SaaS Founder Interview with Sungwon Lim, CEO & Founder of ImpriMed

Tony Zayas 0:06
Hey everybody, welcome to the tech founders show. It’s Tony Zayas here back for another week. I’m joined by Andy Halko. Andy, how’s the weather out there?

Andy Halko 0:16
Pretty nice. Can’t complain. Gotta enjoy the summer. Right? How are you doing? You’re sure doing well doing well.

Tony Zayas 0:20
Air conditioning on? Uh, no. I actually just have some windows open. I was outside a little earlier this morning, but moved inside just in case there’s birds or something crazy going on out there to disrupt the that happened with cast. Yeah, so Well, cool. We have what do we have today? Yeah, so that’d be it’s an area that we have not yet. But we’re talking to Sungwon Lim, he’s the CEO and co founder IMPRIMED. And they provide precision medicines for pet cancer care, which is pretty awesome application of technology. So with that, Sungwon how are you doing?

Sungwon Lim 1:00
Great. How are you guys? I’m doing very well here in Palo Alto. Nice weather here. Yeah,

Tony Zayas 1:06
I bet. Very cool. You guys always have nice weather. And

Andy Halko 1:11
we’re hearing that we’ve got the rain here, which I don’t know if you’ve got as much out there.

Sungwon Lim 1:18
Yeah, yeah. Well, you know, mostly it’s good. You know, too hot and the sun shot sunside. But you know, when you go to the shade, it’s always cool.

Tony Zayas 1:27
There you go. So very cool. Well, thank you for joining us, we’d love to hear just tell us, perhaps the origin. I mean, I share just the very short description of what IMPRIMED does. But tell us about the business. And we’d love to hear the origin. Where did it start? How did you come up with the business and take it to market?

