Nodality Nears Market With Technology to Get the Right Cancer Drug to the Right Patients

Xconomy San Francisco — 

One fascinating new idea in predictive, personalized medicine is starting to gather momentum in South San Francisco. Nodality, a Stanford University spinoff from the lab of biologist Garry Nolan, is now on the cusp of going commercial with technology to help Big Pharma companies, and doctors, better understand why individuals respond differently to drugs, and how to craft the most effective combinations of treatments for an individual patient.

Nodality made news back in March, when it secured a $15.5 million financing led by the venture arm of Pfizer (NYSE: PFE) and Laboratory Corporation of America (NYSE: LH), and which included previous investors Kleiner Perkins Caulfield & Byers, TPG Biotechnology, and Maverick Capital. The money was supposed to go toward Nodality’s commercial rollout of a new test to predict the success of certain treatments for acute myeloid leukemia, and guide physicians trying to treat this deadly disease. The commercial push is now just a couple months away, so I caught up with Nodality CEO David Parkinson earlier this week for an update.

This is certainly a huge problem Nodality is seeking to solve. Only about one out of every 10 cancer drugs that enters clinical trials ever passes all the tests to become an FDA-approved therapy, and scientists often struggle to explain why so many of them fail. Even the ones that do make it on the market often only help about one-fourth of patients, and even with all the molecular biology tools in the world, doctors and drugmakers can seldom predict in advance which patients are likely to respond, and which won’t.

Nodality’s big idea is to run biological samples from individual patients through modern tools for counting cells at high speed, called flow cytometers. With specialty chemicals customized in-house, Nodality uses antibodies to bind to important intracellular proteins that are tagged to show how active they are in a given tumor sample. Armed with that data, Nodality’s team uses proprietary software to look for specific patterns in the biological pathways of a patient’s tumor that will give the doctor a more vivid idea of the malignancy he or she is up against. By knowing what’s happening in the biological pathways, it should provide doctors with better information to form a treatment strategy to keep the individual patient alive and well.

“These are truly predictive tests,” Parkinson says. “We think we have excellent tests, which will allow physicians much more certainty when they talk to patients.”

The company is still being somewhat coy about the business details thus far. Nodality has secured partnerships with two major pharmaceutical companies that it hasn’t yet announced, and it has two more deals that are being discussed, Parkinson says. Several scientific papers that describe insights from the Nodality test for acute myeloid leukemia and chronic lymphocytic leukemia are being reviewed for publication, he says.

One paper that was published in PloS One last month describes how Nodality scientists saw three distinct patterns emerge when acute myeloid leukemia patients got a chemotherapy drug, etoposide, which caused them to respond in different ways. Nodality looked at some of the well-known hot targets in cancer biology today, like the PI3kinase, Jak/Stat, and apoptosis pathways involved in programmed cell death.

Parkinson is quite familiar with all those targets, from the perspective of science, medicine, and business. He is an oncologist by training, with a glowing resume. Before joining the startup in September 2007, he held senior-level positions at Biogen Idec, Amgen, Novartis, and the National Cancer Institute. He played a key role in bringing Novartis’s imatinib (Gleevec) to the market, a huge step forward for treatment of chronic myeloid leukemia.

But, for every hit that shows up in a resume like that, there are plenty of duds that people don’t like to talk as much about. All the experience in drug development made him want to find a way to increase the batting average in development.

“One of the great frustrations I’ve had as a drug development professional is that all too often I felt that the perfectly good drugs we were developing, the molecules were designed and were performing exactly with particular targets, and those targets we knew from biological studies were relevant targets,” Parkinson says. “But on the other hand, the frustration was that as clinical developers we were forced to put them into patients that were categorized in ways that were outdated. It was all based on anatomical, morphological concepts of disease that date to the 1800s.”

Feeling that frustration, Parkinson walked into a lecture hall in December 2006 and was fascinated by something he heard in a talk by Nolan, the Stanford University biologist and scientific founder of South San Francisco-based Rigel Pharmaceuticals (NASDAQ: RIGL). Laboratory instruments were becoming powerful enough to look inside cancer cells of an individual patient with, say, a form of leukemia, and see how it differs at the molecular level from other people with the same diagnosis—and even how one tumor in a single patient can have a variety of different mutations.

Armed with this kind of knowledge, the thinking went, cancer doctors would be able to do away with old-school organ-based classifications like lung cancer or breast cancer, in favor of this more nuanced view of what is going wrong in the biological pathways of an individual breast tumor, and even how those pathways differ from one region of the tumor to another (a concept known as “tumor heterogeneity“). If drug developers and physicians could get a better idea of what kind of tumors they were really dealing with, the success rate ought to climb, Parkinson figured.

“The inefficiency in drug development is not because of lack of targets, or the industry’s ability to create good molecules, be they small molecules or large molecules,” Parkinson says. “The inefficiency largely lies in the fact we don’t have an efficient way to categorize tumor biology in a way that’s relevant to use of therapeutics.”

Over the last three years, Nodality has sought to turn this idea from a laboratory concept into what Parkinson calls a “standardized, industrialized” process which was “no small feat.” The process is performed in house at Nodality, so researchers at a drug company, or doctors at a medical center, need to send their samples in for analysis. Besides the company’s headquarters in South San Francisco, Nodality has a Nashville, TN, office that’s staffed by experts in flow cytometry, many of whom previously worked for Lab Corp, Parkinson says. The company has raised about $50 million since its founding in 2006, and now has about 50 employees.

Nodality is hoping to impress blood cancer experts with its new technology at the American Society of Hematology meeting in Orlando, FL in December. Some big decisions for the company’s commercial strategy—like the cost of its test—still have to be worked out, Parkinson says. Eventually, insurance reimbursement will be a part of the equation—although it’s not necessary when Big Pharma companies are just using the technology for their own research purposes. Assuming Nodality can generate some cash flow from its initial cancer tests, it will have more freedom to pursue some ideas it is chasing in improving the individualized treatment of autoimmune diseases.

It’s all still early in the game for Nodality, and the next year will clearly be crucial for the company’s bid to gain acceptance from physicians. Parkinson pointed out that the company has made sure to collaborate with thought leaders like those who are part of the Eastern Cooperative Oncology Group (ECOG), the kind of people who can influence their peers when it comes to recommending a new technology. Many of them were “appropriately skeptical” when Nodality first started talking to them, but they have joined the cause, and now have helped craft the tests to make sure they are relevant in the clinic, not just the lab.

The potential impact for cancer and autoimmune disease, Parkinson says, could be nothing short of historic. The Nodality approach, he says, could do for cancer and autoimmune disease what high-powered quantitative viral tests did for enabling better HIV treatment strategies in the 1990s.

“I firmly believe the technology we are developing has the capability of giving that kind of efficiency leap or productivity gain to drug development in the cancer and autoimmune space. That’s why I’m doing this,” Parkinson says. “This will change the practice of medicine.”

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