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CuraGen, Glaxo Agree to Pharmacogenomics Deal

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NEW HAVEN, Conn.--A pharmacogenomics collaboration announced last month between Glaxo Wellcome's Research Triangle Park, NC, facility and CuraGen here will utilize CuraGen's integrated genomics processes for the study and selection of compounds for clinical development by Glaxo. Specifically, Glaxo will access CuraGen's suite of genomics technologies, including its gene expression process known as GeneCalling, and a new database product, SeqCalling, which is a comprehensive collection of nearly 75 percent of the expressed genes from selected tissues. By integrating the information derived from the two products, CuraGen will provide Glaxo with insight into possible mechanisms of action or toxicities of candidates in Glaxo's preclinical drug portfolio.

"Understanding how gene expression is altered by a compound can provide powerful clues into the compound's potential efficacy and toxicity prior to costly trials," said Bob Bell, Glaxo's vice-president of US research. Jonathan Rothberg, CuraGen's CEO, added, "This is the first time genomics technologies will evaluate drug activity in animals during preclinicals specifically to guide the selection of appropriate drug candidates for human trials."

The deal calls for CuraGen to receive $2.75 million per year, plus additional milestone and royalty payments throughout the collaboration, which will last as long as five years.

CuraGen also announced last month that, through its SeqCalling process, it has discovered more than 60,000 single-nucleotide polymorphisms (SNP's) in the coding regions of human genes that may be responsible for the development of certain diseases.

Richard Shimkets, CuraGen's senior research scientist, explained that of an estimated 3 million SNP's in the human genome, 200,000 are anticipated to be within the coding regions of genes. Of those, about 10 percent will significantly affect protein sequence, and a fraction of those may determine disease predisposition and drug response. Shimkets said CuraGen's goal is to construct a high-density SNP map of the expressed human genome to better understand disease and treatment options.

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