Toxicity testing in the 21st century: a vision and a strategy

Exploring Proton-Only NMR Experiments and Filters for Daphnia In Vivo: Potential and Limitations

Environmental metabolomics provides insight into how anthropogenic activities have an impact on the health of an organism at the molecular level. Within this field, in vivo NMR stands out as a powerful tool for monitoring real-time changes in an organism's metabolome. Typically, these studies use 2D ¹³C-¹H experiments on ¹³C-enriched organisms. Daphnia are the most studied species, given their widespread use in toxicity testing. However, with COVID-19 and other geopolitical factors, the cost of isotope enrichment increased ~6-7 fold over the last two years, making ¹³C-enriched cultures difficult to maintain. Thus, it is essential to revisit proton-only in vivo NMR and ask, "Can any metabolic information be obtained from Daphnia using proton-only experiments?". Two samples are considered here: living and whole reswollen organisms. A range of filters are tested, including relaxation, lipid suppression, multiple-quantum, J-coupling suppression, 2D ¹H-¹H experiments, selective experiments, and those exploiting intermolecular single-quantum coherence. While most filters improve the ex vivo spectra, only the most complex filters succeed in vivo. If non-enriched organisms must be used, then, DREAMTIME is recommended for targeted monitoring, while IP-iSQC was the only experiment that allowed non-targeted metabolite identification in vivo. This paper is critically important as it documents not just the experiments that succeed in vivo but also those that fail and demonstrates first-hand the difficulties associated with proton-only in vivo NMR.

Toxicity Tests for Chemical Pesticide Registration: Requirement Differences among the United States, the European Union, Japan, and China?

Pesticide registration is a scientific, legal, and administrative process that checks if a pesticide is safe and effective for its intended use before it can be used. The toxicity test is a key part of pesticide registration, which includes human health and ecological effect testing. Different countries adopt their own toxicity test criteria for pesticide registration guidelines. However, these differences, which may help accelerate the progress of pesticide registration and reduce the number of animals used, are yet to be explored and compared. Herein, we outlined the details and compared the differences between the toxicity tests in the United States, the European Union, Japan, and China. Some differences lie in the types and waiver policy, while others are in new approach methodologies (NAMs). On the basis of these differences, there is great potential for the optimization of NAMs during the toxicity tests. It is expected that this perspective can contribute to developing and adopting NAMs.

Integrative toxicogenomics: Advancing precision medicine and toxicology through artificial intelligence and OMICs technology

More information about a person's genetic makeup, drug response, multi-omics response, and genomic response is now available leading to a gradual shift towards personalized treatment. Additionally, the promotion of non-animal testing has fueled the computational toxicogenomics as a pivotal part of the next-gen risk assessment paradigm. Artificial Intelligence (AI) has the potential to provid new ways analyzing the patient data and making predictions about treatment outcomes or toxicity. As personalized medicine and toxicogenomics involve huge data processing, AI can expedite this process by providing powerful data processing, analysis, and interpretation algorithms. AI can process and integrate a multitude of data including genome data, patient records, clinical data and identify patterns to derive predictive models anticipating clinical outcomes and assessing the risk of any personalized medicine approaches. In this article, we have studied the current trends and future perspectives in personalized medicine & toxicology, the role of toxicogenomics in connecting the two fields, and the impact of AI on personalized medicine & toxicology. In this work, we also study the key challenges and limitations in personalized medicine, toxicogenomics, and AI in order to fully realize their potential.

An automatic recognition method of nematode survival rate based on bright field and dark field experimental images

BACKGROUND

The survival rate of experimental animals is a very important index in chemical toxicity evaluation experiments. The calculation of nematode survival rate is used in many experiments.

OBJECTIVE

Traditional survival rate quantification methods require manual counting. This is a time-consuming and laborious work when using 384-well plate for high-throughput chemical toxicity assessment experiments. At present, there is a great need for an automatic method to identify the survival rate of nematodes in the experiment of chemical toxicity evaluation.

METHODS

We designed an automatic nematode survival rate recognition method by combining the bright field experimental image of nematodes and the dark field image of nematodes which is captured after adding Propidium Iodide dye, and used it to calculate the nematode survival rate in different chemical environments. Experiment results show that the survival rate obtained by our automatic counting method is very similar to the survival rate obtained by manual counting.

RESULTS

Through several different chemical experiments, we can see that chemicals with different toxicity have different effects on the survival rate of nematodes. And the survival rate of nematodes under different chemical concentrations has an obvious gradient trend from high concentration to low concentration. In addition, our method can quantify the motility of nematodes. There are also significant differences in the motility of nematodes cultured in different chemical environments. Moreover, the nematode motility under different chemical concentrations showed an obvious gradient change trend from high concentration to low concentration.

CONCLUSION

Our study provides an accurate and efficient nematode survival rate recognition method for chemical toxicology research.

Prediction of drug-induced hepatotoxicity based on histopathological whole slide images

Liver is an important metabolic organ in human body and is sensitive to toxic chemicals or drugs. Adverse reactions caused by drug hepatotoxicity will damage the liver and hepatotoxicity is the leading cause of removal of approved drugs from the market. Therefore, it is of great significance to identify liver toxicity as early as possible in the drug development process. In this study, we developed a predictive model for drug hepatotoxicity based on histopathological whole slide images (WSI) which are the by-product of drug experiments and have received little attention. To better represent the WSIs, we constructed a graph representation for each WSI by dividing it into small patches, taking sampled patches as nodes and calculating the correlation coefficients between node features as the edges of the graph structure. Then a WSI-level graph convolutional network (GCN) was built to effectively extract the node information of the graph and predict the toxicity. In addition, we introduced a gated attention global context vector (gaGCV) to combine the global context to make node features to contain more comprehensive information. The results validated on rat liver in vivo data from the Open TG-GATES show that the use of WSI for the prediction of toxicity is feasible and effective.

Integrated Digital Microfluidics NMR Spectroscopy: A Key Step toward Automated In Vivo Metabolomics

Toxicity testing is currently undergoing a paradigm shift from examining apical end points such as death, to monitoring sub-lethal toxicity in vivo. In vivo nuclear magnetic resonance (NMR) spectroscopy is a key platform in this endeavor. A proof-of-principle study is presented which directly interfaces NMR with digital microfluidics (DMF). DMF is a "lab on a chip" method allowing for the movement, mixing, splitting, and dispensing of μL-sized droplets. The goal is for DMF to supply oxygenated water to keep the organisms alive while NMR detects metabolomic changes. Here, both vertical and horizontal NMR coil configurations are compared. While a horizontal configuration is ideal for DMF, NMR performance was found to be sub-par and instead, a vertical-optimized single-sided stripline showed most promise. In this configuration, three organisms were monitored in vivo using ¹H-¹³C 2D NMR. Without support from DMF droplet exchange, the organisms quickly showed signs of anoxic stress; however, with droplet exchange, this was completely suppressed. The results demonstrate that DMF can be used to maintain living organisms and holds potential for automated exposures in future. However, due to numerous limitations of vertically orientated DMF, along with space limitations in standard bore NMR spectrometers, we recommend future development be performed using a horizontal (MRI style) magnet which would eliminate practically all the drawbacks identified here.

Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations

Explainable machine learning for molecular toxicity prediction is a promising approach for efficient drug development and chemical safety. A predictive ML model of toxicity can reduce experimental cost and time while mitigating ethical concerns by significantly reducing animal and clinical testing. Herein, we use a deep learning framework for simultaneously modeling in vitro, in vivo, and clinical toxicity data. Two different molecular input representations are used; Morgan fingerprints and pre-trained SMILES embeddings. A multi-task deep learning model accurately predicts toxicity for all endpoints, including clinical, as indicated by the area under the Receiver Operator Characteristic curve and balanced accuracy. In particular, pre-trained molecular SMILES embeddings as input to the multi-task model improved clinical toxicity predictions compared to existing models in MoleculeNet benchmark. Additionally, our multitask approach is comprehensive in the sense that it is comparable to state-of-the-art approaches for specific endpoints in in vitro, in vivo and clinical platforms. Through both the multi-task model and transfer learning, we were able to indicate the minimal need of in vivo data for clinical toxicity predictions. To provide confidence and explain the model's predictions, we adapt a post-hoc contrastive explanation method that returns pertinent positive and negative features, which correspond well to known mutagenic and reactive toxicophores, such as unsubstituted bonded heteroatoms, aromatic amines, and Michael receptors. Furthermore, toxicophore recovery by pertinent feature analysis captures more of the in vitro (53%) and in vivo (56%), rather than of the clinical (8%), endpoints, and indeed uncovers a preference in known toxicophore data towards in vitro and in vivo experimental data. To our knowledge, this is the first contrastive explanation, using both present and absent substructures, for predictions of clinical and in vivo molecular toxicity.

An Overview of In Vitro Assays of 64Cu-, 68Ga-, 125I-, and 99mTc-Labelled Radiopharmaceuticals Using Radiometric Counters in the Era of Radiotheranostics

Radionuclides are unstable isotopes that mainly emit alpha (α), beta (β) or gamma (γ) radiation through radiation decay. Therefore, they are used in the biomedical field to label biomolecules or drugs for diagnostic imaging applications, such as positron emission tomography (PET) and/or single-photon emission computed tomography (SPECT). A growing field of research is the development of new radiopharmaceuticals for use in cancer treatments. Preclinical studies are the gold standard for translational research. Specifically, in vitro radiopharmaceutical studies are based on the use of radiopharmaceuticals directly on cells. To date, radiometric β- and γ-counters are the only tools able to assess a preclinical in vitro assay with the aim of estimating uptake, retention, and release parameters, including time- and dose-dependent cytotoxicity and kinetic parameters. This review has been designed for researchers, such as biologists and biotechnologists, who would like to approach the radiobiology field and conduct in vitro assays for cellular radioactivity evaluations using radiometric counters. To demonstrate the importance of in vitro radiopharmaceutical assays using radiometric counters with a view to radiogenomics, many studies based on ⁶⁴Cu-, ⁶⁸Ga-, ¹²⁵I-, and ^99mTc-labeled radiopharmaceuticals have been revised and summarized in this manuscript.

Considerations for application of benchmark dose modeling in radiation research: workshop highlights

BACKGROUND

Exposure to different forms of ionizing radiation occurs in diverse occupational, medical, and environmental settings. Improving the accuracy of the estimated health risks associated with exposure is therefore, essential for protecting the public, particularly as it relates to chronic low dose exposures. A key aspect to understanding health risks is precise and accurate modeling of the dose-response relationship. Toward this vision, benchmark dose (BMD) modeling may be a suitable approach for consideration in the radiation field. BMD modeling is already extensively used for chemical hazard assessments and is considered statistically preferable to identifying low and no observed adverse effects levels. BMD modeling involves fitting mathematical models to dose-response data for a relevant biological endpoint and identifying a point of departure (the BMD, or its lower bound). Recent examples in chemical toxicology show that when applied to molecular endpoints (e.g. genotoxic and transcriptional endpoints), BMDs correlate to points of departure for more apical endpoints such as phenotypic changes (e.g. adverse effects) of interest to regulatory decisions. This use of BMD modeling may be valuable to explore in the radiation field, specifically in combination with adverse outcome pathways, and may facilitate better interpretation of relevant in vivo and in vitro dose-response data. To advance this application, a workshop was organized on June 3rd, 2022, in Ottawa, Ontario that brought together BMD experts in chemical toxicology and the radiation scientific community of researchers, regulators, and policy-makers. The workshop's objective was to introduce radiation scientists to BMD modeling and its practical application using case examples from the chemical toxicity field and demonstrate the BMDExpress software using a radiation dataset. Discussions focused on the BMD approach, the importance of experimental design, regulatory applications, its use in supporting the development of adverse outcome pathways, and specific radiation-relevant examples.

CONCLUSIONS

Although further deliberations are needed to advance the use of BMD modeling in the radiation field, these initial discussions and partnerships highlight some key steps to guide future undertakings related to new experimental work.

Poor Translatability of Biomedical Research Using Animals - A Narrative Review

The failure rate for the translation of drugs from animal testing to human treatments remains at over 92%, where it has been for the past few decades. The majority of these failures are due to unexpected toxicity - that is, safety issues revealed in human trials that were not apparent in animal tests - or lack of efficacy. However, the use of more innovative tools, such as organs-on-chips, in the preclinical pipeline for drug testing, has revealed that these tools are more able to predict unexpected safety events prior to clinical trials and so can be used for this, as well as for efficacy testing. Here, we review several disease areas, and consider how the use of animal models has failed to offer effective new treatments. We also make some suggestions as to how the more human-relevant new approach methodologies might be applied to address this.

Inconsistencies in variable reporting and methods in larval zebrafish behavioral assays

New approaches in developmental neurotoxicity (DNT) screening are needed due to the tens of thousands of chemicals requiring hazard assessments. Zebrafish (Danio rerio) are an alternative vertebrate model for DNT testing, but without a standardized protocol for larval behavioral assays, comparison of results among laboratories is challenging. To evaluate the congruence of protocols across laboratories, we conducted a literature review of DNT studies focusing on larval zebrafish behavior assays and cataloged experimental design consistencies. Our review focused on 51 unique method variables in publications where chemical exposure occurred in early development and subsequent larval locomotor evaluation focused on assays that included a light/dark photoperiod transition. We initially identified 94 publications, but only 31 exclusively met our inclusion criteria which focused on parameters that are important to an assay employed by our laboratory. No publication reported 100% of the targeted variables; only 51 to 86% of those variables were reported in the reviewed publications, with some aspects of the experimental design consistent among laboratories. However, no protocol was exactly the same for any two publications. Many of these variables had more than one parameter/design reported, highlighting the inconsistencies among methods. Overall, there is not only a strong need for the development of a standardized testing protocol for larval zebrafish locomotor assays, but there is also a need for a standardized protocol for reporting experimental variables in the literature. Here we include an extensive guideline checklist for conducting larval zebrafish developmental behavior assays.

Astrocyte-Like Cells Transcriptome Changes After Exposure to a Low and Non-cytotoxic MeHg Concentration

The central nervous system is the main target of MeHg toxicity and glial cells are the first line of defense; however, their true role remains unclear. This study aimed to identify the global map of human glial-like (U87) cells transcriptome after exposure to a non-toxic and non-lethal MeHg concentration and to investigate the related molecular changes. U87 cells were exposed upon 0.1, 0.5, and 1 µM MeHg for 4 and 24 h. Although no changes were observed in the percentage of viable cells, the metabolic viability was significantly decreased after exposure to 1 µM MeHg for 24 h; thus, the non-toxic concentration of 0.1 µM MeHg was chosen to perform microarray analysis. Significant changes in U87 cells transcriptome were observed only after 24 h. The expression of 392 genes was down regulated while 431 genes were up-regulated. Gene ontology showed alterations in biological processes (75%), cellular components (21%), and molecular functions (4%). The main pathways showed by KEGG and Reactome were cell cycle regulation and Rho GTPase signaling. The complex mechanism of U87 cells response against MeHg exposure indicates that even a low and non-toxic concentration is able to alter the gene expression profile.

