Michael C. Laufersweiler

Framework for identifying chemicals with structural features associated with the potential to act as developmental or reproductive toxicants.

Developmental and reproductive toxicity (DART) end points are important hazard end points that need to be addressed in the risk assessment of chemicals to determine whether or not they are the critical effects in the overall risk assessment. These hazard end points are difficult to predict using current in silico tools because of the diversity of mechanisms of action that elicit DART effects and the potential for narrow windows of vulnerability. DART end points have been projected to consume the majority of animals used for compliance with REACH; thus, additional nonanimal predictive tools are urgently needed. This article presents an empirically based decision tree for determining whether or not a chemical has receptor-binding properties and structural features that are consistent with chemical structures known to have toxicity for DART end points. The decision tree is based on a detailed review of 716 chemicals (664 positive, 16 negative, and 36 with insufficient data) that have DART end-point data and are grouped into defined receptor binding and chemical domains. When tested against a group of chemicals not included in the training set, the decision tree is shown to identify a high percentage of chemicals with known DART effects. It is proposed that this decision tree could be used both as a component of a screening system to identify chemicals of potential concern and as a component of weight-of-evidence decisions based on structure-activity relationships (SAR) to fill data gaps without generating additional test data. In addition, the chemical groupings generated could be used as a starting point for the development of hypotheses for in vitro testing to elucidate mode of action and ultimately in the development of refined SAR principles for DART that incorporate mode of action (adverse outcome pathways).

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

ABSTRACT 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. HighlightsExpert judgment in analog selection for read‐across can introduce inconsistent results across experts and hinder transparency.Quantitative similarity scores do not correlate with expert judgement‐based similarity rankings.Rating “rules” can be developed to standardize expert‐judgment based analog rankings.