Scientific Publications

This article reviews the application of multicriteria decision analysis (MCDA) in chemical alternatives assessment (CAA) and presents an overview of how the methodology has been applied within CAA. The study aimed to identify research that uses MCDA to identify the most harmful or least problematic chemicals and evaluate its current use in CAA. The study supports the Partnership for the Assessment of Risks from Chemicals (PARC) in developing a toolbox for safe and sustainable by design (SSbD). 520 studies were analysed, and 21 studies were included. Although MCDA in CAA is still emerging, it shows growth potential in decision analysis and chemical alternatives assessment. The reviewed studies cover various CAA applications and methodological approaches. Multiattribute utility theory (MAUT) is the most often used, followed by Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), ÉLimination Et Choix Traduisant la REalité (ELECTRE), and analytic hierarchy process (AHP). Experimental data and in silico data have been used with roughly equal frequency as input data. Group decision-making involving stakeholders with conflicting interests is rarely addressed, with parameter weighting and problem structuring usually handled by authors, sometimes with expert input. Another little discussed topic is the use of external normalisation of input data. In silico generated predictions on chemical alternatives’ properties come with varying degrees of uncertainty, remaining an issue in CAA with MCDA.

Chemical risk assessment can benefit from integrating informative biomarkers in human biomonitoring (HBM). Beyond exposure biomarkers, effect biomarkers inform on biological reactions in the body, potentially leading to adverse effects, while susceptibility biomarkers address inter-individual variability in exposure. DNA methylation of key genes shows promise as an effect biomarker but this epigenetic mark remains underexplored in the context of chemicals. Similarly, although some genetic polymorphisms are linked to increased chemical susceptibility, genetic biomarkers are rarely included in HBM. This mini-review highlights recent literature supporting the inclusion of genetic and epigenetic biomarkers in HBM. Subsequently, we elaborate on how Oxford Nanopore Technologies as sequencing method can efficiently measure these biomarkers simultaneously, even in non-invasive samples like saliva. Widely used in other fields, this experimental set-up could facilitate the design of large-population studies paving the way for a next generation risk assessment (NGRA) of chemicals.

Children’s lead exposure in three mining-impacted residential areas (Črna, Mežica and Žerjav) was modelled using the Integrated Exposure Uptake Biokinetic Model for Lead in Children (IEUBK). Site-specific environmental and dietary Pb source values were determined for modelling. For the first time, dietary exposure from both market and local foods was studied in detail. Children (Group 1: 24–36 and Group 2: 36–48 months) geometric mean blood lead levels (BLLs) were predicted and lead uptake from multiple sources was quantified according to the different dietary exposure scenarios. Biomonitoring data were used for validation. Site-specific soil, house dust and local food Pb contents are higher than legislative and background levels, remaining a cause for concern. Drinking tap water concentrations and outdoor air contents were found in acceptable levels. The determined dietary exposures, ranging from 0.7 to 3.3 µg/kg bw/day, were above the benchmark dose level of 0.5 µg/kg bw/day for developmental neurotoxicity set for Pb in children, indicating a health concern. In general, the estimated BLLs matched reasonably well with the observed BLLs in the Črna and Mežica area for both age groups and in the Žerjav area for Group 2. For Group 1, in the Žerjav area, the output of the IEUBK model overestimated the actual BLLs of the children. For both groups, the primary exposure pathway in Žerjav is from soil/dust, ranging from 55.3 to 84.8%. In Črna and Mežica, soil/dust exposure ranged between 24.2 and 57.8% and between 26.4 and 61.7%, respectively, indicating that dietary exposure predominates when local foods are included. The results of our study also suggest that using the IEUBK default diet value would reduce the dietary exposure up to 25.2% in Črna, 24.2% in Mežica and 8.6% in Žerjav. One of the main findings is that a diet containing local foods can be an important source of lead in mining-contaminated areas.

