Meet NYU faculty and entrepreneur, Dr. John McDevitt

Dr. John McDevitt is a Professor at the NYU College of Dentistry and the founder of SensoDx. We sat down with Dr. McDevitt to learn more about his entrepreneurial endeavor. 

Tell us about yourself.

So I am the new kid on the block at NYU. I was in Texas for 25 years first at the University of Texas and then at Rice University. And I would say in each of these prior areas that there's been a phase of our research. At the University of Texas, we began to make devices moving into the Texas Medical Center. At Rice, we began to run clinical trials and then we hit a thick glass ceiling with respect to, to what I really felt passionate about which was to help the patient. And so being at NYU, in America's largest city, which is racially diverse with 8.4 million people here. And to be at NYU with a growing interest in engineering is really an ideal opportunity to come here.

What do you look forward to now that you are at NYU?

Basically what we want to do next is take this technology and make it clinically relevant and ultimately help people develop a new diagnostic capacity which hasn’t existed previously.

So a little bit about the technology, this is a platform to digitize biology and it has a capacity to learn. This is a sensor that learns. And then the third part of this is that it brings in connectivity to the patient. So it's mobile health enabled. That last part is about bringing information to the patient and the healthcare providers alike that is complex information but simplifies this. Going back to the whole scheme of things and what we're doing is creating instrumentation like this flu box here that is about the size of a toaster. And this is like David versus Goliath. But this box is about 1 percent of the size of the clinical analyzer, the gold standard that we have in the lab. And so we compare David, we compare Goliath. This is something that they're doing right now to ensure that we have this new technology working as well as the big standard.

But this is one percent of the size is currently about five to ten percent of the cost. And that creates an interesting issue here in terms of scalability and so for the last 10 years, my group has been one of the leading groups in developing non-invasive tests that are using drops of blood, small rather than a vein puncturing. And using saliva of noninvasive samples instead of a blood draw. That 10-year experience has put us in a mind frame that we don't have to have the traditional infrastructure to do the test.

And that's a key step. If we have a noninvasive sampling, we can go anywhere. We can move away from the phlebotomist and say that umbilical cord to the hospital or to the general practitioner and to be in CVS pharmacy or Walgreens or in people's houses or in the hut in Africa—we could be anywhere.

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10 years ago, we were funded by the Gates Foundation to be in sub-Saharan Africa to do an HIV test. That experience made us think a lot about how we scale diagnostic. When you don't have the infrastructure, you don't have the resources now and so fast forward to where we are today and we're thinking about being constantly effective. And yet, really to be like the iPhone of medicine, which is our goal, we need to bridge between lots of different things. So the iPhone of medicine which I think is a great metaphor for us is what Steve Jobs did. In any case, this was to bring together the fields that have not been brought together, like powerful technology and integration of new concepts.

And so the diagnostic industry today is ripe for change. The diagnostic industry doesn't do so well measuring multiple things. If you think about doing a physical and you need a five-test. It's not unusual to have five evacuated containers, five containers loaded up, and those via FedEx go off to LabCorp, and then they put them on separate instruments, and three days later the results come back in. And so it's a lot of inefficiencies with respect to the infrastructure and a number of simple tasks. We are combining five areas, and they're all in this little box.

But at the first of the areas is this point of care technology or near patient testing. We have a technology that can do any measurements of your patient. So that's where we start. And then we bring in new biomarkers, and we're now living the age of openness. The molecules of life are being discovered but they're not impacting people clinically. They're stuck in papers. We have a very big funnel, but nothing comes out of the other side. This is a key step of connecting measurements to the molecules of life.

And then the third piece we bring is microfluidics. There's a little circuitry where miniaturized fluids flow. And it's like the integrated circuit that's on your smartphone, but it processes fluid. So microfluidics is a part of that. And then we have artificial intelligence. Now we have all these biomarkers, there’s a kind need, but what do you do with them? How do you interpret them?

