BY PETER BOWES | LOS ANGELES | JULY 7, 2021 | 0700 PT

If only we could detect all life threatening diseases before symptoms occur and early enough to reverse the course of those conditions.  We are on the cusp of being able to achieve that.  Significant, recent developments, in the field of genomic testing, mean that scientists are able to pinpoint the warning signs for some major degenerative illnesses, in time to make life-saving interventions. In this episode of the LLAMA podcast, we meet Jo Bhakdi, the founder of Quantgene, a Santa Monica based team of researchers – oncologists, clinicians, technologists, data scientists and business innovators  – who’re working to change the paradigm of healthcare.  In conversation with Peter Bowes, Jo discusses his startup company’s mission of defeating cancer and extending human lifespan by ten years, within the next decade. He also reflects on the exploding interest and progress in human longevity research, which has seen the field grow, exponentially, in recent years. 

Recorded: May 30, 2021 | Read a transcript 

Connect with Jo Bhakdi: LinkedIn | Instagram | Twitter | Quantgene: Website | Twitter | Facebook

In this interview we cover:

• The mission of Quantgene “to combine deep genomics, cloud and artificial intelligence into a new technology stack” to help detect dangerous diseases like cancer.
• Why the ability to get genomic data points from DNA is an “enormous game changer” in healthcare.
• The merging of mathematics and complex numeric problems with biology and medicine. 
• Quantgene’s mission of extending the human lifespan by ten years within the next decade. 
• The business model, cautious investors and taking the technology to the next level
• Taking the tests – what does it involve?
• Understanding how medicine and diagnostic work, plus the value of lifestyle information. 
• What it means to overlay the conventional care system with one based on genomic science, such as Quantgene Serenity.
• What about people who would simply rather not know about the diseases they are predisposed or likely to develop? 
• Tying to convince health systems and governments to embrace new ways to diagnose potential ill health. 
• Adopting a radical philosophy about personal longevity. 
• Leveraging the “natural stuff” to live long and well. 
• Given the current science, what is a realistic, maximum lifespan? 

The Live Long and Master Aging podcast, a HealthSpan Media LLC production, shares ideas but does not offer medical advice.  If you have health concerns of any kind, or you are considering adopting a new diet or exercise regime, you should consult your doctor.

TRANSCRIPT

Jo Bhakdi: [00:00:01] People don’t want to die. The key to not dying is life extension through precision medicine, because only then you know what you’re doing and people are willing to pay for it if you show that you can actually move the needle.

Peter Bowes: [00:00:21] Hello again and welcome to LLAMA, the Live Long and Master Aging podcast. My name is Peter Bowes. This is where we explore the science and stories behind human longevity. Now, changing the paradigm of health care is something I think we have to master so we can share the benefits of longevity research to as large a number of people as possible – populations as opposed to individuals. So what do I mean by that? Well, as many guests on this podcast have pointed out, most health care systems around the world are focused on the treatment of diseases as opposed to the detection of potentially life threatening conditions before symptoms occur. That’s a generalisation, but I think it’s fair to say that we live in a disease care world. Well, my guest today is Joe Bhakdi. Jo is the founder Quantgene, a Los Angeles based startup which has a mission to build a future where everyone is protected against most diseases through early detection. It is a lofty goal, but it could happen. Jo, welcome to the Live Long and Master Aging podcast.

Jo Bhakdi: [00:01:25] Hi, Peter. It’s great to be here.

Peter Bowes: [00:01:26] Yeah, good to talk to you. What is Gene Quantgene?

Jo Bhakdi: [00:01:29] Quantgene is a precision medicine company here in Santa Monica. We also have an office in Berlin, Germany, and some clinical sites in Europe and the U.S. And what we are working on is to combine deep genomics, cloud and artificial intelligence into a new technology stack that helps us detect dangerous diseases like cancer. And beyond that, other chronic conditions as so early that we can actually intervene and do something about it.

Peter Bowes: [00:01:56] And the emphasis the the first thing you said, the emphasis is on individualisation in terms of detection. It isn’t a mass style of detection. I think we all know that we’re all individual human beings with quirks and we respond to lifestyle interventions and to food and exercise in very different ways. And that’s what you’re focusing on, the precision nature of what you do.

Jo Bhakdi: [00:02:21] Exactly. And that’s why we you know, I emphasize the technology stack, the combination of technologies we use, but they enable us exactly to do that. If you combine genomics cloud systems, which is, you know, the Internet and software and artificial intelligence at the center of that stands, you know, cloud and AI tools. But genomics is the data source and what that does in medicine to switch medicine from an analog science where someone just listens to your heartbeat or looks you in the eyes or something to something where you get genomic data points from your DNA is a enormous game changer. To just tell you one example, when we do a blood sample for our early cancer detection system, we are getting 10 billion digital data points out of this blood sample. So we literally get a file with 10 billion data points … And then we digest and process that with a very large computing infrastructure. And these data points are pieces of your genome where we identify variants and mutations on all kinds of genomic fragments in your blood that might stem from a tumor cell. And that level of precision is literally millions of times higher than what’s happening currently in conventional medicine. And this level of precision and this amount of data in the end translates exactly in what you say personalized medicine, because as you can imagine, if you if I get 10 billion individual genomic data points from a blood sample, you will never, ever have a blood sample that looks the same. And, you know, each blood sample gives us a fingerprint, something vastly more deep and complex than a fingerprint about you that allows us to see things that were just completely out of the, you know, our visibility of a conventional doctor. And that’s the centerpiece to massively increase precision and with precision early detection, to see tumors, other conditions so early that they are not life threatening and can be removed or can be fought.

