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

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.

Radiation Adverse Outcome pathways (AOPs): examining priority questions from an international horizon-style exercise

PURPOSE

The Organisation for Economic Co-operation and Development (OECD) Adverse Outcome Pathway (AOP) Development Programme is being explored in the radiation field, as an overarching framework to identify and prioritize research needs that best support strengthening of radiation risk assessment and risk management strategies. To advance the use of AOPs, an international horizon-style exercise (HSE) was initiated through the Radiation/Chemical AOP Joint Topical Group (JTG) formed by the OECD Nuclear Energy Agency (NEA) High-Level Group on Low Dose Research (HLG-LDR) under the auspices of the Committee on Radiological Protection and Public Health (CRPPH). The intent of the HSE was to identify key research questions for consideration in AOP development that would help to reduce uncertainties in estimating the health risks following exposures to low dose and low dose-rate ionizing radiation. The HSE was conducted in several phases involving the solicitation of relevant questions, a collaborative review of open-ended candidate questions and an elimination exercise that led to the selection of 25 highest priority questions for the stated purpose. These questions were further ranked by over 100 respondents through an international survey. This final set of questions was judged to provide insights into how the OECD's AOP approach can be put into practice to meet the needs of hazard and risk assessors, regulators, and researchers. This paper examines the 25 priority questions in the context of hazard/risk assessment framework for ionizing radiation.

CONCLUSION

By addressing the 25 priority questions, it is anticipated that constructed AOPs will have a high level of specificity, making them valuable tools for simplifying and prioritizing complex biological processes for use in developing revised radiation hazard and risk assessment strategies.

A workflow to practically apply true dose considerations to in vitro testing for next generation risk assessment

With the move away from safety testing assessment based on data generated in experimental animals the concept of Next Generation Risk Assessment (NGRA) has arisen which instead uses data from in silico and in vitro models. A key uncertainty in risk assessment is the actual dose of test chemical at the target site, and therefore surrogate dose metrics, such as nominal concentration in test media are used to describe in vitro effect (or no-effect) doses. The reliability and accuracy of the risk assessment therefore depends largely on our ability to understand and characterise the relationship between the dose metrics used and the actual biologically effective dose at the target site. The objective of this publication is to use 40 case study chemicals to illustrate how in vitro dose considerations can be applied to characterise the "true dose" and build confidence in the understanding of the biologically effective dose in in vitro test systems for the determination e.g. points of departure (PoDs) for NGRA. We propose a workflow that can be applied to assess whether the nominal test concentration can be considered a conservative dose metric for use in NGRA. The workflow examines the implications of volatility, stability, hydrophobicity, binding to plastic and serum, solubility, and the potential use of in silico models for some of these parameters. For the majority of the case study chemicals we found that the use of nominal concentrations in risk assessment would result in conservative decision making. However, for serval chemicals a potential for underestimation of the risk in humans in vivo based on in vitro nominal effect concentrations was identified, and approaches for refinement by characterisation of the actual effect concentration are proposed.

Thresholds of adversity for endocrine disrupting substances: a conceptual case study

For endocrine disrupting chemicals (EDC) the existence of "safe exposure levels", that is exposure levels that do not present an appreciable risk to human health is most controversially discussed, as is the existence of health-based reference values. Concerns have been especially raised that EDCs might not possess a threshold level such that no exposure level to EDCs can be considered safe. To explore whether or not threshold levels can be identified, we performed a screening exercise on 14 pesticidal and biocidal active substances previously identified as EDCs in the European Union. The respective substances are ideal subjects for case studies to review for endocrine activity and disruptive potential following well-defined regulatory assessment based on solid data to effectually establish adversity as consequence of endocrine disruption. Dimethomorph, metiram and propiconazole for which the weight of evidence demonstrating endocrine disruption was the strongest were used as subjects for further study. Epoxiconazole was additionally selected as its effects on the endocrine system are extensive. For all four substances, analysis of the toxicological data clearly indicated thresholds of adversity below which no adverse effects mediated through an endocrine mechanism were observed. Particular emphasis was placed on mechanistic considerations including homeostasis and the concept of adversity. As a proof of concept this study provides evidence that like other substances of toxicological concern EDCs have threshold levels for adversity. While for some EDCs the respective thresholds might indeed be very low this shows that, data allowing, for other EDCs sufficiently protective reference values can be derived.

Cardiac and neurobehavioral impairments in three phylogenetically distant aquatic model organisms exposed to environmentally relevant concentrations of boscalid

Boscalid (2-Chloro-N-(4'-chlorobiphenyl-2-yl) nicotinamide), a pyridine carboxamide fungicide, is an inhibitor of the complex II of the respiration chain in fungal mitochondria. As boscalid is only moderately toxic for aquatic organisms (LC50 > 1-10 mg/L), current environmental levels of this compound in aquatic ecosystems, in the range of ng/L-μg/L, are considered safe for aquatic organisms. In this study, we have exposed zebrafish (Danio rerio), Japanese medaka (Oryzias latipes) and Daphnia magna to a range of concentrations of boscalid (1-1000 μg/L) for 24 h, and the effects on heart rate (HR), basal locomotor activity (BLA), visual motor response (VMR), startle response (SR), and habituation (HB) to a series of vibrational or light stimuli have been evaluated. Moreover, changes in the profile of the main neurotransmitters have been determined. Boscalid altered HR in a concentration-dependent manner, leading to a positive or negative chronotropic effect in fish and D. magna, respectively. While boscalid decreased BLA and increased VMR in Daphnia, these behaviors were not altered in fish. For SR and HB, the response was more species- and concentration-specific, with Daphnia exhibiting the highest sensitivity. At the neurotransmission level, boscalid exposure decreased the levels of L-aspartic acid in fish larvae and increased the levels of dopaminergic metabolites in D. magna. Our study demonstrates that exposure to environmental levels of boscalid alters cardiac activity, impairs ecologically relevant behaviors, and leads to changes in different neurotransmitter systems in phylogenetically distinct vertebrate and invertebrate models. Thus, the results presented emphasize the need to review the current regulation of this fungicide.

Integrating Mechanistic and Toxicokinetic Information in Predictive Models of Cholestasis

Drug development involves the thorough assessment of the candidate's safety and efficacy. In silico toxicology (IST) methods can contribute to the assessment, complementing in vitro and in vivo experimental methods, since they have many advantages in terms of cost and time. Also, they are less demanding concerning the requirements of product and experimental animals. One of these methods, Quantitative Structure-Activity Relationships (QSAR), has been proven successful in predicting simple toxicity end points but has more difficulties in predicting end points involving more complex phenomena. We hypothesize that QSAR models can produce better predictions of these end points by combining multiple QSAR models describing simpler biological phenomena and incorporating pharmacokinetic (PK) information, using quantitative in vitro to in vivo extrapolation (QIVIVE) models. In this study, we applied our methodology to the prediction of cholestasis and compared it with direct QSAR models. Our results show a clear increase in sensitivity. The predictive quality of the models was further assessed to mimic realistic conditions where the query compounds show low similarity with the training series. Again, our methodology shows clear advantages over direct QSAR models in these situations. We conclude that the proposed methodology could improve existing methodologies and could be suitable for being applied to other toxicity end points.

Linking Mechanistic Effects of Pharmaceuticals and Personal Care Products to Ecologically Relevant Outcomes: A Decade of Progress

There are insufficient toxicity data to assess the ecological risks of many pharmaceuticals and personal care products (PPCPs). While data limitations are not uncommon for contaminants of environmental concern, PPCPs are somewhat unique in that an a priori understanding of their biological activities in conjunction with measurements of molecular, biochemical, or histological responses could provide a foundation for understanding mode(s) of action and predicting potential adverse apical effects. Over the past decade significant progress has been made in the development of new approach methodologies (NAMs) to efficiently quantify these types of endpoints using computational models and pathway-based in vitro and in vivo assays. The availability of open-access knowledgebases to curate biological response (including NAM) data and sophisticated bioinformatics tools to help interpret the information also has significantly increased. Finally, advances in the development and implementation of the adverse outcome pathway framework provide the critical conceptual underpinnings needed to translate NAM data into predictions of the ecologically relevant outcomes required by risk assessors and managers. The evolution and convergence of these various data streams, tools, and concepts provides the basis for a fundamental change in how ecological risks of PPCPs can be pragmatically assessed. Environ Toxicol Chem 2024;43:537-548. © 2022 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Mode of action exploration of reproductive toxicity induced by bisphenol S using human normal ovarian epithelial cells through ERβ-MAPK signaling pathway

BACKGROUND

In the plastics production sector, bisphenol S (BPS) has gained popularity as a replacement for bisphenol A (BPA). However, the mode of action (MOA) of female reproductive toxicity caused by BPS remains unclear and the safety of BPS is controversial.

