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Manchester's 'first step' to perfect drug combinations
Date:10/23/2011

The researchers found a way of identifying ideal drug combinations from billions of others which would prevent inflammation from occurring.

The findings, published 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 defence mechanism.

Inflammation has evolved to help fight infection but can also be very damaging in long term disease, prolonging suffering and ultimately risking 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 thus greatly enhanced. This can be achieved by quickly and effectively identifying combinations of drugs which 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.

The multi-disciplinary team of researchers, led by Professor Douglas Kell, Professor of Bioanalytical Science at The University of Manchester, developed an evolutionary computer programme which rapidly sifted through nine billion different combinations of potential drugs.

Sorting and testing 50 drug combinations at a time using
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Contact: Daniel Cochlin
daniel.cochlin@manchester.ac.uk
44-161-275-8387
University of Manchester
Source:Eurekalert

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