Conventional Toxicity Tests Research Articles

Interpretable attention-based multi-encoder transformer based QSPR model for assessing toxicity and environmental impact of chemicals

The rising demand from consumer goods and pharmaceutical industry is driving a fast expansion of newly developed chemicals. The conventional toxicity testing of unknown chemicals is expensive, time-consuming, and raises ethical concerns. The quantitative structure–property relationship (QSPR) is an efficient computational method because it saves time, resources, and animal experimentation. Advances in machine learning have improved chemical analysis in QSPR studies, but the real-world application of machine learning-based QSPR studies was limited by the unexplainable ‘black box’ feature of the machine learnings. In this study, multi-encoder structure-to-toxicity (S2T)-transformer based QSPR model was developed to estimate the properties of polychlorinated biphenyls (PCBs) and endocrine disrupting chemicals (EDCs). Simplified molecular input line entry systems (SMILES) and molecular descriptors calculated by the Dragon 6 software, were simultaneously considered as input of QSPR model. Furthermore, an attention-based framework is proposed to describe the relationship between the molecular structure and toxicity of hazardous chemicals. The S2T-transformer model achieved the highest R2 scores of 0.918, 0.856, and 0.907 for logarithm of octanol-water partition coefficient (Log KOW), octanol-air partition coefficient (Log KOA), and bioconcentration factor (Log BCF) estimation of PCBs, respectively. Moreover, the attention weights were able to properly interpret the lateral (meta, para) chlorination associated with PCBs toxicity and environmental impact.

Transcriptome analysis of a springtail, Folsomia candida, reveals energy constraint and oxidative stress during petroleum hydrocarbon exposure

Petroleum hydrocarbon (PHC) contamination in soil is ubiquitous and poses harmful consequences to many organisms. The toxicity of PHC-impacted soil is difficult to predict due to variations in mixture composition and the impacts of natural weathering processes. Hence, high-throughput methods to assess PHC-impacted soils is required to expedite land management decisions. Next-generation sequencing is a robust tool that allows researchers to investigate the effects of contaminants on the transcriptome of organisms and identify molecular biomarkers. In this study, the effects of PHCs on conventional endpoints (i.e., survival and reproduction) and gene expression rates of a model springtail species, Folsomia candida were investigated. Age-synchronized F. candida were exposed to ecologically-relevant concentrations of soils spiked with fresh crude oil to calculate the reproductive EC25 and EC50 values using conventional toxicity testing. Soils spiked to these concentrations were then used to evaluate effects on the F. candida transcriptome over a 7-day exposure period. RNA-seq analysis found 98 and 132 differentially expressed genes when compared to the control for the EC25 and EC50 treatment groups, respectively. The majority of up-regulated genes were related to xenobiotic biotransformation reactions and oxidative stress response, while down-regulated genes coded for carbohydrate and peptide metabolic processes. Promotion of the pentose phosphate pathway was also found. Results suggest that the decreased reproduction rates of F. candida exposed to PHCs is due to energy constraints caused by inhibition of carbohydrate metabolic processes and allocation of remaining energy to detoxify xenobiotics. These findings provide insights into the molecular effects in F. candida following exposure to crude oil for seven days and highlight their potential to be used as a high-throughput screening test for PHC-contaminated sites. Adverse molecular effects can be measured as early as 24 h following exposure, whereas conventional toxicity tests may require a minimum of four weeks.

Hi-MGT: A hybrid molecule graph transformer for toxicity identification

Conventional toxicity testing methods that rely on animal experimentation are resource-intensive, time-consuming, and ethically controversial. Therefore, the development of alternative non-animal testing approaches is crucial. This study proposes a novel hybrid graph transformer architecture, termed Hi-MGT, for the toxicity identification. An innovative aggregation strategy, referred to as GNN-GT combination, enables Hi-MGT to simultaneously and comprehensively aggregate local and global structural information of molecules, thus elucidating more informative toxicity information hidden in molecule graphs. The results show that the state-of-the-art model outperforms current baseline CML and DL models on a diverse range of toxicity endpoints and is even comparable to large-scale pretrained GNNs with geometry enhancement. Additionally, the impact of hyperparameters on model performance is investigated, and a systematic ablation study is conducted to demonstrate the effectiveness of the GNN-GT combination. Moreover, this study provides valuable insights into the learning process on molecules and proposes a novel similarity-based method for toxic site detection, which could potentially facilitate toxicity identification and analysis. Overall, the Hi-MGT model represents a significant advancement in the development of alternative non-animal testing approaches for toxicity identification, with promising implications for enhancing human safety in the use of chemical compounds.

