in vitro CL(int)) and measured CL(int in vivo) (obtained using the well-stirred model). When CL was calculated from the regression, 76% and 70% of the compounds were predicted within 2-fold and the AAFE was 1.6 and 1.8 for hepatocytes and microsomes, respectively. We demonstrate that microsomes and hepatocytes are in many cases comparable when scaling of CL is performed from regression. By using the hepatocyte regression, CL for 82% of the compounds in an independent test set (n = 11) were predicted within 2-fold (AAFE 1.4). We suggest that a regression line that adjusts for systematic under-predictions should be the first-hand choice for scaling of CL.
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
顾海东发布了新的文献求助10
刚刚
秋天发布了新的文献求助10
刚刚
刚刚
1秒前
1秒前
汉堡包的应助被伤心的大亩猪采纳,获得10
1秒前
1秒前
PY完成签到,获得积分10
1秒前
sammy_p完成签到,获得积分10
1秒前
neilhua完成签到 ,获得积分10
3秒前
linweiwei发布了新的文献求助10
3秒前
3秒前
4秒前
Lucas的应助被顶天立地采纳,获得10
4秒前
shally发布了新的文献求助10
4秒前
7秒前
dingm2发布了新的文献求助10
8秒前
Emmaanddog发布了新的文献求助10
8秒前
FashionBoy的应助被文献文献采纳,获得10
8秒前
蓝天的应助被包容柏柳采纳,获得10
9秒前
9秒前
顾海东完成签到,获得积分10
9秒前
Yii发布了新的文献求助30
10秒前
12秒前
13秒前
Sun发布了新的文献求助10
13秒前
13秒前
泪西瓜完成签到,获得积分10
14秒前
脑洞疼的应助被迷路的钻石采纳,获得10
14秒前
15秒前
秋天完成签到,获得积分20
15秒前
15秒前
清爽难胜完成签到,获得积分10
16秒前
NexusExplorer的应助被Lunar611采纳,获得10
16秒前
17秒前
kevin发布了新的文献求助50
17秒前
18秒前
18秒前
李爱国的应助被Donghaol采纳,获得10
19秒前
Frim发布了新的文献求助10
19秒前
高分求助中
(应助此贴封号)通过应助OA文献获取积分 10000
Rosenblum, Global Change Biology 800
Acceptability of Printed Boards 600
The Dawn of Philology 520
Organizational Behavior 510
Production Logging: Theoretical and Interpretive Elements 400
A primer on partial least squares structural equation modeling (PLS-SEM) (4th ed.) 310
热门求助领域 (近24小时)
化学 材料科学 医学 生物 计算机科学 工程类 纳米技术 有机化学 化学工程 内科学 物理 生物化学 复合材料 催化作用 细胞生物学 人工智能 心理学 无机化学 基因 遗传学
热门帖子
关注 科研通微信公众号,转发送积分 7823005
求助须知:如何正确求助?哪些是违规求助? 9349614
关于积分的说明 20554124
捐赠科研通 7415619
什么是DOI,文献DOI怎么找? 3333829
关于科研通互助平台的介绍 2479228
邀请新用户注册赠送积分活动 2353979