Weight of evidence evaluation of the metabolism disrupting effects of triphenyl phosphate using an expert knowledge elicitation approach.

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.

Physiology-informed toxicokinetic model for the zebrafish embryo test developed for bisphenols.

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.

From Cohort to Cohort: A Similar Mixture Approach (SMACH) to Evaluate Exposures to a Mixture Leading to Thyroid-Mediated Neurodevelopmental Effects Using NHANES Data.

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.

Predicting Endocrine Disruption Using Conformal Prediction - A Prioritization Strategy to Identify Hazardous Chemicals with Confidence.

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.

Construction of an In Silico Structural Profiling Tool Facilitating Mechanistically Grounded Classification of Aquatic Toxicants.

The performance of chemical safety assessment within the domain of environmental toxicology is often impeded by a shortfall of appropriate experimental data describing potential hazards across the many compounds in regular industrial use. In silico schemes for assigning aquatic-relevant modes or mechanisms of toxic action to substances, based solely on consideration of chemical structure, have seen widespread employment─including those of Verhaar, Russom, and later Bauer (MechoA). Recently, development of a further system was reported by Sapounidou, which, in common with MechoA, seeks to ground its classifications in understanding and appreciation of molecular initiating events. Until now, this Sapounidou scheme has not seen implementation as a tool for practical screening use. Accordingly, the primary purpose of this study was to create such a resource─in the form of a computational workflow. This exercise was facilitated through the formulation of 183 structural alerts/rules describing molecular features associated with narcosis, chemical reactivity, and specific mechanisms of action. Output was subsequently compared relative to that of the three aforementioned alternative systems to identify strengths and shortcomings as regards coverage of chemical space.

A scheme to evaluate structural alerts to predict toxicity - Assessing confidence by characterising uncertainties.

Structure-activity relationships (SARs) in toxicology have enabled the formation of structural rules which, when coded as structural alerts, are essential tools in in silico toxicology. Whilst other in silico methods have approaches for their evaluation, there is no formal process to assess the confidence that may be associated with a structural alert. This investigation proposes twelve criteria to assess the uncertainty associated with structural alerts, allowing for an assessment of confidence. The criteria are based around the stated purpose, description of the chemistry, toxicology and mechanism, performance and coverage, as well as corroborating and supporting evidence of the alert. Alerts can be given a confidence assessment and score, enabling the identification of areas where more information may be beneficial. The scheme to evaluate structural alerts was placed in the context of various use cases for industrial and regulatory applications. The analysis of alerts, and consideration of the evaluation scheme, identifies the different characteristics an alert may have, such as being highly specific or generic. These characteristics may determine when an alert can be used for specific uses such as identification of analogues for read-across or hazard identification.

Development of an Enhanced Mechanistically Driven Mode of Action Classification Scheme for Adverse Effects on Environmental Species.

This study developed a novel classification scheme to assign chemicals to a verifiable mechanism of (eco-)toxicological action to allow for grouping, read-across, and in silico model generation. The new classification scheme unifies and extends existing schemes and has, at its heart, direct reference to molecular initiating events (MIEs) promoting adverse outcomes. The scheme is based on three broad domains of toxic action representing nonspecific toxicity (e.g., narcosis), reactive mechanisms (e.g., electrophilicity and free radical action), and specific mechanisms (e.g., associated with enzyme inhibition). The scheme is organized at three further levels of detail beyond broad domains to separate out the mechanistic group, specific mechanism, and the MIEs responsible. The novelty of this approach comes from the reference to taxonomic diversity within the classification, transparency, quality of supporting evidence relating to MIEs, and that it can be updated readily.