Clinical Applications of Artificial Neural Networks in Pharmacokinetic Modeling

Abstract Artificial neural networks (ANNs) were designed to simulate the biological nervous system, where information is sent via input signals to a processor, resulting in output signals. ANNs are composed of multiple processing units that work together to learn, recognize patterns, and predict data. ANNs do not require regimented experimental design and have the ability to function even with incomplete data. They can be used in multifaceted, nonlinear systems with applications in the field of pharmacokinetic modeling. Pharmacokinetic/pharmacodynamic studies are used to predict meaningful correlations among doses administered, drug concentration levels, and pharmacological response. ANNs can be useful tools in analyzing the data involved in physiological processes, which can have vast amounts of complex variables with nonlinear relationships. ANNs are also convenient for handling data involving dosage form technology because they can simultaneously handle multiple independent and dependent variables without initial definition of causal relationships between the variables and response. The clinical applications of using ANNs in pharmacokinetic modeling are further discussed in this chapter, with specific examples from clinical studies being conducted.