Dataset Information

A Generalized Physiologically Based Kinetic Model for Fish for Environmental Risk Assessment of Pharmaceuticals.

ABSTRACT: An increasing number of pharmaceuticals found in the environment potentially impose adverse effects on organisms such as fish. Physiologically based kinetic (PBK) models are essential risk assessment tools, allowing a mechanistic approach to understanding chemical effects within organisms. However, fish PBK models have been restricted to a few species, limiting the overall applicability given the countless species. Moreover, many pharmaceuticals are ionizable, and fish PBK models accounting for ionization are rare. Here, we developed a generalized PBK model, estimating required parameters as functions of fish and chemical properties. We assessed the model performance for five pharmaceuticals (covering neutral and ionic structures). With biotransformation half-lives (HLs) from EPI Suite, 73 and 41% of the time-course estimations were within a 10-fold and a 3-fold difference from measurements, respectively. The performance improved using experimental biotransformation HLs (87 and 59%, respectively). Estimations for ionizable substances were more accurate than any of the existing species-specific PBK models. The present study is the first to develop a generalized fish PBK model focusing on mechanism-based parameterization and explicitly accounting for ionization. Our generalized model facilitates its application across chemicals and species, improving efficiency for environmental risk assessment and supporting an animal-free toxicity testing paradigm.

SUBMITTER: Wang J

PROVIDER: S-EPMC9118555 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Generalized Physiologically Based Kinetic Model for Fish for Environmental Risk Assessment of Pharmaceuticals.

Wang Jiaqi J Nolte Tom M TM Owen Stewart F SF Beaudouin Rémy R Hendriks A Jan AJ Ragas Ad M J AMJ

Environmental science & technology 20220426 10

An increasing number of pharmaceuticals found in the environment potentially impose adverse effects on organisms such as fish. Physiologically based kinetic (PBK) models are essential risk assessment tools, allowing a mechanistic approach to understanding chemical effects within organisms. However, fish PBK models have been restricted to a few species, limiting the overall applicability given the countless species. Moreover, many pharmaceuticals are ionizable, and fish PBK models accounting for ...[more]

PMID: 35472258

Similar Datasets

Project description:Human biomonitoring (HBM) data can provide insight into co-exposure patterns resulting from exposure to multiple chemicals from various sources and over time. Therefore, such data are particularly valuable for assessing potential risks from combined exposure to multiple chemicals. One way to interpret HBM data is establishing safe levels in blood or urine, called Biomonitoring Equivalents (BE) or HBM health based guidance values (HBM-HBGV). These can be derived by converting established external reference values, such as tolerable daily intake (TDI) values. HBM-HBGV or BE values are so far agreed only for a very limited number of chemicals. These values can be established using physiologically based kinetic (PBK) modelling, usually requiring substance specific models and the collection of many input parameters which are often not available or difficult to find in the literature. The aim of this study was to investigate the suitability and limitations of generic PBK models in deriving BE values for several compounds with a view to facilitating the use of HBM data in the assessment of chemical mixtures at a screening level. The focus was on testing the methodology with two generic models, the IndusChemFate tool and High-Throughput Toxicokinetics package, for two different classes of compounds, phenols and phthalates. HBM data on Danish children and on Norwegian mothers and children were used to evaluate the quality of the predictions and to illustrate, by means of a case study, the overall approach of applying PBK models to chemical classes with HBM data in the context of chemical mixture risk assessment. Application of PBK models provides a better understanding and interpretation of HBM data. However, the study shows that establishing safety threshold levels in urine is a difficult and complex task. The approach might be more straightforward for more persistent chemicals that are analysed as parent compounds in blood but high uncertainties have to be considered around simulated metabolite concentrations in urine. Refining the models may reduce these uncertainties and improve predictions. Based on the experience gained with this study, the performance of the models for other chemicals could be investigated, to improve the accuracy of the simulations.

Project description:With current progress in science, there is growing interest in developing and applying Physiologically Based Kinetic (PBK) models in chemical risk assessment, as knowledge of internal exposure to chemicals is critical to understanding potential effects in vivo. In particular, a new generation of PBK models is being developed in which the model parameters are derived from in silico and in vitro methods. To increase the acceptance and use of these "Next Generation PBK models", there is a need to demonstrate their validity. However, this is challenging in the case of data-poor chemicals that are lacking in kinetic data and for which predictive capacity cannot, therefore, be assessed. The aim of this work is to lay down the fundamental steps in using a read across framework to inform modellers and risk assessors on how to develop, or evaluate, PBK models for chemicals without in vivo kinetic data. The application of a PBK model that takes into account the absorption, distribution, metabolism and excretion characteristics of the chemical reduces the uncertainties in the biokinetics and biotransformation of the chemical of interest. A strategic flow-charting application, proposed herein, allows users to identify the minimum information to perform a read-across from a data-rich chemical to its data-poor analogue(s). The workflow analysis is illustrated by means of a real case study using the alkenylbenzene class of chemicals, showing the reliability and potential of this approach. It was demonstrated that a consistent quantitative relationship between model simulations could be achieved using models for estragole and safrole (source chemicals) when applied to methyleugenol (target chemical). When the PBK model code for the source chemicals was adapted to utilise input values relevant to the target chemical, simulation was consistent between the models. The resulting PBK model for methyleugenol was further evaluated by comparing the results to an existing, published model for methyleugenol, providing further evidence that the approach was successful. This can be considered as a "read-across" approach, enabling a valid PBK model to be derived to aid the assessment of a data poor chemical.

Project description:There is a growing concern that neuroactive chemicals released into the environment can perturb wildlife behaviour. Among these chemicals, pharmaceuticals such as antidepressants and anxiolytics have been receiving increasing attention, as they are specifically prescribed to modify behavioural responses. Many laboratory studies have demonstrated that some of these compounds can affect various aspects of the behaviour of a range of aquatic organisms; however, these investigations are focused on a very small set of neuroactive pharmaceuticals, and they often consider one compound at a time. In this study, to better understand the environmental and toxicological dimension of the problem, we considered all pharmaceuticals explicitly intended to modulate the central nervous system (CNS), and we hypothesised that these compounds have higher probability of perturbing animal behaviour. Based on this hypothesis, we used the classification of pharmaceuticals provided by the British National Formulary (based on their clinical applications) and identified 210 different CNS-acting pharmaceuticals prescribed in the UK to treat a variety of CNS-related conditions, including mental health and sleep disorders, dementia, epilepsy, nausea, and pain. The analysis of existing databases revealed that 84 of these compounds were already detected in surface waters worldwide. Using a biological read-across approach based on the extrapolation of clinical data, we predicted that the concentration of 32 of these neuroactive pharmaceuticals in surface waters in England may be high enough to elicit pharmacological effects in wild fish. The ecotoxicological effects of the vast majority of these compounds are currently uncharacterised. Overall, these results highlight the importance of addressing this environmental challenge from a mixture toxicology and systems perspective. The knowledge platform developed in the present study can guide future region-specific prioritisation efforts, inform the design of mixture studies, and foster interdisciplinary efforts aimed at identifying novel approaches to predict and interpret the ecological implications of chemical-induced behaviour disruption.

Dataset Information

A Generalized Physiologically Based Kinetic Model for Fish for Environmental Risk Assessment of Pharmaceuticals.

Publications

A Generalized Physiologically Based Kinetic Model for Fish for Environmental Risk Assessment of Pharmaceuticals.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets