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High-Throughput Screening to Predict Chemical-Assay Interference
Citation:
Borrel, A., R. Huang, S. Sakamuru, M. Xia, A. Simeonov, K. Mansouri, K. Houck, R. Judson, AND N. Kleinstreuer. High-Throughput Screening to Predict Chemical-Assay Interference. Scientific Reports. Nature Publishing Group, London, Uk, 10:3986, (2020). https://doi.org/10.1038/s41598-020-60747-3
Impact/Purpose:
New approach methods for in vitro toxicity screening evaluates thousands of chemicals across a wide range of assays covering critical biological targets and cellular pathways. Many of these assays, and those used in other in vitro screening programs, rely on luciferase and fluorescence-based readouts that can be susceptible to signal interference by certain chemical structures resulting in false positive outcomes. Included in the Tox21 portfolio are assays specifically designed to measure interference in the form of luciferase inhibition and autofluorescence via multiple wavelengths (red, blue, and green) and under various conditions (cell-free and cell-based, two cell types). Out of 8,305 chemicals tested in the Tox21 interference assays, percent actives ranged from 0.5% (red autofluorescence) to 9.9% (luciferase inhibition). Self-organizing maps and hierarchical clustering were used to relate chemical structural clusters to interference activity profiles. Multiple machine learning algorithms were applied to predict assay interference based on molecular descriptors and chemical properties. The best performing predictive models (accuracies of ~80%) have been included in a web-based tool that will allow users to predict the likelihood of assay interference for any new chemical structure.
Description:
Abstract-The U.S. federal consortium on toxicology in the 21st century (Tox21) produces quantitative, high-throughput screening (HTS) data on thousands of chemicals across a wide range of assays covering critical biological targets and cellular pathways. Many of these assays, and those used in other in vitro screening programs, rely on luciferase and fluorescence-based readouts that can be susceptible to signal interference by certain chemical structures resulting in false positive outcomes. Included in the Tox21 portfolio are assays specifically designed to measure interference in the form of luciferase inhibition and autofluorescence via multiple wavelengths (red, blue, and green) and under various conditions (cell-free and cell-based, two cell types). Out of 8,305 chemicals tested in the Tox21 interference assays, percent actives ranged from 0.5% (red autofluorescence) to 9.9% (luciferase inhibition). Self-organizing maps and hierarchical clustering were used to relate chemical structural clusters to interference activity profiles. Multiple machine learning algorithms were applied to predict assay interference based on molecular descriptors and chemical properties. The best performing predictive models (accuracies of ~80%) have been included in a web-based tool that will allow users to predict the likelihood of assay interference for any new chemical structure.
URLs/Downloads:
DOI: High-Throughput Screening to Predict Chemical-Assay Interference
https://pubmed.ncbi.nlm.nih.gov/32132587/