Environmental Risk Assessment Of Chemicals Research Articles

6PPD induced cardiac dysfunction in zebrafish associated with mitochondrial damage and inhibition of autophagy processes

The compound 6PPD is widely acknowledged for its antioxidative properties; however, concerns regarding its impact on aquatic organisms have spurred comprehensive investigations. In our study, we advanced our comprehension by revealing that exposure to 6PPD could induce cardiac dysfunction, myocardial injury and DNA damage in adult zebrafish. Furthermore, our exploration unveiled that the exposure of cardiomyocytes to 6PPD resulted in apoptosis and mitochondrial injury, as corroborated by analyses using transmission electron microscopy and flow cytometry. Significantly, our study demonstrated the activation of the autophagy pathway in both the heart of zebrafish and cardiomyocytes, as substantiated by transmission electron microscopy and immunofluorescent techniques. Importantly, the increased the expression of P62 in the heart and cardiomyocytes suggested an inhibition of the autophagic process. The reduction in autophagy flux was also verified through in vivo experiments involving the infection of mCherry-GFP-LC3. We further identified that the fusion of autophagosomes and lysosomes was impaired in the 6PPD treatment group. In summary, our findings indicated that the impaired fusion of autophagosomes and lysosomes hampered the autophagic degradation process, leading to apoptosis and ultimately resulting in cardiac dysfunction and myocardial injury. This study discovered the crucial role of the autophagy pathway in regulating 6PPD-induced cardiotoxicity. Synopsis6PPD exposure inhibited the autophagic degradation process and induced mitochondrial injury and apoptosis in the heart of adult zebrafish.

Short-term exposure to environmental levels of nicotine and cotinine impairs visual motor response in zebrafish larvae through a similar mode of action: Exploring the potential role of zebrafish α7 nAChR

The current view is that environmental levels of nicotine and cotinine, commonly in the ng/L range, are safe for aquatic organisms. In this study, 7 days post-fertilization zebrafish embryos have been exposed for 24 h to a range of environmental concentrations of nicotine (2.0 ng/L-2.5 μg/L) and cotinine (50 pg/L–10 μg/L), as well as to a binary mixture of these emerging pollutants. Nicotine exposure led to hyperactivity, decreased vibrational startle response and increased non-associative learning. However, the more consistent effect found for both nicotine and cotinine was a significant increase in light-off visual motor response (VMR). The effect of both pollutants on this behavior occurred through a similar mode of action, as the joint effects of the binary mixture of both chemicals were consistent with the concentration addition concept predictions. The results from docking studies suggest that the effect of nicotine and cotinine on light-off VMR could be mediated by zebrafish α7 nAChR expressed in retina. The results presented in this study emphasize the need to revisit the environmental risk assessment of chemicals including additional ecologically relevant sublethal endpoints.

Roadmap for action on the environmental risk assessment of chemicals for insect pollinators (IPol‐ERA)

Roadmap for action on the environmental risk assessment of chemicals for insect pollinators (IPol‐ERA)

Unlocking secrets of microbial ecotoxicology: recent achievements and future challenges.

Environmental pollution is one of the main challenges faced by humanity. By their ubiquity and vast range of metabolic capabilities, microorganisms are affected by pollution with consequences on their host organisms and on the functioning of their environment. They also play key roles in the fate of pollutants through the degradation, transformation, and transfer of organic or inorganic compounds. Thus, they are crucial for the development of nature-based solutions to reduce pollution and of bio-based solutions for environmental risk assessment of chemicals. At the intersection between microbial ecology, toxicology, and biogeochemistry, microbial ecotoxicology is a fast-expanding research area aiming to decipher the interactions between pollutants and microorganisms. This perspective paper gives an overview of the main research challenges identified by the Ecotoxicomic network within the emerging One Health framework and in the light of ongoing interest in biological approaches to environmental remediation and of the current state of the art in microbial ecology. We highlight prevailing knowledge gaps and pitfalls in exploring complex interactions among microorganisms and their environment in the context of chemical pollution and pinpoint areas of research where future efforts are needed.

Linking ecotoxicological effects on biodiversity and ecosystem functions to impairment of ecosystem services is a challenge: an illustration with the case of plant protection products.

