Predicting Concentrations of Organic Chemicals in Fish by Using Toxicokinetic Models

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 I 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

Predicting exposure concentrations of chemicals with a wide range of volatility and hydrophobicity in different multi-well plate set-ups

Quantification of chemical toxicity in small-scale bioassays is challenging owing to small volumes used and extensive analytical resource needs. Yet, relying on nominal concentrations for effect determination maybe erroneous because loss processes can significantly reduce the actual exposure. Mechanistic models for predicting exposure concentrations based on distribution coefficients exist but require further validation with experimental data. Here we developed a complementary empirical model framework to predict chemical medium concentrations using different well-plate formats (24/48-well), plate covers (plastic lid, or additionally aluminum foil or adhesive foil), exposure volumes, and biological entities (fish, algal cells), focusing on the chemicals’ volatility and hydrophobicity as determinants. The type of plate cover and medium volume were identified as important drivers of volatile chemical loss, which could accurately be predicted by the framework. The model focusing on adhesive foil as cover was exemplary cross-validated and extrapolated to other set-ups, specifically 6-well plates with fish cells and 24-well plates with zebrafish embryos. Two case study model applications further demonstrated the utility of the empirical model framework for toxicity predictions. Thus, our approach can significantly improve the applicability of small-scale systems by providing accurate chemical concentrations in exposure media without resource- and time-intensive analytical measurements.ISSN:2045-232

A Validated Algorithm for Selecting Non-Toxic Chemical Concentrations

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 that 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 embryos. The algorithm provided an NtC that is more protective than NOEC (no-observed-effect-concentration), NEC (modeled no-effect concentration), EC10 and BMD (benchmark dose). Despite focusing on small-scale bioassays here, this study indicates that the NtC algorithm could be used in various systems. Its application to the survival of zebrafish embryos and to 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 source code, and as a free, user-friendly online application.ISSN:1868-8551ISSN:0946-7785ISSN:1868-596

Measured and Modeled Toxicokinetics in Cultured Fish Cells and Application to In Vitro - In Vivo Toxicity Extrapolation

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

Predicting Concentrations of Organic Chemicals in Fish by Using Toxicokinetic Models

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 (<i>Oncorhynchus mykiss</i>) and fathead minnow (<i>Pimephales promelas</i>). 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

Zebrafish early life stages as alternative model to study ‘designer drugs’: Concordance with mammals in response to opioids

The number of new psychoactive substances (NPS) on the illicit drug market increases fast, posing a need to urgently understand their toxicity and behavioural effects. However, with currently available rodent models, NPS assessment is limited to a few substances per year. Therefore, zebrafish (Danio rerio) embryos and larvae have been suggested as an alternative model that would require less time and resources to perform an initial assessment and could help to prioritize substances for subsequent evaluation in rodents. To validate this model, more information on the concordance of zebrafish larvae and mammalian responses to specific classes of NPS is needed. Here, we studied toxicity and behavioural effects of opioids in zebrafish early life stages. Synthetic opioids are a class of NPS that are often used in pain medication but also frequently abused, having caused multiple intoxications and fatalities recently. Our data shows that fentanyl derivatives were the most toxic among the tested opioids, with toxicity in the zebrafish embryo toxicity test decreasing in the following order: butyrfentanyl>3-methylfentanyl>fentanyl>tramadol> O-desmethyltramadol>morphine. Similar to rodents, tramadol as well as fentanyl and its derivatives led to hypoactive behaviour in zebrafish larvae, with 3-methylfentanyl being the most potent. Physico-chemical properties-based predictions of chemicals' uptake into zebrafish embryos and larvae correlated well with the effects observed. Further, the biotransformation pattern of butyrfentanyl in zebrafish larvae was reminiscent of that in humans. Comparison of toxicity and behavioural responses to opioids in zebrafish and rodents supports zebrafish as a suitable alternative model for rapidly testing synthetic opioids