New Approach Methodologies (NAMs) in Next-Generation Risk Assessment (NGRA), integrating toxicokinetics (TK) with toxicodynamics (TD), provides an accurate evaluation of combined chemical exposures. This study assesses pyrethroids, which pose regulatory challenges due to their widespread use and cumulative exposure risks. A tiered NGRA framework was compared with conventional risk assessment (RA) to evaluate regulatory applicability. In Tier 1, ToxCast data established gene and tissue bioactivity indicators, facilitating hypothesis-driven hazard identification. Tier 2 examined combined risk assessments, rejecting the hypothesis of the same mode of action and highlighting inconsistencies in in vitro data and NOAEL/ADI correlations. Tier 3 applied Margin of Exposure (MoE) analysis and TK modeling to realistic exposure estimations for risk assessment screening based on internal doses, identifying tissue-specific pathways as critical risk drivers. Tier 4 refined bioactivity indicators using TK approaches to improve the NAM-based effect assessment, including an in vitro vs. in vivo comparison, with coherent results based on interstitial concentrations, though intracellular estimations remained uncertain. Tier 5 confirmed that dietary exposure in healthy adults is close to but below levels of concern, with bioactivity MoE values remaining below concern thresholds, and in vivo MoEs within the standard safety factors. Nevertheless, the MoEs are insufficient for addressing the additional non-dietary exposure expected from other pyrethroid uses such as biocides or pharmaceuticals. Results demonstrate that NGRA with TK-NAM-based TD offers a nuanced, regulatory-relevant framework for risk assessment. The proposed approach integrates the information on individual pyrethroids using bioactivity indicators; and the re-assessment of regulatory toxicity studies to select organ-relevant NOAELs allowed an improved in vitro-in vivo comparison, demonstrating the capacity of bioactivity-based MoEs for combined exposure assessments. This tiered approach provides key insights for regulatory decision-making, establishing a robust model for evaluating pyrethroids and similar chemical classes.

Chemicals in the EU are mainly regulated based on their intended use. Each legal framework consists of requirements and guidance for hazard- and risk assessment, along with the associated decision processes e.g., registration or authorisation of chemicals for market access in the EU. As a single chemical may have multiple uses, it may be assessed under more than one framework, potentially leading to different assessment outcomes. To address this, the European Commission has introduced the ‘one substance, one assessment’ approach as part of the Chemicals Strategy for Sustainability. The aims of the approach include streamlining risk assessment processes and reducing duplication of work in assessing the same chemical. This study aimed to map the scope of chemicals subject to assessment in multiple legal frameworks and to illustrate the importance of coordination and communication in chemical assessment processes. This was achieved by identifying chemicals that are either registered or have received specific approval for the EU market, and analysing their presence in different legal frameworks. Our findings showed that almost one-tenth of the substances identified were listed under more than one framework. However, there was a notable lack of coherent chemical identifiers available to accurately identify chemicals across the frameworks. Additionally, we identified the presence of phthalates, bisphenols and PFAS in EU frameworks to illustrate how a group-based approach to chemical assessment could be applied across different legal frameworks.

Nephrotoxicity caused by drug or chemical exposure involves complex mechanisms as well as a temporal integration of injury and repair responses in different nephron segments. Distinct cellular transcriptional programs regulate the time-dependent tissue injury and regeneration responses. Whole kidney transcriptome analysis cannot dissect the complex spatio-temporal injury and regeneration responses in the different nephron segments. Here, we used laser capture microdissection of formalin-fixed paraffin embedded sections followed by whole genome targeted RNA-sequencing-TempO-Seq and co-expression gene-network (module) analysis to determine the spatial–temporal responses in rat kidney glomeruli (GM), cortical proximal tubules (CPT) and outer-medulla proximal tubules (OMPT) comparison with whole kidney, after a single dose of the nephrotoxicant cisplatin. We demonstrate that cisplatin induced early onset of DNA damage in both CPT and OMPT, but not GM. Sustained DNA damage response was strongest in OMPT coinciding with OMPT specific inflammatory signaling, actin cytoskeletal remodeling and increased glycolytic metabolism with suppression of mitochondrial activity. Later responses reflected regeneration-related cell cycle pathway activation and ribosomal biogenesis in the injured OMPT regions. Activation of modules containing kidney injury biomarkers was strongest in OMPT, with OMPT Clu expression highly correlating with urinary clusterin biomarker measurements compared the correlation of Kim1. Our findings also showed that whole kidney responses were less sensitive than OMPT. In conclusion, our LCM-TempO-Seq method reveals a detailed spatial mechanistic understanding of renal injury/regeneration after nephrotoxicant exposure and identifies the most representative mechanism-based nephron segment specific renal injury biomarkers.

