Literature DB >> 26914516

Computational Models for Human and Animal Hepatotoxicity with a Global Application Scope.

Denis Mulliner1, Friedemann Schmidt1, Manuela Stolte1, Hans-Peter Spirkl1, Andreas Czich1, Alexander Amberg1.   

Abstract

Hepatic toxicity is a key concern for novel pharmaceutical drugs since it is difficult to anticipate in preclinical models, and it can originate from pharmacologically unrelated drug effects, such as pathway interference, metabolism, and drug accumulation. Because liver toxicity still ranks among the top reasons for drug attrition, the reliable prediction of adverse hepatic effects is a substantial challenge in drug discovery and development. To this end, more effort needs to be focused on the development of improved predictive in-vitro and in-silico approaches. Current computational models often lack applicability to novel pharmaceutical candidates, typically due to insufficient coverage of the chemical space of interest, which is either imposed by size or diversity of the training data. Hence, there is an urgent need for better computational models to allow for the identification of safe drug candidates and to support experimental design. In this context, a large data set comprising 3712 compounds with liver related toxicity findings in humans and animals was collected from various sources. The complex pathology was clustered into 21 preclinical and human hepatotoxicity endpoints, which were organized into three levels of detail. Support vector machine models were trained for each endpoint, using optimized descriptor sets from chemometrics software. The optimized global human hepatotoxicity model has high sensitivity (68%) and excellent specificity (95%) in an internal validation set of 221 compounds. Models for preclinical endpoints performed similarly. To allow for reliable prediction of "truly external" novel compounds, all predictions are tagged with confidence parameters. These parameters are derived from a statistical analysis of the predictive probability densities. The whole approach was validated for an external validation set of 269 proprietary compounds. The models are fully integrated into our early safety in-silico workflow.

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Year:  2016        PMID: 26914516     DOI: 10.1021/acs.chemrestox.5b00465

Source DB:  PubMed          Journal:  Chem Res Toxicol        ISSN: 0893-228X            Impact factor:   3.739


  21 in total

1.  Mechanism-Driven Read-Across of Chemical Hepatotoxicants Based on Chemical Structures and Biological Data.

Authors:  Linlin Zhao; Daniel P Russo; Wenyi Wang; Lauren M Aleksunes; Hao Zhu
Journal:  Toxicol Sci       Date:  2020-04-01       Impact factor: 4.849

2.  Predicting drug-induced liver injury in human with Naïve Bayes classifier approach.

Authors:  Hui Zhang; Lan Ding; Yi Zou; Shui-Qing Hu; Hai-Guo Huang; Wei-Bao Kong; Ji Zhang
Journal:  J Comput Aided Mol Des       Date:  2016-09-17       Impact factor: 3.686

3.  Why Drugs Fail in Late Stages of Development: Case Study Analyses from the Last Decade and Recommendations.

Authors:  Dolly A Parasrampuria; Leslie Z Benet; Amarnath Sharma
Journal:  AAPS J       Date:  2018-03-13       Impact factor: 4.009

4.  Integrating Drug's Mode of Action into Quantitative Structure-Activity Relationships for Improved Prediction of Drug-Induced Liver Injury.

Authors:  Leihong Wu; Zhichao Liu; Scott Auerbach; Ruili Huang; Minjun Chen; Kristin McEuen; Joshua Xu; Hong Fang; Weida Tong
Journal:  J Chem Inf Model       Date:  2017-04-10       Impact factor: 4.956

Review 5.  In Silico Models for Hepatotoxicity.

Authors:  Claire Ellison; Mark Hewitt; Katarzyna Przybylak
Journal:  Methods Mol Biol       Date:  2022

6.  In silico approaches in organ toxicity hazard assessment: current status and future needs in predicting liver toxicity.

Authors:  Arianna Bassan; Vinicius M Alves; Alexander Amberg; Lennart T Anger; Scott Auerbach; Lisa Beilke; Andreas Bender; Mark T D Cronin; Kevin P Cross; Jui-Hua Hsieh; Nigel Greene; Raymond Kemper; Marlene T Kim; Moiz Mumtaz; Tobias Noeske; Manuela Pavan; Julia Pletz; Daniel P Russo; Yogesh Sabnis; Markus Schaefer; David T Szabo; Jean-Pierre Valentin; Joerg Wichard; Dominic Williams; David Woolley; Craig Zwickl; Glenn J Myatt
Journal:  Comput Toxicol       Date:  2021-09-09

7.  Mechanism-driven modeling of chemical hepatotoxicity using structural alerts and an in vitro screening assay.

Authors:  Xuelian Jia; Xia Wen; Daniel P Russo; Lauren M Aleksunes; Hao Zhu
Journal:  J Hazard Mater       Date:  2022-05-20       Impact factor: 14.224

8.  An Algorithm Framework for Drug-Induced Liver Injury Prediction Based on Genetic Algorithm and Ensemble Learning.

Authors:  Bowei Yan; Xiaona Ye; Jing Wang; Junshan Han; Lianlian Wu; Song He; Kunhong Liu; Xiaochen Bo
Journal:  Molecules       Date:  2022-05-12       Impact factor: 4.927

Review 9.  Advances in Engineered Liver Models for Investigating Drug-Induced Liver Injury.

Authors:  Christine Lin; Salman R Khetani
Journal:  Biomed Res Int       Date:  2016-09-20       Impact factor: 3.411

Review 10.  In Silico Prediction of Organ Level Toxicity: Linking Chemistry to Adverse Effects.

Authors:  Mark T D Cronin; Steven J Enoch; Claire L Mellor; Katarzyna R Przybylak; Andrea-Nicole Richarz; Judith C Madden
Journal:  Toxicol Res       Date:  2017-07-15
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