Literature DB >> 31669330

Drug-induced liver injury severity and toxicity (DILIst): binary classification of 1279 drugs by human hepatotoxicity.

Shraddha Thakkar1, Ting Li1, Zhichao Liu1, Leihong Wu1, Ruth Roberts2, Weida Tong3.   

Abstract

Drug-induced liver injury (DILI) is of significant concern to drug development and regulatory review because of the limited success with existing preclinical models. For developing alternative methods, a large drug list is needed with known DILI severity and toxicity. We augmented the DILIrank data set [annotated using US Food and Drug Administration (FDA) drug labeling)] with four literature datasets (N >350 drugs) to generate the largest drug list with DILI classification, called DILIst (DILI severity and toxicity). DILIst comprises 1279 drugs, of which 768 were DILI positives (increase of 65% from DILIrank), whereas 511 were DILI negatives (increase of 65%). The investigation of DILI positive-negative distribution across various therapeutic categories revealed the most and least frequent DILI categories. Thus, we consider DILIst to be an invaluable resource for the community to improve DILI research. Crown
Copyright © 2019. Published by Elsevier Ltd. All rights reserved.

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Year:  2019        PMID: 31669330     DOI: 10.1016/j.drudis.2019.09.022

Source DB:  PubMed          Journal:  Drug Discov Today        ISSN: 1359-6446            Impact factor:   7.851


  18 in total

Review 1.  The Promise of AI for DILI Prediction.

Authors:  Andreu Vall; Yogesh Sabnis; Jiye Shi; Reiner Class; Sepp Hochreiter; Günter Klambauer
Journal:  Front Artif Intell       Date:  2021-04-14

2.  Comparing Machine Learning Algorithms for Predicting Drug-Induced Liver Injury (DILI).

Authors:  Eni Minerali; Daniel H Foil; Kimberley M Zorn; Thomas R Lane; Sean Ekins
Journal:  Mol Pharm       Date:  2020-06-08       Impact factor: 4.939

3.  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

4.  Farnesoid X receptor (FXR) agonists induce hepatocellular apoptosis and impair hepatic functions via FXR/SHP pathway.

Authors:  Tianwei Zhang; Shanshan Feng; Jiahuan Li; Zhitao Wu; Qiangqiang Deng; Wei Yang; Jing Li; Guoyu Pan
Journal:  Arch Toxicol       Date:  2022-03-10       Impact factor: 6.168

5.  Trade-off Predictivity and Explainability for Machine-Learning Powered Predictive Toxicology: An in-Depth Investigation with Tox21 Data Sets.

Authors:  Leihong Wu; Ruili Huang; Igor V Tetko; Zhonghua Xia; Joshua Xu; Weida Tong
Journal:  Chem Res Toxicol       Date:  2021-01-29       Impact factor: 3.739

Review 6.  PXR-mediated idiosyncratic drug-induced liver injury: mechanistic insights and targeting approaches.

Authors:  Jingheng Wang; Monicah Bwayi; Rebecca R Florke Gee; Taosheng Chen
Journal:  Expert Opin Drug Metab Toxicol       Date:  2020-06-16       Impact factor: 4.481

7.  An ensemble learning approach for modeling the systems biology of drug-induced injury.

Authors:  Joaquim Aguirre-Plans; Janet Piñero; Terezinha Souza; Giulia Callegaro; Steven J Kunnen; Ferran Sanz; Narcis Fernandez-Fuentes; Laura I Furlong; Emre Guney; Baldo Oliva
Journal:  Biol Direct       Date:  2021-01-12       Impact factor: 4.540

8.  Prediction of Alternative Drug-Induced Liver Injury Classifications Using Molecular Descriptors, Gene Expression Perturbation, and Toxicology Reports.

Authors:  Wojciech Lesiński; Krzysztof Mnich; Witold R Rudnicki
Journal:  Front Genet       Date:  2021-07-01       Impact factor: 4.599

9.  Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset.

Authors:  Robert Ancuceanu; Marilena Viorica Hovanet; Adriana Iuliana Anghel; Florentina Furtunescu; Monica Neagu; Carolina Constantin; Mihaela Dinu
Journal:  Int J Mol Sci       Date:  2020-03-19       Impact factor: 5.923

10.  Prediction Model of Aryl Hydrocarbon Receptor Activation by a Novel QSAR Approach, DeepSnap-Deep Learning.

Authors:  Yasunari Matsuzaka; Takuomi Hosaka; Anna Ogaito; Kouichi Yoshinari; Yoshihiro Uesawa
Journal:  Molecules       Date:  2020-03-13       Impact factor: 4.411

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