Open AccessA Novel Multispecies Toxicokinetic Modeling Approach in Support of Chemical Risk Assessment
Author(s) -
Annika Mangold-Döring,
Chelsea Grimard,
Derek Green,
Stephanie Petersen,
John W. Nichols,
Natacha S. Hogan,
Lynn P. Weber,
Henner Hollert,
Markus Hecker,
Markus Brinkmann
Publication year - 2021
Publication title -
environmental science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.851
H-Index - 397
eISSN - 1520-5851
pISSN - 0013-936X
DOI - 10.1021/acs.est.1c02055
Subject(s) - bioconcentration , toxicokinetics , environmental chemistry , monte carlo method , environmental science , biochemical engineering , chemistry , bioaccumulation , ecology , biology , statistics , mathematics , toxicity , engineering , organic chemistry
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.