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

BackgroundGiven the increasing exposure of humans to environmental chemicals and the limitations of conventional toxicity test, there is an urgent need to develop next-generation risk assessment methods. ObjectivesThis study aims to establish a novel computational system named Toxicogenomics Scoring System (TGSS) to predict the carcinogenicity of chemicals coupling chemical-gene interactions with multiple cancer transcriptomic datasets. MethodsChemical-related gene signatures were derived from chemical-gene interaction data from the Comparative Toxicogenomics Database (CTD). For each cancer type in TCGA, genes were ranked by their effects on tumorigenesis, which is based on the differential expression between tumor and normal samples. Next, we developed carcinogenicity scores (C-scores) using pre-ranked GSEA to quantify the correlation between chemical-related gene signatures and ranked gene lists. Then we established TGSS by systematically evaluating the C-scores in multiple chemical-tumor pairs. Furthermore, we examined the performance of our approach by ROC curves or prognostic analyses in TCGA and multiple independent cancer cohorts. ResultsForty-six environmental chemicals were finally included in the study. C-score was calculated for each chemical-tumor pair. The C-scores of IARC Group 3 chemicals were significantly lower than those of chemicals in Group 1 (P-value = 0.02) and Group 2 (P-values = 7.49 ×10−5). ROC curves analysis indicated that C-score could distinguish “high-risk chemicals” from the other compounds (AUC = 0.67) with a specificity and sensitivity of 0.86 and 0.57. The results of survival analysis were also in line with the assessed carcinogenicity in TGSS for the chemicals in Group 1. Finally, consistent results were further validated in independent cancer cohorts. ConclusionTGSS highlighted the great potential of integrating chemical-gene interactions with gene-cancer relationships to predict the carcinogenic risk of chemicals, which would be valuable for systems toxicology.

Translating nanoparticle dosimetry from conventional in vitro systems to occupational inhalation exposures

As encouraged by Toxicity Testing in the 21st Century, researchers increasingly apply high-throughput in vitro approaches to identify and characterize nanoparticle hazards, including conventional aqueous cell culture systems to assess respiratory hazards. Translating nanoparticle dose from conventional toxicity testing systems to relevant human exposures remains a major challenge for assessing occupational risk of nanoparticle exposures. Here, we explored existing computational tools and data available to translate nanoparticle dose metrics from cellular test systems to inhalation exposures of silver nanoparticles in humans. We used the Multiple-Path Particle Dosimetry (MPPD) Model to predict nanoparticle deposition of humans exposed to 20 and 110 nm silver nanoparticles at 0.9 μg/m3 over an 8 h period, the proposed National Institute of Occupational Safety and Health (NIOSH) recommended exposure limit (REL). MPPD predicts 8.1 and 3.7 μg of silver deposited in an 8 h period for 20 and 110 nm nanoparticles, respectively, with 20 nm particles displaying nearly 11-fold higher total surface area deposited. Peak deposited nanoparticle concentrations occurred more proximal in the pulmonary tract compared to mass deposition patterns (generation 4 vs. generations 20–21, respectively) due to regional differences in lung lining fluid volumes. Assuming 0.4% nanoparticle dissolution by mass measured in previous studies predicted peak concentrations of silver ions in cells of 1.06 and 0.89 μg/mL for 20 and 110 nm particles, respectively. Both predicted concentrations are below the measured toxic threshold of 1.7 μg/mL of silver ions in cells from in vitro assessments. Assuming 4% dissolution by mass predicted 10-fold higher silver concentrations in tissues, peaking at 10.6 and 8.9 μg/mL, for 20 and 110 nm nanoparticles respectively, exceeding the observed in vitro toxic threshold and highlighting the importance and sensitivity of dissolution rates. Overall, this approach offers a framework for extrapolating nanotoxicity results from in vitro cell culture systems to human exposures. Aligning appropriate dose metrics from in vitro and in vivo hazard characterizations and human pulmonary doses from occupational exposures are critical components for successful nanoparticle risk assessment and worker protection providing guidance for designing future in vitro studies aimed at relevant human exposures.

