Drugs have to be able to deliver their primary effects and not present adverse side effects or toxicity that render them unsafe. But for complex conditions drugs also have to be designed to hit multiple targets. Designing drugs to this kind of multi-target profile is a complex and exceedingly difficult task for medicinal chemistry.
Professor Hopkins and colleagues developed an automated adaptive design approach that can mimic the creative, iterative process of medicinal chemists by using computational evolution of large numbers of compounds. They initially used it to look at an existing drug, Donepezil, which is used in treating Alzheimer's Disease.
"Professor Sir James Black, the Nobel Laureate and former Chancellor of the University, proposed that 'the most fruitful basis for the discovery of a new drug is to start with an old drug' and we followed that advice," said Professor Hopkins.
"We took the structure of Donepezil as a starting point and from there the system evolved its structure, computationally, over many generations to a variety of different profiles across a range of drug targets. The predicted profiles were then tested experimentally and we found that 75% of them were confirmed to be correct.
"This proof of concept shows that we could make significant advances in discovering and designing complex drugs, which could lead to improvements in safety and efficacy, while also potentially reducing the cost of drug discovery, which is a high-risk and expensive process."
Professor Hopkins said improvements in data capture and management were key to developing the research.
"Just a few years ago this would not have been possible because we need the existing drug data to build on and it was not held in a way that it could be analysed like this. But there have been significant devel
|Contact: Roddy Isles|
University of Dundee