Metals and metalloids are widely used in industrial applications, and increasing experimental and epidemiological evidence has linkded metal exposure to adverse health outcomes. However, the underlying mechanisms for these effects have not been fully understood. As part of the Toxicology in 21st century (Tox21) program, we have screened more than 150 metal-containing compounds and their salt forms across over 90 biological endpoints. In this study, we analyzed the comprehensive toxicity of metal compounds using Tox21 screening data to enhance the understanding of their mechanism and molecular pathways involved in molecular initiating events. Integrated data analysis and in vitro confirmation experiments identified three potential novel targets of metal compounds (i.e., sonic hedgehog pathway, thyroid-stimulating hormone receptor, and thyrotropin-releasing hormone receptor). We also found that mercury- and tin-containing substances were highly bioactive. Furthermore, cell painting analysis uncovered metal-induced bioactivity could be classified into two patterns depending on their respective associations to mitochondrial-related morphology changes. Our results provide a comprehensive analysis of metals-association bioactivity data within Tox21 assays, which can be applied to estimate the potency ranges for metal-induced bioactivity that support risk assessment efforts for metal and metalloid exposures. These findings identify previously undercharacterized molecular targets of metal compounds, offering new insights into mechanisms of metal toxicity and informing improved risk assessment methodologies.

Chikungunya virus (CHIKV) is a mosquito-borne RNA virus that has emerged as one of the most important global arboviral threats in the last decade. Although the first CHIKV vaccine has recently been FDA approved for use in healthy adults at increased risk, to date, there are no available antiviral drugs for CHIKV infection. CHIKV nsP2 protease plays a crucial role in the processing of the viral polypeptide precursor to release enzymes required for viral replication, thus making it a promising drug target for antiviral discovery. Here, we established a high-throughput pipeline to identify small molecule inhibitors of nsP2 proteolytic activity. The pipeline is composed of a suite of 1,536-well in vitro assays to support quantitative high-throughput (qHTS) screening campaigns. Specifically, we developed a fluorescence resonance energy transfer (FRET)-based assay using a fluorogenic peptide substrate encompassing an endogenous cleavage site and purified recombinant protease domain (nsP2pro). Using this assay, we interrogated ~ 31,000 unique small molecules, including those in drug repurposing libraries as well as chemically diverse and medicinal chemistry-friendly compounds. Hits were selected for follow-up validation against full-length nsP2 and an additional peptide. FRET-based 1,536-well assays for Papain, hepatitis C virus NS3-4A, and human Furin proteases were implemented to characterize compound selectivity. Notably, we developed a high-throughput cell-based proteolytic assay using a split nanoluciferase reporter to identify cell-active hits. Novel compounds were found to be potential nsP2 inhibitors and molecular docking analyses were performed to explain the binding mode of selected hits. In vitro antiviral activity was evaluated for a subset of compounds using a high-throughput CHIKV infection assay. To our knowledge, the pipeline presented here is unprecedented for CHIKV antiviral discovery research.

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.

Accumulation of lysosomal phospholipids in cells exposed to cationic amphiphilic drugs is characteristic of drug-induced phospholipidosis. The morphological hallmark of phospholipidosis is the appearance of unicentric or multicentric-lamellar bodies when viewed under an electron microscope (EM). The EM method, the gold standard of detecting cellular phospholipidosis, has downsides, namely, low-throughput, high-costs, and unsuitability for screening a large chemical library. This chapter describes a cell-based high-content phospholipidosis assay using the LipidTOX reagent in a high-throughput screening (HTS) platform. This assay has been optimized and validated in HepG2 and HepRG cells, and miniaturized into a 1536-well plate, thus can be used for high-throughput screening (HTS) to identify chemical compounds that induce phospholipidosis.

