Reduction of quantitative systems pharmacology models using artificial neural networks

In this study, we explore the use of artificial neural networks for approximating an input–output relationship within highly dimensional systems models. We illustrate this approach using a model of blood coagulation. The model consists of two components linked together thro ugh a highly dimensional discontinuous interface, which creates a difficulty for model reduction techniques. The proposed approach enables the development of an efficient approximation to complex models with the desired level of accuracy. The technique is applicable to a wide variety of models and p rovides substantial speed boost for use of such models in simulation and control purposes.
Source: Journal of Pharmacokinetics and Pharmacodynamics - Category: Drugs & Pharmacology Source Type: research