Improving the human hazard characterization of chemicals: a Tox21 update

A small-scale data driven and graph neural network based toxicity prediction method of compounds

Toxicity prediction is crucial in drug discovery, helping identify safe compounds and reduce development risks. However, the lack of known toxicity data for most compounds is a major challenge. Recently, data-driven models have gained attention as a more efficient alternative to traditional in vivo and in vitro experiments. In this paper, we propose a small-scale, data-driven toxicity prediction method based on Graph Neural Network (GNN). We introduce a joint learning strategy for multiple toxicity types and construct a graph-based model, JLGCN-MTT, to improve prediction accuracy. In addition, we integrate a transfer learning strategy that leverages data from multiple toxicity types, allowing the model to make reliable predictions even when data for a specific toxicity type is limited. We conducted experiments using data from 3566 compounds in the Tox21 dataset, which contains 12 types of toxicity-related bioactivity data. The experimental results show that JLGCN-MTT outperforms traditional machine learning methods and single-task GNN in all 12 toxicity prediction tasks, with AUC improving by over 10% in 11 tasks. For small-scale data with 50, 100, and 300 training samples, the AUC improved in all cases, with the highest improvement of 11% observed when the sample size was 50. These results demonstrate that the small-scale, data-driven toxicity prediction method we propose can achieve high prediction accuracy.

Application of cytochrome P450 enzyme assays to predict p53 inducers and AChE inhibitors that require metabolic activation

Metabolically active compounds can cause toxicity which would otherwise be undetected using traditional in vitro assays with limited proficiency for xenobiotic metabolism. Introduction of liver microsomes to assay systems enables enhanced identification of compounds that require biotransformation to induce toxicity. Previously, metabolically active compounds from the Tox21 10 K compound library were identified using assays probing two targets, p53 and acetylcholinesterase (AChE), in the presence and absence of human or rat liver microsomes, due to the established roles of cytochrome P450 (CYP) enzymes in human drug metabolism. To further explore the role of metabolic activation, the activities of the identified metabolically active compounds were evaluated against five CYP enzymes: CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4. CYP bioactivities were found to be highly predictive (>80 % accuracy) of compounds that required metabolic activation in these assays. Chemical features significantly enriched in metabolically active compounds, as well as chemical features that were specific for each of the five CYPs, were identified. Product use exposures of the metabolically active compounds were examined in this study, with "pesticides" appearing to be the largest category that may produce harmful metabolites. Additionally, the compound interactions with different CYPs were assessed and frequencies for both classes of compounds, drugs and environmental chemicals, were found to be proportionally similar across the five CYP isoforms.

Novel target identification towards drug repurposing based on biological activity profiles

Rare diseases affect more than 30 million individuals, with the majority facing limited treatment options, elevating the urgency to innovative therapeutic solutions. Addressing these medical challenges necessitates an exploration of novel treatment modalities. Among these, drug repurposing emerges as a promising avenue, offering both potential and risk mitigation. To achieve this goal, we primarily focused on developing predictive models that harness cutting-edge computational techniques to uncover latent relationships between gene targets and chemical compounds towards drug repurposing. Building upon our previous investigation, where we successfully identified gene targets for compounds from the Tox21 in vitro assays, our endeavor expanded to a systematic prediction of potential targets for drug repurposing employing machine learning models built on diverse algorithms such as Support Vector Classifier, K-Nearest Neighbors, Random Forest, and Extreme Gradient Boosting. These models were trained on comprehensive biological activity profile data to predict the relationship between 143 gene targets and over 6000 compounds. Our models demonstrated high accuracy (>0.75), with predictions further validated by using public experimental datasets. Furthermore, several findings were evaluated via case studies. By elucidating these connections, we aim to streamline the drug repurposing process, ultimately catalyzing the discovery of more effective therapeutic interventions for rare diseases.

Lessons learned from evaluating defined chemical mixtures in a high-throughput estrogen receptor assay system

In this article, we provide a proof of concept evaluating the utility of the U.S. Tox21 high-throughput screening approach to assess the hazard of chemical mixtures using 2 estrogen receptor (ER) assays. A subset of chemicals identified in Phase I of the Tox21 program as active in the ER agonist assay were used to design mixtures for testing in Phase II. Individual chemicals and mixtures were evaluated in 2 cell-based ER alpha (ERα) activation assays: One incorporating a transfected ligand-binding domain in an ERα β-lactamase reporter cell line (ER-bla) and the full-length endogenous receptor in the MCF7 cell line with a luciferase reporter gene (ER-luc). Concentration-response data from individual chemicals were used to predict the joint effect based on mixtures modeling methods and were compared with observed mixtures data to assess model fit. The models tended to overpredict mixture responses in the ER-bla assay, whereas predictions were closer to observed responses in the ER-luc assay, indicating that a full-length endogenous ER is a preferred model for high-throughput mixture analysis. Lessons learned from this research include the importance of analyzing the individual chemicals used for predictions and the mixtures in the same experimental paradigm to minimize variation, developing methods for imputing missing values from incomplete concentration-response curves, and establishing criteria to determine when inactive chemicals should be omitted from mixture predictions.

The connection between Bayesian networks and adverse outcome pathway (AOP) networks and how to use it for predicting drug toxicity

There is significant interest in combining adverse outcome pathways (AOPs) with Bayesian networks (BNs) because of their shared representation using directed acyclic graphs (DAGs). However, it has not been verified empirically whether AOP networks are mathematically congruent with BNs. Furthermore, important properties for BNs, such as Markov blankets, have not been emphasized, which is a missed opportunity for simplifying and optimizing the model. Here, we summarize the connection between AOP networks and BNs and explore the implications for toxicity modeling. We also present a case study in drug-related liver toxicity. Our results confirm that AOP networks are congruent mathematically with BNs, with incorporation of the mathematical properties of BN leading to significantly simplified and more efficient models.

Developing a feasible fast-track testing method for developmental neurotoxicity studies: alternative model for risk assessment of micro- and nanoplastics

Micro- and nanoplastics (MNPs) are widespread environmental pollutants that pose significant health risks. They originate from industrial processes, consumer products, and environmental degradation, inducing oxidative stress through cellular dysfunctions such as membrane interaction, internalization, mitochondrial damage, inflammation, metal ion leaching, and impaired antioxidant defense. Despite increasing evidence of their toxicity-particularly developmental neurotoxicity (DNT) and mitochondrial impairment-our understanding remains limited due to the high costs of animal studies, which reduce the overall size of experimental data. This underscores the urgent need for alternative test methods that are cost-effective, rapid, and translational. This review examines new approach methodologies (NAMs) for DNT assessment, addressing the ethical, financial, and translational limitations of animal models. NAMs integrate three complementary non-animal models that enhance conventional testing. First, zebrafish models provide organismal insights into behavioral and neurodevelopmental outcomes at minimal cost. Second, neuronal organoids replicate human-specific neurodevelopmental processes in a 3D system, offering mechanistic insights. Lastly, human cell lines enable high-throughput screening, integrating findings from zebrafish and organoid studies. Establishing a new paradigm for DNT testing is crucial for faster and more efficient toxicity and risk assessments, ultimately protecting public health. Standardizing and gaining regulatory acceptance for NAMs will improve predictive accuracy and broaden their application in environmental toxicology. Advancing these methodologies is essential to addressing the risks of MNP exposure while promoting ethical and sustainable research practices.

