Predicting blood concentration levels of pharmaceutical agents in human subjects can be made difficult by missing data and variability within and between human subjects. Biometricians use a variety of software tools to analyze pharmacokinetic information in order to conduct research about a pharmaceutical agent. This paper is the comparison between using a feedforward backpropagation neural network to predict blood serum concentration levels of the drug tobramycin in pediatric cystic fibrosis and hemotologic-oncologic disorder patients with the most commonly used software for analysis of pharmacokinetics, NONMEM w . Mean squared standard error is used to establish the comparability of the two estimation methods. The motivation for this research is the desire to provide clinicians and pharmaceutical researchers a cost effective, user friendly, and timely analysis tool for effectively predicting blood concentration ranges in human subjects. q 2000 Published by Elsevier Science B.V.
['Kristin M. Tolle', 'Hsinchun Chen', 'Hsiao Hui Chow']
Estimating drug/plasma concentration levels by applying neural networks to pharmacokinetic data sets