"The key problem to solve was how to represent the data so that it included all of the patient's characteristics along with the concentrations of drug and platelet aggregation which change over time and to use a fast back propagation neural network designed for this application," Urquidi-Macdonald says.
The researchers solved the problem and also identified key patient characteristics that contribute significantly to establishing the abciximab dose-effect relationship. These characteristics include, among others, whether the patient smokes or not, ethnicity and the patient's weight.
By comparing the dosages predicted by the new system with dose-effect data from 39 patients who had undergone standard abciximab therapy, the researchers found that the new software offered potential for dose prescription improvement. For example, the software predictions suggest that the targeted degree of platelet inhibition may be achieved in some patients with lower doses, which could translate into a reduced risk for adverse side effects.
The Penn State researcher says, "The software also predicts that administering a smaller initial dose, followed by one or two infusions to keep the platelet concentration at 20 percent of baseline, achieves the same effect as giving the patient a larger initial dose."
In addition, the software predicted that two of the patients tested would not achieve the target response within the tested range of doses.
The researchers write, "The utility of this approach and whether it may provide an improvement in therapeutic outcomes clearly remain to be determined in a randomized, double-blind, prospective clinical trial."
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Contact: Barbara Hale
bah@psu.edu
814-865-9481
Penn State
15-Mar-2004