{"title":"数字健康中的智能计算精确剂量管理。","authors":"Hong Lu, Sara Rosenbaum, Wei Lu","doi":"10.1007/978-3-030-57796-4_26","DOIUrl":null,"url":null,"abstract":"<p><p>Pediatric dosing is not only critical for successful pediatric trials in drug development but also paramount to safety and effective treatment at bedside. Due to the complex pharmacokinetic of children compared to adults, several challenges are posed in managing dosing precisely during drug development and after drug approval to clinicians. In particular, given the real-world practice, understanding the impact of development on the dose-exposure-response relationship is essential in optimizing the dosing to children of different ages. In this paper we propose a novel intelligent computing framework to examine how the growth and maturation create size- and age-dependent variability in pharmacokinetics and pharmacodynamics, and summarize the use of modeling-based approaches for dose finding in pediatric drug development, allowing clinicians to anticipate probable treatment effects and to have a higher likelihood of achieving optimal dose regimens early, as well as reducing the drug development cycling time and cost.</p>","PeriodicalId":101369,"journal":{"name":"Proceedings. International Conference on Intelligent Networking and Collaborative Systems","volume":"1263 ","pages":"269-280"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10619515/pdf/","citationCount":"0","resultStr":"{\"title\":\"Precision Dosing Management with Intelligent Computing in Digital Health.\",\"authors\":\"Hong Lu, Sara Rosenbaum, Wei Lu\",\"doi\":\"10.1007/978-3-030-57796-4_26\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Pediatric dosing is not only critical for successful pediatric trials in drug development but also paramount to safety and effective treatment at bedside. Due to the complex pharmacokinetic of children compared to adults, several challenges are posed in managing dosing precisely during drug development and after drug approval to clinicians. In particular, given the real-world practice, understanding the impact of development on the dose-exposure-response relationship is essential in optimizing the dosing to children of different ages. In this paper we propose a novel intelligent computing framework to examine how the growth and maturation create size- and age-dependent variability in pharmacokinetics and pharmacodynamics, and summarize the use of modeling-based approaches for dose finding in pediatric drug development, allowing clinicians to anticipate probable treatment effects and to have a higher likelihood of achieving optimal dose regimens early, as well as reducing the drug development cycling time and cost.</p>\",\"PeriodicalId\":101369,\"journal\":{\"name\":\"Proceedings. International Conference on Intelligent Networking and Collaborative Systems\",\"volume\":\"1263 \",\"pages\":\"269-280\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10619515/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. International Conference on Intelligent Networking and Collaborative Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/978-3-030-57796-4_26\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2020/8/21 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. International Conference on Intelligent Networking and Collaborative Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/978-3-030-57796-4_26","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2020/8/21 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
Precision Dosing Management with Intelligent Computing in Digital Health.
Pediatric dosing is not only critical for successful pediatric trials in drug development but also paramount to safety and effective treatment at bedside. Due to the complex pharmacokinetic of children compared to adults, several challenges are posed in managing dosing precisely during drug development and after drug approval to clinicians. In particular, given the real-world practice, understanding the impact of development on the dose-exposure-response relationship is essential in optimizing the dosing to children of different ages. In this paper we propose a novel intelligent computing framework to examine how the growth and maturation create size- and age-dependent variability in pharmacokinetics and pharmacodynamics, and summarize the use of modeling-based approaches for dose finding in pediatric drug development, allowing clinicians to anticipate probable treatment effects and to have a higher likelihood of achieving optimal dose regimens early, as well as reducing the drug development cycling time and cost.