Name: Sean McDonald
Position: CEO of Precision Therapeutics
Background: and CEO of Automated Healthcare, 1990 1996
Group President, McKesson HBOC Automation Group, after its acquisition of Automated Healthcare, 1999 2000
Education: MS in Computer Science Engineering, University of Florida, 1985
Pittsburgh-based Precision Therapeutics last week said that it had raised $20 million in a Series B venture capital funding round to commercialize its techniques for guiding chemotherapy.
The company's approach is unusual, in that it uses a combination of familiar molecular technologies alongside cell-based assays and data interpretation to arrive at clinical recommendations.
Pharmacogenomics Reporter spoke to Sean McDonald, the company's CEO, to learn more about the company's technology, as well as how the firm hopes to make use of the venture capital funding.
What sort of work will Precision Therapeutics be doing in pharmacogenomics?
The product is geared toward predicting the therapies patients are likely to respond to in treatment of their cancers, and we have a set of technologies that incorporate a lot of different factors. We're looking at both the individual tumor response through the behavior of the tumor cells themselves, but also what we call patient factors or host factors. [These might] be the metabolism of drugs or how a patient is likely to respond to a particular drug.
We're combining those factors together we're really looking at 'What are the factors that are predictive?' We view ourselves as technologically agnostic, so to speak. Any factor that will help us predict eventual patient outcome is really incorporated into our technology.
What sorts of tests do you conduct?
It really depends on, specifically, the tumor type and the drugs we're looking at. So, we're looking at cell-based phenotypic response to different agents, and then where appropriate, we're looking at specific mutations and how those affect the patient's response to therapy.
But it depends on the particular chemotherapy agent we're talking about. We have a platform that's broadly applicable, though.
How common is it to include cell-based assays in pharmacogenomic research?
I think that historically people have said DNA or RNA or protein or cells or something else holds all the answers, and they run down that road, so to speak. And as I said, we're really saying, 'Hey, a lot of these things hold information, and one of the tricks is going to be to take signals from the patient and the specific cancer cells and combine it to make a predictive system.'
How is a single assay for pharmacogenomic cell-based assays conducted?
The commercial model that's set up is we have technology that separates out the cancer cells, the physician sends us a list of drugs they are considering for that particular patient. We challenge those separated cancer cells through a range of particular agents or combinations together and across ten doses. We read the cells through our imaging system that can track how each individual cancer cell responds. We characterize the behavior of those cancer cell populations and [interpret] output to a prediction.
What will the money fund?
We completed a Series B financing into the company that was led by Quaker Bioscience out of Philadelphia. It was a total of $20 million in financing. They took $10 million of it and existing investors followed on with $10 million as well.
Previously, this company has focused on product development, clinical studies, and working through the reimbursement process. And we will, of course, be continuing those activities to a greater or smaller extent going forward, primarily focused in the future on clinical studies and some product development.
But at this point we're now really beginning the commercialization process. So fundamentally, most of the work will be going towards clinical studies, product development, and commercialization.
What is the foremost project in commercialization?
At this point, we have received some business we did under $5 million of business last year, but we really haven't had a force out in the field or anybody out selling the technology. So it's going to be put toward organizing that sort of effort.
What sorts of clinical studies will you be conducting?
We have clinical studies focused initially on two cancer types, which are both women's cancers ovarian and breast cancers. So, the company has completed several trials. We also have two prospective studies in the field and are about to start a very large third prospective study in breast cancer right now.
So, our focus historically has been in the breast and ovarian area. We're moving now toward looking at some other tumor types where we think the technology might have a lot of benefit.
How are you processing the information to arrive at clinical recommendations?
We have sets of algorithms. The company is set up as a CLIA certified lab, and when we receive specimens, we have a set of technologies that a physician would like tested against a particular tumor. And then [we] make recommendations based on the result of that.
We've put that through a clinical trial to understand the strength of the prediction it has come out very favorable. We have studies in ovarian cancer looking back on historic clinical specimens we've received [that] showed that when a physician follows the result of a test, the women have live three times longer before having a recurrence of the disease. Three times the progression-free interval.
What we're doing is, we're taking that technology, applying it prospectively in a study in ovarian cancer that's where the principal investigator is, at Yale Cancer Center. They're conducting it at twelve sites throughout the country. We're also taking the technologies that showed that predictive value and we're incorporating certain genetic factors that we have found in our research that were also predictive markers.
We have developed and also licensed technology in from Carnegie Mellon University and the University of Pittsburgh School of Medicine. So, we've incorporated that technology, done a lot of in-house development of that, and that is run out of our lab here.
Is this is the first company to use this kind of integrative approach?
I don't know the answer to that. I'd say a lot of people are looking to choose a technology and really ride that horse. A lot of things hold signals, so why not try to read the signals where they exist? I have no agenda to try to certify certain technology we want to build the most predictive system we can.
Who are your competitors?
This whole personalized medicine area, particularly in cancer, is very broad. So a lot of people are looking at clinical problems within this area. I'm not aware of anybody who's doing it like we are, but that doesn't mean that there aren't great technologies out there that are taking a slightly different path on it.