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Gene Network Sciences Using Supercomputing to Match Patients with a Drug That Works

Xconomy Boston — 

Health insurers have wasted billions of dollars on reimbursing drugs that don’t work for certain patients. But Cambridge, MA-based Gene Network Sciences might have a cure for this spending ailment. It is using supercomputing technology to build databases that can match patients with the most suitable drugs or other treatments, company CEO Colin Hill says.

This is a major change for Gene Network Sciences, which has formed a new subsidiary called GNS Healthcare to focus on the healthcare market. Since the Cornell physicists Hill and Iya Khalil formed the startup in 2000, it’s been known mostly for performing computer-simulated drug research with its signature reverse engineering/forward simulation technology for such major companies as Biogen Idec (NASDAQ:BIIB), Johnson & Johnson (NYSE:JNJ), and Pfizer (NYSE:PFE). While Gene Network Sciences is continuing to work in drug research and development, Hill says, the healthcare market has been a major focus for the firm over the past year.

Within the healthcare market, the company is initially seeking partnerships with pharmacy benefit management firms. These outfits, such as CVS Caremark (NYSE:CVS) and Medco Health Solutions (NYSE:MHS), handle prescription drug plans for more than 210 million Americans, the majority of the total U.S. population, according to the Pharmacy Care Management Association, an industry group based in Washington, DC. The PBMs, as they are often called, have already adopted e-prescribing to reduce errors and streamline how doctors order prescriptions for patients, and Hill says these companies have also implemented computer models. But the drug benefit mangers don’t have the artificial intelligence capabilities that his firm offers he says.

“For the payers, it’s really about using innovation to deliver smart, more cost-effective medicines,” Hill says. “The next generation analytics, like GNS Healthcare provides, will match the right drugs to the right patients for the right price.”

The company is taking a different technical approach from its drug R&D work to solve problems for healthcare customers, Hill says. For drug companies, Gene Network Sciences has reverse-engineered detailed biological systems on supercomputers with genomic, proteomic, and clinical data. These computer-simulated systems are then used to perform billions of virtual experiments (what the company calls “forward simulation”) to identify biomarkers of drug efficacy and toxicity to stratify patients in clinical trials. Those biomarkers can later be confirmed in the traditional wet lab environment. A goal here is to apply the firm’s reverse engineering-forward simulation (REFS) technology to speed the often tedious and time-consuming drug discovery and development process, enabling pharmaceutical companies get to market with new products quicker than they could with conventional methods of finding and testing new products.

In the healthcare arena, Gene Network Sciences is making individual patients the starting point for its computer models. The technology would automatically build a computer-simulated model of each patient based on age, gender, medications, allergies, health history, (hypothetically) genetic information, and any other relevant data. When such computer models of patients are integrated into a PBM’s database, the benefit manager might be able to quickly spot when doctors order inappropriate medications or even identify which drugs or combination of drugs are likely to be most effective.

The startup’s technology could help benefit managers eliminate wasteful spending on blockbuster drugs that have proven to be ineffective for significant numbers of patients. Take anti-TNF drugs for multiple sclerosis such as Abbott Laboratories’s adalimumab (Humira), which racked up an estimated $5 billion in sales last year. Hill says that anti-TNF drugs don’t work for more than a third of patients who try them.So his company worked with Biogen (which markets alternative treatments to Abbott’s drug) to identify biomarkers that predict whether patients are likely to benefit from anti-TNFs. Such information could easily be tucked into the systems that Gene Network Sciences wants to build for PBMs, helping them avoid spending a fortune on TNF blockers that are bound to fail for certain individuals.

For Gene Network Sciences, the healthcare field offers certain advantages over the drug R&D market. The company aims to make a major portion of its revenue based on the value its customers gain from using the firm’s technology. In drug research, there are multiple variables that contribute to success of drug discovery, making it difficult to calculate the value the company delivers. But it’s easier to point to a “smoking gun” like the amount of money the startup’s technology could save healthcare payers on, say, avoiding wasted reimbursements on ineffective treatments, Hill says.

Yet the healthcare market is a newer one to the startup. It is still in the process of building systems to prove the worth of its technology to health benefits firms, according to Hill. The CEO declined to name any of the PBMs his firm is working with at the moment, but he says his company might announce its progress in this emerging market later this year.

Next year, Hill says, he expects the healthcare market to be a major revenue source for his firm. The company has been seeking new ways to generate revenue from its technology. In January we covered GNS’s spin-off Fina Technologies, which is applying the reverse engineering/forward simulation technology to build smarter computer models to guide fund managers’ investment strategies on Wall Street.

Michael Gilman, a board member at Gene Network Sciences and the CEO of Cambridge, MA-based biotech Stromedix, says that the cost-saving changes needed in the healthcare system will be data- and evidence-driven. GNS Healthcare fits this market niche perfectly, because it provides the tools to generate and test hypotheses about the most effective and cost-efficient treatment algorithms, he says.

“I think this has the potential to be a huge business for GNS, one that builds directly off their existing biopharma business, and could ultimately feed back into their pharma collaborations,” Gilman says. It is, he says, “A virtuous cycle.”