Peter Bowes: [00:04:33] Which is absolutely the point of this, isn’t it, to detect those potentially killer diseases before we even know that we we have them or even have the potential to have them. And I want to dive into that a little bit further with you. But first of all, just tell me a little bit about yourself and your own background, your own education. What brought you to this point?

Jo Bhakdi: [00:04:50] Yeah, I always joke. I got my medical education in my first 19 years from zero to 19 pre college. I grew up in a family with two very passionate bioscientists and doctors as parents and, you know, got, always my dinner table education from my dad and also my mom, you know, I met my dad is still active and as a research professor for microbiology. Yeah, I learned a lot as a kid on membranes and, you know, toxins and proteins and what’s going on in the cell and bacteria and all these things. And I always find it fascinating. But, you know, when it comes when it came to going to med school, I found too boring. I felt like I have heard enough of that for 19 years. I want to learn there must be more than that in the world. So I actually studied economics because I always wondered who paying for all of that stuff. I don’t know. It came natural to me. The question like how does this work from from a research perspective then went into business. And, you know, after a lot of things I did and, you know, in corporate and investing and in technology investing, it all came back when, you know, a family member asked me for help with a genomics problem. I’m a numbers person, right? I did a lot of financial stuff. I always like numbers and complex patterns. And I always liked the, um, the more diffuse and difficult things. So probabilistic models and things like that, which are very useful in genomics. And then they approached me to ask for help with a sequencing problem, which was a math problem ultimately. And after solving that, it struck me as, yeah, carrying a lot of potential in cancer detection. It’s very you know, basically the question was, if you have a DNA in front of you, how would you tell that it stems from a tumor cell or it is a tumor cell versus not? That’s a very complicated question. You have three point three billion nucleotides in a DNA. Some might be mutated, but at what point would you call it a tumor and at what point would you not call it a tumor just based on the nucleotides that you see? It was a question. We I thought we had much better answers and I did a lot of research around it back in 2014. And so there is just no answer. No one did that before to answer that specific question. And so after we had the solution, it struck me as crazy that we could generate that solution. And that solution wasn’t there before because that is the centerpiece of a new company Quantgene, because with that, you can detect cancer early stage if you can tell people based on a DNA strand and on the mutational patterns on that DND strand, if it’s cancer or not, you made an enormous step forward because from there you can do that with blood samples and it allows you to become, you know, to generate a single molecule, precision approach with a single molecule of DNA would allow you to say if you have a tumor or not in your body, which is vastly more, you know, high resolution than a mammogram or something like that.

Peter Bowes: [00:07:58] So the company you mentioned, the time line that the company started in 2015 is now 2021. You’re still a relatively young company, but in that time, you’ve made a significant amount of progress. And I’m just curious about the the business environment surrounding the launch of a company like this. Is there a lot of support?

Jo Bhakdi: [00:08:18] Yes, fortunately, there is tremendous support. You know, when you talk about extending the healthy human lifespan by 10 years within the next 10 years, which is our mission, my mission, but also Quantgene’s mission, you get a lot of support. It’s creating a lot of goodwill. And I’m super grateful for that because without there is always a leap of faith in the beginning, right. You no one can guarantee it works out. So we got a lot of support raised, roughly 22 million so far and made tremendous progress. So it sounds like a lot of money, but in our space, it’s actually not too much money. So we were very lean in building our sequencing labs, doing the R&D, building the cloud and AI Systems, setting up clinical trials, one of the largest trials in the world now in cell-free DNA a cancer detection. And yeah, now we are taking it to the next level, much more institutional. On the capital partner side, there is a whole new ballgame. But I think we have a good I mean, I you know, I have some experience and that’s why I think we we find good pass here. The challenge is always that if you do something very new, which we do, you don’t fall into any conventional pattern or bucket. If you go to a biotechnology fund, normally they don’t understand really what we are doing because they think very narrowly, OK, you are diagnostics company, you need to get Medicare reimbursement. And then you say this little diagnostic piece to a lot of doctors. And if you don’t do that, we don’t know what you are. And of course, we have a much more complex, much more holistic business approach where we build a medical group, where we work with medical groups and build clinical delivery systems. So it’s a healthcare play as much as it is a biotech problem, because it’s all about how can we get it in front of patients and how can we get patients access to that? And in the first step, that’s not insurance driven because insurance is a whole different problem, right? When they would start paying for preventative care, they just hate it because it’s against their business model. So, you know, we have to innovate the business model. We have to innovate the medical delivery system because of the doctors don’t know what this is. You cannot get it to patients. And we have to solve the biotechnology problem, which is heavily cloud AI centric, which most biotech problems used not to be. So you’re crossing all these different things. You’re doing a consumer product, business model innovation. You’re doing a lot of medical innovation, medical processes, and you’re creating a new form of biotechnology that is much more data driven and artificial intelligence driven than conventional. And then you merged all three together into the real business. And then we created probably the most exciting company, in my opinion, in in the world right now with its upside and everything we can do. And that creates a lot of momentum and attention we get. But when it comes down to the details, a lot of investors don’t know what to make out of this new animal because venture investors also innovation investors still have a hard time to being really innovative. They’re like patterns that they have seen in the past. And of course, real innovations never fit the pattern. So because they’re new. So it’s an it’s an interesting suspense where we definitely get a lot of attention and support. But when it comes down, it’s still not easy to take it to the next level. But I think looking good, but it’s not the easiest.

