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.

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 Potent and Selective Acetylcholinesterase/Butyrylcholinesterase Inhibitors by Virtual Screening.

Acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) play important roles in human neurodegenerative disorders such as Alzheimer's disease. In this study, machine learning methods were applied to develop quantitative structure-activity relationship models for the prediction of novel AChE and BChE inhibitors based on data from quantitative high-throughput screening assays. The models were used to virtually screen an in-house collection of ∼360K compounds. The optimal models achieved good performance with area under the receiver operating characteristic curve values ranging from 0.83 ± 0.03 to 0.87 ± 0.01 for the prediction of AChE/BChE inhibition activity and selectivity. Experimental validation showed that the best-performing models increased the assay hit rate by several folds. We identified 88 novel AChE and 126 novel BChE inhibitors, 25% (AChE) and 53% (BChE) of which showed potent inhibitory effects (IC50 < 5 µM). In addition, structure-activity relationship analysis of the BChE inhibitors revealed scaffolds for chemistry design and optimization. In conclusion, machine learning models were shown to efficiently identify potent and selective inhibitors against AChE and BChE and novel structural series for further design and development of potential therapeutics against neurodegenerative disorders.

Predictive Models for Human Cytochrome P450 3A7 Selective Inhibitors and Substrates.

Inappropriate use of prescription drugs is potentially more harmful in fetuses/neonates than in adults. Cytochrome P450 (CYP) 3A subfamily undergoes developmental changes in expression, such as a transition from CYP3A7 to CYP3A4 shortly after birth, which provides a potential way to distinguish medication effects on fetuses/neonates and adults. The purpose of this study was to build first-in-class predictive models for both inhibitors and substrates of CYP3A7/CYP3A4 using chemical structure analysis. 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 performance varied for each CYP3A7/CYP3A4 inhibitor/substrate model depending on the data set type, model type, rebalancing method, and specific feature set. For the active inhibitor/substrate data set, the optimal models achieved AUC-ROC values ranging from 0.77 ± 0.01 to 0.84 ± 0.01. For the selective inhibitor/substrate data set, the optimal models achieved AUC-ROC values ranging from 0.72 ± 0.02 to 0.79 ± 0.04. The predictive power of the optimal models was validated by compounds with known potencies as CYP3A7/CYP3A4 inhibitors or substrates. In addition, we identified structural features significant for CYP3A7/CYP3A4 selective or common inhibitors and substrates. In summary, the top performing models can be further applied as a tool to rapidly evaluate the safety and efficacy of new drugs separately for fetuses/neonates and adults. The significant structural features could guide the design of new therapeutic drugs as well as aid in the optimization of existing medicine for fetuses/neonates.

Prediction of drug-induced liver injury and cardiotoxicity using chemical structure and in vitro assay data.

Drug-induced liver injury (DILI) and cardiotoxicity (DICT) are major adverse effects triggered by many clinically important drugs. To provide an alternative to in vivo toxicity testing, the U.S. Tox21 consortium has screened a collection of ∼10K compounds, including drugs in clinical use, against >70 cell-based assays in a quantitative high-throughput screening (qHTS) format. In this study, we compiled reference compound lists for DILI and DICT and compared the potential of Tox21 assay data with chemical structure information in building prediction models for human in vivo hepatotoxicity and cardiotoxicity. Models were built with four different machine learning algorithms (e.g., Random Forest, Naïve Bayes, eXtreme Gradient Boosting, and Support Vector Machine) and model performance was evaluated by calculating the area under the receiver operating characteristic curve (AUC-ROC). Chemical structure-based models showed reasonable predictive power for DILI (best AUC-ROC = 0.75 ± 0.03) and DICT (best AUC-ROC = 0.83 ± 0.03), while Tox21 assay data alone only showed better than random performance. DILI and DICT prediction models built using a combination of assay data and chemical structure information did not have a positive impact on model performance. The suboptimal predictive performance of the assay data is likely due to insufficient coverage of an adequately predictive number of toxicity mechanisms. The Tox21 consortium is currently expanding coverage of biological response space with additional assays that probe toxicologically important targets and under-represented pathways that may improve the prediction of in vivo toxicity such as DILI and DICT.

Improving the solubility and antileukemia activity of Wnt/ß-catenin signaling inhibitors by disrupting molecular planarity.

Leukemia cells depend on the Wnt/ß-catenin signaling pathway for their growth. Pyrvinium, a known Wnt signaling inhibitor, has demonstrated promising efficacy in the treatment of the aggressive blast phase chronic myeloid leukemia (BP-CML). We previously developed potent inhibitors 1-2 for the Wnt/ß-catenin signaling pathway. However, the further application of these compounds as anti-leukemia agents is limited by their modest anti-leukemia activity in cells and poor aqueous solubility, due to the high molecular planarity of the chemical scaffold. Here, we reported our efforts in the synthesis and in vitro evaluation of 18 new compounds (4a-r) that have been designed to disrupt the molecular planarity of the chemical scaffold. Several compounds of the series showed significantly improved anti-leukemia activity and aqueous solubility. As a highlight, compounds 4c not only maintained excellent inhibitory potency (IC50 = 1.3 nM) for Wnt signaling but also demonstrated good anti-leukemia potency (IC50 = 0.9 µM) in the CML K562 cells. Moreover, compound 4c had an aqueous solubility of 5.9 µg/mL, which is over 50-fold enhanced compared to its parents 1-2.

Development of Robust Quantitative Structure-Activity Relationship Models for CYP2C9, CYP2D6, and CYP3A4 Catalysis and Inhibition.

Cytochrome P450 enzymes are responsible for the metabolism of >75% of marketed drugs, making it essential to identify the contributions of individual cytochromes P450 to the total clearance of a new candidate drug. Overreliance on one cytochrome P450 for clearance levies a high risk of drug-drug interactions; and considering that several human cytochrome P450 enzymes are polymorphic, it can also lead to highly variable pharmacokinetics in the clinic. Thus, it would be advantageous to understand the likelihood of new chemical entities to interact with the major cytochrome P450 enzymes at an early stage in the drug discovery process. Typical screening assays using human liver microsomes do not provide sufficient information to distinguish the specific cytochromes P450 responsible for clearance. In this regard, we experimentally assessed the metabolic stability of ∼5000 compounds for the three most prominent xenobiotic metabolizing human cytochromes P450, i.e., CYP2C9, CYP2D6, and CYP3A4, and used the data sets to develop quantitative structure-activity relationship models for the prediction of high-clearance substrates for these enzymes. Screening library included the NCATS Pharmaceutical Collection, comprising clinically approved low-molecular-weight compounds, and an annotated library consisting of drug-like compounds. To identify inhibitors, the library was screened against a luminescence-based cytochrome P450 inhibition assay; and through crossreferencing hits from the two assays, we were able to distinguish substrates and inhibitors of these enzymes. The best substrate and inhibitor models (balanced accuracies ∼0.7), as well as the data used to develop these models, have been made publicly available (https://opendata.ncats.nih.gov/adme) to advance drug discovery across all research groups. SIGNIFICANCE STATEMENT: In drug discovery and development, drug candidates with indiscriminate cytochrome P450 metabolic profiles are considered advantageous, since they provide less risk of potential issues with cytochrome P450 polymorphisms and drug-drug interactions. This study developed robust substrate and inhibitor quantitative structure-activity relationship models for the three major xenobiotic metabolizing cytochromes P450, i.e., CYP2C9, CYP2D6, and CYP3A4. The use of these models early in drug discovery will enable project teams to strategize or pivot when necessary, thereby accelerating drug discovery research.

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.

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

The transforming growth factor beta (TGFß) superfamily of secreted signaling molecules and their cognate receptors regulate cell fate and behaviors relevant to many developmental and disease processes. Disruption of TGFß 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 TGFß disruptors in the Tox21 10K compound library. From the primary screening we identified several kinase inhibitors, organometallic compounds, and dithiocarbamates (DTCs) that inhibited TGFß1-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 TGFß/SMAD signaling as part of an integrated qHTS approach for prioritizing putative developmental toxicants.

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.

Development and Validation of a Computational Model for Androgen Receptor Activity.

