Agilent Technologies Inc. (NYSE: A) today announced that Dr. Emmanuel Barillot, director of Bioinformatics and Systems IT at the Institut Curie in Paris, has received an Agilent Thought Leader Award in recognition of his pioneering development of user-friendly, Web-based bioinformatics tools for systems toxicology.
The award will support Dr. Barillot’s work in developing a database of cellular signaling pathways and a visualization tool to help the pharmaceutical industry develop more effective cancer treatments.
The new Web-based visualization tool will augment the institute’s Atlas of Cancer Signaling Networks and will be further enhanced by the combined capabilities of Curie’s NaviCell mapping tool and Agilent’s GeneSpring software and NMR technologies. Together, these tools will enable researchers to identify off-pathway effects that could lead to undesirable outcomes, such as DNA damage.
“Assessing the safety and potential toxicity of drug candidates is a significant hurdle in the extraordinarily complex, labor-intensive drug development process,” said Tony Owen, Agilent’s senior director of life science marketing and market development. “Pharmaceutical companies can spend many years and billions of dollars only to find that a once-promising preclinical therapeutic compound is actually harmful to patients in clinical tests. It is incumbent upon drug developers to find ways to assess drug toxicity much earlier in the process to reduce R&D time, minimize costs and maximize patient benefits.”“The Agilent Thought Leader Award will accelerate the further development of the Curie Atlas of Cancer Signaling Networks, a comprehensive and freely available cancer pathway database, and NaviCell network visualization tool,” said Dr. Barillot. “These tools will enable earlier identification and visualization of toxic interactions between signaling pathways during the discovery and development of therapeutics for the treatment of cancer, thus enabling better informed drug-candidate selection and prioritization. They will also be useful for interpreting high-throughput biological data, like next-generation sequencing profiles of tumors or other samples.”
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