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BioAssay: AID 625286

Drug Induced Liver Injury Prediction System (DILIps) training set; hepatic side effect (HepSE) score for hepatitis

Drug-induced liver injury (DILI) is a significant concern in drug development due to the poor concordance between preclinical and clinical findings of liver toxicity. We hypothesized that the DILI types (hepatotoxic side effects) seen in the clinic can be translated into the development of predictive in silico models for use in the drug discovery phase. We identified 13 hepatotoxic side effects more ..
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 Tested Compounds
 Tested Compounds
All(870)
 
 
Unspecified(870)
 
 
 Tested Substances
 Tested Substances
All(923)
 
 
Unspecified(923)
 
 
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AID: 625286
Data Source: ChEMBL (774792)
Depositor Category: Literature, Extracted
BioAssay Version:
Deposit Date: 2012-09-09
Modify Date: 2014-05-26

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Tested Compounds:
Description:
Title: Translating clinical findings into knowledge in drug safety evaluation--drug induced liver injury prediction system (DILIps).

Abstract: Drug-induced liver injury (DILI) is a significant concern in drug development due to the poor concordance between preclinical and clinical findings of liver toxicity. We hypothesized that the DILI types (hepatotoxic side effects) seen in the clinic can be translated into the development of predictive in silico models for use in the drug discovery phase. We identified 13 hepatotoxic side effects with high accuracy for classifying marketed drugs for their DILI potential. We then developed in silico predictive models for each of these 13 side effects, which were further combined to construct a DILI prediction system (DILIps). The DILIps yielded 60-70% prediction accuracy for three independent validation sets. To enhance the confidence for identification of drugs that cause severe DILI in humans, the "Rule of Three" was developed in DILIps by using a consensus strategy based on 13 models. This gave high positive predictive value (91%) when applied to an external dataset containing 206 drugs from three independent literature datasets. Using the DILIps, we screened all the drugs in DrugBank and investigated their DILI potential in terms of protein targets and therapeutic categories through network modeling. We demonstrated that two therapeutic categories, anti-infectives for systemic use and musculoskeletal system drugs, were enriched for DILI, which is consistent with current knowledge. We also identified protein targets and pathways that are related to drugs that cause DILI by using pathway analysis and co-occurrence text mining. While marketed drugs were the focus of this study, the DILIps has a potential as an evaluation tool to screen and prioritize new drug candidates or chemicals, such as environmental chemicals, to avoid those that might cause liver toxicity. We expect that the methodology can be also applied to other drug safety endpoints, such as renal or cardiovascular toxicity.
(PMID: 22194678)
Comment
Putative Target:

ChEMBL Target ID: 103956
Target Type: PHENOTYPE
Pref Name: Hepatotoxicity
Confidence: Target assigned is non-molecular
Relationship Type: Non-molecular target assigned
Categorized Comment - additional comments and annotations
From ChEMBL:
Assay Type: ADME
Assay Data Source: Scientific Literature
Result Definitions
TIDNameDescriptionHistogramTypeUnit
OutcomeThe BioAssay activity outcomeOutcome
1HepSE_hepatitis activity commentHepSE_hepatitis activity commentString
2HepSE_hepatitis standard flagHepSE_hepatitis standard flagInteger
3HepSE_hepatitis qualifierHepSE_hepatitis qualifierString
4HepSE_hepatitis published valueHepSE_hepatitis published valueFloat
5HepSE_hepatitis standard valueHepSE_hepatitis standard valueFloat

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