Rong Chen, Mark Sale, James Craig, Michael Tomashevskiy, Alex Mazur, Shuhua Hu, Keith Nieforth
{"title":"Pirana和集成PMX工具,一个用于NONMEM、NLME、pyDarwin和RsNLME的工作台。","authors":"Rong Chen, Mark Sale, James Craig, Michael Tomashevskiy, Alex Mazur, Shuhua Hu, Keith Nieforth","doi":"10.1002/psp4.70067","DOIUrl":null,"url":null,"abstract":"<p>Keizer initially described Pirana as a workbench designed to streamline management of NONMEM modeling, visualization, and analysis. Initial versions of Pirana integrated tools included NONMEM and PSN. As new tools have become available to pharmacometricians, new capabilities have been added to Pirana. These capabilities include:\n\n </p><p>In this tutorial, we present a full workflow demonstrating how to use Pirana to build, fit, post-process, and perform VPC on models using NONMEM and NLME. In addition, we show how to use the machine learning-driven pyDarwin package with Pirana to automatically search model structures, random effects, and covariate models.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"14 8","pages":"1298-1309"},"PeriodicalIF":3.0000,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ascpt.onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70067","citationCount":"0","resultStr":"{\"title\":\"Pirana and Integrated PMX Tools, a Workbench for NONMEM, NLME, pyDarwin, and RsNLME\",\"authors\":\"Rong Chen, Mark Sale, James Craig, Michael Tomashevskiy, Alex Mazur, Shuhua Hu, Keith Nieforth\",\"doi\":\"10.1002/psp4.70067\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Keizer initially described Pirana as a workbench designed to streamline management of NONMEM modeling, visualization, and analysis. Initial versions of Pirana integrated tools included NONMEM and PSN. As new tools have become available to pharmacometricians, new capabilities have been added to Pirana. These capabilities include:\\n\\n </p><p>In this tutorial, we present a full workflow demonstrating how to use Pirana to build, fit, post-process, and perform VPC on models using NONMEM and NLME. In addition, we show how to use the machine learning-driven pyDarwin package with Pirana to automatically search model structures, random effects, and covariate models.</p>\",\"PeriodicalId\":10774,\"journal\":{\"name\":\"CPT: Pharmacometrics & Systems Pharmacology\",\"volume\":\"14 8\",\"pages\":\"1298-1309\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2025-07-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ascpt.onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70067\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"CPT: Pharmacometrics & Systems Pharmacology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://ascpt.onlinelibrary.wiley.com/doi/10.1002/psp4.70067\",\"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://ascpt.onlinelibrary.wiley.com/doi/10.1002/psp4.70067","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
Pirana and Integrated PMX Tools, a Workbench for NONMEM, NLME, pyDarwin, and RsNLME
Keizer initially described Pirana as a workbench designed to streamline management of NONMEM modeling, visualization, and analysis. Initial versions of Pirana integrated tools included NONMEM and PSN. As new tools have become available to pharmacometricians, new capabilities have been added to Pirana. These capabilities include:
In this tutorial, we present a full workflow demonstrating how to use Pirana to build, fit, post-process, and perform VPC on models using NONMEM and NLME. In addition, we show how to use the machine learning-driven pyDarwin package with Pirana to automatically search model structures, random effects, and covariate models.