In vivo toxicometabolomics reveals multi-organ and urine metabolic changes in mice upon acuteexposure to human-relevant doses of 3,4-methylenedioxypyrovalerone (MDPV).

3,4-Methylenedioxypyrovalerone (MDPV) is consumed worldwide, despite its potential to cause toxicity in several organs and even death. There is a recognized need to clarify the biological pathways throughwhich MDPV elicits general and target-organ toxicity. In this work, a comprehensive untargeted GC-MS-based metabolomics analysis was performed, aiming to detect metabolic changes in putative target organs (brain, heart, kidneys and liver) but also in urine of mice after acute exposure to human-relevant doses of MDPV. Male CD-1 mice received binge intraperitoneal administrations of saline or MDPV (2.5mg/kg or 5mg/kg) every 2h, for a total of three injections. Twenty-four hours after the first administration, target organs, urine and blood samples were collected for metabolomics, biochemical and histological analysis. Hepatic and renal tissues of MDPV-treated mice showed moderate histopathological changes but no significant differences were found in plasma and tissue biochemical markers of organ injury. In contrast, the multivariate analysis significantly discriminated the organs and urine of MDPV-treated mice from the control (except for the lowest dose in the brain), allowing the identification of a panoply of metabolites. Those levels were significantly deviated in relation to physiological conditions and showed an organ specific response towards the drug. Kidneys and liver showed the greatest metabolic changes. Metabolites related with energetic metabolism, antioxidant defenses and inflammatory response were significantly changed in the liver of MDPV-dosed animals, while the kidneys seem to have developed an adaptive response against oxidative stress caused by MDPV. On the other hand, the dysregulation of metabolites that contribute to metabolic acidosis was also observed in this organ. The heart showed an increase of fatty acid biosynthesis, possibly as an adaptation to maintain the cardiac energy homeostasis. In the brain, changes in 3-hydroxybutyric acid levels may reflect the activation of a neurotoxic pathway. However, the increase in metabolites with neuroprotective properties seems to counteract this change. Metabolic profiling of urine from MDPV-treated mice suggested that glutathione-dependent antioxidant pathways may be particularly involved in the compensatory mechanism to counteract oxidative stress induced by MDPV. Overall, this study reports, for the first time, the metabolic profile of liver, kidneys, heart, brain, and urine of MDPV-dosed mice, providing unique insights into the biological pathways of toxicity. Our findings also underline the value of toxicometabolomics as a robust and sensitive tool for detecting adaptive/toxic cellular responses upon exposure to a physiologically relevant dose of a toxic agent, earlier than conventional toxicity tests.

Transcriptomics in Toxicogenomics, Part I: Experimental Design, Technologies, Publicly Available Data, and Regulatory Aspects.

The starting point of successful hazard assessment is the generation of unbiased and trustworthy data. Conventional toxicity testing deals with extensive observations of phenotypic endpoints in vivo and complementing in vitro models. The increasing development of novel materials and chemical compounds dictates the need for a better understanding of the molecular changes occurring in exposed biological systems. Transcriptomics enables the exploration of organisms’ responses to environmental, chemical, and physical agents by observing the molecular alterations in more detail. Toxicogenomics integrates classical toxicology with omics assays, thus allowing the characterization of the mechanism of action (MOA) of chemical compounds, novel small molecules, and engineered nanomaterials (ENMs). Lack of standardization in data generation and analysis currently hampers the full exploitation of toxicogenomics-based evidence in risk assessment. To fill this gap, TGx methods need to take into account appropriate experimental design and possible pitfalls in the transcriptomic analyses as well as data generation and sharing that adhere to the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. In this review, we summarize the recent advancements in the design and analysis of DNA microarray, RNA sequencing (RNA-Seq), and single-cell RNA-Seq (scRNA-Seq) data. We provide guidelines on exposure time, dose and complex endpoint selection, sample quality considerations and sample randomization. Furthermore, we summarize publicly available data resources and highlight applications of TGx data to understand and predict chemical toxicity potential. Additionally, we discuss the efforts to implement TGx into regulatory decision making to promote alternative methods for risk assessment and to support the 3R (reduction, refinement, and replacement) concept. This review is the first part of a three-article series on Transcriptomics in Toxicogenomics. These initial considerations on Experimental Design, Technologies, Publicly Available Data, Regulatory Aspects, are the starting point for further rigorous and reliable data preprocessing and modeling, described in the second and third part of the review series.

