Abteilung Umwelttoxikologie

Out of the >107 million chemicals already registered with the Chemical Abstracts Services, less than 0.5% are being regulated, and even fewer are evaluated for their safety. Consequently, a new paradigm in risk assessment is urgently needed. It should encompass faster and less costly methods and reduce the number of animals needed for testing. One proposal is to combine computational modeling with small-scale bioassay methods. This chapter describes the methods that link in vitro bioassays using fish cells with physiologically based toxicokinetic (PBTK) modeling in order to predict the acute toxicity, bioaccumulation, and impact of chemicals on fish growth. The main focus is on PBTK modeling; thus all the model equations and parameters available for eight fish species as well as suggestions for possible software implementation will be provided. The PBTK model described here can account for respiratory and dietary uptake routes and for chemical biotransformation processes.

The maximal chemical concentration that causes an acceptably small or no effect in an organism or isolated cells is an often - sought - after value in toxicology. Existing approaches to derive this value have raised several concerns; thus, it is often chosen case - by - case based on personal experience. To overcome this ambiguity, we propose an approach for choosing the non - toxic concentration (NtC) of a chemical in a rational, tractable way. We developed an algorithm that identifies the highest chemical concentration which causes no more than 10% effect (≤ EC10) including the modeled 95% confidence intervals and considering each of the measured biological replicates; and whose toxicity is not significantly different from no effect.The developed algorithm was validated in two steps: by comparing its results with measured and modeled data for 91 dose - response experiments with fish cell lines and/or zebrafish embryos; and by measuring actual effects caused by NtCs in a separate set of experiments using a fish cell line and zebrafish emb ryos. The algorithm provided an NtC that is more protective than NOEC (No - Observed - Effect - Concentration), NEC (modeled No - Effect Concentration), EC10 and Benchmark Dose (BMD). Despite focusing on small scale bioassays here, this study indicates that the NtC algorithm could be used in various systems. Its application on the survival of zebrafish embryos and on metabolic activity in cell lines showed that NtCs can be applied to different effect measurements, time points and levels of biological organization. The algorithm is available as Matlab and R code, and as a free, user friendly online application.

Environmental risk assessment of chemicals is essential but often relies on ethically controversial and expensive methods. We show that tests using cell cultures, combined with modeling of toxicological effects, can replace tests with juvenile fish. Hundreds of thousands of fish at this developmental stage are annually used to assess the influence of chemicals on growth. Juveniles are more sensitive than adult fish, and their growth can affect their chances to survive and reproduce. Thus, to reduce the number of fish used for such tests, we propose a method that can quantitatively predict chemical impact on fish growth based on in vitro data. Our model predicts reduced fish growth in two fish species in excellent agreement with measured in vivo data of two pesticides. This promising step toward alternatives to fish toxicity testing is simple, inexpensive, and fast and only requires in vitro data for model calibration.

Effect concentrations in the toxicity assessment of chemicals with fish and fish cells are generally based on external exposure concentrations. External concentrations as dose metrics, may, however, hamper interpretation and extrapolation of toxicological effects because it is the internal concentration that gives rise to the biological effective dose. Thus, we need to understand the relationship between the external and internal concentrations of chemicals. The objectives of this study were to: (i) elucidate the time-course of the concentration of chemicals with a wide range of physicochemical properties in the compartments of an in vitro test system, (ii) derive a predictive model for toxicokinetics in the in vitro test system, (iii) test the hypothesis that internal effect concentrations in fish (in vivo) and fish cell lines (in vitro) correlate, and (iv) develop a quantitative in vitro to in vivo toxicity extrapolation method for fish acute toxicity. To achieve these goals, time-dependent amounts of organic chemicals were measured in medium, cells (RTgill-W1) and the plastic of exposure wells. Then, the relation between uptake, elimination rate constants, and log K_OW was investigated for cells in order to develop a toxicokinetic model. This model was used to predict internal effect concentrations in cells, which were compared with internal effect concentrations in fish gills predicted by a Physiologically Based Toxicokinetic model. Our model could predict concentrations of non-volatile organic chemicals with log K_OW between 0.5 and 7 in cells. The correlation of the log ratio of internal effect concentrations in fish gills and the fish gill cell line with the log K_OW was significant (r>0.85, p = 0.0008, F-test). This ratio can be predicted from the log K_OW of the chemical (77% of variance explained), comprising a promising model to predict lethal effects on fish based on in vitro data.

Quantification of chemical toxicity continues to be generally based on measured external concentrations. Yet, internal chemical concentrations have been suggested to be a more suitable parameter. To better understand the relationship between the external and internal concentrations of chemicals in fish, and to quantify internal concentrations, we compared three toxicokinetic (TK) models with each other and with literature data of measured concentrations of 39 chemicals. Two one-compartment models, together with the physiologically based toxicokinetic (PBTK) model, in which we improved the treatment of lipids, were used to predict concentrations of organic chemicals in two fish species: rainbow trout (Oncorhynchus mykiss) and fathead minnow (Pimephales promelas). All models predicted the measured internal concentrations in fish within 1 order of magnitude for at least 68% of the chemicals. Furthermore, the PBTK model outperformed the one-compartment models with respect to simulating chemical concentrations in the whole body (at least 88% of internal concentrations were predicted within 1 order of magnitude using the PBTK model). All the models can be used to predict concentrations in different fish species without additional experiments. However, further development of TK models is required for polar, ionizable, and easily biotransformed compounds.