High-Throughput Screening to Predict Chemical-Assay Interference

Alexandre Borrel,Kamel Mansouri,Srilatha Sakamuru,Richard S Judson,Ruili Huang,Nicole C Kleinstreuer,Keith A Houck,Anton Simeonov,Menghang Xia

doi:10.1038/s41598-020-60747-3

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 called InterPred that will allow users to predict the likelihood of assay interference for any new chemical structure and thus increase confidence in HTS data by decreasing false positive testing results.

Highlights

Chemical hazard assessment testing in the twenty-first century has evolved to encompass large high-throughput screening (HTS) research programs, designed to produce quantitative data on the activity of thousands of chemicals across hundreds of biological targets and pathways, a strategy that has long been used in drug discovery
The mechanisms of signal generation from these two common assay formats differ: (1) luciferase expression level is quantitated by the luminescence produced by the luciferase-catalyzed oxidation of added luciferin substrate and (2) fluorescence intensity is measured by excitation at a wavelength matching the fluorescent substrate coupled to quantitation of the wavelength emitted by the excited fluorophore[8]
Considering the importance of highthroughput screening (HTS) platforms in drug discovery and chemical toxicity screening, and the potential impact of false signals derived from these two major interference mechanisms, standardized in silico tools are needed to predict and limit interferent compounds being misinterpreted in fluorescent assay technologies

Summary

Introduction

Chemical hazard assessment testing in the twenty-first century has evolved to encompass large high-throughput screening (HTS) research programs, designed to produce quantitative data on the activity of thousands of chemicals across hundreds of biological targets and pathways, a strategy that has long been used in drug discovery. Many HTS platforms use cell-based assays measuring processes such as cell growth/death, receptor binding, or protein expression while others are cell-free assays that characterize biochemical activity Both formats use a variety of detection technologies including fluorescence and luminescence readouts. Chemicals can interfere by inhibiting luciferase enzymatic activity and possibly by direct www.nature.com/scientificreports oxidation of the luciferin substrate[9] These phenomena are not isolated to a few chemicals, rather more than 5% of PubChem chemical libraries (from over 70,000 tested samples), may have autofluorescence properties[10], and 12% of active chemicals from the NIH Molecular Libraries Small Molecule Repository give paradoxical luminescence changes[11]. Machine learning approaches were leveraged to build statistical quantitative structure–activity relationships (QSAR) models that use selected molecular descriptors to predict the probability of a chemical to interfere with fluorescent intensity or luciferase assays; these open-source models are freely accessible for new chemical prediction via a web-based interface called InterPred (https://sandbox.ntp.niehs.nih.gov/interferences/)

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Conclusion