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

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

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

Distribution of cypermethrin in the well presented as average percentages of the chemical in each compartment over time.

<p>Distribution of cypermethrin in the well presented as average percentages of the chemical in each compartment over time.</p

Regression of model parameters and log K_OW for non-volatile (red •– log k_in, red ^O – log k_out and volatile (blue ▪ – log k_in, blue □ – log k_out) compounds.

<p>Data for volatile compounds were not used for fitting the model and trend lines. Red <b>—</b> – trend lines for log k_in and log k_out (A – plastic: log k_in = 0.2602 · log K_OW+0.5385; log k_out = −0.0388 ·(log K_OW)²+0.0272 · log K_OW+0.7982, B – cells: log k_in = 0.0641 · log K_OW+1.9898, log k_out = −0.0447 · (log K_OW)²+0.0619· log K_OW+0.525).</p

Comparison between measured and modeled bioconcentration factors (log BCF) in fish and fish cells.

<p>Average BCF measured in cells (red ▪), kinetic BCF calculated from our empirical model for rate constants (light blue <b>—</b>), equation: log BCF = −0.0078·(log K_OW)³ +0.1236·(log K_OW)² −0.2073·log K_OW+1.5872, predicted BCF in fish by the Arnot & Gobas model <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092303#pone.0092303-Arnot1" target="_blank">[38]</a> (dark blue <b>- -</b>) and BCF predicted in rainbow trout by the PBTK model (green - •); model parameters: fish weight - 2 g, lipid fraction - 5%, water fraction - 75%). For propiconazole (log K_OW = 3.72) and pentachlorophenol (log K_OW = 5.12), steady-state condition was not reached within 24 hours.</p

The average accumulation of chemicals in cells over time, expressed as percentage of chemical added to the medium at the beginning of the experiment.

<p>Symbols: measured values (replicates are presented in Table S4 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092303#pone.0092303.s001" target="_blank">File S1</a>).</p

External and internal effect concentrations for fathead minnow gills (A, B) and rainbow trout gill cells (C, D).

<p>Significant correlation between effect concentrations and log K_OW were found only for LC50 values (log LC50 = −0.5856·log K_OW+5.2237).</p

Properties and concentrations of the test chemicals used for measuring and predicting chemical concentrations in the RTgill-W1 cell line.

a<p>- logK_ow and log H were taken from EPI Suite: experimental database.</p>b<p>- nominal chemical concentration dosed at the beginning of the experiment</p><p><i>italic font</i>– volatile compounds.</p