There is growing interest in using the ecosystem services framework for environmental risk assessments of chemicals, including plant protection products (PPPs). Although this topic is increasingly discussed in the recent scientific literature, there is still a substantial gap between most ecotoxicological studies and a solid evaluation of potential ecotoxicological consequences on ecosystem services. This was recently highlighted by a collective scientific assessment (CSA) performed by 46 scientific experts who analyzed the international science on the impacts of PPPs on biodiversity, ecosystem functions, and ecosystem services. Here, we first point out the main obstacles to better linking knowledge on the ecotoxicological effects of PPPs on biodiversity and ecological processes with ecosystem functions and services. Then, we go on to propose and discuss possible pathways for related improvements. We describe the main processes governing the relationships between biodiversity, ecological processes, and ecosystem functions in response to effects of PPP, and we define categories of ecosystem functions that could be directly linked with the ecological processes used as functional endpoints in investigations on the ecotoxicology of PPPs. We then explore perceptions on the possible links between these categories of ecosystem functions and ecosystem services among a sub-panel of the scientific experts from various fields of environmental science. We find that these direct and indirect linkages still need clarification. This paper, which reflects the difficulties faced by the multidisciplinary group of researchers involved in the CSA, suggests that the current gap between most ecotoxicological studies and a solid potential evaluation of ecotoxicological consequences on ecosystem services could be partially addressed if concepts and definitions related to ecological processes, ecosystem functions, and ecosystem services were more widely accepted and shared within the ecotoxicology community. Narrowing this gap would help harmonize and extend the science that informs decision-making and policy-making, and ultimately help to better address the trade-off between social benefits and environmental losses caused by the use of PPPs.

The Role of "Physiologically Based Pharmacokinetic Model (PBPK)" New Approach Methodology (NAM) in Pharmaceuticals and Environmental Chemical Risk Assessment.

Physiologically Based Pharmacokinetic (PBPK) models are mechanistic tools generally employed in the pharmaceutical industry and environmental health risk assessment. These models are recognized by regulatory authorities for predicting organ concentration-time profiles, pharmacokinetics and daily intake dose of xenobiotics. The extension of PBPK models to capture sensitive populations such as pediatric, geriatric, pregnant females, fetus, etc., and diseased populations such as those with renal impairment, liver cirrhosis, etc., is a must. However, the current modelling practices and existing models are not mature enough to confidently predict the risk in these populations. A multidisciplinary collaboration between clinicians, experimental and modeler scientist is vital to improve the physiology and calculation of biochemical parameters for integrating knowledge and refining existing PBPK models. Specific PBPK covering compartments such as cerebrospinal fluid and the hippocampus are required to gain mechanistic understanding about xenobiotic disposition in these sub-parts. The PBPK model assists in building quantitative adverse outcome pathways (qAOPs) for several endpoints such as developmental neurotoxicity (DNT), hepatotoxicity and cardiotoxicity. Machine learning algorithms can predict physicochemical parameters required to develop in silico models where experimental data are unavailable. Integrating machine learning with PBPK carries the potential to revolutionize the field of drug discovery and development and environmental risk. Overall, this review tried to summarize the recent developments in the in-silico models, building of qAOPs and use of machine learning for improving existing models, along with a regulatory perspective. This review can act as a guide for toxicologists who wish to build their careers in kinetic modeling.

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

Cadmium (Cd) is a common environmental pollutant that can damage multiple organs, including the kidney. To prevent renal effects, international authorities have set health-based guidance values of Cd from epidemiological studies. To explore the health risk of Cd exposure and whether human equivalent doses (HEDs) derived from in vitro tests match the current guidance values, we integrated renal tubular epithelial cell-based assays with a physiologically based toxicokinetic model combined with the Monte Carlo method. For females, the HEDs (μg/kg/week) derived from KE2 (DNA damage), KE3 (cell cycle arrest), and KE4 (apoptosis) were 0.20 (2.5th-97.5th percentiles: 0.09-0.48), 0.52 (0.24-1.26), and 2.73 (1.27-6.57), respectively; for males the respective HEDs were 0.23 (0.10-0.49), 0.60 (0.27-1.30), and 3.11 (1.39-6.78). Among them, HEDKE4 (female) was close to the tolerable weekly intake (2.5μg/kg/week) set by the European Food Safety Authority. The margin of exposure (MOE) derived from HEDKE4 (female) indicated that risks of renal toxicity for populations living in cadmium-contaminated regions should be of concern. This study provided a new approach methodology (NAM) for environmental chemical risk assessment using in silico and in vitro methods.