This work discusses reuse of chemical data across disciplines and the role of various data initiatives and projects including PARC, NORMAN-SLE, MassBank, WorldFAIR, PSDI and NFDI4Chem to facilitate increased data sharing. Improved machine-readable chemical data supports global research and interdisciplinary methodologies crucial for sustainable development and achievement of UNESCO’s Open Science priorities and the UN Sustainability Development Goals. Examples of success and ongoing approaches include integrating toxicology and chemical exposure data using ontologies, linking specialised chemical data collections with larger repositories such as PubChem, and developing IUPAC International Chemicals Identifier (InChI) extensions for nanomaterials and mixtures. National data infrastructure projects in the UK and Germany focus on digitising and standardising chemical research data management workflows, aiding scientists in data collection, storage, processing, analysis, disclosure, and reuse. These global initiatives aim to enhance chemical data interoperability to solve real-world problems, foster collaboration, and promote innovation while considering sustainable data resources beyond individual projects.

Exposure to respiratory sensitizers (RSs) is the leading cause of occupational asthma. Although the prospective identification of RSs is important, there currently exists no OECD-approved test guideline for this endpoint. The adverse outcome pathway for respiratory sensitization consists of key event (KE) 1: binding of the respiratory sensitizer to a protein, KE2: activation of lung epithelial cells, KE3: activation of dendritic cells, and KE4: T-cell response. Here, we focused on KE2 by investigating whether measuring this KE could contribute to prospectively identify respiratory sensitizers. To mimic real-life exposure, cells were exposed via the air. We used an air–liquid interface model comprising the human bronchial epithelial cell line Calu-3. Exposure to the RS piperazine (3, 18, and 100 mg/m3) dose-dependently increased IL-6 production at dose levels that did not affect the other parameters tested (barrier integrity, cell metabolism, cytotoxicity, and IL-8 production). IL-6 has been linked to asthma in humans. Exposure to the RS chloramine-T (30, 300, and 3000 ng/cm2) showed only minor effects on the parameters tested. Exposure to the RS 2,4-toluene diisocyanate (10, 33, and 100 mg/m3) at the highest dose level clearly affected all parameters tested. Disrupted barrier function has been linked to asthma. In conclusion, this study may possibly suggest that different respiratory sensitizers may differentially impact KE2. Further research is needed to elucidate this.

The persistent nature of the environmental contaminants per- and polyfluoroalkyl substances (PFAS) has recently received considerable attention, particularly because of their adverse effects on immune system functionality in the context of vaccine responses to infectious diseases. Following COVID-19 vaccination, some studies have shown a significant negative correlation between serum PFAS concentrations and the humoral immune response to the SARS-CoV-2 spike protein vaccination. However, the influence of PFAS on the cell-mediated immune response to SARS-CoV-2 spike protein post-COVID-19 vaccination remains underexplored. In the present study, we investigated the impact of a human blood-relevant PFAS mixture, containing perfluorooctane sulfonate (PFOS), perfluorooctanoic acid (PFOA), perfluorohexane sulfonate (PFHxS), perfluorononanoic acid (PFNA), perfluorodecanoic acid (PFDA), and perfluoroundecanoic acid (PFUnDA) on innate (monocytes and NK cells), cell-mediated (T cells) and B cells adaptive immune responses in COVID-19-vaccinated female and male healthy donors. Human peripheral blood mononuclear cells (PBMCs) were exposed to a mixture of the six PFAS at real life concentrations and subsequently stimulated with the SARS-CoV-2 spike peptide. We report a significant upregulation of IFNγ production in T and NK cells, particularly among male donors exposed to high concentrations of the PFAS mixture. Conversely, we observed a decrease in the total B-cell population, particularly among female donors. A significant reduction in the secretion of the pro-inflammatory chemokines MIP-1α (CCL3) and MIP-3α (CCL20) was observed at high PFAS mixture concentrations. Overall, these findings suggest that high PFAS exposure may differentially affect immune responses in a sex-specific manner, with a potential impact on vaccine efficacy.

