开发群体药代动力学模型的全自动工具。

IF 3.1 3区 医学 Q2 PHARMACOLOGY & PHARMACY CPT: Pharmacometrics & Systems Pharmacology Pub Date : 2024-08-27 DOI:10.1002/psp4.13222
Xiaomei Chen, Rikard Nordgren, Stella Belin, Alzahra Hamdan, Shijun Wang, Tianwu Yang, Zhe Huang, Simon J. Carter, Simon Buatois, João A. Abrantes, Andrew C. Hooker, Mats O. Karlsson
{"title":"开发群体药代动力学模型的全自动工具。","authors":"Xiaomei Chen,&nbsp;Rikard Nordgren,&nbsp;Stella Belin,&nbsp;Alzahra Hamdan,&nbsp;Shijun Wang,&nbsp;Tianwu Yang,&nbsp;Zhe Huang,&nbsp;Simon J. Carter,&nbsp;Simon Buatois,&nbsp;João A. Abrantes,&nbsp;Andrew C. Hooker,&nbsp;Mats O. Karlsson","doi":"10.1002/psp4.13222","DOIUrl":null,"url":null,"abstract":"<p>Population pharmacokinetic (PK) models are widely used to inform drug development by pharmaceutical companies and facilitate drug evaluation by regulatory agencies. Developing a population PK model is a multi-step, challenging, and time-consuming process involving iterative manual model fitting and evaluation. A tool for fully automatic model development (AMD) of common population PK models is presented here. The AMD tool is implemented in Pharmpy, a versatile open-source library for pharmacometrics. It consists of different modules responsible for developing the different components of population PK models, including the structural model, the inter-individual variability (IIV) model, the inter-occasional variability (IOV) model, the residual unexplained variability (RUV) model, the covariate model, and the allometry model. The AMD tool was evaluated using 10 real PK datasets involving the structural, IIV, and RUV modules in three sequences. The different sequences yielded generally consistent structural models; however, there were variations in the results of the IIV and RUV models. The final models of the AMD tool showed lower Bayesian Information Criterion (BIC) values and similar visual predictive check plots compared with the available published models, indicating reasonable quality, in addition to reasonable run time. A similar conclusion was also drawn in a simulation study. The developed AMD tool serves as a promising tool for fast and fully automatic population PK model building with the potential to facilitate the use of modeling and simulation in drug development.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"13 10","pages":"1784-1797"},"PeriodicalIF":3.1000,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11494844/pdf/","citationCount":"0","resultStr":"{\"title\":\"A fully automatic tool for development of population pharmacokinetic models\",\"authors\":\"Xiaomei Chen,&nbsp;Rikard Nordgren,&nbsp;Stella Belin,&nbsp;Alzahra Hamdan,&nbsp;Shijun Wang,&nbsp;Tianwu Yang,&nbsp;Zhe Huang,&nbsp;Simon J. Carter,&nbsp;Simon Buatois,&nbsp;João A. Abrantes,&nbsp;Andrew C. Hooker,&nbsp;Mats O. Karlsson\",\"doi\":\"10.1002/psp4.13222\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Population pharmacokinetic (PK) models are widely used to inform drug development by pharmaceutical companies and facilitate drug evaluation by regulatory agencies. Developing a population PK model is a multi-step, challenging, and time-consuming process involving iterative manual model fitting and evaluation. A tool for fully automatic model development (AMD) of common population PK models is presented here. The AMD tool is implemented in Pharmpy, a versatile open-source library for pharmacometrics. It consists of different modules responsible for developing the different components of population PK models, including the structural model, the inter-individual variability (IIV) model, the inter-occasional variability (IOV) model, the residual unexplained variability (RUV) model, the covariate model, and the allometry model. The AMD tool was evaluated using 10 real PK datasets involving the structural, IIV, and RUV modules in three sequences. The different sequences yielded generally consistent structural models; however, there were variations in the results of the IIV and RUV models. The final models of the AMD tool showed lower Bayesian Information Criterion (BIC) values and similar visual predictive check plots compared with the available published models, indicating reasonable quality, in addition to reasonable run time. A similar conclusion was also drawn in a simulation study. The developed AMD tool serves as a promising tool for fast and fully automatic population PK model building with the potential to facilitate the use of modeling and simulation in drug development.</p>\",\"PeriodicalId\":10774,\"journal\":{\"name\":\"CPT: Pharmacometrics & Systems Pharmacology\",\"volume\":\"13 10\",\"pages\":\"1784-1797\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-08-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11494844/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"CPT: Pharmacometrics & Systems Pharmacology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/psp4.13222\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"PHARMACOLOGY & PHARMACY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"CPT: Pharmacometrics & Systems Pharmacology","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/psp4.13222","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
引用次数: 0