BackgroundThough case fatality rate (CFR) is widely used to reflect COVID-19 fatality risk, its use is limited by large temporal and spatial variation. Hospital mortality rate (HMR) is also used to assess the severity of COVID-19, but HMR data is not directly available globally. Alternative metrics are needed for COVID-19 severity and fatality assessment.MethodsWe introduce new metrics for COVID-19 fatality risk measurements/monitoring and a new mathematical model to estimate average hospital length of stay for deaths (Ldead) and discharges (Ldis). Multiple data sources were used for our analyses.FindingsWe propose three, new metrics: hospital occupancy mortality rate (HOMR), ratio of total deaths to hospital occupancy (TDHOR), and ratio of hospital occupancy to cases (HOCR), for dynamic assessment of COVID-19 fatality risk. Estimated Ldead and Ldis for 501,079 COVID-19 hospitalizations in 34 US states between 7 August 2020 and 1 March 2021 were 18·2(95%CI:17·9-18·5) and 14·0(95%CI:13·9-14·0) days, respectively. We found the dramatic changes in COVID-19 CFR observed in 27 countries during early stages of the pandemic were mostly caused by undiagnosed cases. Compared to the first week of November 2021, the week mean HOCRs (mimics hospitalization-to-case ratio) for Omicron variant (58·6% of US new cases as of 25 December 2021) decreased 65·16% in the US as of 16 January 2022.InterpretationThe new and reliable measurements described here could be useful for COVID-19 fatality risk and variant-associated risk monitoring.FundingNo specific funding was associated with the present study.

The potential neurotoxicity from an increasing number of drugs and untested environmental chemicals creates a need to develop reliable and efficient in vitro methods for identifying chemicals that may adversely affect the nervous system. An important process in neurodevelopment is neurite outgrowth, which can be affected by developmental neurotoxicity. Currently, neurite outgrowth assays rely mainly on staining, which requires multiple sample processing steps, particularly washing steps, that may introduce variation and limit throughput. Here, we describe a neurite outgrowth assay that uses induced pluripotent stem cell (iPSC)-derived human cortical glutamatergic neurons and/or spinal motor neurons labeled with green fluorescent protein (GFP) to test compounds in a high-content and high-throughput format. This method enables live and time-lapse imaging of GFP-labeled neurons using an assay plate that is continuously imaged at multiple times after chemical treatment. In this article, we describe how to thaw frozen GFP-labeled neurons, culture them, treat them with a compound of interest, and analyze neurite outgrowth using a high-content imaging platform. In this assay, GFP-labeled iPSC-derived human neurons represent a promising tool for identifying and prioritizing compounds with potential developmental neurotoxicity for further hazard characterization. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA. Basic Protocol 1: Thawing and seeding of iPSC-derived neurons Basic Protocol 2: Compound plate preparation and treatment of neurons Basic Protocol 3: High-content imaging and analysis.

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.

Food, especially animal origin food is the main source of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs), and dioxin-like polychlorinated biphenyls (dl-PCBs) for human exposure. So, a simple, rapid and cheap bioassay method is needed for determination of dioxins in food samples. In this study, we used a new highly sensitive reporter cell line to determine the concentration of dioxins in 33 fish and seafood samples. The samples were extracted by shaking with water/isopropanol (1:1 v/v) and hexane and cleaned-up by a multi layered silica gel column and an alumina column, then analyzed using CBG 2.8D cell line. We compared the results obtained from the CBG 2.8D cell assay to those obtained from conventional High-Resolution Gas Chromatography–High Resolution Mass Spectrometry (HRGC–HRMS) analysis. Good correlations were observed between these two methods (r2=0.93). While the slope of regression line was 1.76, the bioanalytical equivalent (BEQ) values were 1.76 folds higher than WHO-TEQ values and the conversion coefficient was 0.568 (the reciprocal of 1.76). In conclusion, CBG 2.8D cell assay was an applicable method to determine dioxins levels in fish and sea food samples.

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.

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.

