The future of toxicity testing: a focus on in vitro methods using a quantitative high-throughput screening platform

Computational framework for identifying and evaluating mutagenic and xenoestrogenic potential of food additives

Food additives are chemicals incorporated in food to enhance its flavor, color and prevent spoilage. Some of these are associated with substantial health hazards, including developmental disorders, increase cancer risk, and hormone disruption. Hence, this study aimed to comprehend the in-silico toxicology framework for evaluating mutagenic and xenoestrogenic potential of food additives and their association with breast cancer. A total of 2885 food additives were screened for toxicity based on Threshold of Toxicological Concern (TTC), mutagenicity endpoint prediction, and mutagenic structural alerts/toxicophores identification. Ten food additives were identified as having mutagenic potential based on toxicity screening. Furthermore, Protein-Protein Interaction (PPI) analysis identified ESR1, as a key hub gene in breast cancer. KEGG pathway analysis verified that ESR1 plays a significant role in breast cancer pathogenesis. Additionally, competitive interaction studies of the predicted potential mutagenic food additives with the estrogen receptor-α were evaluated at agonist and antagonist binding sites. Indole, Dichloromethane, Trichloroethylene, Quinoline, 6-methyl quinoline, Ethyl nitrite, and 4-methyl quinoline could act as agonists, and Paraldehyde, Azodicarbonamide, and 2-acetylfuranmay as antagonists. The systematic risk assessment framework reported in this study enables the exploration of mutagenic and xenoestrogenic potential associated with food additives for hazard identification and management.

Identification of estrogen receptor agonists among hydroxylated polychlorinated biphenyls using classification-based quantitative structure-activity relationship models

Identification of estrogen receptor (ER) agonists among environmental toxicants is essential for assessing the potential impact of toxicants on human health. Using 2D autocorrelation descriptors as predictor variables, two binary logistic regression models were developed to identify active ER agonists among hydroxylated polychlorinated biphenyls (OH-PCBs). The classifications made by the two models on the training set compounds resulted in accuracy, sensitivity and specificity of 95.9 %, 93.9 % and 97.6 % for ERα dataset and 91.9 %, 90.9 % and 92.7 % for ERβ dataset. The areas under the ROC curves, constructed with the training set data, were found to be 0.985 and 0.987 for the two models. Predictions made by models I and II correctly classified 84.0 % and 88.0 % of the test set compounds and 89.8 % and 85.8% of the cross-validation set compounds respectively. The two classification-based QSAR models proposed in this paper are considered robust and reliable for rapid identification of ERα and ERβ agonists among OH-PCB congeners.

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.

Transcriptome-based prediction of drugs, inhibiting cardiomyogenesis in human induced pluripotent stem cells

Animal studies for embryotoxicity evaluation of potential therapeutics and environmental factors are complex, costly, and time-consuming. Often, studies are not of human relevance because of species differences. In the present study, we recapitulated the process of cardiomyogenesis in human induced pluripotent stem cells (hiPSCs) by modulation of the Wnt signaling pathway to identify a key cardiomyogenesis gene signature that can be applied to identify compounds and/or stress factors compromising the cardiomyogenesis process. Among the 23 tested teratogens and 16 non-teratogens, we identified three retinoids including 13-cis-retinoic acid that completely block the process of cardiomyogenesis in hiPSCs. Moreover, we have identified an early gene signature consisting of 31 genes and associated biological processes that are severely affected by the retinoids. To predict the inhibitory potential of teratogens and non-teratogens in the process of cardiomyogenesis we established the "Developmental Cardiotoxicity Index" (CDI31g) that accurately differentiates teratogens and non-teratogens to do or do not affect the differentiation of hiPSCs to functional cardiomyocytes.

Exploring the potential of ToxCast™ data for mechanism-based prioritization of chemicals in regulatory context: Case study with priority existing chemicals (PECs) under K-REACH

Recent studies have highlighted the potential of ToxCast™ database to mechanism-based prioritization of chemicals. To explore the applicability of ToxCast data in the context of regulatory inventory chemicals, we screened 510 priority existing chemicals (PECs) regulated under the Act on the Registration and Evaluation of Chemical Substances (K-REACH) using ToxCast bioassays. In our analysis, a hit-call data matrix containing 298984 chemical-gene interactions was computed for 949 bioassays with the intended target genes, which enabled the identification of the putative toxicity mechanisms. Based on the reactivity to the chemicals, we analyzed 412 bioassays whose intended target gene families were cytochrome P450, oxidoreductase, transporter, nuclear receptor, steroid hormone, and DNA-binding. We also identified 141 chemicals based on their reactivity in the bioassays. These chemicals are mainly in consumer products including colorants, preservatives, air fresheners, and detergents. Our analysis revealed that in vitro bioactivities were involved in the relevant mechanisms inducing in vivo toxicity; however, this was not sufficient to predict more hazardous chemicals. Overall, the current results point to a potential and limitation in using ToxCast data for chemical prioritization in regulatory context in the absence of suitable in vivo data.

Strategies for automating analytical and bioanalytical laboratories

Analytical measurement methods are used in different areas of production and quality control, diagnostics, environmental monitoring, or in research applications. If direct inline or online measurement methods are not possible, the samples taken have to be processed offline in the manual laboratory. Automated processes are increasingly being used to enhance throughput and improve the quality of results. In contrast to bioscreening, the degree of automation in (bio)analytical laboratories is still low. This is due in particular to the complexity of the processes, the required process conditions, and the complex matrices of the samples. The requirements of the process to be automated itself and numerous other parameters influence the selection of a suitable automation concept. Different automation strategies can be used to automate (bio)analytical processes. Classically, liquid handler-based systems are used. For more complex processes, systems with central robots are used to transport samples and labware. With the development of new collaborative robots, there will also be the possibility of distributed automation systems in the future, which will enable even more flexible automation and use of all subsystems. The complexity of the systems increases with the complexity of the processes to be automated.

Engineered Vasculature for Cancer Research and Regenerative Medicine

Engineered human tissues created by three-dimensional cell culture of human cells in a hydrogel are becoming emerging model systems for cancer drug discovery and regenerative medicine. Complex functional engineered tissues can also assist in the regeneration, repair, or replacement of human tissues. However, one of the main hurdles for tissue engineering, three-dimensional cell culture, and regenerative medicine is the capability of delivering nutrients and oxygen to cells through the vasculatures. Several studies have investigated different strategies to create a functional vascular system in engineered tissues and organ-on-a-chips. Engineered vasculatures have been used for the studies of angiogenesis, vasculogenesis, as well as drug and cell transports across the endothelium. Moreover, vascular engineering allows the creation of large functional vascular conduits for regenerative medicine purposes. However, there are still many challenges in the creation of vascularized tissue constructs and their biological applications. This review will summarize the latest efforts to create vasculatures and vascularized tissues for cancer research and regenerative medicine.

Toxicity prediction: An application of alternative testing and computational toxicology in contaminated groundwater sites in Taiwan

Groundwater contamination remains a global threat due to its toxic effects to humans and the environment. The remediation of contaminated groundwater sites can be costly, thus, identifying the priority areas of concern is important to reduce money spent on resources. In this study, we aimed to identify and rank the priority groundwater sites in a contaminated petrochemical district by combining alternative, non-animal approaches - chemical analysis, cell-based high throughput screening (HTS), and Toxicological Priority Index (ToxPi) computational toxicology tool. Groundwater samples collected from ten different sites in a contaminated district showed pollutant levels below the detection limit, however, hepatotoxic bioactivity was demonstrated in human hepatoma HepaRG cells. Integrating the pollutants information (i.e., pollutant characteristics and concentration data) with the bioactivity data of the groundwater samples, an evidence-based ranking of the groundwater sites for future remediation was established using ToxPi analysis. The currently presented combinatorial approach of screening groundwater sites for remediation purposes can further be refined by including relevant parameters, which can boost the utility of this approach for groundwater screening and future remediation.

