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Protocol: Testing the performance of INVITES-IN, A tool for assessing the internal validity of in vitro studies
Authors
Mathisen Gro Haarklou, Vist Gunn E, Whaley Paul, White Richard A, Husøy Trine, Ames Heather M, Beronius Anna, Di Consiglio Emma, Druwe Ingrid, Hartung Thomas, Hoffmann Sebastian, Hooijmans Carlijn R., Machera Kyriaki, Prieto Pilar, Robin Joshua F, Roggen Erwin, Rooney Andrew A, Roth Nicolas, Spilioti Eliana, Spyropoulou Anastasia, Tcheremenskaia Olga, Testai Emanuela, Vinken Mathieu, Svendsen Camilla
Journal
Evidence-Based Toxicology
Vol. 1
No. 1
2293289
Keywords
Innovative methods and tools , Chemical risk assessment , Hazard assessment, Human health
Date of publication
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A tool for evaluation of internal validity of in vitro studies called INVITES-IN is currently under development. The tool is designed specifically for cell culture studies.

This protocol describes the testing of the performance of INVITES-IN. By performance, we mean the extent to which results of using INVITES-IN are the same for different users (consistency), the amount of time and cognitive effort it takes to apply INVITES-IN (assessor workload), the precision and potential for systematic error in results of applying INVITES-IN (accuracy), and how easy it is to use INVITES-IN (user experience).

The participants in the user testing will be representative for the expected end-users of INVITES-IN which are persons preparing literature reviews including in vitro studies (e.g. in the context of chemical hazard and risk assessments or drug development). All end-users are expected to have experience with in vitro methods.

Data collected from the performance testing will be used for further refinement and development of the release version of INVITES-IN.

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Why adverse outcome pathways need to be FAIR
Authors
Wittwehr Clemens, Clerbaux Laure-Alix, Edwards Stephen, Angrish Michelle, Mortensen Holly, Carusi Annamaria, Gromelski Maciej , Lekka Eftychia, Virvilis Vassilis Virvilis, Martens Marvin, Bonino da Silva Santos Luiz Olavo, Nymark Penny
Journal
ALTEX
Vol. 41
No. 1
50-56
Keywords
Adverse outcome pathways, FAIR data, Machine-actionability, Trust, Visibility
Date of publication
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Adverse outcome pathways (AOPs) provide evidence for demonstrating and assessing causality between measurable toxicological mechanisms and human or environmental adverse effects. AOPs have gained increasing attention over the past decade and are believed to provide the necessary steppingstone for more effective risk assessment of chemicals and materials and moving beyond the need for animal testing. However, as with all types of data and knowledge today, AOPs need to be reusable by machines, i.e., machine-actionable, in order to reach their full impact potential. Machine-actionability is supported by the FAIR principles, which guide findability, accessibility, interoperability, and reusability of data and knowledge. Here, we describe why AOPs need to be FAIR and touch on aspects such as the improved visibility and the increased trust that FAIRification of AOPs provides.

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Acetylcholinesterase inhibition in rats and humans following acute fenitrothion exposure predicted by physiologically based kinetic modeling-facilitated quantitative in vitroto in vivo extrapolation
Journal
Environmental Science & Technology
Vol. 57
No. 49
20521-20531
Keywords
Models, Chemical risk assessment , Hazard assessment, Innovative methods and tools
Date of publication
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Worldwide use of organophosphate pesticides as agricultural chemicals aims to maintain a stable food supply, while their toxicity remains a major public health concern. A common mechanism of acute neurotoxicity following organophosphate pesticide exposure is the inhibition of acetylcholinesterase (AChE). To support Next Generation Risk Assessment for public health upon acute neurotoxicity induced by organophosphate pesticides, physiologically based kinetic (PBK) modeling-facilitated quantitative in vitro to in vivo extrapolation (QIVIVE) approach was employed in this study, with fenitrothion (FNT) as an exemplary organophosphate pesticide. Rat and human PBK models were parametrized with data derived from in silico predictions and in vitro incubations. Then, PBK model-based QIVIVE was performed to convert species-specific concentration-dependent AChE inhibition obtained from in vitro blood assays to corresponding in vivo dose−response curves, from which points of departure (PODs) were derived. The obtained values for rats and humans were comparable with reported no-observed-adverse-effect levels (NOAELs). Humans were found to be more susceptible than rats toward erythrocyte AChE inhibition induced by acute FNT exposure due to interspecies differences in toxicokinetics and toxicodynamics. The described approach adequately predicts toxicokinetics and acute toxicity of FNT, providing a proof-of-principle for applying this approach in a 3R-based chemical risk assessment paradigm.