Under evolutionary pressure, the kinematic and energetic characteristics of animal locomotion have been optimized for survival. We investigated the kinematics and energetic performance of zebrafish eleutheroembryo escape swims triggered by electrical stimuli in fluids of increasing viscosity. Eleutheroembryos exhibited a decrease in both tail movement frequency and swimming velocity in more viscous environments, while the amplitude of body curvature remains constant. We then combined experimental imaging of freely swimming eleutheroembryos with Navier-Stokes numerical simulations. The results showed that the mechanical power output was initially maximal and remained essentially stable with increasing viscosity, while the cost of transport was linearly correlated with viscosity. Eleutheroembryos maximize neuromuscular power output during the fast-start escape response, enabling them to potentially escape predators under all circumstances in a natural environment. This model may be used to identify genetic and toxicological factors that reduce the mechanical power developed by the neuromuscular system or induce a loss of efficiency in its use.

Parkinson’s disease (PD), the second most common neurodegenerative disorder, is characterized by the progressive loss of dopaminergic neurons in the substantia nigra pars compacta, leading to motor and non-motor symptoms. The neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) has been extensively used in different animal species to develop chemical models of PD. This study aimed to evaluate the effects of acute exposure to MPTP (3 × 150 mg/kg, intraperitoneally) on adult zebrafish by assessing the neurochemical, transcriptional, and motor changes associated with PD pathogenesis. MPTP treatment resulted in a significant decrease in brain catecholamines, including dopamine, norepinephrine, and normetanephrine. Additionally, a trend towards decreased levels of dopamine precursors (tyrosine and L-DOPA) and degradation products (3-MT and DOPAC) was also observed, although these changes were not statistically significant. Gene expression analysis showed the downregulation of dbh, while the expression of other genes involved in catecholamine metabolism (th1, th2, mao, comtb) and transport (slc6a3 and slc18a2) remained unaltered, suggesting a lack of dopaminergic neuron degeneration. Behavioral assessments revealed that MPTP-exposed zebrafish exhibited reduced motor activity, consistent with the observed decrease in dopamine levels. In contrast, the kinematic parameters of sharp turning were unaffected. A significant impairment in the sensorimotor gating of the ASR was detected in the MPTP-treated fish, consistent with psychosis. Despite dopamine depletion and behavioral impairments, the absence of neurodegeneration and some hallmark PD motor symptoms suggests limitations in the validity of this model for fully recapitulating PD pathology. Further studies are needed to refine the use of MPTP in zebrafish PD models.

MLinvitroTox is an automated Python pipeline developed for high-throughput hazard-driven prioritization of toxicologically relevant signals detected in complex environmental samples through high-resolution tandem mass spectrometry (HRMS/MS). MLinvitroTox is a machine learning (ML) framework comprising 490 independent XGBoost classifiers trained on molecular fingerprints from chemical structures and target-specific endpoints from the ToxCast/ Tox21 invitroDBv4.1 database. For each analyzed HRMS feature, MLinvitroTox generates a 490-bit bioactivity fingerprint used as a basis for prioritization, focusing the time-consuming molecular identification efforts on features most likely to cause adverse effects. The practical advantages of MLinvitroTox are demonstrated for groundwater HRMS data. Among the 874 features for which molecular fingerprints were derived from spectra, including 630 nontargets, 185 spectral matches, and 59 targets, around 4% of the feature/endpoint relationship pairs were predicted to be active. Cross-checking the predictions for targets and spectral matches with invitroDB data confirmed the bioactivity of 120 active and 6791 nonactive pairs while mislabeling 88 active and 56 non-active relationships. By filtering according to bioactivity probability, endpoint scores, and similarity to the training data, the number of potentially toxic features was reduced by at least one order of magnitude. This refinement makes the analytical confirmation of the toxicologically most relevant features feasible, offering significant benefits for cost-efficient chemical risk assessment.

Scientific Contribution:
In contrast to the classical ML-based approaches for toxicity prediction, MLinvitroTox predicts bioactivity for HRMS features (i.e., distinct m/z signals) based on MS2 fragmentation spectra rather than the chemical structures from the identified features. While the original proof of concept study was accompanied by the release of a MLinvitroTox v1 KNIME workflow, in this study, we release a Python MLinvitroTox v2 package, which, in addition to automation, expands functionality to include predicting toxicity from structures, cleaning up and generating chemical fingerprints, customizing models, and retraining on custom data. Furthermore, as a result of improvements in bioactivity data processing, realized in the concurrently released pytcpl Python package for the custom processing of invitroDBv4.1 input data used for training MLinvitroTox, the current release introduces enhancements in model accuracy, coverage of biological mechanistic targets, and overall interpretability.

