The Importance Of Drug-Drug Interactions

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Drug-drug interactions (DDIs) occurs when two drugs interact with the same gene product. The literature of biological studies contains most of the available information about gene-drug relationships. Identifying drug-drug interactions is an important and challenging problem in computational biology and healthcare research. One approach that has been tried and has been successful is text mining. It provides an attractive solution for identifying gene-drug relationships and aggregating them to predict novel DDIs. There are accurate, structured but limited domain knowledge and noisy, unstructured but abundant textual information available for building predictive models. The greater challenge lies in mining the true patterns embedded in text…show more content…
One study estimated that 29.4% of elderly patients are taking six or more drugs. Drug–drug interactions (DDIs) that lead to adverse reactions are potentially avoidable, if detected early, given that DDIs account for more than 30% of all adverse drug reactions. To address this issue, the U.S. Food and Drug Administration (FDA), World Health Organization, and Health Canada have created large adverse event reporting systems (AERSs) that collect data from clinicians, patients, and pharmaceutical companies. One of the challenges in drug administration and drug development is the need to identify and avoid potential drug–drug interactions (DDIs). During the course of drug development, it is vital to identify possible drug interactions with the new drug compound that is being investigated. Revealing the mechanism behind the drug interactions can provide the necessary scientific evidences for further investigation of the drug compound. The ability to find DDIs and their scientific evidences efficiently and economically can have an impact on the current approach in drug administration and drug development. The reporting systems developed by the Government present an opportunity to monitor drug safety in a large and diverse population of patients. Quantitative signal detection algorithms use these data to flag and prioritize drug-event signals for follow-up analysis via formal pharma co-epidemiological

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