A Quantitative High-Throughput Screening Data Analysis Pipeline for Activity Profiling

Screening (ant)agonistic activities of xenobiotics on the retinoic acid receptor alpha (RARα) using in vitro and in silico analysis

Retinoic acid receptors (RARs) are known as crucial endocrine receptors that could mediate a broad diversity of biological processes. However, the data on endocrine disrupting effects of emerging chemicals by targeting RAR (ant)agonism are far from sufficient. Herein, we investigated the RARα agonistic or antagonistic activities for 75 emerging chemicals of concern, and explored their interactions with this receptor. A recombinant two-hybrid yeast assay was used to examine the RARα activities of the test chemicals, wherein 7 showed effects of RARα agonism and 54 exerted potentials of RARα antagonism. The representative chemicals with RARα agonistic activities, i.e. 4-hydroxylphenol (4-HP) and bisphenol AF (BPAF), significantly increased the mRNA levels of CRABP2 and CYP26A1, while 4 select chemicals with RARα antagonistic potentials, including bisphenol A (BPA), tetrabromobisphenol A (TBBPA), 4-tert-octylphenol (4-t-OP), and 4-n-nonylphenol (4-n-NP), conversely decreased the transcriptional levels of the test genes. The in silico molecular docking analysis using 3 different approaches further confirmed the substantial binding between the chemicals with RARα activities and this nuclear receptor protein. This work highlights the promising strategy for screening endocrine-disrupting effects of emerging chemicals of concern by targeting RARα (ant)agonism.

Prediction of chemical-induced acute toxicity using in vitro assay data and chemical structure

Exposure to various chemicals found in the environment and in the context of drug development can cause acute toxicity. To provide an alternative to in vivo animal toxicity testing, the U.S. Tox21 consortium developed in vitro assays to test a library of approximately 10,000 drugs and environmental chemicals (Tox21 10 K compound library) in a quantitative high-throughput screening (qHTS) approach. In this study, we assessed the utility of Tox21 assay data in comparison with chemical structure information in predicting acute systemic toxicity. Prediction models were developed using four machine learning algorithms, namely Random Forest, Naïve Bayes, eXtreme Gradient Boosting, and Support Vector Machine, and their performance was assessed using the area under the receiver operating characteristic curve (AUC-ROC). The chemical structure-based models as well as the Tox21 assay data demonstrated good predictive power for acute toxicity, achieving AUC-ROC values ranging from 0.83 to 0.93 and 0.73 to 0.79, respectively. We applied the models to predict the acute toxicity potential of the compounds in the Tox21 10 K compound library, most of which were found to be non-toxic. In addition, we identified the Tox21 assays that contributed the most to acute toxicity prediction, such as acetylcholinesterase (AChE) inhibition and p53 induction. Chemical features including organophosphates and carbamates were also identified to be significantly associated with acute toxicity. In conclusion, this study underscores the utility of in vitro assay data in predicting acute toxicity.

Identification of Environmental Compounds That May Trigger Early Female Puberty by Activating Human GnRHR and KISS1R

There has been an alarming trend toward earlier puberty in girls, suggesting the influence of an environmental factor(s). As the reactivation of the reproductive axis during puberty is thought to be mediated by the hypothalamic neuropeptides kisspeptin and gonadotropin-releasing hormone (GnRH), we asked whether an environmental compound might activate the kisspeptin (KISS1R) or GnRH receptor (GnRHR). We used GnRHR or KISS1R-expressing HEK293 cells to screen the Tox21 10K compound library, a compendium of pharmaceuticals and environmental compounds, for GnRHR and KISS1R activation. Agonists were identified using Ca2+ flux and phosphorylated extracellularly regulated kinase (p-ERK) detection assays. Follow-up studies included measurement of genes known to be upregulated upon receptor activation using relevant murine or human cell lines and molecular docking simulation. Musk ambrette was identified as a KISS1R agonist, and treatment with musk ambrette led to increased expression of Gnrh1 in murine and human hypothalamic cells and expansion of GnRH neuronal area in developing zebrafish larvae. Molecular docking demonstrated that musk ambrette interacts with the His309, Gln122, and Gln123 residues of the KISS1R. A group of cholinergic agonists with structures similar to methacholine was identified as GnRHR agonists. When applied to murine gonadotrope cells, these agonists upregulated Fos, Jun, and/or Egr1. Molecular docking revealed a potential interaction between GnRHR and 5 agonists, with Asn305 constituting the most conservative GnRHR binding site. In summary, using a Tox21 10K compound library screen combined with cellular, molecular, and structural biology techniques, we have identified novel environmental agents that may activate the human KISS1R or GnRHR.

Occurrence and Prioritization of Human Androgen Receptor Disruptors in Sewage Sludges Across China

The global practice of reusing sewage sludge in agriculture and its landfill disposal reintroduces environmental contaminants, posing risks to human and ecological health. This study screened sewage sludge from 30 Chinese cities for androgen receptor (AR) disruptors, utilizing a disruptor list from the Toxicology in the 21st Century program (Tox21), and identified 25 agonists and 33 antagonists across diverse use categories. Predominantly, natural products 5α-dihydrotestosterone and thymidine emerged as agonists, whereas the industrial intermediate caprolactam was the principal antagonist. In-house bioassays for identified disruptors displayed good alignment with Tox21 potency data, validating employing Tox21 toxicity data for theoretical toxicity estimations. Potency calculations revealed 5α-dihydrotestosterone and two pharmaceuticals (17β-trenbolone and testosterone isocaproate) as the most potent AR agonists and three dyes (rhodamine 6G, Victoria blue BO, and gentian violet) as antagonists. Theoretical effect contribution evaluations prioritized 5α-dihydrotestosterone and testosterone isocaproate as high-risk AR agonists and caprolactam, rhodamine 6G, and 8-hydroxyquinoline (as a biocide and a preservative) as key antagonists. Notably, 16 agonists and 20 antagonists were newly reported in the sludge, many exhibiting significant detection frequencies, concentrations, and/or toxicities, demanding future scrutiny. Our study presents an efficient strategy for estimating environmental sample toxicity and identifying key toxicants, thereby supporting the development of appropriate sludge management strategies.

Reflected generalized concentration addition and Bayesian hierarchical models to improve chemical mixture prediction

Environmental toxicants overwhelmingly occur together as mixtures. The variety of possible chemical interactions makes it difficult to predict the danger of the mixture. In this work, we propose the novel Reflected Generalized Concentration Addition (RGCA), a piece-wise, geometric technique for sigmoidal dose-responsed inverse functions that extends the use of generalized concentration addition (GCA) for 3+ parameter models. Since experimental tests of all relevant mixtures is costly and intractable, we rely only on the individual chemical dose responses. Additionally, RGCA enhances the classical two-step model for the cumulative effects of mixtures, which assumes a combination of GCA and independent action (IA). We explore how various clustering methods can dramatically improve predictions. We compare our technique to the IA, CA, and GCA models and show in a simulation study that the two-step approach performs well under a variety of true models. We then apply our method to a challenging data set of individual chemical and mixture responses where the target is an androgen receptor (Tox21 AR-luc). Our results show significantly improved predictions for larger mixtures. Our work complements ongoing efforts to predict environmental exposure to various chemicals and offers a starting point for combining different exposure predictions to quantify a total risk to health.