Myrobalan Fruit Extracts Modulate Immunobiochemical Pathways In Vitro

Myrobalan fruits are important ingredients of traditional remedies, such as the Ayurvedic formulation Triphala or the Tibetan formulation Bras bu 3. Myrobalan-containing remedies are described to have positive effects on metabolism, the cardiovascular system, and the immune system. The chemical composition of botanical mixtures can be very complex, and it is often impossible to identify individual compounds as specific active ingredients, which suggests a multi-target mode of action. In this in vitro study, the effect of myrobalan extracts in human cell models was investigated to gain more information about the molecular mechanism of action and to find possible synergistic effects. Direct and indirect antioxidant effects were investigated, and the activation of immunobiochemical metabolic pathways involved in the cellular immune response was examined in cell lines treated with extracts of the fruits of Phyllanthus emblica, Terminalia chebula and Terminalia bellirica, as well as a combination of them. In particular, a synergistic effect on the activation of the endogenous antioxidant defence system was observed with the combined treatment of the three fruit extracts. An integrated transcriptome analysis of cells treated with a combination of fruit extracts confirmed an effect on immune pathways, oxidative stress, and detoxification processes. This study shows the modulation of various signalling pathways and cellular processes that may be part of the multi-target mechanism of individual and combined myrobalan fruit extracts. Although the results are limited to in vitro data, they contribute to a better understanding of how botanical mixtures work and provide hypotheses for further research.

Screening of estrogen receptor activity of per- and polyfluoroalkyl substances based on deep learning and in vivo assessment

Over the past decades, exposure to per- and polyfluoroalkyl substances (PFAS), a group of synthetic chemicals notorious for their environmental persistence, has been shown to pose increased health risks. Despite that some PFAS were reported to have endocrine-disrupting toxicity in previous studies, accurate prediction models based on deep learning and the underlying structural characteristics related to the effect of molecular fluorination remain limited. To address these issues, we proposed a stacking deep learning architecture, GXDNet, that integrates molecular descriptors and molecular graphs to predict the estrogen receptor α (ERα) activities of compounds, enhancing the generalization ability compared to previous models. Subsequently, we screened the ERα activity of 10,067 PFAS molecules using the GXDNet model and identified potential ERα binders. The representative PFAS molecules with the top docking scores showed that the introduction of fluorinated alkane chains significantly increased the binding affinities of parent molecules with ERα, suggesting that the combination of phenol structural fragments and fluorinated alkane chains has a synergistic effect in improving the binding capacity of the ligands to ERα. The binding modes, SHapley Additive Explanations analysis, and attention map emphasized the importance of π-π stacking and hydrogen bonding interactions with the phenol group, while the fluorinated alkane chain enhanced the interaction with the hydrophobic amino acids of the active pocket. Experimental validation using zebrafish models further confirmed the ERα activity of the representative PFAS molecules. Overall, the current computational workflow is beneficial for the toxicological screening of emerging PFAS and accelerating the development of eco-friendly PFAS molecules, thereby mitigating the environmental and health risks associated with PFAS exposure.

Environmental Fate of the Azole Fungicide Fluconazole and Its Persistent and Mobile Transformation Product 1,2,4-Triazole

Fluconazole is a persistent and mobile pharmaceutical azole fungicide observed in natural waters globally. It does not significantly degrade via traditional wastewater treatment, resulting in likely environmental and human exposure and environmental-origin azole fungicide resistance. Indirect photochemistry is known to degrade many recalcitrant contaminants in natural waters but has not been tested for fluconazole. We systematically measured rates and identified products of the indirect photodegradation of fluconazole in genuine and synthetic surface waters with varying nitrate, bicarbonate, and dissolved organic matter using high resolution mass spectrometry. Degradation half-lives of fluconazole ranged from 2 weeks to a year, indicating indirect photochemistry is slow but competitive with other loss processes. The transformation products 1,2,4-triazole and 1,2,4-triazole-1-acetic acid were produced in 30 to 100% yield during fluconazole degradation. These products are far more resistant to indirect photochemistry than fluconazole, with half-lives for 1,2,4-triazole in the environment of between 1 and 3 years when measurable with our methods. These "very persistent very mobile" contaminants are likely formed by most pharmaceutical and agrochemical azole fungicides, are regularly detected in the US and Denmark in monitoring programs and our exposure modeling demonstrates high potential for human exposure through drinking water with uncertain health implications.

Analytical Quality Evaluation of the Tox21 Compound Library

The analytical quality of compounds subjected to high-throughput screening (HTS) impacts accurate interpretation of assay results, with poor quality samples potentially leading to false negatives or positives. The Tox21 "10K" library consists of over 8900 unique compounds, spanning a diverse landscape of environmental and pharmaceutical chemicals, posing opportunities and challenges for analytical quality control (QC) determinations. Tox21 sample plates stored in DMSO at ambient conditions for 0 (T0) and/or 4 months (T4), totaling more than 13K unique sample identifiers (Tox21 IDs), were subjected to various analyses, including liquid and gas chromatography mass spectrometry (LC-MS, GC-MS) and nuclear magnetic resonance (NMR). Results for each sample at T0 or T4 underwent expert review and, where possible, a QC grade conveying purity, identity, and concentration was assigned. Herein, we relate details of the methods applied and report on the original (v0) Tox21 ID level results. Thirteen QC grades were condensed to 5 quality scores to aid global analysis, resulting in reinterpretation and improvement of >700 sample grades. Of the 92% T0 samples successfully graded, 76% exceeded 90% purity. For 76% of samples that were also tested at T4, 89% showed no evidence of sample loss or degradation. Prioritized quality bins were used to summarize thousands of replicate sample-level QC results to a compound-level QC score to support structure-based analyses. ToxPrint chemotype analysis identified structural features enriched in unstable compounds, as well as in high and low quality T0 subsets. Predicted vapor pressure was weakly correlated with low-concentration QC indicators, reflecting likely entanglement with method amenability and quality issues. Finally, an ongoing EPA effort to re-evaluate the original QC spectra is generating insights that will further modify QC grades. Tox21 QC spectra and results will be made available in a new public QC browser, facilitating further evaluation to support HTS interpretation and modeling applications.

Neuroprotective Potential of Origanum majorana L. Essential Oil Against Scopolamine-Induced Memory Deficits and Oxidative Stress in a Zebrafish Model

Origanum majorana L., also known as sweet marjoram, is a plant with multiple uses, both in the culinary field and traditional medicine, because of its major antioxidant, anti-inflammatory, antimicrobial, and digestive properties. In this research, we focused on the effects of O. majorana essential oil (OmEO, at concentrations of 25, 150, and 300 μL/L), evaluating chemical structure as well as its impact on cognitive performance and oxidative stress, in both naive zebrafish (Danio rerio), as well as in a scopolamine-induced amnesic model (SCOP, 100 μM). The fish behavior was analyzed in a novel tank-diving test (NTT), a Y-maze test, and a novel object recognition (NOR) test. We also investigated acetylcholinesterase (AChE) activity and the brain's oxidative stress status. In parallel, we performed in silico predictions (research conducted using computational models) of the pharmacokinetic properties of the main compounds identified in OmEO, using platforms such as SwissADME, pKCSM, ADMETlab 2.0, and ProTox-II. The results revealed that the major compounds were trans-sabinene hydrate (36.11%), terpinen-4-ol (17.97%), linalyl acetate (9.18%), caryophyllene oxide (8.25%), and α-terpineol (6.17%). OmEO can enhance memory through AChE inhibition, reduce SCOP-induced anxiety by increasing the time spent in the top zone in the NTT, and significantly reduce oxidative stress markers. These findings underscore the potential of using O. majorana to improve memory impairment and reduce oxidative stress associated with cognitive disorders, including Alzheimer's disease (AD).

