DallphinAtoM: Physiologically based pharmacokinetics software predicting human PK parameters based on physicochemical properties, in vitro and animal in vivo data

基于生理学的药代动力学模型 广告 计算机科学 药物开发 生物信息学 药代动力学 药理学 药品 计算生物学 生物 生物化学 基因
作者
Sae Kyung Choi,Sehwan Han,So Jin Lee,Byunghee Lim,Soo Hyeon Bae,Seunghoon Han,Dong‐Seok Yim
出处
期刊:Computer Methods and Programs in Biomedicine [Elsevier]
卷期号:216: 106662-106662
标识
DOI:10.1016/j.cmpb.2022.106662
摘要

In silico experiments and simulations using physiologically based pharmacokinetic (PBPK) and allometric approaches have played an important role in pharmaceutical research and drug development. These methods integrate diverse data from preclinical and clinical development, and have been widely applied to in vitro-in vivo extrapolation (IVIVE) of absorption, distribution, metabolism, and excretion (ADME).To develop a user-friendly open tool predicting human PK, we assessed various references on PBPK and allometric methods published so far. They were integrated into a software system named "DallphinAtoM" (Drugs with ALLometry and PHysiology Inside-Animal to huMan), which has a user-friendly platform that can handle complex PBPK models and allometric models with a relatively small amount of essential information of the drug. The models of DallphinAtoM support the integration of data gained during the nonclinical development phase, enable translation from animal to human, and allow the prediction of concentration-time profiles with predicted PK parameters.We presented two illustrative applications using DallphinAtoM: (1) human PK simulation of an orally administered drug using PBPK method; and (2) simulation of intravenous infusion following a two-compartment model using the allometric scaling method.We conclude that this is a straightforward and transparent tool allowing fast and reliable human PK simulation based on the latest knowledge on biochemical processes and physiology and provides valuable information for decision making during the early-phase drug development.
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
shirleeyeahe完成签到,获得积分10
1秒前
1秒前
特特雷珀萨努完成签到 ,获得积分10
1秒前
京阿尼完成签到,获得积分10
1秒前
风雨发布了新的文献求助10
1秒前
orixero应助今非采纳,获得10
1秒前
平常的G完成签到,获得积分10
2秒前
2秒前
小石头完成签到,获得积分10
3秒前
3秒前
YL完成签到 ,获得积分10
3秒前
3秒前
上官若男应助整齐路灯采纳,获得10
3秒前
yyj发布了新的文献求助10
3秒前
细腻的麦片完成签到,获得积分20
4秒前
4秒前
君君完成签到,获得积分10
5秒前
cchen0902完成签到,获得积分10
5秒前
Sara发布了新的文献求助10
5秒前
5秒前
干饭闪电狼完成签到,获得积分10
6秒前
YUZU完成签到,获得积分10
7秒前
123完成签到,获得积分10
8秒前
pcx完成签到,获得积分10
8秒前
phd完成签到,获得积分10
9秒前
9秒前
曹志毅完成签到,获得积分10
9秒前
mito发布了新的文献求助10
10秒前
无悔呀发布了新的文献求助10
10秒前
11秒前
君君发布了新的文献求助10
11秒前
Yang完成签到,获得积分10
12秒前
风雨完成签到,获得积分10
12秒前
12秒前
13秒前
彭于晏应助小西采纳,获得30
13秒前
可爱的函函应助布布采纳,获得10
14秒前
15秒前
轩辕德地发布了新的文献求助10
15秒前
nine发布了新的文献求助30
15秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Social media impact on athlete mental health: #RealityCheck 1020
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 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 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3527849
求助须知:如何正确求助?哪些是违规求助? 3107938
关于积分的说明 9287239
捐赠科研通 2805706
什么是DOI,文献DOI怎么找? 1540033
邀请新用户注册赠送积分活动 716893
科研通“疑难数据库(出版商)”最低求助积分说明 709794