A category approach to predicting the hemolytic effects of ethylene glycol alkyl ethers in repeated-dose toxicity

Yamada, Takashi; Tanaka, Yushiro; Zhang, HuiQi; Hasegawa, Ryuichi; Sakuratani, Yuki; Mekenyan, Ovanes G.; Yamazoe, Yasushi; Yamada, Jun; Hayashi, Makoto

doi:10.2131/jts.37.503

Cited by 9 publications

(1 citation statement)

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“…One of the read-across tools is the HESS system, contained in the OECD QSAR Toolbox. With this tool grouping and read-across for about 40 categories and 20 structural alerts can be performed for repeated toxicity endpoints in mammalians 6 (Yamada et al, 2012 , 2013a , b , 2014 ; Sakuratani et al, 2013a , b ). The further evaluation of the predictions using the HESS system remains an open task.…”

Section: Discussionmentioning

confidence: 99%

Innovative Strategies to Develop Chemical Categories Using a Combination of Structural and Toxicological Properties

et al. 2016

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Interest is increasing in the development of non-animal methods for toxicological evaluations. These methods are however, particularly challenging for complex toxicological endpoints such as repeated dose toxicity. European Legislation, e.g., the European Union's Cosmetic Directive and REACH, demands the use of alternative methods. Frameworks, such as the Read-across Assessment Framework or the Adverse Outcome Pathway Knowledge Base, support the development of these methods. The aim of the project presented in this publication was to develop substance categories for a read-across with complex endpoints of toxicity based on existing databases. The basic conceptual approach was to combine structural similarity with shared mechanisms of action. Substances with similar chemical structure and toxicological profile form candidate categories suitable for read-across. We combined two databases on repeated dose toxicity, RepDose database, and ELINCS database to form a common database for the identification of categories. The resulting database contained physicochemical, structural, and toxicological data, which were refined and curated for cluster analyses. We applied the Predictive Clustering Tree (PCT) approach for clustering chemicals based on structural and on toxicological information to detect groups of chemicals with similar toxic profiles and pathways/mechanisms of toxicity. As many of the experimental toxicity values were not available, this data was imputed by predicting them with a multi-label classification method, prior to clustering. The clustering results were evaluated by assessing chemical and toxicological similarities with the aim of identifying clusters with a concordance between structural information and toxicity profiles/mechanisms. From these chosen clusters, seven were selected for a quantitative read-across, based on a small ratio of NOAEL of the members with the highest and the lowest NOAEL in the cluster (< 5). We discuss the limitations of the approach. Based on this analysis we propose improvements for a follow-up approach, such as incorporation of metabolic information and more detailed mechanistic information. The software enables the user to allocate a substance in a cluster and to use this information for a possible read- across. The clustering tool is provided as a free web service, accessible at http://mlc-reach.informatik.uni-mainz.de.

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Section: Discussionmentioning

confidence: 99%

Innovative Strategies to Develop Chemical Categories Using a Combination of Structural and Toxicological Properties

et al. 2016

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Mind the Gap! A Journey towards Computational Toxicology

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et al. 2016

Molecular Informatics

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Computational methods have advanced toxicology towards the development of target-specific models based on a clear cause-effect rationale. However, the predictive potential of these models presents strengths and weaknesses. On the good side, in silico models are valuable cheap alternatives to in vitro and in vivo experiments. On the other, the unconscious use of in silico methods can mislead end-users with elusive results. The focus of this review is on the basic scientific and regulatory recommendations in the derivation and application of computational models. Attention is paid to examine the interplay between computational toxicology and drug discovery and development. Avoiding the easy temptation of an overoptimistic future, we report our view on what can, or cannot, realistically be done. Indeed, studies of safety/toxicity represent a key element of chemical prioritization programs carried out by chemical industries, and primarily by pharmaceutical companies.

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