High-Throughput Screening to Advance In Vitro Toxicology: Accomplishments, Challenges, and Future Directions

Traditionally, chemical toxicity is determined by in vivo animal studies, which are low throughput, expensive, and sometimes fail to predict compound toxicity in humans. Due to the increasing number of chemicals in use and the high rate of drug candidate failure due to toxicity, it is imperative to develop in vitro, high-throughput screening methods to determine toxicity. The Tox21 program, a unique research consortium of federal public health agencies, was established to address and identify toxicity concerns in a high-throughput, concentration-responsive manner using a battery of in vitro assays. In this article, we review the advancements in high-throughput robotic screening methodology and informatics processes to enable the generation of toxicological data, and their impact on the field; further, we discuss the future of assessing environmental toxicity utilizing efficient and scalable methods that better represent the corresponding biological and toxicodynamic processes in humans.

High-throughput screening of novel TFEB agonists in protecting against acetaminophen-induced liver injury in mice

Macroautophagy (referred to as autophagy hereafter) is a major intracellular lysosomal degradation pathway that is responsible for the degradation of misfolded/damaged proteins and organelles. Previous studies showed that autophagy protects against acetaminophen (APAP)-induced injury (AILI) via selective removal of damaged mitochondria and APAP protein adducts. The lysosome is a critical organelle sitting at the end stage of autophagy for autophagic degradation via fusion with autophagosomes. In the present study, we showed that transcription factor EB (TFEB), a master transcription factor for lysosomal biogenesis, was impaired by APAP resulting in decreased lysosomal biogenesis in mouse livers. Genetic loss-of and gain-of function of hepatic TFEB exacerbated or protected against AILI, respectively. Mechanistically, overexpression of TFEB increased clearance of APAP protein adducts and mitochondria biogenesis as well as SQSTM1/p62-dependent non-canonical nuclear factor erythroid 2-related factor 2 (NRF2) activation to protect against AILI. We also performed an unbiased cell-based imaging high-throughput chemical screening on TFEB and identified a group of TFEB agonists. Among these agonists, salinomycin, an anticoccidial and antibacterial agent, activated TFEB and protected against AILI in mice. In conclusion, genetic and pharmacological activating TFEB may be a promising approach for protecting against AILI.

Recent Advances in Toxicity Prediction: Applications of Deep Graph Learning

The development of new drugs is time-consuming and expensive, and as such, accurately predicting the potential toxicity of a drug candidate is crucial in ensuring its safety and efficacy. Recently, deep graph learning has become prevalent in this field due to its computational power and cost efficiency. Many novel deep graph learning methods aid toxicity prediction and further prompt drug development. This review aims to connect fundamental knowledge with burgeoning deep graph learning methods. We first summarize the essential components of deep graph learning models for toxicity prediction, including molecular descriptors, molecular representations, evaluation metrics, validation methods, and data sets. Furthermore, based on various graph-related representations of molecules, we introduce several representative studies and methods for toxicity prediction from the perspective of GNN architectures and graph pretrained models. Compared to other types of models, deep graph models not only advance in higher accuracy and efficiency but also provide more intuitive insights, which is significant in the development of model interpretation and generalization ability. The graph pretrained models are emerging as they can extract prominent features from large-scale unlabeled molecular graph data and improve the performance of downstream toxicity prediction tasks. We hope this survey can serve as a handbook for individuals interested in exploring deep graph learning for toxicity prediction.

Machine Learning Model for Screening Thyroid Stimulating Hormone Receptor Agonists Based on Updated Datasets and Improved Applicability Domain Metrics

Machine learning (ML) models for screening endocrine-disrupting chemicals (EDCs), such as thyroid stimulating hormone receptor (TSHR) agonists, are essential for sound management of chemicals. Previous models for screening TSHR agonists were built on imbalanced datasets and lacked applicability domain (AD) characterization essential for regulatory application. Herein, an updated TSHR agonist dataset was built, for which the ratio of active to inactive compounds greatly increased to 1:2.6, and chemical spaces of structure-activity landscapes (SALs) were enhanced. Resulting models based on 7 molecular representations and 4 ML algorithms were proven to outperform previous ones. Weighted similarity density (ρ_s) and weighted inconsistency of activities (I_A) were proposed to characterize the SALs, and a state-of-the-art AD characterization methodology AD_SAL{ρ_s, I_A} was established. An optimal classifier developed with PubChem fingerprints and the random forest algorithm, coupled with AD_SAL{ρ_s ≥ 0.15, I_A ≤ 0.65}, exhibited good performance on the validation set with the area under the receiver operating characteristic curve being 0.984 and balanced accuracy being 0.941 and identified 90 TSHR agonist classes that could not be found previously. The classifier together with the AD_SAL{ρ_s, I_A} may serve as efficient tools for screening EDCs, and the AD characterization methodology may be applied to other ML models.

Quantitative Bioactivity Signatures of Dietary Supplements and Natural Products

Dietary supplements and natural products are often marketed as safe and effective alternatives to conventional drugs, but their safety and efficacy are not well regulated. To address the lack of scientific data in these areas, we assembled a collection of Dietary Supplements and Natural Products (DSNP), as well as Traditional Chinese Medicinal (TCM) plant extracts. These collections were then profiled in a series of in vitro high-throughput screening assays, including a liver cytochrome p450 enzyme panel, CAR/PXR signaling pathways, and P-glycoprotein (P-gp) transporter assay activities. This pipeline facilitated the interrogation of natural product-drug interaction (NaPDI) through prominent metabolizing pathways. In addition, we compared the activity profiles of the DSNP/TCM substances with those of an approved drug collection (the NCATS Pharmaceutical Collection or NPC). Many of the approved drugs have well-annotated mechanisms of action (MOAs), while the MOAs for most of the DSNP and TCM samples remain unknown. Based on the premise that compounds with similar activity profiles tend to share similar targets or MOA, we clustered the library activity profiles to identify overlap with the NPC to predict the MOAs of the DSNP/TCM substances. Our results suggest that many of these substances may have significant bioactivity and potential toxicity, and they provide a starting point for further research on their clinical relevance.

