Innovative Strategies to Develop Chemical Categories Using a Combination of Structural and Toxicological Properties
1.AbstractInterest 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 REAC...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2016-09-01
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| Series: | Frontiers in Pharmacology |
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| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fphar.2016.00321/full |
| _version_ | 1818537470034182144 |
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| author | Monika Batke Martin Gütlein Falko Partosch Ursula Gundert-Remy Christoph Helma Stefan Kramer Andreas Maunz Madeleine Seeland Annette Bitsch |
| author_facet | Monika Batke Martin Gütlein Falko Partosch Ursula Gundert-Remy Christoph Helma Stefan Kramer Andreas Maunz Madeleine Seeland Annette Bitsch |
| author_sort | Monika Batke |
| collection | DOAJ |
| description | 1.AbstractInterest 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. |
| first_indexed | 2024-12-11T18:51:09Z |
| format | Article |
| id | doaj.art-0e0db98bc3f84328a4051111cc5ed665 |
| institution | Directory Open Access Journal |
| issn | 1663-9812 |
| language | English |
| last_indexed | 2024-12-11T18:51:09Z |
| publishDate | 2016-09-01 |
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| series | Frontiers in Pharmacology |
| spelling | doaj.art-0e0db98bc3f84328a4051111cc5ed6652022-12-22T00:54:17ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122016-09-01710.3389/fphar.2016.00321211775Innovative Strategies to Develop Chemical Categories Using a Combination of Structural and Toxicological PropertiesMonika Batke0Martin Gütlein1Falko Partosch2Ursula Gundert-Remy3Christoph Helma4Stefan Kramer5Andreas Maunz6Madeleine Seeland7Annette Bitsch8Fraunhofer Institut für Toxikologie und Experimentelle MedizinUniversität MainzUniversitaetCharité Universitätsmedizin Berlin (Berlin, Germany)In silico toxicology GmbHUniversität MainzOncotest GmbHTechnische Universität MünchenFraunhofer Institut für Toxikologie und Experimentelle Medizin1.AbstractInterest 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.http://journal.frontiersin.org/Journal/10.3389/fphar.2016.00321/fullQSARread acrossNon-animal methodsPredictive Clustering Tree (PCT) methodtoxicological and structural similarity |
| spellingShingle | Monika Batke Martin Gütlein Falko Partosch Ursula Gundert-Remy Christoph Helma Stefan Kramer Andreas Maunz Madeleine Seeland Annette Bitsch Innovative Strategies to Develop Chemical Categories Using a Combination of Structural and Toxicological Properties Frontiers in Pharmacology QSAR read across Non-animal methods Predictive Clustering Tree (PCT) method toxicological and structural similarity |
| title | Innovative Strategies to Develop Chemical Categories Using a Combination of Structural and Toxicological Properties |
| title_full | Innovative Strategies to Develop Chemical Categories Using a Combination of Structural and Toxicological Properties |
| title_fullStr | Innovative Strategies to Develop Chemical Categories Using a Combination of Structural and Toxicological Properties |
| title_full_unstemmed | Innovative Strategies to Develop Chemical Categories Using a Combination of Structural and Toxicological Properties |
| title_short | Innovative Strategies to Develop Chemical Categories Using a Combination of Structural and Toxicological Properties |
| title_sort | innovative strategies to develop chemical categories using a combination of structural and toxicological properties |
| topic | QSAR read across Non-animal methods Predictive Clustering Tree (PCT) method toxicological and structural similarity |
| url | http://journal.frontiersin.org/Journal/10.3389/fphar.2016.00321/full |
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