Peter Bowes: [00:11:46] I think it’s fascinating. And you mentioned the perhaps the lack of understanding in the conventional medical world with doctors, perhaps resistance from the insurance company, and again, a lack of understanding perhaps from investors. But these are issues that you have to surmount because ultimately you’ve got to deal with patients. And patients are all very deeply involved in at least the conventional medical world and the insurance world, especially as it applies to the system here in the United States. So you’ve got to kind of close that gap, haven’t you?

Jo Bhakdi: [00:12:19] Yes. And it’s a it’s a very strategic and philosophical question. How do you build this company? How do you keep your eyes on the target, which is a very big target? How can we extend the health human lifespan by 10 years within the next ten years? What is the technology and medical stack we need to achieve that and how can we make it happen? And for me, I am a deep believer in first principles. They allow you to keep the eyes on the target and not get distracted by noise. And my first principle where we align the company to is very simple. People don’t want to die. The key to not dying is life extension through precision medicine because only then you know what you’re doing and people are willing to pay for it. And if you show that you can actually move the needle and if you have something that every human basically wants and every human is willing to pay for it, you don’t need insurance anymore, if you can bring prices down reasonably and if you have a business, even if investors don’t really understand the pattern and it’s too complex for them to understand, what they do understand is, OK, look, we have five thousand people on the waitlist in each of them wants to buy it. And our only problem is we have to streamline the processes and we already have one hundred through. And, you know, soon we have we will have 1000 through. And then we are very streamlined processes because then you can actually bring it back down to business as a general principle from an investment point of view and say even if you don’t fully understand all the details or that don’t match your parents, we have overwhelming demand. It’s profitable and we can scale it to a billion people. So that’s pretty good. And I think that that serves us pretty well to have this big picture. And I think people, you know, start to really get it.

Peter Bowes: [00:14:08] So let’s just delve into what it involves in terms of the use of what you have developed. The technology and I like the analogy of a fingerprint essentially being the end product or at least the detection stage of what you do, very individualized to a person. Can you explain to me from a patient perspective interacting with what you do? What does it involve for the individual?