Prediction of chemical-induced acute toxicity using in vitro assay data and chemical structure

Exposure to various chemicals found in the environment and in the context of drug development can cause acute toxicity. To provide an alternative to in vivo animal toxicity testing, the U.S. Tox21 consortium developed in vitro assays to test a library of approximately 10,000 drugs and environmental chemicals (Tox21 10K compound library) in a quantitative high-throughput screening (qHTS) approach. In this study, we assessed the utility of Tox21 assay data in comparison with chemical structure information in predicting acute systemic toxicity. Prediction models were developed using four machine learning algorithms, namely Random Forest, Naïve Bayes, eXtreme Gradient Boosting, and Support Vector Machine, and their performance was assessed using the area under the receiver operating characteristic curve (AUC-ROC). The chemical structure-based models as well as the Tox21 assay data demonstrated good predictive power for acute toxicity, achieving AUC-ROC values ranging from 0.83 to 0.93 and 0.73 to 0.79, respectively. We applied the models to predict the acute toxicity potential of the compounds in the Tox21 10K compound library, most of which were found to be non-toxic. In addition, we identified the Tox21 assays that contributed the most to acute toxicity prediction, such as acetylcholinesterase (AChE) inhibition and p53 induction. Chemical features including organophosphates and carbamates were also identified to be significantly associated with acute toxicity. In conclusion, this study underscores the utility of in vitro assay data in predicting acute toxicity.

Rapid quantitative high-throughput mouse embryoid body model for embryotoxicity assessment

Individuals are exposed to a wide arrays of hazardous chemicals on a daily basis through various routes, many of which have not undergone comprehensive toxicity assessments. While traditional developmental toxicity tests involving pregnant animals are known for their reliability, they are also associated with high costs and time requirements. Consequently, there is an urgent demand for alternative, cost-efficient, and rapid in vitro testing methods. This study aims to address the challenges related to automating and streamlining the screening of early developmental toxicity of chemicals by introducing a mouse embryoid body test (EBT) model in a 384-ultra low attachment well format. Embryoid bodies (EBs) generated in this format were characterized by a spontaneous differentiation trajectory into cardiac mesoderm by as analyzed by RNA-seq. Assessing prediction accuracy using reference compounds suggested in the ICH S5(R3) guideline and prior studies resulted in the establishment of the acceptance criteria and applicability domain of the EBT model. The results indicated an 84.38% accuracy in predicting the developmental toxicity of 23 positive and 9 negative reference compounds, with an optimized cutoff threshold of 750 µM. Overall, the developed EBT model presents a promising approach for more rapid, high-throughput chemical screening, thereby facilitating well-informed decision-making in environmental management and safety assessments.

Evaluating the Utility of the MSTI Assay in Predicting Compound Promiscuity and Cytotoxicity

Nonspecific reactive chemicals often interfere with the interpretation of high-throughput assay results because of their promiscuity and/or cytotoxicity. Using a high-throughput assay to identify such compounds is necessary to efficiently rule out potential assay artifacts. The MSTI, (E)-2-(4-mercaptostyryl)-1,3,3-trimethyl-3H-indol-1-ium, assay uses a thiol-containing fluorescent probe to screen for electrophile reactivity and could potentially be used to determine nonspecific reactive compounds. The Tox21 10K compound library was previously screened against a panel of ∼80 cell-based and biochemical assays, including the biochemical MSTI assay. In this study, we compared the MSTI assay activity of the Tox21 10K compounds with their promiscuity and cytotoxicity as reflected by their activities across the Tox21 assay panel to determine: (1) if this assay is predictive of a compound's promiscuity and cytotoxicity and (2) what chemical features create inconsistent results between the MSTI assay activity and promiscuity/cytotoxicity (false negatives and false positives). We found that the MSTI assay can predict a chemical's promiscuity/cytotoxicity with a 0.55 sensitivity and 0.97 specificity. Out of 3,407 unique compounds evaluated, we identified 92 false positive and 227 false negative results. Several structural features such as carboxamides and alkyl halides were found to be apparent in 53% (p = 2.4 × 10-07) and 19% (p = 4.3 × 10-06) of the false positives and negatives, respectively. The results of this analysis will help identify the potential challenges of this high-throughput assay and allow researchers to identify if a compound will be cytotoxic or promiscuous in an efficient manner.

Effect driven prioritization of contaminants in wastewater treatment plants across China: A data mining-based toxicity screening approach

A diversity of contaminants of emerging concern (CECs) are present in wastewater effluent, posing potential threats to receiving waters. It is urgent for a holistic assessment of the occurrence and risk of CECs related to wastewater treatment plants (WWTP) on national and regional scales. A data mining-based risk prioritization method was developed to collect the reported contaminants and their respective concentrations in municipal and industrial WWTPs and their receiving waters across China over the past 20 years. A total of 10,781 chemicals were reported in 8336 publications, of which 1037 contaminants were reported with environmental concentrations. While contaminant categories varied across WWTP types (municipal vs. industrial) and regions, pharmaceuticals and cyclic hydrocarbons were the most studied CECs. Contaminant composition in receiving water was closer to that in municipal than industrial WWTPs. Publications on legacy pesticides and polycyclic aromatic hydrocarbons in WWTP decreased recently compared to the past, while pharmaceuticals and perfluorochemicals have received increasing attention, showing a changing concern over time. Detection frequency, concentration, removal efficiency, and toxicity data were integrated for assessing potential risks and prioritizing CECs on national and regional scales using an environmental health prioritization index (EHPi) approach. Among 666 contaminants in municipal WWTP effluent, trichlorfon and perfluorooctanesulfonic acid were with the highest EHPi scores, while 17ɑ-ethinylestradiol and bisphenol A had the highest EHPi scores among 304 contaminants in industrial WWTPs. The prioritized contaminants varied across regions, suggesting a need for tailoring regional measures of wastewater treatment and control.

Advancing toxicity studies of per- and poly-fluoroalkyl substances (pfass) through machine learning: Models, mechanisms, and future directions

Perfluorinated and perfluoroalkyl substances (PFASs), encompassing a vast array of isomeric chemicals, are recognized as typical emerging contaminants with direct or potential impacts on human health and the ecological environment. With the complex and elusive toxicological profiles of PFASs, machine learning (ML) has been increasingly employed in their toxicity studies due to its proficiency in prediction and data analytics. This integration is poised to become a predominant trend in environmental toxicology, propelled by the swift advancements in computational technology. This review diligently examines the literature to encapsulate the varied objectives of employing ML in the toxicity studies of PFASs: (1) Utilizing ML to establish Quantitative Structure-Activity Relationship (QSAR) models for PFASs with diverse toxicity endpoints, facilitating the targeted toxicity prediction of unidentified PFASs; (2) Investigating and substantiating the Adverse Outcome Pathway (AOP) through the synergy of ML and traditional toxicological methods, with this refining the toxicity assessment framework for PFASs; (3) Dissecting and elucidating the features of established ML models to advance Open Research into the toxicity of PFASs, with a primary focus on determinants and mechanisms. The discourse extends to an in-depth examination of ML studies, segregating findings based on their distinct application trajectories. Given that ML represents a nascent paradigm within PFASs research, this review delineates the collective challenges encountered in the ML-mediated study of PFAS toxicity and proffers strategic guidance for ensuing investigations.