Modeling bioconcentration factors in fish with explainable deep learning

The Bioconcentration Factor (BCF) is an important parameter in the environmental risk assessment of chemicals, relevant for industrial and academic research as well as required in many regulatory contexts. It represents the potential of a substance to accumulate in organic tissues or whole animals and is most frequently measured in fish. However, animal welfare reasons, throughput limitations, and costs push the need for alternative methods that allow accurate and reliable estimations of BCF in silico. We present a new deep learning model to predict BCF values from chemical structures, that outperforms currently available models (R2 of 0.68 and RMSE of 0.59 log units on an external test set; R2 of 0.70 and RMSE of 0.74 log units in a demanding cluster split validation). The model is based on molecular representations encoded as CDDD descriptors and exploits a large in-house dataset with measured logD values as an auxiliary task.Additionally, we developed a post-hoc explainability method based on SMILES character substitutions to accompany our predictions with atom-level interpretations. These sensitivity scores highlight the most influential moieties in the molecule and can help to understand the predictions better and design new molecules.

Microbiota-mediated reactivation of triclosan oxidative metabolites in colon tissues

Triclosan (TCS) is a widespread antimicrobial agent that is associated with many adverse health outcomes. Its gut toxicity has been attributed to the molecular modifications mediated by commensal microbes, but microbial transformations of TCS derivatives in the gut lumen are still largely unknown. Aromatic hydroxylation is the predominant oxidative metabolism of TCS that linked to its toxicological effects in host tissues. Here, we aimed to reveal the biological fates of hydroxyl-TCS (OH-TCS) in the colon, where intestinal microbes mainly reside. Unlike the profiles generated via host metabolism, OH-TCS species remain unconjugated in human stools from a cohort study. Through tracking molecular compositions in mouse intestinal tract, elevated abundance of free-form OH-TCS while reduced abundance of conjugated forms was observed in the colon digesta and mucosa. Using antibiotic-treated and germ-free mice, as well as in vitro approaches, we demonstrate that gut microbiota-encoded enzymes efficiently convert glucuronide/sulfate-conjugated OH-TCS, which are generated from host metabolism, back to their bioactive free-forms in colon tissues. Thus, host-gut microbiota metabolic interactions of TCS derivatives were proposed. These results shed light on the crucial roles of microbial metabolism in TCS toxicity, and highlight the importance of incorporating gut microbial transformations in health risk assessment of environmental chemicals.

Machine learning and artificial intelligence in physiologically based pharmacokinetic modeling.

Physiologically based pharmacokinetic (PBPK) models are useful tools in drug development and risk assessment of environmental chemicals. PBPK model development requires the collection of species-specific physiological, and chemical-specific absorption, distribution, metabolism, and excretion (ADME) parameters, which can be a time-consuming and expensive process. This raises a need to create computational models capable of predicting input parameter values for PBPK models, especially for new compounds. In this review, we summarize an emerging paradigm for integrating PBPK modeling with machine learning (ML) or artificial intelligence (AI)-based computational methods. This paradigm includes 3 steps (1) obtain time-concentration PK data and/or ADME parameters from publicly available databases, (2) develop ML/AI-based approaches to predict ADME parameters, and (3) incorporate the ML/AI models into PBPK models to predict PK summary statistics (eg, area under the curve and maximum plasma concentration). We also discuss a neural network architecture "neural ordinary differential equation (Neural-ODE)" that is capable of providing better predictive capabilities than other ML methods when used to directly predict time-series PK profiles. In order to support applications of ML/AI methods for PBPK model development, several challenges should be addressed (1) as more data become available, it is important to expand the training set by including the structural diversity of compounds to improve the prediction accuracy of ML/AI models; (2) due to the black box nature of many ML models, lack of sufficient interpretability is a limitation; (3) Neural-ODE has great potential to be used to generate time-series PK profiles for new compounds with limited ADME information, but its application remains to be explored. Despite existing challenges, ML/AI approaches will continue to facilitate the efficient development of robust PBPK models for a large number of chemicals.

Policy forums and learning in fields underpinned by regulatory science

In policy fields where regulatory science underpins government decision making, barriers to policy learning tend to be more entrenched. Presented with new and improved scientific methods and technologies, a key question increasingly facing regulatory authorities is how best to adapt established procedures without alienating stakeholders who value stability and low regulatory uncertainty? Focusing on the highly institutionalized field of environmental chemical risk assessment and drawing on recent advances in the conceptualization and operationalization of policy learning and policy forums, this paper explores factors linked to individuals and forum dynamics that encourage policy learning in fields underpinned by regulatory science. Using survey research, we analyze the learning experiences reported by policy actors who participated in an international ecotoxicology policy forum. Results suggest that open, balanced, and professional norm-driven forums will not necessarily produce efficient policy learning in fields underpinned by regulatory science due to motivated skepticism insomuch as forum participants are acutely aware that scientific advance may introduce uncertainty into regulatory processes and outcomes. Results also suggest that the longevity of actors’ forum participation is important to facilitate learning.

