In this episode of the Dr. Joy Kong podcast, Dayan, co‑founder of Luventix and founder of Panaton, explains how his company is developing AI‑powered metabolomics diagnostics that analyze raw urine gas‑chromatogram data (≈38,250 data points per sample) from hundreds of patients without first identifying individual biomarkers. The AI model learns patterns across the entire metabolite spectrum to detect over 50 diseases, offering a holistic, system‑level view akin to Eastern medicine. Dayan describes how the technology can be used to select optimal cohorts and predict treatment response in clinical trials, functioning as a companion diagnostic. He outlines the regulatory strategy: initially pursuing a lab‑developed test (LDT) pathway before seeking FDA pre‑market approval, and discusses the FDA’s evolving stance on AI‑driven diagnostics, including challenges of continuous model updates. The conversation also covers the synergy between metabolomics and genomics, positioning metabolomics as a real‑time snapshot of organism state versus the static genetic blueprint. Finally, Dayan shares the commercialization roadmap—future test panels (GI, kidney, neurological) and the current unavailability of the product—while providing the company website (levantix.com) for future updates.
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Unlike the traditional methodology where you first have to identify the biomarker and then develop technology to detect the biomarker in a sample, we operate on the raw data, meaning that we don't necessarily have to identify the specific biomarker. We look at the data pattern across all present analytes.
It's almost like this merging of the system approach in Eastern medicine and then the cool scientific investigation of the Western science.
Technology works the same way. If you give us a group of people that have particular properties of their organism, we are likely to be able to build a model to recognize them by their metabolic profile.
I absolutely love it. I mean, you're really leading the, the future of medicine, right? This is what's, what's so exciting about how things are going to be very different. Hello, welcome back to the Dr. Joy Kong podcast. You know, my mission here is to make sure that you live a longer, happier, and healthier life. So Dayan is a co-founder of Luventix, is a diagnostic startup using AI to detect over 50 diseases from a single urine sample. He has navigated FDA processes and clinical trials for nearly a decade and is also the founder of Panaton, a health IT firm specializing in connecting different healthcare systems and hospitals and labs and clinics all across the US. So thank you so much for being here, Dayan.
It's a pleasure. Thank you very much for having me, Dr. Kong.
So I'm curious, let's just pick one example, let's just say SIBO.
Yeah.
How many of the markers are going to manifest changes among this 10,000 different metabolites?
We're getting into the interesting part.
I'm just curious about the scope.
We do not know. And that is one of actually of the innovations that we're kind of very excited about and one of the things that we claim in our provisional patent in that we, unlike the traditional methodology where you first have to identify the biomarker, and then develop technology to detect the biomarker in a sample. We operate on the raw data, meaning that we don't necessarily have to identify the specific biomarker. We look at the data pattern across all present analytes, and that is the power of the, uh, uh, sort of the, the AI technology that we get the holistically.
Interesting. I'm sorry, it just dawned on me, this is like, um, it's reminding me of Chinese medicine, right? They don't know exactly what compounds are the problem. But on a system level, they know there's something that has a particular tendency, right? It's almost like this merging of, you know, the system approach in Eastern medicine and then, you know, the, the, the cool scientific investigation of the Western science. So I just, it just kind of dawned on me, like, where if we're really going in that direction, then we are bridging the gap almost.
Very interesting you, you brought that up because Again, my, my chief scientist and co-founder and close friend Martin talks about network biology and how the organism is completely interconnected. Historically and traditionally, we've been looking at diseases and isolated biological system in the organism. The reality is a lot more complex. And so when we are able to look at the total picture expressing itself in a digital twin of the organism, which represent— is represented by its metabolic state. We have a much better opportunity to take into account what we call, uh, the, the full dataset without dimensionality reduction. We look at a gas chromatogram, basically is in our case 38,250 data points. Let's say quantitatively how much of a particular molecular weight we're seeing in the sample. We don't know which molecule is expressed at any point in the chromatogram, but we're looking at all those 38,250 data points from 200 patients that have a condition, and the machine learning models that we build are capable of selecting the data correlation, or the, the signal, if you would, that is associated with the presence or the absence of the condition. The technology actually has additional applications which are also very interesting because you, you mentioned clinical trials. You obviously, you have gone through that and, and lived it yourself, but in clinical trials, you It is very, very, very important to select your cohort, you know, the exclusion inclusion criteria of the people that are going to participate in the trial in such a way that you maximize the likelihood that you're going to get a response to a treatment. With our approach, we can build a model that can predict whether or not an individual is going to respond to a treatment or not. In pharmaceuticals, you know, they call this companion diagnostics. Once a, you know, a treatment is brought to market. But during the clinical trial, if you want to reach your endpoint, you're much better off finding people that are going to respond to your treatment to bring, you know, to optimize your cohort. And so technology works the same way. If you give us a group of people that have particular properties of their organism, we are likely to be able to build a model to recognize them by their metabolic profiles.