Synthesis and toxicity of monothiooxalamides against human red blood cells, brine shrimp (Artemia salina), and fruit fly (Drosophila melanogaster)

A new family of monothiooxalamides derived from 2-aminobenzimidazole was synthesized, and their structures were confirmed by 1H and 13C one-dimensional and 2D NMR experiments (COSY, HSQC, and HMBC). The antioxidant capacity was evaluated by free radical scavenging assays: 1,1-diphenyl-2-picrylhydrazyl (DPPH•), 2,2'-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) radical cation (ABTS•+), ferric reducing antioxidant power (FRAP), oxygen radical absorbance capacity (ORAC), and the Fe(II) chelating ability. Our work group has previously reported the synthesis and antioxidant activity of monothiooxalamides derived from 2-aminopyridine (I). In this study, the in vitro hemolytic activity of compounds from the 2-aminopyridine (I) and 2-aminobenzimidazole (II) families was evaluated against human red blood cells (RBCs). The concentration at which monothiooxalamides showed no hemolytic activity was chosen to assess their ability to inhibit free radical-induced membrane damage in human RBCs, acute toxicity in brine shrimp, and in vivo toxicity against Drosophila melanogaster. Compounds with morpholine fragments (1g, 1h, 2g, and 2h) showed time- and concentration-dependent protective effects against radical-induced oxidative hemolysis. Moreover, they had the lowest acute toxicity in the brine shrimp lethality assay and a significant increase in chelating activity compared with the other molecules. In particular, monothiooxalamide 2g showed lower toxicity and can be considered for further biological screening and application trials.

Construction of an in vitro simulated one compartment extravascular administration model and its comparison with classic in vitro administration model in copper chloride induced HepG2 cell death

In this study, we designed an in vitro administration device based on compartment model theory and utilized it to construct an in vitro simulated one compartment extravascular administration model of copper chloride. Within the Cmax range of 3.91-1000.00 μM, the measured concentration-time curves of the simulated one compartment extravascular administration model almost coincide with the corresponding theoretical curves. The measured values of toxicokinetic parameters, including ke, T1/2, ka, T1/2a, Tmax, Cmax, CL, and AUC0-∞ are close to the corresponding theoretical values. The fitting coefficients are >0.9990. In simulated one compartment extravascular administration and classic in vitro administration, copper chloride dose-dependently induced HepG2 cell death. When Cmax/administration concentration is equal, classic in vitro administration induces a higher cell death rate than simulated one compartment extravascular administration. However, there is no significant difference in inducing cell death between the two administration models when area under the curve is equal. In conclusion, the device designed in this study can be used to in vitro simulate one compartment extravascular administration, making in vitro toxicity testing more similar to in vivo scenarios. There are differences in copper chloride induced HepG2 cell death between simulated one compartment extravascular administration and classic in vitro administration.

Utility of life stage-specific chemical risk assessments based on New Approach Methodologies (NAMs)

The safety assessments for chemicals targeted for use or expected to be exposed to specific life stages, including infancy, childhood, pregnancy and lactation, and geriatrics, need to account for extrapolation of data from healthy adults to these populations to assess their human health risk. However, often adequate and relevant toxicity or pharmacokinetic (PK) data of chemicals in specific life stages are not available. For such chemicals, New Approach Methodologies (NAMs), such as physiologically based pharmacokinetic (PBPK) modeling, biologically based dose response (BBDR) modeling, in vitro to in vivo extrapolation (IVIVE), etc. can be used to understand the variability of exposure and effects of chemicals in specific life stages and assess their associated risk. A life stage specific PBPK model incorporates the physiological and biochemical changes associated with each life stage and simulates their impact on the absorption, distribution, metabolism, and elimination (ADME) of these chemicals. In our review, we summarize the parameterization of life stage models based on New Approach Methodologies (NAMs) and discuss case studies that highlight the utility of a life stage based PBPK modeling for risk assessment. In addition, we discuss the utility of artificial intelligence (AI)/machine learning (ML) and other computational models, such as those based on in vitro data, as tools for estimation of relevant physiological or physicochemical parameters and selection of model. We also discuss existing gaps in the available toxicological datasets and current challenges that need to be overcome to expand the utility of NAMs for life stage-specific chemical risk assessment.

Gene-environment interactions within a precision environmental health framework

Understanding the complex interplay of genetic and environmental factors in disease etiology and the role of gene-environment interactions (GEIs) across human development stages is important. We review the state of GEI research, including challenges in measuring environmental factors and advantages of GEI analysis in understanding disease mechanisms. We discuss the evolution of GEI studies from candidate gene-environment studies to genome-wide interaction studies (GWISs) and the role of multi-omics in mediating GEI effects. We review advancements in GEI analysis methods and the importance of large-scale datasets. We also address the translation of GEI findings into precision environmental health (PEH), showcasing real-world applications in healthcare and disease prevention. Additionally, we highlight societal considerations in GEI research, including environmental justice, the return of results to participants, and data privacy. Overall, we underscore the significance of GEI for disease prediction and prevention and advocate for integrating the exposome into PEH omics studies.

Combined Exposures and Mixtures Research: An Enduring NIEHS Priority

BACKGROUND

The National Institute of Environmental Health Sciences (NIEHS) continues to prioritize research to better understand the health effects resulting from exposure to mixtures of chemical and nonchemical stressors. Mixtures research activities over the last decade were informed by expert input during the development and deliberations of the 2011 NIEHS Workshop "Advancing Research on Mixtures: New Perspectives and Approaches for Predicting Adverse Human Health Effects." NIEHS mixtures research efforts since then have focused on key themes including a) prioritizing mixtures for study, b) translating mixtures data from in vitro and in vivo studies, c) developing cross-disciplinary collaborations, d) informing component-based and whole-mixture assessment approaches, e) developing sufficient similarity methods to compare across complex mixtures, f) using systems-based approaches to evaluate mixtures, and g) focusing on management and integration of mixtures-related data.

OBJECTIVES

We aimed to describe NIEHS driven research on mixtures and combined exposures over the last decade and present areas for future attention.

RESULTS

Intramural and extramural mixtures research projects have incorporated a diverse array of chemicals (e.g., polycyclic aromatic hydrocarbons, botanicals, personal care products, wildfire emissions) and nonchemical stressors (e.g., socioeconomic factors, social adversity) and have focused on many diseases (e.g., breast cancer, atherosclerosis, immune disruption). We have made significant progress in certain areas, such as developing statistical methods for evaluating multiple chemical associations in epidemiology and building translational mixtures projects that include both in vitro and in vivo models.

DISCUSSION

Moving forward, additional work is needed to improve mixtures data integration, elucidate interactions between chemical and nonchemical stressors, and resolve the geospatial and temporal nature of mixture exposures. Continued mixtures research will be critical to informing cumulative impact assessments and addressing complex challenges, such as environmental justice and climate change. https://doi.org/10.1289/EHP14340.

High-Efficiency Effect-Directed Analysis Leveraging Five High Level Advancements: A Critical Review

Organic contaminants are ubiquitous in the environment, with mounting evidence unequivocally connecting them to aquatic toxicity, illness, and increased mortality, underscoring their substantial impacts on ecological security and environmental health. The intricate composition of sample mixtures and uncertain physicochemical features of potential toxic substances pose challenges to identify key toxicants in environmental samples. Effect-directed analysis (EDA), establishing a connection between key toxicants found in environmental samples and associated hazards, enables the identification of toxicants that can streamline research efforts and inform management action. Nevertheless, the advancement of EDA is constrained by the following factors: inadequate extraction and fractionation of environmental samples, limited bioassay endpoints and unknown linkage to higher order impacts, limited coverage of chemical analysis (i.e., high-resolution mass spectrometry, HRMS), and lacking effective linkage between bioassays and chemical analysis. This review proposes five key advancements to enhance the efficiency of EDA in addressing these challenges: (1) multiple adsorbents for comprehensive coverage of chemical extraction, (2) high-resolution microfractionation and multidimensional fractionation for refined fractionation, (3) robust in vivo/vitro bioassays and omics, (4) high-performance configurations for HRMS analysis, and (5) chemical-, data-, and knowledge-driven approaches for streamlined toxicant identification and validation. We envision that future EDA will integrate big data and artificial intelligence based on the development of quantitative omics, cutting-edge multidimensional microfractionation, and ultraperformance MS to identify environmental hazard factors, serving for broader environmental governance.

