Horst Spielmann, Elke Genschow, Manfred Liebsch and Willi Halle

To reduce the number of animals used in acute oral toxicity testing, cytotoxicity data (IC50) can be used to determine the starting dose for in vivo testing by applying the standard regression between IC50 and acute oral LD50 values in the Register of Cytotoxicity (RC). In the RC, the correlation between cytotoxicity, represented by the mean IC50 (IC50x), and the acute oral LD50 of rats and/or mice has been determined for 347 chemicals by applying the linear regression model for log-transformed pairs of IC50 versus oral LD50. The standard regression line of the two toxicity parameters is characterised by an intercept a = 0.625 and regression coefficient b = 0.435, and 252 of 347 chemicals (72.6 %) are located within a dose-range differing by not more than 0.699 (factor FG ≤ log 5) from the standard regression line. In the present study, we have used the RC and its IC50/LD50 regression model to predict the LD50 values from cytotoxicity data for nine chemicals which were tested in an evaluation study of the Up and Down Procedure (UDP). For seven of the nine chemicals, LD50 values (mg/kg) predicted from the RC were in the same doserange as LD50 values determined in vivo, while the dose-range differed by more than one order of magnitude for the two remaining chemicals. Thus, the prediction of LD50 values from cytotoxicity data was promising in this limited data set. It is proposed that a tiered in vitro/in vivo testing approach will reduce animal use in the UDP method. As the first step, the in vitro cytotoxicity of a new chemical is determined. By applying the RC regression and adapting it to the sensitivity of a specific cell line, the LD50 value (mg/kg) can be predicted from the IC50 value. The predicted LD50 dose is then used as the starting dose in the UDP. In the RC model, the precision of the prediction increases with decreasing toxic potential, and the majority of industrial chemicals (around 90%) are not toxic according to EU classification criteria.
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