Ultimately, they hope this will lead to the development of tailored therapies for treating inflammation.
Professor Kell, who is also Chief Executive of the Biotechnology and Biological Sciences Research Council, said: "Most diseases have complex causes. This makes their analysis a problem of systems biology, and to find novel therapies multiple targets need to be attacked at once.
"We have devised a strategy, based on Darwinian evolution, to make this considerably easier. Although our immediate interest is inflammation and conditions such as stroke, our approach is universal and is thus applicable to all complex diseases."
Another advantage of choosing ideal drug combinations is that it allows patients to take smaller doses, which reduces potential toxicology concerns.
Professor Kell and his team worked with computer scientists at the University to create the programme. Professor Pedro Mendes explains: "Our experiments were guided by software that is based on an evolutionary algorithm. The algorithm suggests new drug combinations from previous ones by re-mixing their components much like the DNA of a child is a mix of that of their parents.
"The new drug combinations are then tested and the best are selected to continue generating new ones. In each experiment we tested 50 drug combinations, then the software would tell us which new ones to test in the next experiment."
|Contact: Daniel Cochlin|
University of Manchester