Ensemble learning for predicting ex vivo human placental barrier permeability

BackgroundThe placental barrier protects the fetus from exposure to some toxicants and is vital for drug development and risk assessment of environmental chemicals. However, in vivo experiments for assessing the placental barrier permeability of chemicals is not ethically acceptable. Although ex vivo placental perfusion methods provide good alternatives for the assessment of placental barrier permeability, the application to a large number of test chemicals could be time- and resource-consuming. Computational prediction models for ex vivo placental barrier permeability are therefore desirable.MethodsA total of 87 chemicals and corresponding 1444 physicochemical properties were divided into training and test datasets. Three types of algorithms including linear regression, random forest, and ensemble models were applied to develop prediction models for ex vivo placental barrier permeability.ResultsAmong the tested models, the ensemble model integrating the previous two methods performed best for predicting ex vivo human placental barrier permeability with correlation coefficients of 0.887 and 0.825 when considering the applicability domain. An additional test on seven newly curated chemicals from the literature showed a good correlation coefficient of 0.879 which was further improved to 0.921 by considering the variation of experiments.ConclusionIn this study, the first valid predicting model for ex vivo human placental barrier permeability was developed following the OECD guideline. The model is expected to be useful for assessing the human placental barrier permeability and can be integrated with developmental toxicity prediction models for investigating the toxic effects of chemicals on the fetus.

Renewing and improving the environmental risk assessment of chemicals

The processes underpinning the environmental risk assessment (ERA) of chemicals have not changed appreciably in the last 30 years. It is unclear how successful these processes are in protecting the environment from any adverse effects of chemicals. To ascertain if the current methodology can be improved, and if so, how, we invited experts to suggest how the current ERA process could be improved. They were not asked to select from a list of suggestions. The 36 experts made 109 suggestions for improvement, which could be grouped into 33 categories. The category that received the most support, from 12 experts, was to utilise a broader range of scientific information, including all up-to-date information, in ERAs. The second most popular category, supported by 10 experts, was the suggestion to regulate mixtures of chemicals; the current regulatory process involves chemical-by-chemical assessment. Two quite radical proposals were suggested. One was to replace the regulator with artificial intelligence. The other was to establish a new competent authority that would appoint groups of experts, each including representatives of the range of stakeholders, to decide which studies were required, commission those studies, then conduct the ERA based on the results of those studies. These two radical proposals, which the authors support strongly, are not necessarily mutually exclusive. We conclude that the present ERA process could be improved to better protect the environment from the myriad of chemicals in use.

Theme (concept) paper – Advancing the Environmental Risk Assessment of Chemicals to Better Protect Insect Pollinators (IPol‐ERA)

EFSA Supporting PublicationsVolume 19, Issue 5 E200505E Technical reportOpen Access Theme (concept) paper – Advancing the Environmental Risk Assessment of Chemicals to Better Protect Insect Pollinators (IPol-ERA) European Food Safety Authority (EFSA), Corresponding Author European Food Safety Authority (EFSA) SPIDO@efsa.europa.eu Correspondence:SPIDO@efsa.europa.euSearch for more papers by this authorDomenica Auteri, Domenica AuteriSearch for more papers by this authorGuilhem de Seze, Guilhem de SezeSearch for more papers by this authorYann Devos, Yann DevosSearch for more papers by this authorJulia Fabrega, Julia FabregaSearch for more papers by this authorClaudia Heppner, Claudia HeppnerSearch for more papers by this authorMarta Hugas, Marta HugasSearch for more papers by this authorSteve Pagani, Steve PaganiSearch for more papers by this authorAgnès Rortais, Agnès RortaisSearch for more papers by this author European Food Safety Authority (EFSA), Corresponding Author European Food Safety Authority (EFSA) SPIDO@efsa.europa.eu Correspondence:SPIDO@efsa.europa.euSearch for more papers by this authorDomenica Auteri, Domenica AuteriSearch for more papers by this authorGuilhem de Seze, Guilhem de SezeSearch for more papers by this authorYann Devos, Yann DevosSearch for more papers by this authorJulia Fabrega, Julia FabregaSearch for more papers by this authorClaudia Heppner, Claudia HeppnerSearch for more papers by this authorMarta Hugas, Marta HugasSearch for more papers by this authorSteve Pagani, Steve PaganiSearch for more papers by this authorAgnès Rortais, Agnès RortaisSearch for more papers by this author First published: 31 May 2022 https://doi.org/10.2903/sp.efsa.2022.e200505 Disclaimer: This document does not present future project calls as part of EFSA’s work programme, or any future position of EFSA. It aims to support the development of a roadmap for action and its content can be subject to change. AboutPDF ToolsExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Volume19, Issue5May 2022E200505E RelatedInformation

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

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

Integration of Toxicogenomics and Physiologically Based Pharmacokinetic Modeling in Human Health Risk Assessment of Perfluorooctane Sulfonate.