Sungwon Lim 1:48
Sure, happy to do them. Like, first of all, thanks for the invitation. I really enjoyed your show. So this is great opportunity for for me to share our story to your audiences. So actually, funny, you you made a you know, one great sentence to describe our company we are providing a precision medicine for our dogs and cats suffering from cancer. So it’s I know, it’s a kind of refreshing idea. It’s a new application of well, AI and biology to to our pets. So there are so many pets out there who are suffering from cancer, just like human, actually, there are more number of dogs and cats who are who, who are, you know, the suffer from cancer, for example, like, actually, there is a statistics that 6 million dogs and 6 million cats, they are diagnosed with cancer every year, compared to 1.7 million human in the US are newly diagnosed. So it’s a huge number, in total 12 million patients out there. So the how we start this idea is that, you know, I am personally very passionate to cure cancer. So I spent before this implement, I spent more than a decade to develop a new cancer therapeutics, we noble cancer drugs, something like, you know, we are now we are all familiar with a gene therapeutics, something like, you know, mRNA, you know, our nine advisor, you know, it’s it’s all mRNA is one of the gene therapeutics there, of course, it’s a vaccine. Well, I like you know, 10 years ago, you know, not around like 10 years ago, was very passionate about legit therapeutics, and then also I did a PhD developing protein therapeutics for to cure cancer. All these kinds of journey was really, really exciting. You know, you can imagine that, you know, of course, it’s for mice, but we, we humans are using a mice model in the lab, you know, we inject the human cancer in the mice and a big chunk of tumor there. It’s amazing to see that the drug there, I develop in the lab with my pipetting and then make the drug and inject into the mice, and then you can see that the tumor size gradually shrink down. It’s amazing, one of the amazing things that you can actually see, I really enjoy this kind of things. And then I always imagine that how, how it will be amazing if I can bring this drug into the patient bedside after that 10 to 15 years later, that because there is average it takes about 15 to 10 to 15 years from the design to the drug. But it’s actually takes longer than that this is a occasion for a big pharmaceutical, like you know, GSK and then all the other like Pfizer. But, it’s kind of kind of ironic that I was really, really enjoying this kind of new develop cancer, drug development side. But more and more I go deeper inside this field, I always ask my self, are we really really doing really the best thing to use our currently available drugs because there are so many people out there who needs the treatments today, they don’t have time to wait for like 15 years, and then you know, it will be a really, really expensive drug, once it comes comes to the market as a new drug. So most of the people, you can see that they are already you know, they aren’t they don’t have time to wait. So they always trade are traded with the convention, like conventional drug, or what are the available in the clinic right now. So my just like I more and more, I just like obsessed with that kind of question, what is the best way to help the people who really need the treatments right now? So that’s the kind of basis of this company. What we really what we do here at implement is that we optimize and then come up with the best optimal drug for each individual patients. And then there comes a question here that why pets first? I actually just mentioned that had has a huge number of patients out there, this is not an estimate, these are the patients 12 million are the patients who are actually diagnosed in the veterinarian clinic. I see cancer right now as a kind of engineering problem. So more than like 100 years, we have collected a lot of data and a lot of information from biologists. And then you know, cancer biologists, all the molecular biologists, medical doctors, nursing staff, I know the academia, industry, older researches, and we collected all of these great amount of information. And we have a great tool that AI. So why what what I see a cancer, why I see cancer is that we have you know, cancer is engineering problems, that there are a great amount of information out there. The problem here is that, how we can choose the right information for the right application. So I think that that’s how the AI or machine learning can do really, really good. So I really want to make the company are balanced strongly have a strong and balanced team between the the biological, like research scientists and and it`s like a, or like computational scientists. So we have our wet lab team and the dry lab team all together. And then I’m very fortunate to have really, really great people there. But to do the AI or prediction modeling, we need a lot of patients live data, not the liking of that data, which are already published out there patients are right there. But what we need to really is a really great amount of live data, we can track and then refresh data from like patients, and then we do the prediction and then actually get the, you know, proof of that concept or proof of that prediction algorithm by comparing the actual clinical outcomes. So to do all these things in a very short amount of time, it’s really hard to do in human. So that’s why we start with a pets. So you can’t we I can’t I always say no, when I pitch to our investors, to help them to understand our strategy, instead of like, you know, the punching the really, really thick wall to go to the human cancer, cancer care cancer cure, we are building our letter to just go up and then go and then what is great thing about this is that during this going up, like you know, this obstacles, we are actually trading our precious animals, I mean, and then all the knowledge and experience that we are collecting here definitely will help to human patients later. So our what our company is doing, finding out the right drop or the dogs and cats, but I can rephrase that, like, we are very passionate to save our pets first and then next, we’re gonna say our pet owners.

Tony Zayas 9:00
So that’s super fascinating what you guys are doing. So the the, just to be clear, you guys are targeting the pets first but then the goal is to build this model and approach and then be able to treat humans in the same kind of way is that correct?

Sungwon Lim 9:18
So yes, that’s that’s that’s our journey. That’s our kind of scaling up but we I really hate to say that we are kind of like using or our dogs and cats as an intermediate. We are we we are a company provides precision medicine for all the lives who are suffering from cancer. Yeah.

Tony Zayas 9:39
And I would just ask the question, is it because you can get you can take more action faster when treating pets I imagine there’s a lot less you got to go through. So that’s pretty fascinating. I love the approach really cool.

Sungwon Lim 9:55
Yeah, that’s that’s the right thing. That’s that’s exactly why we chose the pet industry first, because we can move a lot faster, of course, a lot more simplified regulations, and then a lot of light, you know, patients out there and then veterinarians,, I can actually because I have, I spent a lot, many, many years in the human side. So I can’t actually, I and honestly, I never imagined that I worked for this path, you know, the cancers. But once I just like plunged into this space, I am amazed by the very narrow influences who are very willing, who are really, really willing to adopt new technology, try a new technology, and they share their, the patient samples, and of course, under the concept formed by the pet, pet parents, but they’re super warm, and they’re very kind, and they’re very well are really, really willing to move forward with a good technologies.