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.

Application of ToxCast/Tox21 data for toxicity mechanism-based evaluation and prioritization of environmental chemicals: Perspective and limitations

In response to the need to minimize the use of experimental animals, new approach methodologies (NAMs) using advanced technology have emerged in the 21st century. ToxCast/Tox21 aims to evaluate the adverse effects of chemicals quickly and efficiently using a high-throughput screening and to transform the paradigm of toxicity assessment into mechanism-based toxicity prediction. The ToxCast/Tox21 database, which contains extensive data from over 1400 assays with numerous biological targets and activity data for over 9000 chemicals, can be used for various purposes in the field of chemical prioritization and toxicity prediction. In this study, an overview of the database was explored to aid mechanism-based chemical prioritization and toxicity prediction. Implications for the utilization of the ToxCast/Tox21 database in chemical prioritization and toxicity prediction were derived. The research trends in ToxCast/Tox21 assay data were reviewed in the context of toxicity mechanism identification, chemical priority, environmental monitoring, assay development, and toxicity prediction. Finally, the potential applications and limitations of using ToxCast/Tox21 assay data in chemical risk assessment were discussed. The analysis of the toxicity mechanism-based assays of ToxCast/Tox21 will help in chemical prioritization and regulatory applications without the use of laboratory animals.

Quantitative in vitro to in vivo extrapolation for developmental toxicity potency of valproic acid analogues

BACKGROUND

The developmental toxicity potential (dTP) concentration from the devTOX quickPredict (devTOX^qP ) assay, a metabolomics-based human induced pluripotent stem cell assay, predicts a chemical's developmental toxicity potency. Here, in vitro to in vivo extrapolation (IVIVE) approaches were applied to address whether the devTOX^qP assay could quantitatively predict in vivo developmental toxicity lowest effect levels (LELs) for the prototypical teratogen valproic acid (VPA) and a group of structural analogues.

METHODS

VPA and a series of structural analogues were tested with the devTOX^qP assay to determine dTP concentration and we estimated the equivalent administered doses (EADs) that would lead to plasma concentrations equivalent to the in vitro dTP concentrations. The EADs were compared to the LELs in rat developmental toxicity studies, human clinical doses, and EADs reported using other in vitro assays. To evaluate the impact of different pharmacokinetic (PK) models on IVIVE outcomes, we compared EADs predicted using various open-source and commercially available PK and physiologically based PK (PBPK) models. To evaluate the effect of in vitro kinetics, an equilibrium distribution model was applied to translate dTP concentrations to free medium concentrations before subsequent IVIVE analyses.

RESULTS

The EAD estimates for the VPA analogues based on different PK/PBPK models were quantitatively similar to in vivo data from both rats and humans, where available, and the derived rank order of the chemicals was consistent with observed in vivo developmental toxicity. Different models were identified that provided accurate predictions for rat prenatal LELs and conservative estimates of human safe exposure. The impact of in vitro kinetics on EAD estimates is chemical-dependent. EADs from this study were within range of predicted doses from other in vitro and model organism data.

CONCLUSIONS

This study highlights the importance of pharmacokinetic considerations when using in vitro assays and demonstrates the utility of the devTOX^qP human stem cell-based platform to quantitatively assess a chemical's developmental toxicity potency.

Application of ARE-reporter systems in drug discovery and safety assessment

The human body is continuously exposed to xenobiotics and internal or external oxidants. The health risk assessment of exogenous chemicals remains a complex and challenging issue. Alternative toxicological test methods have become an essential strategy for health risk assessment. As a core regulator of constitutive and inducible expression of antioxidant response element (ARE)-dependent genes, nuclear factor erythroid 2-related factor 2 (Nrf2) plays a critical role in maintaining cellular redox homeostasis. Consistent with the properties of Nrf2-mediated antioxidant response, Nrf2-ARE activity is a direct indicator of oxidative stress and thus has been used to identify and characterize oxidative stressors and redox modulators. To screen and distinguish chemicals or environmental insults that affect the cellular antioxidant activity and/or induce oxidative stress, various in vitro cell models expressing distinct ARE reporters with high-throughput and high-content properties have been developed. These ARE-reporter systems are currently widely applied in drug discovery and safety assessment. In the present review, we provide an overview of the basic structures and applications of various ARE-reporter systems employed for discovering Nrf2-ARE modulators and characterizing oxidative stressors.

A Flexible Data Evaluation System for Improving the Quality and Efficiency of Laboratory Analysis and Testing

In a chemical analysis laboratory, sample detection via most analytical devices obtains raw data and processes it to validate data reports, including raw data filtering, editing, effectiveness evaluation, error correction, etc. This process is usually carried out manually by analysts. When the sample detection volume is large, the data processing involved becomes time-consuming and laborious, and manual errors may be introduced. In addition, analytical laboratories typically use a variety of analytical devices with different measurement principles, leading to the use of various heterogeneous control software systems from different vendors with different export data formats. Different formats introduce difficulties to laboratory automation. This paper proposes a modular data evaluation system that uses a global unified management and maintenance mode that can automatically filter data, evaluate quality, generate valid reports, and distribute reports. This modular software design concept allows the proposed system to be applied to different analytical devices; its integration into existing laboratory information management systems (LIMS) could maximise automation and improve the analysis and testing quality and efficiency in a chemical analysis laboratory, while meeting the analysis and testing requirements.

Mechanism-driven modeling of chemical hepatotoxicity using structural alerts and an in vitro screening assay

Traditional experimental approaches to evaluate hepatotoxicity are expensive and time-consuming. As an advanced framework of risk assessment, adverse outcome pathways (AOPs) describe the sequence of molecular and cellular events underlying chemical toxicities. We aimed to develop an AOP that can be used to predict hepatotoxicity by leveraging computational modeling and in vitro assays. We curated 869 compounds with known hepatotoxicity classifications as a modeling set and extracted assay data from PubChem. The antioxidant response element (ARE) assay, which quantifies transcriptional responses to oxidative stress, showed a high correlation to hepatotoxicity (PPV=0.82). Next, we developed quantitative structure-activity relationship (QSAR) models to predict ARE activation for compounds lacking testing results. Potential toxicity alerts were identified and used to construct a mechanistic hepatotoxicity model. For experimental validation, 16 compounds in the modeling set and 12 new compounds were selected and tested using an in-house ARE-luciferase assay in HepG2-C8 cells. The mechanistic model showed good hepatotoxicity predictivity (accuracy = 0.82) for these compounds. Potential false positive hepatotoxicity predictions by only using ARE results can be corrected by incorporating structural alerts and vice versa. This mechanistic model illustrates a potential toxicity pathway for hepatotoxicity, and this strategy can be expanded to develop predictive models for other complex toxicities.