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You know, I'm a victim of the Silicon Valley way of saying that we've invented the great next thing, you know, hot water, the wheel, that kind of thing. But the reality is that all of these things are Lego blocks in understanding biology and medicine. We take a metabolic approach. If we marry that with a you know, the genetics approach that people have been pursuing very actively in the last few decades and have heavily invested with, we have an even better understanding of the organism. Because, you know, genetics tell us what we are possibly programmed to do, metabolics tell us what organism is actually doing. It's like the difference between our—
Yeah, so you're referring to metabolomics, right? So like, like what, what exactly is that? Like, because that's a big word.
Metabolomics is simply the study of the metabolism in organism, how our cells produce energy essentially, or, or be behave. So again, you know, the other word that we use a lot these days is multi-omics. That takes all the omics and we combine all the knowledge that we— and all the information that we gather into a bigger, you know, better and better picture of an organism. Coming back sort of down to earth, in reality, we talk a lot about personalized medicine and the technology leaps that we're making in, you know, recently do enable us to get closer and closer to very narrowly personalized medicine, which is a good thing. And make it accessible to, you know, a broader population.
So can people use it already?
No, no, no, it is not available. We, you know, in diagnostics specifically, there's two paths to bringing a new test to market. One is through the FDA process of pre-market approval. The other one is through a process called the LDT, a lab-developed test. We will probably do a lab-developed test first because it's a less complex regulatory path. And so we'll probably do an LDT path, probably with some GI disease or a panel of them, and then we'll continue developing additional conditions, probably in groups of specialties. We may do kidney and urinary tract things next, or neurological, depending on, depending on which, what partners we find first. Bottom line.
Yeah. So has the FDA been pretty supportive supportive of this arena as far as AI-powered diagnostics?
Oh, very much so. It was actually a couple of years ago when the first FDA materials went out for comments on how to regulate, you know, AI-supported diagnostics. It's a very interesting field. One of the interesting things about it is that, you know, the FDA really likes to lock things down, and when they approve something, to be sure that they can control the quality of what comes out, that the drug is exactly the same regardless of which factory makes it, that the test is exactly the same regardless of which lab You know, whether it's a Quest lab or a LabCorp lab or some independent lab performs that test, it's exactly the same. The results are exactly the same as it should be. But with AI-developed products, we're in a different world because literally every 6 months we can improve the model. Just like our colleagues and, you know, OpenAI and Anthropic and Google, you know, now every 2, 3 months there's a new version, you know, there's every 2 months there's, you know, there's better and better models. Very much the same applies to AI-driven diagnostic. We can continuously improve. So the dichotomy there is how does the FDA, how the regulatory body, define the rules of making sure that the next version of the diagnostic model performs at least as well as the previous version without having to go through another clinical trial and 2-year approval cycle, et cetera, et cetera. So how do we build the regulation in such a way that it accomplishes its goal without killing innovation? That's the real challenge. But I think the folks that run the agencies are fully aware of this and highly competent and very much engaged in making these things. I have faith that things are going to work out.
I mean, a lot of people are going for genetic tests, of course. You decided not to go into the genetic direction.
Different branches of scientific inquiry. You know, genetics are a little bit like an architectural drawing. What the house looks like when it's actually built or it looks like after you've lived in it for 20 years, is very different from what the original architectural drawing looks like. And so your genetics are very much the same way. They provide the template, but what actually gets expressed in, in your body is very much influenced by many other factors. And so, you know, think of the, you know, we think of genetics as the predisposition to certain things, but, and metabolics is telling you what the organism is like right now today. Putting the two together is very powerful. Now it's also, I want to be, you know, careful here. Uh, when we talk about genetics, some of the most modern genetics-based diagnostics actually identify the genetic code of tumors or viruses. They don't, they don't rely on the genetic code of the person themselves, but they, they find what is actually expressed, which is actually, again, uh, real-time identification of the presence of, of, uh, biomarkers. So we need to be careful when we talk about genetics, what approach we're talking about.
I see.
Having said that, metabolomics does express the state of the organism at the moment of time much better than your full genome can do.
So thank you for your work. Where can people find you and follow your work and follow your progress?
Our site is levantix.com, and the moment we have Tests available, we will be marketing them. Maybe we even have another, another conversation with you and tell you about the products in the market at that point.
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