Existing approaches for human health risk assessment of chemicals have overall provided a high level of protection for EU citizens. However, the established assessment schemes face numerous challenges. Ethical and scientific concerns about using animals for safety testing have triggered awareness of the need for a paradigm shift, requiring new concepts for chemical risk assessment complementing and, in the long run, replacing existing schemes. Next-Generation Risk Assessment (NGRA) using new approach methodologies (NAMs) is commonly regarded as the way forward. However, adequately meeting regulatory needs is challenging. The European Partnership for the Assessment of Risks from Chemicals (PARC) supports the development of NGRA frameworks and their implementation at all levels, from developing adverse outcome pathways (AOPs), NAMs and integrated approaches to testing and assessments (IATAs), to designing new conceptual approaches and formulating strategic roadmaps. A particular strength of PARC is its focus on interaction and collaboration with stakeholders from all sectors of the chemical risk assessment community to promote cooperation, advance research, increase knowledge of chemical risk assessment and train methodological skills. Its results will help launch European and national strategies to reduce risks posed by hazardous chemicals, to reduce animal testing and to implement NGRA strategies in regulatory practice.

Access to information about chemicals in products and articles is critical for supporting enforcement of chemical regulations, assessing risks from chemicals, allowing informed consumer choices, and enabling product circularity. In this work, we identified and evaluated available databases (DBs) on chemicals in products and articles from the literature using a defined protocol and from European national market surveillance authorities, nongovernmental agencies, and industrial sector groups using questionnaires. This is the first comprehensive review of DBs that provide information about chemicals in products and articles. A majority of these DBs are heterogeneous in terms of scope, ontologies, and data structures. Among the 57 identified DBs, 49 identified specific substances and only 30 reported their concentration in their products. In addition, 35 DBs included hazard information and 27 DBs provided safety information about products or chemicals. The analysis highlights the lack of comprehensive or accessible data on chemicals in products and articles for most categories of products/articles and jurisdictions. The limitations of existing DBs were attributed to scattered regulatory information requirements, a lack of data for unregulated substances, the complexity of supply chain communication, and confidentiality issues. In response to these challenges, we identified opportunities for improving existing information transfer structures and exploring alternative data sources to promote product and article safety and circularity.

The AOP-Wiki, a knowledge database for Adverse Outcome Pathways (AOPs), requires an efficient way to present an overview of its content for the reconstruction of networks by experts in a given domain. We have developed a user-friendly tool using artificial intelligence, the AOP-networkFinder, that retrieves AOPs of interest, modifies them in a desirable manner, and finally visualizes networks built around the retrieved AOPs. Our tool constructs AOP networks by connecting AOPs that use the same Key Events (KEs) in a versatile but controlled manner. Genes related to these KEs are also displayed. The constructed networks can then be exported as images or to Cytoscape for further fine-tuning and statistical analysis. The AOP-networkFinder allows users to comprehensively identify relationships between KEs and visualize the overall structure of an AOP both quickly and easily. This is immensely beneficial to researchers who need to understand the complex interplay between different KEs and the overall pathway they are studying and helps them to build further networks of interest while logging relevant information about changes within the network. These efforts are in line with the FAIR principles, which are crucial attributes for any developed databases and tools for optimizing use and reuse in a dynamically changing landscape of AOP-Wiki.

