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Injectable Biosensors – EP08: Ben Hwang (Profusa)

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Lee: On today’s show, we have Ben Hwang, who is the Chairman and Chief Executive Officer of Profusa. From his early exposure as an undergraduate research fellow at the lab of Leroy Hood at Caltech, where the automated DNA sequencer was developed, to bringing cutting edge life science tools to the market at Life Technologies Corp, acquired by Thermo Fisher Scientific, Ben has seen first time the transformative impact that science and technology have to change our world. Prior to Profusa, Ben served in a variety of leadership roles at Life Technologies Corp, including President of the Asia Pacific Region and Head of the qPCR Division. A former management consultant at McKinsey and Company, Ben earned his M.A. and Ph.D. in Biology from The Johns Hopkins University.

Lee: Hi Ben, welcome to the eighth episode of the Hyper Wellbeing podcast.

Ben: Hey, thanks a million, Lee, for having me. I appreciate it.

Lee: Excellent. I’ve been very interested in Profusa for a number of years. Could you be so kind as to give an introduction to your company, Profusa?

Ben: Well, sure. We are a company based in south San Francisco, California and over the past eight years or so have developed what we considered to be a quite exciting platform. The gist of it is our technology platform allows for the real time unleashing, if you will, or measurement of an individual’s personal biochemistry in realtime, continuous, anywhere and done so in a way that is very non-intrusive to an individual’s life at a cost where it’s incredibly accessible.

Ben: What is important about our platform besides the low cost and unobtrusive nature of using the technology, is that what we measure in realtime also are things that physicians care about. These are values of your biochemistry such as your glucose level, your calcium, sodium, potassium, blood gasses, that physicians are relying upon and using today to make therapeutic choices. Our aspiration is to be able to make those values available in real time for anybody, anywhere.

Lee: Appreciated. I understand that Profusa are unique because they’re doing in-body sensors, injectable sensors, and these are about, well, I would say smaller than a grain of rice or five millimeter by half a millimeter. Is that correct?

Ben: Yeah. One of the unique features of our technology is that the sensors or what allows us to actually measure the biochemistry is actually inside the body. It’s inside the body in way where it does not elicit what’s called a foreign body response. What we place inside the body is very, very small.

Ben: As you said, Lee, they’re about three to five millimeters in length and the diameter is a couple hundred microns. Put that in perspective, it’s the width of a few hairs and the placement of this little sensor under the skin is done so by a typical injection. That’s no more invasive than if you would go to your physician to get a vaccine or get a vial of blood drawn.

Ben: Once that little sliver of sensor or sliver of hydrogel that’s placed under the skin, that sensor is very passive. It actually has no active electronic components. It doesn’t actively ping out a signal. There is no battery on it. It’s literally a material that’s very similar to the material that composes soft contact lenses and we’ve embedded on the material fluorescent-sensing chemistry that senses biochemistry in a very accurate way.

Ben: The big, huge innovation is our approach enables that sensor, the little element under the skin, to evade what is called a foreign body response. A foreign body response is something all of us should all be quite familiar with. Our bodies, through evolution, are exquisitely good, exquisitely good. Almost perfect in fact at determining what belongs inside our body and what doesn’t belong inside our body.

Ben: For the past close to half a century, the industry has been trying very, very hard to try to solve this problem of how do I put sensors inside the body without the body recognize it as being foreign. Because when the body recognizes an element as being foreign, what happens is the immunological response kicks in, your immune cells tries to get rid of that foreign body, that foreign object. When the immune cells not able to do so, because obviously any sensor you put inside the body or any element you put inside the body that is hard, your immune cells are not able to chew it up and get rid of it.

Ben: The body then tries to minimize the potential damage of that foreign element by putting collagen or scar tissue around that to wrap that foreign element up in a way where that foreign element cannot harm the body. That’s exactly what happens to most traditional sensors whether it’s a needle sensor through the skin like current continuous glucose monitors out on the market or some of the implanted sensors that people have tried in the past, is the body recognizes it as foreign, starts encapsulating that sensor with collagen and scar tissue.

Ben: Now pretty soon, even if the sensor still works, the sensor is now measuring the chemistry of the scar tissue rather than the chemistry of the body. The chemistry of the scar tissue, unfortunately, bears little resemblance of actually what’s happening in the rest of the physiology. That’s created a big, big problem for people who are trying to develop biochemical sensors in the past, where the solution is to either coat it with drugs to decrease the amount of encapsulation or you just have to pull it out and change it every two days, seven days, maybe a couple of weeks or so.

Ben: Our approach and what we’ve been able to solve is that foreign body response problem. That little sliver of hydrogel that we put under the skin, Lee, as you said, it is quite small and it’s also architected in a way that the body doesn’t mind it being here. In other words, the body doesn’t see it as foreign. If the body doesn’t see that element as foreign, then that element could stay within under the skin and in place forever. It really doesn’t cause any problem for the function of the sensor.

Ben: Our sensors actually can function for months and years at a time relative to just a few weeks as the best in class sensors are out there in the market today. That really drives a big, big difference in how a user might use it and how an individual might actually adopt it into their lives because it really doesn’t pose any additional burden other than a person living their life the way they normally would.

Lee: I believe it’s 60% porous and it’s implanted eight millimeters below the skin surface, so it’s measuring interstitial fluid.

Ben: Yeah. No, that’s correct. It’s about 60% to 70% porous. There are a couple of features of the sensor that we’ve engineered so that it can overcome that foreign body response. The foreign body response really is the key barrier for long-term continuous sensing as it’s been attempted for the last half a century as I mentioned before.

Ben: One of the key features for our sensor for it to be able to overcome this foreign body response is this microarchitectural porosity. These interconnected pores within the small hydrogel, these interconnected pores within the body encourages the body to grow healthy tissue throughout the interior of the sensor. It basically mimics what is called as extra cellular matrix. It mimics the scaffolding the body naturally would produce to allow cells to grow on that scaffolding which makes the shape of the heart what it is, which makes a kidney the shape of a kidney or the liver the shape of a liver.

