Metabolomics as read-across tool: An example with 3-aminopropanol and 2-aminoethanol.

Read-across and grouping is one of the most commonly used alternative approaches for data gap filling in registrations submitted under the REACH Regulation as defined by the European Chemicals Agency (ECHA) in their 'Read-Across Assessment Framework' (RAAF, 2017). At the same time, the application of read-across is rejected by ECHA frequently due to various reasons. As a major reason hereof, applicants fail to reduce the level of 'remaining uncertainty' intrinsical to every read-across approach compared to testing a substance experimentally. Recently, the use of metabolomics to support read-across cases with biological information has been reported in a case study with phenoxy herbicides (Ravenzwaay et al., 2016). In the present case-study a 'weight-of-evidence' read-across approach from 2-aminoethanol (MEA = 'source') to 3-aminopropanol (3AP = 'target') with metabolomics as 'supporting evidence' reducing the remaining uncertainties is reported. We demonstrate the high structural similarity of the two analogous substances based on the available data and we report how metabolome data add confidence concerning mechanistic similarity in this read-across approach. Finally, the herein described read-across case supported by metabolomics is used to cover the data gaps in repeated dose and reproductive toxicity endpoint of 3AP via weight of evidence for the REACH-registration.

Succinate dehydrogenase inhibitors: in silico flux analysis and in vivo metabolomics investigations show no severe metabolic consequences for rats and humans.

Succinate dehydrogenase complex II inhibitors (SDHIs) are widely used fungicides since the 1960s. Recently, based on published in vitro cell viability data, potential health effects via disruption of the mitochondrial respiratory chain and tricarboxylic acid cycle have been postulated in mammalian species. As primary metabolic impact of SDH inhibition, an increase in succinate, and compensatory ATP production via glycolysis resulting in excess lactate levels was hypothesized. To investigate these hypotheses, genome-scale metabolic models of Rattus norvegicus and Homo sapiens were used for an in silico analysis of mammalian metabolism. Moreover, plasma samples from 28-day studies with the SDHIs boscalid and fluxapyroxad were subjected to metabolome analyses, to assess in vivo metabolite changes induced by SDHIs. The outcome of in silico analyses indicated that mammalian metabolic networks are robust and able to compensate different types of metabolic perturbation, e.g., partial or complete SDH inhibition. Additionally, the in silico comparison of rat and human responses suggested no noticeable differences between both species, evidencing that the rat is an appropriate testing organism for toxicity of SDHIs. Since no succinate or lactate accumulation were found in rats, such an accumulation is also not expected in humans as a result of SDHI exposure.