Andy Halko 10:54
It’s fast. Yeah. And I was really intrigued by your analogy of punching through the wall in a way, you know, that, that that’s applicable for any technology, you know, in many cases where, yeah, there’s this bigger implication, and something that you could really go after, but it’s going to take a lot more effort and time to get there, and how do you step back? And, you know, take a smaller step that can be in that journey. So that’s a really interesting analogy. Was that and I mean, that’s really the core of what you’re thinking and strategy is with this, right?

Sungwon Lim 11:28
Exactly, yeah. Yeah. So and the best thing about like, you know, this kind of, rather than punching, like building up the ladder is that again, like, we are doing something during this kind of journey, you know, there are so many companies out there, you know, teiko like 10 10 or 20 years to build a new therapeutics, and then, of course, they are collecting, and then influencing the people with collecting the knowledge and then sharing that knowledge and the data during that journey. But you know, it’s a, what we are doing right now is that we are actually helping other species know, save, save dogs and cats, who are, you know, suffering from cancer. So, we are not just like punching, or we’re not just like building the ladder, during this kind of process, we are helping, and we are saving the lives, right.

Andy Halko 12:21
So how do you go to market? Is it you know, typically, is it different than on the human side that you have to still go through the same compliance? How is that different? And then is it really going into vet veterinarians? And, you know, making them aware that this is now a product that they could leverage?

Sungwon Lim 12:43
Yeah, that’s great question. So you know, I can do the direct comparison with the human space, and then the veterinary space, because now I experiencing both of them. So veterinary is space, especially I’m just focusing our I’m going to focus in on the cancers, space space here, and the medical space here. So ecosystem, and then the players are very similar here in the bedroom space to there are the customers and then the payers. And then there are also some payers usually here is not the insurance company there are of course, there are a little bit differences here, in the human space, or payers, usually it’s a insurance company, but here in the veterinary space, most of the money is from out of pocket from pet parents. So they are the very, very, they have a very strong voice, and then they have good, you know, power to decide. And then neglige like, you know, this costs were not a negotiation, discuss with a veterinary oncologists and then the primary doctors for the treatment decision too. So that’s kind of different parts. But I know there are doctors who treat the patients there are payers and then there are companies that provide a pharmaceutical companies, the service companies like us, so the ecosystem is very similar, but the difference here is that how we can go into the markets. So the process to go into the market. Of course, if we are a pharmaceutical company, we are regulated by FDA or USDA, depending on the type of drug that we are developing. But we are in a service company and we are in a very unique position right now we are actually in the middle of diagnosis and then the treatment. So what we are trying to do is that once this patient is diagnosed with cancer, the sample comes to us and we are in the middle. Before the treatment we are providing this information. We are a toolbox for the oncologists. So we are providing this information to the doctors to make a better decision. So that’s what we do. So we actually talked to USDA and FDA and then we got to read received the official letter and that you guys are bypassing the current regulations. So feel free to do what you guys do, because we are not developing new therapeutics what we are trying to do is we are trying to optimize that for and personalize the treatments resume for each patient. So that’s kind of differences. And then maybe that’s an actual, that’s kind of unique feature for our company. But usually, I can say that the regulation, if we are, if we are the service company, the regulation is less, less than less well, you know, rigorous and the human side. But that’s, that’s the regulation, you know, that’s a requirement. So what we can do is that we can just sell our products right away. This is the idea, we, cuz we’re gonna charge this amount. And then whatever the thing is, of course, there are doctors who may be adopting that. But we, we did not do that, because we always talk to the veterinary oncologists. And then even though there is no requirement, they always want to see the data. And then clinical validation is same as like human unfortunate human cancer coaches. So they want to see the data, and they want to see the controlled study. That’s why we actually spent the last three, three and half years to provide our service for free until we actually get this competence level. And then until we actually reach out to 2000 patients data in our database.

