Many infectious diseases as well as cancers are strongly influenced by molecular level processes. In several cases, the advent of rapid genetic sequencing, already available in the case of HIV, means that patient-specific treatment based on genetic data becomes conceivable. Targeted therapies use drugs to interfere with specific biomacromolecules involved in disease development. Given the complexity of emergent mutations in such biomacromolecules and in the disease itself, clinicians need to resort to decision support software for patient-specific treatment. Incorporating model based molecular level information into such decision support systems offers the potential to substantially enhance personalised drug treatment by providing first principles based ranking of drug efficacy on a specific patient. Patient specific molecular models of targeted macromolecules are constructed and molecular dynamics simulations are used to rank drug binding affinities. Here we present results from clinically relevant protein variants that arise from two distinct pathologies: HIV and lung carcinoma. Our findings demonstrate the potential for molecular simulations to achieve an accurate ranking of drug binding affinities on clinically relevant time scales and represent the first steps towards the eventual goal of providing data derived from patient specific simulation to enhance clinical decision support systems. The approach gives rapid, robust, and accurate computational results and is dependent on an automated workflow for building, simulating and analysing models distributed over petascale computing resources which are comprised of tens to hundreds of thousands of compute cores.
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[v1] 2012-09-04 05:55:45
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