Orthogonal assay for validation of Tox21 PPARγ data and applicability to in silico prediction model

High-throughput screening data from the Tox21 database is used for prioritizing hazardous chemicals and building in silico-based toxicity prediction models. One of the Tox21 dataset, peroxisome proliferator-activated receptor-gamma (PPARγ), a nuclear receptor superfamily, identified various endpoints in HEK293 cells. PPARγ mediates various toxic effects when its receptors are activated or inhibited by ligands such as thiazolidinedione and GW9662. In this study, an orthogonal assay was constructed to verify the effectiveness of the Tox21 PPARγ data, and the effect of highly reliable data on in silico model construction was investigated. The orthogonal assay was a reporter gene assay based on the PPARγ ligand binding domain in CV-1 cells. Only 39% of agonists and 55% of antagonists had similar responses in CV-1 and HEK293 cells. Thus, the effectiveness of Tox21 data on PPARγ may vary depending on the cell line. However, in silico PLS-DA analysis with only high-reliability data (i.e., the same response in both cell lines), yielded more accurate prediction of the activity of potential chemical ligands, despite the small number of samples. Thus, obtaining reliable chemical screening data for PPARγ through orthogonal analysis, even for only limited chemicals, supports the construction of highly predictive in silico models with improved screening efficiency.

Beyond the behavioural phenotype: Uncovering mechanistic foundations in aquatic eco-neurotoxicology

During the last decade, there has been an increase in awareness of how anthropogenic pollution can alter behavioural traits of diverse aquatic organisms. Apart from understanding profound ecological implications, alterations in neuro-behavioural indices have emerged as sensitive and physiologically integrative endpoints in chemical risk assessment. Accordingly, behavioural ecotoxicology and broader eco-neurotoxicology are becoming increasingly popular fields of research that span a plethora of fundamental laboratory experimentations as well as applied field-based studies. Despite mounting interest in aquatic behavioural ecotoxicology studies, there is, however, a considerable paucity in deciphering the mechanistic foundations underlying behavioural alterations upon exposure to pollutants. The behavioural phenotype is indeed the highest-level integrative neurobiological phenomenon, but at its core lie myriads of intertwined biochemical, cellular, and physiological processes. Therefore, the mechanisms that underlie changes in behavioural phenotypes can stem among others from dysregulation of neurotransmitter pathways, electrical signalling, and cell death of discrete cell populations in the central and peripheral nervous systems. They can, however, also be a result of toxicity to sensory organs and even metabolic dysfunctions. In this critical review, we outline why behavioural phenotyping should be the starting point that leads to actual discovery of fundamental mechanisms underlying actions of neurotoxic and neuromodulating contaminants. We highlight potential applications of the currently existing and emerging neurobiology and neurophysiology analytical strategies that should be embraced and more broadly adopted in behavioural ecotoxicology. Such strategies can provide new mechanistic discoveries instead of only observing the end sum phenotypic effects.

Acute Toxicity Evaluation of the Disinfectant Containing Percarbonate and Tetraacetylethylenediamine by Measuring Behavioral Responses of Small Fish Using Image Analysis

Disinfectants containing percarbonate and tetraacetylethylenediamine (TAED) has been developed as an effective and relatively safe disinfectant to destroy viruses and bacteria in animals and humans, however it is known that most disinfectants can cause danger to living organisms including humans. In the current study, acute toxicity of the disinfectant composed of percarbonate and TAED was assessed by measuring behavioral responses as well as lethal concentrations of aquatic organisms such as medaka and zebrafish when they were exposed to it. First, the breeding water properties were determined by measuring dissolved oxygen (DO) and pH changes over time up to 96 h in acute toxicity tests using the medaka, and the lethal concentration 50% (LC50, 88.39 ppm) was calculated using the lethality rate of the fish. This experiment was conducted in compliance with traditional OECD guidelines. Second, the assessment of behavioral responses (locomotive activity and swimming speed) with the zebrafish were assessed by the image analysis to capture the images per second for three hours, and the collected data were processed using image analysis to calculate the locomotive activity and swimming speed. Finally, the LC50 (135.76 ppm) of the disinfectant to the fish was also measured after three hours. Overall, the data revealed that LC50 of the disinfectant may be affected by the pH of the water exposed to the disinfectant, not by the DO in the water. In addition, the results from the image analysis indicated that the behavioral responses of the fish can further assess the acute toxicity of the disinfectant at concentrations below the LC50 and there was a relationship (R² = 0.85) between the behavioral responses and the survival rate of the fish.

Identifying Protein Features and Pathways Responsible for Toxicity Using Machine Learning and Tox21: Implications for Predictive Toxicology

Humans are exposed to numerous compounds daily, some of which have adverse effects on health. Computational approaches for modeling toxicological data in conjunction with machine learning algorithms have gained popularity over the last few years. Machine learning approaches have been used to predict toxicity-related biological activities using chemical structure descriptors. However, toxicity-related proteomic features have not been fully investigated. In this study, we construct a computational pipeline using machine learning models for predicting the most important protein features responsible for the toxicity of compounds taken from the Tox21 dataset that is implemented within the multiscale Computational Analysis of Novel Drug Opportunities (CANDO) therapeutic discovery platform. Tox21 is a highly imbalanced dataset consisting of twelve in vitro assays, seven from the nuclear receptor (NR) signaling pathway and five from the stress response (SR) pathway, for more than 10,000 compounds. For the machine learning model, we employed a random forest with the combination of Synthetic Minority Oversampling Technique (SMOTE) and the Edited Nearest Neighbor (ENN) method (SMOTE+ENN), which is a resampling method to balance the activity class distribution. Within the NR and SR pathways, the activity of the aryl hydrocarbon receptor (NR-AhR) and the mitochondrial membrane potential (SR-MMP) were two of the top-performing twelve toxicity endpoints with AUCROCs of 0.90 and 0.92, respectively. The top extracted features for evaluating compound toxicity were analyzed for enrichment to highlight the implicated biological pathways and proteins. We validated our enrichment results for the activity of the AhR using a thorough literature search. Our case study showed that the selected enriched pathways and proteins from our computational pipeline are not only correlated with AhR toxicity but also form a cascading upstream/downstream arrangement. Our work elucidates significant relationships between protein and compound interactions computed using CANDO and the associated biological pathways to which the proteins belong for twelve toxicity endpoints. This novel study uses machine learning not only to predict and understand toxicity but also elucidates therapeutic mechanisms at a proteomic level for a variety of toxicity endpoints.

Predicting Prenatal Developmental Toxicity Based On the Combination of Chemical Structures and Biological Data

For hazard identification, classification, and labeling purposes, animal testing guidelines are required by law to evaluate the developmental toxicity potential of new and existing chemical products. However, guideline developmental toxicity studies are costly, time-consuming, and require many laboratory animals. Computational modeling has emerged as a promising, animal-sparing, and cost-effective method for evaluating the developmental toxicity potential of chemicals, such as endocrine disruptors, without the use of animals. We aimed to develop a predictive and explainable computational model for developmental toxicants. To this end, a comprehensive dataset of 1244 chemicals with developmental toxicity classifications was curated from public repositories and literature sources. Data from 2140 toxicological high-throughput screening assays were extracted from PubChem and the ToxCast program for this dataset and combined with information about 834 chemical fragments to group assays based on their chemical-mechanistic relationships. This effort revealed two assay clusters containing 83 and 76 assays, respectively, with high positive predictive rates for developmental toxicants identified with animal testing guidelines (PPV = 72.4 and 77.3% during cross-validation). These two assay clusters can be used as developmental toxicity models and were applied to predict new chemicals for external validation. This study provides a new strategy for constructing alternative chemical developmental toxicity evaluations that can be replicated for other toxicity modeling studies.

