亲爱的研友该休息了!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!身体可是革命的本钱,早点休息,好梦!

Prediction of renal transporter-mediated drug-drug interactions for a drug which is an OAT substrate and inhibitor using PBPK modelling

基于生理学的药代动力学模型 丙磺舒 药理学 药物与药物的相互作用 药代动力学 药品 化学 最大值 有机阴离子转运蛋白1 药物相互作用 运输机 医学 生物化学 基因
作者
Kathryn Ball,Tanguy Jamier,Yannick Parmentier,Claire Denizot,Agnes Mallier,Marylore Chenel
出处
期刊:European Journal of Pharmaceutical Sciences [Elsevier]
卷期号:106: 122-132 被引量:20
标识
DOI:10.1016/j.ejps.2017.05.055
摘要

A PBPK modelling approach was used to predict organic anion transporter (OAT) mediated drug-drug interactions involving S44121, a substrate and an inhibitor of OAT1 and OAT3. Model predictions were then compared to the results of a clinical DDI study which was carried out to investigate the interaction of S44121 with probenecid, tenofovir and ciprofloxacin. PBPK models were developed and qualified using existing clinical data, and inhibition constants were determined in vitro. The model predictions for S44121 as an OAT inhibitor were similar to the results obtained from the clinical DDI study, with no interaction observed for tenofovir or ciprofloxacin in the presence of S44121. An observed AUC ratio of 2.2 was obtained for S44121 in the presence of probenecid, which was slightly higher than the model predicted AUC ratio of 1.6. A DDI study in the monkey was also carried out for the interaction between S44121 and probenecid, since the monkey has previously been reported to be a good preclinical model for OAT-mediated DDI. However, this study highlighted a species difference in the major route of S44121 elimination between monkey (mainly hepatic metabolism) and human (mainly renal excretion of unchanged drug), rendering a comparison between the two DDI studies difficult. Overall, for S44121 the PBPK modelling approach gave a better prediction of the extent of DDI than the static predictions based on inhibitor Cmax and IC50, therefore this can be considered a potentially valuable tool within drug development.

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
Eatanicecube完成签到,获得积分10
刚刚
山与完成签到,获得积分20
4秒前
凤迎雪飘完成签到,获得积分10
17秒前
40秒前
九个烧卖发布了新的文献求助10
46秒前
陶陶子完成签到 ,获得积分10
48秒前
科目三应助虾鱼采纳,获得10
50秒前
SciGPT应助虾鱼采纳,获得10
1分钟前
屿2完成签到,获得积分10
1分钟前
读读读发布了新的文献求助10
1分钟前
1分钟前
Owen应助科研通管家采纳,获得10
1分钟前
1分钟前
1分钟前
研友_LJaXX8完成签到,获得积分10
1分钟前
1分钟前
研友_LJaXX8发布了新的文献求助10
1分钟前
屿2发布了新的文献求助10
1分钟前
华仔应助晨曦采纳,获得10
2分钟前
李爱国应助卷卷采纳,获得10
2分钟前
2分钟前
虾鱼发布了新的文献求助10
2分钟前
完美世界应助英俊大树采纳,获得10
2分钟前
2分钟前
卷卷发布了新的文献求助10
2分钟前
2分钟前
虾鱼发布了新的文献求助10
3分钟前
情怀应助lsl采纳,获得30
3分钟前
3分钟前
3分钟前
小点点cy_发布了新的文献求助10
3分钟前
3分钟前
落后易烟发布了新的文献求助10
3分钟前
3分钟前
loii应助科研通管家采纳,获得10
3分钟前
loii应助科研通管家采纳,获得10
3分钟前
Owen应助科研通管家采纳,获得10
3分钟前
3分钟前
英姑应助stq1997采纳,获得10
3分钟前
英俊大树发布了新的文献求助10
3分钟前
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
Handbook of pharmaceutical excipients, Ninth edition 5000
Aerospace Standards Index - 2026 ASIN2026 3000
Signals, Systems, and Signal Processing 610
Discrete-Time Signals and Systems 610
Principles of town planning : translating concepts to applications 500
Wearable Exoskeleton Systems, 2nd Edition 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 纳米技术 有机化学 物理 生物化学 化学工程 计算机科学 复合材料 内科学 催化作用 光电子学 物理化学 电极 冶金 遗传学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 6058607
求助须知:如何正确求助?哪些是违规求助? 7891263
关于积分的说明 16296923
捐赠科研通 5203328
什么是DOI,文献DOI怎么找? 2783899
邀请新用户注册赠送积分活动 1766552
关于科研通互助平台的介绍 1647129