Ben: That’s actually done through this scaffold called the extra cellular matrix. Our sensor basically mimics that extra cellular matrix, so the body doesn’t see it as foreign. It drives healthy tissue throughout the interior of that hydrogel. We place that sensor within 2 to 8, 10 millimeters under the skin because we want to have the sensor close enough to the surface of the skin so that the light signal could come through the skin easily and we could actually detect it.

Ben: What works great also is, and therefore in that 2 to 8, 2 to 10 millimeter under the skin distance in that compartment of the body, if you will, that is the space that’s called, as you mentioned, the interstitial space. The interstitial fluid that we actually measure is the fluid that bathe the cells. It’s the environment, the local environment that your cells actually experience.

Ben: It’s important that we measure the activity or the biochemistry within that area, within the interstitial fluid, because it’s what the cells experience and so any increase or decline in any particular biochemistry or biomarker that the cell will experience would have a lot more clinical relevance for your health and for your activity.

Lee: Being in interstitial fluid, you can potentially measure electrolytes, glucose, oxygen, lactate, pH, ethanol. Any others that come to your mind? I mean potentially.

Ben: Yeah. Really, we have a pretty robust pipeline in the company. We’re relatively early stages and so we have oxygen, interstitial fluid, dissolved oxygen that’s approved in Europe. We have glucose in our pipeline that’s in clinical studies now. We have a lactate program that actually is about to get into the clinical study. We’ve demonstrated on a benchshop a variety of other analytes. You mentioned a few, ethanol, pH, calcium, potassium, sodium and a variety of others.

Ben: The key to what we could actually measure really is two-fold or quite frankly three. One is, obviously, because we are in the interstitial fluid, whatever it is that we’re measuring need to be available in the interstitial fluid. For example, I’m not going to be able to measure, to be able to do red blood cell count, which is one of the things that a blood draw normally could do. We won’t be able to count the number of red blood cells because that information is not attainable in the interstitial fluid. As long as the biomarker they were looking at is available in the interstitial fluid, we have a shot at getting it.

Ben: Two, it needs to be in the high enough abundance so that the lock and key model, the sensing chemistry can sense it. Then three, obviously, is that the sensing chemistry itself can be produced. Fortunately for us, those requirements actually are really amenable to a technological solution for measurement because as I mentioned, most important biochemistries are available in the interstitial fluid, because those are all things that need to be available to the cells for the cell’s function and activity and survival.

Ben: Interstitial fluid is actually a great space to make those measurements. The fact is that most of those elements are available in abundance for the cells to actually have access to or for the cells to actually get rid of.

Ben: The third is because these biochemistries are important, the ability to detect it in traditional clinical chemistry fashions, those lock and key and those detection chemicals have been developed for the most part. We could just go ahead and pick off of those menu and do our modification to them to tailor to our system so it will work. The potential application actually is incredibly broad and we’re quite excited about that [crosstalk 00:14:01].

Lee: This seems a breakthrough you have. If I compare it to, say, Abbott’s FreeStyle Libre or Dexcom’s G6 continuous glucose monitor, these are patches which have a pin on them. An electrode actually that breaks through the skin, directly into the interstitial fluid. Then these all suffer from the foreign body response. Hence why they have a very limited time within the body.

Lee: What you’re doing is splitting the electrode a piece apart. It goes into the body and you use an external reader. That electrode, as you had said earlier, it doesn’t elicit the foreign body response, but you do need a specific reader to supply to read optics of that electrode. Could you explain a little bit about the three stages of the sensor, the optical excitation, fluorescent submission and then for photo detection and data processing? Could you just give some kind of a sample idea as far as possible those three stages please?

Ben: Sure. No, sure, Lee, I’m happy to. First of all, let me take a step back here and just say you mentioned a couple of giants in the industry with Abbott and Medtronic and Dexcom. We feel incredibly lucky and fortunate to be in a space where there’s just a bunch of really great science and wonderful, dedicated scientists to try to solve some fundamental problems in chronic disease management.

Ben: Being in the industry and in a group where we’re building off of the approaches and the experiences of the shoulders of some giants, makes our lives really easy and makes our work really quite exciting. Their approach of continuous monitoring of glucose, which is really the best in class in the standard today, both in terms of technology as well as application is quite inspiring for everybody here at Profusa.

Ben: You’re right. What we’ve done is build off of that thinking and say, “Can we detach and separate out the sensing component with the detection component?” Because if you could do that, you can miniaturize a sensing component in a way and you have much greater latitude to actually work around how do you solve this foreign body response problem only in the sensing element.

Ben: Then now, what’s left is the trick of getting the data or getting the signal from that sensing element from inside the body to outside the body and be able to capture and detect it. We think that technologically if you disaggregate those two components, it actually is a much easier problem to solve those inherent problems.

Ben: To your point, there is three stages for us to translate biochemistry concentration levels into a signal that we actually can translate into a data point. The first step is the sensing itself. That magic lies on the sensor that I’ve described earlier. It’s fluorescence-based. Basically, the sliver of hydrogel that has the porosity on it that we put under the surface of the skin that is injected, it sits there passively.

Ben: Think of that scaffolding as a stage and on that stage is decorated with a bunch of sensing chemistry, and they’re all just kind of sitting there. The binding and unbinding of the thing that you want to measure. For oxygen sensor is the sensing chemistry that binds specifically the oxygen, is sitting on a scaffolding and there’s a bunch of free oxygen floating around.

Ben: Bind on to the chemistry sometimes and won’t bind on to the chemistry sometime. That interaction, that binding you went, it’s just constantly happening in the background, and that’s okay. Any time you actually want to take the data point, because the binding chemistry that we’ve decorated on that scaffolding is fluorescence in nature, and when the oxygen is bound versus the oxygen is unbound, that fluorescence characteristic changes, it’s different.

Ben: What we then have is on the surface of the skin, a very simple optical reader. That’s just a fancy way of saying it’s a little device no bigger than, let’s say, a watch that people are actually used to wearing. By the way, the path of that technology is pretty straightforward where it could be as small as a thin little Band-Aid that sits on the surface of the skin.

