Cogenics, Algynomics team up on pain panel

NEWTON, Mass.—Clinical Data Inc. announced in mid-October an agreement withAlgynomics that will allow Clinical Data's Cogenics division to use Algynomic's proprietary panel of genetic markers to allow clients to examine the genetic basis or underpinnings of human pain sensitivity, pain conditions and responses to existing and new pain therapeutics and treatments.

"We have found that using genetics and the psychological profile of patients, we are able to very accurately predict those people most sensitive to pain and those likely to develop chronic pain," says Dr. Luda Diatchenko, a researcher at the University of North Carolina (UNC). "This information can help determine the different approaches needed for different people to properly treat their pain."

The basis of the panel to be marketed by Cogenics are more than 3,000 SNPs that have been identified by the research of Drs. William Maixner and Diatchenko conducted at the UNC. Patents to the genetic information developed in their research are held by UNC, which has licensed these patents to Algynomics.

"There are some genes involved in pain that are known among researchers and some are in the public domain," says Bob Bondaryk, senior VP and GM of Cogenics. "But what is unique about Algynomics and the services we will offer are the SNPs themselves."

Cogenics believes there is a significant market for the service. "When we take a look at it and try to outline the market, we believe the number is in the hundreds of research labs that could take advantage of this," says Bondaryk.

For now, the panel will be a one-size-fits-all solution, Bondaryk notes.

"There are about 3,200 SNPs and that is a pretty broad collection to be working with," he says. "As we get the chips out and go into validation studies we hope to be able to cut down the number of SNPs on each chip down to make it more manageable."

Diatchenko hopes Algynomics is now taking its first steps to provide a basis for wider study of pain.

"Pain is an understudied area," she says. "Our hope is that we can help provide a better understanding of how it works for researchers looking to develop new treatments and be able to accurately predict who will respond better to particular treatments."