Evaluating the utility of a high throughput thiol-containing fluorescent probe to screen for reactivity: A case study with the Tox21 library

High-throughput screening (HTS) assays for bioactivity in the Tox21 program aim to evaluate an array of different biological targets and pathways, but a significant barrier to interpretation of these data is the lack of high-throughput screening (HTS) assays intended to identify non-specific reactive chemicals. This is an important aspect for prioritising chemicals to test in specific assays, identifying promiscuous chemicals based on their reactivity, as well as addressing hazards such as skin sensitisation which are not necessarily initiated by a receptor-mediated effect but act through a non-specific mechanism. Herein, a fluorescence-based HTS assay that allows the identification of thiol-reactive compounds was used to screen 7,872 unique chemicals in the Tox21 10K chemical library. Active chemicals were compared with profiling outcomes using structural alerts encoding electrophilic information. Random Forest classification models based on chemical fingerprints were developed to predict assay outcomes and evaluated through 10-fold stratified cross validation (CV). The mean CV Balanced Accuracy of the validation set was 0.648. The model developed shows promise as a tool to screen untested chemicals for their potential electrophilic reactivity based solely on chemical structural features.

Machine learning approaches to predict drug efficacy and toxicity in oncology

In recent years, there has been a surge of interest in using machine learning algorithms (MLAs) in oncology, particularly for biomedical applications such as drug discovery, drug repurposing, diagnostics, clinical trial design, and pharmaceutical production. MLAs have the potential to provide valuable insights and predictions in these areas by representing both the disease state and the therapeutic agents used to treat it. To fully utilize the capabilities of MLAs in oncology, it is important to understand the fundamental concepts underlying these algorithms and how they can be applied to assess the efficacy and toxicity of therapeutics. In this perspective, we lay out approaches to represent both the disease state and the therapeutic agents used by MLAs to derive novel insights and make relevant predictions.

Lestaurtinib induces DNA damage that is related to estrogen receptor activation

A number of chemicals in the environment pose a threat to human health. Recent studies indicate estradiol induces DNA damage through the activation of the estrogen receptor alpha (ERα). Given that many environmental chemical compounds act like hormones once they enter the human body, it is possible that they induce DNA damage in the same way as estradiol, which is of great concern to females with the BRCA1 mutation. In this study, we developed an antibody-based high content method measuring γH2AX, a biomarker for DNA damage, to test a subset of 907 chemical compounds in MCF7 cells. The assay was optimized for a 1536 well plate format and had a satisfactory assay performance with Z-factor of 0.67. From the screening, we identified 128 compounds that induce γH2AX expression in the cells. These compounds were further examined for their γH2AX induction in the presence of an ER inhibitor, tamoxifen. After tamoxifen treatment, four compounds induced less γH2AX expression compared to those without tamoxifen treatment, suggesting these compounds induced γH2AX that is related to ERα activation. These four compounds were chosen for further studies to assess their ERα activating capability and c-MYC induction. Only lestaurtinib, a selective tyrosine kinase inhibitor, induced ERα activation, which was confirmed by both ERα beta-lactamase reporter gene assay and molecular docking analysis. Lestaurtinib also increased c-MYC expression, a target gene of ERα signaling, measured by the quantitative PCR method. This data suggests that lestaurtinib acts as a DNA damage inducer that is related to ERα activation.

Epidemiology meets toxicogenomics: Mining toxicologic evidence in support of an untargeted analysis of pesticides exposure and Parkinson's disease

BACKGROUND

Pesticides have been widely used in agriculture for more than half a century. However, with thousands currently in use, most have not been adequately assessed for influence Parkinson's disease (PD).

OBJECTIVES

Here we aimed to assess biologic plausibility of 70 pesticides implicated with PD through an agnostic pesticide-wide association study using a data mining approach linking toxicology and toxicogenomics databases.

METHODS

We linked the 70 targeted pesticides to quantitative high-throughput screening assay findings from the Toxicology in the 21st Century (Tox21) program and pesticide-related genetic/disease information with the Comparative Toxicogenomics Database (CTD). We used the CTD to determine networks of genes each pesticide has been linked to and assess enrichment of relevant gene ontology (GO) annotations. With Tox21, we evaluated pesticide induced activity on a series of 43 nuclear receptor and stress response assays and two cytotoxicity assays.

RESULTS

Overall, 59 % of the 70 pesticides had chemical-gene networks including at least one PD gene/gene product. In total, 41 % of the pesticides had chemical-gene networks enriched for ≥ 1 high-priority PD GO terms. For instance, 23 pesticides had chemical-gene networks enriched for response to oxidative stress, 21 for regulation of neuron death, and twelve for autophagy, including copper sulfate, endosulfan and chlorpyrifos. Of the pesticides tested against the Tox21 assays, 79 % showed activity on ≥ 1 assay and 11 were toxic to the two human cell lines. The set of PD-associated pesticides showed more activity than expected on assays testing for xenobiotic homeostasis, mitochondrial membrane permeability, and genotoxic stress.

CONCLUSIONS

Overall, cross-database queries allowed us to connect a targeted set of pesticides implicated in PD via epidemiology to specific biologic targets relevant to PD etiology. This knowledge can be used to help prioritize targets for future experimental studies and improve our understanding of the role of pesticides in PD etiology.

Tox21-Based Comparative Analyses for the Identification of Potential Toxic Effects of Environmental Pollutants

Chemical pollution has become a prominent environmental problem. In recent years, quantitative high-throughput screening (qHTS) assays have been developed for the fast assessment of chemicals' toxic effects. Toxicology in the 21st Century (Tox21) is a well-known and continuously developing qHTS project. Recent reports utilizing Tox21 data have mainly focused on setting up mathematical models for in vivo toxicity predictions, with less attention to intuitive qHTS data visualization. In this study, we attempted to reveal and summarize the toxic effects of environmental pollutants by analyzing and visualizing Tox21 qHTS data. Via PubMed text mining, toxicity/structure clustering, and manual classification, we detected a total of 158 chemicals of environmental concern (COECs) from the Tox21 library that we classified into 13 COEC groups based on structure and activity similarities. By visualizing these COEC groups' bioactivities, we demonstrated that COECs frequently displayed androgen and progesterone antagonistic effects, xenobiotic receptor agonistic roles, and mitochondrial toxicity. We also revealed many other potential targets of the 13 COEC groups, which were not well illustrated yet, and that current Tox21 assays may not correctly classify known teratogens. In conclusion, we provide a feasible method to intuitively understand qHTS data.

Use of Tox21 Screening Data to Evaluate the COVID-19 Drug Candidates for Their Potential Toxic Effects and Related Pathways

Currently, various potential therapeutic agents for coronavirus disease-2019 (COVID-19), a global pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), are being investigated worldwide mainly through the drug repurposing approach. Several anti-viral, anti-bacterial, anti-malarial, and anti-inflammatory drugs were employed in randomized trials and observational studies for developing new therapeutics for COVID-19. Although an increasing number of repurposed drugs have shown anti-SARS-CoV-2 activities in vitro, so far only remdesivir has been approved by the US FDA to treat COVID-19, and several other drugs approved for Emergency Use Authorization, including sotrovimab, tocilizumab, baricitinib, paxlovid, molnupiravir, and other potential strategies to develop safe and effective therapeutics for SARS-CoV-2 infection are still underway. Many drugs employed as anti-viral may exert unwanted side effects (i.e., toxicity) via unknown mechanisms. To quickly assess these drugs for their potential toxicological effects and mechanisms, we used the Tox21 in vitro assay datasets generated from screening ∼10,000 compounds consisting of approved drugs and environmental chemicals against multiple cellular targets and pathways. Here we summarize the toxicological profiles of small molecule drugs that are currently under clinical trials for the treatment of COVID-19 based on their in vitro activities against various targets and cellular signaling pathways.

