Prediction of In Vivo Pharmacokinetic Parameters and Time–Exposure Curves in Rats Using Machine Learning from the Chemical Structure

体内 均方误差 药代动力学 药物发现 生物利用度 广告 机器学习 计算机科学 外推法 人工智能 药理学 生物系统 化学 数学 统计 生物 生物化学 生物技术
作者
Olga Obrezanova,Anton Martinsson,Thomas M. Whitehead,Samar Mahmoud,Andreas Bender,Filip Miljković,Piotr Grabowski,Ben Irwin,Ioana Oprisiu,G. J. Conduit,Matthew Segall,G. Smith,Beth Williamson,Susanne Winiwarter,Nigel Greene
出处
期刊:Molecular Pharmaceutics [American Chemical Society]
卷期号:19 (5): 1488-1504 被引量:46
标识
DOI:10.1021/acs.molpharmaceut.2c00027
摘要

Animal pharmacokinetic (PK) data as well as human and animal in vitro systems are utilized in drug discovery to define the rate and route of drug elimination. Accurate prediction and mechanistic understanding of drug clearance and disposition in animals provide a degree of confidence for extrapolation to humans. In addition, prediction of in vivo properties can be used to improve design during drug discovery, help select compounds with better properties, and reduce the number of in vivo experiments. In this study, we generated machine learning models able to predict rat in vivo PK parameters and concentration-time PK profiles based on the molecular chemical structure and either measured or predicted in vitro parameters. The models were trained on internal in vivo rat PK data for over 3000 diverse compounds from multiple projects and therapeutic areas, and the predicted endpoints include clearance and oral bioavailability. We compared the performance of various traditional machine learning algorithms and deep learning approaches, including graph convolutional neural networks. The best models for PK parameters achieved R2 = 0.63 [root mean squared error (RMSE) = 0.26] for clearance and R2 = 0.55 (RMSE = 0.46) for bioavailability. The models provide a fast and cost-efficient way to guide the design of molecules with optimal PK profiles, to enable the prediction of virtual compounds at the point of design, and to drive prioritization of compounds for in vivo assays.
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
量子星尘发布了新的文献求助10
刚刚
爆米花应助Master采纳,获得10
1秒前
Pom发布了新的文献求助10
2秒前
3秒前
DzongKha完成签到,获得积分10
3秒前
4秒前
XYX发布了新的文献求助10
6秒前
7秒前
YvonneL完成签到,获得积分10
8秒前
8秒前
退役干饭王完成签到 ,获得积分10
8秒前
Carol发布了新的文献求助10
8秒前
9秒前
NexusExplorer应助秃顶双马尾采纳,获得10
10秒前
11秒前
11秒前
ZWGS发布了新的文献求助10
12秒前
别卷了完成签到 ,获得积分10
12秒前
CodeCraft应助健壮的怜烟采纳,获得10
13秒前
14秒前
14秒前
张道恒发布了新的文献求助10
15秒前
所所应助Tung采纳,获得10
16秒前
han应助阿良采纳,获得10
16秒前
laity完成签到,获得积分10
17秒前
xxx发布了新的文献求助10
17秒前
17秒前
18秒前
19秒前
19秒前
123发布了新的文献求助10
21秒前
23秒前
欢呼寻冬完成签到 ,获得积分10
24秒前
nj完成签到,获得积分10
24秒前
momo发布了新的文献求助10
24秒前
汌舟完成签到,获得积分10
25秒前
启点发布了新的文献求助10
25秒前
26秒前
酷波er应助蔡翌文采纳,获得10
26秒前
26秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
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 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3988920
求助须知:如何正确求助?哪些是违规求助? 3531290
关于积分的说明 11253247
捐赠科研通 3269903
什么是DOI,文献DOI怎么找? 1804830
邀请新用户注册赠送积分活动 882027
科研通“疑难数据库(出版商)”最低求助积分说明 809052