| 1. |
- Audouze, Karine, et al.
(author)
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Evidenced-Based Approaches to Support the Development of Endocrine-Mediated Adverse Outcome Pathways : Challenges and Opportunities
- 2021
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In: Frontiers in Toxicology. - : Frontiers Media S.A.. - 2673-3080. ; 3
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Journal article (peer-reviewed)abstract
- A transformation of regulatory toxicology is underway to meet the demands of testing increasing numbers of chemicals whilst reducing reliance on in vivo models. This transformation requires a shift from chemical safety assessment largely based on direct empirical observation of apical toxicity outcomes in whole organisms to predictive approaches in which outcomes and risks are inferred from accumulated mechanistic understanding.
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| 2. |
- Beausoleil, Claire, et al.
(author)
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Weight of evidence evaluation of the metabolism disrupting effects of triphenyl phosphate using an expert knowledge elicitation approach
- 2024
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In: Toxicology and Applied Pharmacology. - : Elsevier. - 0041-008X .- 1096-0333. ; 489
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Journal article (peer-reviewed)abstract
- Identification of Endocrine-Disrupting Chemicals (EDCs) in a regulatory context requires a high level of evidence. However, lines of evidence (e.g. human, in vivo, in vitro or in silico) are heterogeneous and incomplete for quantifying evidence of the adverse effects and mechanisms involved. To date, for the regulatory appraisal of metabolism-disrupting chemicals (MDCs), no harmonised guidance to assess the weight of evidence has been developed at the EU or international level. To explore how to develop this, we applied a formal Expert Knowledge Elicitation (EKE) approach within the European GOLIATH project. EKE captures expert judgment in a quantitative manner and provides an estimate of uncertainty of the final opinion. As a proof of principle, we selected one suspected MDC -triphenyl phosphate (TPP) - based on its related adverse endpoints (obesity/adipogenicity) relevant to metabolic disruption and a putative Molecular Initiating Event (MIE): activation of peroxisome proliferator activated receptor gamma (PPARγ). We conducted a systematic literature review and assessed the quality of the lines of evidence with two independent groups of experts within GOLIATH, with the objective of categorising the metabolic disruption properties of TPP, by applying an EKE approach. Having followed the entire process separately, both groups arrived at the same conclusion, designating TPP as a “suspected MDC” with an overall quantitative agreement exceeding 85%, indicating robust reproducibility. The EKE method provides to be an important way to bring together scientists with diverse expertise and is recommended for future work in this area.
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| 3. |
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| 4. |
- Chelcea, Ioana C., 1994-, et al.
(author)
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Physiology-informed toxicokinetic model for the zebrafish embryo test developed for bisphenols
- 2023
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In: Chemosphere. - : Elsevier. - 0045-6535 .- 1879-1298. ; 345
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Journal article (peer-reviewed)abstract
- Zebrafish embryos (ZFE) is a widely used model organism, employed in various research fields including toxicology to assess e.g., developmental toxicity and endocrine disruption. Variation in effects between chemicals are difficult to compare using nominal dose as toxicokinetic properties may vary. Toxicokinetic (TK) modeling is a means to estimate internal exposure concentration or dose at target and to enable extrapolation between experimental conditions and species, thereby improving hazard assessment of potential pollutants. In this study we advance currently existing TK models for ZFE with physiological ZFE parameters and novel experimental bisphenol data, a class of chemicals with suspected endocrine activity. We developed a five-compartment model consisting of water, plastic, chorion, yolk sack and embryo in which surface area and volume changes as well as the processes of biotransformation and blood circulation influence mass fluxes. For model training and validation, we measured internal concentrations in ZFE exposed individually to BPA, bisphenol AF (BPAF) and Z (BPZ). Bayesian inference was applied for parameter calibration based on the training data set of BPZ. The calibrated TK model predicted internal ZFE concentrations of the majority of external test data within a 5-fold error and half of the data within a 2-fold error for bisphenols A, AF, F, and tetrabromo bisphenol A (TBBPA). We used the developed model to rank the hazard of seven bisphenols based on predicted internal concentrations and measured in vitro estrogenicity. This ranking indicated a higher hazard for BPAF, BPZ, bisphenol B and C (BPB, BPC) than for BPA.
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| 5. |
- Sapounidou, Maria, et al.
(author)
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From Cohort to Cohort : A Similar Mixture Approach (SMACH) to Evaluate Exposures to a Mixture Leading to Thyroid-Mediated Neurodevelopmental Effects Using NHANES Data
- 2023
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In: Toxics. - : MDPI. - 2305-6304. ; 11:4
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Journal article (peer-reviewed)abstract
- Prenatal exposure to a mixture (MIX N) of eight endocrine-disrupting chemicals has been associated with language delay in children in a Swedish pregnancy cohort. A novel approach was proposed linking this epidemiological association with experimental evidence, where the effect of MIX N on thyroid hormone signaling was assessed using the Xenopus eleuthero-embryonic thyroid assay (XETA OECD TG248). From this experimental data, a point of departure (PoD) was derived based on OECD guidance. Our aim in the current study was to use updated toxicokinetic models to compare exposures of women of reproductive age in the US population to MIX N using a Similar Mixture Approach (SMACH). Based on our findings, 66% of women of reproductive age in the US (roughly 38 million women) had exposures sufficiently similar to MIX N. For this subset, a Similar Mixture Risk Index (SMRIHI) was calculated comparing their exposures to the PoD. Women with SMRIHI > 1 represent 1.1 million women of reproductive age. Older women, Mexican American and other/multi race women were less likely to have high SMRIHI values compared to Non-Hispanic White women. These findings indicate that a reference mixture of chemicals identified in a Swedish cohort—and tested in an experimental model for establishment of (PoDs)—is also of health relevance in a US population.
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| 6. |
- Sapounidou, Maria, et al.
(author)
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Predicting Endocrine Disruption Using Conformal Prediction : A Prioritization Strategy to Identify Hazardous Chemicals with Confidence
- 2023
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In: Chemical Research in Toxicology. - : American Chemical Society (ACS). - 0893-228X .- 1520-5010. ; 36:1, s. 53-65
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Journal article (peer-reviewed)abstract
- Receptor-mediated molecular initiating events (MIEs) and their relevance in endocrine activity (EA) have been highlighted in literature. More than 15 receptors have been associated with neurodevelopmental adversity and metabolic disruption. MIEs describe chemical interactions with defined biological outcomes, a relationship that could be described with quantitative structure-activity relationship (QSAR) models. QSAR uncertainty can be assessed using the conformal prediction (CP) framework, which provides similarity (i.e., nonconformity) scores relative to the defined classes per prediction. CP calibration can indirectly mitigate data imbalance during model development, and the nonconformity scores serve as intrinsic measures of chemical applicability domain assessment during screening. The focus of this work was to propose an in silico predictive strategy for EA. First, 23 QSAR models for MIEs associated with EA were developed using high-throughput data for 14 receptors. To handle the data imbalance, five protocols were compared, and CP provided the most balanced class definition. Second, the developed QSAR models were applied to a large data set (∼55,000 chemicals), comprising chemicals representative of potential risk for human exposure. Using CP, it was possible to assess the uncertainty of the screening results and identify model strengths and out of domain chemicals. Last, two clustering methods, t-distributed stochastic neighbor embedding and Tanimoto similarity, were used to identify compounds with potential EA using known endocrine disruptors as reference. The cluster overlap between methods produced 23 chemicals with suspected or demonstrated EA potential. The presented models could be utilized for first-tier screening and identification of compounds with potential biological activity across the studied MIEs.
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