Human activities severely impact biodiversity, particularly in freshwater lakes. These habitats provide critical ecosystem services and, at the same time, suffer from river inflow, agricultural runoff, and urban discharge. DNA-based techniques are preferred for monitoring biodiversity due to their effectiveness. However, pinpointing the causes of biodiversity decline across landscapes poses challenges due to the complex interactions between biodiversity and environmental drivers. In this study, we used an explainable multimodal machine learning approach that can integrate different types of data, such as biological, chemical, and physical data, to discover potential causes of biodiversity dynamics. This is done by identifying relationships between environmental drivers—plant protection products, physico-chemical parameters and typology- and community biodiversity changes in 52 lake ecosystems. By analyzing benthic and pelagic lake communities, we found significant correlations between biodiversity and environmental drivers, such as plant protection products. Furthermore, our analysis allowed us to identify factors within these drivers responsible for biodiversity dynamics. Specifically, insecticides and fungicides were identified as the most important factors, followed by 43 physico-chemical factors, including many heavy metals. Our holistic, data-driven approach provides insights into large-scale biodiversity changes and could inform conservation efforts and regulatory interventions to protect biodiversity from pollution.

Assessment of potential impacts of chemicals on the environment traditionally involves regulatory standard data requirements for acute aquatic toxicity testing using algae, daphnids, and fish (e.g., Organisation for Economic Co-operation and Development [OECD] test guidelines 201, 202, and 203, respectively), representing different trophic levels. In line with the societal goal to replace or reduce vertebrate animal testing, alternative bioassays were developed to replace testing with fish: the fish cell line RTgill-W1 acute toxicity assay (OECD test guideline 249) and the zebrafish embryo acute toxicity test (zFET, OECD test guideline 236). However, previous studies revealed the lower sensitivity of the RTgill-W1 cell line assay and zFET for some neurotoxic chemicals and allyl alcohol, which is presumably biotransformed in fish to the more toxic acrolein (which is predicted well through the cell line assay). To provide an additional alternative to acute fish toxicity, in this study we analyzed historic ecotoxicity data for fish and daphnids from the EnviroTox Database. We found a considerable variability in acute fish median lethal concentration and acute daphnids median effect concentration values, particularly for neurotoxic chemicals. Comparing sensitivity of these taxonomic groups according to different neurotoxicity classification schemes indicates that fish rarely represent the most sensitive trophic level of the two. Exceptions here most prominently include a few cyclodiene compounds, which are no longer marketed, and a chemical group that could be identified through structural alerts. Moreover, daphnids are more sensitive than fish to acrolein. This analysis highlights the potential of the Daphnia acute toxicity test, which is usually a standard regulatory data requirement, in safeguarding the environmental protection level provided by the RTgill-W1 cell line assay and the zFET. This research, rooted in decades of efforts to replace the fish acute toxicity test, shifts the focus from predicting fish toxicity one-to-one to emphasizing the protectiveness of alternative methods, paving the way for further eliminating vertebrate tests in environmental toxicology.

Endocrine-disrupting chemicals (EDCs) are natural or synthetic substances that are able to interfere with hormonal systems and alter their physiological signaling. EDCs have been recognized as a public health issue due to their widespread use, environmental persistence and the potential levels of long-term exposure with implications in multiple pathological conditions. Their reported adverse effects pose critical concerns about their use, warranting their strict regulation. This is the case of bisphenol A (BPA), a well-known EDC whose tolerable daily intake (TDI) was re-evaluated in 2023 by the European Food Safety Authority (EFSA), and the immune system has been identified as the most sensitive to BPA exposure. Increasing scientific evidence indicates that EDCs can interfere with several hormone receptors, pathways and interacting proteins, resulting in a complex, cell context-dependent response that may differ among tissues. In this regard, the neuronal and immune systems are important targets of hormonal signaling and are now emerging as critical players in endocrine disruption. Here, we use BPA and its analogs as proof-of-concept EDCs to address their detrimental effects on the immune and nervous systems and to highlight complex interrelationships within the immune–neuroendocrine network (INEN). Finally, we propose that Receptor for Activated C Kinase 1 (RACK1), an important target for EDCs and a valuable screening tool, could serve as a central hub in our toxicology model to explain bisphenol-mediated adverse effects on the INEN.

Introduction
The European Partnership for the Assessment of Risks from Chemicals (PARC) is a 7-year multinational partnership aimed at consolidating and strengthening European Union’s (EU) research and innovation capacity for chemical risk assessment (RA) to protect human health and the environment. It consists of nine work packages (WP) involving more than 200 participating organisations from 29 countries. PARC is currently mapping the most relevant needs in the field of European chemical RA to steer PARC’s future activities in the coming years. The present study aims to gather the perspectives of WP/Task/Project Leaders of PARC to understand their experience during the first prioritisation round of PARC activities and to identify potential points of improvement for future rounds.