Ben: Any time you actually want to take a data point, a light is emitted from that little device. Just a little short pulse of light. Very similar to how Fitbit or Apple Watch or any of the smartwatches out there that are taking pulses and taking blood oxygen levels today. Just a little pulse of light going through the skin.

Ben: That light will illuminate that sensor under the surface of the skin. Illuminate those for us these molecules and excite that fluorescence molecule to emit now another wavelength of light. That different wavelength, different color that comes back through the skin, that same device that we have on the skin will capture it through a photo detector.

Ben: The photo detector is just a simple, a fancy word for something that actually detects light like a camera would. What traditionally would be film now, it’s just electronic that captures light that comes back. That photo detector on our device that catches the light that actually comes back will process that light signal and be able to get you, based on internal calibrations and calibrations in the laboratory, a value, a number that tells you how much oxygen in concentration or how much glucose in concentration is in that interstitial environment where the sensor sits today.

Ben: Because this is done by light, the old joke is, “It’s very difficult to turn off the light before you actually get into bed while the room is still light.” Light happens very, very quickly. While I’m taking a long time describing this phenomenon, the process of actually how our sensors take a measurement, emit light that allows a photo detector to actually take a measurement, that happens so quickly that you could actually take a measurement two, three times a second without any problems to the system.

Ben: By taking these discreet measurements, but you can take discreet measurements very rapidly, you could actually now piece together a continuous stream of biochemistry data. It’s like taking a blood test every second, every five seconds of your lives without actually having to go through the pain of getting a vial of blood drawn, but getting that same richness and information out. That’s how our technology actually …

Lee: It clearly seems to work with science fiction, and you so casually state it. Does it seem like a science fiction to you, Ben?

Ben: It’s such a compliment, Lee, when you say that when others actually describe that. It doesn’t seem like science fiction to us, only because, one, it’s reality and, two, we understand the science behind this so well.

Ben: We’ve been very fortunate over the lifetime of our company to not only have gotten great support from like-minded investors and individuals who actually … An organization we actually have alignment to the mission that we have. We also have been very fortunate to have gotten support through government organizations such as the NIH and DARPA to have given us a funding support. Actually, to the tune of more than $30 million to date from the government.

Ben: These processes, obviously, are incredibly competitive especially through the governmental agencies. There are grant applications. They’re reviewed by the best and the brightest in the relevant scientific field. They’re competitive. They’re scored and benchmarked against other applications.

Ben: Then we are very fortunate to be able to come out on top. What seems like science fiction in many ways is actually reality and it’s reality based on technologies that have been vetted through very rigorous and publicly-disclosed forums. We’re quite pleased with that.

Ben: I think when we hear this notion that it sounds a lot like science fiction, we feel like we’ve done something then because we understand the technology is sound. We have technology that is on the market and that’s been approved in Europe, so this approach is validated. It’s validated through multiple, very rigorous approaches. It’s validated through multiple very …

Lee: Is that your calendar? [crosstalk 00:23:58].

Ben: It is.

Lee: [crosstalk 00:24:01] is a demanding one. It’s been getting quite aggressive.

Ben: It’s blown up. Sorry.

Lee: I’ll give you a second to sort it out. No problem.

It’s like taking a blood test every second, every five seconds of your lives without actually having to go through the pain of getting a vial of blood drawnBen Hwang

Ben: It’s validated through multiple very rigorous processes and procedures. When we hear the description as a science fiction, I think it just means the word creating something really, really transformative and bringing high technology to life, and that’s very exciting to us.

Lee: It definitely seems science fiction to me. I can’t even begin to imagine even some of the large blocks you’ve needed to solve like calibration. It would seem impossible to get something clinical-grade calibrated at the level you’re talking of.

Ben: Yeah. No. Yeah, let’s talk about it a little bit. It’s such a great question. It’s clearly something that we work very hard at. The challenge of getting a biochemical signal through the skin from inside the body to outside the body has been two-fold. One, we describe ad nauseam, which is this whole notion that if you measure inside the body, you listen to this foreign body response and you have to overcome that. That’s something that we’ve overcome.

Ben: Then the second is this, to your point, this notion of calibration. That’s mostly driven and rooted by our bodies are incredibly complex. To use a fancy word, the heterogeneity of the tissue is such that it’s difficult to find uniformity. Also, my body is very different than your body. Quite frankly, even within my body, the tissue and the skin in my forearm is very different than the tissue in the skin in my upper room.

Ben: To be able to calibrate those differences so that those differences do not compound the signal and what we’re measuring is a very accurate and specific signal in this incredibly complicate milieu of biochemistry and the environment that we’re trying to decode is quite challenging. Approach has actually been two-fold.

Ben: One is we measure, and the way we report the signal that’s coming out through light is fancier that I probably represented. We don’t measure the return light relative to its intensity. In other words, if we were not looking at the return light from the sensor to see how bright it is or how dim it is and therefore inferring a concentration. Because if we do that, then anything that blocks that light coming back being stronger or lighter could confound that signal.

Ben: What we measure is actually what is called a lifetime decay of our specific dye and that’s very specific to our dye. It’s not confounded or diminished by any of the other elements in the skin that might contribute to that signal. We spend a lot of time in the chemistry itself to actually make it so that we can measure the lifetime decay versus the brightness.

Ben: Then the other way we do it is we just have different calibration scheme. For one analyte, we don’t just measure one thing. For one analyte, we measure the contributions of other things around it and subtract or normalize against those other noise.

Lee: It sounds incredible, Ben. As far as I understand, each sensor measures one analyte, but you’re now trying to take one sensor and stock up multiple chemistries so you can measure multiple biomarkers with one injection, with one sensor.

Ben: Yeah. Since we’re doing this by light …

Lee: I mean it seems to be exponentially harder. Am I misunderstanding here by putting multiple chemistries on one single sensor? That seems to be an order of magnitude more difficult.

Ben: No, it actually isn’t. We routinely do two. In our parlance, we call that multiplexing. We routinely do two without any issues and we believe we could do many, many more. That’s just because we’re measuring by light. We’re getting signal by light.