Identification of active or inactive agonists of tumor suppressor protein based on Tox21 library

Exposure of cells to xenobiotic human-made products can lead to genotoxicity and cause DNA damage. It is an urgent need to quickly identify the chemicals that cause DNA damage, and their toxicity should be predicted. In this study, recursive partitioning (RP), binary logistic regression, and one machine learning approach, namely, random forest (RF) classifier, were used to predict the active and inactive compounds of a total 5036 data based on the assay conducted by a β-lactamase reporter gene under control of the p53 response element (p53RE) from Tox21 library. Results show that the binary logistic regression model with a threshold of 0.5 has a high accuracy rate (83%) to distinguish active and inactive compounds. The RF classifier method has satisfactory results, with an accuracy rate (84.38%) approximately higher than that of binary logistic regression. The models established can identify compounds that induce DNA damage and activate p53, and provide a scientific basis for the risk assessment of organic chemicals in the environment.

Repurposing drugs as COVID-19 therapies: a toxicity evaluation

Drug repurposing is an appealing method to address the Coronavirus 2019 (COVID-19) pandemic because of the low cost and efficiency. We analyzed our in-house database of approved drug screens and compared their activity profiles with results from a severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) cytopathic effect (CPE) assay. The activity profiles of the human ether-à-go-go-related gene (hERG), phospholipidosis (PLD), and many cytotoxicity screens were found significantly correlated with anti-SARS-CoV-2 activity. hERG inhibition is a nonspecific off-target effect that has contributed to promiscuous drug interactions, whereas drug-induced PLD is an undesirable effect linked to hERG blockers. Thus, this study identifies preferred drug candidates as well as chemical structures that should be avoided because of their potential to induce toxicity. Lastly, we highlight the hERG liability of anti-SARS-CoV-2 drugs currently enrolled in clinical trials.

A gene expression biomarker for predictive toxicology to identify chemical modulators of NF-κB

The nuclear factor-kappa B (NF-κB) is a transcription factor with important roles in inflammation, immune response, and oncogenesis. Dysregulation of NF-κB signaling is associated with inflammation and certain cancers. We developed a gene expression biomarker predictive of NF-κB modulation and used the biomarker to screen a large compendia of gene expression data. The biomarker consists of 108 genes responsive to tumor necrosis factor α in the absence but not the presence of IκB, an inhibitor of NF-κB. Using a set of 450 profiles from cells treated with immunomodulatory factors with known NF-κB activity, the balanced accuracy for prediction of NF-κB activation was > 90%. The biomarker was used to screen a microarray compendium consisting of 12,061 microarray comparisons from human cells exposed to 2,672 individual chemicals to identify chemicals that could cause toxic effects through NF-κB. There were 215 and 49 chemicals that were identified as putative or known NF-κB activators or suppressors, respectively. NF-κB activators were also identified using two high-throughput screening assays; 165 out of the ~3,800 chemicals (ToxCast assay) and 55 out of ~7,500 unique compounds (Tox21 assay) were identified as potential activators. A set of 32 chemicals not previously associated with NF-κB activation and which partially overlapped between the different screens were selected for validation in wild-type and NFKB1-null HeLa cells. Using RT-qPCR and targeted RNA-Seq, 31 of the 32 chemicals were confirmed to be NF-κB activators. These results comprehensively identify a set of chemicals that could cause toxic effects through NF-κB.

High-Throughput Chemical Screening and Structure-Based Models to Predict hERG Inhibition

Chemical inhibition of the human ether-a -go-go-related gene (hERG) potassium channel leads to a prolonged QT interval that can contribute to severe cardiotoxicity. The adverse effects of hERG inhibition are one of the principal causes of drug attrition in clinical and pre-clinical development. Preliminary studies have demonstrated that a wide range of environmental chemicals and toxicants may also inhibit the hERG channel and contribute to the pathophysiology of cardiovascular (CV) diseases. As part of the US federal Tox21 program, the National Center for Advancing Translational Science (NCATS) applied a quantitative high throughput screening (qHTS) approach to screen the Tox21 library of 10,000 compounds (~7871 unique chemicals) at 14 concentrations in triplicate to identify chemicals perturbing hERG activity in the U2OS cell line thallium flux assay platform. The qHTS cell-based thallium influx assay provided a robust and reliable dataset to evaluate the ability of thousands of drugs and environmental chemicals to inhibit hERG channel protein, and the use of chemical structure-based clustering and chemotype enrichment analysis facilitated the identification of molecular features that are likely responsible for the observed hERG activity. We employed several machine-learning approaches to develop QSAR prediction models for the assessment of hERG liabilities for drug-like and environmental chemicals. The training set was compiled by integrating hERG bioactivity data from the ChEMBL database with the Tox21 qHTS thallium flux assay data. The best results were obtained with the random forest method (~92.6% balanced accuracy). The data and scripts used to generate hERG prediction models are provided in an open-access format as key in vitro and in silico tools that can be applied in a translational toxicology pipeline for drug development and environmental chemical screening.

Identification of Compounds for Butyrylcholinesterase Inhibition

Butyrylcholinesterase (BChE) is a nonspecific cholinesterase enzyme that hydrolyzes choline-based esters. BChE plays a critical role in maintaining normal cholinergic function like acetylcholinesterase (AChE) through hydrolyzing acetylcholine (ACh). Selective BChE inhibition has been regarded as a viable therapeutic approach in Alzheimer's disease. As of now, a limited number of selective BChE inhibitors are available. To identify BChE inhibitors rapidly and efficiently, we have screened 8998 compounds from several annotated libraries against an enzyme-based BChE inhibition assay in a quantitative high-throughput screening (qHTS) format. From the primary screening, we identified a group of 125 compounds that were further confirmed to inhibit BChE activity, including previously reported BChE inhibitors (e.g., bambuterol and rivastigmine) and potential novel BChE inhibitors (e.g., pancuronium bromide and NNC 756), representing diverse structural classes. These BChE inhibitors were also tested for their selectivity by comparing their IC50 values in BChE and AChE inhibition assays. The binding modes of these compounds were further studied using molecular docking analyses to identify the differences between the interactions of these BChE inhibitors within the active sites of AChE and BChE. Our qHTS approach allowed us to establish a robust and reliable process to screen large compound collections for potential BChE inhibitors.

