A Stochastic Model for the Ethanol Pharmacokinetics

The aim of this study was to propose a new stochastic model to study the time course of ethanol elimination in human bodies.
The times and amount of alcohol ingested are assumed to be random in controllable intervals. Constant elimination rate follows zero order kinetics and is replaced by first order kinetics when the effects of alcohol increase due to alcohol ingestion. Simulation studies of three different models were made to compare the statistical characteristics of the ethanol effects obtained using analytical expressions. For each model, three cases were considered depending on the drinking pattern and by classifying the drinker as heavy, normal or sparse.
From the model formulation, we noted that as the rate of drinking increases for a given elimination rate, the expected time between overflows goes towards zero. Furthermore, as the average amount of alcohol in each drink increases, the corresponding time between overflows decreases.
Variations in times of alcohol intakes as well as the amount of alcohol consumption can be accounted through the final created formula. The model proves that overflows occur when alcohol is ingested before the adverse effects of alcohol from the previous drink are completely eliminated. Being the first stochastic model of such a kind, we do hope that it will throw more light on interpreting experimental data of alcohol abuse.