Ben: As long as we could get the emitted signal in different wavelengths, we’re able to take a picture or detect via our photo detector multiple data streams simultaneously. It’s just a difference between taking a black and white picture versus taking a color picture.

Ben: If we can have an oxygen signal that’s, let’s say, giving off one wavelength and then a glucose signal is giving off its signal at a second wavelength, so on and so forth, our photo detector is able to pick that up. There’s one advantage to our approach of using optics to get our sensor’s signal out.

Ben: For those of you in the audience and any for you who probably know a lot about the automated sequencing and how sequencing is actually being done too, that’s exactly the approach. They do one reaction, but you’re able to tease out four different signals from four different basis discreetly all at the same time.

Ben: It’s been done over and over and over again and it will be no different than for us. The ability to do multiple streams on one sliver of hydrogel with one Band-Aid on the surface of your skin is a pretty [crosstalk 00:29:44].

Lee: This show is not by orthodox healthcare, but because this is such a … Well, I’d go back to call and point the science fiction. Though I think you’re going to turn traditional healthcare upside down the long-term once we have implantable and you have continuous access to body chemistry. Because take today, I don’t know how often average American goes to, say, fasting blood glucose tested or cholesterol or other body chemistries, but I would imagine we’re talking years.

Lee: Your diet alone can drastically change your blood chemistries on a week if not by day basis and the choices you make of exercise, not when you sleep for how long, how long you fast. There’s this absurd disconnect between these episodic, delayed, one-off measurements taken by a clinician every few years and what’s going on in your body in response to how you’re living your life and the choices that you’re making.

Lee: It’s hard to see orthodox healthcare surviving that onslaught, which has to take place one day when this is more widespread in the market, do you agree?

Ben: I agree 100%. If I may, let me add more color to that. Our wellbeing and this notion that we ought to be able to leverage technology to allow us to live happier and healthier lives is not something new. Unfortunately, most of the technological advancement that’s occurred over the last 50 years or so have impacted aspects of our lives and really fundamentally changed them from episodic engagement and brick and mortar engagement into realtime decision-making, has really impacted only parts of our lives that are outside of our body but really not within our body. Healthcare is still delivered in a profoundly historical wave.

Lee: Yeah. Institutional. Top down. It’s not networked.

Ben: Exactly. You get the benefit of the healthcare system only when you go visit a physician. During that face-to-face interaction. You get the benefit …

Lee: As you know, it’s also very late in the journey. My last guest, Nathan Price, that’s what we’re talking about. You end up in the healthcare system very late in the journey. You get blood chemistries measured after something is wrong, after you have symptoms.

Ben: Exactly. It’s all in the rear view mirror. Everything is done in the rear view mirror. Very few things are actually done proactively. What happens is it actually forces you to think about the population in two very distinct buckets. Either you’re a patient when you’re under physician’s care or you’re a consumer when you’re not.

Ben: We believe that our technology really could fundamentally change that paradigm, because to your point … Listen, if somebody is developing diabetes and let’s say I am about to become a diabetes patient and I’m about to suffer from diabetes, the day before I go to see a doctor to get a glucose challenge, to get the diagnosis that I have diabetes. Then the date before and the day after, there’s nothing magical between those two days.

Ben: It’s not as if I didn’t have diabetes the day before my doctor visit. All of a sudden, after that, I’m diagnosed with it. I’m most likely have been on the path to become a diabetic patient for the last 20 years. It’s very different than having a broken bone. Before an event, I’m healthy and after an event, I had a traumatic event and I actually have a broken bone. For me …

Lee: Yeah, that’s the same for all chronic disease.

Ben: Everything. For asthmatic conditions, for heart condition and glucose. Having an ability in realtime to actually measure an individual’s biochemistry in a way where it tracks the progression of your wellbeing from something that’s completely healthy, in a state that’s completely healthy to your marching down to this clinical definition. To triggering that clinical definition.

Ben: Having that knowledge base and having that data will allow an individual … Or quite frankly, the healthcare community to be able to engage much earlier to flatten out that curve if you will and prevent the triggering of that clinical diagnosis, which triggers a bunch of expense and then bad things coming down the line.

Ben: I think the vision that we’re talking about, the ability to do that, to have realtime information that allows you to change your behavior or create actions or activities that influences or prevents bad things from happening, I think that vision has been around. It’s driven the digital health wave. It’s driven the telemedicine wave.

Ben: That vision has been around for a while, but what’s not been around and what’s not been available to make that vision a reality is how do you make those decisions, what is the data. What the data set and the clinical so what if you will that when you see a number coming off of your device and that number is 168, what does that number 168 means and what would a doctor do about the number of 168? Whatever it is that you’re measuring, whether there’s 168 steps or 168 milligrams per deciliter of glucose or 168 micromoles of oxygen.

Ben: That number has to be meaningful. It has to be meaningful in a way that links up to a body of knowledge that doctors actually care about so you could get the right advice. On top of that, it has to be done in a way – that data has to be gathered if you will – in a way where an individual doesn’t mind it gathered.

Ben: Another barrier has always been I could get that information, but gosh, it would just … It’s a pain to get it. Either I have to manage the technology in a way that I’m not used to or have to charge something and put it on my body. Or I have to actually do something special and wear something special on me. Because it’s intrusive to somebody’s lives, most of us would not have the compliance or the reason to actually do it for any extended prior of time.

Ben: Then the third barrier is the technology actually has to be accessible. Accessible in a way that’s not just available on the shelf, but accessible in that economically it’s not a huge investment, if you will, for an individual to actually have access to technology. A continuous glucose monitor that could benefit a bunch of people but it costs $4000 a year will likely not get the kind of adoption as if a technology that’s widely available but only cost $80 or it costs nothing but every month cost you the same as a price of a Netflix subscription and something that a lot of people could actually afford and have access to.

Ben: Unless you could hit all three that you gather data in realtime that a doctor actually could care about or a clinical community care about. You could get real knowledge and real meaningful, so what’s out of them. Do so in a way where you’re not putting undue imposition into a person’s life to adopt it so that adoption is not an issue. The person who just live their lives without having to babysit some device.