A Comparative Multi-System Approach to Characterizing Bioactivity of Commonly Occurring Chemicals

A 2019 retrospective study analyzed wristband personal samplers from fourteen different communities across three different continents for over 1530 organic chemicals. Investigators identified fourteen chemicals (G14) detected in over 50% of personal samplers. The G14 represent a group of chemicals that individuals are commonly exposed to, and are mainly associated with consumer products including plasticizers, fragrances, flame retardants, and pesticides. The high frequency of exposure to these chemicals raises questions of their potential adverse human health effects. Additionally, the possibility of exposure to mixtures of these chemicals is likely due to their co-occurrence; thus, the potential for mixtures to induce differential bioactivity warrants further investigation. This study describes a novel approach to broadly evaluate the hazards of personal chemical exposures by coupling data from personal sampling devices with high-throughput bioactivity screenings using in vitro and non-mammalian in vivo models. To account for species and sensitivity differences, screening was conducted using primary normal human bronchial epithelial (NHBE) cells and early life-stage zebrafish. Mixtures of the G14 and most potent G14 chemicals were created to assess potential mixture effects. Chemical bioactivity was dependent on the model system, with five and eleven chemicals deemed bioactive in NHBE and zebrafish, respectively, supporting the use of a multi-system approach for bioactivity testing and highlighting sensitivity differences between the models. In both NHBE and zebrafish, mixture effects were observed when screening mixtures of the most potent chemicals. Observations of BMC-based mixtures in NHBE (NHBE BMC Mix) and zebrafish (ZF BMC Mix) suggested antagonistic effects. In this study, consumer product-related chemicals were prioritized for bioactivity screening using personal exposure data. High-throughput high-content screening was utilized to assess the chemical bioactivity and mixture effects of the most potent chemicals.

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

The U.S. Tox21 program has developed in vitro assays to test large collections of environmental chemicals in a quantitative high-throughput screening (qHTS) format, using triplicate 15-dose titrations to generate over 100 million data points to date. Counterscreens are also employed to minimize interferences from non-target-specific assay artifacts, such as compound autofluorescence and cytotoxicity. New data analysis approaches are needed to integrate these data and characterize the activities observed from these assays. Here, we describe a complete analysis pipeline that evaluates these qHTS data for technical quality in terms of signal reproducibility. We integrate signals from repeated assay runs, primary readouts and counterscreens to produce a final call on on-target compound activity.

Large-Scale Transcriptional Profiling of Molecular Perturbations Reveals Cell Line Specific Responses and Implications for Environmental Screening

Cell-based bioassays represent nearly half of all high-throughput screens currently conducted for risk assessment of environmental chemicals. However, there has long been a concern about the sensitivity and heterogeneity among cell lines, which were explored only in a limited manner. Here, we address this question by conducting a large-scale transcriptome analysis of the responses of discrete cell lines to specific molecules. We report the collections of >223 300 gene expression profiles from a wide array of cell lines exposed to 2243 compounds. Our results demonstrate distinct responses among cell lines at both the gene and the pathway levels. Temporal variations for a very large proportion of compounds occur as well. High sensitivity and/or heterogeneity is either cell line-specific or universal depending on the modes of action of the compounds. Among 12 representative pathways analyzed, distinct cell-chemical interactions exist. On one hand, lung carcinoma cells are always best suited for glucocorticoid receptor agonist identification, while on the other hand, high sensitivity and heterogenic features are universal for histone deacetylase inhibitors and ATPase inhibitors. Our data provide novel insights into the understanding of cell-specific responses and interactions between cells and xenobiotics. The findings have substantial implications for the design, execution, and interpretation of high-throughput screening assays in (eco)toxicology.

Toward the Proactive Design of Sustainable Chemicals: Ionic Liquids as a Prime Example

The tailorable and often unique properties of ionic liquids (ILs) drive their implementation into a broad variety of seminal technologies. The modular design of ILs allows in this context a proactive selection of structures that favor environmental sustainability─ideally without compromising their technological performance. To achieve this objective, the whole life cycle must be taken into account and various aspects considered simultaneously. In this review, we discuss how the structural design of ILs affects their environmental impacts throughout all stages of their life cycles and scrutinize the available data in order to point out knowledge gaps that need further research activities. The design of more sustainable ILs starts with the selection of the most beneficial precursors and synthesis routes, takes their technical properties and application specific performance into due account, and considers its environmental fate particularly in terms of their (eco)toxicity, biotic and abiotic degradability, mobility, and bioaccumulation potential. Special emphasis is placed on reported structure-activity relationships and suggested mechanisms on a molecular level that might rationalize the empirically found design criteria.

Advanced preclinical models for evaluation of drug-induced liver injury - consensus statement by the European Drug-Induced Liver Injury Network [PRO-EURO-DILI-NET]

Drug-induced liver injury (DILI) is a major cause of acute liver failure (ALF) and one of the leading indications for liver transplantation in Western societies. Given the wide use of both prescribed and over the counter drugs, DILI has become a major health issue for which there is a pressing need to find novel and effective therapies. Although significant progress has been made in understanding the molecular mechanisms underlying DILI, our incomplete knowledge of its pathogenesis and inability to predict DILI is largely due to both discordance between human and animal DILI in preclinical drug development and a lack of models that faithfully recapitulate complex pathophysiological features of human DILI. This is exemplified by the hepatotoxicity of acetaminophen (APAP) overdose, a major cause of ALF because of its extensive worldwide use as an analgesic. Despite intensive efforts utilising current animal and in vitro models, the mechanisms involved in the hepatotoxicity of APAP are still not fully understood. In this expert Consensus Statement, which is endorsed by the European Drug-Induced Liver Injury Network, we aim to facilitate and outline clinically impactful discoveries by detailing the requirements for more realistic human-based systems to assess hepatotoxicity and guide future drug safety testing. We present novel insights and discuss major players in APAP pathophysiology, and describe emerging in vitro and in vivo pre-clinical models, as well as advanced imaging and in silico technologies, which may improve prediction of clinical outcomes of DILI.

In silico prediction of nuclear receptor binding to polychlorinated dibenzofurans and its implication on endocrine disruption in humans and wildlife

Polychlorinated dibenzofurans (PCDFs) are known to cause endocrine disruption in humans and wildlife but the mechanisms underlying this disruption have not been adequately investigated. In this paper, the susceptibility of the endocrine system to disruption by PCDF congeners via nuclear receptor binding was studied using molecular docking simulation. Findings revealed that some PCDF congeners exhibit high probabilities of binding to androgen receptor in its agonistic and antagonistic conformations. In depth molecular docking analysis of the receptor-ligand complexes formed by PCDFs with androgen receptor in its agonistic and antagonistic conformations showed that, these complexes were stabilized by electrostatic, van der Waals, pi-effect and hydrophobic interactions. It was also observed that PCDF molecules mimic the modes of interaction observed in androgen-testosterone and androgen-bicalutamide complexes, utilizing between 65 and 83% of the amino acid residues used by the co-crystallized ligands for binding. This computational study suggests that some PCDF congeners may act as agonists and antagonists of androgen receptor in humans and wildlife via inapproprate binding to the receptor.