Tony Zayas 16:19
So so long, would you say that that’s kind of the future of medicine? Is this personalization? Because that’s a fascinating concept. You know, I think of I always said, I always say, you know, doctors, I simplified a little bit, right, but doctors, you come in with pain, they give you, you know, one of a few pain medications, you come in with inflammation, they give you an anti inflammatory, so on and so forth. Right? super simplification. But what you’re talking about is really treating based on the person, right, and their, their, their biology, as well as what they have, and then matching that up with personalized treatment. Is that the following? Is that somewhere along the lines of what you’re doing?

Sungwon Lim 17:09
Yeah, exactly. So I’m a strong advocate. And then I think I advocate for the precision medicine, especially for you know, integrating all the data together. And not only just based on precision medicine is just like basically personalizing the treatment or diagnosis for each individual patient, because every patient has a different response to the same drug. That’s actually very, extremely important for cancer patients, because they, they don’t have time to trip to spend time to trade with the waste their time with on effect effective drugs. So for them, we really need to come up with effective treatments upfront. So that’s what we are trying to do. And then, of course, you know, they are, it’s a kind of great concept, always, it’s been a great concept of precision medicine, personalized medicine, it’s been a great concept for a long time. But 2016, you know, there was a president’s budget, that’s for the Precision Medicine Initiative, a huge amount of money goes here. And then people now people, like, the lay audience also know that the precision of the term the precision medicine, so I think that’s a big trend here. And of course, we need to go for that, in all this, you know, we are personalizing ads, and then building mobile app targeting targeted apps, all these kind of things that is actually happening in the in the medical side, and which is definitely a lot more critical.

Andy Halko 18:44
I’ve been hearing a lot you know about AI and mathematics being used in you know, gene therapy and understanding, you know, all of these, you know, cells, and it goes a little bit over my head. But what can you talk to me about how medicine is now using things like mathematics and AI to help solve problems?

Sungwon Lim 19:09
That’s a That’s a great question. And that’s why I am very passionate. And then I’m still learning. I’m actually my background is not a data scientist, more than me. So go ahead a little bit a little bit so I can help you because I actually went through that kind of process to learning and then get used to get familiarized with AI thanks to our data scientists. So AI, I think I can share the kind of why I really wanted to build this AI integrate AI into our company. I think that kind of journey can help help you understand that and then maybe describe how AI can help us. So I have a I had a personal experience in my master’s degree the lab was consists of like, exactly a very similar landscape of what IMPRIMED is currently doing. So there is a wet lab scientists who work in the lab, actually dealing with clinical samples. And then the the dry lab team who analyze and like virtual cell design the virtual cell, and they always just like sticking onto the computer, and then they do all the simulations, and then try to find out the unexpected targets on rational targets. So it actually worked really, really well for my project. So dry lab team found out but two really great targets, we never human may not be able to think about that target, because it’s far from our, like, mainstream, and then, and it works modification, like genetic modification of that in this micro organism, for this is a great amount of you know, that should the goal. So, I was so amazed by that kind of concept of working together with a computational biologist, and then the that well, that scientists working together, you know, definitely, this without this well of scientists, AI, or this kind of computer scientists, they bring they okay, can always bring up some new targets, it cannot be validated without this wealth of scientists. So it’s a really, really great concept. So I really wanted to build that. But I did not know about AI. I just always heard like, as I as as Andy mentioned, I was one of the people like, overwhelmed by machine learning and AI, oh, this kind of thing. But I know the concept that machine learning can, you know, do all this kind of like collect the, you know, AI and machine learning can do is collect all this information together, and then find out the good features and then make the kind of there are so many, we can say that it’s black box, we put all these different things there. And they let the computer be trained by all the ground truth data, which for us, it’s actual patients clinical outcome data, in the in the clinic, after the drug injection, how did patients did, all these kinds of information put together and then these machine trained, and then they stop and they just like, without, you know, beyond the humans rational approach, they can bring the computer or this algorithm can bring up some great equation that can digest these input variables. So what as I mentioned, like I see the cancer, right now we collect, we have a lot of information. So maybe this machine learning can do this digestion. And then if we put all these control data, they can do their job. And then we have a great power to collect the our dog cancer patients actual risk clinical response, very fast. So that actually worked. So AI here, I was amazed by it. And then what we are doing is a prediction, it’s which is very, very hard, the prediction cannot be perfect. What we are, what we are trying to do is that like, you know, increase a prediction accuracy more and more with a lot larger data sets. So that’s our daily job here. And then, you know, all the conventional methods have been like have an older limitation. So far, we have a witness that and I studied that, like this kind of input is not like one to one correlated with a certain clinical outcome. So we need a lot of input, because cancer is a systematic disease. So we need all these kind of information and let the computer which is trained by all the ground truth data, and let the computer do their job, and then spit out a prediction score. If you inject this drug into this certain patient, what is the likelihood of success? Success means that what is the likelihood? What is the probability probability of reducing the tumor size, once injected this drug, so each drug in our AI model, each drug has its own success rates that success success probability? So based on that we rank the drug, and then the doctors can see that, oh, these are the viable option for this specific patient. And these are maybe we should avoid this because it’s not effective, predictive, not effective. Does that answer your question?

