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ICCVAM Biennial Report 2020-2021

Biennial Progress Report 2020-2021 Interagency Coordinating Committee on the Validation of Alternative Methods
https://ntp.niehs.nih.gov/go/928049

Tools to Predict Chemical-assay Interference

Assays used in Tox21 and other HTS programs rely on luciferase and fluorescence-based readouts. These can be susceptible to signal interference by certain chemical structures resulting in false positive outcomes. However, the Tox21 portfolio includes assays specifically designed to measure interference in the form of luciferase inhibition and autofluorescence via multiple wavelengths and under various conditions. EPA, NIEHS, and NCATS scientists applied multiple machine learning algorithms to predict assay interference based on molecular descriptors and chemical properties (Borrel et al. 2020a). The best performing predictive models were incorporated into a web-based tool called InterPred (Borrel et al. 2020b) that allows users to predict the likelihood of assay interference for any new chemical structure. InterPred increases confidence in HTS data by decreasing false positive testing results.

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