摘要

群体药代动力学(PK)模型被广泛用于为制药公司的药物开发提供信息,并为监管机构的药物评估提供便利。开发群体 PK 模型是一个多步骤、具有挑战性且耗时的过程,其中涉及反复的人工模型拟合和评估。本文介绍了一种用于常见群体 PK 模型的全自动模型开发(AMD)工具。AMD 工具是在 Pharmpy 中实现的,Pharmpy 是一个多功能的药物计量学开源库。它由不同的模块组成,负责开发群体 PK 模型的不同组成部分,包括结构模型、个体间变异性 (IIV) 模型、事件间变异性 (IOV) 模型、残余未解释变异性 (RUV) 模型、协变量模型和异构模型。使用 10 个真实 PK 数据集对 AMD 工具进行了评估,涉及三个序列中的结构、IOV 和 RUV 模块。不同序列产生的结构模型基本一致;但 IIV 和 RUV 模型的结果存在差异。AMD 工具的最终模型显示出较低的贝叶斯信息标准(BIC)值,与现有的已发表模型相比,视觉预测检查图相似,表明除了运行时间合理外,质量也合理。模拟研究也得出了类似的结论。所开发的 AMD 工具是快速、全自动建立群体 PK 模型的理想工具,有望促进建模和模拟在药物开发中的应用。
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
A fully automatic tool for development of population pharmacokinetic models

Population pharmacokinetic (PK) models are widely used to inform drug development by pharmaceutical companies and facilitate drug evaluation by regulatory agencies. Developing a population PK model is a multi-step, challenging, and time-consuming process involving iterative manual model fitting and evaluation. A tool for fully automatic model development (AMD) of common population PK models is presented here. The AMD tool is implemented in Pharmpy, a versatile open-source library for pharmacometrics. It consists of different modules responsible for developing the different components of population PK models, including the structural model, the inter-individual variability (IIV) model, the inter-occasional variability (IOV) model, the residual unexplained variability (RUV) model, the covariate model, and the allometry model. The AMD tool was evaluated using 10 real PK datasets involving the structural, IIV, and RUV modules in three sequences. The different sequences yielded generally consistent structural models; however, there were variations in the results of the IIV and RUV models. The final models of the AMD tool showed lower Bayesian Information Criterion (BIC) values and similar visual predictive check plots compared with the available published models, indicating reasonable quality, in addition to reasonable run time. A similar conclusion was also drawn in a simulation study. The developed AMD tool serves as a promising tool for fast and fully automatic population PK model building with the potential to facilitate the use of modeling and simulation in drug development.

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
CiteScore
5.00
自引率
11.40%
发文量
146
审稿时长
8 weeks
期刊最新文献
Clinical study design strategies to mitigate confounding effects of time-dependent clearance on dose optimization of therapeutic antibodies. Exploration of the potential impact of batch-to-batch variability on the establishment of pharmacokinetic bioequivalence for inhalation powder drug products. Population pharmacokinetics of selexipag for dose selection and confirmation in pediatric patients with pulmonary arterial hypertension. Issue Information Exposure-response modeling of liver fat imaging endpoints in non-alcoholic fatty liver disease populations administered ervogastat alone and co-administered with clesacostat.
×
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