Methods
Three online 90-min focus group discussion (FGD) sessions were conducted between the 3rd and 9th of May 2023. Each session was attended by 4-5 participants with at least one representative from each PARC WPs 4, 5 and 6 (n = 13). The sessions were recorded and transcribed, then analysed in NVivo 12 software using thematic analysis.

Results
Some important aspects for the prioritisation of activities that were mentioned include: (1) having a transparent prioritisation process even though each WP might need different prioritisation criteria, (2) balancing the fulfilment of short-term regulatory needs and anticipating long-term needs in chemical RA, (3) maintaining alignment and synergy between the WPs and with other relevant EU initiatives to avoid duplication and to ensure continuity of work and (4) making sure that PARC can effectively respond to requests from different PARC stakeholders.

Conclusions
The next round of PARC research activity steering process will provide an opportunity to implement the various improvements identified. PARC should utilise the advantage of having stakeholders from different backgrounds (e.g., risk assessors, policymakers, regulatory bodies, academia, etc.) within its consortium and its advising bodies to prioritise projects and activities that will support its overall objectives. These recommendations could also be of interest outside PARC in the context of prioritising research and innovation needs related to chemical RA.

The acoustic startle response (ASR) is leaded by a sudden and intense acoustic stimulus. ASR has several forms of plasticity, including habituation and sensorimotor gating. Although ASR and its plasticity have been intensively studied in zebrafish (Danio rerio) larvae, information in adult zebrafish is still very scarce. In this manuscript we present Zebra_K, a new automated high-content kinematic analysis platform for assessing ASR, its habituation and prepulse inhibition (PPI), a quantitative measure of sensorimotor gating, in adult zebrafish. The analysis of the kinematic parameters of ASR in adult zebrafish has shown a single response wave consistent with the short-latency C-bend described in zebrafish larvae. Moreover, protocols have been designed and validated in Zebra_K for the analysis of sensitivity, habituation and PPI of this response. Then, the effect of the time of day and the gender on zebrafish ASR plasticity has been analyzed for the first time. Females exhibited higher responsiveness and a lower habituation and PPI than males, a result consistent with the gender effect described in other animal models and in humans. This platform has also been used to determine the effect of a pharmacological modulators of ASR plasticity, the NMDA-receptor antagonist ketamine. As described in other animal models, ketamine increased the responsiveness to the acoustic stimuli, decreasing habituation and leading to complete abolition of PPI. These results enhance the interest of using adult zebrafish to assess the potential effect of environmental pollutants on ASR plasticity.

The main objective of the ZebraCool programme was to create a positive attitude and curiosity towards science by bringing experimental activities within schools using an introductory cognitive and sensory approach. This innovative programme was offered at all levels of primary and secondary education including vocational high schools. Thematic workshops can be carried out on various themes such as comparative anatomy and embryology, molecular biology and evolution, or toxicology and endocrine disruptors. They were on an ad hoc basis or as part of an annual school project using zebrafish as a model. This animal was a very attractive entry point for the educator to motivate students to appreciate biology, in particular in the field of molecular biology and evolution. For each practical workshop, the student was an actor in his/her learning, which was intended to arouse the curiosity and desire to understand and learn. The programme was based on close collaboration between class teachers and programme educators to adapt workshops’ content to the school curriculum. Students conducted their own experiments, formulated and tested hypotheses, learned laboratory techniques, collected and analysed data. ZebraCool scientific activities fell within a conceptual framework of evolutionary biology through which participants perceived their own inner fish through the comparison of biological processes between humans and zebrafish.

Background
Asthma and allergic diseases are among the common causes of morbidity and mortality globally. Various environmental pollutants are linked to the development of asthma and allergic diseases. Evidence on the role of oxidative stress and immune markers in the association of environmental pollutants with asthma and allergy is scant. We examined cross-sectional associations between environmental pollutants and asthma and allergy, investigated mixture effects and possible mediation by oxidative stress or immune markers.

