Matched Molecular Pairs Analysis for the Identification of Analogues for the Predictions of Genotoxicity of Plant Protection Product Residues via Read-Across

Background: Plant protection products (PPPs) are used for prevention of crop infestation by disease and pests. Exposure to PPPs can be to the active ingredient or residues in food items. For active ingredients and residues the impact on human health has to be evaluated. Genotoxicity is one of the key effects that must be investigated, specifically the ability of the active ingredient and its residues to cause either gene mutation, or structural and/or numerical chromosomal aberrations. European Food Safety Authority (EFSA) guidance proposes a workflow to estimate the genotoxic potential of PPP residues for dietary risk assessment, within which read-across can be used [1]. To address this, we have previously shown how metabolic similarity, derived via expert judgement of compound class specific metabolism datasets, can be used as a basis for read-across [2-5].

Matched Molecular Pairs Analysis (MMPA): MMPA offers a systematic method for the identification of structural activity relationships through the identification of "pairs" of molecules that differ by a single atom or functional group [6]. This approach has been widely used in drug discovery to develop structure-activity rules for the prediction of drug properties. However, it has not been systematically used to develop structural rules for the prediction of metabolism. In the current work we have applied this method to the analysis of our recently published dataset of sulphonyl urea metabolites and residues [2]. In this work we will show how MMPA leads to a data-driven set of structural rules that define the metabolic transformations present in metabolism datasets. Importantly, we will also show how MMPA compliments expert judgement when it comes to defining such structural rules. This will be exemplified by comparison of the MMPA structural rules to those we defined by expert judgement for the same sulphonyl urea dataset [2]. Read-across case studies using the newly derived MMPA rules will also be presented.

The research presented in this project is funded by CropLife Europe.

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