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Quantitative structure-activity relationship models for genotoxicity prediction based on combination evaluation strategies for toxicological alternative experiments.

ABSTRACT: Mutagenicity exerts adverse effects on humans. Conventional methods cannot simultaneously predict the toxicity of a large number of compounds. Most mutagenicity prediction models are based on a single experimental type and lack other experimental combination data as support, resulting in limited application scope and predictive ability. In this study, we partitioned data from GENE-TOX, CPDB, and Chemical Carcinogenesis Research Information System according to the weight-of-evidence method for modelling. In our data set, in vivo and in vitro experiments in groups as well as prokaryotic and eukaryotic cell experiments were included in accordance with the ICH guideline. We compared the two experimental combinations mentioned in the weight-of-evidence method and reintegrated the experimental data into three groups. Nine sub-models and three fusion models were established using random forest (RF), support vector machine (SVM), and back propagation (BP) neural network algorithms. When fusing base models under the same algorithm according to the ensemble rules, all models showed excellent predictive performance. The RF, SVM, and BP fusion models reached a prediction accuracy rate of 83.4%, 80.5%, 79.0% respectively. The area under the curve (AUC) reached 0.853, 0.897, 0.865 respectively. Therefore, the established fusion QSAR models can serve as an early warning system for mutagenicity of compounds.

SUBMITTER: Yang X

PROVIDER: S-EPMC8044236 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description: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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Quantitative structure-activity relationship models for genotoxicity prediction based on combination evaluation strategies for toxicological alternative experiments.

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