In This Issue, Volume 9, Issue 8

Computational Toxicology, 2018

Adverse Outcome Pathways (AOPs) establish a connection between a molecular initiating event (MIE) and an adverse outcome. Detailed understanding of the MIE provides the ideal data for determining chemical properties required to elicit the MIE. This study utilized high-throughput screening data from the ToxCast program, coupled with chemical structural information, to generate chemical clusters using three similarity methods pertaining to nine MIEs within an AOP network for hepatic steatosis. Three case studies demonstrate the utility of the mechanistic information held by the MIE for integrating biological and chemical data. Evaluation of the chemical clusters activating the glucocorticoid receptor identified activity differences in chemicals within a cluster. Comparison of the estrogen receptor results with previous work showed that bioactivity data and structural alerts can be combined to improve predictions in a customizable way where bioactivity data are limited. The aryl hydrocarbon receptor (AHR) highlighted that while structural data can be used to offset limited data for new screening efforts, not all ToxCast targets have sufficient data to define robust chemical clusters. In this context, an alternative to additional receptor assays is proposed where assays for proximal key events downstream of AHR activation could be used to enhance confidence in active calls. These case studies illustrate how the AOP framework can support an iterative process whereby in vitro toxicity testing and chemical structure can be combined to improve toxicity predictions. In vitro assays can inform the development of structural alerts linking chemical structure to toxicity. Consequently, structurally related chemical groups can facilitate identification of assays that would be informative for a specific MIE. Together, these activities form a virtuous cycle where the mechanistic basis for the in vitro results and the breadth of the structural alerts continually improve over time to better predict activity of chemicals for which limited toxicity data exist.

Chemistry & Biodiversity, 2009

Current Opinion in Chemical Biology, 2010

Journal of Medicinal Chemistry, 2010

This report describes a number of substructural features which can help to identify compounds that appear as frequent hitters (promiscuous compounds) in many biochemical high throughput screens. The compounds identified by such substructural features are not recognized by filters commonly used to identify reactive compounds. Even though these substructural features were identified using only one assay detection technology, such compounds have been reported to be active from many different assays. In fact, these compounds are increasingly prevalent in the literature as potential starting points for further exploration, whereas they may not be.

Environmental Health Perspectives, 2007

BACKGROUND: The propensity of compounds to produce adverse health effects in humans is generally evaluated using animal-based test methods. Such methods can be relatively expensive, lowthroughput, and associated with pain suffered by the treated animals. In addition, differences in species biology may confound extrapolation to human health effects. OBJECTIVE: The National Toxicology Program and the National Institutes of Health Chemical Genomics Center are collaborating to identify a battery of cell-based screens to prioritize compounds for further toxicologic evaluation. METHODS: A collection of 1,408 compounds previously tested in one or more traditional toxicologic assays were profiled for cytotoxicity using quantitative high-throughput screening (qHTS) in 13 human and rodent cell types derived from six common targets of xenobiotic toxicity (liver, blood, kidney, nerve, lung, skin). Selected cytotoxicants were further tested to define response kinetics. RESULTS: qHTS of these compounds produced robust and reproducible results, which allowed cross-compound, cross-cell type, and cross-species comparisons. Some compounds were cytotoxic to all cell types at similar concentrations, whereas others exhibited species-or cell type-specific cytotoxicity. Closely related cell types and analogous cell types in human and rodent frequently showed different patterns of cytotoxicity. Some compounds inducing similar levels of cytotoxicity showed distinct time dependence in kinetic studies, consistent with known mechanisms of toxicity. CONCLUSIONS: The generation of high-quality cytotoxicity data on this large library of known compounds using qHTS demonstrates the potential of this methodology to profile a much broader array of assays and compounds, which, in aggregate, may be valuable for prioritizing compounds for further toxicologic evaluation, identifying compounds with particular mechanisms of action, and potentially predicting in vivo biological response. KEY WORDS: 1,536-well, cell viability, NTP 1,408 compound library, PubChem, qHTS, RT-CES.

