In this study, we aimed to characterise exposure to pyrethroids, organophosphates, and tebuconazole through multiple pathways in 110 parent–child pairs participating in the CELSPAC–SPECIMEn study.

First, we estimated the daily intake (EDI) of pesticides based on measured urinary metabolites. Second, we compared EDI with estimated pesticide intake from food. We used multiple linear regression to identify the main predictors of urinary pesticide concentrations. We also assessed the relationship between urinary pesticide concentrations and organic and non-organic food consumption while controlling for a range of factors. Finally, we employed a model to estimate inhalation and dermal exposure due to spray drift and volatilization after assuming pesticide application in crop fields.

EDI was often higher in children in comparison to adults, especially in the winter season. A comparison of food intake estimates and EDI suggested diet as a critical pathway of tebuconazole exposure, less so in the case of organophosphates. Regression models showed that consumption per g of peaches/apricots was associated with an increase of 0.37% CI [0.23% to 0.51%] in urinary tebuconazole metabolite concentrations. Consumption of white bread was associated with an increase of 0.21% CI [0.08% to 0.35%], and consumption of organic strawberries was inversely associated (-61.52% CI [-79.34% to -28.32%]), with urinary pyrethroid metabolite concentrations. Inhalation and dermal exposure seemed to represent a relatively small contribution to pesticide exposure as compared to dietary intake.

In our study population, findings indicate diet plays a significant role in exposure to the analysed pesticides. We found an influence of potential exposure due to spray drift and volatilization among the subpopulation residing near presumably sprayed crop fields to be minimal in comparison. However, the lack of data indicating actual spraying occurred during the critical 24-hour period prior to urine sample collection could be a significant contributing factor.

Machine Learning (ML) is increasingly applied to fill data gaps in assessments to quantify impacts associated with chemical emissions and chemicals in products. However, the systematic application of ML-based approaches to fill chemical data gaps is still limited, and their potential for addressing a wide range of chemicals is unknown. We prioritized chemical-related parameters for chemical toxicity characterization to inform ML model development based on two criteria: (1) each parameter's relevance to robustly characterize chemical toxicity described by the uncertainty in characterization results attributable to each parameter and (2) the potential for ML-based approaches to predict parameter values for a wide range of chemicals described by the availability of chemicals with measured parameter data. We prioritized 13 out of 38 parameters for developing ML-based approaches, while flagging another nine with critical data gaps. For all prioritized parameters, we performed a chemical space analysis to assess further the potential for ML-based approaches to predict data for diverse chemicals considering the structural diversity of available measured data, showing that ML-based approaches can potentially predict 8–46% of marketed chemicals based on 1–10% with available measured data. Our results can systematically inform future ML model development efforts to address data gaps in chemical toxicity characterization.

The use of machine learning for predicting ecotoxicological outcomes is promising, but underutilized. The curation of data with informative features requires both expertise in machine learning as well as a strong biological and ecotoxicological background, which we consider a barrier of entry for this kind of research. Additionally, model performances can only be compared across studies when the same dataset, cleaning, and splittings were used. Therefore, we provide ADORE, an extensive and well-described dataset on acute aquatic toxicity in three relevant taxonomic groups (fish, crustaceans, and algae). The core dataset describes ecotoxicological experiments and is expanded with phylogenetic and species-specific data on the species as well as chemical properties and molecular representations. Apart from challenging other researchers to try and achieve the best model performances across the whole dataset, we propose specific relevant challenges on subsets of the data and include datasets and splittings corresponding to each of these challenge as well as in-depth characterization and discussion of train-test splitting approaches.

Paraoxonase 1 (PON1) is an antioxidant enzyme linked to metabolic disorders by genome-wide association studies in humans. Exposure to metabolic disrupting chemicals (MDCs) such as bisphenol A (BPA), together with genetic and dietary factors, can increase the risk of metabolic disorders. The objective of this study was to investigate how PON1 responds to the metabolic changes and oxidative stress caused by a western diet, and whether exposure to BPA alters the metabolic and PON1 responses. Zebrafish larvae at 14 days post fertilization were fed a custom-made western diet with and without aquatic exposure to two concentrations of BPA for 5 days. A combination of western diet and 150 μg/L BPA exposure resulted in a stepwise increase in weight, length and oxidative stress, suggesting that BPA amplifies the western diet-induced metabolic shift. PON1 arylesterase activity was increased in all western diet and BPA exposure groups and PON1 lactonase activity was increased when western diet was combined with exposure to 1800 μg/L BPA. Both PON1 activities were positively correlated to oxidative stress. Based on our observations we hypothesize that a western diet caused a shift towards fatty acid-based metabolism, which was increased by BPA exposure. This shift resulted in increased oxidative stress, which in turn was associated with a PON1 activity increase as an antioxidant response. This is the first exploration of PON1 responses to metabolic challenges in zebrafish, and the first study of PON1 in the context of MDC exposure in vertebrates.