Jo Bhakdi: [00:14:32] Well, if you decide you want to do that, we have you know, it’s right now we are trying to get to a monthly pricing here. So it’s going to be two hundred twenty five dollars a month or something. Roughly. It’s in it’s an annual checkup that you do and that goes on for some time. So the way you do it is you sign up and then you get a first intake form where you start entering medical information about yourself. That is very important for the data system, then you will have a counseling session with a genetic counselor and a medical expert to explain how it works and what it is and also informed consent that you understand, OK, give you if you if you do genomics on you, there might be findings that you don’t like and that create anxiety. So are you OK with that or do you not want to know certain things? So there’s a certain, you know, informed consent. Very important. Then you will get a kit where you basically collect saliva sample for doing whole exome sequencing. So part of the service is also, which is very variable to look at all your twenty thousand health on regular genes and all variants we found on germline. So on your hereditary side and then on top of that you will get a blood draw where a nurse comes to your home, draws three tubes of blood, sounds a lot, but it’s not a big difference to one. You’re sitting, takes 20 seconds longer. And then we have two laboratory processes, one where you saliva is being processed for your whole exome ad, for your germline genes. We look through 30 million locations on twenty thousand genes, busy your entire genome that is coding. And in parallel, we do what’s called a liquid biopsy, which is our you know what? We took a long time to develop where we get these 10 billion data points out of your blood. And once we have these results, they go into the serenity engine, which is our cloud system and the AI where there’s a lot of data crunching going on. So we run your sample against very extensive cohorts of clinical trial samples that we generated. So we can basically take now your mutational pattern that we find in the blood with a single molecule precision. So we know at that specific location we found two and a half thousand copies of DNA in your blood cell, free outside cells that stem from cells that have died, including potential tumor cells. And out of these two and a half thousand copies, let’s say three carry a variant that is associated with cancer. That’s not a bad thing necessarily, but that’s good to know. And then we collected over thousands and thousands of locations. This exact variant frequencies that I call like is zero point one percent is something. And then we ran this entire pattern against these cohorts. And see what type of pattern do you have that said, look, most like early stage pancreatic cancer, for example, or does it most look like normal people or does it look like pancreatitis also and not just cancer? Does it happen like it does look like an inflammatory, inflammatory condition or what? What is it? And that’s a probabilistic thing. It’s very complex. Right. So there’s no clear answer. It doesn’t say, oh, you have that cancer or you don’t. It says, well, after running all these things, here’s how it looks like. And then on our side, they are trained physicians, genomic specialists, medical specialists who start looking at that like, well, Peter’s pattern, you know, there are two orange flags, no red flag, but two orange flags that are a little concerning. And then they take into consideration the other two data dimensions, which is your your whole exome, what is your hereditary, you know, condition and predispositions and the medical intake. Are you a cancer survivor? Did you have cancer in the past? Are you currently diagnosed with cancer then? It’s a whole different thing. Then we look for different questions. Or are you at high risk? Right. Are you a heavy smoker, obese and all kinds of family history? And then we overlay all these things and we look at your current patterns, but we also understand your risk predisposition genetically and we understand your risk profile medically just on your side. And then if you put this all together and have a lot of statistical models that are hopefully smart, you get to your orange flags or red flags or green flags and they are being interpreted by trained physicians. And they say, Peter, here’s the situation. We think you right now, there is no reason to go into any downstream diagnostics. Right. So we do not recommend a colonoscopy outside your normal colonoscopy. We don’t recommend abdominal ultrasounds or CT scans because we based on these results, we don’t think there’s a reason to do that. But we see two orange flags. What about putting you a little bit on a more strict nutritional regime to see if we can actually bring this down? Because we saw on your medical intake that you eat two steaks a day and drink, you know, two whiskeys so that and there are flags we see could implicate that you have colon mutational issues. I’m just making this up right now as a case, so they could say, why don’t we cut down a little bit on your stuff, increase a little fibre, get a little bit more rid of the alcohol and do a test again in six months and see if we see any changes. So I told you basically more a complex case. Normally, you know, most cases are green and they say, well, it’s looking pretty good or red. It’s like, well, you definitely should do an abdominal ultrasound here to see what’s going on. But it becomes a very high resolution game. And that’s exactly what we want. We want higher resolution, highly personalized precision medicine based on billions of data points across three very orthogonal dimensions to see, you know, what’s going on, to give you a much, much more rewarding medical feedback than your conventional primary care docs saying like, oh, Peter what what’s your problem like? I don’t know. I don’t have a problem. I just want to know if I have a problem. I don’t know what to do with you. Go home. So that’s the idea to provide patients with a much deeper inside of what’s actually going on.

Peter Bowes: [00:20:54] So crucial to the analysis of the data that you are managed to this picture of someone, this fingerprint that you’re managing to build, crucial to the analysis of that is external information. It is the knowledge of the lifestyle of the person, as you said, whether they’re eating two steaks a day, whether they perhaps spend too much time in the sunshine or too little time in the sunshine, that’s crucial to giving the patients something meaningful for them to go away with.

