The importance of mathematical modelling in chemical risk assessment and the associated quantification of uncertainty

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.

Uncertainty assessment of proarrhythmia predictions derived from multi-level in silico models

In silico methods can be used for an early assessment of arrhythmogenic properties of drug candidates. However, their use for decision-making is conditioned by the possibility to estimate the predictions' uncertainty. This work describes our efforts to develop uncertainty quantification methods for the predictions produced by multi-level proarrhythmia models. In silico models used in this field usually start with experimental or predicted IC50 values that describe drug-induced ion channel blockade. Using such inputs, an electrophysiological model computes how the ion channel inhibition, exerted by a drug in a certain concentration, translates to an altered shape and duration of the action potential in cardiac cells, which can be represented as arrhythmogenic risk biomarkers such as the APD90. Using this framework, we identify the main sources of aleatory and epistemic uncertainties and propose a method based on probabilistic simulations that replaces single-point estimates predicted using multiple input values, including the IC50s and the electrophysiological parameters, by distributions of values. Two selected variability types associated with these inputs are then propagated through the multi-level model to estimate their impact on the uncertainty levels in the output, expressed by means of intervals. The proposed approach yields single predictions of arrhythmogenic risk biomarkers together with value intervals, providing a more comprehensive and realistic description of drug effects on a human population. The methodology was tested by predicting arrhythmogenic biomarkers on a series of twelve well-characterised marketed drugs, belonging to different arrhythmogenic risk classes.

Residual behaviors and metabolic pathway of ethylparaben in Drosophila melanogaster

OBJECTIVE

Parabens are commonly used as preservatives in foodstuffs, cosmetics, and pharmaceutical products. The widespread use of parabens has led to their leaking into the environment. Concerns about the safety of parabens have recently increased due to their potential endocrine-disrupting effects as an emerging contaminant. Thus, it is necessary to study the metabolism of parabens in vivo.

METHODS

In this study, Drosophila melanogaster in males and females were exposed to ethylparaben (EP) concentration group (300 mg/L, 700 mg/L, and 1000 mg/L), and control group (0 mg/L) by the capillary feeding assay (CAFE). We quantified the activity of the detoxification-related carboxylesterase (CarE). The contents of EP metabolites in D. melanogaster, including p-hydroxybenzoic acid (PHBA), methylparaben (MP), and intact EP were carried out by high-performance liquid chromatography (HPLC). The regression model between EP metabolites (PHBA and MP) and CarE was developed using the Fourier series fitting method.

RESULTS

The general level of EP metabolites (PHBA, MP, and intact EP) accumulation was accounted for 5.6-11.5% in D. melanogaster. As EP accumulated, the activity of CarE increased, and the activity of CarE in females was higher than males, which is inconsistent with the result of EP intake dose. Additionally, there were significant differences in the proportion of EP metabolites between female and male flies, and the results of sex comparison were different depending on the EP treated groups and EP metabolites. In general, PHBA of EP hydrolytic product and MP of EP transesterification product in D. melanogaster were 41.4-63.9% and 10.4-24.6%, respectively. In terms of the rest of the EP existed in intact form and ranged from 22.4% to 34.0%. Moreover, the EP metabolites in the conjugated form were higher than those in the free form. The regression model between EP metabolites and CarE was established, showing that the CarE activity can be used to estimate the content of PHBA and MP.

CONCLUSION

The result indicates that the EP can accumulate in the body through food. Hydrolysis is the main metabolic pathway of EP in D. melanogaster, and transesterification is another metabolic pathway of EP. Additionally, the EP metabolites in flies mainly exist in conjugated form. Furthermore, the Fourier series fitting method model between EP metabolites and CarE, providing theoretical support to study the dose-effect relationship between metabolites of parabens and CarE. This study not only provides a mathematical basis for the safety evaluation of parabens, but also provides support for the further study of the toxicological effects of parabens.