Andy Halko 24:18
It Yeah, that’s great. It’s, you know, it’s really about what, you know, finding hypothesis faster, and then testing that, and using that, those predictions to better understand, you know, a higher likelihood of something being effective.

Sungwon Lim 24:35
Exactly.

Andy Halko 24:36
Is that a good summary?

Sungwon Lim 24:38
So a summary, I should have answer like that.

Andy Halko 24:43
Well, what uh, one thing I’m curious about what this so I get it. I love AI and I love machine learning and the growth of that. Where do you think, you know, is the wet lab where the creativity comes in because I have to believe that there’s something to the side scientific process that there has to be the innovation and creativity of ideas and things that just can’t even be trained into a system. And so do you think that’s where that gray balances between the wet and dry lab?

Sungwon Lim 25:14
Exactly, yeah. Yeah, well, definitely. That’s, that’s the beauty of our company. And then that’s too I think I really highly recommend the other comoany to also take a look into this kind of well balanced between the, the wet lab team and the dry lab team. Because for example, all this kind of AI, you know, we need the input variable, something that we need to input put into this black box for the data scientist, teams AI model. So this AI model cannot be wrong. Even it would be it, it would not have been developed without this well app team. Because the way that we started our company is that now I’m the biologic biological back, I have a biological background only, of course, I wanted to be a computer game programmer that computer gamer computer game programmer when I was young, but I switched to the bioengineer later. Anyway, most of my career path was a biological science. So it was kind of natural for me to understand the clinical needs I in the, in the medical science, the biggest challenge for this kind of prediction, the cancer prediction, or cancer, precision medicine, is that, you know, most of the people are using the genetic mutations in the cancer cells, and then do the prediction based on that I did not, you know, I actually witnessed this great DNA information, genetic information is great, but it was not enough. So what I really wanted to do is that we need to see the cell as a system, and then keep the cells alive, take the patient’s cancer cells outside, doctors always do that in the tumor biopsy, and take the cells alive, and then treat, they actually treat the all different drugs on it, to find out the right drug and then measure the cancer cells behavior against anti cancer drugs, finding out the neutrals, bad information is really, really crucial. Because it’s kind of you know, not the not the sole prediction, it’s kind of evidence based, because we actually see that this patient’s cancer cell actually killed by specific drugs. So that information is very important. But the problem here is that cancer cells just naturally die, once it is taken out of the body. That’s why we cannot see that the company like us ours in too many companies like ours, which is called functional precision medicine, rather than the genetic precision medicine, we are, I am a strong advocate for the puncture precision medicine side. So what I did for the first four and a half months, once I founded this company is developing the new like liquids for to kick the cancer cells alive outside of the body. So in this media, we can keep the cells alive, up to seven days, without 80% viability without this, within four days, without this media, within four days, the cancer cells just die. So it’s a too short amount of time to test all the different drugs. So we need at least a one week period. So without this, this is kind of like initiation of this company. And this enable, oh innate, this media enable all these kind of downstream like AI model development, all these candidate drug testing all this can things and then that’s how it was initiated this company and then let you know, our data scientists or dry lab team can develop the the the new model prediction models, and they will have scientists also continuously providing this input raw data, and this kind of really big, you know, circuit so called here that said, it’s a loop. You know, you know, that mentioned, machine learning always needs to be like continuously evolved and developed with a new data sets. So that’s how, yeah, that’s why it’s very important to be balanced between these two things.