Revealing Adverse Outcome Pathways from Public High-Throughput Screening Data to Evaluate New Toxicants by a Knowledge-Based Deep Neural Network Approach

Traditional experimental testing to identify endocrine disruptors that enhance estrogenic signaling relies on expensive and labor-intensive experiments. We sought to design a knowledge-based deep neural network (k-DNN) approach to reveal and organize public high-throughput screening data for compounds with nuclear estrogen receptor α and β (ERα and ERβ) binding potentials. The target activity was rodent uterotrophic bioactivity driven by ERα/ERβ activations. After training, the resultant network successfully inferred critical relationships among ERα/ERβ target bioassays, shown as weights of 6521 edges between 1071 neurons. The resultant network uses an adverse outcome pathway (AOP) framework to mimic the signaling pathway initiated by ERα and identify compounds that mimic endogenous estrogens (i.e., estrogen mimetics). The k-DNN can predict estrogen mimetics by activating neurons representing several events in the ERα/ERβ signaling pathway. Therefore, this virtual pathway model, starting from a compound's chemistry initiating ERα activation and ending with rodent uterotrophic bioactivity, can efficiently and accurately prioritize new estrogen mimetics (AUC = 0.864-0.927). This k-DNN method is a potential universal computational toxicology strategy to utilize public high-throughput screening data to characterize hazards and prioritize potentially toxic compounds.

Automation for Life Science Laboratories

The automation of processes in all areas of the life sciences will continue to increase in the coming years due to an ever increasing number of samples to be processed, an increasing need to protect laboratory personnel from infectious material and increasing cost pressure. Depending on the requirements of the respective application, different concepts for automation systems are available, which have a different degree of automation with regard to data handling, transportation tasks, and the processing of the samples. Robots form a central component of these automation concepts. Classic stationary robots from the industrial sector will increasingly be replaced by new developments in the field of light-weight robots. In addition, mobile robots will also be of particular importance in the automation of life science laboratories in the future, especially for transportation tasks between different manual and (partially) automated stations. With an increasing number of different, highly diverse processes, the need for special devices and system components will also increase. This applies to both, the handling of the labware and the processing of the samples. In contrast to previous automation strategies with a highly parallel approach, future developments will increasingly be characterized by individual sample handling.

Applications of advanced neuro-behavioral analysis strategies in aquatic ecotoxicology

Despite mounting evidence of pleiotropic ecological risks, the understanding of the eco-neurotoxic impact of most industrially relevant chemicals is still very limited. In particularly the acute and chronic exposures to industrial pollutants on nervous systems and thus potential alterations in ecological fitness remain profoundly understudied. Since the behavioral phenotype is the highest-level and functional manifestation of integrated neurological functions, the alterations in neuro-behavioral traits have been postulated as very sensitive and physiologically integrative endpoints to assess eco-neurotoxicological risks associated with industrial pollutants. Due to a considerable backlog of risk assessments of existing and new production chemicals there is a need for a paradigm shift from high cost, low throughput ecotoxicity test models to next generation systems amenable to higher throughput. In this review we concentrate on emerging aspects of laboratory-based neuro-behavioral phenotyping approaches that can be amenable for rapid prioritizing pipelines. We outline the importance of development and applications of innovative neuro-behavioral assays utilizing small aquatic biological indicators and demonstrate emerging concepts of high-throughput chemo-behavioral phenotyping. We also discuss new analytical approaches to effectively and rapidly evaluate the impact of pollutants on higher behavioral functions such as sensory-motor assays, decision-making and cognitive behaviors using innovative model organisms. Finally, we provide a snapshot of most recent analytical approaches that can be applied to elucidate mechanistic rationale that underlie the observed neuro-behavioral alterations upon exposure to pollutants. This review is intended to outline the emerging opportunities for innovative multidisciplinary research and highlight the existing challenges as well barriers to future development.

In Vitro Liver Toxicity Testing of Chemicals: A Pragmatic Approach

The liver is among the most frequently targeted organs by noxious chemicals of diverse nature. Liver toxicity testing using laboratory animals not only raises serious ethical questions, but is also rather poorly predictive of human safety towards chemicals. Increasing attention is, therefore, being paid to the development of non-animal and human-based testing schemes, which rely to a great extent on in vitro methodology. The present paper proposes a rationalized tiered in vitro testing strategy to detect liver toxicity triggered by chemicals, in which the first tier is focused on assessing general cytotoxicity, while the second tier is aimed at identifying liver-specific toxicity as such. A state-of-the-art overview is provided of the most commonly used in vitro assays that can be used in both tiers. Advantages and disadvantages of each assay as well as overall practical considerations are discussed.

Predictive modeling of estrogen receptor agonism, antagonism, and binding activities using machine- and deep-learning approaches

As defined by the World Health Organization, an endocrine disruptor is an exogenous substance or mixture that alters function(s) of the endocrine system and consequently causes adverse health effects in an intact organism, its progeny, or (sub)populations. Traditional experimental testing regimens to identify toxicants that induce endocrine disruption can be expensive and time-consuming. Computational modeling has emerged as a promising and cost-effective alternative method for screening and prioritizing potentially endocrine-active compounds. The efficient identification of suitable chemical descriptors and machine-learning algorithms, including deep learning, is a considerable challenge for computational toxicology studies. Here, we sought to apply classic machine-learning algorithms and deep-learning approaches to a panel of over 7500 compounds tested against 18 Toxicity Forecaster assays related to nuclear estrogen receptor (ERα and ERβ) activity. Three binary fingerprints (Extended Connectivity FingerPrints, Functional Connectivity FingerPrints, and Molecular ACCess System) were used as chemical descriptors in this study. Each descriptor was combined with four machine-learning and two deep- learning (normal and multitask neural networks) approaches to construct models for all 18 ER assays. The resulting model performance was evaluated using the area under the receiver- operating curve (AUC) values obtained from a fivefold cross-validation procedure. The results showed that individual models have AUC values that range from 0.56 to 0.86. External validation was conducted using two additional sets of compounds (n = 592 and n = 966) with established interactions with nuclear ER demonstrated through experimentation. An agonist, antagonist, or binding score was determined for each compound by averaging its predicted probabilities in relevant assay models as an external validation, yielding AUC values ranging from 0.63 to 0.91. The results suggest that multitask neural networks offer advantages when modeling mechanistically related endpoints. Consensus predictions based on the average values of individual models remain the best modeling strategy for computational toxicity evaluations.

Tryptophanol-Derived Oxazolopyrrolidone Lactams as Potential Anticancer Agents against Gastric Adenocarcinoma

Gastric cancer is one of the deadliest cancers in modern societies, so there is a high level of interest in discovering new drugs for this malignancy. Previously, we demonstrated the ability of tryptophanol-derived polycyclic compounds to activate the tumor suppressor protein p53, a relevant therapeutic target in cancer. In this work, we developed a novel series of enantiomerically pure tryptophanol-derived small molecules to target human gastric adenocarcinoma (AGS) cells. From an initial screening of fourteen compounds in AGS cell line, a hit compound was selected for optimization, leading to two derivatives selective for AGS gastric cells over other types of cancer cells (MDA-MB-231, A-549, DU-145, and MG-63). More importantly, the compounds were non-toxic in normal cells (HEK 293T). Additionally, we show that the growth inhibition of AGS cells induced by these compounds is mediated by apoptosis. Stability studies in human plasma and human liver microsomes indicate that the compounds are stable, and that the major metabolic transformations of these molecules are mono- and di-hydroxylation of the indole ring.

