Dr. Mirna Urquidi-Macdonald, professor of engineering science and mechanics, says, "While we tried our approach first with abciximab, it may be applicable to other medicines that have a narrow therapeutical range between under dosing and overdosing."
The approach is described in the January issue of the journal, Clinical Pharmacology and Therapeutics. The authors are Urquidi-Macdonald, who worked on the project during a sabbatical at the National Institute on Aging, Gerontology Research Center in Baltimore, Md.; Dr. Donald E. Mager, National Institute on Aging, Gerontology Research Center, Baltimore, Md.; Dr. Mary A. Mascelli, Centocor Inc., Malvern, Pa.; Bart Frederick, Centocor, Inc., Malvern; Dr. Jane Freedman, Division of Cardiology, Boston University School of Medicine; Dr. Desmond J. Fitzgerald, The Royal College of Surgeons in Ireland; Dr. Neal S. Kleiman, Division of Cardiology, Baylor College of Medicine, Houston, Texas; and Dr. Darrell R. Abernethy, National Institute on Aging, Gerontology Research Center,.
The new approach is based on neural network software that can "learn" when given a large body of data on which to train. Using a fast back-propagation neural network and data from 8 patients undergoing coronary angioplasty and 30 healthy patients, the researchers trained the software to predict the best dose strategy for an individual patient based on 17 characteristics. These include, race, sex, age, weight, stable angina, previous myocardial infarction, diabetes, hypertension, hypercholesterolemia, smoking, prior coronary angioplasty, coronary artery bypass graft, statins, beta blocker, nitrates, calcium antagonists and diuretics.
Abciximab lessens the risk of heart attack by reducing the chance tha
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Contact: Barbara Hale
bah@psu.edu
814-865-9481
Penn State
15-Mar-2004