Scientific Reports

The U.S. federal consortium on toxicology in the 21 st century (Tox21) produces quantitative, highthroughput 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. 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. Such efforts, exemplified by the federal Tox21 research consortium 1,2 , are intended to facilitate rapid chemical hazard screening, predictive computational toxicology using machine learning and artificial intelligence techniques, and human-relevant systems biology models that provide mechanistic insight into chemical toxicity (e.g. 3). The Tox21 program and other such HTS initiatives rely upon an array of biological assays, i.e. analytical measurement procedures defined by a set of reagents that produce a detectable signal, allowing a biological process to be quantified 4. 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. Fluorescence-based assays are one of the leading technologies in terms of widespread application reported in PubChem 5,6. Indeed, the routinely used fluorescence intensity-based format allows for optimization of speed, accuracy, reproducibility and sensitivity of assays 7. Luminescence is frequently used as a readout from luciferase-based reporter gene assays and provides high sensitivity due to lack of background activity in mammalian cell lines. 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. Each are subject to different interferent chemicals, i.e. chemicals that modulate the signal intensity without any biological action. There are two main mechanisms by which a compound can directly interfere with a fluorescent assay: quenching, i.e. chemicals absorb light directly, and autofluorescence, i.e. chemicals emit light that overlaps the fluorophore spectrum 4. With respect to luciferase, chemicals can interfere by inhibiting luciferase enzymatic activity and possibly by direct

Journal of Chemical Information and Modeling, 2013

The efficiency of automated compound screening is heavily influenced by the design and the quality of the screening libraries used. We recently reported on the assembly of one diverse and one target-focused lead-like screening library. Using data from 15 enzyme-based screenings conducted using these libraries, their performance was investigated. Both libraries delivered screening hits across a range of targets, with the hits distributed across the entire chemical space represented by both libraries. On closer inspection, however, hit distribution was uneven across the chemical space, with enrichments observed in octants characterized by compounds at the higher end of the molecular weight and lipophilicity spectrum for lead-like compounds, while polar and sp 3 -carbon atom rich compounds were underrepresented among the screening hits. Based on these observations, we propose that screening libraries should not be evenly distributed in lead-like chemical space but be enriched in polar, aliphatic compounds. In conjunction with variable concentration screening, this could lead to more balanced hit rates across the chemical space and screening hits of higher ligand efficiency will be captured. Apart from chemical diversity, both screening libraries were shown to be clean from any pan-assay interference (PAINS) behavior. Even though some compounds were flagged to contain PAINS structural motifs, some of these motifs were demonstrated to be less problematic than previously suggested. To maximize the diversity of the chemical space sampled in a screening campaign, we therefore consider it justifiable to retain compounds containing PAINS structural motifs that were apparently clean in this analysis when assembling screening libraries.

Journal of Medicinal Chemistry, 2007

High-throughput screening (HTS) is the primary technique for new lead identification in drug discovery and chemical biology. Unfortunately, it is susceptible to false-positive hits. One common mechanism for such false-positives is the congregation of organic molecules into colloidal aggregates, which nonspecifically inhibit enzymes. To both evaluate the feasibility of large-scale identification of aggregate-based inhibition and quantify its prevalence among screening hits, we tested 70 563 molecules from the National Institutes of Health Chemical Genomics Center (NCGC) library for detergent-sensitive inhibition. Each molecule was screened in at least seven concentrations, such that dose-response curves were obtained for all molecules in the library. There were 1274 inhibitors identified in total, of which 1204 were unambiguously detergentsensitive. We identified these as aggregate-based inhibitors. Thirty-one library molecules were independently purchased and retested in secondary low-throughput experiments; 29 of these were confirmed as either aggregators or nonaggregators, as appropriate. Finally, with the dose-response information collected for every compound, we could examine the correlation between aggregate-based inhibition and steep doseresponse curves. Three key results emerge from this study: first, detergent-dependent identification of aggregate-based inhibition is feasible on the large scale. Second, 95% of the actives obtained in this screen are aggregate-based inhibitors. Third, aggregate-based inhibition is correlated with steep dose-response curves, although not absolutely. The results of this screen are being released publicly via the PubChem database.

Toxicology Reports, 2018

Therefore, chemical intervention strategies to eliminate bioactivation are often interactive processes; their success depends largely on a close working relationship between drug chemists, pharmacologists and researchers in metabolic science.

Journal of Biomolecular Screening, 2013

Although small-molecule drug discovery efforts have focused largely on enzyme, receptor, and ion-channel targets, there has been an increase in such activities to search for protein-protein interaction (PPI) disruptors by applying high-throughout screening (HTS)–compatible protein-binding assays. However, a disadvantage of these assays is that many primary hits are frequent hitters regardless of the PPI being investigated. We have used the AlphaScreen technology to screen four different robust PPI assays each against 25,000 compounds. These activities led to the identification of 137 compounds that demonstrated repeated activity in all PPI assays. These compounds were subsequently evaluated in two AlphaScreen counter assays, leading to classification of compounds that either interfered with the AlphaScreen chemistry (60 compounds) or prevented the binding of the protein His-tag moiety to nickel chelate (Ni2+-NTA) beads of the AlphaScreen detection system (77 compounds). To further t...