A screening approach for the evaluation of tobacco-free 'modern oral' nicotine products using Real Time Cell Analysis

In many regulated industries there is an increasing pressure to provide timely and robust risk assessment data to support product launches. Real-time cell analysis (RTCA) is a tool that allows for the fast and relatively labour-free cytotoxic assessment of test compounds, compared to traditional methods. Here, we propose an application for the RTCA platform to provide a screening approach, to evaluate the cytotoxic potential of tobacco-free nicotine pouches, also termed modern oral product (MOP), to determine the contribution of differing nicotine strengths (4-11 mg) and a range of available flavour types from multiple markets, on overall product toxicity. Aqueous extracts were prepared for all products using 1 pouch in 20 mL cell culture media and applied to the cell system for 24 h. Test extract nicotine concentrations reflected the increases in product nicotine strength; however, these changes were not present in the same magnitude in the cytotoxicity data obtained from both primary human gingival fibroblasts (HGF) and an NCI-H292 human bronchial epithelial continuous cell line. Furthermore, across the range of flavours and product nicotine strengths tested, H292 cells whilst not the target organ for oral product use, accurately predicted the results seen in HGFs and could be considered a useful surrogate for fast screening studies. H292 cells are more easily cultured and for longer periods, offering a more compatible test system. In conclusion, the data demonstrate the utility of the RTCA platform for the quick assessment of a large range of product variants. Furthermore, for a cytotoxicity measure with this test product, the simple H292 cell line can predict outcomes in the more complex HGF and provide useful pre-clinical cytotoxicity screening data to inform the risk assessment of MOPs and the relative contribution of flavourings, nicotine and other components.

A Critical Perspective on 3D Liver Models for Drug Metabolism and Toxicology Studies

The poor predictability of human liver toxicity is still causing high attrition rates of drug candidates in the pharmaceutical industry at the non-clinical, clinical, and post-marketing authorization stages. This is in part caused by animal models that fail to predict various human adverse drug reactions (ADRs), resulting in undetected hepatotoxicity at the non-clinical phase of drug development. In an effort to increase the prediction of human hepatotoxicity, different approaches to enhance the physiological relevance of hepatic in vitro systems are being pursued. Three-dimensional (3D) or microfluidic technologies allow to better recapitulate hepatocyte organization and cell-matrix contacts, to include additional cell types, to incorporate fluid flow and to create gradients of oxygen and nutrients, which have led to improved differentiated cell phenotype and functionality. This comprehensive review addresses the drug-induced hepatotoxicity mechanisms and the currently available 3D liver in vitro models, their characteristics, as well as their advantages and limitations for human hepatotoxicity assessment. In addition, since toxic responses are greatly dependent on the culture model, a comparative analysis of the toxicity studies performed using two-dimensional (2D) and 3D in vitro strategies with recognized hepatotoxic compounds, such as paracetamol, diclofenac, and troglitazone is performed, further highlighting the need for harmonization of the respective characterization methods. Finally, taking a step forward, we propose a roadmap for the assessment of drugs hepatotoxicity based on fully characterized fit-for-purpose in vitro models, taking advantage of the best of each model, which will ultimately contribute to more informed decision-making in the drug development and risk assessment fields.

Bromamine T (BAT) Exerts Stronger Anti-Cancer Properties than Taurine (Tau)

Simple Summary

Taurine (Tau) has been shown to inhibit cancer growth. However, the mechanisms that underlie the growth-inhibitory effects of Tau remain obscure in both colon and breast cancer. In parallel, N-bromotaurine (TauNHBr) and a stable active bromine molecule, bromamine T (BAT), appear to exert strong anti-inflammatory effects. To our knowledge, this is the first study that evaluates the anti-cancer effects of BAT and its underlying mechanisms. To gain a comprehensive picture of the cytotoxic effect of BAT on colon and breast cancer, we compared its effect with that of Tau. Our data support that BAT exerts a superior anti-cancer effect than Tau, through the induction of cell death, probably due to the activation of distinct mitogen-activated protein kinase (MAPK) family members. Interestingly, BAT inhibits colon carcinogenesis in vivo to a greater extent than Tau. Our data significantly add to the use of BAT as a novel therapeutic modality in colon and breast cancer.

Abstract

Background: Taurine (Tau) ameliorates cancer pathogenesis. Researchers have focused on the functional properties of bromamine T (BAT), a stable active bromine molecule. Both N-bromotaurine (TauNHBr) and BAT exert potent anti-inflammatory properties, but the landscape remains obscure concerning the anti-cancer effect of BAT. Methods: We used Crystal Violet, colony formation, flow cytometry and Western blot experiments to evaluate the effect of BAT and Tau on the apoptosis and autophagy of cancer cells. Xenograft experiments were used to determine the in vivo cytotoxicity of either agent. Results: We demonstrated that both BAT and Tau inhibited the growth of human colon, breast, cervical and skin cancer cell lines. Among them, BAT exerted the greatest cytotoxic effect on both RKO and MDA-MB-468 cells. In particular, BAT increased the phosphorylation of c-Jun N-terminal kinases (JNK½), p38 mitogen-activated protein kinase (MAPK), and extracellular-signal-regulated kinases (ERK½), thereby inducing mitochondrial apoptosis and autophagy in RKO cells. In contrast, Tau exerted its cytotoxic effect by upregulating JNK½ forms, thus triggering mitochondrial apoptosis in RKO cells. Accordingly, colon cancer growth was impaired in vivo. Conclusions: BAT and Tau exerted their anti-tumor properties through the induction of (i) mitochondrial apoptosis, (ii) the MAPK family, and iii) autophagy, providing novel anti-cancer therapeutic modalities.

Induction of AhR transactivation by PBDD/Fs and PCDD/Fs using a novel human-relevant, high-throughput DRhuman CALUX reporter gene assay

Polychlorinated dioxins and dibenzofurans (PCDD/Fs) are highly toxic contaminants that are strictly regulated and monitored in the environment and food to reduce human exposure. Recently, the increasing occurrence of polybrominated dioxins and dibenzofurans (PBDD/Fs) in the environment is raising concerns about the impact on human health by the combined exposure to chlorinated and brominated analogues of dioxins. Toxicological properties of PBDD/Fs relative to PCDD/Fs have not been firmly established, and brominated dioxins are not included in routine monitoring programs. In this study, we set out to determine human-relevant congener-specific potency values for a range of brominated and chlorinated dioxin congeners, based on their aryl hydrocarbon receptor (AhR)-mediated mode of toxic action. Transactivation of the AhR was measured using dioxin-responsive (DR) CALUX reporter gene assays. Because of known species-differences in dioxin-mediated toxicity, we developed and used a HepG2 human liver cell-based DR human CALUX assay that is a variant of the rodent-based DR CALUX. The assay was found to be highly inducible and stable, with low variations between independent measurements. Using both DR CALUX assays in an automated high-throughput mode we found that overall PBDD/Fs were as potent as PCDD/Fs in inducing AhR transactivation, but congener-specific differences were observed. We also observed species-specific differences in sensitivity and potency when comparing DR human REP values to those obtained in the rat-based DR CALUX. Finally, we observed significant differences between WHO-TEF values and DR human REP values, suggesting that actual WHO-TEF values may underestimate the hazards associated with exposure of humans to dioxins.

Risk Characterization and Probabilistic Concentration-Response Modeling of Complex Environmental Mixtures Using New Approach Methodologies (NAMs) Data from Organotypic in Vitro Human Stem Cell Assays

BACKGROUND

Risk assessment of chemical mixtures or complex substances remains a major methodological challenge due to lack of available hazard or exposure data. Therefore, risk assessors usually infer hazard or risk from data on the subset of constituents with available toxicity values.