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

The US Environmental Protection Agency Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool is a fast, freely available, online screening application that allows researchers and regulators to extrapolate toxicity information across species. For biological targets in model systems such as human cells, mice, rats, and zebrafish, toxicity data are available for a variety of chemicals. Through the evaluation of protein target conservation, this tool can be used to extrapolate data generated from such model systems to thousands of other species lacking toxicity data, yielding predictions of relative intrinsic chemical susceptibility. The latest releases of the tool (versions 2.0-6.1) have incorporated new features that allow for the rapid synthesis, interpretation, and use of the data for publication plus presentation-quality graphics. Among these features are customizable data visualizations and a comprehensive summary report designed to summarize SeqAPASS data for ease of interpretation. This paper describes the protocol to guide users through submitting jobs, navigating the various levels of protein sequence comparisons, and interpreting and displaying the resulting data. New features of SeqAPASS v2.0-6.0 are highlighted. Furthermore, two use-cases focused on transthyretin and opioid receptor protein conservation using this tool are described. Finally, SeqAPASS' strengths and limitations are discussed to define the domain of applicability for the tool and highlight different applications for cross-species extrapolation.

In vivo singlet state filtered nuclear magnetic resonance: towards monitoring toxic responses inside living organisms

In line with recent paradigm shifts in toxicity testing, in vivo nuclear magnetic resonance (NMR) is a powerful tool for studying the biological impacts and perturbations caused by toxicants in living organisms. However, despite the excellent molecular insights that can be obtained through this technique, in vivo NMR applications are hampered by considerable experimental challenges such as poor line shape and spectral overlap. Here, we demonstrate the application of singlet-filtered NMR to target specific metabolites and facilitate the study of metabolite fluxes in living Daphnia magna, an aquatic keystone species and model organism. Informed by mathematical simulations and experiments on ex vivo organisms, singlet state NMR is used to monitor the flux of metabolites such as d-glucose and serine in living D. magna, during the environmentally relevant processes of anoxic stress and reduced food availability. Overall, singlet state NMR is shown to have significant future potential for studying metabolic processes in vivo.

Health risk assessment of cadmium exposure by integration of an in silico physiologically based toxicokinetic model and in vitro tests

Cadmium (Cd) is a common environmental pollutant that can damage multiple organs, including the kidney. To prevent renal effects, international authorities have set health-based guidance values of Cd from epidemiological studies. To explore the health risk of Cd exposure and whether human equivalent doses (HEDs) derived from in vitro tests match the current guidance values, we integrated renal tubular epithelial cell-based assays with a physiologically based toxicokinetic model combined with the Monte Carlo method. For females, the HEDs (μg/kg/week) derived from KE2 (DNA damage), KE3 (cell cycle arrest), and KE4 (apoptosis) were 0.20 (2.5th-97.5th percentiles: 0.09-0.48), 0.52 (0.24-1.26), and 2.73 (1.27-6.57), respectively; for males the respective HEDs were 0.23 (0.10-0.49), 0.60 (0.27-1.30), and 3.11 (1.39-6.78). Among them, HED_KE4 (female) was close to the tolerable weekly intake (2.5 μg/kg/week) set by the European Food Safety Authority. The margin of exposure (MOE) derived from HED_KE4 (female) indicated that risks of renal toxicity for populations living in cadmium-contaminated regions should be of concern. This study provided a new approach methodology (NAM) for environmental chemical risk assessment using in silico and in vitro methods.

High-throughput detection of craniofacial defects in fluorescent zebrafish

Losses and malformations of cranial neural crest cell (cNCC) derivatives are a hallmark of several common brain and face malformations. Nevertheless, the etiology of these cNCC defects remains unknown for many cases, suggesting a complex basis involving interactions between genetic and/or environmental factors. However, the sheer number of possible factors (thousands of genes and hundreds of thousands of toxicants) has hindered identification of specific interactions. Here, we develop a high-throughput analysis that will enable faster identification of multifactorial interactions in the genesis of craniofacial defects. Zebrafish embryos expressing a fluorescent marker of cNCCs (fli1:EGFP) were exposed to a pathway inhibitor standard or environmental toxicant, and resulting changes in fluorescence were measured in high-throughput using a fluorescent microplate reader to approximate cNCC losses. Embryos exposed to the environmental Hedgehog pathway inhibitor piperonyl butoxide (PBO), a Hedgehog pathway inhibitor standard, or alcohol (ethanol) exhibited reduced fli1:EGFP fluorescence at one day post fertilization, which corresponded with craniofacial defects at five days post fertilization. Combining PBO and alcohol in a co-exposure paradigm synergistically reduced fluorescence, demonstrating a multifactorial interaction. Using pathway reporter transgenics, we show that the plate reader assay is sensitive at detecting alterations in Hedgehog signaling, a critical regulator of craniofacial development. We go on to demonstrate that this technique readily detects defects in other important cell types, namely neurons. Together, these findings demonstrate this novel in vivo platform can predict developmental abnormalities and multifactorial interactions in high-throughput.

Toxicogenomics scoring system: TGSS, a novel integrated risk assessment model for chemical carcinogenicity prediction

BACKGROUND

Given the increasing exposure of humans to environmental chemicals and the limitations of conventional toxicity test, there is an urgent need to develop next-generation risk assessment methods.

OBJECTIVES

This study aims to establish a novel computational system named Toxicogenomics Scoring System (TGSS) to predict the carcinogenicity of chemicals coupling chemical-gene interactions with multiple cancer transcriptomic datasets.

METHODS

Chemical-related gene signatures were derived from chemical-gene interaction data from the Comparative Toxicogenomics Database (CTD). For each cancer type in TCGA, genes were ranked by their effects on tumorigenesis, which is based on the differential expression between tumor and normal samples. Next, we developed carcinogenicity scores (C-scores) using pre-ranked GSEA to quantify the correlation between chemical-related gene signatures and ranked gene lists. Then we established TGSS by systematically evaluating the C-scores in multiple chemical-tumor pairs. Furthermore, we examined the performance of our approach by ROC curves or prognostic analyses in TCGA and multiple independent cancer cohorts.

RESULTS

Forty-six environmental chemicals were finally included in the study. C-score was calculated for each chemical-tumor pair. The C-scores of IARC Group 3 chemicals were significantly lower than those of chemicals in Group 1 (P-value = 0.02) and Group 2 (P-values = 7.49 ×10^-5). ROC curves analysis indicated that C-score could distinguish "high-risk chemicals" from the other compounds (AUC = 0.67) with a specificity and sensitivity of 0.86 and 0.57. The results of survival analysis were also in line with the assessed carcinogenicity in TGSS for the chemicals in Group 1. Finally, consistent results were further validated in independent cancer cohorts.

CONCLUSION

TGSS highlighted the great potential of integrating chemical-gene interactions with gene-cancer relationships to predict the carcinogenic risk of chemicals, which would be valuable for systems toxicology.

Prediction of inotropic effect based on calcium transients in human iPSC-derived cardiomyocytes and machine learning

Functional changes to cardiomyocytes are undesirable during drug discovery and identifying the inotropic effects of compounds is hence necessary to decrease the risk of cardiovascular adverse effects in the clinic. Recently, approaches leveraging calcium transients in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) have been developed to detect contractility changes, induced by a variety of mechanisms early during drug discovery projects. Although these approaches have been able to provide some predictive ability, we hypothesised that using additional waveform parameters could offer improved insights, as well as predictivity. In this study, we derived 25 parameters from each calcium transient waveform and developed a modified Random Forest method to predict the inotropic effects of the compounds. In total annotated data for 48 compounds were available for modelling, out of which 31 were inotropes. The results show that the Random Forest model with a modified purity criterion performed slightly better than an unmodified algorithm in terms of the Area Under the Curve, giving values of 0.84 vs 0.81 in a cross-validation, and outperformed the ToxCast Pipeline model, for which the highest value was 0.76 when using the best-performing parameter, PW10. Our study hence demonstrates that more advanced parameters derived from waveforms, in combination with additional machine learning methods, provide improved predictivity of cardiovascular risk associated with inotropic effects.

