Search

The quantitative analysis and physicochemical description of time- and dose-dependent in vivo drug effects is problematic as observed effects depend on both pharmacodynamics and pharmacokinetics. These factors depend in a different manner on the physicochemical properties of investigated drugs. Obviously in vivo effects of drug series are governed by highly non-linear and extremely complicated relationships. The exact form of these relations usually can be given only for some restricted experimental conditions and generally may be assumed to be unknown. As the function relating in vivo hypotensive effects of the title compounds observed in cats, to dose, time and physicochemical properties could not be modelled explicitly, an attempt has been made to train an Artificial Neural Network (ANN) to find the unknown non-linear relationship between input variables and observed % effect values (raw data, ±20–25% error). Although each drug was applied at only two different doses and “% effect” was observed at few time points, the weights characterising the ANN model enabled the calculation/prediction of (i) complete time-activity profiles (reflecting mainly pharmacokinetics); (ii) physicochemical property dependent activities. Thus, a maximum of information was extracted from raw in vivo data by this approach which may be useful in saving animal experiments in drug development. However, complete dose-response curves could not be obtained due to insufficient data in terms of applied doses. [ABSTRACT FROM AUTHOR]