Application of Neural Networks to Population Pharmacokinetic Data Analysis

Abstract

0 This research examined the applicability of using a neural network approach to analyze population pharmacokinetic data. Such data were collected retrospectively from pediatric patients who had received tobramycin for the treatment of bacterial infection. The information collected included patient-related demographic variables (age, weight, gender, and other underlying illness), the individual's dosing regimens (dose and dosing interval), time of blood drawn, and the resulting tobramycin concentration. Neural networks were trained with this information to capture the relationships between the plasma tobramycin levels and the following factors: patient-related demographic factors, dosing regimens, and time of blood drawn. The data were also analyzed using a standard population pharmacokinetic modeling program, NON-MEM. The observed vs predicted concentration relationships obtained from the neural network approach were similar to those from NONMEM. The residuals of the predictions from neural network analyses showed a positive correlation with that from NONMEM. Average absolute errors were 33.9 and 37.3% for neural networks and 39.9% for NONMEM. Average prediction errors were found to be 2.59 and −5.01% for neural networks and 17.7% for NONMEM. We concluded that neural networks were capable of capturing the relationships between plasma drug levels and patient-related prognostic factors from routinely collected sparse withinpatient pharmacokinetic data. Neural networks can therefore be considered to have potential to become a useful analytical tool for population pharmacokinetic data analysis.

Mapping the Dose–Effect Relationship of Orbofiban from Sparse Data with an Artificial Neural Network

“…3,4 NNs have been shown to approximate the PK/PD profiles generated from simulations of several structural PK/PD models 5 and to be useful for population PK data analysis. 6 Furthermore, NNs have been used to develop population PK/PD relationships from Phase-II data, as demonstrated by Haidar et al 7 Although these examples avoid the need for specifying a structural PK/PD model, the inputs to the PD networks typically include some PK measure, thereby necessitating a piece-wise approach of predicting PK and then PD. In contrast, several studies have reported the development of NNs that directly map the dose-effect relationship from drug dosing information and readily available patient characteristics.…”

Artificial Neural Network in Drug Delivery and Pharmaceutical Research

“…Due to their computation versatility, ANNs have been utilized as analytical tool for population pharmacokinetic data analysis and was found superior to NONMEM with lesser average absolute errors and significantly lesser average prediction errors than NONMEM [66,116]. ANNs have been tested in prediction of Hussain et al tested application of ANNs for prediction of PK parameters from those determined in animal studies [117].…”

Decision Support Systems for Pharmaceutical Formulation Development Based on Artificial Neural Networks

“…Pharmaceutical applications of ANNs are still far from being routine, however ANNs are gradually coming into the focus in different pharmacy areas: pharmacokinetics (Brier & Aronoff, 1996;Brier & Żurada, 1995;Chow et al, 1997;Gobburu & Chen, 1996;VengPedersen & Modi, 1992), drug discovery and structure-activity relationships (Huuskonen. et al, 1997;Polański, 2003;Taskinen & Yliruusi, 2003), pharmacoeconomics and epidemiology (Polak & Mendyk, 2004;Kolarzyk et al, 2006), in vitro in vivo correlation (Dowell et al, 1999) and pharmaceutical technology (Behzadia et al 2009;Hussain et al, 1991;Bourquin et al, 1998aBourquin et al, , 1998bBourquin et al, , 1998cChen et al, 1999;Gašperlin et al, 2000;Kandimalla et al, 1999;Mendyk & Jachowicz, 2005Rocksloh et al, 1999;Takahara et al, 1997;Takayama et al, 2003;Türkoğlu et al, 1995).…”