基于生理学的药代动力学建模:转化研究和调节毒理学的一个有前途的工具

Kiara Fairman , Miao Li , Shruti V. Kabadi , Annie Lumen
{"title":"基于生理学的药代动力学建模:转化研究和调节毒理学的一个有前途的工具","authors":"Kiara Fairman ,&nbsp;Miao Li ,&nbsp;Shruti V. Kabadi ,&nbsp;Annie Lumen","doi":"10.1016/j.cotox.2020.03.001","DOIUrl":null,"url":null,"abstract":"<div><p><span>Computational pharmacokinetic modeling methods, such as physiologically based pharmacokinetic (PBPK) modeling, have shown great promise for use in translational research as well as regulatory assessments. PBPK models are assumption-based simplifications of the complex biological system modeled and have high data demands for model parameterization and verification. However, unlike empirical models that rely on multiple observations from a single system, PBPK models uniquely allow for data to be obtained from multiple platforms (</span><em>in silico, in vitro</em>, and <em>in vivo</em><span>). Furthermore, these data are integrated by the principles of physiology and pharmacology/toxicology to make predictions in domains with sparse observations. Our article provides an overview of scientific utility of PBPK modeling in translational research and regulatory toxicology<span> using some case examples that highlight the important role of PBPK model-based predictions in contributing to regulatory assessments of diverse types of chemicals, ranging from food and environmental chemicals to drugs intended for use in veterinary and human medicine. At present, collective efforts are ongoing for establishing uniformity, consistency, and transparency within many areas of PBPK modeling, and with continuing advances in the field of computational pharmacokinetic, PBPK modeling has the potential to contribute to reliable alternatives to animal testing in the future.</span></span></p></div>","PeriodicalId":93968,"journal":{"name":"Current opinion in toxicology","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.cotox.2020.03.001","citationCount":"4","resultStr":"{\"title\":\"Physiologically based pharmacokinetic modeling: A promising tool for translational research and regulatory toxicology\",\"authors\":\"Kiara Fairman ,&nbsp;Miao Li ,&nbsp;Shruti V. Kabadi ,&nbsp;Annie Lumen\",\"doi\":\"10.1016/j.cotox.2020.03.001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p><span>Computational pharmacokinetic modeling methods, such as physiologically based pharmacokinetic (PBPK) modeling, have shown great promise for use in translational research as well as regulatory assessments. PBPK models are assumption-based simplifications of the complex biological system modeled and have high data demands for model parameterization and verification. However, unlike empirical models that rely on multiple observations from a single system, PBPK models uniquely allow for data to be obtained from multiple platforms (</span><em>in silico, in vitro</em>, and <em>in vivo</em><span>). Furthermore, these data are integrated by the principles of physiology and pharmacology/toxicology to make predictions in domains with sparse observations. Our article provides an overview of scientific utility of PBPK modeling in translational research and regulatory toxicology<span> using some case examples that highlight the important role of PBPK model-based predictions in contributing to regulatory assessments of diverse types of chemicals, ranging from food and environmental chemicals to drugs intended for use in veterinary and human medicine. At present, collective efforts are ongoing for establishing uniformity, consistency, and transparency within many areas of PBPK modeling, and with continuing advances in the field of computational pharmacokinetic, PBPK modeling has the potential to contribute to reliable alternatives to animal testing in the future.</span></span></p></div>\",\"PeriodicalId\":93968,\"journal\":{\"name\":\"Current opinion in toxicology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2020-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1016/j.cotox.2020.03.001\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current opinion in toxicology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2468202020300176\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current opinion in toxicology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2468202020300176","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

摘要

计算药代动力学建模方法,如基于生理的药代动力学(PBPK)建模,在转化研究和监管评估中显示出巨大的应用前景。PBPK模型是对复杂生物系统建模的基于假设的简化,对模型参数化和验证有很高的数据要求。然而,与依赖于单个系统的多个观察结果的经验模型不同,PBPK模型独特地允许从多个平台(在体内、体外和体内)获得数据。此外,这些数据通过生理学和药理学/毒理学原理进行整合,以在观测稀疏的领域进行预测。我们的文章概述了PBPK模型在转化研究和监管毒理学中的科学应用,并使用了一些案例,突出了基于PBPK模型的预测在促进不同类型化学品的监管评估中的重要作用,范围从食品和环境化学品到用于兽药和人类药物的药物。目前,在PBPK建模的许多领域中,人们正在共同努力建立一致性、一致性和透明度,随着计算药代动力学领域的不断进步,PBPK建模有可能在未来为动物实验提供可靠的替代方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Physiologically based pharmacokinetic modeling: A promising tool for translational research and regulatory toxicology

Computational pharmacokinetic modeling methods, such as physiologically based pharmacokinetic (PBPK) modeling, have shown great promise for use in translational research as well as regulatory assessments. PBPK models are assumption-based simplifications of the complex biological system modeled and have high data demands for model parameterization and verification. However, unlike empirical models that rely on multiple observations from a single system, PBPK models uniquely allow for data to be obtained from multiple platforms (in silico, in vitro, and in vivo). Furthermore, these data are integrated by the principles of physiology and pharmacology/toxicology to make predictions in domains with sparse observations. Our article provides an overview of scientific utility of PBPK modeling in translational research and regulatory toxicology using some case examples that highlight the important role of PBPK model-based predictions in contributing to regulatory assessments of diverse types of chemicals, ranging from food and environmental chemicals to drugs intended for use in veterinary and human medicine. At present, collective efforts are ongoing for establishing uniformity, consistency, and transparency within many areas of PBPK modeling, and with continuing advances in the field of computational pharmacokinetic, PBPK modeling has the potential to contribute to reliable alternatives to animal testing in the future.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Current opinion in toxicology
Current opinion in toxicology Toxicology, Biochemistry
CiteScore
8.50
自引率
0.00%
发文量
0
审稿时长
64 days
期刊最新文献
Adverse outcome pathway networks as the basis for the development of new approach methodologies: Liver toxicity as a case study New approach methodologies (NAMs) in drug safety assessment: A vision of the future Editorial: Transforming toxicology one cell at a time: A special issue on the application of scRNA-seq to the study of environmental response Editorial Board Practical lessons of the 3Rs: Learning from the past and looking toward the future
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1