Shadia Zaman, Sirarat Sarntivijai, Darrell R Abernethy
{"title":"利用生物医学本体整合生物知识,以学习和预测药物不良反应。","authors":"Shadia Zaman, Sirarat Sarntivijai, Darrell R Abernethy","doi":"10.1177/1177625017696075","DOIUrl":null,"url":null,"abstract":"<p><p>Drug-induced toxicity is a major public health concern that leads to patient morbidity and mortality. To address this problem, the Food and Drug Administration is working on the PredicTox initiative, a pilot research program on tyrosine kinase inhibitors, to build mechanistic and predictive models for drug-induced toxicity. This program involves integrating data acquired during preclinical studies and clinical trials within pharmaceutical company development programs that they have agreed to put in the public domain and in publicly available biological, pharmacological, and chemical databases. The integration process is accommodated by biomedical ontologies, a set of standardized vocabularies that define terms and logical relationships between them in each vocabulary. We describe a few programs that have used ontologies to address biomedical questions. The PredicTox effort is leveraging the experience gathered from these early initiatives to develop an infrastructure that allows evaluation of the hypothesis that having a mechanistic understanding underlying adverse drug reactions will improve the capacity to understand drug-induced clinical adverse drug reactions.</p>","PeriodicalId":73138,"journal":{"name":"Gene regulation and systems biology","volume":"11 ","pages":"1177625017696075"},"PeriodicalIF":0.0000,"publicationDate":"2017-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1177625017696075","citationCount":"14","resultStr":"{\"title\":\"Use of Biomedical Ontologies for Integration of Biological Knowledge for Learning and Prediction of Adverse Drug Reactions.\",\"authors\":\"Shadia Zaman, Sirarat Sarntivijai, Darrell R Abernethy\",\"doi\":\"10.1177/1177625017696075\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Drug-induced toxicity is a major public health concern that leads to patient morbidity and mortality. To address this problem, the Food and Drug Administration is working on the PredicTox initiative, a pilot research program on tyrosine kinase inhibitors, to build mechanistic and predictive models for drug-induced toxicity. This program involves integrating data acquired during preclinical studies and clinical trials within pharmaceutical company development programs that they have agreed to put in the public domain and in publicly available biological, pharmacological, and chemical databases. The integration process is accommodated by biomedical ontologies, a set of standardized vocabularies that define terms and logical relationships between them in each vocabulary. We describe a few programs that have used ontologies to address biomedical questions. The PredicTox effort is leveraging the experience gathered from these early initiatives to develop an infrastructure that allows evaluation of the hypothesis that having a mechanistic understanding underlying adverse drug reactions will improve the capacity to understand drug-induced clinical adverse drug reactions.</p>\",\"PeriodicalId\":73138,\"journal\":{\"name\":\"Gene regulation and systems biology\",\"volume\":\"11 \",\"pages\":\"1177625017696075\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-03-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1177/1177625017696075\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Gene regulation and systems biology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1177/1177625017696075\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2017/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Gene regulation and systems biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/1177625017696075","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2017/1/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
Use of Biomedical Ontologies for Integration of Biological Knowledge for Learning and Prediction of Adverse Drug Reactions.
Drug-induced toxicity is a major public health concern that leads to patient morbidity and mortality. To address this problem, the Food and Drug Administration is working on the PredicTox initiative, a pilot research program on tyrosine kinase inhibitors, to build mechanistic and predictive models for drug-induced toxicity. This program involves integrating data acquired during preclinical studies and clinical trials within pharmaceutical company development programs that they have agreed to put in the public domain and in publicly available biological, pharmacological, and chemical databases. The integration process is accommodated by biomedical ontologies, a set of standardized vocabularies that define terms and logical relationships between them in each vocabulary. We describe a few programs that have used ontologies to address biomedical questions. The PredicTox effort is leveraging the experience gathered from these early initiatives to develop an infrastructure that allows evaluation of the hypothesis that having a mechanistic understanding underlying adverse drug reactions will improve the capacity to understand drug-induced clinical adverse drug reactions.