METHODS

Human normal ovarian epithelial cell line, IOSE80, were exposed to BPS at human-relevant levels for short-term exposure at 24 h or 48 h, or for long-term exposure at 28 days, either alone or together with five signaling pathway inhibitors: ICI 18,2780 (estrogen receptor [ER] antagonist), G15 (GPR30 specific inhibitor), U0126 (extracellular regulated protein kinase [ERK] 1/2 inhibitor), SP600125 (c-Jun N-terminal kinase [JNK] inhibitor) or SB203580 (p38 mitogen‑activated protein kinase [p38MAPK] inhibitor). MOA through ERβ-MAPK signaling pathway interruption was explored, and potential thresholds were estimated by the benchmark dose method.

RESULTS

For short-term exposure, BPS exposure at human-relevant levels elevated the ESR2 and MAPK8 mRNA levels, along with the percentage of the G0/G1 phase. For long-term exposure, BPS raised the MAPK1 and EGFR mRNA levels, the ERβ, p-ERK, and p-JNK protein levels, and the percentage of the G0/G1 phase, which was partly suppressed by U0126. The benchmark dose lower confidence limit (BMDL) of the percentage of the S phase after 24 h exposure was the lowest among all the BMDLs of a good fit, with BMDL5 of 9.55 μM.

CONCLUSIONS

The MOA of female reproductive toxicity caused by BPS at human-relevant levels might involve: molecular initiating event (MIE)-BPS binding to ERβ receptor, key event (KE)1-the interrupted expression of GnRH, KE2-the activation of JNK (for short-term exposure) and ERK pathway (for long-term exposure), KE3-cell cycle arrest (the increased percentage of the G0/G1 phase), and KE4-interruption of cell proliferation (only for short-term exposure). The BMDL of the percentage of the S phase after 24 h exposure was the lowest among all the BMDLs of a good fit, with BMDL5 of 9.55 μM.

Southeast Asia's environmental challenges: emergence of new contaminants and advancements in testing methods

Emerging contaminants, including pharmaceuticals, personal care products, microplastics, and per- and poly-fluoroalkyl substances, pose a major threat to both ecosystems and human health in Southeast Asia. As this region undergoes rapid industrialization and urbanization, the increasing presence of unconventional pollutants in water bodies, soil, and various organisms has become an alarming concern. This review comprehensively examines the environmental challenges posed by emerging contaminants in Southeast Asia and recent progress in toxicity testing methods. We discuss the diverse range of emerging contaminants found in Southeast Asia, shedding light on their causes and effects on ecosystems, and emphasize the need for robust toxicological testing methods. This review is a valuable resource for researchers, policymakers, and environmental practitioners working to mitigate the impacts of emerging contaminants and secure a sustainable future for Southeast Asia.

Multidimensional assessment of the biological effects of electronic cigarettes on lung bronchial epithelial cells

Cigarette smoke (CS) exposure is known to cause injury to respiratory tract epithelial cells and is a contributing factor in the development of chronic obstructive pulmonary disease and lung cancer. Electronic cigarettes (e-cigarettes) are gaining popularity as a potential substitute for conventional cigarettes due to their potential for aiding smoking cessation. However, the safety of e-cigarettes remains uncertain, and scientific evidence on this topic is still limited. In this study, we aimed to investigate the effects of CS and e-cigarette smoke (ECS) of different flavors on human lung bronchial epithelial cells. Real-time smoke exposure was carried out using an air-liquid interface system, and cell viability was assessed. RNA-Seq transcriptome analysis was performed to compare the differences between CS and ECS. The transcriptome analysis revealed a significantly higher number of differentially expressed genes in CS than in ECS. Moreover, the impact of mint-flavored e-cigarettes on cells was found to be greater than that of tobacco-flavored e-cigarettes, as evidenced by the greater number of differentially expressed genes. These findings provide a reference for future safety research on traditional cigarettes and e-cigarettes, particularly those of different flavors. The use of omics-scale methodologies has improved our ability to understand the biological effects of CS and ECS on human respiratory tract epithelial cells, which can aid in the development of novel approaches for smoking cessation and lung disease prevention.

Protecting Human and Animal Health: The Road from Animal Models to New Approach Methods

Animals and animal models have been invaluable for our current understanding of human and animal biology, including physiology, pharmacology, biochemistry, and disease pathology. However, there are increasing concerns with continued use of animals in basic biomedical, pharmacological, and regulatory research to provide safety assessments for drugs and chemicals. There are concerns that animals do not provide sufficient information on toxicity and/or efficacy to protect the target population, so scientists are utilizing the principles of replacement, reduction, and refinement (the 3Rs) and increasing the development and application of new approach methods (NAMs). NAMs are any technology, methodology, approach, or assay used to understand the effects and mechanisms of drugs or chemicals, with specific focus on applying the 3Rs. Although progress has been made in several areas with NAMs, complete replacement of animal models with NAMs is not yet attainable. The road to NAMs requires additional development, increased use, and, for regulatory decision making, usually formal validation. Moreover, it is likely that replacement of animal models with NAMs will require multiple assays to ensure sufficient biologic coverage. The purpose of this manuscript is to provide a balanced view of the current state of the use of animal models and NAMs as approaches to development, safety, efficacy, and toxicity testing of drugs and chemicals. Animals do not provide all needed information nor do NAMs, but each can elucidate key pieces of the puzzle of human and animal biology and contribute to the goal of protecting human and animal health. SIGNIFICANCE STATEMENT: Data from traditional animal studies have predominantly been used to inform human health safety and efficacy. Although it is unlikely that all animal studies will be able to be replaced, with the continued advancement in new approach methods (NAMs), it is possible that sometime in the future, NAMs will likely be an important component by which the discovery, efficacy, and toxicity testing of drugs and chemicals is conducted and regulatory decisions are made.

Missing Genomic Resources for the Next Generation of Environmental Risk Assessment

Environmental risk assessment traditionally relies on a wide range of in vivo testing to assess the potential hazards of chemicals in the environment. These tests are often time-consuming and costly and can cause test organisms' suffering. Recent developments of reliable low-cost alternatives, both in vivo- and in silico-based, opened the door to reconsider current toxicity assessment. However, many of these new approach methodologies (NAMs) rely on high-quality annotated genomes for surrogate species of regulatory risk assessment. Currently, a lack of genomic information slows the process of NAM development. Here, we present a phylogenetically resolved overview of missing genomic resources for surrogate species within a regulatory ecotoxicological risk assessment. We call for an organized and systematic effort within the (regulatory) ecotoxicological community to provide these missing genomic resources. Further, we discuss the potential of a standardized genomic surrogate species landscape to enable a robust and nonanimal-reliant ecotoxicological risk assessment in the systems ecotoxicology era.

Phytochemical composition, toxicological profiling and effect on pup birth weight of Corchorus olitorius leaf extract in rats: Implications for fetal macrosomia control

ETHNOPHARMACOLOGICAL RELEVANCE

Corchorus olitorius is used in ethnomedicine to arrest threatened miscarriage, ease labour, and promote smooth childbirth.

AIM OF THE STUDY

To evaluate the phytochemical composition, toxicity profile, and effect of Corchorus olitorius L. leaf extract (COLE) on fetal macrosomia control in rats.