OBJECTIVES

We evaluated the validity of the widely used traditional mixtures risk assessment paradigms, Independent Action (IA) and Concentration Addition (CA), with new approach methodologies (NAMs) data from human cell-based in vitro assays.

METHODS

A diverse set of 42 chemicals was tested both individually and as mixtures for functional and cytotoxic effects in vitro. A panel of induced pluripotent stem cell (iPSCs)-derived models (hepatocytes, cardiomyocytes, endothelial, and neurons) and one primary cell type (HUVEC) were used. Bayesian concentration-response modeling of individual chemicals or their mixtures was performed for a total of 47 phenotypes to derive point-of-departure (POD) values. Probabilistic IA or CA was conducted to estimate the mixture effects based on the bioactivity profiles from the individual chemicals and compared with mixture bioactivity.

RESULTS

All mixtures showed significant bioactivity, even though some were constructed using individual chemical concentrations considered "low" or "safe." Even though CA is much more accurate as a predictor of mixture effects in comparison with IA, with CA-based POD typically within an order of magnitude of the actual mixture, in some cases, the bioactivity of the mixtures appeared to be much greater than that of their components under either additivity assumption.

DISCUSSION

These results suggest that CA is a preferred first approximation for predicting mixture toxicity when data for all constituents are available. However, because the accuracy of additivity assumptions varies greatly across phenotypes, we posit that mixtures and complex substances need to be directly tested for their hazard potential. NAMs provide a practical solution that rapidly yields highly informative data for mixtures risk assessment. https://doi.org/10.1289/EHP7600.

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.

3-D Human Renal Tubular Organoids Generated from Urine-Derived Stem Cells for Nephrotoxicity Screening

The development of human cell-based systems to replace the use of rodents or the two-dimensional culture of cells for studying nephrotoxicity is urgently needed. Human urine-derived stem cells were differentiated into renal tubular epithelial cells in three-dimensional (3-D) culture after being induced by a kidney extracellular matrix. Levels of CYP2E1 and KIM-1 in 3-D organoids were significantly increased in response to acetone and cisplatin. This 3-D culture system provides an alternative tool for nephrotoxicity screening and research.

Application of In Vitro Metabolism Activation in High-Throughput Screening

In vitro methods which incorporate metabolic capability into the assays allow us to assess the activity of metabolites from their parent compounds. These methods can be applied into high-throughput screening (HTS) platforms, thereby increasing the speed to identify compounds that become active via the metabolism process. HTS was originally used in the pharmaceutical industry and now is also used in academic settings to evaluate biological activity and/or toxicity of chemicals. Although most chemicals are metabolized in our body, many HTS assays lack the capability to determine compound activity via metabolism. To overcome this problem, several in vitro metabolic methods have been applied to an HTS format. In this review, we describe in vitro metabolism methods and their application in HTS assays, as well as discuss the future perspectives of HTS with metabolic activity. Each in vitro metabolism method has advantages and disadvantages. For instance, the S9 mix has a full set of liver metabolic enzymes, but it displays high cytotoxicity in cell-based assays. In vitro metabolism requires liver fractions or the use of other metabolically capable systems, including primary hepatocytes or recombinant enzymes. Several newly developed in vitro metabolic methods, including HepaRG cells, three-dimensional (3D) cell models, and organ-on-a-chip technology, will also be discussed. These newly developed in vitro metabolism approaches offer significant progress in dissecting biological processes, developing drugs, and making toxicology studies quicker and more efficient.

Collecting e-cigarette aerosols for in vitro applications: A survey of the biomedical literature and opportunities to increase the value of submerged cell culture-based assessments

Electronic nicotine delivery systems (ENDS) are being developed as potentially reduced‐risk alternatives to the continued use of combustible tobacco products. Because of the widespread uptake of ENDS—in particular, e‐cigarettes—the biological effects, including the toxic potential, of their aerosols are under investigation. Preclinically, collection of such aerosols is a prerequisite for testing in submerged cell culture‐based in vitro assays; however, despite the growth in this research area, there is no apparent standardized collection method for this application. To this end, through an Institute for in vitro Sciences, Inc. workshop initiative, we surveyed the biomedical literature catalogued in PubMed® to map the types of methods hitherto used and reported publicly. From the 47 relevant publications retrieved, we identified seven distinct collection methods. Bubble‐through (with aqueous solvents) and Cambridge filter pad (CFP) (with polar solvents) collection were the most frequently cited methods (57% and 18%, respectively), while the five others (CFP + bubble‐through; condensation; cotton filters; settle‐upon; settle‐upon + dry) were cited less often (2–10%). Critically, the collected aerosol fractions were generally found to be only minimally characterized chemically, if at all. Furthermore, there was large heterogeneity among other experimental parameters (e.g., vaping regimen). Consequently, we recommend that more comprehensive research be conducted to identify the method(s) that produce the fraction(s) most representative of the native aerosol. We also endorse standardization of the aerosol generation process. These should be regarded as opportunities for increasing the value of in vitro assessments in relation to predicting effects on human health.

Collection of e‐cigarette aerosols is a prerequisite to enable testing in submerged culture‐based in vitro assays; however, there is no standardized method for this. Thus, we surveyed the biomedical literature to map the types of published methods. Bubble‐through and Cambridge filter pad methods were most common, although there was heterogeneity among other parameters, and moreover, the resulting fractions were only minimally characterized. Comprehensive research is required to identify the method(s) that produce the fraction(s) most representative of the native aerosol.

In vitro and In vivo toxicity analysis of zinc selenium/zinc sulfide (ZnSe/ZnS) quantum dots

Despite the versatility of quantum dots (QDs) in optoelectronics and biomedical field, their toxicity risks remain a considerable hindrance for clinical applications. Cytotoxicity of Cadmium containing QDs is well documented and reveals that they are toxic to cells. Reports suggest that the presence of toxic elements at the QD core (e.g., cadmium, selenium) is responsible for its toxicity in in vivo and in vitro levels. Hence, here the toxicity of heavy metal free ZnSe/ZnS QDs on two scenarios were assessed, (i) HEK cells as in vitro system and (ii) Swiss Albino mice as in vivo model. Before toxicity analysis, QDs subjected to various optical and physico-chemical characterization methods such as absorption and emission spectra analysis, observation under U.V light, TEM, DLS, Zeta potential, FTIR, Raman and XPS spectra, ICP-OES, TGA and DTG curve. It is very necessary to characterize the synthesized QDs because their toxicity greatly influenced by the physico-chemical properties. On checking the vulnerability of HEK cells on exposure to ZnSe/ZnS QDs, the obtained results disclose that ZnSe/ZnS QDs showed merest impact on cellular viability at a concentration less than 100 μg/ml. Acute toxicity of 10 mg/kg ZnSe/ZnS QDs was studied in mice and no clinical or behavioural changes were observed. It did not induce any changes in haematological parameters and any loss of body or organ weight. Moderate pathological changes were evident only in the liver, all others organs like kidney, spleen and brain did not show any manifestations of toxicity. Current work lays substantial bedrock for safe biomedical and environmental application of ZnSe/ZnS QDs in near future.

