Identifying chemicals based on receptor binding/bioactivation/mechanistic explanation associated with potential to elicit hepatotoxicity and to support structure activity relationship-based read-across.

The liver is the most common target organ in toxicology studies. The development of chemical structural alerts for identifying hepatotoxicity will play an important role in in silico model prediction and help strengthen the identification of analogs used in structure activity relationship (SAR)- based read-across. The aim of the current study is development of an SAR-based expert-system decision tree for screening of hepatotoxicants across a wide range of chemistry space and proposed modes of action for clustering of chemicals using defined core chemical categories based on receptor-binding or bioactivation. The decision tree is based on âˆ¼ 1180 different chemicals that were reviewed for hepatotoxicity information. Knowledge of chemical receptor binding, metabolism and mechanistic information were used to group these chemicals into 16 different categories and 102 subcategories: four categories describe binders to 9 different receptors, 11 categories are associated with possible reactive metabolites (RMs) and there is one miscellaneous category. Each chemical subcategory has been associated with possible modes of action (MOAs) or similar key structural features. This decision tree can help to screen potential liver toxicants associated with core structural alerts of receptor binding and/or RMs and be used as a component of weight of evidence decisions based on SAR read-across, and to fill data gaps.

Structure activity relationship (SAR) toxicological assessments: The role of expert judgment.

Structure activity relationships (SAR) and read-across are widely used animal alternative approaches for filling toxicological data gaps. A framework describing the use of expert judgment in evaluating analogs for SAR has been published and widely cited, however, reliance on expert judgment can introduce inconsistent results across experts and hinder transparency. Here we explore the use of a quantitative similarity score between an analog and a Structure of Interest (SOI) to see if these scores correlate with the expert judgement-based suitability rankings. We find these global similarity scores representing a "whole-molecule" view of similarity to be insensitive to differences in local structure which may be important for toxicity, and, therefore, cannot be substituted for expert judgement-based similarity rankings. In this paper, we suggest that the next step in the progression of SAR approaches retains the insights from expert judgment, but facilitates consistency and transparency through the development of rating "rules". This report outlines and defines analog rating rules for several compound categories. While not comprehensive, the exercises demonstrate the development of rules for categories with a large spread in molecular weight and alkyl chain length and explains the advantages that we see in this approach compared to relying solely on a computational approach or an unstructured expert judgement approach. These rules may be incorporated into analog searching work flows to define boundaries for analogs "suitable" for read-across.