Methods
We used data from the Flemish Environment and Health Study 2016–2020 (FLEHS IV), including 409 adolescents aged 13–16 years. Fifty-four pollutants, including metals, phthalates, Di(isononyl) cyclohexane-1,2-dicarboxylate (DINCH), bisphenols, currently used and legacy pesticides, flame retardants, per- and polyfluoroalkyl substances (PFAS), polyaromatic hydrocarbons (PAHs), and polychlorinated biphenyls (PCBs) were analyzed. Outcomes were self-reported asthma, rhinitis, eczema, allergies, respiratory infection, and airway inflammation, measured through fractional exhaled nitric oxide (FeNO). Single pollutant models using multiple regression analysis and multipollutant models using Bayesian Kernel Machine Regression (BKMR) were fitted. As sensitivity analysis, Bayesian model averaging (BMA) and elastic net (ENET) models were also performed. For Bayesian models, posterior inclusion probabilities (PIP) were used to identify the most important chemicals. Mediation analysis was performed to investigate the role of oxidative stress, measured by urinary 8-hydroxy-2' -deoxyguanosine (8-OHdG), and immune markers (eosinophils, basophils, InterLeukin 8, InterLeukin 6, and Interferon-ᵧ in blood).

Results
In single pollutant models, FeNO was significantly higher by 20% (95% CI: 6, 36%) and 13% (95% CI: 2, 25%) per interquartile range (IQR) fold in mono-n-butyl phthalate (MnBP) and mono-benzyl phthalate (MBzP), respectively. In BKMR analysis, the group PIPs indicated phthalates and DINCH as the most important group (group PIP = 0.509), with MnBP being the most important pollutant within that group (conditional PIP = 0.564; %change = 28%; 95%CI: 6, 54%). Similar patterns were observed in all multipollutant models. Eosinophil count mediated 37.8% (p = 0.018) and 27.9% (p = 0.045) of the association between MBzP and FeNO, and the association between MnBP and FeNO, respectively. 8-OHdG plays a significant mediating role in the association of 2,4-Dichlorophenoxyacetic acid (2,4-D) (55.4%), 3,5,6-Trichloro-2-pyridinol (TCPY) (48.1%), and 1-Naphthylamine (1-NAP) (32.7%) with rhinitis, while the total effects of these chemicals on rhinitis were not statistically significant.

Conclusions
This study found associations between phthalates, MnBP and MBzP, and elevated FeNO, which appeared to be mediated by eosinophil count. 8-OHdG plays a significant mediating role in the association between 2,4-D, TCPY, and 1-NAP with rhinitis, while their direct effects remain non-significant. Use of inflammatory and oxidative stress markers can enhance the understanding of inflammatory processes in asthma and allergic diseases due to environmental pollutants.

Dietary risk assessment of food contaminants requires a well-established understanding of the exposure in a heterogeneous population. There are many methods for estimating human exposure to food contaminants, such as intake calculations and internal biomarkers of exposure measured in individuals. However, those methods are expensive, partly invasive, and often provide a momentary exposure snapshot. Physiologically Based Kinetic (PBK) modelling is increasingly used to overcome those challenges that traditional human exposure methods encounter. Still, PBK models are often restricted to certain life stages (e.g., children, adolescents, adults). This study outlines a strategy for implementing nonlinear organ growths in age-specific PBK models to enhance dietary risk assessment from lifetime exposure. To this end, lifetime physiological equations calculating organ growth for both sexes were inventoried from literature and a library was established for 24 organs. We then assessed total lifelong mercury exposure via foodstuff by combining two existing age-specific PBK models for methylmercury (MeHg) and inorganic mercury (iHg) that simulated internal exposure to total mercury, the speciation typically measured in hair and urine. We implemented a set of physiological equations in the PBK model that fitted best the total mercury measured in individuals' organs, hair, and urine from heterogeneous populations. For refined dietary risk assessment, we ultimately estimated total mercury concentration in hair and urine based on i) maximum limits defined by the regulation for MeHg in seafood, ii) the health-based guidance values for MeHg and iHg, and iii) realistic intakes considering French demographic parameters and food consumption data. These exposure scenarios demonstrated that total mercury concentrations in hair and urine estimated from realistic intakes are below critical effect level measures at all ages. The result of this study is the creation of easily accessible tools in Excel and R that facilitate the implementation of physiological equations in Next Generation PBK models.