And so for the last five years, we've been running all these trials, and we have big data now. And so we take the big data and we basically do Netflix analysis. You choose a movie and Netflix has collected all this data and they see that you like chick flicks, and it says, I've seen this pattern before, we can recommend the next movie. So we've just run six trials, five thousand patients at clinical sites including NYU. Again why I'm here is because NYU was one of those sites and we like the interactions that we had and the people here and the point realized, America's largest city, America's largest dental program, which just something I’m part of. And the Bluestones Center which is in the building next door - is the world's largest oral cancer center. And so this yellow chip is an oral cancer chip. But staying with the thought of these five areas, so we have Netflix and it's about recognizing a pattern and getting back to that pattern. These trials are our curriculum to the learn diseases, and once we see a pattern, and we do a well-defined trial, then we use machine learning and then we extract out the mathematical algorithm. Again, in a simple way it's Netflix. But it's about patterns and we're able to capture diseases earlier than they've ever been caught before.

The last piece is after we see that pattern, and we can say, “You're on the verge of getting cancer. You don't have cancer yet, you have dysplasia.” And so if we capture this now, and you go to your oncologist, they'll do a small surgery to remove the lesion that you have which is not yet cancer and hasn’t spread. That's really the ideal.

The key is early detection. So we get that with this Netflix, this passage to do this test. But once we have that information now we have to take complex information and I don't want to dumb it down but that's kind of what it is. We simplify it. Just like Netflix defines the recommendations, so we get that and we deliver information via mobile. And also to the health care provider to create a risk analysis, that’s analogous to a student's GPA or test score. So all this complex biomarker stuff that patients certainly don’t understand, and it's a challenge for doctors to understand.

At what level does one need the training to read it?

So this is set so it can be done by someone with a modest education. Right this minute, what you see is a research blue box. So it is now running through all these trials. Our lab is treating this for oral cancer, cardiac heart disease, drug abuse, prostate cancer, ovarian cancer, etc.

It's a good operation. This is inspired by a floppy disk or computer disk of the three-and-a-half-inch disk, and now credit card or ATM.  

How easy this is to use is really key. If you think about what happened 40 years ago, maybe a little bit longer than that, to diabetic patients. The point when the glucometer, the portable glucose measurement system was developed, and you spend time, and this was one of the most frequent tests done on it for good reason. But the diabetic patients' lives have been changed. They now can monitor their own blood which is totally cool.

It’s empowering. And the glucometer costs $8 to make. They may charge $60 or $50 or $35. But it’s an unbelievably cost-effective. There’s a needle stick, and then there's a 10 cent strip that works on that. And there's no pipe padding so you don't have to be a chemist. And so that that concept is what we're developing here, that we have things like a needle stick. And a drop of blood transfer to the target zone. We put the sample in here and we close a little flap, and then the cartridge goes into the analyzer, and then the analyzer reads what kind of card this is. So there's a QR code, a 2D barcode here and it says ‘oh I'm a cardiac chip, and OK I’m a cardiac chip, I'm going to run this sequence.” And so these are all programmable.

I grew up in Silicon Valley so I think what the microelectronics industry has done to change our society. But part of that was to generate the integrated circuit. And the integrated circuit now it's such a powerful tool that we have Moore's law, and it gets better, cheaper, faster each year. And that's what we want to do for diagnostics: better cheaper faster. So more information, content each year. Tapping into the biomarkers, tapping into the Netflix, where, as we acquire more and more data, we step back in and we say “Wait a minute we're capturing the disease earlier, earlier and earlier.”

And so our dream or vision is to start here, in Langone. With people like Judy Hochman who's running the largest cardiac trial in the United States. And we're also working with Jeff Berger who does open heart surgeries. And Jeff has recently challenged us to help him find which patients are at risk when he does open heart surgery.

So it's a fascinating question he posed to us just a couple of months ago: Who's going to die?

After the surgery, who's destined to be dying, because he wants to make that not happen, right. And so we now have samples that we've trained on doing exactly what we've done before to see what's the fingerprint of the good outcome and a bad outcome. And if there's a trajectory for bad outcomes. It's not that Jeff can play God, but that he can intervene in a more aggressive way.

And so that's pretty cool. Now what we do. But what’s very cool is what he will be able to do when we provide him with more information in time. So that's exactly how we want to interject here to be right smack in the middle of the practice of medicine to help clinicians with their state of the art problems, especially those that are time sensitive.

