Département Toxicologie de l’environnement

Cationic surfactants are used in many industrial processes and in consumer products with concurrent release into the aquatic environment, where they may accumulate in aquatic organisms to regulatoryly relevant thresholds. Here, we aimed to better understand the bioconcentration behavior of three selected cationic surfactants, namely N,N-dimethyldecylamine (T10), N-methyldodecylamine (S12), and N,N,N-trimethyltetradecylammonium cation (Q14), in the cells of fish liver (RTL-W1) and gill (RTgill-W1) cell lines. We conducted full mass balances for bioconcentration tests with the cell cultures, in which the medium, the cell surface, the cells themselves, and the plastic compartment were sampled and quantified for each surfactant by HPLC MS/MS. Accumulation in/to cells correlated with the surfactants' alkyl chain lengths and their membrane lipid–water partitioning coefficient, D_MLW. Cell-derived bioconcentration factors (BCF) of T10 and S12 were within a factor of 3.5 to in vivo BCF obtained from the literature, while the cell-derived BCF values for Q14 were >100 times higher than the in vivo BCF. From our experiments, rainbow trout cell lines appear as a suitable conservative in vitro screening method for bioconcentration assessment of cationic surfactants and are promising for further testing.

Permanent rainbow trout (Oncorhynchus mykiss) cell lines represent potential in vitro alternatives to experiments with fish. We here developed a method to assess the bioaccumulation potential of anionic organic compounds in fish, using the rainbow trout liver-derived RTL-W1 cell line. Based on the availability of high quality in vivo bioconcentration (BCF) and biomagnification (BMF) data and the substances’ charge state at physiological pH, four anionic compounds were selected: pentachlorophenol (PCP), diclofenac (DCF), tecloftalam (TT) and benzotriazol-tert-butyl-hydroxyl-phenyl propanoic acid (BHPP). The fish cell line acute toxicity assay (OECD TG249) was used to derive effective concentrations 50 % and non-toxic exposure concentrations to determine exposure concentrations for bioaccumulation experiments. Bioaccumulation experiments were performed over 48 h with a total of six time points, at which cell, medium and plastic fractions were sampled and measured using high resolution tandem mass spectrometry after online solid phase extraction. Observed cell internal concentrations were over-predicted by K_OW-derived predictions while pH-dependent octanol–water partitioning (D_OW) and membrane lipid-water partitioning (D_MLW) gave better predictions of cell internal concentrations. Measured medium and cell internal concentrations at steady state were used to calculate RTL-W1-based BCF, which were compared to D_OW- or D_MLW-based model approaches and in vivo data. With the exception of PCP, the cell-derived BCF best compared to D_OW-based model predictions, which were higher than predictions based on D_MLW. All methods predicted the in vivo BCF for diclofenac well. For PCP, the cell-derived BCF was lowest although all BCF predictions underestimated the in vivo BCF by ≥ 1 order of magnitude. The RTL-W1 cells, and all other prediction methods, largely overestimated in vivo BMF, which were available for PCP, TT and BHPP. We conclude that the RTL-W1 cell line can supplement BCF predictions for anionic compounds. For BMF estimations, however, in vitro-in vivo extrapolations need adaptation or a multiple cell line approach.

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.

Permanent fish cell lines constitute a promising complement or substitute for fish in the environmental risk assessment of chemicals. We demonstrate the potential of a set of cell lines originating from rainbow trout (Oncorhynchus mykiss) to aid in the prediction of chemical bioaccumulation in fish, using benzo[a]pyrene (BaP) as a model chemical. We selected three cell lines from different tissues to more fully account for whole-body biotransformation in vivo: the RTL-W1 cell line, representing the liver as major site of biotransformation, and the RTgill-W1 (gill) and RTgutGC (intestine) cell lines, as important environment-organism interfaces, which likely influence chemical uptake. All three cell lines were found to effectively biotransform BaP. However, rates of in vitro clearance differed, with the RTL-W1 cell line being most efficient, followed by RTgutGC. Co-exposures with α-naphthoflavone as potent inhibitor of biotransformation, assessment of CYP1A catalytic activity, and the progression of cellular toxicity upon prolonged BaP exposure revealed that BaP is handled differently in the RTgill-W1 compared to the other two cell lines. Application of the cell-line-derived in vitro clearance rates into a physiology-based toxicokinetic model predicted a BaP bioconcentration factor (BCF) of 909-1057 compared to 920 reported for rainbow trout in vivo.

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.

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.