This protocol describes the design and development of a tool for evaluation of the internal validity of in vitro studies, which is needed to include the data as evidence in systematic reviews and chemical risk assessments. The tool will be designed specifically to be applied to cell culture studies, including, but not restricted to, studies meeting the new approach methodology (NAM) definition. The tool is called INVITES-IN (IN VITro Experimental Studies INternal validity).
In this protocol, three of the four studies that will be performed to create the release version of INVITES-IN are described. In the first study, evaluation of existing assessment tools will be combined with focus group discussions to identify how characteristics of the design or conduct of an in vitro study can affect its internal validity. Bias domains and items considered to be of relevance for in
vitro studies will be identified. In the second study, group agreement on internal validity domains and items of importance for in vitro studies will be identified via a modified Delphi methodology. In the third study, the draft version of the tool will be created, based on the data on relevance and importance of bias domains and items collected in Studies 1 and 2. A separate protocol will be prepared for the fourth study, which includes the user testing and validation of the tool, and collection of users’ experience.

Here, we present an in vitro test battery to analyze chemicals for their potential to induce liver triglyceride accumulation, a hallmark of liver steatosis. We describe steps for using HepG2 and HepaRG human hepatoma cells in conjunction with a combination of several in vitro assays covering the different molecular initiating events and key events of the respective adverse outcome pathway. This protocol is suitable for assessing single substance effects as well as mixtures allowing their classification as steatotic or non-steatotic. 

Epidemiological studies have shown associations between prenatal exposure to lead (Pb) and neurodevelopmental effects in young children. Prenatal exposure is generally characterized by measuring the concentration in the umbilical cord at delivery or in the maternal blood during pregnancy. To assess internal Pb exposure during prenatal life, we developed a pregnancy physiologically based pharmacokinetic (p-PBPK) model that to simulates Pb levels in blood and target tissues in the fetus, especially during critical periods for brain development. An existing Pb PBPK model was adapted to pregnant women and fetuses. Using data from literature, both the additional maternal bone remodeling, that causes Pb release into the blood, and the Pb placental transfers were estimated by Bayesian inference. Additional maternal bone remodeling was estimated to start at 21.6 weeks. Placental transfers were estimated between and L.day^-1 at delivery with high interindividual variability. Once calibrated, the p-PBPK model was used to simulate fetal exposure to Pb. Internal fetal exposure greatly varies over the pregnancy with two peaks of Pb levels in blood and brain at the end of the 1^st and 3^rd trimesters. Sensitivity analysis shows that the fetal blood lead levels are affected by the maternal burden of bone Pb via maternal bone remodeling and by fetal bone formation at different pregnancy stages. Coupling the p-PBPK model with an effect model such as an adverse outcome pathway could help to predict the effects on children’s neurodevelopment.

Proper brain development is based on the orchestration of key neurodevelopmental processes (KNDP), including the for-mation and function of neural networks. If at least one KNDP is affected by a chemical, an adverse outcome is expected. To enable a higher testing throughput than the guideline animal experiments, a developmental neurotoxicity (DNT) in vitrotesting battery (DNT IVB) comprising a variety of assays that model several KNDPs was set up. Gap analysis revealed the need for a human-based assay to assess neural network formation and function (NNF). Therefore, we established the human NNF (hNNF) assay. A co-culture comprised of human induced pluripotent stem cell (hiPSC)-derived excitatory and inhibitory neurons as well as primary human astroglia was differentiated for 35 days on microelectrode arrays (MEA), and spontaneous electrical activity, together with cytotoxicity, was assessed on a weekly basis after washout of the compounds 24 h prior to measurements. In addition to the characterization of the test system, the assay was challenged with 28 com-pounds, mainly pesticides, identifying their DNT potential by evaluation of specific spike-, burst-, and network parameters. This approach confirmed the suitability of the assay for screening environmental chemicals. Comparison of benchmark con-centrations (BMC) with an NNF in vitro assay (rNNF) based on primary rat cortical cells revealed differences in sensitivity. Together with the successful implementation of hNNF data into a postulated stressor-specific adverse outcome pathway (AOP) network associated with a plausible molecular initiating event for deltamethrin, this study suggests the hNNF assay as a useful complement to the DNT IVB.