MATERIALS AND METHODS

The chemical constituents of COLE were determined using gas chromatography-mass spectrometry (GC-MS). A single dose of up to 5000 mg/kg was administered in the acute toxicity test, and the rats were monitored for 14 days. In the sub-acute toxicity study, rats were treated with the extract for 28 days, and liver function, renal function, lipid profile, and serum antioxidant parameters, coupled with liver and kidney histology, were used to assess the toxicity potential of the extract. In the pup birth weight study, treatment of pregnant rats lasted until the birth of pups and continued for an extra 4 weeks. Rats in group 1 served as a control, whereas rats in groups 2, 3, and 4 received daily doses of 200, 400, and 800 mg/kg body weight of COLE, respectively, via oral gavage.

RESULTS

Bioactive compounds such as D-limonene, phytol, hexadecanoic acid, 9-octadecenoic acid (Z), dodecanoic acid, and ethyl 9,12,15-octadecatrienoate with well-known pharmacological activities were detected. LD50 of COLE was >5000 mg/kg. COLE decreased body weight and increased red blood cell, packed cell volume, and haemoglobin values when compared with the control (p < 0.05). COLE significantly decreased alkaline phosphatase, alanine aminotransferase, and aspartate aminotransferase but did not cause any significant (p > 0.05) changes in other biochemical (liver and kidney) parameters when compared to control. COLE decreased total cholesterol, triglycerides, low-density lipoprotein cholesterol, and increased high-density lipoprotein cholesterol when compared with the control (p < 0.05). Birth weights of pups were significantly decreased in the COLE-treated groups when compared with the control (p < 0.05), but the observed difference terminated by the fourth week of weaning.

CONCLUSION

The results showed that COLE is not toxic but has several bioactive compounds with known pharmacological activities and therefore may be a safe oral agent for fetal macrosomia control.

Development and application of consensus in silico models for advancing high-throughput toxicological predictions

Computational toxicology models have been successfully implemented to prioritize and screen chemicals. There are numerous in silico (quantitative) structure-activity relationship ([Q]SAR) models for the prediction of a range of human-relevant toxicological endpoints, but for a given endpoint and chemical, not all predictions are identical due to differences in their training sets, algorithms, and methodology. This poses an issue for high-throughput screening of a large chemical inventory as it necessitates several models to cover diverse chemistries but will then generate data conflicts. To address this challenge, we developed a consensus modeling strategy to combine predictions obtained from different existing in silico (Q)SAR models into a single predictive value while also expanding chemical space coverage. This study developed consensus models for nine toxicological endpoints relating to estrogen receptor (ER) and androgen receptor (AR) interactions (i.e., binding, agonism, and antagonism) and genotoxicity (i.e., bacterial mutation, in vitro chromosomal aberration, and in vivo micronucleus). Consensus models were created by combining different (Q)SAR models using various weighting schemes. As a multi-objective optimization problem, there is no single best consensus model, and therefore, Pareto fronts were determined for each endpoint to identify the consensus models that optimize the multiple-criterion decisions simultaneously. Accordingly, this work presents sets of solutions for each endpoint that contain the optimal combination, regardless of the trade-off, with the results demonstrating that the consensus models improved both the predictive power and chemical space coverage. These solutions were further analyzed to find trends between the best consensus models and their components. Here, we demonstrate the development of a flexible and adaptable approach for in silico consensus modeling and its application across nine toxicological endpoints related to ER activity, AR activity, and genotoxicity. These consensus models are developed to be integrated into a larger multi-tier NAM-based framework to prioritize chemicals for further investigation and support the transition to a non-animal approach to risk assessment in Canada.

Application of interpretable machine learning models to improve the prediction performance of ionic liquids toxicity

With the wide application prospect of ionic liquids (ILs) as solvent in the future industry, in order to promote green and sustainable chemical engineering, the toxicity problem of common concern has been systematically modeled. Machine learning has promoted the development of chemical property prediction model with its powerful data processing ability. Two typical ensemble learning models, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), were used to model the toxicity of ILs to Vibrio fischeri in this work. The model's hyperparameters were fine-tuned using Bayesian optimization, and its robustness was enhanced through the 5-fold cross validation. The results of the model comparison showed that the XGBoost model exhibited good generalization ability. In addition, the SHapley Additive exPlanations (SHAP) method was used to explain the model in more detail and the XGBoost model was used to supplement the toxicity value matrix of 1590 ILs.

Establishing short-term occupational exposure limits (STELs) for sensory irritants using predictive and in silico respiratory rate depression (RD50) models

Sensory irritation is a health endpoint that serves as the critical effect basis for many occupational exposure limits (OELs). Schaper 1993 described a significant relationship with high correlation between the measured exposure concentration producing a 50% respiratory rate decrease (RD50) in a standard rodent assay and the American Conference of Governmental Industrial Hygienists (ACGIH®) Threshold Limit Values (TLVs®) as time-weighted averages (TWAs) for airborne chemical irritants. The results demonstrated the potential use of the RD50 values for deriving full-shift TWA OELs protective of irritant responses. However, there remains a need to develop a similar predictive model for deriving workplace short-term exposure limits (STELs) for sensory irritants. The aim of our study was to establish a model capable of correlating the relationship between RD50 values and published STELs to prospectively derive short-term exposure OELs for sensory irritants. A National Toxicology Program (NTP) database that included chemicals with both an RD50 and established STELs was used to fit several linear regression models. A strong correlation between RD50s and STELs was identified, with a predictive equation of ln (STEL) (ppm) = 0.86 * ln (RD50) (ppm) - 2.42 and an R2 value of 0.75. This model supports the use of RD50s to derive STELs for chemicals without existing exposure recommendations. Further, for data-poor sensory irritants, predicted RD50 values from in silico quantitative structure activity relationship (QSAR) models can be used to derive STELs. Hence, in silico methods and statistical modeling can present a path forward for establishing reliable OELs and improving worker safety and health.

A novel strategy for the study on molecular mechanism of prostate injury induced by 4,4'-sulfonyldiphenol based on network toxicology analysis

The study aimed to investigate the underlying molecular mechanisms of prostate injury induced by 4,4'-sulfonyldiphenol (BPS) exposure and propose a novel research strategy to systematically explore the molecular mechanisms of toxicant-induced adverse health effects. By utilizing the ChEMBL, STITCH, and GeneCards databases, a total of 208 potential targets associated with BPS exposure and prostate injury were identified. Through screening the potential target network in the STRING database and Cytoscape software, we determined 21 core targets including AKT1, EGFR, and MAPK3. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses conducted through the DAVID database illustrated that the potential targets of BPS in prostatic toxicity were primarily enriched in cancer signaling pathways and calcium signaling pathways. These findings suggest that BPS may actively participate in the occurrence and development of prostate inflammation, prostatic hyperplasia, prostate cancer, and other aspects of prostate injury by regulating prostate cancer cell apoptosis and proliferation, activating inflammatory signaling pathways, and modulating prostate adipocytes and fibroblasts. This research provides a theoretical basis for understanding the molecular mechanism of underlying BPS-induced prostatic toxicity and establishes a foundation for the prevention and treatment of prostatic diseases associated with exposure to plastic products containing BPS and certain BPS-overwhelmed environments.