Quantum-to-Classical Neural Network Transfer Learning Applied to Drug Toxicity Prediction

Toxicity is a roadblock that prevents an inordinate number of drugs from being used in potentially life-saving applications. Deep learning provides a promising solution to finding ideal drug candidates; however, the vastness of chemical space coupled with the underlying O ( n 3 ) matrix multiplication means these efforts quickly become computationally demanding. To remedy this, we present a hybrid quantum-classical neural network for predicting drug toxicity utilizing a quantum circuit design that mimics classical neural behavior by explicitly calculating matrix products with complexity O ( n 2 ) . Leveraging the Hadamard test for efficient inner product estimation rather than the conventionally used swap test, we reduce the number of qubits by half and remove the need for quantum phase estimation. Directly computing matrix products quantum mechanically allows for learnable weights to be transferred from a quantum to a classical device for further training. We apply our framework to the Tox21 data set and show that it achieves commensurate predictive accuracy to the model's fully classical O ( n 3 ) analogue. Additionally, we demonstrate that the model continues to learn, without disruption, once transferred to a fully classical architecture. We believe that combining the quantum advantage of reduced complexity and the classical advantage of noise-free calculation will pave the way for more scalable machine learning models.

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

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

Pluripotent stem cells for target organ developmental toxicity testing

Prenatal developmental toxicity research focuses on understanding the potential adverse effects of environmental agents, drugs, and chemicals on the development of embryos and fetuses. Traditional methods involve animal testing, but ethical concerns and the need for human-relevant models have prompted the exploration of alternatives. Pluripotent stem cells (PSCs) are versatile cells with the unique ability to differentiate into any cell type, serving as a foundational tool for studying human development. Two-dimensional (2D) PSC models are often chosen for their ease of use and reproducibility for high-throughput screening. However, they lack the complexity of an in vivo environment. Alternatively, three-dimensional (3D) PSC models, such as organoids, offer tissue architecture and intercellular communication more reminiscent of in vivo conditions. However, they are complicated to produce and analyze, usually requiring advanced and expensive techniques. This review discusses recent advances in the use of human PSCs differentiated into brain and heart lineages and emerging tools and methods that can be combined with PSCs to help address important scientific questions in the area of developmental toxicology. These advancements and new approach methods align with the push for more relevant and predictive developmental toxicity assessment, combining innovative techniques with organoid models to advance regulatory decision-making.

How neurobehavior and brain development in alternative whole-organism models can contribute to prediction of developmental neurotoxicity

Developmental neurotoxicity (DNT) is not routinely evaluated in chemical risk assessment because current test paradigms for DNT require the use of mammalian models which are ethically controversial, expensive, and resource demanding. Consequently, efforts have focused on revolutionizing DNT testing through affordable novel alternative methods for risk assessment. The goal is to develop a DNT in vitro test battery amenable to high-throughput screening (HTS). Currently, the DNT in vitro test battery consists primarily of human cell-based assays because of their immediate relevance to human health. However, such cell-based assays alone are unable to capture the complexity of a developing nervous system. Whole organismal systems that qualify as 3 R (Replace, Reduce and Refine) models are urgently needed to complement cell-based DNT testing. These models can provide the necessary organismal context and be used to explore the impact of chemicals on brain function by linking molecular and/or cellular changes to behavioural readouts. The nematode Caenorhabditis elegans, the planarian Dugesia japonica, and embryos of the zebrafish Danio rerio are all suited to low-cost HTS and each has unique strengths for DNT testing. Here, we review the strengths and the complementarity of these organisms in a novel, integrative context and highlight how they can augment current cell-based assays for more comprehensive and robust DNT screening of chemicals. Considering the limitations of all in vitro test systems, we discuss how a smart combinatory use of these systems will contribute to a better human relevant risk assessment of chemicals that considers the complexity of the developing brain.

Comparing Transcriptomic Points of Departure to Apical Effect Concentrations For Larval Fathead Minnow Exposed to Chemicals with Four Different Modes Of Action

It is postulated that below a transcriptomic-based point of departure, adverse effects are unlikely to occur, thereby providing a chemical concentration to use in screening level hazard assessment. The present study extends previous work describing a high-throughput fathead minnow assay that can provide full transcriptomic data after exposure to a test chemical. One-day post-hatch fathead minnows were exposed to ten concentrations of three representatives of four chemical modes of action: organophosphates, ecdysone receptor agonists, plant photosystem II inhibitors, and estrogen receptor agonists for 24 h. Concentration response modeling was performed on whole body gene expression data from each exposure, using measured chemical concentrations when available. Transcriptomic points of departure in larval fathead minnow were lower than apical effect concentrations across fish species but not always lower than toxic effect concentrations in other aquatic taxa like crustaceans and insects. The point of departure was highly dependent on measured chemical concentration which were often lower than the nominal concentration. Differentially expressed genes between chemicals within modes of action were compared and often showed statistically significant overlap. In addition, reproducibility between identical exposures using a positive control chemical (CuSO4) and variability associated with the transcriptomic point of departure using in silico sampling were considered. Results extend a transcriptomic-compatible fathead minnow high-throughput assay for possible use in ecological hazard screening.

Gap-Δenergy, a New Metric of the Bond Energy State, Assisting to Predict Molecular Toxicity

Molecular toxicity is a critical feature of drug development. It is thus very important to develop computational models to evaluate the toxicity of small molecules. The accuracy of toxicity prediction largely depends on the quality of molecular representation; however, current methods for this purpose do not address this issue well. Here, we introduce a new metric, gap-Δenergy, which is designed to quantify the intermolecular bond energy difference with atom distance. We next find significant variations in the gap-Δenergy distribution among different types of molecules. Moreover, we show that this metric is able to distinguish the toxic small molecules. We collected data sets of toxic and exogenous small molecules and presented a novel index, namely, global toxicity, to evaluate the overall toxicity of molecules. Based on molecular descriptors and the proposed gap-Δenergy metric, we further constructed machine learning models that were trained with 7816 small molecules. The XGBoost-based model achieved the best performance with an AUC score of 0.965 and an F1 score of 0.849 on the test set (1954 small molecules), which outperformed the model that did not use gap-Δenergy features, with a sensitivity score increase of 3.2%.

Expanding Predictive Capacities in Toxicology: Insights from Hackathon-Enhanced Data and Model Aggregation

In the realm of predictive toxicology for small molecules, the applicability domain of QSAR models is often limited by the coverage of the chemical space in the training set. Consequently, classical models fail to provide reliable predictions for wide classes of molecules. However, the emergence of innovative data collection methods such as intensive hackathons have promise to quickly expand the available chemical space for model construction. Combined with algorithmic refinement methods, these tools can address the challenges of toxicity prediction, enhancing both the robustness and applicability of the corresponding models. This study aimed to investigate the roles of gradient boosting and strategic data aggregation in enhancing the predictivity ability of models for the toxicity of small organic molecules. We focused on evaluating the impact of incorporating fragment features and expanding the chemical space, facilitated by a comprehensive dataset procured in an open hackathon. We used gradient boosting techniques, accounting for critical features such as the structural fragments or functional groups often associated with manifestations of toxicity.

