Evolutionary Computing Used to Identify Potentially Effective Drug Combinations

University of Manchester researchers and colleagues have found a way to identify potentially ideal drug combinations (from billions of others) that would prevent inflammation from occurring. The findings, published online on October 23, 2011 in Nature Chemical Biology, could be the first step in the development of new drug combinations to combat severe diseases and conditions. Most non-infectious disease, such as cancer, stroke, and Alzheimer's are worsened by inflammation, which is the body's natural defense mechanism. Inflammation has evolved to help fight infection but can also be very damaging in long-term disease, prolonging suffering and ultimately possibly contributing to premature death. After a stroke, the body reacts to the injury as if it were an infection, causing further damage. By blocking the inflammation, the chances of survival or higher quality of life following a stroke are greatly enhanced. This can be achieved by quickly and effectively identifying combinations of drugs that can be used together. Existing 'clot-busting' stroke drugs are only effective if administered within three hours after the stroke – often very difficult to achieve as people are often unaware they are having a stroke – and even then do not completely solve the problem, often leaving sufferers with serious disabilities. However, using ideal drug combinations the researchers suggest they can block inflammation and therefore greatly reduce the damage caused by non-communicable diseases such as stroke. Although the researchers have initially concentrated on stroke, they believe the process can be applied to all drugs and for a huge variety of diseases.
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