Air/liquid interphase technique as an alternative in vitro testing strategy for detecting biological effects of volatile compounds. First results and future perspectives of an ongoing prevalidation study

The increasing demand for assessing inhalation toxicity hazards calls for new testing strategies comprising both in vitro and in vivo assays. The aim of this ongoing pre-validation study is to perform a multi-laboratory evaluation of an air/liquid interface culturing and exposure technique for testing the acute cytotoxic and genotoxic effects of gases on a biological cell model. A549 cells, grown on microporous membranes at the air/liquid interface, were exposed to several test atmospheres (NO2, SO2, formaldehyde, or ozone). Gas-mediated cytotoxicity was assessed after a one hour exposure via electronic cell counting (CASY®technology). Analysis of dose-response relationships showed a good reproducibility within and between the laboratories for all four gases. Comparison of the derived EC50 values with published LC50 values revealed a tight quantitative relationship between in vitro cytotoxicity and in vivo lethality. To evaluatethe performance and reliability of electronic cell counting, the cytotoxicity of SO2 was additionally assessed using two well established viability assays, the Neutral Red Uptake assay and the CellTiter-Blue® assay (Promega). A high correlation was found between EC50 values obtained with all three viability assays. The release of IL-6, IL-8, and MCP1 was analysed in order to evaluate the inflammatory effect of SO2. Genotoxicity assessment via Comet assay demonstrated reproducible dose-response relationships for SO2 and formaldehyde. No such dose-dependent genotoxicity could be observed for NO2 and ozone. The results of the present pre-validation study are promising with respect to the reliability and relevance of the proposed in vitro method for inhalation toxicity testing. Extended prevalidation is underway to establish a tested training set of compounds sufficiently large to allow for optimization of the developed prediction model.


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