Abstract
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.
Highlights
Internal dose metrics are considered more predictive of biological responses than external doses when assessing and managing risks of chemicals to human health and the environment [1]
A “familiar uncertainty” based on the conceptual model parameters used, as well as the dose metrics applied is present in the traditional model
In this paper we describe a strategy for deriving a Physiologically Based Kinetic (PBK) model for a data-poor chemical using a read-across approach, which can be applied where in vivo kinetic data are not available for validation
Summary
Internal dose metrics are considered more predictive of biological responses than external doses when assessing and managing risks of chemicals to human health and the environment [1]. Model structure reflects a more detailed mechanistic understanding of biology and biochemistry, but this is accompanied by more “unfamiliar uncertainties” (for example, uncer tainty relating to the relevance, reliability and variability of the in vitro and in silico methods from which model parameters are generated) These generation PBK models, in the ideal situation, promise increased predictive potential, as well as mechanistic insights due to inclusion of mechanistic processes and emerging (human-relevant) data, but introduce additional challenges for risk assessors attempting to re view and use these more detailed and complex models in support of regulatory decision making. To address the lack of uptake of PBK models by the regulatory sector, a group of experts in the field proposed a way forward for model evaluation, establishing a list of elements that could be used to assess the validity of gener ation PBK models [3] Among these elements the read-across approach was proposed and is further illustrated here. This has been described in the recently published Organisation for Economic Coop eration and Development (OECD) PBK model guidance document [4]
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