The complex field of systems biology might be in need of some clarification. Cambridge, MA-based Selventa has been renamed from its former moniker Genstruct as part of an effort to clarify to pharmaceutical companies and others in the life sciences industry how the firm uses computational methods to help its customers match patients with the best drugs.
In fact, the new name, Selventa, comes from the Finnish verb that literally means “to clarify,” said David de Graaf, the company’s chief scientist. For those of you who had not even heard of Genstruct, the firm has been around since 2002 and has raised venture capital from well-known life sciences investors Flagship Ventures and Pappas Ventures.
To hear de Graaf and his colleagues, their company is doing a lot more than undergoing a name change. (They also say that the company is already profitable, which leads me to believe that this new name is in no way a last-ditch effort to reinvent a struggling operation.) The firm is stepping up efforts publish information about its research and technology, having kept the details of how its software works largely under wraps in the past. And the firm is also offering its customers licenses to use its software in their labs, in addition to charging fees for services it provides.
Selventa is also looking to structure its deals with drug companies in a way that rewards the firm for the successes its partners achieve with its technology. This is a major change from the company’s previous focus on making money on a fee-for-service basis, company executives say.
“We want to put our money where our mouth is,” de Graaf says. “We obviously need to [do business] in a way that keeps the doors open and the lights on, but we want to be paid based on success because we very much believe that we can help our partners achieve success.”
Selventa’s technology could accelerate and improve the prospects of the notoriously long and expensive journeys taken to develop a new drug. It takes around $1 billion and 10 years for a company to bring a new drug to market, and the high cost includes expenses from the majority of compounds that fail at some point in the development cycle. The firm’s technology is intended to help drug developers pair optimal treatments with patients—before expensive clinical research such as drug trials are done.
The company’s computational methods use existing data from patients with specific diseases. It then aims to stratify patient populations based on different drivers or mechanisms of disease. The firm builds computer models of the disease for each patient group from … Next Page »
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