t these 15 proteins, resulting in over 35 million docking simulations. Now in the second phase, this number has been whittled down to 5000 promising candidate molecules and the team is running free energy calculations for each protein target. From these calculations, 50 of the best binding compounds for each target protein will be tested in biochemical assays to confirm the computer predictions. Active compounds will then be evaluated in cell culture assays and other preclinical tests with the ultimate goal of moving promising new drug candidates into clinical trials in humans with regulatory drug approval as the end result, says Watowich.
Not only does this research aim to attack the high attrition rate for compounds moving through the development process by providing more promising drug candidates that should have an improved probability of regulatory approval, Watowich says it also aims to show it is possible to accelerate drug discovery by completing accurate free energy of binding calculations on highly distributed computer resources. 'If successful, our grid-based drug discovery approach can be applied to almost any disease with defined molecular triggers,' he adds.
The end of experiments?
So does this all point to grid-based computer simulations trumping traditional lab-based work? Jasinski says it is important to see the two as complementary. 'Today's science demands both approaches. A computer can certainly offer certain efficiencies that reduce time and expense dramatically. At some point though, it becomes necessary to perform experiments with physical matter.' Likewise, he says there are very few things in life that are infallible. While there are advantages to electronic simulations, they are, in the end, only as good as the accuracy of the computational model, he says. False positives do exist, and it requires additional research to eliminate these.
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In the case of finding new drugs, Watowich adds that the candidates from grid-based research are really no different to leads that have entered the pipeline through more conventional paths. They will still require significant medicinal chemistry effort to be developed into drug candidates. However, he says, the greater number of grid-generated leads does mean an increased probability that at least one will become an approved treatment.
Modelling limitations aside, Jurisica believes that computation is going to 'revolutionise' medicine. He points out that it was exactly this kind of 'brute force computing' that enabled the identification of thousands of prognostic signatures in lung cancer. Indeed, this success has led to Jurisica's team preparing a new WCG proposal where the main idea is to combine the knowledge of physical protein interactions, protein disorder and other properties with failed crystallisations to predict co-crystallisation experiments.
Tackling the hardest problems
Watowich points out that drug discovery projects for any number of neglected, rare and infectious diseases are effectively endless. 'The computing power of the World Community Grid will allow researchers to think beyond the confines of traditional computing and launch large computing efforts that accurately model physical processes.' He adds that 'the relatively low cost of the WCG makes it ideal for studying diseases that do not appear to offer an attractive return on investment to large pharmaceutical companies'.
Not only is the WCG an 'incredible resource' for researchers who may have no other means to test their ideas, notes Jasinski, it is also a starting block for attacking diseases that have been traditionally hard-placed to treat. One such disease is Aids. According to the Global Health Council, in 2008, 33.4 million people around the world were living with HIV and 2.7 million people were newly infected with the virus. While the disease can be treated with drugs, there is no cure. But perhaps more problematic, is that the virus, often called a 'sloppy copier', is constantly evolving 