Literature DB >> 35188639

In Silico Models for Hepatotoxicity.

Claire Ellison1, Mark Hewitt2, Katarzyna Przybylak3.   

Abstract

In this chapter, we review the state of the art of predicting human hepatotoxicity using in silico techniques. There has been significant progress in this area over the past 20 years but there are still some challenges ahead. Principally, these challenges are our partial understanding of a very complex biochemical system and our ability to emulate that in a predictive capacity. Here, we provide an overview of the published modeling approaches in this area to date and discuss their design, strengths and weaknesses. It is interesting to note the diversity in modeling approaches, whether they be statistical algorithms or evidenced-based approaches including structural alerts and pharmacophore models. Irrespective of modeling approach, it appears a common theme of access to appropriate, relevant, and high-quality data is a limitation to all and is likely to continue to be the focus of future research.
© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Expert system; Hepatotoxicity; In silico or computational prediction; Liver; QSAR

Mesh:

Year:  2022        PMID: 35188639     DOI: 10.1007/978-1-0716-1960-5_14

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  56 in total

1.  TIMES-SS--a promising tool for the assessment of skin sensitization hazard. A characterization with respect to the OECD validation principles for (Q)SARs and an external evaluation for predictivity.

Authors:  Grace Patlewicz; Sabcho D Dimitrov; Lawrence K Low; Petra S Kern; Gergana D Dimitrova; Mike I H Comber; Aynur O Aptula; Richard D Phillips; Jay Niemelä; Charlotte Madsen; Eva B Wedebye; David W Roberts; Paul T Bailey; Ovanes G Mekenyan
Journal:  Regul Toxicol Pharmacol       Date:  2007-03-25       Impact factor: 3.271

Review 2.  Structure alerts for carcinogenicity, and the Salmonella assay system: a novel insight through the chemical relational databases technology.

Authors:  Romualdo Benigni; Cecilia Bossa
Journal:  Mutat Res       Date:  2008-07-11       Impact factor: 2.433

3.  In silico prediction of drug safety: despite progress there is abundant room for improvement.

Authors:  William J Egan; Gregor Zlokarnik; Peter D J Grootenhuis
Journal:  Drug Discov Today Technol       Date:  2004-12

Review 4.  In silico models for drug-induced liver injury--current status.

Authors:  Katarzyna R Przybylak; Mark T D Cronin
Journal:  Expert Opin Drug Metab Toxicol       Date:  2012-01-17       Impact factor: 4.481

Review 5.  Idiosyncratic drug hepatotoxicity.

Authors:  Neil Kaplowitz
Journal:  Nat Rev Drug Discov       Date:  2005-06       Impact factor: 84.694

Review 6.  Review of liver injury associated with dietary supplements.

Authors:  Felix Stickel; Kerstin Kessebohm; Rosemarie Weimann; Helmut K Seitz
Journal:  Liver Int       Date:  2011-01-11       Impact factor: 5.828

Review 7.  Toxic hepatitis in occupational exposure to solvents.

Authors:  Giulia Malaguarnera; Emanuela Cataudella; Maria Giordano; Giuseppe Nunnari; Giuseppe Chisari; Mariano Malaguarnera
Journal:  World J Gastroenterol       Date:  2012-06-14       Impact factor: 5.742

Review 8.  Mechanisms of drug-induced liver injury.

Authors:  Michael P Holt; Cynthia Ju
Journal:  AAPS J       Date:  2006-02-03       Impact factor: 4.009

Review 9.  Today's Challenges to De-Risk and Predict Drug Safety in Human "Mind-the-Gap".

Authors:  Richard J Weaver; Jean-Pierre Valentin
Journal:  Toxicol Sci       Date:  2019-02-01       Impact factor: 4.849

Review 10.  Models of Drug Induced Liver Injury (DILI) - Current Issues and Future Perspectives.

Authors:  Lucija Kuna; Ivana Bozic; Tomislav Kizivat; Kristina Bojanic; Margareta Mrso; Edgar Kralj; Robert Smolic; George Y Wu; Martina Smolic
Journal:  Curr Drug Metab       Date:  2018       Impact factor: 3.731

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