High-throughput screening assays for SARS-CoV-2 drug development: Current status and future directions

In response to the ongoing coronavirus disease 2019 (COVID-19) pandemic, a panel of assays has been developed and applied to screen collections of approved and investigational drugs for anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) activity in a quantitative high-throughput screening (qHTS) format. In this review, we applied data-driven approaches to evaluate the ability of each assay to identify potential anti-SARS-CoV-2 leads. Multitarget assays were found to show advantages in terms of accuracy and efficiency over single-target assays, whereas target-specific assays were more suitable for investigating compound mechanisms of action. Moreover, strict filtering with counter screens might be more detrimental than beneficial in identifying true positives. Thus, developing novel HTS assays acting simultaneously against multiple targets in the SARS-CoV-2 life cycle will benefit anti-COVID-19 drug discovery.

Assessing how in vitro assay types predict in vivo toxicology data

In vivo animal bioassays are increasingly being supplemented with in vitro assays to serve as the new standard for chemical toxicity tests. Despite this shift, investigators face challenges related to increased reliance on in vitro data. The aim of this study was to deploy a streamlined method to assess the ability of in vitro data to predict similar results as in vivo data by correlating chemical toxicity rankings obtained using Benchmark Doses and Benchmark Dose Lower Limits (BMD(L)s) derived from in vivo and in vitro assays. In vitro and in vivo assay characteristics were assessed for their impact on the predictive ability of in vitro data. Minimum best-fit BMD(L)s were calculated for chemicals using Environmental Protection Agency's (EPA's) Benchmark Dose Software (BMDS). Forty-one chemicals met the inclusion criteria of this study. Relative chemical toxicity rankings were assessed through Kappa statistics, Pearson correlations, and/or Ordinary Least Squares (OLS) regressions. Results illustrated likely ability of in vitro data to predict similar results as short-term in vivo data. Further, rankings derived from in vitro cytotoxicity assays, unlike stress response assays, significantly correlated with rankings derived from short-term in vivo assays. These results support the use of in vitro data as a prioritization tool within toxicity testing.

Predictive Models to Identify Small Molecule Activators and Inhibitors of Opioid Receptors

Opioid receptors (OPRs) are the main targets for the treatment of pain and related disorders. The opiate compounds that activate these receptors are effective analgesics but their use leads to adverse effects, and they often are highly addictive drugs of abuse. There is an urgent need for alternative chemicals that are analgesics and to reduce/avoid the unwanted effects in order to relieve the public health crisis of opioid addiction. Here, we aim to develop computational models to predict the OPR activity of small molecule compounds based on chemical structures and apply these models to identify novel OPR active compounds. We used four different machine learning algorithms to build models based on quantitative high throughput screening (qHTS) data sets of three OPRs in both agonist and antagonist modes. The best performing models were applied to virtually screen a large collection of compounds. The model predicted active compounds were experimentally validated using the same qHTS assays that generated the training data. Random forest was the best classifier with the highest performance metrics, and the mu OPR (OPRM)-agonist model achieved the best performance measured by AUC-ROC (0.88) and MCC (0.7) values. The model predicted actives resulted in hit rates ranging from 2.3% (delta OPR-agonist) to 15.8% (OPRM-agonist) after experimental confirmation. Compared to the original assay hit rate, all models enriched the hit rate by ≥2-fold. Our approach produced robust OPR prediction models that can be applied to prioritize compounds from large libraries for further experimental validation. The models identified several novel potent compounds as activators/inhibitors of OPRs that were confirmed experimentally. The potent hits were further investigated using molecular docking to find the interactions of the novel ligands in the active site of the corresponding OPR.

Profiling the Tox21 Chemical Collection for Acetylcholinesterase Inhibition

BACKGROUND

Inhibition of acetylcholinesterase (AChE), a biomarker of organophosphorous and carbamate exposure in environmental and occupational human health, has been commonly used to identify potential safety liabilities. So far, many environmental chemicals, including drug candidates, food additives, and industrial chemicals, have not been thoroughly evaluated for their inhibitory effects on AChE activity. AChE inhibitors can have therapeutic applications (e.g., tacrine and donepezil) or neurotoxic consequences (e.g., insecticides and nerve agents).

OBJECTIVES

The objective of the current study was to identify environmental chemicals that inhibit AChE activity using in vitro and in silico models.

METHODS

To identify AChE inhibitors rapidly and efficiently, we have screened the Toxicology in the 21st Century (Tox21) 10K compound library in a quantitative high-throughput screening (qHTS) platform by using the homogenous cell-based AChE inhibition assay and enzyme-based AChE inhibition assays (with or without microsomes). AChE inhibitors identified from the primary screening were further tested in monolayer or spheroid formed by SH-SY5Y and neural stem cell models. The inhibition and binding modes of these identified compounds were studied with time-dependent enzyme-based AChE inhibition assay and molecular docking, respectively.

RESULTS

A group of known AChE inhibitors, such as donepezil, ambenonium dichloride, and tacrine hydrochloride, as well as many previously unreported AChE inhibitors, such as chelerythrine chloride and cilostazol, were identified in this study. Many of these compounds, such as pyrazophos, phosalone, and triazophos, needed metabolic activation. This study identified both reversible (e.g., donepezil and tacrine) and irreversible inhibitors (e.g., chlorpyrifos and bromophos-ethyl). Molecular docking analyses were performed to explain the relative inhibitory potency of selected compounds.

CONCLUSIONS

Our tiered qHTS approach allowed us to generate a robust and reliable data set to evaluate large sets of environmental compounds for their AChE inhibitory activity. https://doi.org/10.1289/EHP6993.

Evaluation of chemical compounds that inhibit neurite outgrowth using GFP-labeled iPSC-derived human neurons

Due to the increasing number of drugs and untested environmental compounds introduced into commercial use, there is recognition for a need to develop reliable and efficient screening methods to identify compounds that may adversely impact the nervous system. One process that has been implicated in neurodevelopment is neurite outgrowth; the disruption of which can result in adverse outcomes that persist later in life. Here, we developed a green fluorescent protein (GFP) labeled neurite outgrowth assay in a high-content, high-throughput format using induced pluripotent stem cell (iPSC) derived human spinal motor neurons and cortical glutamatergic neurons. The assay was optimized for use in a 1536-well plate format. Then, we used this assay to screen a set of 84 unique compounds that have previously been screened in other neurite outgrowth assays. This library consists of known developmental neurotoxicants, environmental compounds with unknown toxicity, and negative controls. Neurons were cultured for 40 h and then treated with compounds at 11 concentrations ranging from 1.56 nM to 92 μM for 24 and 48 h. Effects of compounds on neurite outgrowth were evaluated by quantifying total neurite length, number of segments, and maximum neurite length per cell. Among the 84 tested compounds, neurite outgrowth in cortical neurons and motor neurons were selectively inhibited by 36 and 31 compounds, respectively. Colchicine, rotenone, and methyl mercuric (II) chloride inhibited neurite outgrowth in both cortical and motor neurons. It is interesting to note that some compounds like parathion and bisphenol AF had inhibitory effects on neurite outgrowth specifically in the cortical neurons, while other compounds, such as 2,2',4,4'-tetrabromodiphenyl ether and caffeine, inhibited neurite outgrowth in motor neurons. The data gathered from these studies show that GFP-labeled iPSC-derived human neurons are a promising tool for identifying and prioritizing compounds with developmental neurotoxicity potential for further hazard characterization.

