Making massive models
Biogen Idec to process inflammatory disease data using Gene Network Sciences' REFS software
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CAMBRIDGE, Mass.—With the goal of reverse engineering an inflammatory disease model and using it to identify targets and biomarkers in inflammation, systems biology company Gene Network Sciences Inc. (GNS) has signed a deal in which GNS use its proprietary software platform to process clinical data provided by biotechnology company Biogen Idec Inc.
According to the agreement, GNS—which works with IBM to employ supercomputers to probe vast amounts of raw human biological data—will use its proprietary Reverse Engineering/Forward Simulation (REFS) technology to analyze genetic, genomic and clinical data provided by Biogen Idec and use it to rapidly discover new drug targets and biomarkers. A forward simulation model generated directly from the Biogen Idec data will be licensed to Biogen Idec at the conclusion of this project. Financial terms of the agreement were not disclosed.
Dr. Ronenn Roubenoff, senior director of Immunology Medical Research at Biogen Idec, says the company was looking for a partner with the expertise and capacity to handle heavy computational data. GNS, one of the industry's first computational modeling companies, was the perfect complement to Biogen Idec's interest in discovering new immunological drug targets from human data rather than animal data, Roubenoff says.
"We want to get the most information possible out of the biotechnologies we have available," Roubenoff says. "I don't think we have made the best possible use of the data we have. I think most people have stuck to the tried-and-true statistical approach of looking at one gene at a time, but this partnership will allow us to do that in a massively parallel way so we can really look at the interactions between these genes."
The collaboration will focus initially on rheumatoid arthritis, Roubeoff says, because "patient outcomes are so well defined and the disease is active in the blood."
There is also the possibility of expanding the agreement to other inflammatory diseases.
"Diseases where we can tap into what the immune system is doing in the blood are easier to explore than diseases where we have to go after specific tissues, like kidney or lung inflammation," Roubenoff says. "If it works, there will be the appetite to do it again with additional diseases. We think that autoimmune diseases have been one area where biotech products and drugs have been most effective."
Because GNS' REFS technology is capable of taking raw "omic" data and running billions of calculations in a short period of time to find potential biomarkers and promising drug targets, the partnership offers significant potential to the Biogen Idec drug development program, says GNS Co-Founder and Vice President of Research and Development Iya Khalil. The computer-assembled models generated by REFS are queried rapidly through billions of in silico experiments to discover the highest-impact molecular targets for the disease being studied and the corresponding efficacy and toxicity markers related to specific drug treatments, she explains.
"What makes this partnership so interesting is that we are reverse engineering models of the inflammatory response process and using those models to predict what is happening in patients," Khalil says. "This will enable us to discover key targets and biomarkers for inflammatory response as well as biomarkers that may be specifically for one patient or a subset of patients. We believe this could be a new paradigm for drug discovery and could be applied to other diseases, because combining clinical data with a powerful computational tool for generating a disease model can be used for any project." DDN
According to the agreement, GNS—which works with IBM to employ supercomputers to probe vast amounts of raw human biological data—will use its proprietary Reverse Engineering/Forward Simulation (REFS) technology to analyze genetic, genomic and clinical data provided by Biogen Idec and use it to rapidly discover new drug targets and biomarkers. A forward simulation model generated directly from the Biogen Idec data will be licensed to Biogen Idec at the conclusion of this project. Financial terms of the agreement were not disclosed.
Dr. Ronenn Roubenoff, senior director of Immunology Medical Research at Biogen Idec, says the company was looking for a partner with the expertise and capacity to handle heavy computational data. GNS, one of the industry's first computational modeling companies, was the perfect complement to Biogen Idec's interest in discovering new immunological drug targets from human data rather than animal data, Roubenoff says.
"We want to get the most information possible out of the biotechnologies we have available," Roubenoff says. "I don't think we have made the best possible use of the data we have. I think most people have stuck to the tried-and-true statistical approach of looking at one gene at a time, but this partnership will allow us to do that in a massively parallel way so we can really look at the interactions between these genes."
The collaboration will focus initially on rheumatoid arthritis, Roubeoff says, because "patient outcomes are so well defined and the disease is active in the blood."
There is also the possibility of expanding the agreement to other inflammatory diseases.
"Diseases where we can tap into what the immune system is doing in the blood are easier to explore than diseases where we have to go after specific tissues, like kidney or lung inflammation," Roubenoff says. "If it works, there will be the appetite to do it again with additional diseases. We think that autoimmune diseases have been one area where biotech products and drugs have been most effective."
Because GNS' REFS technology is capable of taking raw "omic" data and running billions of calculations in a short period of time to find potential biomarkers and promising drug targets, the partnership offers significant potential to the Biogen Idec drug development program, says GNS Co-Founder and Vice President of Research and Development Iya Khalil. The computer-assembled models generated by REFS are queried rapidly through billions of in silico experiments to discover the highest-impact molecular targets for the disease being studied and the corresponding efficacy and toxicity markers related to specific drug treatments, she explains.
"What makes this partnership so interesting is that we are reverse engineering models of the inflammatory response process and using those models to predict what is happening in patients," Khalil says. "This will enable us to discover key targets and biomarkers for inflammatory response as well as biomarkers that may be specifically for one patient or a subset of patients. We believe this could be a new paradigm for drug discovery and could be applied to other diseases, because combining clinical data with a powerful computational tool for generating a disease model can be used for any project." DDN
