Evaluating In Vitro-In Vivo Extrapolation of Toxicokinetics

Abstract

Prioritizing the risk posed by thousands of chemicals potentially present in the environment requires exposure, toxicity, and toxicokinetic (TK) data, which are often unavailable. Relatively high throughput, in vitro TK (HTTK) assays and in vitro-to-in vivo extrapolation (IVIVE) methods have been developed to predict TK, but most of the in vivo TK data available to benchmark these methods are from pharmaceuticals. Here we report on new, in vivo rat TK experiments for 26 non-pharmaceutical chemicals with environmental relevance. Both intravenous and oral dosing were used to calculate bioavailability. These chemicals, and an additional 19 chemicals (including some pharmaceuticals) from previously published in vivo rat studies, were systematically analyzed to estimate in vivo TK parameters (e.g., volume of distribution [Vd], elimination rate). For each of the chemicals, rat-specific HTTK data were available and key TK predictions were examined: oral bioavailability, clearance, Vd, and uncertainty. For the non-pharmaceutical chemicals, predictions for bioavailability were not effective. While no pharmaceutical was absorbed at less than 10%, the fraction bioavailable for non-pharmaceutical chemicals was as low as 0.3%. Total clearance was generally more under-estimated for nonpharmaceuticals and Vd methods calibrated to pharmaceuticals may not be appropriate for other chemicals. However, the steady-state, peak, and time-integrated plasma concentrations of nonpharmaceuticals were predicted with reasonable accuracy. The plasma concentration predictions improved when experimental measurements of bioavailability were incorporated. In summary, HTTK and IVIVE methods are adequately robust to be applied to high throughput in vitro toxicity screening data of environmentally relevant chemicals for prioritizing based on human health risks.

Database of pharmacokinetic time-series data and parameters for 144 environmental chemicals

“…There is increasing acceptance of the use of in vitro-derived TK for chemical risk prioritization 13,14 and the design of human clinical trials 15 . However, there is still need for careful model evaluation to determine which chemicals these techniques might work for, and how well 16,17 .…”

“…Investigating every premise of a model would be time-intensive, and still would not demonstrate whether a modelling approach provides a unique description of the system modelled or an accurate prediction of dose-response curves in any non-modelled scenario 18 . While systems do exist for understanding the confidence with which in vitro-in vivo extrapolation (IVIVE) may be applied to pharmaceuticals (e.g., Riede, et al 19 ), it is simpler and more informative to compare modelling results with at least one observed instance of a relevant in vivo exposure scenario 14,15,17 . Unfortunately, the relative lack of structured, non-pharmaceutical in vivo pharmacokinetic data makes systematic evaluation of the performance of IVIVE for environmental chemicals difficult 17 .…”

Improving natural product research translation: From source to clinical trial

“…[88][89][90][91][92] These approaches, as outlined by the Tox21 Federal collaboration and the U.S. Environmental Protection Agency's ToxCast program, [93][94][95][96] can be used to focus high-throughput in vitro and in vivo testing on compounds and concentrations that are more likely to be biologically active and appropriate for each model. Data from more highly characterized products can be integrated into dynamic models, and in vitro to in vivo extrapolation (IVIVE) can be used to predict biological responses.…”

“…Data from more highly characterized products can be integrated into dynamic models, and in vitro to in vivo extrapolation (IVIVE) can be used to predict biological responses. [88][89][90][91][92] These approaches, as outlined by the Tox21 Federal collaboration and the U.S. Environmental Protection Agency's ToxCast program, [93][94][95][96] can be used to focus high-throughput in vitro and in vivo testing on compounds and concentrations that are more likely to be biologically active and appropriate for each model. 97,98 Such integrated approaches could be further developed to probabilistically model the pharmacological and toxicological kinetics and dynamics of complex NPs.…”

Improving Natural Product Research Translation: from Source to Clinical Trial

“…When information on NP kinetics, dynamics, bioavailability, clearance, and pharmacologic activities is limited, one useful approach is to apply computational or in silico methods to leverage information from products with known chemistry, mechanisms of action, and activities. Data from more highly characterized products can be integrated into dynamic models, and in vitro to in vivo extrapolation (IVIVE) can be used to predict biological responses (87)(88)(89)(90)(91). These approaches, as outlined by the Tox21 Federal collaboration and the U.S. Environmental Protection Agency's ToxCast program (92)(93)(94)(95), can be used to focus high-throughput in vitro, and in vivo testing on compounds and concentrations that are more likely to be biologically active and appropriate for each model (96,97).…”