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ECO37: D-BASS – Developing a Bioaccumulation Assessment Strategy for Surfactants

Principal Investigator

Prof. Steven Droge

Institute for Biodiversity and Ecosystem Dynamics (IBED)

University of Amsterdam

P.O. Box 94248, 1090 GE Amsterdam , The Netherlands

Telephone: +31 20 525 7439

E-mail: s.t.j.droge@uva.nl – steven.droge@gmail.com

Collaborators

Prof. Pim de Voogt; Dr. John Parsons, University of Amsterdam, Institute for Biodiversity and Ecosystem Dynamics

Prof. Michael McLachlan, Stockholm University, Department of Environmental Science and Analytical Chemistry

Dr. Jon Arnot; Dr. James Armitage, ARC Arnot Research & Consulting Inc.

Dr. Mark Bonnell, Senior Science Advisor, Environment and Climate Change Canada

Dr. John Nichols, US-Environmental Protection Agency

Description

The general objective of this proposed research is therefore to validate the combined use of (a) measured partition coefficients, (b) in vitro intrinsic hepatic clearance rates, and (c) a recently improved bioaccumulation model for ionogenic compounds, against measured bioconcentration factors of surfactants in fish.

To satisfy this objective, the project will:

  1. Expand the existing set of membrane-water partition/distribution ratios (Kmw/Dmw) to all major surfactant classes, with an experimentally feasible range of alkyl chain lengths, and align these with COSMOmic simulated Kmw values to confirm extrapolation to longer alkyl chain length analogues.
  2. Evaluate the need for additional surrogate tissue-water partition coefficients (fish-muscle protein, storage lipid, leverage results from a preceding ERASM funded project performed by Partner 1), and measure these for representative surfactants of classes where this would improve confidence and accuracy of model-based whole body and tissue specific bioconcentration factors (BCFs).
  3. Expand the existing set of in vitro hepatic clearance rates to all major surfactant classes using the rainbow trout S9 liver homogenate assay (RT-S9), with experimentally feasible range of alkyl chain lengths, and define guidance rules and/or structure-activity relationships for each class in as much detail as possible. Options to include S9 enzyme fractions from other tissues (gut RT-S9, gill RT-S9) may be included for several key surfactants, as currently being studied by Partner 5.
  4. Generate in vivo fish bioaccumulation data with rainbow trout for a selection of (non-radiolabelled) surfactants, a) from classes for which experimental BCF are absent or only of low quality, b) with an experimentally feasible range of alkyl chain lengths, and c) for which key distribution ratios and clearance rates have been determined in a consistent way in either the current study, the LRI-ECO.21 project, or in high quality published studies.
  5. Evaluate the inclusion of surfactants in the applicability domain of mecha-nistic bioconcentration model for ionogenic organic chemicals (BIONIC v2).
  6. Improve a tiered approach for the prediction of bioconcentration factors for surfactants in fish, based on weight-of-evidence decisions on the key model parameters that can be obtained via various predictive tools and experimental assays. The available values for key model parameters will be reviewed at the end of the project for the most important classes of surfactants, along with guidance on the weight-of-evidence approach.

Work content:

The proposed research will build on the improved BIONIC model and input parameter data streams leading to new models for specific surfactants types that can be viewed as a tiered modelling approach. The research required to implement the tiered approach is divided into three Work Packages (WPs):

WP1. Measuring key input parameters for the BCF model – The main objective of WP1 is to apply standardized experimental methods to generate new empirical measurements for chemicals representing the major surfactant classes to improve the bioaccumulation assessment of surfactants (i.e. partition coefficients/distribution ratios and in vitro biotransformation rate constant estimates);

WP2. Measuring in vivo tissue distribution and steady state fish BCFs – The main objective of WP2 is to generate high quality BCF data for a series of surfactant analogues that will be used to validate model predictions based on measured key parameters alike the ones in WP1. It is proposed to perform up to 4 separate BCF studies, each applying mixtures of 4-5 chemicals, varying key conditions. Our goal is to test three different surfactant mixtures, with one mixture tested at two different bulk water pH. Initially, a small set of relatively large fish (~500 g) will expose for several weeks to a single surfactant mixture that covers the relevant property domain. This size should provide sufficient blood sample to determine the volume of distribution, which strongly supports in vivo with in vitro/in silico predictions, and to examine sampling feasibility in tests with smaller fish. Afterwards, more detailed uptake studies with chemical mixtures will be performed for sufficient time to reach steady state equilibrium (SS), with a large number of smaller fish. The main reason for using smaller fish is that the uptake kinetics (i.e., time to steady-state) are much faster meaning that testing can be completed in a shorter time period. A depuration phase will be included to evaluate model predictions of elimination kinetics based on kB, the gill exchange model, and tissue-water partitioning.

WP3. Use modeling insights to refine the mechanistic bioaccumulation models for surfactants – The main objectives of WP 3 are to (i) support decisions on the experimental design of the in vivo BCF studies, (ii) to support the development of new structure-activity relationships for key parameters in the chemical domain of surfactants, (iii) to discuss selection/application of the key parameters into the model in a tiered modelling scheme (alike Figure 1), and (iv) to compare BCF model outcomes with in vivo BCF results. WP 3 includes developing a guidance document on model parameterization and related tools for surfactants.

Timeline: March 2017 > August 2019

LRI funding: € 500K

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