Testing thousands of chemicals to identify potential androgen receptor (AR) agonists or antagonists would cost millions of dollars and take decades to complete using current validated methods. High-throughput in vitro screening (HTS) and computational toxicology approaches can more rapidly and inexpensively identify potential androgen-active chemicals. We integrated 11 HTS ToxCast/Tox21 in vitro assays into a computational network model to distinguish true AR pathway activity from technology-specific assay interference. The in vitro HTS assays probed perturbations of the AR pathway at multiple points (receptor binding, coregulator recruitment, gene transcription, and protein production) and multiple cell types. Confirmatory in vitro antagonist assay data and cytotoxicity information were used as additional flags for potential nonspecific activity. Validating such alternative testing strategies requires high-quality reference data. We compiled 158 putative androgen-active and -inactive chemicals from a combination of international test method validation efforts and semiautomated systematic literature reviews. Detailed in vitro assay information and results were compiled into a single database using a standardized ontology. Reference chemical concentrations that activated or inhibited AR pathway activity were identified to establish a range of potencies with reproducible reference chemical results. Comparison with existing Tier 1 AR binding data from the U.S. EPA Endocrine Disruptor Screening Program revealed that the model identified binders at relevant test concentrations (<100 µM) and was more sensitive to antagonist activity. The AR pathway model based on the ToxCast/Tox21 assays had balanced accuracies of 95.2% for agonist (n = 29) and 97.5% for antagonist (n = 28) reference chemicals. Out of 1855 chemicals screened in the AR pathway model, 220 chemicals demonstrated AR agonist or antagonist activity and an additional 174 chemicals were predicted to have potential weak AR pathway activity.

Characterization of three human cell line models for high-throughput neuronal cytotoxicity screening.

More than 75 000 man-made chemicals contaminate the environment; many of these have not been tested for toxicities. These chemicals demand quantitative high-throughput screening assays to assess them for causative roles in neurotoxicities, including Parkinson's disease and other neurodegenerative disorders. To facilitate high throughput screening for cytotoxicity to neurons, three human neuronal cellular models were compared: SH-SY5Y neuroblastoma cells, LUHMES conditionally-immortalized dopaminergic neurons, and Neural Stem Cells (NSC) derived from human fetal brain. These three cell lines were evaluated for rapidity and degree of differentiation, and sensitivity to 32 known or candidate neurotoxicants. First, expression of neural differentiation genes was assayed during a 7-day differentiation period. Of the three cell lines, LUHMES showed the highest gene expression of neuronal markers after differentiation. Both in the undifferentiated state and after 7 days of neuronal differentiation, LUHMES cells exhibited greater cytotoxic sensitivity to most of 32 suspected or known neurotoxicants than SH-SY5Y or NSCs. LUHMES cells were also unique in being more susceptible to several compounds in the differentiating state than in the undifferentiated state; including known neurotoxicants colchicine, methyl-mercury (II), and vincristine. Gene expression results suggest that differentiating LUHMES cells may be susceptible to apoptosis because they express low levels of anti-apoptotic genes BCL2 and BIRC5/survivin, whereas SH-SY5Y cells may be resistant to apoptosis because they express high levels of BCL2, BIRC5/survivin, and BIRC3 genes. Thus, LUHMES cells exhibited favorable characteristics for neuro-cytotoxicity screening: rapid differentiation into neurons that exhibit high level expression neuronal marker genes, and marked sensitivity of LUHMES cells to known neurotoxicants. Copyright © 2016 John Wiley & Sons, Ltd.

Identification of known drugs targeting the endoplasmic reticulum stress response.

The endoplasmic reticulum (ER), a multifunctional organelle, plays a central role in cellular signaling, development, and stress response. Dysregulation of ER homeostasis has been associated with human diseases, such as cancer, inflammation, and diabetes. A broad spectrum of stressful stimuli including hypoxia as well as a variety of pharmacological agents can lead to the ER stress response. In this study, we have developed a stable ER stress reporter cell line that stably expresses a ß-lactamase reporter gene under the control of the ER stress response element (ESRE) present in the glucose-regulated protein, 78 kDa (GRP78) gene promoter. This assay has been optimized and miniaturized into a 1536-well plate format. In order to identify clinically used drugs that induce ER stress response, we screened approximately 2800 drugs from the NIH Chemical Genomics Center Pharmaceutical Collection (NPC library) using a quantitative high-throughput screening (qHTS) platform. From this study, we have identified several known ER stress inducers, such as 17-AAG (via HSP90 inhibition), as well as several novel ER stress inducers such as AMI-193 and spiperone. The confirmed drugs were further studied for their effects on the phosphorylation of eukaryotic initiation factor 2α (eIF2α), the X-box-binding protein (XBP1) splicing, and GRP78 gene expression. These results suggest that the ER stress inducers identified from the NPC library using the qHTS approach could shed new lights on the potential therapeutic targets of these drugs.

High-throughput genotoxicity assay identifies antioxidants as inducers of DNA damage response and cell death.

