Critical Variables to be Considered when Attempting to Estimate Blood Ethanol Concentrations in Rats from g/kg Exposure Data

Abstract

BackgroundIn rodent studies of ethanol (EtOH) consumption where blood sample collection does not occur, there is often mention of likely BECs based on prior studies. These studies may vary in dose(s) used, age/sex/species, or administration route. Often, intake studies may presume that binge-levels were achieved without knowing that BECs exceeded 80 mg% (binge threshold). In human studies, estimated BECs (eBECs) have been derived using complex formulas that consider EtOH consumption level and the weight and sex of the individual.\n\nMethodThree datasets were used to derive eBECs using a conversion factor (CF) that considers gram (g) of EtOH per kilogram (kg) of animal weight and other variables that may influence BECs such as age, sex, dose, route, vehicle, chronicity, and timing post-exposure. Regression analyses were also conducted for each dataset, building regression models with BEC as the response and other variables in the study specific to each dataset as predictor variables.\n\nResultsDataset1 assessed age, sex and post-injection time point. Both CF and regression analyses determined that different CFs should be used for 10- and 30-min post-administration time points. Dataset2 assessed age, dose, vehicle and post-intubation time point. Depending on the post-intubation time point, several CFs were used to derive eBECs. When weight was not used as a regression variable, data across approaches corresponded, with age differences emerging later in elimination phase. In Dataset3 that used BECs from a repeated intake study, chronic exposure influenced CFs, although regression analysis did not yield similar findings.\n\nConclusionsAlthough eBECs can be derived, critical variables vary with subject and test conditions and do not always concur with results of regression analyses. Although, not designed to replace assessment of BECs when sample collection is possible, the CF approach may prove useful when estimating BECs in studies where assessments are not feasible.