<i>In silico</i>models for drug-induced liver injury – current status

Przybylak, Katarzyna R.; Cronin, Mark T.D.

doi:10.1517/17425255.2012.648613

Cited by 80 publications

(71 citation statements)

References 96 publications

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“…Drug-induced liver injury (DILI) are detrimental adverse effects caused by marketed drugs toward patients' liver (Przybylak and Cronin, 2012). DILI is a major challenge to the pharmaceutical industry, regulatory bodies and physicians (Chen et al, 2014a).…”

Section: Introductionmentioning

confidence: 99%

“…Alanine and aspartate aminotransferase (ALT and AST), alkaline phosphatase (ALP), total bilirubin (TBIL) and γ-glutamyltransferase (GGT) are considered the reference biomarkers and are widely employed for the detection of DILI, providing supporting information in pre-clinical and clinical toxicity studies for drug development (US FDA, 2009; Tonomura et al, 2015). However, they are not always specific and sensitive in recognizing liver diseases provoked by DILI or other causes such as viruses (Przybylak and Cronin, 2012). Gene-expression profiling has now been proposed for more accurate evaluation of DILI (Blomme et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

“…Despite the objective hurdles to modeling DILI, some in silico tools for the prediction of hepatotoxicity have been developed through most of them are commercial. The models for in silico assessment of hepatotoxicity have been recently reviewed by Przybylak and Cronin (2012) and Chen et al (2014b). …”

Section: Introductionmentioning

confidence: 99%

“…Considering the model structure, in silico models can be divided in two main groups: statistical and expert-based. In the first case the models are built on the basis of an automated algorithm; in the second case the human expert, exploiting his/her understanding of toxicological mechanisms, outlines the relationship between the chemical structure and the biological activity (Przybylak and Cronin, 2012). The commercial software Derek for Windows (Lhasa Limited) and CASE Ultra (MultiCASE Inc) contain modules for the prediction of hepatotoxicity.…”

Section: Introductionmentioning

confidence: 99%

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A New Structure-Activity Relationship (SAR) Model for Predicting Drug-Induced Liver Injury, Based on Statistical and Expert-Based Structural Alerts

et al. 2016

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The prompt identification of chemical molecules with potential effects on liver may help in drug discovery and in raising the levels of protection for human health. Besides in vitro approaches, computational methods in toxicology are drawing attention. We built a structure-activity relationship (SAR) model for evaluating hepatotoxicity. After compiling a data set of 950 compounds using data from the literature, we randomly split it into training (80%) and test sets (20%). We also compiled an external validation set (101 compounds) for evaluating the performance of the model. To extract structural alerts (SAs) related to hepatotoxicity and non-hepatotoxicity we used SARpy, a statistical application that automatically identifies and extracts chemical fragments related to a specific activity. We also applied the chemical grouping approach for manually identifying other SAs. We calculated accuracy, specificity, sensitivity and Matthews correlation coefficient (MCC) on the training, test and external validation sets. Considering the complexity of the endpoint, the model performed well. In the training, test and external validation sets the accuracy was respectively 81, 63, and 68%, specificity 89, 33, and 33%, sensitivity 93, 88, and 80% and MCC 0.63, 0.27, and 0.13. Since it is preferable to overestimate hepatotoxicity rather than not to recognize unsafe compounds, the model's architecture followed a conservative approach. As it was built using human data, it might be applied without any need for extrapolation from other species. This model will be freely available in the VEGA platform.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A New Structure-Activity Relationship (SAR) Model for Predicting Drug-Induced Liver Injury, Based on Statistical and Expert-Based Structural Alerts

et al. 2016

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“…Excellent reviews have recently been published about adverse outcome pathways [7], multi-scale computational models [8], and cheminformatic models [9,10].…”

Section: Introductionmentioning

confidence: 99%

Unraveling cellular pathways contributing to drug-induced liver injury by dynamical modeling

Kuijper

Yang

Water

et al. 2016

Expert Opinion on Drug Metabolism & Toxicology

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Introduction: Drug-induced liver injury (DILI) is a significant threat to human health and a major problem in drug development. It is hard to predict due to its idiosyncratic nature and which does not show up in animal trials. Hepatic adaptive stress response pathway activation is generally observed in drug-induced liver injury. Dynamical pathway modeling has the potential to foresee adverse effects of drugs before they go in trial. Ordinary differential equation modeling can offer mechanistic insight, and allows us to study the dynamical behavior of stress pathways involved in DILI. Areas covered: This review provides an overview on the progress of the dynamical modeling of stress and death pathways pertinent to DILI, i.e. pathways relevant for oxidative stress, inflammatory stress, DNA damage, unfolded proteins, heat shock and apoptosis. We also discuss the required steps for applying such modeling to the liver. Expert opinion: Despite the strong progress made since the turn of the century, models of stress pathways have only rarely been specifically applied to describe pathway dynamics for DILI. We argue that with minor changes, in some cases only to parameter values, many of these models can be repurposed for application in DILI research. Combining both dynamical models with in vitro testing might offer novel screening methods for the harmful side-effects of drugs. ARTICLE HISTORY

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Utility of High‐Resolution Mass Spectrometry for New Drug Discovery Applications

Emary¹,

Zhang²

2013

Mass Spectrometry for Drug Discovery and Drug Development

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In silicomodels for drug-induced liver injury – current status

Cited by 80 publications

References 96 publications

A New Structure-Activity Relationship (SAR) Model for Predicting Drug-Induced Liver Injury, Based on Statistical and Expert-Based Structural Alerts

A New Structure-Activity Relationship (SAR) Model for Predicting Drug-Induced Liver Injury, Based on Statistical and Expert-Based Structural Alerts

Unraveling cellular pathways contributing to drug-induced liver injury by dynamical modeling

Utility of High‐Resolution Mass Spectrometry for New Drug Discovery Applications

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