Profiling the Tox21 Chemical Collection for Acetylcholinesterase Inhibition

Terpenoids from Alpinia galanga and their acetylcholinesterase inhibitory activity

A new labdane diterpene (1), two new norsesquiterpenoids (2-3), as well as eight known terpenoids (4-11) were isolated from the seeds of Alpinia galanga (Zingiberaceae). Their structures and absolute configurations were elucidated by 1D, 2D NMR, MS, and comparison of their experimental and calculated electronic circular dichroism (ECD). The acetylcholinesterase (AChE) inhibitory activities of all the isolated compounds (1-11) were evaluated and the result showed that compounds 6 and 9 had inhibitory activity against AChE, with IC50 values at 295.70 and 183.91 μM, whereas other compounds did not show any inhibitory activity.

NeuTox: A weighted ensemble model for screening potential neuronal cytotoxicity of chemicals based on various types of molecular representations

Chemical-induced neurotoxicity has been widely brought into focus in the risk assessment of chemical safety. However, the traditional in vivo animal models to evaluate neurotoxicity are time-consuming and expensive, which cannot completely represent the pathophysiology of neurotoxicity in humans. Cytotoxicity to human neuroblastoma cell line (SH-SY5Y) is commonly used as an alternative to animal testing for the assessment of neurotoxicity, yet it is still not appropriate for high throughput screening of potential neuronal cytotoxicity of chemicals. In this study, we constructed an ensemble prediction model, termed NeuTox, by combining multiple machine learning algorithms with molecular representations based on the weighted score of Particle Swarm Optimization. For the test set, NeuTox shows excellent performance with an accuracy of 0.9064, which are superior to the top-performing individual models. The subsequent experimental verifications reveal that 5,5'-isopropylidenedi-2-biphenylol and 4,4'-cyclo-hexylidenebisphenol exhibited stronger SH-SY5Y-based cytotoxicity compared to bisphenol A, suggesting that NeuTox has good generalization ability in the first-tier assessment of neuronal cytotoxicity of BPA analogs. For ease of use, NeuTox is presented as an online web server that can be freely accessed via http://www.iehneutox-predictor.cn/NeuToxPredict/Predict.

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.

Definition of the Neurotoxicity-Associated Metabolic Signature Triggered by Berberine and Other Respiratory Chain Inhibitors

To characterize the hits from a phenotypic neurotoxicity screen, we obtained transcriptomics data for valinomycin, diethylstilbestrol, colchicine, rotenone, 1-methyl-4-phenylpyridinium (MPP), carbaryl and berberine (Ber). For all compounds, the concentration triggering neurite degeneration correlated with the onset of gene expression changes. The mechanistically diverse toxicants caused similar patterns of gene regulation: the responses were dominated by cell de-differentiation and a triggering of canonical stress response pathways driven by ATF4 and NRF2. To obtain more detailed and specific information on the modes-of-action, the effects on energy metabolism (respiration and glycolysis) were measured. Ber, rotenone and MPP inhibited the mitochondrial respiratory chain and they shared complex I as the target. This group of toxicants was further evaluated by metabolomics under experimental conditions that did not deplete ATP. Ber (204 changed metabolites) showed similar effects as MPP and rotenone. The overall metabolic situation was characterized by oxidative stress, an over-abundance of NADH (>1000% increase) and a re-routing of metabolism in order to dispose of the nitrogen resulting from increased amino acid turnover. This unique overall pattern led to the accumulation of metabolites known as biomarkers of neurodegeneration (saccharopine, aminoadipate and branched-chain ketoacids). These findings suggest that neurotoxicity of mitochondrial inhibitors may result from an ensemble of metabolic changes rather than from a simple ATP depletion. The combi-omics approach used here provided richer and more specific MoA data than the more common transcriptomics analysis alone. As Ber, a human drug and food supplement, mimicked closely the mode-of-action of known neurotoxicants, its potential hazard requires further investigation.

Identification and Prioritization of Environmental Organic Pollutants: From an Analytical and Toxicological Perspective

Exposure to environmental organic pollutants has triggered significant ecological impacts and adverse health outcomes, which have been received substantial and increasing attention. The contribution of unidentified chemical components is considered as the most significant knowledge gap in understanding the combined effects of pollutant mixtures. To address this issue, remarkable analytical breakthroughs have recently been made. In this review, the basic principles on recognition of environmental organic pollutants are overviewed. Complementary analytical methodologies (i.e., quantitative structure-activity relationship prediction, mass spectrometric nontarget screening, and effect-directed analysis) and experimental platforms are briefly described. The stages of technique development and/or essential parts of the analytical workflow for each of the methodologies are then reviewed. Finally, plausible technique paths and applications of the future nontarget screening methods, interdisciplinary techniques for achieving toxicant identification, and burgeoning strategies on risk assessment of chemical cocktails are discussed.

