Citation:

Pradeep, P., K. Mansouri, G. Patlewicz, AND R. Judson. A Systematic Evaluation of Analogs for the Read-across Prediction of Estrogenicity (Future Tox III). Presented at Future Tox III, Arlington, VA, November 19 - 20, 2015.

Impact/Purpose:

presentation at the FutureTox III meeting

Description:

Read-across is a data gap filling technique which is commonly used within category and analog approaches to predict a biological property for a target data-poor chemical using known information from similar (analog) chemical(s). Analog identification and evaluation are critical steps in deriving a robust read-across prediction. Potential source analogs are typically identified based on structural similarity. Despite the available framework for read-across, there are few guiding principles for evaluating the scientific validity of the analogs. This case study used a two-tiered approach for selection of structurally related analogs to read-across estrogenicity for target chemicals of interest. Firstly, ‘k’ structural analogs were selected using 3 different chemical descriptor approaches (Pubchem, Leadscope and MoSS) based on Tanimoto similarity. Secondly, the analogs were evaluated and screened for consistency in their measured estrogenicity and phys-chem properties with the target chemical. This case study demonstrates the approach outlined above using a dataset of 22 hindered phenols as test chemicals and a chemical universe of ~3700 phenols from the CERAPP project as an inventory of potential source analogs. The results demonstrate that none of the 3 descriptor approaches can be considered as an unequivocal method for analog identification for read across estrogenicity predictions. Future work includes an analysis of the scientific validity and the uncertainty associated with the selection of source analogs using an overlap of analogs from the 3 descriptor approaches.

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