Neural networks in pharmacodynamic modeling. Is current modeling practice of complex kinetic systems at a dead end?

Neural networks (NN) are computational systems implemented in software or hardware that attempt to simulate the neurological processing abilities of biological systems, in particular the brain. Computational NN are classified as parallel distributed processing systems that for many tasks are recognized to have superior processing capability to the classical sequential Von Neuman computer model. NN are recognized mainly in terms of their adaptive learning and self-organization features and their nonlinear processing capability and are considered most suitable to deal with complex multivariate systems that are poorly understood and difficult to model by classical inductive, logically structured modeling techniques. A NN is applied to demonstrate one of the potentially many applications of NN for modeling complex kinetic systems. The NN was used to predict the effect of alfentanil on the heart rate resulting from a complex infusion scheme applied to six rabbits. Drug input-drug effect data resulting from a repeated, triple infusion rate scheme lasting from 30 to 180 min was used to train the NN to recognize and emulate the input-effect behavior of the system. With the NN memory fixed from the 30- to 180-min learning phase the NN was then tested for its ability to predict the effect resulting from a multiple infusion rate scheme applied in the subsequent 180 to 300 min of the experiment. The NN's ability to emulate the system (30-180 min) was excellent and its predictive extrapolation capability (180-300 min) was very good (mean relative prediction accuracy of 78%). The NN was best in predicting the higher intensity effect and was able to identify and predict an overshoot phenomenon likely caused by a withdrawal effect from acute tolerance. Current modeling philosophy and practice is discussed on the basis of the alternative offered by NN in the modeling of complex kinetic systems. In modeling such systems it is questioned whether traditional modeling practice that insists on structure relevance and conceptually pleasing structures has any practical advantages over the empirical NN approach that largely ignores structure relevance but concentrates on the emulation of the behavior of the kinetic system. The traditional searching for appropriate models of complex kinetic systems is a painstakingly slow process. In contrast, the search for empirical models using NN will continue to improve, limited only by technological advances supporting the very promising NN developments.