In vitro profiling of pesticides within the Tox21 10K compound library for bioactivity and potential toxicity

Pesticides include a diverse class of toxic chemicals, often having numerous modes of actions when used in agriculture against targeted organisms to control insect infestation, halt unwanted vegetation, and prevent the spread of disease. In this study, the in vitro assay activity of pesticides within the Tox21 10K compound library were examined. The assays in which pesticides showed significantly more activities than non-pesticide chemicals revealed potential targets and mechanisms of action for pesticides. Furthermore, pesticides that showed promiscuous activity against many targets and cytotoxicity were identified, which warrant further toxicological evaluation. Several pesticides were shown to require metabolic activation, demonstrating the importance of introducing metabolic capacity to in vitro assays. Overall, the activity profiles of pesticides highlighted in this study can contribute to the knowledge gaps surrounding pesticide mechanisms and to the better understanding of the on- and off-target organismal effects of pesticides.

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.

Validation of Acetylcholinesterase Inhibition Machine Learning Models for Multiple Species

Acetylcholinesterase (AChE) is an important enzyme and target for human therapeutics, environmental safety, and global food supply. Inhibitors of this enzyme are also used for pest elimination and can be misused for suicide or chemical warfare. Adverse effects of AChE pesticides on nontarget organisms, such as fish, amphibians, and humans, have also occurred as a result of biomagnifications of these toxic compounds. We have exhaustively curated the public data for AChE inhibition data and developed machine learning classification models for seven different species. Each set of models were built using up to nine different algorithms for each species and Morgan fingerprints (ECFP6) with an activity cutoff of 1 μM. The human (4075 compounds) and eel (5459 compounds) consensus models predicted AChE inhibition activity using external test sets from literature data with 81% and 82% accuracy, respectively, while the reciprocal cross (76% and 82% percent accuracy) was not species-specific. In addition, we also created machine learning regression models for human and eel AChE inhibition to return a predicted IC₅₀ value for a queried molecule. We did observe an improved species specificity in the regression models, where a human support vector regression model of human AChE inhibition (3652 compounds) predicted the IC₅₀s of the human test set to a better extent than the eel regression model (4930 compounds) on the same test set, based on mean absolute percentage error (MAPE = 9.73% vs 13.4%). The predictive power of these models certainly benefits from increasing the chemical diversity of the training set, as evidenced by expanding our human classification model by incorporating data from the Tox21 library of compounds. Of the 10 compounds we tested that were predicted active by this expanded model, two showed >80% inhibition at 100 μM. This machine learning approach therefore offers the ability to rapidly score massive libraries of molecules against the models for AChE inhibition that can then be selected for future in vitro testing to identify potential toxins. It also enabled us to create a public website, MegaAChE, for single-molecule predictions of AChE inhibition using these models at megaache.collaborationspharma.com.

The prediction of molecular toxicity based on BiGRU and GraphSAGE

The prediction of molecules toxicity properties plays an crucial role in the realm of the drug discovery, since it can swiftly screen out the expected drug moleculars. The conventional method for predicting toxicity is to use some in vivo or in vitro biological experiments in the laboratory, which can easily pose a threat significant time and financial waste and even ethical issues. Therefore, using computational approaches to predict molecular toxicity has become a common strategy in modern drug discovery. In this article, we propose a novel model named MTBG, which primarily makes use of both SMILES (Simplified molecular input line entry system) strings and graph structures of molecules to extract drug molecular feature in the field of drug molecular toxicity prediction. To verify the performance of the MTBG model, we opt the Tox21 dataset and several widely used baseline models. Experimental results demonstrate that our model can perform better than these baseline models.

