An international team of scientists have used the latest genetic sequencing techniques and big computation to give a glimpse of the future of personalised medicine.
Using ‘supercomputers’, Professor Peter Coveney, Director of UCL’s Centre for Computational Science and colleagues from UCL and Rutgers University, simulated the shape of a key protein involved in HIV infection in an individual patient and then ranked the drug molecules most likely to block the activity.
In the future, it is expected that this type of patient-specific drug selection will become routine.
The team took as their target the HIV protease molecule, which is critical in helping to build the viral particle, or virion, in a cell that will eventually break out to infect the next cell. The protease has a slightly different shape in each individual, in particular in the protein's active zone where it slices the components that will form the next virion.
This is a consequence of the very specific genetic sequence of the virus in that person, but unless that shape is known, there is uncertainty as to which particular drug will bind to the protease and stop it in its tracks.
Although the idea sounds simple, working out how each drug molecule would fit into the patient's shape-specific protease protein required enormous computing capability.
Professor Coveney said: “We're having to run upwards of 50 simulations of these models, each one of which needs a hundred cores on a computer. So that's a machine with 5,000 cores, and then you run the calculations for about 12 to 18 hours. You get a huge amount of output data, and then do post-processing and analysis to get the ranking. A doctor need not know about any of this complexity; all they'd be interested in would be the list of best-to-worst drugs for that patient.
“We show that it's possible to take a genomic sequence from a patient; use that to build the accurate, patient-specific, three-dimensional structure of the patient's protein; and then match that protein to the best drug available from a set. In other words, to rank those drugs - to be able to say to a doctor 'this drug is the one that's going to bind most efficiently to that site. The other ones, less so”.
There are currently nine US Federal Drug Administration-approved HIV-protease inhibitors on the market. The UCL-Rutgers project ranked seven of them in its proof of principle experiment.