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

农田障碍物的精确识别是无人农业车辆必不可少的关键技术之一.针对果园环境复杂难以准确检测出障碍物信息的问题,提出了一种改进单次多重检测器(Single shot multibox detector,SSD)深度学习目标检测方法,对田间障碍物中的行人进行识别.使用轻量化网络MobileNetV2作为SSD模型中的基础网络,以减少提取图像特征过程中所花费的时间及运算量,辅助网络层以反向残差结构结合空洞卷积作为基础结构进行位置预测,在综合多尺度特征的同时避免下采样操作带来的信息损失,基于Tensorflow深度学习框架,在卡耐基梅隆大学国家机器人工程中心的果园行人检测开放数据集上进行不同运动状态(运动、静止)、不同姿态(正常、非正常)和不同目标面积(大、中、小)的田间行人识别精度和识别速度的对比试验.试验表明,当IOU阀值为0.4时,改进的SSD模型田间行人检测模型的平均准确率和召回率分别达到了97.46％和91.65％,高于改进前SSD模型的96.87％和88.51％,并且参数量减少至原来的1/7,检测速度提高了187.5％,检测速度为62.50帧/s,模型具有较好的鲁棒性,可以较好地实现田间环境下行人的检测,为无人农机的避障决策提供依据.

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.

High-content screening (HCS) technology combining automated microscopy and quantitative image analysis can address biological questions in academia and the pharmaceutical industry. Various HCS experimental applications have been utilized in the research field of in vitro toxicology. In this review, we describe several HCS application approaches used for studying the mechanism of compound toxicity, highlight some challenges faced in the toxicological community, and discuss the future directions of HCS in regards to new models, new reagents, data management, and informatics. Many specialized areas of toxicology including developmental toxicity, genotoxicity, developmental neurotoxicity/neurotoxicity, hepatotoxicity, cardiotoxicity, and nephrotoxicity will be examined. In addition, several newly developed cellular assay models including induced pluripotent stem cells (iPSCs), three-dimensional (3D) cell models, and tissues-on-a-chip will be discussed. New genome-editing technologies (e.g., CRISPR/Cas9), data analyzing tools for imaging, and coupling with high-content assays will be reviewed. Finally, the applications of machine learning to image processing will be explored. These new HCS approaches offer a huge step forward in dissecting biological processes, developing drugs, and making toxicology studies easier.

Airborne persistent toxic substances are associated with health impacts resulting from air pollution, for example, dioxins, dioxin like polychlorinated biphenyls, and certain polycyclic aromatic hydrocarbons (PAHs), which activate aryl hydrocarbon receptors (AhR) and thereby produce adverse outcomes. Thus, a bioassay for evaluating AhR activation is required for risk assessment of ambient-air samples, and for this purpose, we developed a new and sensitive recombinant mouse hepatoma cell line, CBG2.8D, in which a novel luciferase-reporter plasmid containing two copies of a newly designed dioxin-responsive domain and a minimal promoter derived from a native gene were integrated. The minimal detection limit for 2,3,7,8-tetrachlorodibenzo-p-dioxin with this assay system was 0.1 pM. We used CBG2.8D to determine dioxin levels in 45 ambient-air samples collected in Beijing. The measured bioanalytical equivalent (BEQ) values were closely correlated with the toxic equivalent values obtained from chemical analysis. In haze ambient-air samples, the total activation of aryl hydrocarbon receptors (TAA) was considerably higher than the BEQof dioxin-rich fractions, according to the results of the cell-based bioassay. Notably, the haze samples contained abundant amounts of PAHs, whose relative toxicity equivalent was correlated with the TAA; this finding suggests that PAHs critically contribute to the AhR-related biological impacts of haze ambient-air samples.