Tony Zayas 29:18
So I have a question about your, your the team makeup, you’re obviously tackling very complex challenges and problems. And the solution you’re bringing together is a lot of complexity to that. You have the data science kind of side of the house, and then there’s also the biotech and then you’re running, you know, the business side as well. So what does that what does your team look like? And I know you’re a co founder side, I guess started out just like to hear about who else you know, the other co founder, co founders, and just I’d like to hear the blend of who’s involved in this.

Sungwon Lim 29:55
Yeah, I can start with our co founders. So I know them through Stanford, when I did the PhD, actually, I might plan I actually, I grew up in Korea, South Korea, and then I came to the US like 11 years ago. And then I did masters and PhD here in the Bay Area, I’ve just stuck in to barrier and just fascinated with all this environment here. So my plan, I actually came to us just to do the, the gret, the advanced the academic career, and then do the startup later. So that’s my like, goal to why I came to, to the west. But my plan was that, okay, do the PhD to a to equip myself with, you know, strong weapon and their communication skill set with other, you know, high level researchers, and then work in Jinan tag or for like, at least three years to just learning experience the big company, buy the company in the US, and then do the start my own company. But it actually came earlier than that. During my PhD, I actually met a great people there, I just came up with this idea. I mean, I really want to help, but you know, so obsessed with this kind of what is the best way to help the current current people. So that’s the initiation in 2012, and then met great people there, and all the co founders, I met through the Stanford, and then one of the, the main co founder, his name is Johny Crew. He’s a professor in Korea now, he actually we, he is a 21 year old friend, I’ve been friends for 21 years, last 20 years, we went to college together in the same class. And then we actually, it’s quite instantly we, we started our PhD and the end of the PhD at the same time, same period. So he did the defense right, one day after mean, so we initiate that same day, we listen to the same classes. So he is one of the most influential people, a few people who influenced my life, and then I respect him really a lot. And he, he came back to Korea to be a professor, and then he’s leading the IMPRIMED Korea team. So he is a great scientist, and then great data, that data scientist or biological, chemical engineer, all this kind of thing, he is a kind of, you know, really, really best to discussion partner for our technology. So that’s a and then also we have other co founders who have co founding team, of course, they are like, not, if not too much active because they are positioning as advisor, because they have their own job like David is working in Stanford Hospital as a humanly, not Pat human lymphoma, the doctor, yeah, you know, may not is working in the electrical engineering company out there. So that those are the co co founding team. But you know, right now we have, again, the wet lab scientist, who, and then we have dry lab sign dry lab scientist, and I really want to emphasize that I always feel kind of guilty, and then feel sorry, to our commercial operations team, because business cannot be wrong without this team. Because, you know, I, whenever I talk about technology, I always emphasize these, well, left team and dry lab team, but commercial operations and business operations teams, we have great people, they’re very, very dedicated, and very enthusiastic. And then who makes all of this kind of work out, you know, central flow and all this workflow, go. So I really appreciate those people, we have a VP of commercial operations, and then we have two very talented marketing designer, and then we have a great business operations manager. So these are the people who consist our company, and then every single person is contributing a lot. And then you I think, you know, that definitely, I bet you know, that all this kind of startups. Everyone has a very different wet hair. Different, you know, very different hats, right? Oh, definitely. So, yeah, that’s what we do.

Tony Zayas 34:23
So along those lines, what is your role as CEO, with all those different disciplines, and you know, everything going into this business? What is your role look like? And how has that changed? You know, since since the idea and the start.