Pathogen inactivation method using ultraviolet C light

Since the end of the 1990s pathogen inactivation methods were being gradually implemented into routine work of blood transfusion establishments in many countries with regard to blood components dedicated for clinical use. The developed pathogen inactivation methods were either based on chemical compounds eg. the solvent detergent (SD) inactivation method or on photochemical and photodynamic reactions eg. inactivation methods with methylene blue, amotosalen hydrochloride and riboflavin. Blood components inactivated with any of the above mentioned methods still had traces of chemical compounds although removal steps were added (an exception here is the method with riboflavin). Attempts were therefore undertaken to develop an inactivation method based not on chemical compounds but on specific wavelength irradiation. An example of such inactivation method solely based on properties of short-wave UVC-light (UVC) with no photosensitizing chemicals is the Theraflex UV-Platelets system dedicated to platelet concentrates (PCs). The system uses 254 nm wavelength irradiation which is not absorbed by proteins so conventional toxicity tests are not required. The method is effective for clinically significant both G (+) and G (-) bacteria as well as viruses and protozoa. Clinical trials demonstrated reduced recovery of UVC-irradiated platelets and shorter survival time in the recipient’s organism.

Machine learning for mixture toxicity analysis based on high-throughput printing technology

Analysis on mixture toxicity (Mix-tox) of the multi-chemical space is constantly followed with interest for many researchers. Conventional toxicity tests with time-consuming and costly operations make researchers can only establish some toxicity prediction models aiming to a limited sampling dimension. The rapid development of machine learning (ML) algorithm will accelerate the exploration of many fields involving toxicity analysis. Rather than the model calculation capacity, the challenge of this process mainly comes from the lack of toxicology big-data to perform toxicity perception through the ML model. In this paper, a full strategy based a standardized high-throughput experiment was developed for Mix-tox analysis throughout the whole routine, from big-sample dataset design, model building, and training, to the toxicity prediction. Using the concentration variates as input and bio-luminescent inhibition rate as output, it turned out that a well-trained random forest algorithm was successfully applied to assess the mixtures’ toxicity effect, suggesting its value in facilitating adoption of Mix-tox analysis.

Deep learning for predicting toxicity of chemicals: a mini review

Humans and wildlife inhabit a world with panoply of natural and synthetic chemicals. Alarmingly, only a limited number of chemicals have undergone comprehensive toxicological evaluation due to limitations of traditional toxicity testing. High-throughput screening assays provide a higher-speed alternative for conventional toxicity testing. Advancement of high-throughput bioassay technology has greatly increased chemical toxicity data volumes in the past decade, pushing toxicology research into a “big data” era. However, traditional data analysis methods fail to effectively process large data volumes, presenting both a challenge and an opportunity for toxicologists. Deep learning, a machine learning method leveraging deep neural networks (DNNs), is a proven useful tool for building quantitative structure–activity relationship (QSAR) models for toxicity prediction utilizing these new large datasets. In this mini review, a brief technical background on DNNs is provided, and the current state of chemical toxicity prediction models built with DNNs is reviewed. In addition, relevant toxicity data sources are summarized, possible limitations are discussed, and perspectives on DNN utilization in chemical toxicity prediction are given.

Caging of planktonic rotifers in microfluidic environment for sub-lethal aquatic toxicity tests.

Quantification of neuro-behavioural responses of intact small model organisms has been proposed as a sensitive, sub-lethal alternative to conventional toxicity testing. Such bioassays are characterized by a high physiological and ecological relevance, short response times, increased sensitivity, and non-invasive nature. Despite a significant potential for predictive aquatic toxicology analysis of behavioural traits of micro-invertebrates in microfluidic environment has received little attention. In this work, we demonstrate a new Lab-on-a-Chip technology capable of effectively caging freshwater rotifers Brachionus calyciflorus for real-time video-microscopy analysis. We demonstrate that behavioural bioassays performed under microfluidic perfusion can significantly enhance the sensitivity of conventional ecotoxicology test protocols.

