<i>In silico</i> Prediction of Drug Induced Liver Toxicity Using Substructure Pattern Recognition Method

Zhang, Chen; Cheng, Feixiong; Li, Weihua; Liu, Guixia; Lee, Philip W.; Tang, Yun

doi:10.1002/minf.201500055

Cited by 79 publications

(75 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…23,28 Liew et al 23 collected available drugs in the market listed in the U.S. FDA Orange Book and checked the adverse hepatic effects using the Micromedex Healthcare Series; a total of 1274 compounds were collected with DILI class labels. Zhang et al 28 obtained a large diverse DILI database containing 1317 unique molecules from publications and LTKB. To ensure the consistency of data quality, we prepared Liew's data with a procedure similar to Zhang.…”

Section: Data Collection and Preparationmentioning

confidence: 99%

The development and application of in silico models for drug induced liver injury

Chen

Song

et al. 2018

RSC Adv.

View full text Add to dashboard Cite

show abstract

Section: Data Collection and Preparationmentioning

confidence: 99%

The development and application of in silico models for drug induced liver injury

Chen

Song

et al. 2018

RSC Adv.

View full text Add to dashboard Cite

show abstract

“…Accurate and timely prediction of drug induced liver injury remains a challenging research topic with a great potential impact in drug R&D. A number of efforts have been made to build in silico models that can predict DILI [32][33][34]. Since the datasets and feature selection vary, a direct comparison between these methods and our approach may be inaccurate.…”

Section: Discussionmentioning

confidence: 99%

A compound attributes-based predictive model for drug induced liver injury in humans

Liu

Gao

2020

PLoS ONE

View full text Add to dashboard Cite

Drug induced liver injury (DILI) is one of the key safety concerns in drug development. To assess the likelihood of drug candidates with potential adverse reactions of liver, we propose a compound attributes-based approach to predicting hepatobiliary disorders that are routinely reported to US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). Specifically, we developed a support vector machine (SVM) model with recursive feature extraction, based on physicochemical and structural properties of compounds as model input. Cross validation demonstrates that the predictive model has a robust performance with averaged 70% of both sensitivity and specificity over 500 trials. An independent validation was performed on public benchmark drugs and the results suggest potential utility of our model for identifying safety alerts. This in silico approach, upon further validation, would ultimately be implemented, together with other in vitro safety assays, for screening compounds early in drug development. OPEN ACCESSCitation: Liu Y, Gao H, He YD (2020) A compound attributes-based predictive model for drug induced liver injury in humans. PLoS ONE 15(4): e0231252.

show abstract

“…Few public datasets on DILI are available. We focused on the following data sources since they were easily detectable and downloadable from the web and they were reliable since already used by other authors (Chen et al, 2013; Hewitt et al, 2013; Zhang et al, 2016). …”

Section: Methodsmentioning

confidence: 99%

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

et al. 2016

View full text Add to dashboard Cite

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.

show abstract

In silico Prediction of Drug Induced Liver Toxicity Using Substructure Pattern Recognition Method

Cited by 79 publications

References 54 publications

The development and application of in silico models for drug induced liver injury

The development and application of in silico models for drug induced liver injury

A compound attributes-based predictive model for drug induced liver injury in humans

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

Contact Info

Product

Resources

About