IVIVE-PBPK based new approach methodology for addressing early life toxicity induced by Bisphenol A

Bisphenol A (BPA) is a known endocrine disruptor mimicking natural estrogens with the potential to affect human health, especially during prenatal and postnatal exposure at or below current acceptable daily intake levels. Different adverse effects of BPA are still under investigation, and multiple mechanisms of action remain unexplored. This may be one of the reasons for the continuously changing tolerable daily intake (TDI) of BPA with the emergence of new adverse health effects over time. In addition, translational modelling through in vitro-in vivo extrapolation (IVIVE) can act as prerequisite bridge for translating in-vitro finding into human risk assessment. The objective of this study was to conduct in-vitro experiments and utilize an IVIVE-pregnancy physiologically based pharmacokinetic (P-PBPK) modeling to investigate developmental neurotoxicity and embryotoxicity in humans. The data obtained from human embryonic stem cells-based assays (study conducted between October 2020-2021) were used for the IVIVE-P-PBPK models to obtain the human equivalent doses (HEDs) which were further extrapolated to reference doses (RfDs). The results showed that simulated mean RfDs (μg/kg/day) derived from the HSD3B1 and NFATC2 gene of embryotoxicity and neurodevelopmental toxicity tests, respectively, were 4.94 and 5.18. The simulated RfDs were close to the temporary-tolerable daily intake (t-TDI) recommended by European Food Safety Authority (EFSA) in 2015 (t-TDI: 4 μg/kg·bw) and higher than the TDI of 2023 (0.2 ng/kg·bw). In conclusion, in-vitro toxicogenomics dose-response data combined with PBPK modeling can become a promising alternative new approach methodology (NAM) to support decision-making in chemical risk assessment. Based on the simulated RfDs derived from this NAM, the t-TDI set by EFSA in 2015 may be considered a safe exposure limit for mothers and fetuses at the current BPA intake levels in Chinese mothers. This study provided an animal-free new strategy for NAMs based risk assessment by combining toxicogenomics and computational toxicology.

Changes in chemical occurrence, concentration, and bioactivity in the Colorado River before and after replacement of the Moab, Utah wastewater treatment plant

Long-term (2010-19) water-quality monitoring on the Colorado River downstream from Moab Utah indicated the persistent presence of Bioactive Chemicals (BC), such as pesticides and pharmaceuticals. This stream reach near Canyonlands National Park provides critical habitat for federally endangered species. The Moab wastewater treatment plant (WWTP) outfall discharges to the Colorado River and is the nearest potential point-source to this reach. The original WWTP was replaced in 2018. In 2016-19, a study was completed to determine if the new plant reduced BC input to the Colorado River at, and downstream from, the outfall. Water samples were collected before and after the plant replacement at sites upstream and downstream from the outfall. Samples were analyzed for as many as 243 pesticides, 109 pharmaceuticals, 20 hormones, 51 wastewater indicator chemicals, 20 metals, and 8 nutrients. BC concentrations, hazard quotients (HQs), and exposure activity ratios (EARs) were used to identify and prioritize contaminants for their potential to have adverse biological effects on the health of native and endangered wildlife. There were 22 BC with HQs >1, mostly metals and hormones; and 23 BC with EARs >0.1, mostly hormones and pharmaceuticals. Most high HQs or EARs were associated with samples collected at the WWTP outfall site prior to its replacement. Discharge from the new plant had reduced concentrations of nutrients, hormones, pharmaceuticals, and other BC. For example, all 16 of the hormones detected at the WWTP outfall site had maximum concentrations in samples collected prior to the WWTP replacement. The WWTP replacement had less effect on instream concentrations of metals and pesticides, BC whose sources are less directly tied to domestic wastewater. Study results indicate that improved WWTP technology can create substantial reductions in concentrations of non-regulated BC such as pharmaceuticals, in addition to regulated contaminants such as nutrients.

The mediating role of AKT/ERK/JNK signaling on the malignant phenotype of microcystin-LR in gastric adenocarcinoma cells

Microcystin-leucine arginine (MC-LR), a widely distributed and highly toxic environmental pollutant, plays crucial roles in cancer malignancy by activating characteristically toxic signaling pathways. Traditional animal-based toxicity evaluation methods have proven insufficient for identifying the specific role of these signaling pathways. Therefore, this study aimed to uncover the regulatory relationship between the toxic pathways and the progression of gastric cancer (GC). The findings provide novel avenues for conducting in vitro toxicity tests based on the investigated pathways. We found that MC-LR promoted the migration and invasion of SGC-7901 cells while simultaneously inhibiting their apoptosis in a dose-dependent manner. This observed cytotoxicity was primarily mediated through the AKT, JNK, and ERK signaling pathways. By using a mediation analysis model, we determined that AKT and ERK exhibited competitive effects in MC-LR-treated GC malignancy, while AKT and JNK acted independently from one another. This study establishes an in vitro toxicity test model of MC-LR based on toxicity-related pathways and underscores the pivotal roles of AKT, ERK, and JNK signaling in MC-LR toxicity. The findings offer a novel, fundamental framework for conducting chemical toxicity risk assessment.

Machine Learning-Based Hazard-Driven Prioritization of Features in Nontarget Screening of Environmental High-Resolution Mass Spectrometry Data

Nontarget high-resolution mass spectrometry screening (NTS HRMS/MS) can detect thousands of organic substances in environmental samples. However, new strategies are needed to focus time-intensive identification efforts on features with the highest potential to cause adverse effects instead of the most abundant ones. To address this challenge, we developed MLinvitroTox, a machine learning framework that uses molecular fingerprints derived from fragmentation spectra (MS2) for a rapid classification of thousands of unidentified HRMS/MS features as toxic/nontoxic based on nearly 400 target-specific and over 100 cytotoxic endpoints from ToxCast/Tox21. Model development results demonstrated that using customized molecular fingerprints and models, over a quarter of toxic endpoints and the majority of the associated mechanistic targets could be accurately predicted with sensitivities exceeding 0.95. Notably, SIRIUS molecular fingerprints and xboost (Extreme Gradient Boosting) models with SMOTE (Synthetic Minority Oversampling Technique) for handling data imbalance were a universally successful and robust modeling configuration. Validation of MLinvitroTox on MassBank spectra showed that toxicity could be predicted from molecular fingerprints derived from MS2 with an average balanced accuracy of 0.75. By applying MLinvitroTox to environmental HRMS/MS data, we confirmed the experimental results obtained with target analysis and narrowed the analytical focus from tens of thousands of detected signals to 783 features linked to potential toxicity, including 109 spectral matches and 30 compounds with confirmed toxic activity.

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.

Advancing food safety risk assessment in China: development of new approach methodologies (NAMs)

Novel techniques and methodologies are being developed to advance food safety risk assessment into the next-generation. Considering the shortcomings of traditional animal testing, new approach methodologies (NAMs) will be the main tools for the next-generation risk assessment (NGRA), using non-animal methodologies such as in vitro and in silico approaches. The United States Environmental Protection Agency and the European Food Safety Authority have established work plans to encourage the development and application of NAMs in NGRA. Currently, NAMs are more commonly used in research than in regulatory risk assessment. China is also developing NAMs for NGRA but without a comprehensive review of the current work. This review summarizes major NAM-related research articles from China and highlights the China National Center for Food Safety Risk Assessment (CFSA) as the primary institution leading the implementation of NAMs in NGRA in China. The projects of CFSA on NAMs such as the Food Toxicology Program and the strategies for implementing NAMs in NGRA are outlined. Key issues and recommendations, such as discipline development and team building, are also presented to promote NAMs development in China and worldwide.

Harnessing Semi-Supervised Machine Learning to Automatically Predict Bioactivities of Per- and Polyfluoroalkyl Substances (PFASs)

Many per- and polyfluoroalkyl substances (PFASs) pose significant health hazards due to their bioactive and persistent bioaccumulative properties. However, assessing the bioactivities of PFASs is both time-consuming and costly due to the sheer number and expense of in vivo and in vitro biological experiments. To this end, we harnessed new unsupervised/semi-supervised machine learning models to automatically predict bioactivities of PFASs in various human biological targets, including enzymes, genes, proteins, and cell lines. Our semi-supervised metric learning models were used to predict the bioactivity of PFASs found in the recent Organisation of Economic Co-operation and Development (OECD) report list, which contains 4730 PFASs used in a broad range of industries and consumers. Our work provides the first semi-supervised machine learning study of structure-activity relationships for predicting possible bioactivities in a variety of PFAS species.