Targeted Per- and Polyfluoroalkyl substances (PFAS) assessments for high throughput screening: Analytical and testing considerations to inform a PFAS stock quality evaluation framework

Per- and polyfluoroalkyl substances (PFAS) represent a large chemical class lacking hazard, toxicokinetic, and exposure information. To accelerate PFAS hazard evaluation, new approach methodologies (NAMs) comprised of in vitro high-throughput toxicity screening, toxicokinetic data, and computational modeling are being employed in read across strategies to evaluate the larger PFAS landscape. A critical consideration to ensure robust evaluations is a parallel assessment of the quality of the screening stock solutions, where dimethyl sulfoxide (DMSO) is often the diluent of choice. Challenged by the lack of commercially available reference standards for many of the selected PFAS and reliance on mass spectrometry approaches for such an evaluation, we developed a high-throughput framework to evaluate the quality of screening stocks for 205 PFAS selected for these NAM efforts. Using mass spectrometry coupled with either liquid or gas chromatography, a quality scoring system was developed that incorporated observations during mass spectral examination to provide a simple pass or fail notation. Informational flags were used to further describe findings regarding parent analyte presence through accurate mass identification, evidence of contaminants and/or degradation, or further describe characteristics such as isomer presence. Across the PFAS-DMSO stocks tested, 148 unique PFAS received passing quality scores to allow for further in vitro testing whereas 57 received a failing score primarily due to detection issues or confounding effects of DMSO. Principle component analysis indicated vapor pressure and Henry's Law Constant as top indicators for a failed quality score for those analyzed by gas chromatography. Three PFAS in the hexafluoropropylene oxide family failed due to degradation in DMSO. As the PFAS evaluated spanned over 20 different structural categories, additional commentary describes analytical observations across specific groups related to PFAS stock composition, detection, stability, and methodologic considerations that will be useful for informing future analytical assessment and downstream HTS efforts. The high-throughput stock quality scoring workflow presented holds value as a tool to evaluate chemical presence and quality efficiently and for informing data inclusion in PFAS or other NAM screening efforts.

Drug Metabolism of Hepatocyte-like Organoids and Their Applicability in In Vitro Toxicity Testing

Emerging advances in the field of in vitro toxicity testing attempt to meet the need for reliable human-based safety assessment in drug development. Intrahepatic cholangiocyte organoids (ICOs) are described as a donor-derived in vitro model for disease modelling and regenerative medicine. Here, we explored the potential of hepatocyte-like ICOs (HL-ICOs) in in vitro toxicity testing by exploring the expression and activity of genes involved in drug metabolism, a key determinant in drug-induced toxicity, and the exposure of HL-ICOs to well-known hepatotoxicants. The current state of drug metabolism in HL-ICOs showed levels comparable to those of PHHs and HepaRGs for CYP3A4; however, other enzymes, such as CYP2B6 and CYP2D6, were expressed at lower levels. Additionally, EC50 values were determined in HL-ICOs for acetaminophen (24.0−26.8 mM), diclofenac (475.5−>500 µM), perhexiline (9.7−>31.5 µM), troglitazone (23.1−90.8 µM), and valproic acid (>10 mM). Exposure to the hepatotoxicants showed EC50s in HL-ICOs comparable to those in PHHs and HepaRGs; however, for acetaminophen exposure, HL-ICOs were less sensitive. Further elucidation of enzyme and transporter activity in drug metabolism in HL-ICOs and exposure to a more extensive compound set are needed to accurately define the potential of HL-ICOs in in vitro toxicity testing.

Twenty-eight day repeated exposure of human 3D bronchial epithelial model to heated tobacco aerosols indicates decreased toxicological responses compared to cigarette smoke

Tobacco harm reduction (THR) involves providing adult smokers with potentially reduced harm modes of nicotine delivery as alternatives to smoking combustible cigarettes. Heated tobacco products (HTPs) form a category with THR potential due to their ability to deliver nicotine and flavours through heating, not burning, tobacco. By eliminating burning, heated tobacco does not produce smoke but an aerosol which contains fewer and lower levels of harmful chemicals compared to cigarette smoke. In this study we assessed the in vitro toxicological profiles of two prototype HTPs' aerosols compared to the 1R6F reference cigarette using the 3D human (bronchial) MucilAir™ model. To increase consumer relevance, whole aerosol/smoke exposures were delivered repeatedly across a 28 day period (16, 32, or 48 puffs per exposure). Cytotoxicity (LDH secretion), histology (Alcian Blue/H&E; Muc5AC; FoxJ1 staining), cilia active area and beat frequency and inflammatory marker (IL-6; IL-8; MMP-1; MMP-3; MMP-9; TNFα) levels were assessed. Diluted 1R6F smoke consistently induced greater and earlier effects compared to the prototype HTP aerosols across the endpoints, and in a puff dependent manner. Although some significant changes across the endpoints were induced by exposure to the HTPs, these were substantially less pronounced and less frequently observed, with apparent adaptive responses occurring over the experimental period. Furthermore, these differences between the two product categories were observed at a greater dilution (and generally lower nicotine delivery range) for 1R6F (1R6F smoke diluted 1/14, HTP aerosols diluted 1/2, with air). Overall, the findings demonstrate the THR potential of the prototype HTPs through demonstrated substantial reductions in toxicological outcomes in in vitro 3D human lung models.

Case Study in 21st-Century Ecotoxicology: Using In Vitro Aromatase Inhibition Data to Predict Reproductive Outcomes in Fish In Vivo

To reduce the use of intact animals for chemical safety testing, while ensuring protection of ecosystems and human health, there is a demand for new approach methodologies (NAMs) that provide relevant scientific information at a quality equivalent to or better than traditional approaches. The present case study examined whether bioactivity and associated potency measured in an in vitro screening assay for aromatase inhibition could be used together with an adverse outcome pathway (AOP) and mechanistically based computational models to predict previously uncharacterized in vivo effects. Model simulations were used to inform designs of 60-h and 10-21-day in vivo exposures of adult fathead minnows (Pimephales promelas) to three or four test concentrations of the in vitro aromatase inhibitor imazalil ranging from 0.12 to 260 µg/L water. Consistent with an AOP linking aromatase inhibition to reproductive impairment in fish, exposure to the fungicide resulted in significant reductions in ex vivo production of 17β-estradiol (E2) by ovary tissue (≥165 µg imazalil/L), plasma E2 concentrations (≥74 µg imazalil/L), vitellogenin (Vtg) messenger RNA expression (≥165 µg imazalil/L), Vtg plasma concentrations (≥74 µg imazalil/L), uptake of Vtg into oocytes (≥260 µg imazalil/L), and overall reproductive output in terms of cumulative fecundity, number of spawning events, and eggs per spawning event (≥24 µg imazalil/L). Despite many potential sources of uncertainty in potency and efficacy estimates based on model simulations, observed magnitudes of apical effects were quite consistent with model predictions, and in vivo potency was within an order of magnitude of that predicted based on in vitro relative potency. Overall, our study suggests that NAMs and AOP-based approaches can support meaningful reduction and refinement of animal testing. Environ Toxicol Chem 2023;42:100-116. © 2022 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Toxicogenomics Data for Chemical Safety Assessment and Development of New Approach Methodologies: An Adverse Outcome Pathway-Based Approach

