کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
2079888 | 1079901 | 2016 | 6 صفحه PDF | دانلود رایگان |

• DILIrank contains the largest number of drugs (N = 1036) ranked by their risk for causing DILI.
• The existing drug labeling-based DILI annotation was enhanced by weighing evidence of causality.
• Drugs were classified as verified vMost-, vLess-, vNo-DILI-concern, leaving out terminology ‘Ambiguous DILI-concern’ drugs.
• DILIrank is invaluable for the development of predictive models using emerging technologies.High-throughput methods are powerful tools to develop predictive models for assessing drug-induced liver injury (DILI). However, the development of predictive models requires a drug reference list with an accurate annotation of DILI risk in humans. We previously developed a DILI annotation schema based on information curated from the US Food and Drug Administration (FDA)-approved drug labeling for 287 drugs. In this article, we refine the schema by weighing the evidence of causality (i.e., a verification process to evaluate a drug as the cause of DILI) and generate a data set that ranks the DILI risk (DILIrank) in humans for 1036 FDA-approved drugs, providing the largest annotated data set of such drugs in the public domain.
Journal: Drug Discovery Today - Volume 21, Issue 4, April 2016, Pages 648–653