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

Monika Batke,Stefan Kramer,Ursula Gundert-Remy,Madeleine Seeland,Christoph Helma,Falko Partosch,Andreas Maunz,Annette Bitsch,Martin Gütlein

doi:10.3389/fphar.2016.00321

Abstract

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.

Highlights

At present, there is increasing interest in developing alternative methods for toxicological evaluations that do not require the testing of animals
Regulatory agencies increasingly accept in silico results on genotoxicity (Cassano et al, 2014; Aiba nee Kaneko et al, 2015; Jolly et al, 2015).”In the absence of toxicological data, grouping of substances and read-across approaches are encouraged in the REACH legislation to predict complex endpoints, as resulting from repeated dose toxicity testing
A data analysis performed by ECHA shows that data gaps exist for one-third (32.9%) of endpoints of the so-called phase-in substances

Summary

Introduction

There is increasing interest in developing alternative methods for toxicological evaluations that do not require the testing of animals. Animal testing is banned in the cosmetic legislation and non-animal testing methods have to be used to fulfill the legal request for safe products (7th amendment to the European Union’s Cosmetics Directive 76/768/EE). Both legislations underscore the need for non-animal tools and methods predicting the inherent toxic properties of chemical substances. Like grouping of substances and read-across (ECHA, 2014) are described under Section 1.5 of the above-mentioned Annex XI of Regulation (EC) No 1907/2006 In this legislative text, similarity is defined based on a common functional group, common precursor or degradation product or a constant pattern in the potency of properties across the category

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