APPLICATION OF NEURAL NETWORKS TO PHARMACODYNAMICS

Neural networks (NN) are computational systems implemented in software or hardware that attempt to simulate the neurological processing abilities of biological systems. A synopsis is presented of the operational characteristics, structures, and applications of NN. The NN technology has primarily been aimed at recognition science (e.g., handwriting, voice, signal, picture, image, pattern, etc.). It is pointed out that NN may also be particularly suitable to deal with pharmacokinetic (PK) and pharmacodynamic (PD) systems, especially in cases such as multivariate PK/PD population kinetics when the systems are so complex that modeling by a conventional structured model building technique is very troublesome. The main practical advantage of NN is the intrinsic ability to closely emulate virtually any multivariate system, including nonlinear systems, independently of structural/physiologic relevance. Thus, NN are most suitable to model the behavior of complex kinetic systems and unsuitable to model the structure. In a practical sense, this structure limitation may be inconsequential because NN in its multivariate formulation may consider any physiologic, clinical, or population variable that may influence the kinetic behavior. The application of NN in PD is demonstrated in terms of the ability of an NN to predict, by extrapolation, the central nervous system (CNS) activity of alfentanil. The drug was infused by a complex computer-controlled infusion scheme over 180 min with simultaneous recording af the CNS effect quantified by a fast Fourier transform power spectrum analysis. The NN was trained to recognize (emulate) the drug input-drug effect behavior of the PD system with the input-effect data for the 180 min as a training set. The trained NN was then used to predict, by extrapolation, the CNS effect in the subsequent 120-min period that contained a complex infusion scheme to provide an extra challenge in testing how well the trained NN could predict the effect. The relative prediction performance of the NN was 66% (SD = 32, n = 6), which indicates an excellent prediction considering the intrinsic high degree of fluctuations in the effect variable. The NN was best in predicting the high intensity effects and also learned to recognize an overshoot phenomenon indicative of development of short-term tolerance.