Mechanistic toxicology provides a powerful approach to inform on the safety of chemicals and the development of safe-by-design compounds. Although toxicogenomics supports mechanistic evaluation of chemical exposures, its implementation into the regulatory framework is hindered by uncertainties in the analysis and interpretation of such data. The use of mechanistic evidence through the adverse outcome pathway (AOP) concept is promoted for the development of new approach methodologies (NAMs) that can reduce animal experimentation. However, to unleash the full potential of AOPs and build confidence into toxicogenomics, robust associations between AOPs and patterns of molecular alteration need to be established. Systematic curation of molecular events to AOPs will create the much-needed link between toxicogenomics and systemic mechanisms depicted by the AOPs. This, in turn, will introduce novel ways of benefitting from the AOPs, including predictive models and targeted assays, while also reducing the need for multiple testing strategies. Hence, a multi-step strategy to annotate AOPs is developed, and the resulting associations are applied to successfully highlight relevant adverse outcomes for chemical exposures with strong in vitro and in vivo convergence, supporting chemical grouping and other data-driven approaches. Finally, a panel of AOP-derived in vitro biomarkers for pulmonary fibrosis (PF) is identified and experimentally validated.

A walk in the PARC: developing and implementing 21st century chemical risk assessment in Europe

Current approaches for the assessment of environmental and human health risks due to exposure to chemical substances have served their purpose reasonably well. Nevertheless, the systems in place for different uses of chemicals are faced with various challenges, ranging from a growing number of chemicals to changes in the types of chemicals and materials produced. This has triggered global awareness of the need for a paradigm shift, which in turn has led to the publication of new concepts for chemical risk assessment and explorations of how to translate these concepts into pragmatic approaches. As a result, next-generation risk assessment (NGRA) is generally seen as the way forward. However, incorporating new scientific insights and innovative approaches into hazard and exposure assessments in such a way that regulatory needs are adequately met has appeared to be challenging. The European Partnership for the Assessment of Risks from Chemicals (PARC) has been designed to address various challenges associated with innovating chemical risk assessment. Its overall goal is to consolidate and strengthen the European research and innovation capacity for chemical risk assessment to protect human health and the environment. With around 200 participating organisations from all over Europe, including three European agencies, and a total budget of over 400 million euro, PARC is one of the largest projects of its kind. It has a duration of seven years and is coordinated by ANSES, the French Agency for Food, Environmental and Occupational Health & Safety.

Single-chirality of single-walled carbon nanotubes (SWCNTs) through chromatography and its potential biological applications

Gel chromatography is used to separate single-chirality and selective-diameter SWCNTs. We also explore the use of photothermal therapy and biosensor applications based on single-chirality, selected-diameter, and unique geometric shape.

DARTpaths, an in silico platform to investigate molecular mechanisms of compounds

SUMMARY

Xpaths is a collection of algorithms that allow for the prediction of compound-induced molecular mechanisms of action by integrating phenotypic endpoints of different species; and proposes follow-up tests for model organisms to validate these pathway predictions. The Xpaths algorithms are applied to predict developmental and reproductive toxicity (DART) and implemented into an in silico platform, called DARTpaths.

AVAILABILITY AND IMPLEMENTATION

All code is available on GitHub https://github.com/Xpaths/dartpaths-app under Apache license 2.0, detailed overview with demo is available at https://www.vivaltes.com/dartpaths/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Machine Learning Toxicity Prediction: Latest Advances by Toxicity End Point

Machine learning (ML) models to predict the toxicity of small molecules have garnered great attention and have become widely used in recent years. Computational toxicity prediction is particularly advantageous in the early stages of drug discovery in order to filter out molecules with high probability of failing in clinical trials. This has been helped by the increase in the number of large toxicology databases available. However, being an area of recent application, a greater understanding of the scope and applicability of ML methods is still necessary. There are various kinds of toxic end points that have been predicted in silico. Acute oral toxicity, hepatotoxicity, cardiotoxicity, mutagenicity, and the 12 Tox21 data end points are among the most commonly investigated. Machine learning methods exhibit different performances on different data sets due to dissimilar complexity, class distributions, or chemical space covered, which makes it hard to compare the performance of algorithms over different toxic end points. The general pipeline to predict toxicity using ML has already been analyzed in various reviews. In this contribution, we focus on the recent progress in the area and the outstanding challenges, making a detailed description of the state-of-the-art models implemented for each toxic end point. The type of molecular representation, the algorithm, and the evaluation metric used in each research work are explained and analyzed. A detailed description of end points that are usually predicted, their clinical relevance, the available databases, and the challenges they bring to the field are also highlighted.

Pilot testing and optimization of a larval fathead minnow high throughput transcriptomics assay

Concentrations at which global gene expression profiles in cells or animals exposed to a test substance start to differ significantly from those of controls have been proposed as an alternative point of departure for use in screening level hazard assessment. The present study describes pilot testing of a high throughput compatible transcriptomics assay with larval fathead minnows. One day post hatch fathead minnows were exposed to eleven different concentrations of three metals, three selective serotonin reuptake inhibitors, and four neonicotinoid-like compounds for 24 h and concentration response modeling was applied to whole body gene expression data. Transcriptomics-based points of departure (tPODs) were consistently lower than effect concentrations reported in apical endpoint studies in fish. However, larval fathead minnow-based tPODs were not always lower than concentrations reported to elicit apical toxicity in other aquatic organisms like crustaceans or insects. Random in silico subsampling of data from the pilot assays was used to evaluate various assay design and acceptance considerations such as transcriptome coverage, number of replicate individuals to sequence per treatment, and minimum number of differentially expressed genes to produce a reliable tPOD estimate. Results showed a strong association between the total number of genes for which a concentration response relationship could be derived and the overall variability in the resulting tPOD estimates. We conclude that, for our current assay design and analysis pipeline, tPODs based on fewer than 15 differentially expressed genes are likely to be unreliable for screening and that interindividual variability in gene expression profiles appears to be a more significant driver of tPOD variability than sample size alone. Results represent initial steps toward developing high throughput transcriptomics assays for use in ecological hazard screening.

The Ecotoxicology of Microbial Insecticides and Their Toxins in Genetically Modified Crops: An Overview

The use of microbial insecticides and their toxins in biological control and transgenic plants has increased their presence in the environment. Although they are natural products, the main concerns are related to the potential impacts on the environment and human health. Several assays have been performed worldwide to investigate the toxicity or adverse effects of these microbial products or their individual toxins. This overview examines the published data concerning the knowledge obtained about the ecotoxicity and environmental risks of these natural pesticides. The data presented show that many results are difficult to compare due to the diversity of measurement units used in the different research data. Even so, the products and toxins tested present low toxicity and low risk when compared to the concentrations used for pesticide purposes. Complementary studies should be carried out to assess possible effects on human health.