Trade-off Predictivity and Explainability for Machine-Learning Powered Predictive Toxicology: An in-Depth Investigation with Tox21 Data Sets

Selecting a model in predictive toxicology often involves a trade-off between prediction performance and explainability: should we sacrifice the model performance to gain explainability or vice versa. Here we present a comprehensive study to assess algorithm and feature influences on model performance in chemical toxicity research. We conducted over 5000 models for a Tox21 bioassay data set of 65 assays and ∼7600 compounds. Seven molecular representations as features and 12 modeling approaches varying in complexity and explainability were employed to systematically investigate the impact of various factors on model performance and explainability. We demonstrated that end points dictated a model's performance, regardless of the chosen modeling approach including deep learning and chemical features. Overall, more complex models such as (LS-)SVM and Random Forest performed marginally better than simpler models such as linear regression and KNN in the presented Tox21 data analysis. Since a simpler model with acceptable performance often also is easy to interpret for the Tox21 data set, it clearly was the preferred choice due to its better explainability. Given that each data set had its own error structure both for dependent and independent variables, we strongly recommend that it is important to conduct a systematic study with a broad range of model complexity and feature explainability to identify model balancing its predictivity and explainability.

Characterization of human pregnane X receptor activators identified from a screening of the Tox21 compound library

The pregnane X receptor (PXR; NR1I2) is an important nuclear receptor whose main function is to regulate enzymes within drug metabolism. The main drug metabolizing enzyme regulated by PXR, cytochrome P450 (CYP) 3A4, accounts for the metabolism of nearly 50% of all marketed drugs. Recently, PXR has also been identified as playing a role in energy homeostasis, immune response, and cancer. Due to its interaction with these important roles, alongside its drug-drug interaction function, it is imperative to identify compounds which can modulate PXR. In this study, we screened the Tox21 10,000 compound collection to identify hPXR agonists using a stable hPXR-Luc HepG2 cell line. A pharmacological study in the presence of a PXR antagonist was performed to confirm the activity of the chosen potential hPXR agonists in the same cells. Finally, metabolically competent cell lines - HepaRG and HepaRG-PXR-Knockout (KO) - were used to further confirm the potential PXR activators. We identified a group of structural clusters and singleton compounds which included potentially novel hPXR agonists. Of the 21 selected compounds, 11 potential PXR activators significantly induced CYP3A4 mRNA expression in HepaRG cells. All of these compounds lost their induction when treating HepaRG-PXR-KO cells, confirming their PXR activation. Etomidoline presented as a potentially selective agonist of PXR. In conclusion, the current study has identified 11 compounds as potentially novel or not well-characterized PXR activators. These compounds should further be studied for their potential effects on drug metabolism and drug-drug interactions due to the immense implications of being a PXR agonist.

Systematic Identification of Molecular Targets and Pathways Related to Human Organ Level Toxicity

The mechanisms leading to organ level toxicities are poorly understood. In this study, we applied an integrated approach to deduce the molecular targets and biological pathways involved in chemically induced toxicity for eight common human organ level toxicity end points (carcinogenicity, cardiotoxicity, developmental toxicity, hepatotoxicity, nephrotoxicity, neurotoxicity, reproductive toxicity, and skin toxicity). Integrated analysis of in vitro assay data, molecular targets and pathway annotations from the literature, and toxicity-molecular target associations derived from text mining, combined with machine learning techniques, were used to generate molecular targets for each of the organ level toxicity end points. A total of 1516 toxicity-related genes were identified and subsequently analyzed for biological pathway coverage, resulting in 206 significant pathways (p-value <0.05), ranging from 3 (e.g., developmental toxicity) to 101 (e.g., skin toxicity) for each toxicity end point. This study presents a systematic and comprehensive analysis of molecular targets and pathways related to various in vivo toxicity end points. These molecular targets and pathways could aid in understanding the biological mechanisms of toxicity and serve as a guide for the design of suitable in vitro assays for more efficient toxicity testing. In addition, these results are complementary to the existing adverse outcome pathway (AOP) framework and can be used to aid in the development of novel AOPs. Our results provide abundant testable hypotheses for further experimental validation.

NURA: A curated dataset of nuclear receptor modulators

Nuclear receptors (NRs) are key regulators of human health and constitute a relevant target for medicinal chemistry applications as well as for toxicological risk assessment. Several open databases dedicated to small molecules that modulate NRs exist; however, depending on their final aim (i.e., adverse effect assessment or drug design), these databases contain a different amount and type of annotated molecules, along with a different distribution of experimental bioactivity values. Stemming from these considerations, in this work we aim to provide a unified dataset, NURA (NUclear Receptor Activity) dataset, collecting curated information on small molecules that modulate NRs, to be intended for both pharmacological and toxicological applications. NURA contains bioactivity annotations for 15,247 molecules and 11 selected NRs, and it was obtained by integrating and curating data from toxicological and pharmacological databases (i.e., Tox21, ChEMBL, NR-DBIND and BindingDB). Our results show that NURA dataset is a useful tool to bridge the gap between toxicology- and medicinal-chemistry-related databases, as it is enriched in terms of number of molecules, structural diversity and covered atomic scaffolds compared to the single sources. To the best of our knowledge, NURA dataset is the most exhaustive collection of small molecules annotated for their modulation of the chosen nuclear receptors. NURA dataset is intended to support decision-making in pharmacology and toxicology, as well as to contribute to data-driven applications, such as machine learning. The dataset and the data curation pipeline can be downloaded free of charge on Zenodo at the following DOI: https://doi.org/10.5281/zenodo.3991561.

Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling

Advancing a new drug to market requires substantial investments in time as well as financial resources. Crucial bioactivities for drug candidates, including their efficacy, pharmacokinetics (PK), and adverse effects, need to be investigated during drug development. With advancements in chemical synthesis and biological screening technologies over the past decade, a large amount of biological data points for millions of small molecules have been generated and are stored in various databases. These accumulated data, combined with new machine learning (ML) approaches, such as deep learning, have shown great potential to provide insights into relevant chemical structures to predict in vitro, in vivo, and clinical outcomes, thereby advancing drug discovery and development in the big data era.

