Prediction of Synergistic Toxicity of Binary Mixtures toVibrio fischeriBased on Biomolecular Interaction Networks

Abstract

The paradigm of chemical safety assessment is shifting from ‘chemical management focusing on single chemicals’ to ‘product management extending to mixtures and articles’. However, because of the enormous combinatorial complexity, testing the toxicity of all conceivable mixture products is currently not feasible. There exist only few models that allow predicting the synergistic toxicity potentially caused by toxicological interactions among components. In this study, we present a novel approach to qualitatively predict the synergistic toxicity of binary mixtures to Vibrio fischeri. On the basis of information derived from protein–chemical and protein–protein interaction networks, we trained machine learning models for classifying chemical mixtures to have synergistic or nonsynergistic toxicity with accuracies and an area under the receiver operating characteristic (ROC) curve (AUC) up to 0.73. The numbers of shared targets and their neighborhood were found to be the most important features for classifying chemicals into synergistic and nonsynergistic groups.

Predicting acute contact toxicity of organic binary mixtures in honey bees (A. mellifera) through innovative QSAR models

“…This manuscript presents three innovative QSAR models (two regression-and one classification-based) developed for the prediction of combined toxicity of binary mixtures in honey bees to date, notwithstanding that other models for predicting binary mixtures toxicity already exist in the literature (Kim et al, 2018;Qin et al, 2018;Muratov et al, 2012;Tian et al, 2013;Toropova et al, 2012;Wang et al, 2018a).…”

A screening study of the spatial distribution and cumulative toxicity of agricultural pesticides in the European Union’s waters

“…In the light of this evidence, the literature tends to suggest CA as the first step in ecological risk assessment irrespective of the MoA, as it provides a precautionary but not overprotective estimation for pesticide mixtures (Junghans et al, 2006;Backhaus and Faust, 2012;Schell et al, 2018) while more complex approaches may not be applicable widely due to data limitations (e.g. Kim et al, 2018). Another recent review confirmed that synergisms with high deviations from CA based predictions are rare and the use of CA as default approach is recommended, still keeping in mind some exceptions for specific classes of chemicals (Martin et al, 2021).…”

Joint Virtual Special Issue on Computational Toxicology

“…18 The authors considered ion extraction from the surface of a specimen to aqueous media and water dissociation on the surface to come with new descriptors to model toxicity of nanomaterials. The analysis of whether bioinformatics knowledge can help to predict synergetic toxicity of binary mixtures was investigated by Kim et al 19 The author used protein−chemical and protein− protein interaction networks to characterize shared biological targets and their neighborhoods for analyzed mixtures, which were the most important features for classifying chemicals into synergistic and nonsynergistic groups.…”