A Novel Multispecies Toxicokinetic Modeling Approach in Support of Chemical Risk Assessment

Abstract

Standardized laboratory tests with a limited number of model species are a key component of chemical risk assessments. These surrogate species cannot represent the entire diversity of native species, but there are practical and ethical objections against testing chemicals in a large variety of species. In previous research, we have developed a multispecies toxicokinetic model to extrapolate chemical bioconcentration across species by combining single-species physiologically based toxicokinetic (PBTK) models. This "top-down" approach was limited, however, by the availability of fully parameterized single-species models. Here, we present a "bottom-up" multispecies PBTK model based on available data from 69 freshwater fishes found in Canada. Monte Carlo-like simulations were performed using statistical distributions of model parameters derived from these data to predict steady-state bioconcentration factors (BCFs) for a set of well-studied chemicals. The distributions of predicted BCFs for 1,4-dichlorobenzene and dichlorodiphenyltrichloroethane largely overlapped those of empirical data, although a tendency existed toward overestimation of measured values. When expressed as means, predicted BCFs for 26 of 34 chemicals (82%) deviated by less than 10-fold from measured data, indicating an accuracy similar to that of previously published single-species models. This new model potentially enables more environmentally relevant predictions of bioconcentration in support of chemical risk assessments.

Keywords: PBTK model; bioaccumulation; cross-species extrapolation; database development; physiologically based toxicokinetic model.

Figure 1.

Conceptional representation of the multi-species physiologically-based toxicokinetic (PBTK) model framework. Statistical distributions of model input parameters are resampled using random number generators (RNG) to obtain a set of ecologically plausible “virtual species objects.” Simulations performed using these “virtual species objects” are then complied to produce multi-species model outputs, including the distribution of predicted bioconcentration factors (BCFs).

Figure 2.

Histograms of (A) oxygen consumption rate (VO₂; mg h⁻¹ kg⁻¹) and (B) cardiac output (Qc; L h⁻¹ kg⁻¹) with fitted statistical probability density functions. The red line represents the fit to a normal distribution, the blue line is the fit to a gamma distribution, and the purple line is the fit to a uniform distribution. The best-fit distributions are highlighted with an asterisk: that for VO₂ is a normal distribution while that for Q_c is a gamma distribution.

Figure 3.

Comparison of modeled and measured multi-species bioconcentration factor (BCF) values for (A) 1,4-dichlorobenzene (PDB) and (B) dichlorodiphenyltrichloroethane (DDT). For each chemical, two datasets (modeled: blue and measured: red) were plotted as a scatterplot and half violin plot. Scatterplot results were randomly displaced along the Y axis to permit discrimination of values. Half violin plots were created using the Python package Seaborn.

Figure 4.

Relationship between modeled and measured multi-species BCF values for various chemicals. Mean values of multi-species bioconcentration factor (BCF) from modeled predictions and measured literature data for 34 chemicals (Supporting Information, Table S12) were plotted on a logarithmic scale. The solid line represents the line of equality; dashed lines represent a 10-fold deviation from equality. Letters and abbreviations: a = 1,2-difluorobenzene, b = 1,2-dibromobenzene, c = hexachloro-1,3-butadiene, d = fenvalerate, e = permethrin, f = 2,3,7,8-tetrachlorodibenzofuran, PDB = 1,4-dichlorobenzene, DDT = dichlorodiphenyltrichloroethane.