Conotoxin Prediction: New Features to Increase Prediction Accuracy

Conotoxins are toxic, disulfide-bond-rich peptides from cone snail venom that target a wide range of receptors and ion channels with multiple pathophysiological effects. Conotoxins have extraordinary potential for medical therapeutics that include cancer, microbial infections, epilepsy, autoimmune diseases, neurological conditions, and cardiovascular disorders. Despite the potential for these compounds in novel therapeutic treatment development, the process of identifying and characterizing the toxicities of conotoxins is difficult, costly, and time-consuming. This challenge requires a series of diverse, complex, and labor-intensive biological, toxicological, and analytical techniques for effective characterization. While recent attempts, using machine learning based solely on primary amino acid sequences to predict biological toxins (e.g., conotoxins and animal venoms), have improved toxin identification, these methods are limited due to peptide conformational flexibility and the high frequency of cysteines present in toxin sequences. This results in an enumerable set of disulfide-bridged foldamers with different conformations of the same primary amino acid sequence that affect function and toxicity levels. Consequently, a given peptide may be toxic when its cysteine residues form a particular disulfide-bond pattern, while alternative bonding patterns (isoforms) or its reduced form (free cysteines with no disulfide bridges) may have little or no toxicological effects. Similarly, the same disulfide-bond pattern may be possible for other peptide sequences and result in different conformations that all exhibit varying toxicities to the same receptor or to different receptors. We present here new features, when combined with primary sequence features to train machine learning algorithms to predict conotoxins, that significantly increase prediction accuracy.

Computer-assisted drug discovery of potential natural inhibitors of the SARS-CoV-2 RNA-dependent RNA polymerase through a multi-phase in silico approach

BACKGROUND

The COVID-19 pandemic has led to significant loss of life and economic disruption worldwide. Currently, there are limited effective treatments available for this disease. SARS-CoV-2 RNA-dependent RNA polymerase (SARS-CoV-2 RdRp) has been identified as a potential target for drug development against COVID-19. Natural products have been shown to possess antiviral properties, making them a promising source for developing drugs against SARS-CoV-2.

OBJECTIVES

The objective of this study is to identify the most effective natural inhibitors of SARS-CoV-2 RdRp among a set of 4924 African natural products using a multi-phase in silico approach.

METHODS

The study utilized remdesivir (RTP), the co-crystallized ligand of RdRp, as a starting point to select compounds that have the most similar chemical structures among the examined set of compounds. Molecular fingerprints and structure similarity studies were carried out in the first part of the study. The second part of the study included molecular docking against SARS-CoV-2 RdRp (PDB ID: 7BV2) and Molecular Dynamics (MD) simulations including the calculation of RMSD, RMSF, Rg, SASA, hydrogen bonding, and PLIP. Moreover, the calculations of Molecular mechanics with generalised Born and surface area solvation (MM-GBSA) Lennard-Jones and Columbic electrostatic interaction energies have been conducted. Additionally, in silico ADMET and toxicity studies were performed to examine the drug likeness degrees of the selected compounds.

RESULTS

Eight compounds were identified as the most effective natural inhibitors of SARS-CoV-2 RdRp. These compounds are kaempferol 3-galactoside, kaempferol 3-O-β-D-glucopyranoside, mangiferin methyl ether, luteolin 7-O-β-D-glucopyranoside, quercetin-O-β-D-3-glucopyranoside, 1-methoxy-3-indolylmethyl glucosinolate, naringenin, and asphodelin A 4'-O-β-D-glucopyranoside.

CONCLUSION

The results of this study provide valuable information for the development of natural product-based drugs against COVID-19. However, the elected compounds should be further studied in vitro and in vivo to confirm their efficacy in treating COVID-19.

Genotoxicity assessment of eight nitrosamines using 2D and 3D HepaRG cell models

N-nitrosamine impurities have been increasingly detected in human drugs. This is a safety concern as many nitrosamines are mutagenic in bacteria and carcinogenic in rodent models. Typically, the mutagenic and carcinogenic activity of nitrosamines requires metabolic activation by cytochromes P450 enzymes (CYPs), which in many in vitro models are supplied exogenously using rodent liver homogenates. There are only limited data on the genotoxicity of nitrosamines in human cell systems. In this study, we used metabolically competent human HepaRG cells, whose metabolic capability is comparable to that of primary human hepatocytes, to evaluate the genotoxicity of eight nitrosamines [N-cyclopentyl-4-nitrosopiperazine (CPNP), N-nitrosodibutylamine (NDBA), N-nitrosodiethylamine (NDEA), N-nitrosodimethylamine (NDMA), N-nitrosodiisopropylamine (NDIPA), N-nitrosoethylisopropylamine (NEIPA), N-nitroso-N-methyl-4-aminobutyric acid (NMBA), and N-nitrosomethylphenylamine (NMPA)]. Under the conditions we used to culture HepaRG cells, three-dimensional (3D) spheroids possessed higher levels of CYP activity compared to 2D monolayer cells; thus the genotoxicity of the eight nitrosamines was investigated using 3D HepaRG spheroids in addition to more conventional 2D cultures. Genotoxicity was assessed as DNA damage using the high-throughput CometChip assay and as aneugenicity/clastogenicity in the flow-cytometry-based micronucleus (MN) assay. Following a 24-h treatment, all the nitrosamines induced DNA damage in 3D spheroids, while only three nitrosamines, NDBA, NDEA, and NDMA, produced positive responses in 2D HepaRG cells. In addition, these three nitrosamines also caused significant increases in MN frequency in both 2D and 3D HepaRG models, while NMBA and NMPA were positive only in the 3D HepaRG MN assay. Overall, our results indicate that HepaRG spheroids may provide a sensitive, human-based cell system for evaluating the genotoxicity of nitrosamines.

Comparison of in silico, in vitro, and in vivo toxicity benchmarks suggests a role for ToxCast data in ecological hazard assessment

Large repositories of in vitro bioactivity data such as US EPA's Toxicity Forecaster (ToxCast) provide a wealth of publicly accessible toxicity information for thousands of chemicals. These data can be used to calculate point-of-departure (POD) estimates via concentration-response modeling that may serve as lower bound, protective estimates of in vivo effects. However, the data are predominantly based on mammalian models and discussions to date about their utility have largely focused on potential integration into human hazard assessment, rather than application to ecological risk assessment. The goal of the present study was to compare PODs based on (1) quantitative structure-activity relationships (QSARs), (2) the 5th centile of the activity concentration at cutoff (ACC), and (3) lower-bound cytotoxic burst (LCB) from ToxCast, with the distribution of in vivo PODs compiled in the Ecotoxicology Knowledgebase (ECOTOX). While overall correlation between ToxCast ACC5 and ECOTOX PODs for 649 chemicals was weak, there were significant associations among PODs based on LCB and ECOTOX, LCB and QSARs, and ECOTOX and QSARs. Certain classes of compounds showed moderate correlation across datasets (eg, antimicrobials/disinfectants), while others, such as organophosphate insecticides, did not. Unsurprisingly, more precise classifications of the data based on ECOTOX effect and endpoint type (eg, apical vs biochemical; acute vs chronic) had a significant effect on overall relationships. Results of this research help to define appropriate roles for data from new approach methodologies in chemical prioritization and screening of ecological hazards.

Cutting without a Knife: A Slice-Selective 2D 1H-13C HSQC NMR Sequence for the Analysis of Inhomogeneous Samples

Nuclear magnetic resonance (NMR) is a powerful technique with applications ranging from small molecule structure elucidation to metabolomics studies of living organisms. Typically, solution-state NMR requires a homogeneous liquid, and the whole sample is analyzed as a single entity. While adequate for homogeneous samples, such an approach is limited if the composition varies as would be the case in samples that are naturally heterogeneous or layered. In complex samples such as living organisms, magnetic susceptibility distortions lead to broad 1H line shapes, and thus, the additional spectral dispersion afforded by 2D heteronuclear experiments is often required for metabolite discrimination. Here, a novel, slice-selective 2D, 1H-13C heteronuclear single quantum coherence (HSQC) sequence was developed that exclusively employs shaped pulses such that only spins in the desired volume are perturbed. In turn, this permits multiple volumes in the tube to be studied during a single relaxation delay, increasing sensitivity and throughput. The approach is first demonstrated on standards and then used to isolate specific sample/sensor elements from a microcoil array and finally study slices within a living earthworm, allowing metabolite changes to be discerned with feeding. Overall, slice-selective NMR is demonstrated to have significant potential for the study of layered and other inhomogeneous samples of varying complexity. In particular, its ability to select subelements is an important step toward developing microcoil receive-only arrays to study environmental toxicity in tiny eggs, cells, and neonates, whereas localization in larger living species could help better correlate toxin-induced biochemical responses to the physical localities or organs involved.