Ben: Then do so in a way where economically, it’s not going to break somebody’s bank. That anybody could actually afford it. If you could hit all three of those, then I think you could actually bring this vision of changing the way healthcare is thought about. Empowering individuals to be able to make healthcare decisions and decisions about their wellbeing and do so in a really meaningful way that changes how insurance companies, healthcare providers and such think about keeping a population or individual healthy.

Ben: So far, the technology landscape has not been able to come up with something that hits all three of those categories. We at Profusa I think are really proud of being able to lead that way because our technology really hits all three of those categories quite nicely.

Lee: Well, I’m honored to have you, Ben. Ben, I’m not sure if you’re aware of my story, because I mentioned or alluded to it on a few shows now. I won’t cover it, but what I will say is I was lucky enough I accidentally get cheap access to my blood chemistry. No physician in between. That changed my life for the better.

Lee: It’s a good part of why we’re talking today, because once the barrier was gone of having to see a physician and having episodic measurement and being able to tweak my diet, tweak my lifestyle and get the data back relatively quickly; I mean relative to present day. I’ve been able to vastly improve my blood chemistry, improve my sense of wellbeing. Obviously, I’m excited when you’re talking about an injectable sensor that can do continuous tracking of blood chemistry. I hope that in the long run what you’re going to do is you’re going to remove the barrier between people and their own blood chemistry and you’re going to reduce … Oh, in terms of reducing barrier, I hope, one, you reduce the cost and, two, eventually I hope we don’t need medical practitioners in between us now we’re on body data.

Ben: Yeah. Exactly, Lee. I would probably add to the last statement where … Listen, it’s my firm belief that the medical practitioners will always be our partners in this journey. I think what we want to be able to do is not to remove the medical practitioner as intermediary, but rather create a platform where that knowledge base and where the practitioner playing the intermediary and the outcome of that, the insights from that, the decisions from that medical practitioner actually accrues benefit to an individual without that individual going to a doctor or doing anything out other than just living his or her life.

Ben: Right now, for me to get that benefit and for you to get that benefit, you got to get into the car and drive somewhere. I want that benefit. I think these wonderful physicians who are doing the clinical research, who are actually taking care of patients and creating that insight is something that is going to be really difficult to replace.

Ben: As a matter of fact, I want to make them even more productive and better. I want to get their knowledge base in my pocket. I want to get their advice in my year any time anywhere that I actually want. I think we do that through technologies like this. We do that through technologies like this where the advice that comes through your phone is driven by data, but also backed by an algorithm or backed by insights from a physician community that delivers that insight to you without you having to actually call a doctor every time you actually want that insight.

Ben: I think if you do that, if you actually do that, you take away the risk of deploying a technology like this. Yet, you actually could increase the benefit of those interactions across time, across geography and across the evolution, if you will, of disease states or disease progression within the individual.

Ben: Then I think you actually have something. Then I think the epidemics that we actually are experiencing and the high cost and the high cost burden of keeping a population healthy can actually be driven down dramatically, because you become much more efficient at using your most expensive resources and you’re actually much more efficient at deploying your most knowledgeable resources across a broader base of individual.

Lee: I hear what you’re saying, but I think that the healthcare we have today is more incentivized towards a treatment model, i.e., procedures, pills and acute care. I don’t know if you’ve heard previous episodes, but the gist of this podcast is my belief, I strongly find founded belief, after many years of research and conversations that a secondary healthcare is emerging, which I might call healthcare for healthy people or hyper-wellbeing. Or I may say Wellness-as-a-Service.

Lee: I think you’re going to have a separate healthcare and it’s focused on prediction, prevention and optimization, because healthcare today does not do optimization of blood chemistry. It does a very poor job of prevention of chronic disease. It does near and nil prediction. Prediction, prevention and optimization I see as a data science problem. The healthcare of the 20th century type problems.

Lee: I think you’re going to have a secondary healthcare that’s founded upon data science. Brad Perkins, who’s my first guest, I was more along the lines of the machine knows best. More swinging towards the data feeding into algorithms and less people. Brad has pushed me towards a sort of intermediate angle because he’s very strong on what we actually need as a new breed of clinicians who are actually data scientists. This is a long-term view.

Ben: Lee, actually, I cannot agree with you more. If I was inarticulate in my last set of comments, let me try to reorient it a little bit. I agree. I absolutely think that the healthcare paradigm or at least the economics of the healthcare paradigm is going to actually change, and it has to.

Ben: It has to because as we live longer, chronic conditions by definition is just going to grow, and also because the latter stages of chronic disease management are usually the more expensive stages. As we live longer, the cost burden on our system is just going to go up and up if you’re just treating the disease and condition, and so it’s not sustainable.

Ben: We see that today already both in terms of reimbursement philosophies here or there or innovative programs from insurance companies or other payers to try to invest in areas and arenas where prevention becomes more important rather than treatment.

Ben: What’s happening though is there’s a little bit of a chicken and the egg. The reason that the healthcare system is set up this way is because modalities of prevention has never been effective or robust enough to be able to move the needle to say, “Yeah, not only can you create the prevention and invest in it to create good outcomes, but a lot of people are actually going to do it.”

Ben: We’ve never been successful because I think technology … Whether it’s technology or culture, it’s just not been there. A lot of, if not most of the clinical research budget, most of the knowledge-based generation in the clinical community and medical community really is about finding the symptoms and then go on and treating those symptoms and then trying to get somebody better. That embeds into the economics reimbursement.

Ben: I think as folks like you, folks like my colleagues at Profusa and others, start to leverage and think about what are the powers that we can bring to bear from a technology perspective as well as proving out that prevention is beneficial by leveraging this technology. I think actually you will create this following, because prevention is always going to be cheaper.

Ben: If you could create a solution that gets better outcomes at a lower price via prevention or via technology, then you’re going to force the payers of the world. Whether it’s a government, whether it’s a public health agency, whether it’s an individual, families or insurance company or capitated systems, you’re going to force the payers to actually look for more innovative solutions because economically it’s actually works out better for the same, if not better outcome of the populations that they actually serve.

