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EU Initiative to Develop 'Virtual Patient' Models for Personalized Medicine IT


To develop the IT side of personalized medicine, researchers at the University of Manchester in the UK are using a massive computer network to create "virtual patients." These computational patient models will contain genetic data along with traditional health metrics. These computer models are intended to be prototypes of next-generation, easy-to-use health records for personalized medicine. The Manchester team is part of the pan-European IT Future of Medicine initiative, a 10-year project that unites more than 25 academic institutions and industrial partners.

"The information gathered from sequencing an individual's genome, or measuring the levels of certain chemicals in their blood, or a more comprehensive screen of quantities of proteins and metabolites within their serum will be used to create an individual mathematical model of that person, the virtual patient," says Daniel Jameson, project manager for ITFoM. "A large part of the vision here is to model bottom-up from the molecules, [so] the final virtual patient will undoubtedly be a multi-scale model, with layers of different granularity."

Eventually, Jameson says, the project will produce a range of individualized models that physicians can query, along with an entire IT ecosystem that could support personalized genomics data in the clinic. "When a clinical decision needs to be made about the treatment of an individual, the physician will be in a position to ask 'What if I combine these two drugs?' or 'What if they reduce their sugar intake?'" Jameson says. To do this, he and his colleagues intend to develop a human-computer interface, the IT infrastructure to support asking these questions, the technology to facilitate processing of these data both at the point of acquisition and as model outputs, as well as the processing power needed to run these simulations on what will be a massive scale.

Before they can roll out a workable IT model for all this data, there are some hurdles the researchers must face. "Firstly [is] the challenge of developing models that support effective prediction for an organism that is massively complex and not necessarily experimentally tractable," Jameson says. Then, he adds, "there is the sheer scale of what is required and the sheer number of people from multiple disciplines who will have to be involved — coordinating all of this effectively across multiple institutions and companies in multiple countries."

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