The Toxicity of Nanoparticles to Organisms in Freshwater.

Nanotechnology is a rapidly growing industry yielding many benefits to society. However, aquatic environments are at risk as increasing amounts of nanoparticles (NPs) are contaminating waterbodies causing adverse effects on aquatic organisms. In this review, the impacts of environmental exposure to NPs, the influence of the physicochemical characteristics of NPs and the surrounding environment on toxicity and mechanisms of toxicity together with NP bioaccumulation and trophic transfer are assessed with a focus on their impacts on bacteria, algae and daphnids. We identify several gaps which need urgent attention in order to make sound decisions to protect the environment. These include uncertainty in both estimated and measured environmental concentrations of NPs for reliable risk assessment and for regulating the NP industry. In addition toxicity tests and risk assessment methodologies specific to NPs are still at the research and development stage. Also conflicting and inconsistent results on physicochemical characteristics and the fate and transport of NPs in the environment suggest the need for further research. Finally, improved understanding of the mechanisms of NP toxicity is crucial in risk assessment of NPs, since conventional toxicity tests may not reflect the risks associated with NPs. Behavioural effects may be more sensitive and would be efficient in certain situations compared with conventional toxicity tests due to low NP concentrations in field conditions. However, the development of such tests is still lacking, and further research is recommended.

Epigenetic toxicity of environmental chemicals upon exposure during development - Bisphenol A and valproic acid may have epigenetic effects

As of 2017, chemical substances registered in Chemical Abstracts Service (CAS) exceed 100 million, which is increasing yearly. The safety of chemical substances is adequately managed by regulations based on scientific information from toxicity tests. However, there are substances reported to have "biological effects" even though they are judged to be nontoxic in conventional toxicity tests. Therefore, it is necessary to consider a new concept on toxicity, "epigenetic toxicity". In this review, we explain about epigenetic toxicity using bisphenol A (BPA) and valproic acid (VPA) as examples. We also discuss the problems associated with the judgment of epigenetic toxicity. Currently, epigenetic changes can only be detected by biochemical methods, which are labor-intensive. Therefore, we are developing reporter mice that can be used to detect epigenetic toxicity during conventional toxicity tests. In addition, we consider that linking epigenomic changes with phenotypic changes is important, because causality is important for toxicity evaluation. Therefore, we are developing an artificial epigenome-editing technology. If we can develop a safety-assessment system by incorporating epigenetic evaluation into toxicity tests, we can increase the safety of both food and environmental chemical substances. The practical application of such a new safety-assessment system will be increasingly important in the future.

Immunotoxicity, genotoxicity and epigenetic toxicity of nanomaterials: New strategies for toxicity testing?

The unique properties of nanomaterials (NMs) are beneficial in numerous industrial and medical applications. However, they could also induce unintended effects. Thus, a proper strategy for toxicity testing is essential in human hazard and risk assessment. Toxicity can be tested in vivo and in vitro; in compliance with the 3Rs, alternative strategies for in vitro testing should be further developed for NMs. Robust, standardized methods are of great importance in nanotoxicology, with comprehensive material characterization and uptake as an integral part of the testing strategy. Oxidative stress has been shown to be an underlying mechanism of possible toxicity of NMs, causing both immunotoxicity and genotoxicity. For testing NMs in vitro, a battery of tests should be performed on cells of human origin, either cell lines or primary cells, in conditions as close as possible to an in vivo situation. Novel toxicity pathways, particularly epigenetic modification, should be assessed along with conventional toxicity testing methods. However, to initiate epigenetic toxicity screens for NM exposure, there is a need to better understand their adverse effects on the epigenome, to identify robust and reproducible causal links between exposure, epigenetic changes and adverse phenotypic endpoints, and to develop improved assays to monitor epigenetic toxicity.

