{"title":"生成基于模型的虚拟临床试验的实用指南","authors":"M. Craig, J. Gevertz, I. Kareva, K. Wilkie","doi":"10.3389/fsysb.2023.1174647","DOIUrl":null,"url":null,"abstract":"Mathematical modeling has made significant contributions to drug design, development, and optimization. Virtual clinical trials that integrate mathematical models to explore patient heterogeneity and its impact on a variety of therapeutic questions have recently risen in popularity. Here, we outline best practices for creating virtual patients from mathematical models to ultimately implement and execute a virtual clinical trial. In this practical guide, we discuss and provide examples of model design, parameter estimation, parameter sensitivity, model identifiability, and virtual patient cohort creation. Our goal is to help researchers adopt these approaches to further the use of virtual population-based analysis and virtual clinical trials.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"A practical guide for the generation of model-based virtual clinical trials\",\"authors\":\"M. Craig, J. Gevertz, I. Kareva, K. Wilkie\",\"doi\":\"10.3389/fsysb.2023.1174647\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Mathematical modeling has made significant contributions to drug design, development, and optimization. Virtual clinical trials that integrate mathematical models to explore patient heterogeneity and its impact on a variety of therapeutic questions have recently risen in popularity. Here, we outline best practices for creating virtual patients from mathematical models to ultimately implement and execute a virtual clinical trial. In this practical guide, we discuss and provide examples of model design, parameter estimation, parameter sensitivity, model identifiability, and virtual patient cohort creation. Our goal is to help researchers adopt these approaches to further the use of virtual population-based analysis and virtual clinical trials.\",\"PeriodicalId\":73109,\"journal\":{\"name\":\"Frontiers in systems biology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in systems biology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3389/fsysb.2023.1174647\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in systems biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/fsysb.2023.1174647","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A practical guide for the generation of model-based virtual clinical trials
Mathematical modeling has made significant contributions to drug design, development, and optimization. Virtual clinical trials that integrate mathematical models to explore patient heterogeneity and its impact on a variety of therapeutic questions have recently risen in popularity. Here, we outline best practices for creating virtual patients from mathematical models to ultimately implement and execute a virtual clinical trial. In this practical guide, we discuss and provide examples of model design, parameter estimation, parameter sensitivity, model identifiability, and virtual patient cohort creation. Our goal is to help researchers adopt these approaches to further the use of virtual population-based analysis and virtual clinical trials.