So that's what we would be doing this year. Five years from now, we’re going to acquire a very simple mathematical thing, a formula. It's illness minus wellness.

If people are very well, they’re not ill. And if people are very ill, they're not well. So if we understand this we understand wellness too. So the same algorithms transform very simply. Although our society is very focused on illness today there is a pathway to wellness. And we are getting involved in some very exciting initiatives now that are about this. It doesn't happen overnight. And one of the relationships aside from Langone, and I'm very excited about it, is working with the Lutheran community clinics. That creates an unbelievable opportunity for us now because there's something a kind of deja-vu dream.

To be successful in community clinics here in the United States is no longer about Africa, I'm talking about the United States assimilation. We have a lot of similar issues of underserved populations that don't see traditional health care. In many cases, they're ticking time bombs with potential for very big health care costs for our society. And if we capture the diseases earlier on and engage these patients, and they want to be engaged, they'll know they have to be engaged. Though, capturing their diseases early on has the potential to have huge cost savings for the State of New York, and for the whole United States. And for the insurance providers, and the US government, and for the patient, too. And the community clinics can be more profitable too by having this expanded level of engagement and enable to do what they need to do to have these underserved patients come in when they need to. So there's an interesting alignment factor that are evolving now in the other kind of initiative. I’ll mention it just briefly here. The City of New York and Manhattan is in the process of helping us establish the Manhattan cardiac scorecard test facility. That is the unique play to make, and we basically, as we're coming here last year, thinking about how do we make New York City the heart healthy city in America. And it kind of helps that no one can park right.

Walkability is huge. Relative to where I just came from in Houston, people there drive in air-conditioned cars and parking lot and they're seven pounds heavier on average than in New York City. But that's not the whole story. We want to provide a tool with this particular blue card is a cardiac scorecard that is at the tail end of fast 50 patient trial that we just completed. And that has given us a holistic understanding of cardiovascular disease. And we have four algorithms for heart attack, Q coronaries, and heart failure. Those three or four are kind of late in the disease, and then we have risk assessment.

This is the one minus trajectory, that you can call cardiac wellness. This is maybe a little bit of an overstatement, but I don't think it is, In my personal opinion it's not, but that we don’t really have good cardiac wellness tools. We have a recommendation from the American Heart Association that Americans do seven things, and 0.1 percent of the population can do that. So 99.9% are dying of heart attacks each year. So the recommendations let's say are hard to follow. So we want to have a flashlight that is bright that people can use. And so that's what this cardiac scorecard is about. It's about taking cholesterol, which is the dominant biomarker and that’s now used for cardiac wellness and it really doesn't work that well. CRP which is another biomarker again doesn't work that well. It's not that information rich. We have a scorecard that is more information rich, it’s more informative. So it's Netflix after it's been trained. So that's that part of what we're doing and it's us. So we've been talking a lot about the technology.

SensoDx is a startup company that is, its goal is to get the stuff out the lab. We publish lots of papers, do the academic thing, which is what we’ve got to do, and we’ve been well funded. But if it stays in our lab and we just publish papers, we’re not where we want to be. I would consider us being failures if we stop there. If the paper is nice but when we're this close to helping people we need to work extra hours, we need to put in the 16 hour days to make that happen. And so we've launched this company called SensoDx last year, and SensoDx now has more four commercialization brands from NIH, and NIH has been a very generous sponsor and also are partnering with large dental providers that are helping us get to the finish line. And we've chosen two signature applications, although I've shown you six things here on the table. We've chosen the cardiac score card and then the oral cancer as our two signature applications to try to get out the door first. Cardiac heart disease is the biggest killer in the United States and also global, so that’s an obvious choice. And then for oral cancer, we have America's largest, or the world's largest, oral cancer center in Blue Stone. So that’s part of the reason we’re doing that. But the other part of this.

But the other part of this is we have oral cancer as one of the most expensive and worst outcomes of all cancers. And it's and it's stayed this way for the last 50 years. The outcomes are about the same, it's just got more and more expensive. So we don't have a good screen for oral cancer yet. We've just completed, like cardiac, another thousand patients trial. We've done Netflix there, so we know what the signatures are for oral cancer and how to capture the disease early on.

