{"title":"生物监测和数字数据技术作为加强动物研究翻译的机会。","authors":"Erwin B Defensor, Maria A Lim, Laura R Schaevitz","doi":"10.1093/ilar/ilab018","DOIUrl":null,"url":null,"abstract":"<p><p>The failure of animal studies to translate to effective clinical therapeutics has driven efforts to identify underlying cause and develop solutions that improve the reproducibility and translatability of preclinical research. Common issues revolve around study design, analysis, and reporting as well as standardization between preclinical and clinical endpoints. To address these needs, recent advancements in digital technology, including biomonitoring of digital biomarkers, development of software systems and database technologies, as well as application of artificial intelligence to preclinical datasets can be used to increase the translational relevance of preclinical animal research. In this review, we will describe how a number of innovative digital technologies are being applied to overcome recurring challenges in study design, execution, and data sharing as well as improving scientific outcome measures. Examples of how these technologies are applied to specific therapeutic areas are provided. Digital technologies can enhance the quality of preclinical research and encourage scientific collaboration, thus accelerating the development of novel therapeutics.</p>","PeriodicalId":56299,"journal":{"name":"Ilar Journal","volume":"62 1-2","pages":"223-231"},"PeriodicalIF":3.1000,"publicationDate":"2021-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Biomonitoring and Digital Data Technology as an Opportunity for Enhancing Animal Study Translation.\",\"authors\":\"Erwin B Defensor, Maria A Lim, Laura R Schaevitz\",\"doi\":\"10.1093/ilar/ilab018\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The failure of animal studies to translate to effective clinical therapeutics has driven efforts to identify underlying cause and develop solutions that improve the reproducibility and translatability of preclinical research. Common issues revolve around study design, analysis, and reporting as well as standardization between preclinical and clinical endpoints. To address these needs, recent advancements in digital technology, including biomonitoring of digital biomarkers, development of software systems and database technologies, as well as application of artificial intelligence to preclinical datasets can be used to increase the translational relevance of preclinical animal research. In this review, we will describe how a number of innovative digital technologies are being applied to overcome recurring challenges in study design, execution, and data sharing as well as improving scientific outcome measures. Examples of how these technologies are applied to specific therapeutic areas are provided. Digital technologies can enhance the quality of preclinical research and encourage scientific collaboration, thus accelerating the development of novel therapeutics.</p>\",\"PeriodicalId\":56299,\"journal\":{\"name\":\"Ilar Journal\",\"volume\":\"62 1-2\",\"pages\":\"223-231\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2021-12-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ilar Journal\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://doi.org/10.1093/ilar/ilab018\",\"RegionNum\":3,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"VETERINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ilar Journal","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1093/ilar/ilab018","RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"VETERINARY SCIENCES","Score":null,"Total":0}
Biomonitoring and Digital Data Technology as an Opportunity for Enhancing Animal Study Translation.
The failure of animal studies to translate to effective clinical therapeutics has driven efforts to identify underlying cause and develop solutions that improve the reproducibility and translatability of preclinical research. Common issues revolve around study design, analysis, and reporting as well as standardization between preclinical and clinical endpoints. To address these needs, recent advancements in digital technology, including biomonitoring of digital biomarkers, development of software systems and database technologies, as well as application of artificial intelligence to preclinical datasets can be used to increase the translational relevance of preclinical animal research. In this review, we will describe how a number of innovative digital technologies are being applied to overcome recurring challenges in study design, execution, and data sharing as well as improving scientific outcome measures. Examples of how these technologies are applied to specific therapeutic areas are provided. Digital technologies can enhance the quality of preclinical research and encourage scientific collaboration, thus accelerating the development of novel therapeutics.
期刊介绍:
The ILAR Journal is the peer-reviewed, theme-oriented publication of the Institute for Laboratory Animal Research (ILAR), which provides timely information for all who study, use, care for, and oversee the use of animals in research. The journal publishes original articles that review research on animals either as direct subjects or as surrogates for humans. According to policy, any previously unpublished animal research reported in the ILAR Journal will have been conducted according to the scientific, technical, and humanely appropriate guidelines current at the time the research was conducted in accordance with the Guide for the Care and Use of Laboratory Animals or other guidance provided by taxonomically-oriented professional societies (e.g., American Society of Mammalogy) as referenced in the Guide.