Jo Bhakdi: [00:21:21] Absolutely. And there is another thing here that is very important to understand. So glad that you brought it up here. That’s because most people are not fully aware of how medicine and diagnostics actually work. So if you do a conventional test, a PSA test, someone came up with a number and says, well, if it’s seven, it’s bad. If it’s three, it’s not great. But you’re below a threshold. That is the old school old way of doing medicine that is highly problematic in a precision medicine world because it is not reflective of your true personalized risk. And in order to make sense of any result, you need context. So the human brain does that right. If you, for example, say, you know, if you are scared of terrorists and you try to spot terrorists and OK, this what is the pattern of a terrorist? Well, behaves like a little awkward, looks away and tries to not be seen, but looks like very aggressive and make some aggressive moves. OK, fine. We get this pattern right. If you just have that pattern and just look for that pattern and you see that pattern and have no context, contextual information, you know you will. Next thing you know, you arrest like a two year old baby girl because she did that. But the contact will say, no, stop. The pattern is true, but only for like young men or something between 18 and 25. And say oh fine that was important to know. No, don’t arrest little baby girl. They’re unlikely to be terrorists. So our brain does this automatically. It’s never just one pattern. It’s always an overlay of many parents. And what that means is if we get a liquid biopsy sample, back the question if that’s a red flag or an orange flag or maybe green. It’s not just the liquid biopsy pattern that we get from the blood. You need to overlay your other risk profiles. And for the statisticians or math people who are listening, in the end, it’s a question of positive predictive value. The question is, if I do something with you, you want to know only one thing in the end, what are the odds that I have a problem? Is it one percent or is it 90 percent in in order to determine these odds? It’s kind of a Bayesian problem. It’s not just one test is the test. Yeah. And then the probability, if that test is positive, that you have a problem, not someone else. And an important question to determine that PPV this positivey value is before you do the test, what is the underlying risk that you have because of that risk is very high and the test comes back positive. The probability that you have something is much higher than if your risk is tremendously low and the test comes back positive. It’s like pregnancy tests. If you again, if you do a pregnancy test with five year olds, five year old boys, a bunch of them eventually come back positive. It’s just a question of diagnostics, right? The odds that the five year old boys pregnant are just zero. So it doesn’t matter if it’s positive or not, it will be zero. But, you know, if it’s a twenty two year old girl, it’s just higher. That’s what people sometimes overlook. And a pregnancy test is a great example. I mean, what are the odds you’re pregnant if you have a positive pregnancy test? I tell you, it is important. Who took that test, 80 year old guys. It’s very unlikely they’re pregnant, but it’s definitely possible that the pregnancy test is positive. So that is why it’s so important to overlay these things into it statistically sound and know. OK, if you have a BRCA mutation germline, so if your normal genome has a  BRCA variant so your odds of getting GYN cancers as a woman is very, very high, 80, 90 percent in your life, then, you know, when something comes back flagged in liquid biopsy, it has a very different meaning from someone who is at a very low risk. And the same is true for your lifestyle and so on. So normally in normal diagnostics and biotech, the biotech people don’t deal with these problems. They say we don’t deal with that. We give you a tool. You are the physician. You have to figure that out. The problem in this new world of extreme precision medicine is if you a conventional physician, you are not trained in artificial intelligence and cloud systems and genomics. You have no chance getting that right. I mean, what are you going to do, read through 10 billion data points and then think about what it means is just not happening. And so that is the gap. That is what’s holding precision medicine back the most, that there is no company that integrates these different questions and makes sure in the end we, the patient needs to know if there’s a problem or not. And if you don’t get there, it’s useless.

Peter Bowes: [00:26:05] And that brings me to exactly the point that was going through my mind, as you were just saying, that let’s say there’s a red flag, an orange flag for pancreatic cancer. That clearly is very significant for the patient. And as we as we all know, pancreatic cancer is an extremely serious condition and needs to be looked into further. That patient is dealing with a doctor in the conventional medicine system, to use a phrase, and it’s a concern of mine that that framework that they’re working in already isn’t geared up to fully understand what you and what your diagnostics are telling them.

Jo Bhakdi: [00:26:42] Exactly. And that is why we designed the whole system as something that goes far beyond the diagnostic. It goes into what we call serenity, the future of medicine, where you overlay our conventional care system with a much more sophisticated, much more advanced system of medicine that we also design so we do not replace the conventional system. You’re still going through the conventional system, but there is a layer above that that you get as a client where we steer and oversee what’s happening. We can’t control it directly. We can’t force people to do stuff, but we can get them information and get the patient information. We can say, well, maybe your primary care doc now is very confused because they don’t know what to do next because there is no early detection of pancreatic cancer. Therefore, there is no good known downstream diagnostics path. Right. If you say, oh, it might be pancreatic cancer, then most people don’t even know what to do. You know, OK, what do I do? An ultrasound endoscopy? I don’t know. Can I just send you there should be do a protein test. Many people are not totally clear what to do because that whole system doesn’t exist. So then at that point, if you are a customer of Serenity, we guide you through that process and we guide your physicians. So that is very important. And we are also, you know, already working on more downstream products, because if you if you actually then get diagnosed with cancer, that’s a turning point also for Serenity. Right? We we cannot provide for the same price like a full on oncology advisory, because it is just we need to be really on top. You need to then go a little out of your way to make sure this works. But we we always develop technology and clinical in parallel because we don’t want to throw technologies out and then no one knows what to do with them. You need advisory. You need medical advisory on top of the tech, all the way from early detection to a high risk screening to diagnosis to post diagnosis and oncology treatment, for example. And we are building better both paths out. And that’s why I’m so excited about Quantgene. I think that is there is a game changer because no one does that right now. Our technology stack expands across the entire course of disease. It’s kind of an end to end solution from detection or early detection all the way to treatment and hopefully remission, because once you are in treatment for a cancer, of course, the game doesn’t stop. Then it starts like the precision medicine game. Do you want deep insight into your mutational profile? You want to understand the AI base? What are the best combination treatments? Based on my unique tumor profile, all of these things are absolutely immature right now. If you go to any standard hospital, like often, they don’t even sequence you. They don’t even know your mutation profile. If they do, they don’t know your germline, which is very important. That can also inform treatment. Even if they know all of that, they don’t know always what the best treatment combination is, because that science is also getting very complex. Right. What if you have a target, you have breast cancer, you find one target, a triple negative or something, and then that informs an FDA recommended drug for breast cancer. But what if you also found two additional mutations that are known in another cancer in lung cancer and lung cancer to block that drug, but it’s not approved for breast cancer, right? Normally it’s like whatever, it’s breast cancer, just give it up anyway. But on a molecular basis, you know. Exactly. It cannot work if it doesn’t work in lung cancer. It also cannot work in breast cancer because the drug is getting blocked by a protein that is definitely found now to be mutated in breast cancer. So there’s just no doubt it’s going to fail. But you get this drug anyway. And if you get chemo drugs that are guaranteed to fail, that’s not good, but it’s just not yet and standard of care because it might have been found three years ago. And it’s not in standard practice yet. But you don’t care. You’re the patient. You don’t care if it’s standard practice. You don’t want to die and get a chemo drug that will fail.