Alternatives to Fish Acute Whole Effluent Toxicity (WET) Testing: Predictability of RTgill-W1 Cells and Fathead Minnow Embryos with Actual Wastewater Samples

Toxicity assays using fish cells and embryos continue to gain momentum as a more ethical and informative alternative to fish acute toxicity testing. The goal of our study was to test the accuracy of RTgill-W1 cells and the fathead minnow (Pimephales promelas) embryos to predict actual whole effluent toxicity (WET) in the fathead minnow larvae. The three models were compared concurrently using samples of various origins and treatment types. Additionally, the toxicity of reference toxicants (Cd, Cu, NH3-N, 3,4-dichloraniline, and benzalkonium chloride) spiked into a nontoxic wastewater was compared. The toxicity of reference toxicants was tested in isosmotic and hypoosmotic exposure media in RTgill-W1 cells. Of the 28 wastewater samples, 14 induced a toxic response in fish larvae. Embryos predicted 11 of the 14 wastewater samples toxic to the larvae, whereas RTgill-W1 cells predicted the toxicity of all 14 toxic samples to the larvae. In addition, embryos and RTgill-W1 cells predicted toxicity in two and six additional samples, respectively, that were nontoxic to larvae. Exposures in hypoosmotic medium significantly increased sensitivity of RTgill-W1 cells to all reference toxicants, excluding benzalkonium chloride, compared to exposures in isosmotic medium and showed toxicity levels similar to that in larvae. Thus, hypoosmotic exposure medium should be considered for aquatic toxicity testing applications. Overall, both gill cell and embryo models predicted toxicity in the majority of wastewater samples toxic to larvae and demonstrated their applicability for regulatory WET testing.

Enhancing developmental and reproductive toxicity knowledge: A new AOP stemming from glutathione depletion

Integrated approaches to testing and assessments (IATAs) have been proposed as a method to organise new approach methodologies in order to replace traditional animal testing for chemical safety assessments. To capture the mechanistic aspects of toxicity assessments, IATAs can be framed around the adverse outcome pathway (AOP) concept. To utilise AOPs fully in this context, a sufficient number of pathways need to be present to develop fit for purpose IATAs. In silico approaches can support IATA through the provision of predictive models and also through data integration to derive conclusions using a weight-of-evidence approach. To examine the maturity of a developmental and reproductive toxicity (DART) AOP network derived from the literature, an assessment of its coverage was performed against a novel toxicity dataset. A dataset of diverse compounds, with data from studies performed according to OECD test guidelines TG-421 and TG-422, was curated to test the performance of an in silico model based on the AOP network - allowing for the identification of knowledge gaps within the network. One such gap in the knowledge was filled through the development of an AOP stemming from the molecular initiating event 'glutathione reaction with an electrophile' leading to male fertility toxicity. The creation of the AOP provided the mechanistic rationale for the curation of pre-existing structural alerts to relevant key events. Integrating this new knowledge and associated alerts into the DART AOP network will improve its coverage of DART-relevant chemical space. In addition, broadening the coverage of AOPs for a particular regulatory endpoint may facilitate the development of, and confidence in, robust IATAs.

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.

Regulatory landscape of alternatives to animal testing in food safety evaluations with a focus on the western world

Traditional animal models are increasingly being replaced by new approach methodologies (NAMs) which focus on predicting toxicity of chemicals based on mechanistic data rather than apical endpoint data usually obtained from animal models. Beyond in vitro genetic tests, however, only a handful of NAMs have been successfully implemented in regulatory decision-making processes, mostly in the cosmetics and chemicals sector. Regulatory guidance on food safety testing in many jurisdictions still relies on data obtained from animal studies. This is due to the lack of validated models to predict systemic toxicity, which is essential to develop health-based guidance values for food additives. Other factors limiting the adoption of NAMs into food safety assessment include sector legislation lagging behind scientific progress, and lack of training and expertise to use the new models. While regulatory and industry bodies are working to combat these challenges, more needs to be done before these models can be used as standalone tools for regulatory decision-making. This review summarizes the current state and challenges of regulatory acceptance of NAMs for decision-making, and the efforts by governing bodies and industry to transition from animal testing for food safety assessments.

An in silico toxicogenomic approach in constructing the aflatoxin B1-mediated regulatory network of hub genes in hepatocellular carcinoma

Aflatoxin B1 (AFB1) can cause hepatocellular carcinoma (HCC) through a mutagenic mode of action but can also lead to global changes in gene expression; however, the AFB1 network of molecular pathways involved in HCC is not known. Here, we used toxicogenomic data from human liver cells exposed to AFB1 to infer the network of AFB1-responsive molecular pathways involved in HCC. The following computational tools: STRING, MCODE, cytoHubba, iRegulon, kinase enrichment tool KEA3, and DAVID were used to identify protein-protein interaction network, hub genes, transcription factors (TFs), upstream kinases, and biological processes (BPs). Predicted molecular events were validated with an external dataset, whereas the hub genes in HCC were validated using the UALCAN database. The results revealed an association between AFB1 and the hub genes involved in the cell cycle. We identified TFs that regulate the hub genes and linked them with upstream kinases including cyclin-dependent kinases, mitogen-activated protein kinase 1, and AKT. This approach enabled the construction of the AFB1-mediated regulatory network consisting of upstream kinases, TFs, hub genes, and BPs, thus revealing the signaling hierarchy and information flow that may contribute to AFB1-induced HCC. This could be a useful tool in predicting the molecular mechanisms involved in chemical-induced diseases when available toxicogenomic data exist.

An in silico insight on the mechanistic aspects of gelsenicine toxicity: A reverse screening study pointing to the possible involvement of acetylcholine binding receptor

Gelsedine-type alkaloids are highly toxic plant secondary metabolites produced by shrubs belonging to the Gelsemium genus. Gelsenicine is one of the most concerning gelsedine-type alkaloids with a lethal dose lower than 1 mg/Kg in mice. Several reported episodes of poisoning in livestock and fatality cases in humans due to the usage of Gelsemium plants extracts were reported. Also, gelsedine-type alkaloids were found in honey constituting a potential food safety issue. However, their toxicological understanding is scarce and the molecular mechanism underpinning their toxicity needs further investigations. In this context, an in silico approach based on reverse screening, docking and molecular dynamics successfully identified a possible gelsenicine biological target shedding light on its toxicodynamics. In line with the available crystallographic data, it emerged gelsenicine could target the acetylcholine binding protein possibly acting as a partial agonist against α7 nicotinic acetylcholine receptor (AChR). Overall, these results agreed with evidence previously reported and prioritized AChR for further dedicated analysis.

