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SAN DIEGO—Eidogen-Sertanty, a computational drug discovery solutions provider, announced recently that it has entered into a collaborative service project with KAI Pharmaceuticals, a South San Francisco, Calif.-based drug discovery company focused on the development of Protein Kinase C (PKC) modulators for a variety of diseases—particularly cardiovascular disease.
 
Under the terms of the agreement, Eidogen-Sertanty will use the Target Informatics Platform and EVE Comparative Visualization software within its DirectDesign Discovery Service to perform structural characterization and comparative binding site analysis for PKC regulatory domains. This will reportedly allow KAI to further enhance its unique insights into PKC isozyme selectivity, as well as to advance KAI's strategies for pharmacophore-based design of modulators of PKC activity.
 
In the end, it will also save them money, says Eidogen-Sertanty's CEO, Dr. Steven Muskal. "This DirectDesign collaboration—and we will be announcing another one soon with another company—allows companies to outsource work to us on a project-by-project basis," he says, "which can be very cost-effective. They contract with us, and we throw every technology under that sun that we have at that project."
 
"The unique attributes of Eidogen-Sertanty's EVE Visualization Software provide powerful capabilities for protein comparison and pharmacophore mapping," says Derek Maclean, senior scientist at KAI Pharmaceuticals. "We view our relationship with Eidogen-Sertanty as a potentially important part of our drug design strategy."
 
Companies could buy or make the software and do the work themselves, of course, but Muskal says that licensing software is already pricey, and the resources needed to leverage such software can cost companies two to three times as much as the licensing fees themeselves—something that he says even medium-size pharmaceutical companies often have difficulty budgeting.
 
In addition to financial savings, Muskal says DirectDesign collaborations like the one with KAI ease the need for additional employees.
 
"When I was doing research computing, I found it was much easier to fire a contactor or collaborator than an employee who is not performing," he says. "Today, pharma struggles with staffing issues. So we increase the efficiency of their staffing and interactions because when they need work done for a project, they come to us. If we don't perform, they can let us go easily and move on. This kind of focus on efficiency and outsourcing in preclinical discovery is starting to really take root in the industry, and we're here to help facilitate that."

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