Moon A, Smith T. A preliminary evaluation of neural network analysis for pharmacodynamic modeling of the dosing of the hydroxymethylglutaryl coenzyme A-reductase inhibitors simvastatin and atorvastatin.
Clin Ther 2002;
24:653-61. [PMID:
12017409 DOI:
10.1016/s0149-2918(02)85140-2]
[Citation(s) in RCA: 4] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
BACKGROUND
Neural networks have been used in diagnosing and treating many diseases, including the diagnosis of myocardial infarction and insulin dosing in diabetes mellitus.
OBJECTIVE
The goal of this study was to develop a preliminary pharmacodynamic model for dosing of the hydroxymethylglutaryl coenzyme A (HMG-CoA)-reductase inhibitors simvastatin and atorvastatin using neural network analysis.
METHODS
Using NeuralSIM neural network software (NeuralWare, Carnegie, Pa), lipid panels from patients at a hospital lipid clinic were entered as inputs for the model, and the doses of simvastatin or atorvastatin that achieved those lipid results were used as outputs. The network was trained using 2 different data sets and was run using a subset of the data as inputs. The results of the neural network run (predictions) were compared with the actual doses used as directed by the hospital pharmacist in accordance with the lipid clinic protocol.
RESULTS
Complete data sets were available for 17 patients (11 men, 6 women). The mean age of these patients was 56.7 years (range, 40-67 years). The neural network model based on data set 1 predicted a dose that was within 95% of the actual dose 7 of 12 times and predicted use of the drug actually used 13 of 19 (68.4%) times. The neural network based on data set 2 predicted use of the drug actually used 10 of 17 (58.8%) times, but the predicted doses never approximated the actual doses by > or = 90%.
CONCLUSIONS
A neural network model for the dosing of the HMG-CoA-reductase inhibitors simvastatin and atorvastatin demonstrated an ability to predict appropriate dosing, but inclusion of other factors (eg, age, body weight, sex) and a larger sample size may be necessary for development of a more accurate model.
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