Predictive Models for Human Organ Toxicity Based on In Vitro Bioactivity Data and Chemical Structure

Traditional toxicity testing reliant on animal models is costly and low throughput, posing a significant challenge with the increasing numbers of chemicals that humans are exposed to in the environment. The purpose of this investigation was to build optimal prediction models for various human in vivo/organ-level toxicity end points (extracted from ChemIDPlus) using chemical structure and Tox21 in vitro quantitative high-throughput screening (qHTS) bioactivity assay data. Several supervised machine learning algorithms were applied to model 14 human toxicity end points pertaining to vascular, kidney, ureter and bladder, and liver organ systems. Three metrics were used to evaluate model performance: area under the receiver operating characteristic curve (AUC-ROC), balanced accuracy (BA), and Matthews correlation coefficient (MCC). The top four models, with AUC-ROC values >0.8, were derived for endocrine (0.90 ± 0.00), musculoskeletal (0.88 ± 0.02), peripheral nerve and sensation (0.85 ± 0.01), and brain and coverings (0.83 ± 0.02) toxicities, whereas the best model AUC-ROC values were >0.7 for the remaining 10 toxicities. Model performance was found to be dependent on the specific data set, model type, and feature selection method used. In addition, chemical structure and assay data showed different levels of contribution to the prediction of different toxicity end points. Although in vitro assay data, when combined with chemical structure, slightly improved the predictive accuracy for most end points (11 out of 14), a noteworthy finding was the near equal success of the structure-only models, which do not require Tox21 qHTS screening data, and the relatively poor performance of assay-only models. Thus, the top-performing structure-only models from this study could be applied for hazard screening of large sets of chemicals for potential human toxicity, whereas the largest assay contributions to models (i.e., cellular targets) could be used, along with the top-contributing structural features, to provide insight into toxicity mechanisms.

High-Throughput Screening to Predict Chemical-Assay Interference

The U.S. federal consortium on toxicology in the 21^st 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.

Two-Dimensional Cellular and Three-Dimensional Bio-Printed Skin Models to Screen Topical-Use Compounds for Irritation Potential

Assessing skin irritation potential is critical for the safety evaluation of topical drugs and other consumer products such as cosmetics. The use of advanced cellular models, as an alternative to replace animal testing in the safety evaluation for both consumer products and ingredients, is already mandated by law in the European Union (EU) and other countries. However, there has not yet been a large-scale comparison of the effects of topical-use compounds in different cellular skin models. This study assesses the irritation potential of topical-use compounds in different cellular models of the skin that are compatible with high throughput screening (HTS) platforms. A set of 451 topical-use compounds were first tested for cytotoxic effects using two-dimensional (2D) monolayer models of primary neonatal keratinocytes and immortalized human keratinocytes. Forty-six toxic compounds identified from the initial screen with the monolayer culture systems were further tested for skin irritation potential on reconstructed human epidermis (RhE) and full thickness skin (FTS) three-dimensional (3D) tissue model constructs. Skin irritation potential of the compounds was assessed by measuring tissue viability, trans-epithelial electrical resistance (TEER), and secretion of cytokines interleukin 1 alpha (IL-1α) and interleukin 18 (IL-18). Among known irritants, high concentrations of methyl violet and methylrosaniline decreased viability, lowered TEER, and increased IL-1α secretion in both RhE and FTS models, consistent with irritant properties. However, at low concentrations, these two compounds increased IL-18 secretion without affecting levels of secreted IL-1α, and did not reduce tissue viability and TEER, in either RhE or FTS models. This result suggests that at low concentrations, methyl violet and methylrosaniline have an allergic potential without causing irritation. Using both HTS-compatible 2D cellular and 3D tissue skin models, together with irritation relevant activity endpoints, we obtained data to help assess the irritation effects of topical-use compounds and identify potential dermal hazards.

DeepSnap-Deep Learning Approach Predicts Progesterone Receptor Antagonist Activity With High Performance

The progesterone receptor (PR) is important therapeutic target for many malignancies and endocrine disorders due to its role in controlling ovulation and pregnancy via the reproductive cycle. Therefore, the modulation of PR activity using its agonists and antagonists is receiving increasing interest as novel treatment strategy. However, clinical trials using the PR modulators have not yet been found conclusive evidences. Recently, increasing evidence from several fields shows that the classification of chemical compounds, including agonists and antagonists, can be done with recent improvements in deep learning (DL) using deep neural network. Therefore, we recently proposed a novel DL-based quantitative structure-activity relationship (QSAR) strategy using transfer learning to build prediction models for agonists and antagonists. By employing this novel approach, referred as DeepSnap-DL method, which uses images captured from 3-dimension (3D) chemical structure with multiple angles as input data into the DL classification, we constructed prediction models of the PR antagonists in this study. Here, the DeepSnap-DL method showed a high performance prediction of the PR antagonists by optimization of some parameters and image adjustment from 3D-structures. Furthermore, comparison of the prediction models from this approach with conventional machine learnings (MLs) indicated the DeepSnap-DL method outperformed these MLs. Therefore, the models predicted by DeepSnap-DL would be powerful tool for not only QSAR field in predicting physiological and agonist/antagonist activities, toxicity, and molecular bindings; but also for identifying biological or pathological phenomena.

Identification and Profiling of Environmental Chemicals That Inhibit the TGFβ/SMAD Signaling Pathway

The transforming growth factor beta (TGFβ) superfamily of secreted signaling molecules and their cognate receptors regulate cell fate and behaviors relevant to many developmental and disease processes. Disruption of TGFβ signaling during embryonic development can, for example, affect morphogenesis and differentiation through complex pathways that may be SMAD (Small Mothers Against Decapentaplegic) dependent or SMAD independent. In the present study, the SMAD Binding Element (SBE)-beta lactamase (bla) HEK 293T cell line, which responds to the activation of the SMAD2/3/4 complex, was used in a quantitative high-throughput screening (qHTS) assay to identify potential TGFβ disruptors in the Tox21 10K compound library. From the primary screening we identified several kinase inhibitors, organometallic compounds, and dithiocarbamates (DTCs) that inhibited TGFβ1-induced SMAD signaling of reporter gene activation independent of cytotoxicity. Counterscreen of SBE antagonists on human embryonic neural stem cells demonstrated cytotoxicity, providing additional evidence to support evaluation of these compounds for developmental toxicity. We profiled the inhibitory patterns of putative SBE antagonists toward other developmental signaling pathways, including wingless-related integration site (WNT), retinoic acid α receptor (RAR), and sonic hedgehog (SHH). The profiling results from SBE-bla assay identify chemicals that disrupt TGFβ/SMAD signaling as part of an integrated qHTS approach for prioritizing putative developmental toxicants.

