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BioAssay: AID 588210

Human drug-induced liver injury (DILI) modelling dataset from Ekins et al

Drug-induced liver injury (DILI) is one of the most important reasons for drug development failure at both preapproval and postapproval stages. There has been increased interest in developing predictive in vivo, in vitro, and in silico models to identify compounds that cause idiosyncratic hepatotoxicity. In the current study, we applied machine learning, a Bayesian modeling method with extended more ..
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 Tested Compounds
 Tested Compounds
All(524)
 
 
Unspecified(524)
 
 
 Tested Substances
 Tested Substances
All(555)
 
 
Unspecified(555)
 
 
AID: 588210
Data Source: ChEMBL (736783)
Depositor Category: Literature, Extracted
BioAssay Version:
Deposit Date: 2011-09-18
Modify Date: 2014-12-06

Data Table ( Complete ):           View All Data
Tested Compounds:
Description:
Title: A predictive ligand-based Bayesian model for human drug-induced liver injury.

Abstract: Drug-induced liver injury (DILI) is one of the most important reasons for drug development failure at both preapproval and postapproval stages. There has been increased interest in developing predictive in vivo, in vitro, and in silico models to identify compounds that cause idiosyncratic hepatotoxicity. In the current study, we applied machine learning, a Bayesian modeling method with extended connectivity fingerprints and other interpretable descriptors. The model that was developed and internally validated (using a training set of 295 compounds) was then applied to a large test set relative to the training set (237 compounds) for external validation. The resulting concordance of 60%, sensitivity of 56%, and specificity of 67% were comparable to results for internal validation. The Bayesian model with extended connectivity functional class fingerprints of maximum diameter 6 (ECFC_6) and interpretable descriptors suggested several substructures that are chemically reactive and may also be important for DILI-causing compounds, e.g., ketones, diols, and alpha-methyl styrene type structures. Using Smiles Arbitrary Target Specification (SMARTS) filters published by several pharmaceutical companies, we evaluated whether such reactive substructures could be readily detected by any of the published filters. It was apparent that the most stringent filters used in this study, such as the Abbott alerts, which captures thiol traps and other compounds, may be of use in identifying DILI-causing compounds (sensitivity 67%). A significant outcome of the present study is that we provide predictions for many compounds that cause DILI by using the knowledge we have available from previous studies. These computational models may represent cost-effective selection criteria before in vitro or in vivo experimental studies.
(PMID: 20843939)
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
1Hepatotoxicity activity commentHepatotoxicity activity commentString
2Hepatotoxicity standard flagHepatotoxicity standard flagInteger
3Hepatotoxicity qualifierHepatotoxicity qualifierString
4Hepatotoxicity published valueHepatotoxicity published valueFloat
5Hepatotoxicity standard valueHepatotoxicity standard valueFloat

Data Table (Concise)
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