Screening androgen receptor agonists of fish species using machine learning and molecular model in NORMAN water-relevant list

Androgen receptor (AR) agonists have strong endocrine disrupting effects in fish. Most studies mainly investigate AR binding capacity using human AR in vitro. However, there is still few methods to rapidly predict AR agonists in aquatic organisms. This study aimed to screen AR agonists of fish species using machine learning and molecular models in water-relevant list from NORMAN, a network of reference laboratories for monitoring contaminants of emerging concern in the environment. In this study, machine learning approaches (e.g., Deep Forest (DF)), Random Forests and artificial neural networks) were applied to predict AR agonists. Zebrafish, fathead minnow, mosquitofish, medaka fish and grass carp are all important aquatic model organisms widely used to evaluate the toxicity of new pollutants, and the molecular models of ARs from these five fish species were constructed to further screen AR agonists using AlphaFold2. The DF method showed the best performances with 0.99 accuracy, 0.97 sensitivity and 1 precision. The Asn705, Gln711, Arg752, and Thr877 residues in human AR and the corresponding sites in ARs from the five fish species were responsible for agonist binding. Overall, 245 substances were predicted as suspect AR agonists in the five fish species, including, certain glucocorticoids, cholesterol metabolites, and cardiovascular drugs in the NORMAN list. Using machine learning and molecular modeling hybrid methods rapidly and accurately screened AR agonists in fish species, and helping evaluate their ecological risk in fish populations.

Inhibition of Metalloproteinases Extends Longevity and Function of In Vitro Human iPSC-Derived Skeletal Muscle

In vitro culture longevity has long been a concern for disease modeling and drug testing when using contractable cells. The dynamic nature of certain cells, such as skeletal muscle, contributes to cell surface release, which limits the system's ability to conduct long-term studies. This study hypothesized that regulating the extracellular matrix (ECM) dynamics should be able to prolong cell attachment on a culture surface. Human induced pluripotent stem cell (iPSC)-derived skeletal muscle (SKM) culture was utilized to test this hypothesis due to its forceful contractions in mature muscle culture, which can cause cell detachment. By specifically inhibiting matrix metalloproteinases (MMPs) that work to digest components of the ECM, it was shown that the SKM culture remained adhered for longer periods of time, up to 80 days. Functional testing of myofibers indicated that cells treated with the MMP inhibitors, tempol, and doxycycline, displayed a significantly reduced fatigue index, although the fidelity was not affected, while those treated with the MMP inducer, PMA, indicated a premature detachment and increased fatigue index. The MMP-modulating activity by the inhibitors and inducer was further validated by gel zymography analysis, where the MMP inhibitor showed minimally active MMPs, while the inducer-treated cells indicated high MMP activity. These data support the hypotheses that regulating the ECM dynamics can help maximize in vitro myotube longevity. This proof-of-principle strategy would benefit the modeling of diseases that require a long time to develop and the evaluation of chronic effects of potential therapeutics.

Reflected generalized concentration addition and Bayesian hierarchical models to improve chemical mixture prediction

Environmental toxicants overwhelmingly occur together as mixtures. The variety of possible chemical interactions makes it difficult to predict the danger of the mixture. In this work, we propose the novel Reflected Generalized Concentration Addition (RGCA), a piece-wise, geometric technique for sigmoidal dose-responsed inverse functions that extends the use of generalized concentration addition (GCA) for 3+ parameter models. Since experimental tests of all relevant mixtures is costly and intractable, we rely only on the individual chemical dose responses. Additionally, RGCA enhances the classical two-step model for the cumulative effects of mixtures, which assumes a combination of GCA and independent action (IA). We explore how various clustering methods can dramatically improve predictions. We compare our technique to the IA, CA, and GCA models and show in a simulation study that the two-step approach performs well under a variety of true models. We then apply our method to a challenging data set of individual chemical and mixture responses where the target is an androgen receptor (Tox21 AR-luc). Our results show significantly improved predictions for larger mixtures. Our work complements ongoing efforts to predict environmental exposure to various chemicals and offers a starting point for combining different exposure predictions to quantify a total risk to health.

Elucidating the effects of pure glyphosate and a commercial formulation on early life stages of zebrafish using a complete biomarker approach: All-or-nothing!

The search for new methods in the toxicology field has increased the use of early life stages of zebrafish (Danio rerio) as a versatile organism model. Here, we use early stages of zebrafish to evaluate glyphosate as pure active ingredient and within a commercial formulation in terms of oxidative stress. Biomarkers involved in the oxidative status were evaluated along with other markers of neurotoxicity, genotoxicity, cytotoxicity, energy balance and motor performance, and the selected tools were evaluated by its sensitivity in determining early-warning events. Zebrafish embryos exposed to glyphosate active ingredient and glyphosate-based formulation were under oxidative stress, but only the commercial formulation delayed the embryogenesis, affected the cholinergic neurotransmission and induced DNA damage. Both altered the motor performance of larvae at very low concentrations, becoming larvae hypoactive. The energy balance was also impaired, as embryos under oxidative stress had lower lipids reserves. Although data suggest that glyphosate-based formulation has higher toxicity than the active ingredient itself, the most sensitive biomarkers detected early-warning effects at very low concentrations of the active ingredient. Biochemical biomarkers of defense system and oxidative damage were the most sensitive tools, detecting pro-oxidant responses at very low concentrations, along with markers of motor performance that showed high sensitivity and high throughput, suitable for detecting early effects linked to neurotoxicity. Alterations on morphology during embryogenesis showed the lowest sensitivity, thus morphological alterations appeared after several alterations at biochemical levels. Tools evaluating DNA damage and cell proliferation showed mid-sensitivity, but low throughput, thus they could be used as complementary markers.

Pyruvate dehydrogenase is a potential mitochondrial off-target for gentamicin based on in silico predictions and in vitro inhibition studies

During the drug development process, organ toxicity leads to an estimated failure of one-third of novel chemical entities. Drug-induced toxicity is increasingly associated with mitochondrial dysfunction, but identifying the underlying molecular mechanisms remains a challenge. Computational modeling techniques have proven to be a good tool in searching for drug off-targets. Here, we aimed to identify mitochondrial off-targets of the nephrotoxic drugs tenofovir and gentamicin using different in silico approaches (KRIPO, ProBis and PDID). Dihydroorotate dehydrogenase (DHODH) and pyruvate dehydrogenase (PDH) were predicted as potential novel off-target sites for tenofovir and gentamicin, respectively. The predicted targets were evaluated in vitro, using (colorimetric) enzymatic activity measurements. Tenofovir did not inhibit DHODH activity, while gentamicin potently reduced PDH activity. In conclusion, the use of in silico methods appeared a valuable approach in predicting PDH as a mitochondrial off-target of gentamicin. Further research is required to investigate the contribution of PDH inhibition to overall renal toxicity of gentamicin.

A novel multi-omics data analysis of dose-dependent and temporal changes in regulatory pathways due to chemical perturbation: a case study on caffeine

Comprehensive analysis of multi-omics data can reveal alterations in regulatory pathways induced by cellular exposure to chemicals by characterizing biological processes at the molecular level. Data-driven omics analysis, conducted in a dose-dependent or dynamic manner, can facilitate comprehending toxicity mechanisms. This study introduces a novel multi-omics data analysis designed to concurrently examine dose-dependent and temporal patterns of cellular responses to chemical perturbations. This analysis, encompassing preliminary exploration, pattern deconstruction, and network reconstruction of multi-omics data, provides a comprehensive perspective on the dynamic behaviors of cells exposed to varying levels of chemical stimuli. Importantly, this analysis is adaptable to any number of omics layers, including site-specific phosphoproteomics. We implemented this analysis on multi-omics data obtained from HepG2 cells exposed to a range of caffeine doses over varying durations and identified six response patterns, along with their associated biomolecules and pathways. Our study demonstrates the effectiveness of the proposed multi-omics data analysis in capturing multidimensional patterns of cellular response to chemical perturbation, enhancing understanding of pathway regulation for chemical risk assessment.