Ben: I completely agree with you. That’s what’s going to happen. When that wave occurs, and I think beginnings of that today, then you will have medical practitioners really rethinking and revamping the way that they practice as well as the way that they’re trained. We see that in certain specialties today.

Ben: The patient population is in the vascular space and so a lot of interventional cardiologists or interventional radiologists are going into vascular surgeon space because that’s where the patient population is. Doctors will change their behavior and major practice based on where the market, if you will, takes them. Where the patient needs actually take them. That will happen organically.

Ben: I think it’s a very exciting time to be in this space, because you’re right, physicians will follow. All I’m saying is the danger of that migration, if you will, is that you leave the intellectual insights behind. That you leave decades of experience or a physician knowledge base or some medical education behind by trying to do too much with the artificial intelligence and machine learning on what the data actually tells you.

Ben: I think your guess is spot on. The way to hit that balance is leveraged each party for what they’re good at. What data science is really good at is look at patterns in large numbers much more efficiently than humans can. What people are really good at is actually drawing the connections and creating the creative so whats if you will off of the dataset and be able to guide the decisions and choices not within the norm but actually outside of the norm.

Ben: If you are able to have physicians give you the input into the algorithm and let the machines do what they’re really good at is punch large number of datasets with that input, I think you actually have a winning formula.

Ben: I never want to cut the physicians out. It’s just using them in a very different way and leveraging their knowledge base to patient populations or individuals who normally would not be under their care.

Lee: I hear you and I understand. I understand that’s the approach taken … Part of my issue is I know quite a few physicians, a family and they actually know very little about nutrition. Most chronic disease, a primary driver, if not the primary driver is actually our nutritional intake for example.

Lee: As you pointed out, these are not injuries you’re getting or a virus or a bacteria. Instead, these diseases are taking time 20, 30, 40 years to build. I just don’t feel that regular physicians are going to work in the domain of prediction, prevention and optimization.

Lee: Some will go along for the ride as Brad Perkins pointed out. I do think there will be some overlap by some way in which orthodox healthcare will try and intervene earlier. I do think you’ll have all of that e-monitoring, et cetera, take place. I think that will happen.

Lee: I think the real excitement is once you start measuring people’s biology continuously, soaking into the cloud, running algorithms on it. Maybe running avatars of people’s biology in between blood draws and trying to precision guide people’s life and environment. Most disease today is caused by our environment. That’s including diet.

Lee: I think what we’re needing is machine software to tell us, “Hey, how to modify the environment?” Particularly for our biology. I’m a big fan of ancestral medicine, but I also understand that we have our own unique, say, genetics and epigenetics that could be better much to our environment. It would be nice if machines guided us towards what is optimal for us. For example, what time to go to bed. What blue light to get. What red light to get, et cetera.

Lee: I just do see that machine-driven world coming together for optimization prediction and prevention. I hope I don’t take you too far off, but I like to pull back. You had mentioned diabetes. I know that your wife had gestational diabetes. Could you tell me about that episode you once mentioned with salad dressing, Ben?

Ben: Yeah. My wife and I were very, very fortunate to have three wonderful children. When she was pregnant with our youngest daughter who’s four years old, Gracie, she was diagnosed with gestational diabetes.

Ben: She was incredibly compliant by the way. She was a great patient. She would check her blood sugar religiously. Try to keep her blood sugar in line so that Gracie could grow to be a healthy individual, which she is. Through that process, she actually learned a lot about herself. One of the things she actually learned was her diet.

Ben: When you suffer from diabetes or when you’re around somebody who actually is suffering from diabetes, there are certain things that are obvious. Do not eat that sugary doughnut. Don’t drink that Coke. What’s funny is there are certain things that are not so obvious. Is that salad okay? Well, it turns out that, that salad is okay depending on the salad dressing.

Ben: Is that sushi okay? Well, it turns out, that sushi is okay depending on what fish and what you actually put with that fish. She have experience where she thought she was doing great by trying to control her glycemic level by going on a salad diet and it turns out that, that Thai salad dressing was just loaded with sugar. Now, it tasted tart and so she didn’t think it was sugar-laden and then her blood sugar went through the roof when she actually came home and learned something about it.

Ben: I think, Lee, to the topic of realtime biochemistry and the insight that it actually could provide for you, that’s exactly it. That’s one of the great use cases, which is it educates an individual on what is appropriate and what is inappropriate and there are landmines around us all the time. There are landmines around us all the time where we may not be making the optimal choices, even though we may think we are.

Ben: I think measuring your blood chemistry in realtime to be able to give you that body of information is what I was referring when I say this information could actually allow you to make realtime decisions and change your behavior and actions that could align your choices to the better health outcome.

Lee: A few more questions here, Ben. I don’t want to overshoot the time we have, but I do have a few more questions. This injector, it’s a unique injection kit. At the moment, it is what I would term a medical procedure. Do you think that will ever get reduce to something someone could do at home? Do you think that could ever become a possibility?

Ben: Yes. It’s not just the possibility, but it’s definitely central to our product development sense. There are a variety of precedents already out there on self-deployment inside the home. Yeah. Somebody could definitely do that to me.

Lee: That’s fantastic, Ben. I had no idea, because the injector looked quite complicated from them, which I saw of it. In terms of cost, I have no idea of cost. I don’t need costs specifically from you. Is this going to trend towards being affording by the consumer like, say, Libre patches are … In fact, I would argue that Libre patches are not affordable or the Dexcom ones. I don’t know where to fit on the price point, but I would like to know if that price point will get “cheap.”

Ben: Yeah. The good news is, as I mentioned before, the accessibility is an issue that we actually think about and talk about quite a bit. Our ambition is that our technology ought to be able to touch the billion people around the world. To be able to achieve that type of scale, you need to be able to have an economic model, or at least a model where technology, where accessibility can be readily achieved and price point cannot be a barrier or shouldn’t be a barrier.

Ben: I won’t share any of the specifics with your audience, but let’s just say that from a Western viewpoint, let’s say in the US, if you’re in the US market or European market or you’re living in the US or living in Europe, I would say adopting a technology like ours will not create any more burden than your ability to enjoy your Netflix or Spotify subscription.