Behavioral and physiological toxicity thresholds of a freshwater vertebrate (Heteropneustes fossilis) and invertebrate (Branchiura sowerbyi), exposed to zinc oxide nanoparticles (nZnO): A General Unified Threshold model of Survival (GUTS)

The toxic effects of Zinc oxide nanoparticles (nZnO) on Branchiura sowerbyi and Heteropneustes fossilis, was assessed in a 96-hour acute exposure regime using behavioral (including loss-of balance and clumping tendencies) and physiological (mucus secretion and oxygen consumption) endpoints. While the relationship between behavioral, physiological biomarkers, and exposure concentrations was assessed using correlation analysis, nZnO toxicity was further predicted using the General Unified Threshold model for Survival (GUTS). The time-dependent lethal limits for acute nZnO toxicity (LC₅₀) on B. sowerbyi were estimated to be 0.668, 0.588, 0.448, and 0.400 mg/l, respectively, at 24, 48, 72, and 96 h whereas for H. fossilis the LC50 values are 0.954, 0.905, 0.874 and 0.838 mg/l. Threshold effect values i.e., LOEC (Lowest Observed Effect Concentration), NOEC (No Observed Effect Concentration), and MATC (Maximum Acceptable Toxicant Concentration) threshold effect values at 96 h were higher for fish compared to the oligochaete. For B. sowerbyi, the GUTS-SD (stochastic death) model is a better predictor of nanoparticle exposure effects compared to the GUTS-IT (individual tolerance) model, however in the case of H. fossilis, the reverse pattern was observed. Oxygen consumption rate was negatively correlated to mortality under acute exposure duration. The strong negative correlation between mortality and oxygen consumption strongly suggests a metabolic-toxicity pathway for nZnO exposure effects. The higher toxicity threshold values i.e., LOEC, NOEC, and MATC for fish compared to the oligochaete invertebrate indicates greater risks for invertebrates compared to vertebrates, with resultant implications for local habitat trophic relationships.

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

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

Gene-alcohol interactions in birth defects

Most human birth defects are thought to result from complex interactions between combinations of genetic and environmental factors. This is true even for conditions that, at face value, may appear simple and straightforward, like fetal alcohol spectrum disorders (FASD). FASD describe the full range of structural and neurological disruptions that result from prenatal alcohol exposure. While FASD require alcohol exposure, evidence from human and animal model studies demonstrate that additional genetic and/or environmental factors can influence the embryo's susceptibility to alcohol. Only a limited number of alcohol interactions in birth defects have been identified, with many sensitizing genetic and environmental factors likely yet to be identified. Because of this, while unsatisfying, there is no definitively "safe" dose of alcohol for all pregnancies. Determining these other factors, as well as mechanistically characterizing known interactions, is critical for better understanding and preventing FASD and requires combined scrutiny of human and model organism studies.

Toxicokinetics, dose-response, and risk assessment of nanomaterials: Methodology, challenges, and future perspectives

The rapid growth of nanomaterial applications has raised safety concerns for human health. A number of studies have been conducted to assess the toxicokinetics, toxicology, dose-response, and risk assessment of different nanomaterials using in vitro and in vivo animal and human models. However, current studies cannot meet the demand for efficient assessment of toxicokinetics, dose-response relationships, or the toxicological risk arising from the rapidly increasing number of newly synthesized nanomaterials. In this article, we review the methods for conducting toxicokinetics, hazard identification, dose-response, exposure, and risk assessment studies of nanomaterials, identify the knowledge gaps, and discuss the challenges remaining. We provide the rationale behind the appropriate design of nanomaterial plasma toxicokinetic and tissue distribution studies, including caveats on the interpretation and correlation of in vitro and in vivo toxicology studies. The potential of using physiologically based pharmacokinetic (PBPK) models to extrapolate toxicokinetic and toxicity findings from in vitro to in vivo and from animals to humans is discussed, and the knowledge gaps of PBPK modeling for nanomaterials are identified. While challenges still exist, there has been progress in the toxicokinetics, hazard identification, and risk assessment of nanomaterials in the past two decades. Recent advancements in the field are highlighted with relevant examples. We also share latest guidelines as well as our perspectives on future studies needed to characterize the toxicokinetics, toxicity, and dose-response relationship in support of nanomaterial risk assessment. This article is categorized under: Toxicology and Regulatory Issues in Nanomedicine > Toxicology of Nanomaterials Toxicology and Regulatory Issues in Nanomedicine > Regulatory and Policy Issues in Nanomedicine.

Confirmation of high-throughput screening data and novel mechanistic insights into FXR-xenobiotic interactions by orthogonal assays

Toxicology in the 21st Century (Tox21) is a federal collaboration employing a high-throughput robotic screening system to test 10,000 environmental chemicals. One of the primary goals of the program is prioritizing toxicity evaluations through in vitro high-throughput screening (HTS) assays for large numbers of chemicals already in commercial use for which little or no toxicity data is available. Within the Tox21 screening program, disruption in nuclear receptor (NR) signaling represents a particular area of interest. Given the role of NR's in modulating a wide range of biological processes, alterations of their activity can have profound biological impacts. Farnesoid X receptor (FXR) is a member of the nuclear receptor superfamily that has demonstrated importance in bile acid homeostasis, glucose metabolism, lipid homeostasis and hepatic regeneration. In this study, we re-evaluated 24 FXR agonists and antagonists identified through Tox21 using select orthogonal assays. In transient transactivation assays, 7/8 putative agonists and 4/4 putative inactive compounds were confirmed. Likewise, we confirmed 9/12 antagonists tested. Using a mammalian two hybrid approach we demonstrate that both FXR agonists and antagonists facilitate FXRα-coregulator interactions suggesting that differential coregulator recruitment may mediate activation/repression of FXRα mediated transcription. Additionally, we tested the ability of select FXR agonists and antagonists to facilitate hepatic transcription of FXR gene targets Shp and Bsep in a teleost (Medaka) model. Through application of in vitro cell-based assays, in silico modeling and in vivo gene expressions, we demonstrated the molecular complexity of FXR:ligand interactions and confirmed the ability of diverse ligands to modulate FXRα, facilitate differential coregulator recruitment and activate/repress receptor-mediated transcription. Overall, we suggest a multiplicative approach to assessment of nuclear receptor function may facilitate a greater understanding of the biological and mechanistic complexities of nuclear receptor activities and further our ability to interpret broad HTS outcomes.

Evaluation of Metabolism of a Defined Pesticide Mixture through Multiple In Vitro Liver Models

The evaluation of exposure to multiple contaminants in a mixture presents a number of challenges. For example, the characterization of chemical metabolism in a mixture setting remains a research area with critical knowledge gaps. Studies of chemical metabolism typically utilize suspension cultures of primary human hepatocytes; however, this model is not suitable for studies of more extended exposures and donor-to-donor variability in a metabolic capacity is unavoidable. To address this issue, we utilized several in vitro models based on human-induced pluripotent stem cell (iPSC)-derived hepatocytes (iHep) to characterize the metabolism of an equimolar (1 or 5 µM) mixture of 20 pesticides. We used iHep suspensions and 2D sandwich cultures, and a microphysiological system OrganoPlate^® 2-lane 96 (Mimetas^TM) that also included endothelial cells and THP-1 cell-derived macrophages. When cell culture media were evaluated using gas and liquid chromatography coupled to tandem mass spectrometry methods, we found that the parent molecule concentrations diminished, consistent with metabolic activity. This effect was most pronounced in iHep suspensions with a 1 µM mixture, and was lowest in OrganoPlate^® 2-lane 96 for both mixtures. Additionally, we used ion mobility spectrometry-mass spectrometry (IMS-MS) to screen for metabolite formation in these cultures. These analyses revealed the presence of five primary metabolites that allowed for a more comprehensive evaluation of chemical metabolism in vitro. These findings suggest that iHep-based suspension assays maintain higher metabolic activity compared to 2D sandwich and OrganoPlate^® 2-lane 96 model. Moreover, this study illustrates that IMS-MS can characterize in vitro metabolite formation following exposure to mixtures of environmental contaminants.

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.