Peter Bowes: [00:30:41] Right, exactly. Because the patient is is interested really only in the final outcome.

Jo Bhakdi: [00:30:45] Exactly. And so a normal oncologist is vastly outnumbered by the science and complexity. Right. They can’t do all that sequencing. They can you can’t even read these things up because they become a combinatorial problem. Like there is no specifics there. There’s one study showing that drug will fail. And the other thing shows that works if you have the other mutation with their drug and the FDA approves it. So how do we even know that in another cancer, it’s shown to fail, right it’s getting very complicated? And then let’s say you read upon it and say, OK, not that drug, OK, but what then? Like, what is the alternative? So that is why we have AI systems in precision oncology that help us to actually determine that they read twenty five million publications per every two weeks. Good luck doing that as a doctor. And they understand what they mean. They weigh them mathematically and they look at the combination treatments and then give us a list, a ranked list of the best likely match for your full mutational pattern in the tumor, but ninety nine point five percent of oncologists have never heard of that because it’s new. It’s it became possible basically in December last year. So, you know, that’s why you want, in the end, the precision, you know, companies that are specialized on getting that all in, digesting it and giving you that these kinds of insights, especially if you have cancer, but of course, also in early detection. That’s important.

Peter Bowes: [00:32:15] You’ve indicated and it’s clear just listening to you how excited you are by Quantgene, the work that you’re doing and the potential of this technology and this area of science. And to me, it is fascinating. It takes preventative medicine to an entirely new level. I’m just curious. I don’t really know the answer to this. I’m just wondering what proportion of the audience perhaps listening to this conversation or similar conversations are equally excited.  And by that I mean, I think there’s a certain section of the audience and by audience, I mean populations around the world who simply don’t want to know. There will be people listening to this saying I’d rather not know to that detail what’s actually happening in my body and I’d rather just get on with my life. Now, clearly, there is the potential to tell them a tremendous amount. But I think you’re perhaps coming up against human nature with some people.

Jo Bhakdi: [00:33:10] Well, I think that’s an excellent point. I think there’s a very simple response to that. There are two types of knowledge. One is the old school. I’m saying old school, even though the vast majority of physicians are not there yet. So for them, it’s the future. For us, the past is just germline, risk predispositions. So it was like, OK, do I really want to know that I have an 80 percent chance of getting Alzheimer’s before 60? Well, probably not. That’s a very big burden because you can’t do anything. It’s not happening now. And you’re basically screwing up a little bit your life through that knowledge because there’s no a pathway of action. You can prepare yourself for it, but you don’t want to live a life. Are you prepared for, you know, Alzheimer’s before sixty? I mean, do you have much more fun things to do?

Peter Bowes: [00:34:01] Because you’re looking you’re living your life. You’re looking at the potential systems, you’re looking at the potential symptoms every day. I mean, clearly memory loss and just stressing yourself, You would think.

Jo Bhakdi: [00:34:12] Exactly. So that’s the first type of genomics knowledge that is scary. And I totally understand why some people don’t want to know that. The second type of knowledge is, OK, we can put a screen in extremely high resolution genomic screen over your body every year to detect any form of tumor at the earliest stage so we can send you to the hospital and you will be cancer free in one week and never deal with it again. With that cancer, it’s very hard for me to see why you would not want to know that. So, you know, it’s basically one is one is like, oh, you know, if you think in bigger terms, it’s like telling a nation, OK, there will be an asteroid and you will all die next year. You can’t do anything that’s not very useful knowledge. Well, maybe it is, but I don’t I wouldn’t like to know that. The other thing is, I know there’s an asteroid coming and we can we have three options on the table, but we only have one week to act. That’s knowledge where everyone’s like, yeah, I’ve got to really know that knowledge because I don’t want to be in this first situation. I want to be in the second situation so we can take action and avoid the problem. So that’s how we see it. Like at Quantgene we want to take that whole genomics game to the next level through liquid biopsy, for example, very different from 23 and Me and these systems where we are fully action driven. Right. We only design products and solutions to keep people safe, not solutions to just tell people terrible things that are going to happen where you can’t do anything about it. And that might be sometimes a side effect. That’s why you get informed consent and why you can opt out of these kinds of information pieces. But we are really focused on the other side of things. We want to make sure if there’s anything going wrong in your body that you want to know this much, much earlier than you would normally know. No trace down the problem and remove the problem.