Human ATAD5 is a biomarker for identifying genotoxic compounds because ATAD5 protein levels increase posttranscriptionally in response to DNA damage. We screened over 4,000 compounds with a cell-based quantitative high-throughput ATAD5-luciferase assay detecting genotoxic compounds. We identified 22 antioxidants, including resveratrol, genistein, and baicalein, that are currently used or investigated for the treatment of cardiovascular disease, type 2 diabetes, osteopenia, osteoporosis, and chronic hepatitis, as well as for antiaging. Treatment of dividing cells with these compounds induced DNA damage and resulted in cell death. Despite their genotoxic effects, resveratrol, genistein, and baicalein did not cause mutagenesis, which is a major side effect of conventional anticancer drugs. Furthermore, resveratrol and genistein killed multidrug-resistant cancer cells. We therefore propose that resveratrol, genistein, and baicalein are attractive candidates for improved chemotherapeutic agents.

Drug repurposing screen identifies lestaurtinib amplifies the ability of the poly (ADP-ribose) polymerase 1 inhibitor AG14361 to kill breast cancer associated gene-1 mutant and wild type breast cancer cells.

INTRODUCTION: Breast cancer is a devastating disease that results in approximately 40,000 deaths each year in the USA. Current drug screening and chemopreventatitive methods are suboptimal, due in part to the poor specificity of compounds for cancer cells. Poly (ADP-ribose) polymerase 1 (PARP1) inhibitor (PARPi)-mediated therapy is a promising approach for familial breast cancers caused by mutations of breast cancer-associated gene-1 and -2 (BRCA1/2), yet drug resistance frequently occurs during the treatment. Moreover, PARPis exhibit very little effect on cancers that are proficient for DNA repair and clinical efficacy for PARPis as single-agent therapies has yet to be illustrated. METHODS: Using a quantitative high-throughput screening approach, we screened a library containing 2,816 drugs, most of which are approved for human or animal use by the Food and Drug Administration (FDA) or other countries, to identify compounds that sensitize breast cancer cells to PARPi. After initial screening, we performed further cellular and molecular analysis on lestaurtinib, which is an orally bioavailable multikinase inhibitor and has been used in clinical trials for myeloproliferative disorders and acute myelogenous leukemia. RESULTS: Our study indicated that lestaurtinib is highly potent against breast cancers as a mono-treatment agent. It also strongly enhanced the activity of the potent PARPi AG14361 on breast cancer cell growth both in vitro and in vivo conditions. The inhibition of cancer growth is measured by increased apoptosis and reduced cell proliferation. Consistent with this, the treatment results in activation of caspase 3/7, and accumulation of cells in the G2 phase of the cell cycle, irrespective of their BRCA1 status. Finally, we demonstrated that AG14361 inhibits NF-κB signaling, which is further enhanced by lestaurtinib treatment. CONCLUSIONS: Lestaurtinib amplifies the ability of the PARP1 inhibitor AG14361 to kill BRCA1 mutant and wild-type breast cancer cells, at least in part, by inhibiting NF-κB signaling. Each of these drugs has been approved for clinical trials for several different cancers, thus, their combination treatment should be applicable for a breast cancer trial in the future.

Using Reporter Gene Assays to Screen and Identify Chemical Compounds that Modulate Estrogen Receptor Activity.

Estrogen receptor alpha (ERα) is a nuclear receptor that is expressed mainly in the breast, uterus, and ovary, among several other organs. ERα plays important roles in reproduction, mammary gland formation, and glucose homeostasis. Disruption of ERα may result in adverse outcomes, such as cancer, impaired fertility, and abnormal fetal growth. Therefore, identifying compounds that modulate ERα is of great interest due to their potential-endocrine disrupting capability and pharmaceutical applications. To rapidly test tens of thousands of compounds, high-throughput screening assays are essential. Here, we describe high-throughput screening methods, including plating and treatment of cells in 384-well and 1536-well plates and analysis of the resulting data. The two cell lines used, MCF7-VM7Luc4E2 and HEK293-ERα-bla, have been described previously. MCF7-VM7Luc4E2 cells are a stable luciferase reporter gene cell line expressing full-length endogenous estrogen receptor in the MCF7 cell line background, and HEK293-ERα-bla cells stably express an ERα ligand-binding domain/GAL4 DNA-binding domain fusion regulating a UAS ß-lactamase reporter gene. These cell lines can be used to identify and confirm ERα modulators. Published 2024. This article is a U.S. Government work and is in the public domain in the USA. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Establishment of a high-throughput ERα reporter gene assay with luminescence readout to identify activators and inhibitors of estrogen receptor α Basic Protocol 2: Use of an orthogonal assay with fluorescence readout to confirm potential estrogen receptor activators or inhibitors.