Bio-based surfactants derived from pectin

Surfactants derived from renewable resources and synthesized using renewable feedstock and sustainable methods have become a major research focus over the past decade in the surfactant industry. This research presents an approach for rapidly converting readily available polysaccharides, like pectin derived from fruit waste, into safely biodegradable surface-active polymers. Commercially available pectin was modified with n-alkyl amines having different alkyl chain lengths using potassium carbonate as a catalyst. The effect of pectin molecular weight, alkyl chain length and degree of substitution (DS) on surface-active properties of the modified pectin derivatives was studied. Surface tension decreased slightly with lowering molecular weight, whereas interfacial tension decreased dramatically. Cytotoxicity evaluations using human dermal fibroblast, HepG2 and Jurkat cells demonstrated that these polysaccharide-based surfactants exhibit lower cytotoxicity compared to the conventional surfactants such as octyl phenol ethoxylates (i.e., Triton™ X-100), and therefore are more environmentally friendly. Biodegradation studies show that all modified pectins are "ultimately biodegradable" except for Pectin-amide C8 (1:10).

Recent Advances in Drug Discovery Toxicology

Major advances in scientific discovery and insights that stem from the development and use of new techniques and models can bring remarkable progress to conventional toxicology. Although animal testing is still considered as the "gold standard" in traditional toxicity testing, there is a necessity for shift from animal testing to alternative methods regarding the drug safety testing owing to the emerging state-of-art techniques and the proposal of 3Rs (replace, reduce, and refine) towards animal welfare. This review describes some recent research methods in drug discovery toxicology, including in vitro cell and organ-on-a-chip, imaging systems, model organisms (C. elegans, Danio rerio, and Drosophila melanogaster), and toxicogenomics in modern toxicology testing.

Advancing Computational Toxicology by Interpretable Machine Learning

Chemical toxicity evaluations for drugs, consumer products, and environmental chemicals have a critical impact on human health. Traditional animal models to evaluate chemical toxicity are expensive, time-consuming, and often fail to detect toxicants in humans. Computational toxicology is a promising alternative approach that utilizes machine learning (ML) and deep learning (DL) techniques to predict the toxicity potentials of chemicals. Although the applications of ML- and DL-based computational models in chemical toxicity predictions are attractive, many toxicity models are "black boxes" in nature and difficult to interpret by toxicologists, which hampers the chemical risk assessments using these models. The recent progress of interpretable ML (IML) in the computer science field meets this urgent need to unveil the underlying toxicity mechanisms and elucidate the domain knowledge of toxicity models. In this review, we focused on the applications of IML in computational toxicology, including toxicity feature data, model interpretation methods, use of knowledge base frameworks in IML development, and recent applications. The challenges and future directions of IML modeling in toxicology are also discussed. We hope this review can encourage efforts in developing interpretable models with new IML algorithms that can assist new chemical assessments by illustrating toxicity mechanisms in humans.

Occurrence of emerging contaminants in pet hair and indoor air: integrative health risk assessment using multiple ToxCast endpoints

Indoor exposome is a growing concern, including a mixture of legacy and emerging contaminants. Recent studies suggest that indoor pollutants may accumulate in pet hair, a part of indoor exposome, increasing health risks to pet owners; however, the source and hazards of pollutants associated with pet hair are largely unknown. Here, we found that hydrophobic pollutants often had higher indoor concentrations than hydrophilic ones, and polycyclic aromatic hydrocarbons (PAHs) were the most dominant fractions (61.1%) in indoor air exposome while polycyclic musks (PCMs) had the highest concentrations among all contaminant classes in indoor dust (1559 ± 1598 ng g-1 dw) and pet hair (2831 ± 2458 ng g-1 dw). The levels of hygiene-related contaminants (PCMs, current-use pesticides (CUPs), and antibiotics) were higher in pet hair than dust due to direct contact during applications. Health risk assessment using toxicity thresholds from high-throughput screening data showed that human health risks from the five classes of indoor contaminants (PAHs, PCMs, organophosphate esters, CUPs, and antibiotics) via inhalation, ingestion, and dermal contact were within acceptable limits, but the children may be exposed to a higher risk than the adults. The thresholds estimated from the ToxCast data using endpoint sensitivity distribution make the exposome risk assessment feasible in the absence of benchmarks, which is beneficial for including a mixture of emerging pollutants in risk assessment.

Reevaluation of 2,3,7,8-Tetrachlorodibenzo-p-Dioxin Equivalency Factors for Dioxin-Like Polychlorinated Dibenzo-p-Dioxins, Polychlorinated Dibenzofurans, and Polychlorinated Biphenyls for Fishes

An expert meeting was organized by the World Health Organization (WHO) in 1997 to streamline assessments of risk posed by mixtures of dioxin-like chemicals (DLCs) through development of 2,3,7,8-tetrachlorodibenzo-p-dioxin (2,3,7,8-TCDD) equivalency factors (TEFs) for mammals, birds, and fishes. No reevaluation has been performed for fish TEFs. Therefore, the objective of the present study was to reevaluate the TEFs for fishes based on an updated database of relative potencies (RePs) for DLCs. Selection criteria consistent with the WHO meeting resulted in 53 RePs across 14 species of fish ultimately being considered. Of these RePs, 70% were not available at the time of the WHO meeting. These RePs were used to develop updated TEFs for fishes based on a similar decision process as used at the WHO meeting. The updated TEF for 16 DLCs was greater than the WHO TEF, but only four differed by more than an order of magnitude. Measured concentrations of DLCs in four environmental samples were used to compare 2,3,7,8-TCDD equivalents (TEQs) calculated using the WHO TEFs relative to the updated TEFs. The TEQs for none of these environmental samples differed by more than an order of magnitude. Therefore, present knowledge supports that the WHO TEFs are suitable potency estimates for fishes. However, the updated TEFs pull from a larger database with a greater breadth of data and as a result offer greater confidence relative to the WHO TEFs. Risk assessors will have different criteria in the selection of TEFs, and the updated TEFs are not meant to immediately replace the formal WHO TEFs; but those who value a larger database and increased confidence in TEQs could consider using the updated TEFs. Environ Toxicol Chem 2023;42:2215-2228. © 2023 Wiley Periodicals LLC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

A vision for safer food contact materials: Public health concerns as drivers for improved testing

Food contact materials (FCMs) and food contact articles are ubiquitous in today's globalized food system. Chemicals migrate from FCMs into foodstuffs, so called food contact chemicals (FCCs), but current regulatory requirements do not sufficiently protect public health from hazardous FCCs because only individual substances used to make FCMs are tested and mostly only for genotoxicity while endocrine disruption and other hazard properties are disregarded. Indeed, FCMs are a known source of a wide range of hazardous chemicals, and they likely contribute to highly prevalent non-communicable diseases. FCMs can also include non-intentionally added substances (NIAS), which often are unknown and therefore not subject to risk assessment. To address these important shortcomings, we outline how the safety of FCMs may be improved by (1) testing the overall migrate, including (unknown) NIAS, of finished food contact articles, and (2) expanding toxicological testing beyond genotoxicity to multiple endpoints associated with non-communicable diseases relevant to human health. To identify mechanistic endpoints for testing, we group chronic health outcomes associated with chemical exposure into Six Clusters of Disease (SCOD) and we propose that finished food contact articles should be tested for their impacts on these SCOD. Research should focus on developing robust, relevant, and sensitive in-vitro assays based on mechanistic information linked to the SCOD, e.g., through Adverse Outcome Pathways (AOPs) or Key Characteristics of Toxicants. Implementing this vision will improve prevention of chronic diseases that are associated with hazardous chemical exposures, including from FCMs.