There's like 30 million people in the United States. 10 percent of the time they go to the dentist but not to their general practitioner, and those patients are unengaged. A lot of them, like community clinics, can be walking time bombs they don't know yet. So we were in the process of thinking how can we empower them to provide things like a cardiac screen to do a wellness screen that would capture cardiac disease before it’s too late.

And cardiac heart of heart disease is a great example where early disease detection has to come into play. But it's not helping as much as it should today because one of three people who has cardiac heart disease gets the first symptom by dying. The penultimate symptom is death and death arise and no one's happy. Families not happy spouses not happy. This happened to all my uncles, it happened to my grandfather, I never knew my grandfather on my mother’s side. So it's, you know , it's personal to a lot of people. And so we'd like to help other people to capture these diseases before they get out of control.

So early disease detection would be powered by looking at multiple things: biomarkers or any large trials, talking to clinicians, and now that I'm in the same place. So in the NYU College of Dentistry, I’m chair of the biomaterials department.

A part of my appointment is with Tandon. My main appointment is with the NYU College of Dentistry. I have a new appointment evolving in Langone. With all of these things - it's about a bridge. The most important thing that we do is to talk to the clinicians.

You can see we're already pretty good engineers. We understand that piece but we need to understand the kind of problem that Jeff Berger brought to us, which is a really neat problem that he has with patients. And it’s not a neat problem for the patients, but it is a neat problem for us to dream about, of having an outcome that can meant life or death, a way to treat that on a timeframe that we need to. Open heart surgery is a very dramatic operation, and decisions are happening in real time. And trauma is another area where decisions need to be made really quickly and stroke is another area.

So those are very significant things that we can think about training our device to do to have immediate impact. But again long term vision is that we help the wellness in the street evolve and, you know, make an effective tool. You know, FitBit is okay, and I'm a runner too, so I'm really into this issue of monitoring health, but FitBit is kind of interesting. There are some recent studies that have shown that people have been thinking about using FitBit as a way to stay healthy but tying to medical treatment is not well established. So there's a fatigue that can happen in a lot of people like myself are thinking, how do you take that information and put it in a form that can impact people's health care?

And I think if you go to your doctor today and show how many miles you’ve run, like that, they’ll say you're fit. But what people are doing now, so how do we get the rest of the population in that mode? And even if you’re using FitBit, how does that translate into a score. How do you know if you're losing fitness?  You’re losing cardiovascular fitness. It's probably something more than your pace. Because your pace could go down because you're getting potential heart failure. So we have to pull that part.

What role if any, did NYU’s entrepreneurial ecosystem play in your journey?

NYU is actually very savvy. I’d say over the last 10 years that academic institutions in general have become more savvy because there's been more successes. But it's tough to do, and the pressure is on the academic person are significant. In many cases, a young professor goes through with the goal to get tenure, and the entrepreneurial part of it is often an afterthought. It's not a big part of the decision. I can say as I was going up for tenure I was warned not to spend too much time pursuing my passion of helping patients because no one's going to count it. And so I think it’s maybe a little bit different now, but the reality of the situation is that young people going through the academics ranks have to realize that the publication and the grant writing is more important than the entrepreneurial piece. But after that happens, a lot of people in academia are realizing that there is an opportunity for a success on the publication side and on the startup side. So NYU has a lot of great programs to support these kind of initiatives. And Frank as a key player, the Leslie eLab, his staff has been fabulous. I think we're talking today in part because of Frank. Frank is a resource for NYU. It’s a savvy resource.

Part of this is because the leadership here, there have been successes that have led to royalties coming back to NYU. So the institution can see if there is a pathway to have successes, and that allows NYU to put a bit more budget into that. It takes resources to put the patents in, to make the right decisions on the intellectual property, and to support the faculty, to steer the patent submissions. So I’d give NYU an A-plus mark for what they're doing in this space. There's a neat kind of work now, and we’re going on at the Leslie eLab. Frank has played a leadership role, and that’s key. I’ve done three companies now. SensoDx is the third company that I’ve been involved in. And I’m happy to help other people here at NYU. And each time you do it you learn something. Just jumping in and getting started is a key step.