Validation of an SH-SY5Y Cell-Based Acetylcholinesterase Inhibition Assay for Water Quality Assessment

The acetylcholinesterase (AChE) inhibition assay has been frequently applied for environmental monitoring to capture insecticides such as organothiophosphates (OTPs) and carbamates. However, natural organic matter such as dissolved organic carbon (DOC) co-extracted with solid-phase extraction from environmental samples can produce false-negative AChE inhibition in free enzyme-based AChE assays. We evaluated whether disturbance by DOC can be alleviated in a cell-based AChE assay using differentiated human neuroblastoma SH-SY5Y cells. The exposure duration was set at an optimum of 3 h considering the effects of OTPs and carbamates. Because loss to the airspace was expected for the more volatile OTPs (chlorpyrifos, diazinon, and parathion), the chemical loss in this bioassay setup was investigated using solid-phase microextraction followed by chemical analysis. The three OTPs were relatively well retained (loss <34%) during 3 h of exposure in the 384-well plate, but higher losses occurred on prolonged exposure, accompanied by slight cross-contamination of adjacent wells. Inhibition of AChE by paraoxon-ethyl was not altered in the presence of up to 68 mg_c /L Aldrich humic acid used as surrogate for DOC. Binary mixtures of paraoxon-ethyl and water extracts showed concentration-additive effects. These experiments confirmed that the matrix in water extracts does not disturb the assay, unlike purified enzyme-based AChE assays. The cell-based AChE assay proved to be suitable for testing water samples with effect concentrations causing 50% inhibition of AChE at relative enrichments of 0.5-10 in river water samples, which were distinctly lower than corresponding cytotoxicity, confirming the high sensitivity of the cell-based AChE inhibition assay and its relevance for water quality monitoring. Environ Toxicol Chem 2022;41:3046-3057. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Data-driven learning of narcosis mode of action identifies a CNS transcriptional signature shared between whole organism Caenorhabditis elegans and a fish gill cell line

With the large numbers of man-made chemicals produced and released in the environment, there is a need to provide assessments on their potential effects on environmental safety and human health. Current regulatory frameworks rely on a mix of both hazard and risk-based approaches to make safety decisions, but the large number of chemicals in commerce combined with an increased need to conduct assessments in the absence of animal testing makes this increasingly challenging. This challenge is catalysing the use of more mechanistic knowledge in safety assessment from both in silico and in vitro approaches in the hope that this will increase confidence in being able to identify modes of action (MoA) for the chemicals in question. Here we approach this challenge by testing whether a functional genomics approach in C. elegans and in a fish cell line can identify molecular mechanisms underlying the effects of narcotics, and the effects of more specific acting toxicants. We show that narcosis affects the expression of neuronal genes associated with CNS function in C. elegans and in a fish cell line. Overall, we believe that our study provides an important step in developing mechanistically relevant biomarkers which can be used to screen for hazards, and which prevent the need for repeated animal or cross-species comparisons for each new chemical.

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.

The SH-SY5Y human neuroblastoma cell line, a relevant in vitro cell model for investigating neurotoxicology in human: focus on organic pollutants

Investigation of the toxicity triggered by chemicals on the human brain has traditionally relied on approaches using rodent in vivo models and in vitro cell models including primary neuronal cultures and cell lines from rodents. The issues of species differences between humans and rodents, the animal ethical concerns and the time and cost required for neurotoxicity studies on in vivo animal models, do limit the use of animal-based models in neurotoxicology. In this context, human cell models appear relevant in elucidating cellular and molecular impacts of neurotoxicants and facilitating prioritization of in vivo testing. The SH-SY5Y human neuroblastoma cell line (ATCC® CRL-2266^TM) is one of the most used cell lines in neurosciences, either undifferentiated or differentiated into neuron-like cells. This review presents the characteristics of the SH-SY5Y cell line and proposes the results of a systematic review of literature on the use of this in vitro cell model for neurotoxicity research by focusing on organic environmental pollutants including pesticides, 2, 3, 7, 8-tetrachlorodibenzo-p-dioxin (TCDD), flame retardants, PFASs, parabens, bisphenols, phthalates, and PAHs. Organic environmental pollutants are widely present in the environment and increasingly known to cause clinical neurotoxic effects during fetal & child development and adulthood. Their effects on cultured SH-SY5Y cells include autophagy, cell death (apoptosis, pyroptosis, necroptosis, or necrosis), increased oxidative stress, mitochondrial dysfunction, disruption of neurotransmitter homeostasis, and alteration of neuritic length. Finally, the inherent advantages and limitations of the SH-SY5Y cell model are discussed in the context of chemical testing.