Real time monitoring of oxygen uptake of hepatocytes in a microreactor using optical microsensors

Most in vitro test systems for the assessment of toxicity are based on endpoint measurements and cannot contribute much to the establishment of mechanistic models, which are crucially important for further progress in this field. Hence, in recent years, much effort has been put into the development of methods that generate kinetic data. Real time measurements of the metabolic activity of cells based on the use of oxygen sensitive microsensor beads have been shown to provide access to the mode of action of compounds in hepatocytes. However, for fully exploiting this approach a detailed knowledge of the microenvironment of the cells is required. In this work, we investigate the cellular behaviour of three types of hepatocytes, HepG2 cells, HepG2-3A4 cells and primary mouse hepatocytes, towards their exposure to acetaminophen when the availability of oxygen for the cell is systematically varied. We show that the relative emergence of two modes of action, one NAPQI dependent and the other one transient and NAPQI independent, scale with expression level of CYP3A4. The transient cellular response associated to mitochondrial respiration is used to characterise the influence of the initial oxygen concentration in the wells before exposure to acetaminophen on the cell behaviour. A simple model is presented to describe the behaviour of the cells in this scenario. It demonstrates the level of control over the role of oxygen supply in these experiments. This is crucial for establishing this approach into a reliable and powerful method for the assessment of toxicity.

Endocrine-disrupting chemicals: economic, regulatory, and policy implications

Endocrine-disrupting chemicals (EDCs) substantially cost society as a result of increases in disease and disability but-unlike other toxicant classes such as carcinogens-have yet to be codified into regulations as a hazard category. This Series paper examines economic, regulatory, and policy approaches to limit human EDC exposures and describes potential improvements. In the EU, general principles for EDCs call for minimisation of human exposure, identification as substances of very high concern, and ban on use in pesticides. In the USA, screening and testing programmes are focused on oestrogenic EDCs exclusively, and regulation is strictly risk-based. Minimisation of human exposure is unlikely without a clear overarching definition for EDCs and relevant pre-marketing test requirements. We call for a multifaceted international programme (eg, modelled on the International Agency for Research in Cancer) to address the effects of EDCs on human health-an approach that would proactively identify hazards for subsequent regulation.

Identifying and Characterizing Stress Pathways of Concern for Consumer Safety in Next-Generation Risk Assessment

Many substances for which consumer safety risk assessments need to be conducted are not associated with specific toxicity modes of action, but rather exhibit nonspecific toxicity leading to cell stress. In this work, a cellular stress panel is described, consisting of 36 biomarkers representing mitochondrial toxicity, cell stress, and cell health, measured predominantly using high content imaging. To evaluate the panel, data were generated for 13 substances at exposures consistent with typical use-case scenarios. These included some that have been shown to cause adverse effects in a proportion of exposed humans and have a toxicological mode-of-action associated with cellular stress (eg, doxorubicin, troglitazone, and diclofenac), and some that are not associated with adverse effects due to cellular stress at human-relevant exposures (eg, caffeine, niacinamide, and phenoxyethanol). For each substance, concentration response data were generated for each biomarker at 3 timepoints. A Bayesian model was then developed to quantify the evidence for a biological response, and if present, a credibility range for the estimated point of departure (PoD) was determined. PoDs were compared with the plasma Cmax associated with the typical substance exposures, and indicated a clear differentiation between "low" risk and "high" risk chemical exposure scenarios. Developing robust methods to characterize the in vitro bioactivity of xenobiotics is an important part of non-animal safety assessment. The results presented in this work show that the cellular stress panel can be used, together with other new approach methodologies, to identify chemical exposures that are protective of consumer health.

Bioprospecting for Antibacterial Drugs: a Multidisciplinary Perspective on Natural Product Source Material, Bioassay Selection and Avoidable Pitfalls

Bioprospecting is the exploration, extraction and screening of biological material and sometimes indigenous knowledge to discover and develop new drugs and other products. Most antibiotics in current clinical use (eg. β-lactams, aminoglycosides, tetracyclines, macrolides) were discovered using this approach, and there are strong arguments to reprioritize bioprospecting over other strategies in the search for new antibacterial drugs. Academic institutions should be well positioned to lead the early stages of these efforts given their many thousands of locations globally and because they are not constrained by the same commercial considerations as industry. University groups can lack the full complement of knowledge and skills needed though (eg. how to tailor screening strategy to biological source material). In this article, we review three key aspects of the bioprospecting literature (source material and in vitro antibacterial and toxicity testing) and present an integrated multidisciplinary perspective on (a) source material selection, (b) legal, taxonomic and other issues related to source material, (c) cultivation methods, (d) bioassay selection, (e) technical standards available, (f) extract/compound dissolution, (g) use of minimum inhibitory concentration and selectivity index values to identify progressible extracts and compounds, and (h) avoidable pitfalls. The review closes with recommendations for future study design and information on subsequent steps in the bioprospecting process.

Identification of tetracyclic lactams as NMDA receptor antagonists with potential application in neurological disorders

N-Methyl-d-aspartate receptors (NMDARs) are crucial for the normal function of the central nervous system (CNS), and fundamental in memory and learning-related processes. The overactivation of these receptors is associated with numerous neurodegenerative and psychiatric disorders. Therefore, NMDAR is considered a relevant therapeutic target for many CNS disorders. Herein, we report the synthesis and pharmacological evaluation of a new scaffold with antagonistic activity for NMDAR. Specifically, a chemical library of eighteen 1-aminoindan-2-ol tetracyclic lactams was synthesized and screened as NMDAR antagonists. The compounds were obtained by chiral pool synthesis using enantiomerically pure 1-aminoindan-2-ols as chiral inductors, and their stereochemistry was proven by X-ray crystallographic analysis of two target compounds. Most compounds reveal NMDAR antagonism, and eleven compounds display IC₅₀ values in a Ca²⁺ entry-sensitive fluo-4 assay in the same order of magnitude of memantine, a clinically approved NMDAR antagonist. Docking studies suggest that the novel compounds can act as NMDAR channel blockers since there is a compatible conformation with MK-801 co-crystallized with NMDAR channel. In addition, we show that the tetracyclic 1-aminoindan-2-ol derivatives are brain permeable and non-toxic, and we identify promising hits for further optimization as modulators of the NMDAR function.

A multiparametric organ toxicity predictor for drug discovery

The assessment of major organ toxicities through in silico predictive models plays a crucial role in drug discovery. Computational tools can predict chemical toxicities using the knowledge gained from experimental studies which drastically reduces the attrition rate of compounds during drug discovery and developmental stages. The purpose of in silico predictions for drug leads and anticipating toxicological endpoints of absorption, distribution, metabolism, excretion and toxicity, clinical adverse impacts and metabolism of pharmaceutically active substances has gained widespread acceptance in academia and pharmaceutical industries. With unrestricted accessibility to powerful biomarkers, researchers have an opportunity to contemplate the most accurate predictive scores to evaluate drug's adverse impact on various organs.A multiparametric model involving physico-chemical properties, quantitative structure-activity relationship predictions and docking score was found to be a more reliable predictor for estimating chemical toxicities with potential to reflect atomic-level insights. These in silico models provide informed decisions to carry out in vitro and in vivo studies and subsequently confirms the molecules clues deciphering the cytotoxicity, pharmacokinetics, and pharmacodynamics and organ toxicity properties of compounds. Even though the drugs withdrawn by USFDA at later phases of drug discovery which should have passed all the state-of-the-art experimental approaches and currently acceptable toxicity filters, there is a dire need to interconnect all these molecular key properties to enhance our knowledge and guide in the identification of leads to drug optimization phases. Current computational tools can predict ADMET and organ toxicities based on pharmacophore fingerprint, toxicophores and advanced machine-learning techniques.