The NCATS Pharmaceutical Collection: a 10-year update

The National Center for Advancing Translational Sciences (NCATS) Pharmaceutical Collection (NPC), a comprehensive collection of clinically approved drugs, was made a public resource in 2011. Over the past decade, the NPC has been systematically profiled for activity across an array of pathways and disease models, generating an unparalleled amount of data. These data have not only enabled the identification of new repurposing candidates with several in clinical trials, but also uncovered new biological insights into drug targets and disease mechanisms. This retrospective provides an update on the NPC in terms of both successes and lessons learned. We also report our efforts in bringing the NPC up-to-date with drugs approved in recent years.

Identification of Compounds That Inhibit Estrogen-Related Receptor Alpha Signaling Using High-Throughput Screening Assays

The nuclear receptor, estrogen-related receptor alpha (ERRα; NR3B1), plays a pivotal role in energy homeostasis. Its expression fluctuates with the demands of energy production in various tissues. When paired with the peroxisome proliferator-activated receptor γ coactivator 1α (PGC-1α), the PGC/ERR pathway regulates a host of genes that participate in metabolic signaling networks and in mitochondrial oxidative respiration. Unregulated overexpression of ERRα is found in many cancer cells, implicating a role in cancer progression and other metabolism-related diseases. Using high throughput screening assays, we screened the Tox21 10K compound library in stably transfected HEK293 cells containing either the ERRα-reporter or the reporter plus PGC-1α expression plasmid. We identified two groups of antagonists that were potent inhibitors of ERRα activity and/or the PGC/ERR pathway: nine antineoplastic agents and thirteen pesticides. Results were confirmed using gene expression studies. These findings suggest a novel mechanism of action on bioenergetics for five of the nine antineoplastic drugs. Nine of the thirteen pesticides, which have not been investigated previously for ERRα disrupting activity, were classified as such. In conclusion, we demonstrated that high-throughput screening assays can be used to reveal new biological properties of therapeutic and environmental chemicals, broadening our understanding of their modes of action.

Use of high-throughput enzyme-based assay with xenobiotic metabolic capability to evaluate the inhibition of acetylcholinesterase activity by organophosphorous pesticides

The inhibition of acetylcholinesterase (AChE) has pharmaceutical applications as well as potential neurotoxic effects. The in vivo metabolites of some chemicals including organophosphorus pesticides can become more potent AChE inhibitors compared to their parental compounds. To account for the effects of biotransformation, we have developed and characterized a high-throughput screening method for identifying AChE inhibitors that become active or more potent following xenobiotic metabolism. In this study, an enzyme-based assay was developed in 1536-well plates using recombinant human AChE combined with human or rat liver microsomes. The AChE activity was measured by two methods with different readouts: colorimetric and fluorescent. The assay exhibited exceptional performance characteristics including large assay signal window, low well-to-well variability and high reproducibility. The performance of the assays with microsomes was characterized by testing a group of known AChE inhibitors including parent compounds and their metabolites. Large potency differences between the parent compounds and the metabolites were observed in the assay with microsome addition. Both assay readouts were required for maximal sensitivity. These results demonstrate that this platform is a promising method to profile large numbers of chemicals that require metabolic activation for inhibiting AChE activity.

Identification of Modulators That Activate the Constitutive Androstane Receptor From the Tox21 10K Compound Library

The constitutive androstane receptor (CAR; NR1I3) is a nuclear receptor involved in all phases of drug metabolism and disposition. However, recently it's been implicated in energy metabolism, tumor progression, and cancer therapy as well. It is, therefore, important to identify compounds that induce human CAR (hCAR) activation to predict drug-drug interactions and potential therapeutic usage. In this study, we screen the Tox21 10,000 compound collection to characterize hCAR activators. A potential novel structural cluster of compounds was identified, which included nitazoxanide and tenonitrozole, whereas known structural clusters, such as flavones and prazoles, were also detected. Four compounds, neticonazole, diphenamid, phenothrin, and rimcazole, have been identified as novel hCAR activators, one of which, rimcazole, shows potential selectivity toward hCAR over its sister receptor, the pregnane X receptor (PXR). All 4 compounds translocated hCAR from the cytoplasm into the nucleus demonstrating the first step to CAR activation. Profiling these compounds as hCAR activators would enable an estimation of drug-drug interactions, as well as identify prospective therapeutically beneficial drugs.

Comprehensive Analyses and Prioritization of Tox21 10K Chemicals Affecting Mitochondrial Function by in-Depth Mechanistic Studies

BACKGROUND

A central challenge in toxicity testing is the large number of chemicals in commerce that lack toxicological assessment. In response, the Tox21 program is re-focusing toxicity testing from animal studies to less expensive and higher throughput in vitro methods using target/pathway-specific, mechanism-driven assays.

OBJECTIVES

Our objective was to use an in-depth mechanistic study approach to prioritize and characterize the chemicals affecting mitochondrial function.

METHODS

We used a tiered testing approach to prioritize for more extensive testing 622 compounds identified from a primary, quantitative high-throughput screen of 8,300 unique small molecules, including drugs and industrial chemicals, as potential mitochondrial toxicants by their ability to significantly decrease the mitochondrial membrane potential (MMP). Based on results from secondary MMP assays in HepG2 cells and rat hepatocytes, 34 compounds were selected for testing in tertiary assays that included formation of reactive oxygen species (ROS), upregulation of p53 and nuclear erythroid 2-related factor 2/antioxidant response element (Nrf2/ARE), mitochondrial oxygen consumption, cellular Parkin translocation, and larval development and ATP status in the nematode Caenorhabditis elegans.

RESULTS

A group of known mitochondrial complex inhibitors (e.g., rotenone) and uncouplers (e.g., chlorfenapyr), as well as potential novel complex inhibitors and uncouplers, were detected. From this study, we identified four not well-characterized potential mitochondrial toxicants (lasalocid, picoxystrobin, pinacyanol, and triclocarban) that merit additional in vivo characterization.

CONCLUSIONS

The tier-based approach for identifying and mechanistically characterizing mitochondrial toxicants can potentially reduce animal use in toxicological testing. https://doi.org/10.1289/EHP2589.

An Integrated Chemical Environment to Support 21st-Century Toxicology

SUMMARY: Access to high-quality reference data is essential for the development, validation, and implementation of in vitro and in silico approaches that reduce and replace the use of animals in toxicity testing. Currently, these data must often be pooled from a variety of disparate sources to efficiently link a set of assay responses and model predictions to an outcome or hazard classification. To provide a central access point for these purposes, the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods developed the Integrated Chemical Environment (ICE) web resource. The ICE data integrator allows users to retrieve and combine data sets and to develop hypotheses through data exploration. Open-source computational workflows and models will be available for download and application to local data. ICE currently includes curated in vivo test data, reference chemical information, in vitro assay data (including Tox21^TM/ToxCast™ high-throughput screening data), and in silico model predictions. Users can query these data collections focusing on end points of interest such as acute systemic toxicity, endocrine disruption, skin sensitization, and many others. ICE is publicly accessible at https://ice.ntp.niehs.nih.gov. https://doi.org/10.1289/EHP1759.