Vesicular Glutamate Release from Feeder-FreehiPSC-Derived Neurons

Human-induced pluripotent stem cells (hiPSCs) represent one of the main and powerful tools for the in vitro modeling of neurological diseases. Standard hiPSC-based protocols make use of animal-derived feeder systems to better support the neuronal differentiation process. Despite their efficiency, such protocols may not be appropriate to dissect neuronal specific properties or to avoid interspecies contaminations, hindering their future translation into clinical and drug discovery approaches. In this work, we focused on the optimization of a reproducible protocol in feeder-free conditions able to generate functional glutamatergic neurons. This protocol is based on a generation of neuroprecursor cells differentiated into human neurons with the administration in the culture medium of specific neurotrophins in a Geltrex-coated substrate. We confirmed the efficiency of this protocol through molecular analysis (upregulation of neuronal markers and neurotransmitter receptors assessed by gene expression profiling and expression of the neuronal markers at the protein level), morphological analysis, and immunfluorescence detection of pre-synaptic and post-synaptic markers at synaptic boutons. The hiPSC-derived neurons acquired Ca²⁺-dependent glutamate release properties as a hallmark of neuronal maturation. In conclusion, our study describes a new methodological approach to achieve feeder-free neuronal differentiation from hiPSC and adds a new tool for functional characterization of hiPSC-derived neurons.

A framework for establishing scientific confidence in new approach methodologies

Robust and efficient processes are needed to establish scientific confidence in new approach methodologies (NAMs) if they are to be considered for regulatory applications. NAMs need to be fit for purpose, reliable and, for the assessment of human health effects, provide information relevant to human biology. They must also be independently reviewed and transparently communicated. Ideally, NAM developers should communicate with stakeholders such as regulators and industry to identify the question(s), and specified purpose that the NAM is intended to address, and the context in which it will be used. Assessment of the biological relevance of the NAM should focus on its alignment with human biology, mechanistic understanding, and ability to provide information that leads to health protective decisions, rather than solely comparing NAM-based chemical testing results with those from traditional animal test methods. However, when NAM results are compared to historical animal test results, the variability observed within animal test method results should be used to inform performance benchmarks. Building on previous efforts, this paper proposes a framework comprising five essential elements to establish scientific confidence in NAMs for regulatory use: fitness for purpose, human biological relevance, technical characterization, data integrity and transparency, and independent review. Universal uptake of this framework would facilitate the timely development and use of NAMs by the international community. While this paper focuses on NAMs for assessing human health effects of pesticides and industrial chemicals, many of the suggested elements are expected to apply to other types of chemicals and to ecotoxicological effect assessments.

SETApp: A machine learning and image analysis based application to automate the sea urchin embryo test

Since countless xenobiotic compounds are being found in the environment, ecotoxicology faces an astounding challenge in identifying toxicants. The combination of high-throughput in vivo/in vitro bioassays with high-resolution chemical analysis is an effective way to elucidate the cause-effect relationship. However, these combined strategies imply an enormous workload that can hinder their implementation in routine analysis. The purpose of this study was to develop a new high throughput screening method that could be used as a predictive expert system that automatically quantifies the size increase and malformation of the larvae and, thus, eases the application of the sea urchin embryo test in complex toxicant identification pipelines such as effect-directed analysis. For this task, a training set of 242 images was used to calibrate the size-increase and malformation level of the larvae. Two classification models based on partial least squares discriminant analysis (PLS-DA) were built and compared. Moreover, Hierarchical PLS-DA shows a high proficiency in classifying the larvae, achieving a prediction accuracy of 84 % in validation. The scripts built along the work were compiled in a user-friendly standalone app (SETApp) freely accessible at https://github.com/UPV-EHU-IBeA/SETApp. The SETApp was tested in a real case scenario to fulfill the tedious requirements of a WWTP effect-directed analysis.

In vitro evaluation of the inhalation toxicity of the cosmetic ingredient aluminum chlorohydrate

Aluminum chlorohydrate (ACH) is a major aerosol component frequently used as the active ingredient in antiperspirants, and in vivo studies have raised a concern about its inhalation toxicity. Still, few studies have addressed its effects on the human respiratory tract. Therefore, we developed a study on ACH inhalation toxicity using an in vitro human alveolar cell model (A549 cells) with molecular and cellular markers of oxidative stress, immunotoxicity, and epigenetic changes. The chemical characterization of ACH suspensions indicated particle instability and aggregation; however, side-scatter analysis demonstrated significant particle uptake in cells exposed to ACH. Exposure of A549 cells to non-cytotoxic concentrations of ACH (0.25, 0.5, and 1 mg/ml) showed that ACH induced reactive oxygen species. Moreover, ACH upregulated TNF, IL6, IL8, and IL1A genes, but not the lncRNAs NEAT1 and MALAT1. Finally, no alterations on the global DNA methylation pattern (5-methylcytosine and 5-hydroxymethylcytosine) or the phosphorylation of histone H2AX (γ-H2AX) were observed. Our data suggest that ACH may induce oxidative stress and inflammation on alveolar cells, and A549 cells may be useful to identify cellular and molecular events that may be associated with adverse effects on the lungs. Still, further research is needed to ensure the inhalation safety of ACH.

Correlation Analysis of Variables From the Atherosclerosis Risk in Communities Study

The need to test chemicals in a timely and cost-effective manner has driven the development of new alternative methods (NAMs) that utilize in silico and in vitro approaches for toxicity prediction. There is a wealth of existing data from human studies that can aid in understanding the ability of NAMs to support chemical safety assessment. This study aims to streamline the integration of data from existing human cohorts by programmatically identifying related variables within each study. Study variables from the Atherosclerosis Risk in Communities (ARIC) study were clustered based on their correlation within the study. The quality of the clusters was evaluated via a combination of manual review and natural language processing (NLP). We identified 391 clusters including 3,285 variables. Manual review of the clusters containing more than one variable determined that human reviewers considered 95% of the clusters related to some degree. To evaluate potential bias in the human reviewers, clusters were also scored via NLP, which showed a high concordance with the human classification. Clusters were further consolidated into cluster groups using the Louvain community finding algorithm. Manual review of the cluster groups confirmed that clusters within a group were more related than clusters from different groups. Our data-driven approach can facilitate data harmonization and curation efforts by providing human annotators with groups of related variables reflecting the themes present in the data. Reviewing groups of related variables should increase efficiency of the human review, and the number of variables reviewed can be reduced by focusing curator attention on variable groups whose theme is relevant for the topic being studied.

Principles of risk decision-making

Risk management decisions in public health require consideration of a number of complex, often conflicting factors. The aim of this review was to propose a set of 10 fundamental principles to guide risk decision-making. Although each of these principles is sound in its own right, the guidance provided by different principles might lead the decision-maker in different directions. For example, where the precautionary principle advocates for preemptive risk management action under situations of scientific uncertainty and potentially catastrophic consequences, the principle of risk-based decision-making encourages decision-makers to focus on established and modifiable risks, where a return on the investment in risk management is all but guaranteed in the near term. To evaluate the applicability of the 10 principles in practice, one needs to consider 10 diverse risk issues of broad concern and explore which of these principles are most appropriate in different contexts. The 10 principles presented here afford substantive insight into the process of risk management decision-making, although decision-makers will ultimately need to exercise judgment in reaching appropriate risk decisions, accounting for all of the scientific and extra-scientific factors relevant to the risk decision at hand.