Lee: That fits very well into a Wellness-as-a-Service price point for the average consumer.

Ben: Exactly. Or it won’t cost you anymore than, let’s say, going on and buying a pair of running shoes, which you probably would do. You probably should do anyways if you’re going to go exercise. Or it probably wouldn’t cost more than a very nice meal for two at a restaurant. It will be at a point where price will not be a barrier for anybody who wants to adopt it.

Lee: It sounds amazing. When you say one billion people, it actually sounds achievable, Ben, which seems ludicrous to say sounds achievable, that volume.

Ben: Yeah. We think so too. It’s obviously an ambitious goal, but I think … We wake up in the morning … My colleagues and I wake up every morning and go to work with an incredible degree of enthusiasm because we believe that fundamentally our technology really [inaudible 00:57:27] the way healthcare is actually delivered.

Ben: I think there’s no bigger problem that face us today. I think there is no better opportunity that face us today that we leave behind a world where our children could grow up to be healthier and happier for a lot longer. I don’t think we should ever give up on the hope and the aspiration to bring health and well being to not just the people who could afford it but people who are less fortunate than we are.

Lee: This is very concrete though what you’re doing, because at the moment, since 2008, we’ve been able to measure, say, heart rate or respiration, but we haven’t had access to our own body data, our own blood chemistry, and our food choices massively impact our blood chemistry quite quickly and other lifestyle choices. We haven’t had access to that, Ben.

Ben: Absolutely. Then when you think about it culturally speaking, we all eat different types of foods and have different bent if you will depending on where you live. Unfortunately, food choices and lifestyle choices are also very much driven by the socioeconomic status of an individual wherever you actually are.

Ben: Having an accessible, accurate and deployable way of monitoring individual’s health either through a food intake or environment and measuring the outcome of that via biochemistry, I think, can fundamentally change the way global health is actually viewed. Not just in places like the US, but places like Sub-Saharan Africa, places like more rural India and rural China, where traditionally, this kind of information is actually not available.

Ben: That type of mission is actually … That aspect of the mission is incredibly important to us. Creating technology that empowers the individual to make healthcare decisions locally, and it’s why we work really, really hard to make sure that while there may be other barriers logistically and mechanically, other barriers that prevents these technologies to be deployed that cost should not be one of them. We’re fortunate that our technology is inherently amenable to that value engineering if you will taking cost out.

Lee: To crack that one billion mark, you’re going to need to get the data to the mobile phone, to the smartphone with minimum friction.

Ben: Yes.

Lee: I’ll presume that’s more … [crosstalk 00:59:58] realize that one and that’s to be under way.

Ben: Not only is it under way, it is how the data is actually transmitted. Right now, the data is going from a sensor via light to the read on the surface of the skin and then via Bluetooth to the phone.

Lee: It’s Bluetooth. Okay.

Ben: Then at that point, you could actually figure out whether you’re in control as an individual. Do you want that data to go to the cloud for other purposes or do you want to just keep it local? We, as a company, went down that path because, to your point Lee, it’s very insightful that you have to leverage the phone.

Ben: If you look at the phone industry or telecommunication industry, there’s a really interesting phenomenon that has occurred, which is the deployment of landlines, while that’s the progression of centralized operators to landlines, so you have phones inside the house to cellular communication in the car, to cellular communications in the pocket.

Ben: That migration, that progression if you will in the US and the Western world did not occur in rural India, rural Chian, Sub-Saharan Africa, parts of Africa. They just leapfrogged over the landline phase and everybody has a phone in their pocket now. What a wide deployment of phone, the ease of access to the cellular network and the phones in individual’s pockets, we felt that it was really important for our data to be transmitted that way.

Lee: Yeah. The smartphone is struggling on the front of innovation and that’s why they’re getting similar. They’re getting less exciting. I do see that health is the next wave of the mobile phone. The mobile phone being health and wellness companion and clinically validated in many cases, health companion.

Lee: In terms of measuring that interstitial fluid, what we want to get a handle on is the likes of measuring our stress, our metabolism, diet, dehydration, et cetera. I presume you’re looking at achieving those insights.

Ben: Yeah. Lee, exactly. You touched upon earlier regarding the environment and the technology that we have available to us. Our approach at Profusa is … The philosophical approach is obviously we want to create a platform to unlock the stream of biochemistry data that reside the inside every one of us.

Ben: The higher level approach really is that data ought to augment the other streams of information that’s all around us to help us make much better choices. That what we’re measuring is a biochemical endpoint, but that biochemical end point ought to be married to other data and other information that provides context to what’s happening inside the body. Pollen count, geo location, altitude, activity level, your voice, how you’re feeling by your voice.

Lee: Absolutely, I agree. Feelings or emotion is extremely important.

Ben: That’s right. If you stitch all of that together, then you could actually marry. When your physical behavior or level activities in this way and that happens in this external environment at this time and point of the day that occurs after when you didn’t have a good night sleep which creates some stress as depicted or picked up by your voice. Just the voice as well as the word choice patterns. That links to an end-stage biochemistry that has this biochemical outcome that links to a biochemistry signature therefore means that your physical health is actually declining a little bit. You’re not in the healthy zone if you will.

Ben: That holistic picture makes the body’s data much more powerful because then it becomes actionable. You could either get more sleep. Prevent being in the presence of that environment. Get rid of or change your context or at least warn you, “Hey, are you feeling that context?” Because you know that’s always going to happen, then you know what to do about it. That becomes much more powerful. For us, we look at our data stream as a critical piece of that puzzle, but it’s just one piece of the puzzle that [crosstalk 01:04:36].

Lee: Ben, it’s a hugely important feat, blood chemistry. I could talk about longer and I’m very wary of time though, but blood chemistry has been underutilized. That’s why I was talking about with my last guest. There’s a lot of excitement around genetic test and our microbiome testing, et cetera, but regular cheap blood chemistry is largely untapped.