Use of Tox21 screening data to profile PFAS bioactivities on nuclear receptors, cellular stress pathways, and cytochrome p450 enzymes.

Per- and poly-fluoroalkyl substances (PFAS) are synthetic chemicals widely used in commercial products. PFAS are a global concern due to their persistence in the environment and extensive associations with adverse health outcomes. While legacy PFAS have been extensively studied, many non-legacy PFAS lack sufficient toxicity information. In this study, we first analyzed the bioactivity of PFAS using Tox21 screening data surveying more than 75 assay endpoints (e.g., nuclear receptors, stress response, and metabolism) to understand the toxicity of non-legacy PFAS and investigate potential new targets of PFAS. From the Tox21 screening data analysis, we confirmed several known PFAS targets/pathways and identified several potential novel targets/pathways of PFAS. To confirm the effect of PFAS on these novel targets/pathways, we conducted several cell- and enzyme-based assays in the follow-up studies. We found PFAS inhibited cytochromes P450s (CYPs), especially CYP2C9 with IC50 values of < 1 µM. Considering PFAS affected other targets/pathways at > 10 µM, PFAS have a higher affinity to CYP2C9. This PFAS-CYP2C9 interaction was further investigated using molecular docking analysis. The result suggested that PFAS directly bind to the active sites of CYP2C9. These findings have important implications to understand the mechanism of PFAS action and toxicity.

Development and validation of CYP26A1 inhibition assay for high-throughput screening.

All-trans retinoic acid (atRA) is an endogenous ligand of the retinoic acid receptors, which heterodimerize with retinoid X receptors. AtRA is generated in tissues from vitamin A (retinol) metabolism to form a paracrine signal and is locally degraded by cytochrome P450 family 26 (CYP26) enzymes. The CYP26 family consists of three subtypes: A1, B1, and C1, which are differentially expressed during development. This study aims to develop and validate a high throughput screening assay to identify CYP26A1 inhibitors in a cell-free system using a luminescent P450-Glo assay technology. The assay performed well with a signal to background ratio of 25.7, a coefficient of variation of 8.9%, and a Z-factor of 0.7. To validate the assay, we tested a subset of 39 compounds that included known CYP26 inhibitors and retinoids, as well as positive and negative control compounds selected from the literature and/or the ToxCast/Tox21 portfolio. Known CYP26A1 inhibitors were confirmed, and predicted CYP26A1 inhibitors, such as chlorothalonil, prochloraz, and SSR126768, were identified, demonstrating the reliability and robustness of the assay. Given the general importance of atRA as a morphogenetic signal and the localized expression of Cyp26a1 in embryonic tissues, a validated CYP26A1 assay has important implications for evaluating the potential developmental toxicity of chemicals.

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.

Systematic study of mitochondrial toxicity of environmental chemicals using quantitative high throughput screening.

A goal of the Tox21 program is to transit toxicity testing from traditional in vivo models to in vitro assays that assess how chemicals affect cellular responses and toxicity pathways. A critical contribution of the NIH Chemical Genomics center (NCGC) to the Tox21 program is the implementation of a quantitative high throughput screening (qHTS) approach, using cell- and biochemical-based assays to generate toxicological profiles for thousands of environmental compounds. Here, we evaluated the effect of chemical compounds on mitochondrial membrane potential in HepG2 cells by screening a library of 1,408 compounds provided by the National Toxicology Program (NTP) in a qHTS platform. Compounds were screened over 14 concentrations, and results showed that 91 and 88 compounds disrupted mitochondrial membrane potential after treatment for 1 or 5 h, respectively. Seventy-six compounds active at both time points were clustered by structural similarity, producing 11 clusters and 23 singletons. Thirty-eight compounds covering most of the active chemical space were more extensively evaluated. Thirty-six of the 38 compounds were confirmed to disrupt mitochondrial membrane potential using a fluorescence plate reader, and 35 were confirmed using a high content imaging approach. Among the 38 compounds, 4 and 6 induced LDH release, a measure of cytotoxicity, at 1 or 5 h, respectively. Compounds were further assessed for mechanism of action (MOA) by measuring changes in oxygen consumption rate, which enabled the identification of 20 compounds as uncouplers. This comprehensive approach allows for the evaluation of thousands of environmental chemicals for mitochondrial toxicity and identification of possible MOAs.