In silico toxicology: computational methods for the prediction of chemical toxicity

Determining the toxicity of chemicals is necessary to identify their harmful effects on humans, animals, plants, or the environment. It is also one of the main steps in drug design. Animal models have been used for a long time for toxicity testing. However, in vivo animal tests are constrained by ti...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Wiley interdisciplinary reviews. Computational molecular science Jg. 6; H. 2; S. 147 - 172
Hauptverfasser: Raies, Arwa B., Bajic, Vladimir B.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hoboken, USA Wiley Periodicals, Inc 01.03.2016
Wiley Subscription Services, Inc
Schlagworte:
Advanced Review
Advanced Reviews
Algorithms
Animal models
Artificial intelligence
Biocompatibility
Chemicals
Chemoinformatics
Computation
Computer Algorithms and Programming
Computer applications
Computer simulation
Databases and Expert Systems
Drug development
Ethics
Expert systems
Failures
In vivo methods and tests
Information science
Information systems
Modelling
Prediction models
Software
Tests
Toxicity
Toxicity testing
Toxicity tests
Toxicology
ISSN:1759-0876, 1759-0884
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Determining the toxicity of chemicals is necessary to identify their harmful effects on humans, animals, plants, or the environment. It is also one of the main steps in drug design. Animal models have been used for a long time for toxicity testing. However, in vivo animal tests are constrained by time, ethical considerations, and financial burden. Therefore, computational methods for estimating the toxicity of chemicals are considered useful. In silico toxicology is one type of toxicity assessment that uses computational methods to analyze, simulate, visualize, or predict the toxicity of chemicals. In silico toxicology aims to complement existing toxicity tests to predict toxicity, prioritize chemicals, guide toxicity tests, and minimize late‐stage failures in drugs design. There are various methods for generating models to predict toxicity endpoints. We provide a comprehensive overview, explain, and compare the strengths and weaknesses of the existing modeling methods and algorithms for toxicity prediction with a particular (but not exclusive) emphasis on computational tools that can implement these methods and refer to expert systems that deploy the prediction models. Finally, we briefly review a number of new research directions in in silico toxicology and provide recommendations for designing in silico models. WIREs Comput Mol Sci 2016, 6:147–172. doi: 10.1002/wcms.1240 This article is categorized under: Computer and Information Science > Chemoinformatics Computer and Information Science > Databases and Expert Systems Computer and Information Science > Computer Algorithms and Programming
Bibliographie:ArticleID:WCMS1240
istex:F4BB6FBA53A97AF369DED5F6E193A8024C89C171
Table S1. Different types of PK and PD models. Table S2. Different method to determine uncertainty factors. Table S3. Different types of models and molecular descriptors in the QSAR family.
King Abdullah University of Science and Technology
ark:/67375/WNG-F7S4L715-D
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ObjectType-Article-2
ObjectType-Feature-3
content type line 23
ObjectType-Review-1
Conflict of interest: The authors have declared no conflicts of interest for this article.
ISSN:1759-0876
1759-0884
DOI:10.1002/wcms.1240