Cyto-genotoxic and DNA methylation changes induced by different crystal phases of TiO2-np in bronchial epithelial (16-HBE) cells

With the increase in use of TiO2-np, a better understanding of their safety is important. In the present study the effect of different crystal phases of TiO2-np (anatase, rutile and anatase: rutile mixture; 20–26nm) were studied for cyto-genotoxicity and global DNA methylation and hydroxymethylation. Cytotoxic response was observed at a concentration of 25μg/ml for the particles tested. Results of comet and micronucleus (with and without CytB) assays revealed significant genotoxic effect of these particles. Flow cytometry revealed cell cycle arrest in the S-phase. Based on the results, toxicity of the particles could be correlated with their physico-chemical properties (i.e. smaller size and hydrodynamic diameter and larger surface area), anatase form being the most toxic. From the results of the cyto-genotoxicity assays, concentrations were determined for the epigenetic study. Effect on global DNA methylation and hydroxymethylation levels were studied at cyto-genotoxic (25μg/ml), genotoxic (12.5μg/ml) and sub cyto-genotoxic (3.25μg/ml) concentrations using LC–MS/MS analysis. Though no significant changes were observed for 3h treatment schedule; significant hypomethylation were observed at 24h for anatase (significant at 3.25 and 25μg/ml), rutile (significant at 3.25 and 25μg/ml) and anatase: rutile mixture (significant at 25μg/ml) forms. The results suggest that epigenetic changes could occur at sub cyto-genotoxic concentrations. And hence for complete characterization of nanoparticle toxicity, epigenetic studies should be performed along with conventional toxicity testing methods.

Disrupt toxicity testing

Dimensions of time that are relevant to molecular mechanisms are not incorporated into conventional toxicity testing methodologies. Historically, this was due to technological limitations. These limitations no longer exist. Application of real-time imaging and chemical sensor technologies presents an opportunity to overcome the challenges that have stalled essential transformation of toxicity testing methodology.

Dose: Time-to-effect analyses can identify hazardous chemicals at an early stage of product development.

Expenditure for discovery and development of a new crop protection product is now approaching the $ 300 million mark, while at the same time underpinning information gaps in environmental safety assessment. Large information gaps also exist for the safety of a vast number of existing chemicals in commerce. Addressing safety information gaps of new and existing chemicals with increased conventional toxicity testing would skyrocket costs and animal usage and contravene the 3Rs principles to refine, reduce, and replace animal testing. There is a great need to improve predictive safety assessment with minimum animal use. Recent progress in the understanding of the molecular biology of chemical dose response relationships, which has confirmed general applicability of the Druckrey-Kupfmuller theorem for receptor-mediated toxicity, can now be employed at an early stage of product development to identify (and eliminate) hazardous chemicals using short-term chronic dose response tests in invertebrates. Time-to-effect (TTE) approaches can identify hazardous chemicals with time-cumulative toxicity. This approach would shift research and development programs to chemicals with ascertained strictly dosedependent toxicity, for which no-observable-adverse-effect levels (NOAELs) can be reliably established with short-term tests, which could make a myriad of resource-intensive animal studies obsolete.

Quantifying the Sensitivity of Soil Microbial Communities to Silver Sulfide Nanoparticles Using Metagenome Sequencing.

Soils are a sink for sulfidised-silver nanoparticles (Ag2S-NPs), yet there are limited ecotoxicity data for their effects on microbial communities. Conventional toxicity tests typically target a single test species or function, which does not reflect the broader community response. Using a combination of quantitative PCR, 16S rRNA amplicon sequencing and species sensitivity distribution (SSD) methods, we have developed a new approach to calculate silver-based NP toxicity thresholds (HCx, hazardous concentrations) that are protective of specific members (operational taxonomic units, OTUs) of the soil microbial community. At the HC20 (80% of species protected), soil OTUs were significantly less sensitive to Ag2S-NPs compared to AgNPs and Ag+ (5.9, 1.4 and 1.4 mg Ag kg-1, respectively). However at more conservative HC values, there were no significant differences. These trends in OTU responses matched with those seen in a specific microbial function (rate of nitrification) and amoA-bacteria gene abundance. This study provides a novel molecular-based framework for quantifying the effect of a toxicant on whole soil microbial communities while still determining sensitive genera/species. Methods and results described here provide a benchmark for microbial community ecotoxicological studies and we recommend that future revisions of Soil Quality Guidelines for AgNPs and other such toxicants consider this approach.

In response: Government perspective.

In response: Government perspective.

Development of an experiential education program for the environmental education using the conventional environmental toxicity test

Development of an experiential education program for the environmental education using the conventional environmental toxicity test