Synergistic cytotoxicity of binary combinations of inorganic and organic disinfection byproducts assessed by real-time cell analysis

Chlorine, chlorine dioxide, and ozone are widely used as disinfectants in drinking water treatments. However, the combined use of different disinfectants can result in the formation of various organic and inorganic disinfection byproducts (DBPs). The toxic interactions, including synergism, addition, and antagonism, among the complex DBPs are still unclear. In this study, we established and verified a real-time cell analysis (RTCA) method for cytotoxicity measurement on Chinese hamster ovary (CHO) cell. Using this convenient and accurate method, we assessed the cytotoxicity of a series of binary combinations consisting of one of the 3 inorganic DBPs (chlorite, chlorate, and bromate) and one of the 32 regulated and emerging organic DBPs. The combination index (CI) of each combination was calculated and evaluated by isobolographic analysis to reflect the toxic interactions. The results confirmed the synergistic effect on cytotoxicity in the binary combinations consisting of chlorite and one of the 5 organic DBPs (2 iodinated DBPs (I-DBPs) and 3 brominated DBPs (Br-DBPs)), chlorate and one of the 4 organic DBPs (3 aromatic DBPs and dibromoacetonitrile), and bromate and one of the 3 organic DBPs (2 I-DBPs and dibromoacetic acid). The possible synergism mechanism of organic DBPs on the inorganic ones may be attributed to the influence of organic DBPs on cell membrane and cell antioxidant system. This study revealed the toxic interactions among organic and inorganic DBPs, and emphasized the latent adverse outcomes in the combined use of different disinfectants.

Artificial Intelligence-Based Toxicity Prediction of Environmental Chemicals: Future Directions for Chemical Management Applications

Recently, research on the development of artificial intelligence (AI)-based computational toxicology models that predict toxicity without the use of animal testing has emerged because of the rapid development of computer technology. Various computational toxicology techniques that predict toxicity based on the structure of chemical substances are gaining attention, including the quantitative structure-activity relationship. To understand the recent development of these models, we analyzed the databases, molecular descriptors, fingerprints, and algorithms considered in recent studies. Based on a selection of 96 papers published since 2014, we found that AI models have been developed to predict approximately 30 different toxicity end points using more than 20 toxicity databases. For model development, molecular access system and extended-connectivity fingerprints are the most commonly used molecular descriptors. The most used algorithm among the machine learning techniques is the random forest, while the most used algorithm among the deep learning techniques is a deep neural network. The use of AI technology in the development of toxicity prediction models is a new concept that will aid in achieving a scientific accord and meet regulatory applications. The comprehensive overview provided in this study will provide a useful guide for the further development and application of toxicity prediction models.

Investigation of a derived adverse outcome pathway (AOP) network for endocrine-mediated perturbations

An adverse outcome pathway (AOP) is a compact representation of the available mechanistic information on observed adverse effects upon environmental exposure. Sharing of events across individual AOPs has led to the emergence of AOP networks. Since AOP networks are expected to be functional units of toxicity prediction, there is current interest in their development tailored to specific research question or regulatory problem. To this end, we have developed a detailed workflow to construct an endocrine-relevant AOP (ED-AOP) network based on the existing information available in AOP-Wiki. We propose a cumulative weight of evidence (WoE) score for each ED-AOP based on the WoE scores assigned to key event relationships (KERs) by AOP-Wiki, revealing gaps in AOP development. Connectivity analysis of the ED-AOP network comprising 48 AOPs reveals 7 connected components and 12 isolated AOPs. Subsequently, we apply standard network measures to perform an in-depth analysis of the two largest connected components of the ED-AOP network. Notably, the graph-theoretic analyses led to the identification of important events including points of convergence or divergence in the ED-AOP network. These findings can suggest potential adverse outcomes and facilitate the development of new endpoints or assays for chemical risk assessment. Detailed analysis of the largest component in the ED-AOP network gives insights on the systems-level perturbations caused by endocrine disruption, emergent paths, and stressor-event associations. In sum, the derived ED-AOP network can provide a broader view of the biological events disrupted by endocrine disruption, as well as facilitate better risk assessment of environmental chemicals.

Assessment of biosafety and toxicity of hydrophilic gel for implantation in experimental in vitro and in vivo models

Background

The assessment of biosafety of pharmacologically active substances is crucial for determining the feasibility of their medical use. There are controversial issues regarding the use of substances of different origins as implants.

Methods

We have conducted the comprehensive studies to determine the in vivo toxicity and in vitro genotoxicity of new generation of hydrophilic gel for implantation (production name of the substance “Activegel”) to detail its characteristics and assess its biosafety.

Results

In vivo studies have shown the absence of clinical manifestations of intoxication in animals and no abnormalities in their physiological condition, general and biochemical blood tests. Evaluation of the site of the gel application showed no inflammatory reaction and evidenced on normal state of tissues of animal skin. The results of the genotoxicity test indicated that the gel did not affect the parameters of DNA comets and the formation of micronuclei, accordingly, had no genotoxic effect on human peripheral blood lymphocytes. When studying the effect of the gel on malignantly transformed cells in vitro, it was found that the gel for implantation did not change the proliferative activity and viability of human breast cancer cells.

Conclusions

Comprehensive in vitro and in vivo study using various experimental model systems showed that the hydrophilic gel for implantation “Activegel” is non-toxic.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40360-022-00577-3.

Building a Network of Adverse Outcome Pathways (AOPs) Incorporating the Tau-Driven AOP Toward Memory Loss (AOP429)

The adverse outcome pathway (AOP) concept was first proposed as a tool for chemical hazard assessment facilitating the regulatory decision-making in toxicology and was more recently recommended during the BioMed21 workshops as a tool for the characterization of crucial endpoints in the human disease development. This AOP framework represents mechanistically based approaches using existing data, more realistic and relevant to human biological systems. In principle, AOPs are described by molecular initiating events (MIEs) which induce key events (KEs) leading to adverse outcomes (AOs). In addition to the individual AOPs, the network of AOPs has been also suggested to beneficially support the understanding and prediction of adverse effects in risk assessment. The AOP-based networks can capture the complexity of biological systems described by different AOPs, in which multiple AOs diverge from a single MIE or multiple MIEs trigger a cascade of KEs that converge to a single AO. Here, an AOP network incorporating a recently proposed tau-driven AOP toward memory loss (AOP429) related to sporadic (late-onset) Alzheimer’s disease is constructed. This proposed AOP network is an attempt to extract useful information for better comprehending the interactions among existing mechanistic data linked to memory loss as an early phase of sporadic Alzheimer’s disease pathology.

Integration of Bioinspired Fibrous Strands with 3D Spheroids for Environmental Hazard Monitoring

Numerous methods have been introduced to produce 3D cell cultures that can reduce the need for animal experimentation. This study presents a unique 3D culture platform that features bioinspired strands of electrospun nanofibers (BSeNs) and aquatic cell lines to compensate for shortcomings in the current cell spheroid generation techniques. The use of BSeNs in 3D zebrafish liver cell cultures is found to improve liver and reproductive functions through spheroid-based in vitro assays such as whole transcriptome sequencing and reproductive toxicity testing, with optimized properties exhibiting results similar to those obtained for fish embryo acute toxicity (FET, OECD TG 236) following exposure to environmental endocrine-disrupting chemicals (17β-Estradiol (E2), 4-hydroxytamoxifen (4-HT), and bisphenol compounds (bisphenol A (BPA) and 9,9-Bis(4-hydroxyphenyl)fluorene (BPFL)). These findings indicate that the beneficial effects of bioinspired materials that closely mimic ECM environments can yield efficient zebrafish cells with intrinsic functions and xenobiotic metabolism similar to those of zebrafish embryos. As a closer analog for the in vivo conditions that are associated with exposure to potentially hazardous chemicals, the straightforward culture model introduced in this study shows promise as an alternative tool that can be used to further eco-environmental assessment.