Use of read-across to simplify the toxicological assessment of a complex mixture of lysimeter leachate metabolites on the basis of chemical similarity and ADME behavior.

This paper describes the further development of a read-across approach applicable to the toxicological assessment of structurally-related xenobiotic metabolites. The approach, which can be applied in the absence of definitive identification of all the individual metabolites, draws on the use of chemical descriptors and multi-variate statistical analysis to define a composite "chemical space" and to classify and characterize closely-related subgroups within this. In this example, consideration of the descriptors driving grouping, combined with empirical evidence for lack of significant further biotransformation of metabolites, leads to the conclusion that, in the absence of any specific structural alerts, the relative toxicity of metabolites within a single grouping will be determined by their relative systemic exposure as described by their ADME characteristics. The in vivo testing of a smaller number of exemplars, selected to have representative ADME properties for each grouping, is sufficient, therefore, to evaluate the toxicity of the remainder. The approach is exemplified using the metabolites of the herbicide S-metolachlor, detected in the leachate of a soil lysimeter.

High-resolution accurate mass spectrometry as a technique for characterization of complex lysimeter leachate samples.

Lysimeter studies can be used to identify and quantify soil degradates of agrochemicals (metabolites) that have the potential to leach to groundwater. However, the apparent metabolic profile of such lysimeter leachate samples will often be significantly more complex than would be expected in true groundwater samples. This is particularly true for S-metolachlor, which has an extremely complex metabolic pathway. Consequently, it was not practically possible to apply a conventional analytical approach to identify all metabolites in an S-metolachlor lysimeter study, because there was insufficient mass to enable the use of techniques such as nuclear magnetic resonance. Recent advances in high-resolution accurate mass spectrometry, however, allow innovative screening approaches to characterize leachate samples to a greater extent than previously possible. Leachate from the S-metolachlor study was screened for accurate masses (±5 ppm of the nominal mass) corresponding to more than 400 hypothetical metabolite structures. A refined list of plausible metabolites was constructed from these data to provide a comprehensive description of the most likely metabolites present. The properties of these metabolites were then evaluated using a principal component analysis model, based on molecular descriptors, to visualize the entire chemical space and to cluster the metabolites into a number of subclasses. This characterization and principal component analysis evaluation enabled the selection of suitable representative metabolites that were subsequently used as exemplars to assess the toxicological relevance of the leachate as a whole. Environ Toxicol Chem 2016;35:1401-1412. © 2015 SETAC.