Artificial Intelligence Lab, Department of MIS, University of Arizona / 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 individualâ 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, NONMEM.
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.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/105273 |
| Date | 07 1900 |
| Creators | Chow, Hsiao-Hui, Tolle, Kristin M., Roe, Denise J., Elsberry, Victor, Chen, Hsinchun |
| Publisher | American Chemical Society and American Pharmaceutical Association |
| Source Sets | University of Arizona |
| Language | English |
| Detected Language | English |
| Type | Journal Article (Paginated) |
Page generated in 0.0022 seconds