Toxicogenomics and physiologically based pharmacokinetic (PBPK) models are useful approaches in chemical risk assessment, but the methodology to incorporate toxicogenomic data into a PBPK model to inform risk assessment remains to be developed. This study aimed to develop a probabilistic human health risk assessment approach by integrating toxicogenomic dose-response data and PBPK modeling using perfluorooctane sulfonate (PFOS) as a case study. Based on the available human in vitro and mouse in vivo toxicogenomic data, we identified the differentially expressed genes (DEGs) at each exposure paradigm/duration. Kyoto Encyclopedia of Genes and Genomes and disease ontology enrichment analyses were conducted on the DEGs to identify significantly enriched pathways and diseases. The dose-response data of DEGs were analyzed using the Bayesian benchmark dose (BMD) method. Using a previously published PBPK model, the gene BMDs were converted to human equivalent doses (HEDs), which were summarized to pathway and disease HEDs and then extrapolated to reference doses (RfDs) by considering an uncertainty factor of 30 for mouse in vivo data and 10 for human in vitro data. The results suggested that the median RfDs at different exposure paradigms were similar to the 2016 U.S. Environmental Protection Agency's recommended RfD, while the RfDs for the most sensitive pathways and diseases were closer to the recent European Food Safety Authority's guidance values. In conclusion, genomic dose-response data and PBPK modeling can be integrated to become a useful alternative approach in risk assessment of environmental chemicals. This approach considers multiple endpoints, provides toxicity mechanistic insights, and does not rely on apical toxicity endpoints.

Toward a Framework for Environmental Fate and Exposure Assessment of Polymers.

Development of risk-assessment methodologies for polymers is an emerging regulatory priority to prevent negative environmental impacts; however, the diversity and complexity of polymers require adaptation of existing environmental risk-assessment approaches. The present review discusses the challenges and opportunities for the fate and exposure assessment of polymers in the context of regulatory environmental risk assessment of chemicals. The review discusses the applicability and adequacy for polymers of existing fate parameters used for nonpolymeric compounds and proposes additional parameters that could inform the fate of polymers. The significance of these parameters in various stages of an exposure-assessment framework is highlighted, with classification of polymers as solid or dissolved being key for identification of those parameters most relevant to environmental fate. Considerations to address the key limitations and knowledge gaps are then identified and discussed, specifically the complexity of polymer identification, with the need for characterization of the most significant parameters for polymer grouping and prioritization; the complexity of polymer degradation in the environment, with the need to incorporate the fate and hazards of degradation products into risk assessment; the requirement for development and standardization of analytical methods for characterization of polymer fate properties and degradation products; and the need to develop exposure modeling approaches for polymers. Environ Toxicol Chem 2022;41:515-540. © 2021 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Obituary: Dr Gordon Charles Hard (1931-2021).

Dr Gordon Hard, of Tairua, New Zealand, a preeminent international toxicology thought leader and international consultant in toxicologic pathology of the kidney, passed on November 22, 2021. He was a key player in shaping and developing the global field of toxicologic pathology and the role of toxicologic pathology in risk assessment of environmental chemicals and pharmaceuticals.

Renewing and Improving the Environmental Risk Assessment of Chemicals

Renewing and Improving the Environmental Risk Assessment of Chemicals

In Silico Methods for Environmental Risk Assessment: Principles, Tiered Approaches, Applications, and Future Perspectives.

This chapter aims to introduce the reader to the basic principles of environmental risk assessment of chemicals and highlights the usefulness of tiered approaches within weight of evidence approaches in relation to problem formulation i.e., data availability, time and resource availability. In silico models are then introduced and include quantitative structure-activity relationship (QSAR) models, whichsupport filling data gaps when no chemical property or ecotoxicological data are available. In addition, biologically-based models can be applied in more data rich situations and these include generic or species-specific models such as toxicokinetic-toxicodynamic models, dynamic energy budget models, physiologically based models, and models for ecosystem hazard assessment i.e.species sensitivity distributionsand ultimately for landscape assessment i.e.landscape-based modeling approaches. Throughout this chapter, particular attention is given to provide practical examples supporting the application ofsuch in silico models in real-world settings. Future perspectives are discussed to address environmental risk assessment in a more holistic manner particularly for relevant complex questions, such as the risk assessment of multiple stressors and the development of harmonized approaches to ultimately quantify the relative contribution and impactof singlechemicals, multiple chemicals and multiple stressors on living organisms.