Disruptive 3D in vitro models for respiratory disease investigation: A state-of-the-art approach focused on SARS-CoV-2 infection

COVID-19, along with most respiratory diseases in the medical field, demonstrates significant ability to take its toll on global population. There is a particular difficulty in studying these conditions, which stems especially from the short supply of in vitro models for detailed investigation, the specific therapeutic knowledge required for disease scrutinization and the occasional need of BSL-3 [Biosafety Level 3] laboratories for research. Based on this, the process of drug development is hampered to a great extent. In the scenario of COVID-19, this difficulty is even more substantial on account of the current undefinition regarding the exact role of the ACE2 [Angiotensin-converting enzyme 2] receptor upon SARS-CoV-2 kinetics in human cells and the great level of demand in the investigation process of ACE2, which usually requires the laborious and ethically complicated usage of transgenic animal models overexpressing the receptor. Moreover, the rapid progression of the aforementioned diseases, especially COVID-19, poses a crucial necessity for adequate therapeutic solutions emergence. In this context, the work herein presented introduces a groundbreaking set of 3D models, namely spheroids and MatriWell cell culture inserts, whose remarkable ability to mimic the in vivo environment makes them highly suitable for respiratory diseases investigation, particularly SARS-CoV-2 infection. Using MatriWells, we developed an innovative platform for COVID-19 research: a pulmonary air-liquid interface [ALI] associated with endothelial (HUVEC) cells. Infection studies revealed that pulmonary (BEAS-2B) cells in the ALI reached peak viral load at 24h and endothelial cells, at 48h, demonstrating lung viral replication and subsequent hematogenous dissemination, which provides us with a unique and realistic framework for studying COVID-19. Simultaneously, the spheroids were used to address the understudied ACE2 receptor, aiming at a pronounced process of COVID-19 investigation. ACE2 expression not only increased spheroid diameter by 20% (p<0.001) and volume by 60% (p≤0.0001) but also led to a remarkable 640-fold increase in intracellular viral load (p≤0.01). The previously mentioned finding supports ACE2 as a potential target for COVID-19 treatment. Lastly, we observed a higher viral load in the MatriWells compared to spheroids (150-fold, p<0.0001), suggesting the MatriWells as a more appropriate approach for COVID-19 investigation. By establishing an advanced method for respiratory tract conditions research, this work paves the way toward an efficacious process of drug development, contributing to a change in the course of respiratory diseases such as COVID-19.

Alternatives to animal testing in toxicity testing: Current status and future perspectives in food safety assessments

The development of alternative methods to animal testing has gained great momentum since Russel and Burch introduced the "3Rs" concept of Reduction, Refinement, and Replacement of animals in safety testing in 1959. Several alternatives to animal testing have since been introduced, including but not limited to in vitro and in chemico test systems, in silico models, and computational models (e.g., [quantitative] structural activity relationship models, high-throughput screens, organ-on-chip models, and genomics or bioinformatics) to predict chemical toxicity. Furthermore, several agencies have developed robust integrated testing strategies to determine chemical toxicity. The cosmetics sector is pioneering the adoption of alternative methodologies for safety evaluations, and other sectors are aiming to completely abandon animal testing by 2035. However, beyond the use of in vitro genetic testing, agencies regulating the food industry have been slow to implement alternative methodologies into safety evaluations compared with other sectors; setting health-based guidance values for food ingredients requires data from systemic toxicity, and to date, no standalone validated alternative models to assess systemic toxicity exist. The abovementioned models show promise for assessing systemic toxicity with further research. In this paper, we review the current alternatives and their applicability and limitations in food safety evaluations.

Benzo[b]fluoranthene induces male reproductive toxicity and apoptosis via Akt-Mdm2-p53 signaling axis in mouse Leydig cells: Integrating computational toxicology and experimental approaches

This study aims to explore the male reproductive toxicity of Benzo[b]fluoranthene (BbF) and related mechanisms. The results of computational toxicology analysis indicated male reproductive toxicity of BbF was related to apoptosis of Leydig cells and that Akt/p53 pathway might play a key role. In experiments, BbF induced testosterone decline, decreased concentration and motility of sperm and aggravated testicular pathological injury in mice. Besides, BbF led to apoptosis in Leydig cells, and decreased expressions of p-Akt and Bcl2, while improving the expressions of p53, Bax and Cleaved Caspase-3 in vivo and in vitro. Further, compared with BbF group, Akt activator SC79 significantly reduced cell apoptosis rate, improved cell viability, promoted the expressions of p-Akt and p-Mdm2, and reversed the above molecular expressions. Similarly, p53 inhibitor Pifithrin-α also significantly enhanced the cell vitality, alleviated the apoptosis of TM3 cells induced by BbF, and decreased the expressions of Bax and Cleaved Caspase-3, with the up-regulation of Bcl2. To sum up, by inhibiting Akt-Mdm2 signaling, BbF activated the p53-mediated mitochondrial apoptosis pathway, further inducing the apoptosis of Leydig cells, therefore resulting in testosterone decline and male reproductive damage. Besides, this study provided a valid mode integrating computational toxicology and experimental approaches in toxicity testing.

MolFPG: Multi-level fingerprint-based Graph Transformer for accurate and robust drug toxicity prediction

Drug toxicity prediction is essential to drug development, which can help screen compounds with potential toxicity and reduce the cost and risk of animal experiments and clinical trials. However, traditional handcrafted feature-based and molecular-graph-based approaches are insufficient for molecular representation learning. To address the problem, we developed an innovative molecular fingerprint Graph Transformer framework (MolFPG) with a global-aware module for interpretable toxicity prediction. Our approach encodes compounds using multiple molecular fingerprinting techniques and integrates Graph Transformer-based molecular representation for feature learning and toxic prediction. Experimental results show that our proposed approach has high accuracy and reliability in predicting drug toxicity. In addition, we explored the relationship between drug features and toxicity through an interpretive analysis approach, which improved the interpretability of the approach. Our results highlight the potential of Graph Transformers and multi-level fingerprints for accelerating the drug discovery process by reliably, effectively alarming drug safety. We believe that our study will provide vital support and reference for further development in the field of drug development and toxicity assessment.

Integrating Concentration-Dependent Toxicity Data and Toxicokinetics To Inform Hepatotoxicity Response Pathways

Failure of animal models to predict hepatotoxicity in humans has created a push to develop biological pathway-based alternatives, such as those that use in vitro assays. Public screening programs (e.g., ToxCast/Tox21 programs) have tested thousands of chemicals using in vitro high-throughput screening (HTS) assays. Developing pathway-based models for simple biological pathways, such as endocrine disruption, has proven successful, but development remains a challenge for complex toxicities like hepatotoxicity, due to the many biological events involved. To this goal, we aimed to develop a computational strategy for developing pathway-based models for complex toxicities. Using a database of 2171 chemicals with human hepatotoxicity classifications, we identified 157 out of 1600+ ToxCast/Tox21 HTS assays to be associated with human hepatotoxicity. Then, a computational framework was used to group these assays by biological target or mechanisms into 52 key event (KE) models of hepatotoxicity. KE model output is a KE score summarizing chemical potency against a hepatotoxicity-relevant biological target or mechanism. Grouping hepatotoxic chemicals based on the chemical structure revealed chemical classes with high KE scores plausibly informing their hepatotoxicity mechanisms. Using KE scores and supervised learning to predict in vivo hepatotoxicity, including toxicokinetic information, improved the predictive performance. This new approach can be a universal computational toxicology strategy for various chemical toxicity evaluations.

Systematic computational toxicity analysis of the ozonolytic degraded compounds of azo dyes: Quantitative structure-activity relationship (QSAR) and adverse outcome pathway (AOP) based approach

The present study identifies and analyses the degraded products of three azo dyes (Reactive Orange 16, Reactive Red 120, and Direct Red 80) and proffers their in silico toxicity predictions. In our previously published work, the synthetic dye effluents were degraded using an ozonolysis-based Advanced Oxidation Process. In the present study, the degraded products of the three dyes were analysed using GC-MS at endpoint strategy and further subjected to in silico toxicity analysis using Toxicity Estimation Software Tool (TEST), Prediction Of TOXicity of chemicals (ProTox-II), and Estimation Programs Interface Suite (EPI Suite). Several physiological toxicity endpoints, such as hepatotoxicity, carcinogenicity, mutagenicity, cellular and molecular interactions, were considered to assess the Quantitative Structure-Activity Relationships (QSAR) and adverse outcome pathways. The environmental fate of the by-products in terms of their biodegradability and possible bioaccumulation was also assessed. Results of ProTox-II suggested that the azo dye degradation products are carcinogenic, immunotoxic, and cytotoxic and displayed toxicity towards Androgen Receptor and Mitochondrial Membrane Potential. TEST results predicted LC50 and IGC50 values for three organisms Tetrahymena pyriformis, Daphnia magna, and Pimephales promelas. EPISUITE software via the BCFBAF module surmises that the degradation products' bioaccumulation (BAF) and bioconcentration factors (BCF) are high. The cumulative inference of the results suggests that most degradation by-products are toxic and need further remediation strategies. The study aims to complement existing tests to predict toxicity and prioritise the elimination/reduction of harmful degradation products of primary treatment procedures. The novelty of this study is that it streamlines in silico approaches to predict the nature of toxicity of degradation by-products of toxic industrial affluents like azo dyes. These approaches can assist the first phase of toxicology assessments for any pollutant for regulatory decision-making bodies to chalk out appropriate action plans for their remediation.

