Quantitative structure-activity relationship models for predicting drug-induced liver injury based on FDA-approved drug labeling annotation and using a large collection of drugs.

@article{Chen2013QuantitativeSR,
  title={Quantitative structure-activity relationship models for predicting drug-induced liver injury based on FDA-approved drug labeling annotation and using a large collection of drugs.},
  author={Minjun Chen and Huixiao Hong and Hong Fang and Reagan J. Kelly and Guangxu Zhou and J{\"u}rgen Borlak and Weida Tong},
  journal={Toxicological sciences : an official journal of the Society of Toxicology},
  year={2013},
  volume={136 1},
  pages={
          242-9
        }
}
  • Minjun Chen, H. Hong, +4 authors W. Tong
  • Published 1 November 2013
  • Biology
  • Toxicological sciences : an official journal of the Society of Toxicology
Drug-induced liver injury (DILI) is one of the leading causes of the termination of drug development programs. Consequently, identifying the risk of DILI in humans for drug candidates during the early stages of the development process would greatly reduce the drug attrition rate in the pharmaceutical industry but would require the implementation of new research and development strategies. In this regard, several in silico models have been proposed as alternative means in prioritizing drug… 
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61 Citations

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Predicting the Risks of Drug-Induced Liver Injury in Humans Utilizing Computational Modeling
TLDR
This chapter introduces the drug label-based approach to annotate the DILI risk associated with individual drugs and then upon these annotations the authors developed a series of predictive models that could be used to assess the potential of DilI risk, including the “rule-of-two” model, DILi score model, and conventional and modified Quantitative structure–activity relationship (QSAR) models.
Development of Decision Forest Models for Prediction of Drug-Induced Liver Injury in Humans Using A Large Set of FDA-approved Drugs
TLDR
This work developed and compared 2-class and 3-class DILI prediction models using the machine learning algorithm of Decision Forest (DF) with Mold2 structural descriptors and suggested that the 3- class model presents a better option than the binary model for drug safety evaluation.
An In Silico Model for Predicting Drug-Induced Hepatotoxicity
TLDR
A high-quality QSAR model for predicting the DILI risk of xenobiotics by incorporating the use of eight effective classifiers and molecular descriptors provided by Marvin is reported, indicating that the model outperformed prior studies significantly.
DILIrank: the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans.
Integrating Drug's Mode of Action into Quantitative Structure-Activity Relationships for Improved Prediction of Drug-Induced Liver Injury
TLDR
The MOA-DILI approach has the potential to significantly improve predictive outcomes and to reveal complex relationships between MOAs and DILI, all of which would be helpful in developing D ILI predictive models in drug screening and for risk assessment of industrial chemicals.
In Silico Prediction of Drug-Induced Liver Injury Based on Adverse Drug Reaction Reports
  • Xiangwei Zhu, Shao Li
  • Chemistry
    Toxicological sciences : an official journal of the Society of Toxicology
  • 2017
TLDR
The curated hepatotoxicity dataset along with the predictive classification models presented here should provide insight into future studies of DILI.
Toward predictive models for drug-induced liver injury in humans: are we there yet?
TLDR
This review focuses on the current state of efforts in developing predictive models from diverse data sources for potential use in detecting human hepatotoxicity, and aims to provide perspectives on how to further improve DILI prediction.
A testing strategy to predict risk for drug-induced liver injury in humans using high-content screen assays and the ‘rule-of-two’ model
TLDR
A new testing strategy was developed to improve DILI prediction by employing in vitro assays that was combined with the RO2 model and improved the accuracies by 10 % and reduced the number of drugs requiring experimental assessment by approximately 20 %, as compared to the HCS assay alone.
A New Structure-Activity Relationship (SAR) Model for Predicting Drug-Induced Liver Injury, Based on Statistical and Expert-Based Structural Alerts
TLDR
A structure-activity relationship (SAR) model for evaluating hepatotoxicity that might be applied without any need for extrapolation from other species is built using human data and freely available in the VEGA platform.
Predicting drug-induced liver injury: The importance of data curation
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