Feature - Tackling HIV via the grid |
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The United Kingdom's National Grid Service (NGS) played a key role in helping scientists simulate the efficacy of an HIV drug in blocking a key protein used by the virus. The experiment by Peter Coveney, Ileana Stoica and Kashif Sadiq from the Department of Chemistry at University College London, performed a sequence of simulation steps across the UK's NGS and America's TeraGrid. The team ran a large number of simulations to predict how strongly the drug saquinavir would bind to three resistant mutants of HIV-1 protease and its original strain-or "wild-type" protease (one of the proteins produced by the virus to propagate itself). Saquinavir is a known inhibitor of HIV-1 protease as it blocks the maturation step of the HIV life cycle. Several drugs have the ability to inhibit HIV-1 protease but doctors currently have no direct way of matching the right drug to the right strain, as the virus rapidly mutates in each patient to create a unique version. Even though doctors do have the ability to obtain a patient's protein sequences through methods such as genotypic assays, in the end, they are still forced to prescribe a "cocktail" containing a variety of drugs, choosing their ingredients by making use of historical data and various rules of thumb obtained from past experience. They then typically work through something of a trial-and-error process to see which drug works best, by analyzing the patient's immune response. |
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What grids could do A long-held goal of physicians is to replace this "shotgun" strategy with individualized, patient specific treatments custom-tailored to each person's unique genotype. Professor Peter Coveney said: "This study represents a first step towards the ultimate goal of 'on-demand' medical computing, where doctors could one day 'borrow' supercomputing time from the national grid to make critical decisions on life-saving treatments." "For example, for an HIV patient, a doctor could perform an assay to establish the patient's genotype and then rank the available drugs' efficacy against that patient's profile based upon a rapid set of large-scale simulations, enabling the doctor to tailor the treatment accordingly." Coveney admits that there are questions about the application, such as how many computing resources could be devoted to helping individual patients, and at what price. He said, "At present, such simulations-requiring a substantial amount of computing power-might prove costly for the (UK's) National Health Service, but technological advances and those in the economics of computing would bring costs down." -Gillian Sinclair, National Grid Service |
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