Scientists develop new model that can predict early adverse drug reactions
All medications have side-effects from common aspirin to herbal remedies and from standard anticancer drugs to experimental immunosuppressants. However, predicting important side effects, serious adverse drug reactions, ADRs, is with current understanding nearly impossible. However, a neural network technology trained with past data could give drug companies and health-care workers a new tool to spot the potential for ADRs with any given medication.
Writing in the International Journal of Medical Engineering and Informatics, at team from the University of Medicine and Dentistry of New Jersey, has developed a new model that tests show is 99.87 percent accurate in predicting adverse drug reactions among 10,000 observations and 100 percent for non-serious ADRs.
Peng-fang Yen and colleagues Dinesh Mital and Shankar Srinivasan explain how obligatory warning labels on medication packaging often serve only to cause concern among patients, while products withdrawn from the market because of repeated ADRs repeatedly undermine the pharmaceutical industry. From the medical industry’s point of view and the perspective of patients, this is a growing concern that might be remedied with new technology, saving lives, reputations and health-care costs.
The Food Drug Administration (FDA) in USA and the World Health Organization (WHO) monitor the safety of medications continuously. However, technology that could identify possible ADRs at the earliest possible stage of drug development, licensing and marketing is urgently needed, especially given the potential risks to patients in emerging areas of health-care and the potential risks to shareholder confidence.
More source:
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Submited at Tuesday, March 29th, 2011 at 5:00 am on Uncategorized by ethan
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