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Ecotoxicological Evaluation of Bisphenol A and Alternatives: A Comprehensive In Silico Modelling Approach
Journal
Journal of Xenobiotics
Vol. 13
No. 4
719-739
Keywords
Bisphenol A (BPA), BPA alternatives, Ecotoxicity assessment, In silico models, Principal component analysis (PCA), Environmental impact, Models, Chemical risk assessment
Date of publication
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Bisphenol A (BPA), a compound widely used in industrial applications, has raised concerns due to its environmental impact. As a key component in the manufacture of polycarbonate plastics and epoxy resins used in many consumer products, concerns about potential harm to human health and the environment are unavoidable. This study seeks to address these concerns by evaluating a range of potential BPA alternatives, focusing on their ecotoxicological properties. The research examines 76 bisphenols, including BPA derivatives, using a variety of in silico ecotoxicological models, although it should be noted that these models were not developed exclusively for this particular class of compounds. Consequently, interpretations should be made with caution. The results of this study highlight specific compounds of potential environmental concern and underscore the need to develop more specific models for BPA alternatives that will allow for more accurate and reliable assessment.

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Current-use pesticide exposure pathways in Czech adults and children from the CELSPAC-SPECIMEn cohort
Authors
Šulc Libor, Figueiredo Daniel, Huss Anke, Kalina Jiří, Gregor Petr, Janoš Tomáš, Šenk Petr, Dalecká Andrea, Andrýsková Lenka, Kodeš Vít, Čupr Pavel
Journal
Environment International
Vol. 181
No. 108297
Keywords
Current-use pesticides, HBM4EU, Dietary exposure, Pesticide application, Environmental exposure, Organic diet, Exposure assessment, Models, Human biomonitoring, Human health
Date of publication
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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.

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Combined chronic dietary exposure to four nephrotoxic metals exceeds tolerable intake levels in the adult population of 10 European countries
Authors
Sprong R. Corinne, Van den Brand Annick D., Van Donkersgoed Gerda, Blaznik Urska, Christodoulou Despo, Crépet Amélie, Da Graca Dias Maria, Jensen Bodil Hamborg, Moretto Angelo, Rauscher-Gabernig Elke, Ruprich Jiri, Sokolic Darja, Van Klaveren Jacob D., Luijten Mirjam, Mengelers Marcel J.B.
Journal
Food Additives and Contaminants Part A
Vol. 40
No. 12
1568-1588
Keywords
Chemical risk assessment , Exposure assessment, Mixtures
Date of publication
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A mixture risk assessment (MRA) for four metals relevant to chronic kidney disease (CKD) was performed. Dietary exposure to cadmium or lead alone exceeded the respective reference values in the majority of the 10 European countries included in our study. When the dietary exposure to those metals and inorganic mercury and inorganic arsenic was combined following a classical or personalised modified reference point index (mRPI) approach, not only high exposure (95th percentile) estimates but also the mean exceeded the tolerable intake of the mixture in all countries studied. Cadmium and lead contributed most to the combined exposure, followed by inorganic arsenic and inorganic mercury. The use of conversion factors for inorganic arsenic and inorganic mercury from total arsenic and total mercury concentration data was a source of uncertainty. Other uncertainties were related to the use of different principles to derive reference points. Yet, MRA at the target organ level, as performed in our study, could be used as a way to efficiently prioritise assessment groups for higher-tier MRA. Since the combined exposure to the four metals exceeded the tolerable intake, we recommend a refined MRA based on a common, specific nephrotoxic effect and relative potency factors (RPFs) based on a similar effect size.

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Potential for machine learning to address data gaps in human toxicity and ecotoxicity characterization
Authors
von Borries Kerstin, Holmquist Hanna, Kosnik Marissa, Katie Beckwith, Jolliet Olivier, Goodman Jonathan, Fantke Peter
Journal
Environmental Science and Technology
Vol. 57 (46)
18259–18270
Keywords
Models, Afe and sustainable-by-design
Date of publication
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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.

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A benchmark dataset for machine learning in ecotoxicology
Authors
Schür Christoph, Gasser Lilian, Perez-Cruz Fernando, Schirmer Kristin, Baity-Jesi Marco
Journal
Nature Scientific Data
Vol. 10
No. 718 (2023)
Keywords
Machine learning, Ecotoxicology, Benchmark, Scientificdata, Fish, Algae, Crustaceans, Predictive toxicology, Hazard assessment, Models
Date of publication
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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.

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Acceptance criteria for new approach methods in toxicology and human health-relevant life science research - part I
Authors
Holzer Anna-Katharina, Dreser Nadine, Pallocca Giorgia, Mangerich Aswin, Stacey Glyn, Dipalo Michele, Van de Water Bob, Rovida Costanza, Wirtz Petra H., Van Vugt Barbara, Panzarella Giulia, Hartung Thomas, Terron Andrea, Mangas Iris, Herzler Matthias, Marx-Stoelting Philip, Coecke Sandra, Leist Marcel
Journal
ALTEX
Vol. 40
No. 4
706–712.
Keywords
FAIR data, Models, Chemical risk assessment , Developmental neurotoxicity, Hazard assessment
Date of publication
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