Peter Bowes: [00:36:13] Let me ask you this, Jo. You say you and your company have a mission of extending human lifespan. And with lifespan, of course, comes Healthspan as well, extending it by 10 years within the next ten years. So if you assume that and acknowledge that the average lifespan, at least in the Western world, is about 80 years old, 79, 80 at the moment, do you think it’s realistic that by 2031 we will be looking at an average lifespan of about 90?

Jo Bhakdi: [00:36:39] I think it’s absolutely realistic with the right systems in place, and I think it’s absolutely realistic to design these systems and make them work. Is it realistic to scale this to the entire population by then? That’s clearly not realistic. So they are aspirational things in our mission and they are realistic things in our mission. And my approach here is, well, if we can get to a point where we show statistically that we extended the healthy human lifespan of the Serenity customer base by that by then or hopefully five years within five, I’d like to show we are on track that helps us build the firepower to bring it to everyone faster. So I think I always think as a technologist, it’s like a little bit like Tesla. Right. Is it realistic that all people have amazing electric cars in 10 years? Of course not. But can you build something that demonstrates in very stark terms to everyone that it’s absolutely possible? And the only reason you don’t have it is because he didn’t buy it yet or someone else didn’t make it available. That’s what we want to get it. We want to have overwhelming evidence and demonstration that this is technologically possible. And in 10 years. And in order to demonstrate that there’s only one way, you need a million or two million or five million customers who all live 10 years longer than average, which is statistically very easy to show if it’s true. And then you have something where, you know, Medicare and all all the people who are normally resistant to much innovation can’t resist it anymore because it’s too outrageous. It’s like, OK, fine, really. You are not you’re not going to pay for this. So that’s what I learned. I mean, that is, you know, if you go to all these insurance systems or governments and try to convince them of something smart, you just have no chance. Right. It’s just not how the system works is they have very different incentives, very different thinking patterns. So you can’t just go there like, oh, look, we keep all the patients say, oh, fine, no problem here. So you need to find a much more savvy model. And I think the savvy model is OK. We build a more perfect system of precision, preventative care ourselves. We find people who are willing to pay 200 bucks a month for it. We are building this system out with them, train the system and make it really, really good. And then we start a campaign, a public campaign around that and say, look, guys, here’s the evidence. We build a clean new system and we can make the system work. And after two years, which will look on average, we already had one point five years extension compared to the comparison to, what, in five years, maybe four years and in nine years, maybe a nine point five years? In 10 years. Ten years. And they say, look, look what’s happening. What are you going to say about that? Like it’s time to bring this to everyone. So that’s kind of the strategy here.

Peter Bowes: [00:39:32] And presumably the younger the better for people to undergo these deep diagnostic tests, because presumably, if you’re learning things about yourself in your 20s and 30s and repeating the test as you get older, then you’re clearly much better placed to extend your your healthspan and your lifespan.

Jo Bhakdi: [00:39:50] Yeah, absolutely. I think from a tactical medical perspective, of course, 50 plus is the most recommended age because then it gets kind of more serious in terms of probabilistic, you know, frameworks, you’re just more likely to have a problem, but you’re absolutely right. If you start this with 35, you will have a much more robust data set because something we didn’t discuss statistically, this is about combining these three different dimensions to get to actual positive predictive value, but it’s also building true personalized precision medicine. So we need your baseline. If I compare mine with yours, it’s helpful, but it’s not as good as comparing mine with mine last year. So the more you’re building up this base, the more you investing into having a really sound system of statistical evidence for yourself.

Peter Bowes: [00:40:42] And let me ask you from a personal perspective, from everything that you’ve learned so far in your career with an eye on your own longevity is longevity, first of all, something from your own perspective that you think about and what you will be like your state of health will be like in in the decades to come. And is there something especially about the science that you maybe think about every day and apply to yourself with your own lifestyle?