What are some of the lessons you learned along the way that could be helpful to faculty entrepreneurs?

Well a couple of things I would say is to get tenure first. There are exceptions for this, but this is my recommendation, so that it doesn’t get in the way. And then jump in. Don’t worry too much about hitting perfection. It depends on the areas. So I'm focusing more on diagnostics which are the area I know well, but software has very different rules, and the barriers are much lower. Just jumping in and getting a minimum viable product and getting something out there really quickly is key.

But for the areas that I'm involved in, they involve FDA approval. And so life science things that impact patients, my strong recommendation is to get the science and engineering and medical things to a good point evolution. Don’t go out and that takes discipline to do.

If one can build partners. That’s another key thing. It can be a partner with a national lab, or a company or dental provider. Partnerships define usually smart money. In my experience it's not just money that's key, it’s smart money. And that can help by providing resources by defining the regulatory pathway. by making sure that whenever you build after blood sweat and tears are put into this that it's something is going to buy. There's a lot of entrepreneurs that go down that pathway. A lot of money goes in, and after tens of millions put in, one finds out that this wasn’t the right product. The customers don't want this, they want something a little bit different. And then everyone is frustrated at that point. Getting that definition right, the right product, that's hard. It's actually very hard to do from an academic seat.

I go to meetings all the time where biosciences, researchers, professing a solution which is often not a solution. So the academic goal is rarely the commercial goal.

Hardest thing for academics?

I don't want to insult academia. It's very complex. And so and it takes a huge amount of effort. So the other part of my advice is you got to know that you really want to do it. Something besides money that has to drive you. You have to have an unbelievable passion, you have to be willing to work a decade for 14-16 hour days to do everything that you do, and put a big effort on top of that to get it the finish line.

In your perspective, what is the single most important trait for entrepreneurs?

The first thing that comes to mind is passion but, I think that term is overused. Jim Collins’ Good to Great, has the intersection of all the things that are really key to entrepreneurship. Passion, what society needs, and what you're good at. That has been a guiding principle so encapsulating all three of these things: I’d say focus. To be focused on an end, that you want to work. I think that will make you more likely for success.

So we were comfortable with that notion. So going back to why I’m here in New York City. Initially I wanted the sub-saharan Africa, the peace process which again in hindsight, was a bit ambitious. We launched a 42 million dollar company do to this, which was successful in making instrument, but not successful in scaling to the size of Africa. There's politics, corruption and whole bunch of things that got in the way. Also, the kind of a business model challenge. So that was unbelievably frustrating for me to put in that much effort and then to realize it's not going to scale. So that is the scar on my back. And so when I realized that, I came to the conclusion that I have to work on US applications, that have a big financial potential. Then, that way I can be successful in Africa. It was still a dream of mine. But it's with the understanding that the iPhone was created in the United States. And it scaled, and now we have 7 billion smartphones. But it was this capacity to scale, it’s Moore’s Law. And so the more enlightened approach we have now is to scale things here in the United States, but to do it with a commercial driver. So in order for us to be successful, since it has to be successful, there has to be a market driver. And the market driver will drive down cost.

What is next for you?

So that to that aspect of scalability is clearly part of what we're thinking about right now. If you look at the diagnostic industry, it's very fragmented. There's not one gadget that goes everywhere. There’s not one device that does many tasks.

Yes, we have a very unique platform that can do cell-based test. So going back to the CP4 counting we did for Africa for the AIDS initiative, back then was reprogramed, and we raised the bar to do the oral cancer. So this is the mythology on a chip. So we replace a three day test with something that's non-invasive, psychology test. And then we have a series of other kinds of tests, immunoacid general chemistry things that are all there all happening here. By the way, this is one percent of the size of one instrument. And so now you begin to think, there's a potential for traction and the potential for this to go lots of places. And so the scalability, the better cheaper faster, is common in micro-electronics. It's not common in diagnostics. But that's our vision, that we do that, that we make everything here go from 10 boxes to a million boxes. Price goes down tremendously. As it has in the smartphones. So we’ll be pushing in that direction.