Lee: The last speaker was talking about applying AI to standard cheap blood chemistry and achieving amazing results for people. When you start speaking about that holistic data picture, which I love and wholly agree with. It’s tremendously exciting when you can capture a person’s movement, gestures, intonation, feeling, mind state and stitch altogether. Then there’s the next stage where you could feed into AI systems to do multivariate analysis.

Ben: Yes.

Lee: Yeah, you end up with machines working out the causes of human disease and the causes of human depression. You actually end up with machines knowing humans better than humans know humans.

Ben: Listen, as I mentioned earlier, we’re building off of shoulders of giants here. I think this entire healthcare industry is an exciting, exciting place. We look at the importance of the genomics data. We look at the importance of the microbiome data because they are important, but I put them in the category of genomics and microbiome information are like atlases, the old … I’m old enough to remember that when I die …

Lee: Yeah, I agree with you there.

Ben: Get a book of Rand McNally Atlas every single year and the realtime continuous biochemistry data is like Waze or Google Maps where it actually creates new realtime information. It allows you to actually make changes in your choices to try to circumvent an accident [crosstalk 01:06:36].

Lee: That’s a good analogy, Ben, and it’s one I strongly agree with also. In terms of blood chemistry, so you can measure like urea, creatinine, what about hormones like cortisol? A lot of people say cortisol dysregulation. It’s kind of timely and expensive to work out when people have such circadian rhythm disruption. It’s affecting cortisol. I.e., they’re not making in the morning and they’re making it in the afternoon or they’re making some at night when they shouldn’t be making any.

Ben: Listen, everything that you actually mentioned are possible as long as they are available in the interstitial fluid like hormone, cortisol. Actually, it would be. They would be very important markets to actually measure.

that biochemical end point ought to be married to other data and other information that provides context to what’s happening inside the body. Pollen count, geo location, altitude, activity level, your voice, how you’re feeling by your voiceBen Hwang

Ben: Here’s the potential power of this platform that people have not looked at. It talks a little bit about what … It touches on how you actually were talking about machine learning, artificial intelligence, what machines can actually tell you.

Ben: It is possible and quite frankly likely that cortisol concentrations going up and down or any hormone concentration going up and down and biomarkers going up and down. It’s like that they’re not singular events. In other words, they’re not moving up and down in isolation. That there are other biomarkers that likely will trace or move up and down in the body as a response to or as a precursor to the hormones and other biomarkers going up and down.

Ben: In other words, that their movements are linked. That you actually get the … It’s a dance, if you will. Biomarker A and B always move a certain way before you see a spike in biomarker C. The likely scenario is as we develop more and more flavors of our biochemistry sensors. As we get more and more information in that we’ll be able to predict and be able to monitor many more biochemical markers than the ones that we actually have to measure. That we could actually infer with high degree of accuracy what other markers are likely to occur.

Lee: I agree.

Ben: Looking at what we have already. We may not have to measure cortisol to be able to get the benefit of knowing how cortisol is moving in the body.

Lee: Yes. I don’t know if you saw

Ben: I have not.

Lee: I’ll put in the show notes and I’ll send you a link. I think you’ll be quite interested in what they’re doing. The final few questions I have for you here. When is glucose going to be in the market? Because that’s obviously … I think that’s number one on a consumer list is glucose.

Ben: Yeah. As you know, Lee, in our industry here, being on the market is really driven by when we could get regulatory approval. What we control is when we actually submit and apply. What we cannot control is when the European regulatory bodies or what FDA is going to say yes and okay. We’re optimistic because there’s clear path of approval here.

Ben: It’s just difficult for me to actually tell when the approval is going to happen because we can’t control what questions you’re going to ask and what they’re going to do. I can say that we are in human studies and human trials right now and they’re going really, really well. We are optimistic that over the next 12 to 18 months, we’ll be able to submit.

Lee: Is that submission in Europe, in the US? Is that CE or FDA or on what by Asia?

Ben: Yeah, it’s both. Most likely, the current way, if you will, or current strategy for most med-tech companies is to seek European approval first and then seek US approval second. You know this as well as I do. The submission process is really not running a race where everybody lines up on the starting line and then there’s a gun and then boom, you start a submission.

Ben: We are constantly engaging with the FDA and the CE regulatory bodies even now to talk about our platforms. To talk about our strategy. To talk about what concerns that they actually may have so that as we develop our sensors and as we actually go in and get the clinical evidence required for the submission, we’re doing so not in a vacuum but with the guidance of the regulatory body. One can argue that through the submission process if you will. The engagement in the conversation is already ongoing. We are engaged with regulatory bodies all over the world for those purposes.

Lee: Should I be watching it in 2019?

Ben: I appreciate you’re keeping an eye on this. I hope you’re more than just watching out for us. I hope you’re rooting for us as well. What were going to make a bit different. Yeah, watch out in 2019.

Lee: Well, I do believe. In fact, I’ll say I know. I’ll go this far. The majority of humans in developed countries will have implantable biosensors for continuous long-term monitoring of blood chemistries. That’s not an opinion. That’s just a given. We can debate the privacy. We can debate the ethics. We can debate how it will be used, but I think it has to happen.

Ben: I agree. I think that demand is going to be there. Whether it’s through the individuals or through the payers or through some healthcare programs. I think the demand is going to be there. It just makes too much sense for it.

Lee: It does. It’s back to that whole car analogy a lot of people give saying, “Hey, a car has 200 sensors operating continuously but we don’t have our own for the human body.”

Ben: That’s exactly right.

Lee: I think you’re creating the future, the far out future. I think it’s a more positive future, Ben. I think you’re aware of that. I can hear that in this conversation. I greatly appreciate your time today and I’m going to wish you the best of luck with the approvals that you need to go through with the company.

Ben: Well, thank you very much, Lee. It’s always a lot of fun for me to talk to you and to folks like you, so thank you for the opportunity for me to share what our company is doing and hopefully this has been productive and useful for you and your [crosstalk 01:13:14].

Lee: It certainly has been. I really appreciate the update, Ben. Thank you so much for your time. Thank you, Ben.

Ben: Thanks for thinking of us. Thanks for thinking of us, Lee. Thanks.