Jo Bhakdi: [00:41:06] For sure? I mean, that’s not a I’m yeah. I might be on the extreme end of things here, obviously, I think. But is more than a lot three things. Number one, I believe in what we are building at Quantgene. I think that is the first lowest hanging fruit is to have precision diagnostics on a genomics level and AI driven because that’s the first low hanging fruit. How to extend is life about 10 years. If you ask me what our plan is after this decade, I think then it goes much more into active life extension, which is more proteomics space, where it’s more about actually inventing things you inject and stuff like that to reset your body. That’s going to be also very quantitative science. I have I think we have some very good ideas there, but this is going to take a few years before it becomes more serious. So that’s the second dimension. I think there are things how you can bring it from 90 to 100 or 110 that will require a whole different technology stack and it goes beyond diagnostics. And then third, for myself, I mean, I have a very I have a pretty radical philosophy. I understand that the idea that you can inject something or eat something specific right now that extends your life is not true. I think we will have that in the future. But to design that is not something we can biohack or something. It needs to be a very deliberate, very extensive R&D effort. That is pretty clear to me at this point what it needs to be. But it’s not going to be easy. It’s going to cost a lot of money and be a little complicated. So there is no trick to just eat something and then live longer. So right now, what we can do is two things. First of all, precision genomics and like early detection, because that gets rid of a whole set of big risks. And second, leverage your natural powers as much as possible. So the unnatural stuff is simply not available right now. So I would forget about that. So it’s really about leveraging the natural stuff and the natural stuff avoid the four big problems, right? Metabolic disease, cancer, cardiovascular and of course, neurodegenerative. In my opinion, cancer is a special case where it’s all about early detection. The other three are lifestyle, nutrition and exercise, all three of them, plus a little infectious diseases maybe. And on that side, I mean, you know, I live in here in L.A. everyone’s about micro dosing, mushrooms and all kinds of stuff. I am not the biggest believer in that. I think you should neither. You should like medium dose chicken breast and broccoli. And, you know, you should you should eat healthy. And, you know, in other things like this body positivity thing is also horrific for me. Like, people don’t even understand what being obese or overweight does to your body. It’s just, is like a perversion of what? I don’t know what what went wrong with people about to tell people it’s fine to be obese. They’re just crazy. I mean, it’s like telling people it’s fine to jump off a bridge. It’s like, well, it depends what fine is, but it’s not healthy. And, you know, I think to be absolutely on top of your game and take this extremely seriously is we are you know, we are the life extension is just the you need to be happy, socially balanced with friends and family. You need to have very, very good nutrition, which means just very normal natural nutrition, whole foods like the whole thing. Everyone knows the trick. But, you know, you have to go with it and exercise. Not crazy, not like marathons. You can do that if you want, but it’s more about a balance exercise and I think don’t go crazy and radical without. You should be radical about balancing these things out. You should have radical balance and not accept being in a bad mood, bad shape, bad energy. So we need to all become a little Californian about it, without the drugs. And I take this extremely seriously. I’m very hypochondriac when it comes to my nutrition and basic well-being. Not that I’m great at it. I’m just I’m very obsessed with it. I’m a little stressed because we’re running a pretty complicated thing here. But I know every day I’m stressed or it wrong, it’s going to, you know, kill me a little sooner. I have no illusions about that. And I think that’s very important. We have to understand that.

Peter Bowes: [00:45:37] I think it’s fascinating. And just a closing thought.  I’d be intrigued about your view on this. What do you think the in terms of aging and a maximum lifespan, what do you think ultimately the human body is capable of?

Jo Bhakdi: [00:45:50] I think we can relatively easily push it over 90 with just being very healthy, being on top of our game and having all kinds of advanced diagnostics and early detection. That’s where this brings us to. If you want to push it over 100, I think you’re running against a very natural problem that is around 100, 110, where you just die to break through that barrier. I mean, it’s a hundred percent possible, there’s no doubt about that. But in order to do that, there, you need like severe interventions. And that means. I mean, it’s out of question in the end, you need some magic elixir that you inject in people that will do the job. The big question is, what is that? And, you know, I think it is about resetting your there is also no doubt that death is artificially designed because you have organisms that live forever, no probablem to live forever in theory. So your body is kind of designed to have a certain lifespan. It’s very clear why? Because evolutionary it’s a total disaster if you don’t die. So you had you need the turnover. You need to get rid of the old genes and give the mutations a chance. Otherwise you become non adaptive and just die of causes as a species. But of course, humanity moves into this whole new thing we are doing to not like this whole game is done now, there’s a new paradigm, of course, humanity can adopt genetically easily very soon. Like synthetically, if you think that’s a good idea or not, it plays no role because it’s definitely going to happen, then we are pushing into that new area where you can also talk about much more extensive life extension. And I think it’s all about understanding this aging clock, which I think we sufficiently to some extent understand. It’s a systemic clock. It’s not one thing. It’s the entire system situation, which is the entirety of all your proteins and all your fats and lipids that are in your body, the exact quantities and constellations and that informs a lot of things. And if we learn how to reset that clock, which will be very complicated but possible, then it will work. And we already have the mice experience, parabiosis, you know sharing blood streams and suddenly things happen because that’s exactly what that does. If I inject you with young blood or replace your entire blood with young blood, you will feel amazing. There is just no doubt about it and you will look much younger. Everything will be much younger because it’s not really doable. So but we tested with mice, so solving that problem will not automatically lead to eternal life. Right. you still have degradation and stuff, but that will give you another 10, 20 years or something and then you can go from there.

Peter Bowes: [00:48:42] Jo this has been a fascinating conversation, you’re doing some really good work. Thank you very much indeed.

Jo Bhakdi: [00:48:48] Thanks a lot, Peter. I appreciate it.

Peter Bowes: [00:48:50] And if you’d like to delve a little deeper and there is much to dive into, I’ll put a link to Quantgene in the show notes of this episode at the Live Longer Master Aging website. That’s LLAMApodcast.com LLAMApodcast.com In social media you’ll find us at @LLAMApodcast. You can contact me @PeterBowes. The LLAMA podcast is a Healthspan Media production were available on all of the major podcasting platforms, including Audible at audible.com. You might listen to books there. You can also download this podcast free of charge. Wherever you find us, take care. And thanks so much for listening.