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 environmental chemicals that activate p53 signaling after in vitro metabolic activation

Currently, approximately 80,000 chemicals are used in commerce. Most have little-to-no toxicity information. The U.S. Toxicology in the 21st Century (Tox21) program has conducted a battery of in vitro assays using a quantitative high-throughput screening (qHTS) platform to gain toxicity information on environmental chemicals. Due to technical challenges, standard methods for providing xenobiotic metabolism could not be applied to qHTS assays. To address this limitation, we screened the Tox21 10,000-compound (10K) library, with concentrations ranging from 2.8 nM to 92 µM, using a p53 beta-lactamase reporter gene assay (p53-bla) alone or with rat liver microsomes (RLM) or human liver microsomes (HLM) supplemented with NADPH, to identify compounds that induce p53 signaling after biotransformation. Two hundred and seventy-eight compounds were identified as active under any of these three conditions. Of these 278 compounds, 73 gave more potent responses in the p53-bla assay with RLM, and 2 were more potent in the p53-bla assay with HLM compared with the responses they generated in the p53-bla assay without microsomes. To confirm the role of metabolism in the differential responses, we re-tested these 75 compounds in the absence of NADPH or with heat-attenuated microsomes. Forty-four compounds treated with RLM, but none with HLM, became less potent under these conditions, confirming the role of RLM in metabolic activation. Further evidence of biotransformation was obtained by measuring the half-life of the parent compounds in the presence of microsomes. Together, the data support the use of RLM in qHTS for identifying chemicals requiring biotransformation to induce biological responses.

High-throughput screening for identifying acetylcholinesterase inhibitors: Insights on novel inhibitors and the use of liver microsomes

Rapid, higher throughput, and predictive toxicological methods are needed to assess vast numbers of chemicals with unknown safety profiles. A current effort towards this goal is Toxicology in the 21st Century (Tox21), a United States government consortium using a battery of in vitro assays to screen a library of 10,000 compounds relevant to food, drug, and environmental safety. Recently, we implemented in vitro assays for measuring acetylcholinesterase (AChE) inhibition, a mechanism of toxicity, into Tox21's high-throughput screening campaign (Li S., et al. Environ Health Persp 2021;129:047008, doi:10.1289/EHP6993). In this Commentary, we provide detailed insights on two topics related to our article: (1) prioritizing recently discovered AChE inhibitors from our screening based upon physiological relevance and (2) incorporating human liver microsomes into the AChE inhibition assay to identify metabolically active AChE inhibitors.

Acetylcholinesterase Inhibition Assays for High-Throughput Screening

Acetylcholinesterase (AChE) hydrolyzes acetylcholine (ACh), a vital neurotransmitter that regulates muscle movement and brain function, including memory, attention, and learning. Inhibition of AChE activity can cause a variety of adverse health effects and toxicity. Identifying AChE inhibitors quickly and efficiently warrants developing AChE inhibition assays in a quantitative, high-throughput screening (qHTS) platform. In this chapter, protocols for multiple homogenous AChE inhibition assays used in a qHTS system are provided. These AChE inhibition assays include a (1) human neuroblastoma (SH-SY5Y) cell-based assay with fluorescence or colorimetric detection; (2) human recombinant AChE with fluorescence or colorimetric detection; and (3) combination of human recombinant AChE and liver microsomes with colorimetric detection, which enables detection of test compounds requiring metabolic activation to become AChE inhibitors. Together, these AChE assays can help identify, prioritize, and predict chemical hazards in large compound libraries using qHTS systems.

Prediction Models for Agonists and Antagonists of Molecular Initiation Events for Toxicity Pathways Using an Improved Deep-Learning-Based Quantitative Structure-Activity Relationship System

In silico approaches have been studied intensively to assess the toxicological risk of various chemical compounds as alternatives to traditional in vivo animal tests. Among these approaches, quantitative structure-activity relationship (QSAR) analysis has the advantages that it is able to construct models to predict the biological properties of chemicals based on structural information. Previously, we reported a deep learning (DL) algorithm-based QSAR approach called DeepSnap-DL for high-performance prediction modeling of the agonist and antagonist activity of key molecules in molecular initiating events in toxicological pathways using optimized hyperparameters. In the present study, to achieve high throughput in the DeepSnap-DL system-which consists of the preparation of three-dimensional molecular structures of chemical compounds, the generation of snapshot images from the three-dimensional chemical structures, DL, and statistical calculations-we propose an improved DeepSnap-DL approach. Using this improved system, we constructed 59 prediction models for the agonist and antagonist activity of key molecules in the Tox21 10K library. The results indicate that modeling of the agonist and antagonist activity with high prediction performance and high throughput can be achieved by optimizing suitable parameters in the improved DeepSnap-DL system.