DILIrank: the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans
•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-...
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| Published in | Drug discovery today Vol. 21; no. 4; pp. 648 - 653 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
Elsevier Ltd
01.04.2016
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| Subjects | |
| Online Access | Get full text |
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| Abstract | •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. |
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| AbstractList | •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. |
| Author | Chen, Minjun Thakkar, Shraddha Tong, Weida Yu, Ke Suzuki, Ayako Hu, Chuchu |
| Author_xml | – sequence: 1 givenname: Minjun surname: Chen fullname: Chen, Minjun organization: Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA – sequence: 2 givenname: Ayako surname: Suzuki fullname: Suzuki, Ayako organization: Department of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, USA – sequence: 3 givenname: Shraddha surname: Thakkar fullname: Thakkar, Shraddha organization: Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA – sequence: 4 givenname: Ke surname: Yu fullname: Yu, Ke organization: Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA – sequence: 5 givenname: Chuchu surname: Hu fullname: Hu, Chuchu organization: Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA – sequence: 6 givenname: Weida surname: Tong fullname: Tong, Weida email: weida.tong@fda.hhs.gov organization: Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26948801$$D View this record in MEDLINE/PubMed |
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| Snippet | •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... |
| SourceID | proquest pubmed crossref elsevier |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 648 |
| SubjectTerms | Chemical and Drug Induced Liver Injury Databases, Factual Drug Labeling Humans Pharmaceutical Preparations - classification Risk |
| Title | DILIrank: the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans |
| URI | https://dx.doi.org/10.1016/j.drudis.2016.02.015 https://www.ncbi.nlm.nih.gov/pubmed/26948801 https://www.proquest.com/docview/1781541871 |
| Volume | 21 |
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