Angiogenesis is a multi-step physiological process which is involved in a large number of normal and disease state processes; In vitro angiogenesis models provide useful tools to study these processes, one of which is the analysis of tubule formation. Tubules formed in co-culture assays composed of both endothelial and stroma-producing cells were significantly more heterogeneous and more closely resembled capillaries than mono cell culture models utilizing only endothelial cells to generate tubules in an extracellular matrix. Current co-culture models using primary cells have donor variability and inconsistent results due to lot-to-lot variation. In this study, we established an in vitro co-culture model system consisting of an assay-ready mixture of an aortic endothelial cell line TeloHAEC-GFP and an hTERT immortalized adipose-derived mesenchymal stem cell line in a specially formulated medium containing VEGF supplement. Both cell lines were immortalized by hTERT alone and have been well-characterized; showing that the cells retain the most important characteristic of their primary counterparts. The new co-culture system forms functional tubular structures in less than 7 days, additionally, the hTERT-MSC cells which surround the tubular structures have undergone transformation indicated by elevated positive αSMA staining, indicating that the system has physiological relevance. Notably, our results showed the co-culture system has minimal lot-to-lot variation indicated by the treatment of three lots with the anti-cancer drug, ramucirumab. More importantly, the tubular formation efficiency is reduced or blocked by well-known anti-cancer drugs such as sunitinib and bevacizumab. We also tested four HIF-1 inhibitors identified in previous high-throughput screens and found that those compounds inhibited tubule formation in the co-culture system. These results suggest that the co-culture system can mimic the hypoxic environment in solid tumors. Previously the authors optimized the system for 384-well performance and here we report further optimization of the system into a 1536-well high-throughput format and a shortening of the assay time frame to 3 days. Using this format, we evaluated 2816 drugs from The National Center for Advancing Translational Sciences (NCATS) Pharmaceutical Collection (NPC), and 35 potent inhibitors (IC50 ≤1 μM) were identified. Moreover, many known angiogenesis inhibitors were identified, such as topotecan, docetaxel, and bortezomib. Several potential novel angiogenesis inhibitors were also identified from this study. Among the inhibitors, some compounds were proven to be involved in the hypoxia-inducible factor-1α and the nuclear factor-kappa B pathways. These results demonstrate that the co-culture model described in this report provides a consistent and robust in vitro system for antiangiogenic drug screening. Citation Format: Chaozhong Zou, Shuaizhang LI, Chia-Wen Hsu, Menghang Xia, Metewo S. Enuameh. Development of a high-throughput screening co-culture angiogenesis assay system using hTERT immortalized primary cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3868.

Recent reports have noted that a number of compounds that block the human Ether-à-go-go related gene (hERG) ion channel also induce phospholipidosis (PLD). To explore a hypothesis explaining why most PLD inducers are also hERG inhibitors, a modeling approach was undertaken with data sets comprised of 4096 compounds assayed for hERG inhibition and 5490 compounds assayed for PLD induction. To eliminate the chemical domain effect, a filtered data set of 567 compounds tested in quantitative high-throughput screening (qHTS) format for both hERG inhibition and PLD induction was constructed. Partial least squares (PLS) modeling followed by 3D-SDAR mapping of the most frequently occurring bins and projection on to the chemical structure suggested that both adverse effects are driven by similar structural features, namely two aromatic rings and an amino group forming a three-center toxicophore. Non-parametric U-tests performed on the original 3D-SDAR bins indicated that the distance between the two aromatic rings is the main factor determining the differences in activity; at distances of up to about 5.5 Å, a phospholipidotic compound would also inhibit hERG, while at longer distances, a sharp reduction of the PLD-inducing potential leaves only a well-pronounced hERG blocking effect. The hERG activity itself diminishes after the distance between the centroids of the two aromatic rings exceeds 12.5 Å. Further comparison of the two toxicophores revealed that the almost identical aromatic rings to amino group distances play no significant role in distinguishing between PLD and hERG activity. The hypothesis that the PLD toxicophore appears to be a subset of the hERG toxicophore explains why about 80% of all phospholipidotic chemicals (the remaining 20% are thought to act via a different mechanism) also inhibit the hERG ion channel. These models were further validated in large-scale qHTS assays testing 1085 chemicals for their PLD-inducing potential and 1570 compounds for hERG inhibition. After removal of the modeling and experimental inconclusive compounds, the area under the receiver-operating characteristic (ROC) curve was 0.92 for the PLD model and 0.87 for the hERG model. Due to the exceptional ability of these models to recognize safe compounds (negative predictive values of 0.99 for PLD and 0.94 for hERG were achieved), their use in regulatory settings might be particularly useful.