An ancestral molecular response to nanomaterial particulates

The varied transcriptomic response to nanoparticles has hampered the understanding of the mechanism of action. Here, by performing a meta-analysis of a large collection of transcriptomics data from various engineered nanoparticle exposure studies, we identify common patterns of gene regulation that impact the transcriptomic response. Analysis identifies deregulation of immune functions as a prominent response across different exposure studies. Looking at the promoter regions of these genes, a set of binding sites for zinc finger transcription factors C2H2, involved in cell stress responses, protein misfolding and chromatin remodelling and immunomodulation, is identified. The model can be used to explain the outcomes of mechanism of action and is observed across a range of species indicating this is a conserved part of the innate immune system.

Investigation on the potential adverse outcome pathway of the sensitive endpoint for nephrotoxicity induced by gardenia yellow based on an integrated strategy using bioinformatics analysis and in vitro testing validation

To explore the potential the adverse outcome pathway of Gardenia Yellow (GY)-induced sensitive endpoint for nephrotoxicity, an integrated strategy was applied in the present study. Using bioinformatic analysis, based on the constructed Protein-protein interaction networks, Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis on the core target network were performed to illustrate the potential gene targets and signal pathways. Then, the most enriched pathway was validated with Cell counting kit-8 assays and Western blot analysis in embryonic kidney epithelial 293 cell models. According to the findings, GY may interact with 321 targets related to the endpoint. The five targets on the top ranking in the PPI network were STAT3, SRC, HRAS, AKT1, EP300. Among them, PI3K/Akt was the most enriched pathway. In vitro testing showed that GY exerted a proliferative effect on the cell variability in a dose-dependent manner. GY at concentration of 1000 μg/ml and stimulation for 30 min can significantly enhance the expression of phosphorylated Akt. Thus, after the quantitative weight of evidence evaluation, Akt phosphorylation induced PI3K/Akt activation was speculated as a molecular initiating event leading to a proliferative and inflammatory response in renal tubular epithelial cells.

Exploring the Potential of Broadband Complementary Metal Oxide Semiconductor Micro-Coil Nuclear Magnetic Resonance for Environmental Research

With sensitivity being the Achilles' heel of nuclear magnetic resonance (NMR), the superior mass sensitivity offered by micro-coils can be an excellent choice for tiny, mass limited samples such as eggs and small organisms. Recently, complementary metal oxide semiconductor (CMOS)-based micro-coil transceivers have been reported and demonstrate excellent mass sensitivity. However, the ability of broadband CMOS micro-coils to study heteronuclei has yet to be investigated, and here their potential is explored within the lens of environmental research. Eleven nuclei including 7Li, 19F, 31P and, 205Tl were studied and detection limits in the low to mid picomole range were found for an extended experiment. Further, two environmentally relevant samples (a sprouting broccoli seed and a D. magna egg) were successfully studied using the CMOS micro-coil system. 13C NMR was used to help resolve broad signals in the 1H spectrum of the 13C enriched broccoli seed, and steady state free precession was used to improve the signal-to-noise ratio by a factor of six. 19F NMR was used to track fluorinated contaminants in a single D. magna egg, showing potential for studying egg-pollutant interactions. Overall, CMOS micro-coil NMR demonstrates significant promise in environmental research, especially when the future potential to scale to multiple coil arrays (greatly improving throughput) is considered.

Magnetic resonance imaging and ultrasound elastography in the context of preclinical pharmacological research: significance for the 3R principles

The 3Rs principles-reduction, refinement, replacement-are at the core of preclinical research within drug discovery, which still relies to a great extent on the availability of models of disease in animals. Minimizing their distress, reducing their number as well as searching for means to replace them in experimental studies are constant objectives in this area. Due to its non-invasive character in vivo imaging supports these efforts by enabling repeated longitudinal assessments in each animal which serves as its own control, thereby enabling to reduce considerably the animal utilization in the experiments. The repetitive monitoring of pathology progression and the effects of therapy becomes feasible by assessment of quantitative biomarkers. Moreover, imaging has translational prospects by facilitating the comparison of studies performed in small rodents and humans. Also, learnings from the clinic may be potentially back-translated to preclinical settings and therefore contribute to refining animal investigations. By concentrating on activities around the application of magnetic resonance imaging (MRI) and ultrasound elastography to small rodent models of disease, we aim to illustrate how in vivo imaging contributes primarily to reduction and refinement in the context of pharmacological research.

Aspirin ameliorates the neurotoxicity of benzo[a]pyrene in mice and HT22 cells: Possible role of miRNA-mRNA network

Benzo[a]pyrene (B[a]P) is neurotoxic, however, the mechanism and potential prevention are yet not clear. This study explored the miRNA-mRNA network in the B[a]P-induced neurotoxicity in mice and HT22 cells and the intervention of aspirin (ASP). HT22 cells were treated for 48 h with DMSO, B[a]P (20 μM), or both B[a]P (20 μM) and ASP (4 μM). Following B[a]P treatment, compared to the DMSO controls, HT22 cells showed injured cell morphology, reduced cell viability and neurotrophic factor concentrations, and increased LDH leakage, Aβ_1-42, and inflammatory factor concentrations, which were improved by ASP. RNA sequencing and qPCR verified the significant differences of miRNA and mRNA profiles following B[a]P treatment, which were rescued by ASP. Bioinformatics analysis suggested the miRNA-mRNA network could be involved in the neurotoxicity of B[a]P and the intervention of ASP. The neurotoxicity and neuroinflammation were induced in mice's brains by B[a]P, and the target miRNA and mRNA were proved to be consistent with in vitro, which were ameliorated by ASP. The findings demonstrate a possible role of miRNA-mRNA network in the B[a]P-induced neurotoxicity. If this is confirmed by additional experiments, it will provide a promising pathway of intervention against B[a]P, using ASP or other agents with fewer toxic effects.

Environmentally relevant doses of endocrine disrupting chemicals affect male fertility by interfering with sertoli cell glucose metabolism in mice

The growing global deterioration in several aspects of human health has been partly attributed to hazardous effects of endocrine-disrupting chemicals (EDCs) exposure. Therefore, experts and government regulatory agencies have consistently advocated for studies on the combined effects of EDCs that model human exposure to multiple environmental chemicals in real life. Here, we investigated how low concentrations of bisphenol A (BPA), and phthalates compounds affect the Sertoli cell glucose uptake/lactate production in the testis and male fertility. An EDC mixture containing a detected amount of each chemical compound in humans, called daily exposure (DE), and DE increased in magnitude by 25 (DE25), 250 (DE250), and 2500 (DE2500), and corn oil (control) were administered for six weeks to male mice. We found that DE activated estrogen receptor beta (Erβ) and glucose-regulated protein 78 (Grp 78) and disrupted the estradiol (E2) balance. In addition, DE25, DE250, and DE2500 doses of the EDC mixture via binding with Sertoli cells' estrogen receptors (ERs) inhibited the glucose uptake and lactate production processes by downregulating glucose transporters (GLUTs) and glycolytic enzymes. As a result, endoplasmic reticulum stress (ERS), marked by unfolded protein response (UPR) activation, was induced. The accompanying upregulation of activating transcription factor 4 (ATF4), inositol requiring enzyme-1 (IRE1), C/EBP homologous protein (CHOP), and mitogen-activated protein kinase (MAPK) signaling promoted antioxidant depletion, testicular cell apoptosis, abnormal regulation of the blood-testis barrier, and decreased sperm count. Therefore, these findings suggest that human and wildlife exposure to multiple environmental chemicals can produce a wide range of reproductive health complications in male mammals.

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.