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

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

Recent Advances in Toxicity Prediction: Applications of Deep Graph Learning

The development of new drugs is time-consuming and expensive, and as such, accurately predicting the potential toxicity of a drug candidate is crucial in ensuring its safety and efficacy. Recently, deep graph learning has become prevalent in this field due to its computational power and cost efficiency. Many novel deep graph learning methods aid toxicity prediction and further prompt drug development. This review aims to connect fundamental knowledge with burgeoning deep graph learning methods. We first summarize the essential components of deep graph learning models for toxicity prediction, including molecular descriptors, molecular representations, evaluation metrics, validation methods, and data sets. Furthermore, based on various graph-related representations of molecules, we introduce several representative studies and methods for toxicity prediction from the perspective of GNN architectures and graph pretrained models. Compared to other types of models, deep graph models not only advance in higher accuracy and efficiency but also provide more intuitive insights, which is significant in the development of model interpretation and generalization ability. The graph pretrained models are emerging as they can extract prominent features from large-scale unlabeled molecular graph data and improve the performance of downstream toxicity prediction tasks. We hope this survey can serve as a handbook for individuals interested in exploring deep graph learning for toxicity prediction.

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

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

Environmental and health impacts of functional graphenic materials and their ultrasonically altered products

Graphenic materials have excited the scientific community due to their exciting mechanical, thermal, and optoelectronic properties for a potential range of applications. Graphene and graphene derivatives have demonstrated application in areas stretching from composites to medicine; however, the environmental and health impacts of these materials have not been sufficiently characterized. Graphene oxide (GO) is one of the most widely used graphenic derivatives due to a relatively easy and scalable synthesis, and the ability to tailor the oxygen containing functional groups through further chemical modification. In this paper, ecological and health impacts of fresh and ultrasonically altered functional graphenic materials (FGMs) were investigated. Model organisms, specifically Escherichia coli, Bacillus subtilis, and Caenorhabditis elegans, were used to assess the consequences of environmental exposure to fresh and ultrasonically altered FGMs. FGMs were selected to evaluate the environmental effects of aggregation state, degree of oxidation, charge, and ultrasonication. The major findings indicate that bacterial cell viability, nematode fertility, and nematode movement were largely unaffected, suggesting that a wide variety of FGMs may not pose significant health and environmental risks.

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.

Evaluating pharmaceuticals and other organic contaminants in the Lac du Flambeau Chain of Lakes using risk-based screening techniques

In an investigation of pharmaceutical contamination in the Lac du Flambeau Chain of Lakes (hereafter referred to as "the Chain"), few contaminants were detected; only eight pharmaceuticals and one pesticide were identified among the 110 pharmaceuticals and other organic contaminants monitored in surface water samples. This study, conducted in cooperation with the Lac du Flambeau Tribe's Water Resource Program, investigated these organic contaminants and potential biological effects in channels connecting lakes throughout the Chain, including the Moss Lake Outlet site, adjacent to the wastewater treatment plant lagoon. Of the 6 sites monitored and 24 samples analyzed, sample concentrations and contaminant detection frequencies were greatest at the Moss Lake Outlet site; however, the concentrations and detection frequencies of this study were comparable to other pharmaceutical investigations in basins with similar characteristics. Because established water-quality benchmarks do not exist for the pharmaceuticals detected in this study, alternative screening-level water-quality benchmarks, developed using two U.S. Environmental Protection Agency toxicological resources (ToxCast database and ECOTOX knowledgebase), were used to estimate potential biological effects associated with the observed contaminant concentrations. Two contaminants (caffeine and thiabendazole) exceeded the prioritization threshold according to ToxCast alternative benchmarks, and four contaminants (acetaminophen, atrazine, caffeine, and carbamazepine) exceeded the prioritization threshold according to ECOTOX alternative benchmarks. Atrazine, an herbicide, was the most frequently detected contaminant (79% of samples), and it exhibited the strongest potential for biological effects due to its high estimated potency. Insufficient toxicological information within ToxCast and ECOTOX for gabapentin and methocarbamol (which had the two greatest concentrations in this study) precluded alternative benchmark development. This data gap presents unknown potential environmental impacts. Future research examining the biological effects elicited by these two contaminants as well as the others detected in this study would further elucidate the ecological relevance of the water chemistry results generated though this investigation.

Evaluation of Complex Mixture Toxicity in the Milwaukee Estuary (WI, USA) Using Whole-Mixture and Component-Based Evaluation Methods

Anthropogenic activities introduce complex mixtures into aquatic environments, necessitating mixture toxicity evaluation during risk assessment. There are many alternative approaches that can be used to complement traditional techniques for mixture assessment. Our study aimed to demonstrate how these approaches could be employed for mixture evaluation in a target watershed. Evaluations were carried out over 2 years (2017-2018) across 8-11 study sites in the Milwaukee Estuary (WI, USA). Whole mixtures were evaluated on a site-specific basis by deploying caged fathead minnows (Pimephales promelas) alongside composite samplers for 96 h and characterizing chemical composition, in vitro bioactivity of collected water samples, and in vivo effects in whole organisms. Chemicals were grouped based on structure/mode of action, bioactivity, and pharmacological activity. Priority chemicals and mixtures were identified based on their relative contributions to estimated mixture pressure (based on cumulative toxic units) and via predictive assessments (random forest regression). Whole mixture assessments identified target sites for further evaluation including two sites targeted for industrial/urban chemical mixture effects assessment; three target sites for pharmaceutical mixture effects assessment; three target sites for further mixture characterization; and three low-priority sites. Analyses identified 14 mixtures and 16 chemicals that significantly contributed to cumulative effects, representing high or medium priority targets for further ecotoxicological evaluation, monitoring, or regulatory assessment. Overall, our study represents an important complement to single-chemical prioritizations, providing a comprehensive evaluation of the cumulative effects of mixtures detected in a target watershed. Furthermore, it demonstrates how different tools and techniques can be used to identify diverse facets of mixture risk and highlights strategies that can be considered in future complex mixture assessments. Environ Toxicol Chem 2023;42:1229-1256. © 2023 SETAC.

The Precision Toxicology Initiative

The goal of PrecisionTox is to overcome conceptual barriers to replacing traditional mammalian chemical safety testing by accelerating the discovery of evolutionarily conserved toxicity pathways that are shared by descent among humans and more distantly related animals. An international consortium is systematically testing the toxicological effects of a diverse set of chemicals on a suite of five model species comprising fruit flies, nematodes, water fleas, and embryos of clawed frogs and zebrafish along with human cell lines. Multiple forms of omics and comparative toxicology data are integrated to map the evolutionary origins of biomolecular interactions, which are predictive of adverse health effects, to major branches of the animal phylogeny. These conserved elements of adverse outcome pathways (AOPs) and their biomarkers are expect to provide mechanistic insight useful for regulating groups of chemicals based on their shared modes of action. PrecisionTox also aims to quantify risk variation within populations by recognizing susceptibility as a heritable trait that varies with genetic diversity. This initiative incorporates legal experts and collaborates with risk managers to address specific needs within European chemicals legislation, including the uptake of new approach methodologies (NAMs) for setting precise regulatory limits on toxic chemicals.