Effects of Chemicals in Reporter Gene Bioassays with Different Metabolic Activities Compared to Baseline Toxicity

Abstract

High-throughput cell-based bioassays are used for chemical screening and risk assessment. Chemical transformation processes caused by abiotic degradation or metabolization can reduce the chemical concentration or, in some cases, lead to the formation of more toxic transformation products. Unaccounted loss processes may falsify the bioassay results. Capturing the formation and effects of transformation products is important for relating the in vitro effects to in vivo. Reporter gene cell lines are believed to have low metabolic activity, but inducibility of cytochrome P450 (CYP) enzymes has been reported. Baseline toxicity is the minimal toxicity a chemical can have and is caused by the incorporation of the chemical into cell membranes. In the present study, we improved an existing baseline toxicity model based on a newly defined critical membrane burden derived from freely dissolved effect concentrations, which are directly related to the membrane concentration. Experimental effect concentrations of 94 chemicals in three bioassays (AREc32, ARE-bla and GR-bla) were compared with baseline toxicity by calculating the toxic ratio (TR). CYP activities of all cell lines were determined by using fluorescence-based assays. Only ARE-bla showed a low basal CYP activity and inducibility and AREc32 showed a low inducibility. Overall cytotoxicity was similar in all three assays despite the different metabolic activities indicating that chemical metabolism is not relevant for the cytotoxicity of the tested chemicals in these assays. Up to 28 chemicals showed specific cytotoxicity with TR > 10 in the bioassays, but baseline toxicity could explain the effects of the majority of the remaining chemicals. Seven chemicals showed TR < 0.1 indicating inaccurate physicochemical properties or experimental artifacts like chemical precipitation, volatilization, degradation, or other loss processes during the in vitro bioassay. The new baseline model can be used not only to identify specific cytotoxicity mechanisms but also to identify potential problems in the experimental performance or evaluation of the bioassay and thus improve the quality of the bioassay data.

Figure 1

Experimental derivation of nominal baseline toxicity QSARs for neutral and anionic chemicals for AREc32, ARE-bla, and GR-bla cell lines. (A) Logarithmic reciprocal IC_10,free (log 1/IC_10,free) of test chemicals were plotted against logarithmic liposome–water distribution ratios (log D_lip/w). The black solid line is the linear regression of the data points, and the dotted black line is the linear regression with a slope fixed to 1, which was used to derive the critical membrane concentration for baseline toxicity (IC_10,membrane). (B) Logarithmic reciprocal IC_10,membrane (log 1/IC_10,membrane) of test chemicals were plotted against log D_lip/w. The solid gray line indicates the constant CMB of 26 mmol/L_lip derived from the linear regression from A. (C) Logarithmic reciprocal IC_10,nom (log 1/IC₁₀) of test chemicals plotted against log D_lip/w derived with the mass-balance models for neutral and anionic chemicals. The red solid line indicates the generic QSAR for AREc32 and ARE-bla for neutral chemicals, and the red dotted line indicates the generic anionic QSAR. The green solid line indicates the QSAR for GR-bla for neutral chemicals, and the green dotted line indicates the anionic QSAR for GR-bla.

Figure 2

Results of EROD, EFCOD and BFCOD assay for AREc32, ARE-bla, and GR-bla cells without chemical exposure and after exposure to omeprazole or benzo[a]pyrene (A) and for rat liver S9 as a positive control (B). CYP activity was measured as the amount of resorufin (n_resorufin, EROD) or the amount of 7-hydroxy-4-trifluoromethylcoumarin (n_HFC, EFCOD and BFCOD) formed per minute and per mg_protein.

Figure 3

Specificity ratios (SR) of oxidative stress response activation in ARE-bla plotted against SR in AREc32. SR_cytotoxicity is shown when the cytotoxicity could be determined in both assays (black circles). For chemicals without measured cytotoxicity in at least one assay, SR_baseline was used (gray circles). Chemicals which showed oxidative stress response activation only in one assay are indicated with white circles. The solid black line indicates a perfect agreement of SR from both cell lines, and the dashed black lines indicate a deviation by a factor of 10.

Figure 4

Comparison of cytotoxicity (log 1/IC₁₀) of all cell lines. (A) All measured log 1/IC₁₀ values from all assays.. (B) Log 1/IC₁₀ measured in AREc32 or ARE-bla plotted against 1/IC₁₀ measured in GR-bla. Red circles indicate results for AREc32, blue triangles indicate results for ARE-bla, and green squares indicate results for GR-bla. The black line in (B) indicates a perfect agreement of the results of the cell lines. The gray area indicates a deviation of a factor of 10. HCP = hexachlorophene, CdCl₂ = cadmium chloride, VPA = valproic acid, HU = hydroxyurea.

Figure 5

Cytotoxicity of test chemicals compared to baseline toxicity. Logarithmic reciprocal IC_{10,confluency} and logarithmic reciprocal IC_10,ToxBLAzer were plotted against the logarithmic liposome–water distribution ratios (log  D_lip/w) of the test chemicals. The black line indicates IC_10,baseline, and the gray area indicates a toxic ratio (TR) between 0.1 and 10. (A, D) Data from AREc32 assay for neutral (A) and anionic chemicals (D). (B, E) Data from ARE-bla assay for neutral (B) and anionic chemicals (E). (C, F) Data from GR-bla assay for neutral (C) and anionic chemicals (F).

Figure 6

Testing strategies for single chemical screening in high-throughput in vitro bioassays.