Sungwon Lim 34:38
Yeah. So I really like the quote from my own friend who, who built it, who sold his startup, we really successfully exit accident in IT space, and he now became a VC and he is actually the partner of one of the investor group that invested in our company. I like to his quotes that he always said, like, I’m using a CEO, as the chief executive executive officer, right? I’m using a CEO for for different, you know, my CEO stands for Chief, etc. Officer. So I’m doing all different, like, you know, the Mr. miscellaneous things and all this kind of hassles, all these kind of things, I really like that kind of quotes. And then I want to become that kind of the CEO, of course, you know, big decisions, making a vision, and then all this kind of, you know, penetrate our my, you know, philosophical and visiot throughout the team, and that always listened to all the team members, those are kind of general things differently, and the most important things that I need to do. But I always want to let our team member do their job. So research scientists just focus on the research. And then you know, the marketing designer on commercial operations, can they do their sales and marketing and PR, and then data scientist just focus, like laser focus on that the model development, all these campaigns, all the outside, like, HR, you know, the payroll, and then the financial? And then, of course, meeting the investors and then meeting outside people reading all these kinds of things. I’m doing that. Yeah.

Andy Halko 36:27
How have you found the investment process, I mean, most of the founders we talked to, you know, describe it as a second job is that it really a full time job to find investors to, you know, raise the money to stay in touch with people and handled that. So you’re a guy with a, you know, the the medical background, you’ve got this medical and science company that probably rely needs a lot of hands on hands on work, but then you’ve got to do this aspect of raising money. How do you balance that?

Sungwon Lim 37:02
Yeah, well, I completely agree with thoughtco. There, other you know, founders and CEOs, you know, I will say, it’s, it’s not a second job. I mean, it’s actually, when there is a fundraising, it’s the first needs to be the first job. So my biggest role is to lead this community keep going this great journey. So to do that, we need the financial supports, that’s why we are doing the fundraising and then meeting the investors. I actually really enjoyed this investor pitch and the meeting the investors, I’m actually, I think it I heard from our other, you know, like, friends, CEOs, and they just described as a pain in the ass. But for me, it’s really, really exciting. I have meeting actually a, my XML file that I so far, within the last four years, throughout the two rounds of fundraising, I met the 260 VCs, in the Bay Area, and then internationally. So you can imagine that every time we meet investors, we have a little bit different, but almost, you know, pitching the same materials. And then always, I still enjoy the with the same slide that if I met the investor today, and then this morning, and then in the afternoon, again, if I do the repeat all this kind of presentation again, I’m still very excited about that, especially I’m very excited about featuring our team members slide and all campaign. I think I’m very lucky to have this kind of, you know, preference and joy to meet people and investors. But I learned a lot throughout the incubator and accelerator so as you described, I’m like, kind of like engineer geek and I thought that I am very, very, you know, very good at explaining difficult things and easily to the to all the all the other like lay audience but I realized that I I was not I learned a lot when we first joined the first incubator and then they they just taught me a lot how I can just change their language change the term and then explain a more you know, easily in the from the starting from the concept and then to the details, all these kind of things. So throughout this accelerator and then incubator, I learned a lot. I’m still learning I have to learn a lot more. But now I’m enjoying a lot more than before, so that I can actually see the investors understand what I’m talking about.

Andy Halko 39:44
I’m curious to go back a little bit. We talked about the go to market and then we talked about AI and I want to connect maybe potentially two dots as you talked about it taking 10 to 15 years, right for something to really get into the market. But then we talk about AI, and machine learning these other pieces that speed up the hypothesis, the testing, and all of this, you know, do you see that that is ever going to change? Because we’re I, you know, I would assume the science is happening more rapidly. And I would assume patients have the demand to want these things to go through faster if they’re going to be successful. So I don’t know, can you maybe see, is there a connection between those dots of the length of time of go to market and how we’re being able to incorporate AI and machine learning?

Sungwon Lim 40:37
Yeah, I mean, that’s a great question. And then I do agree with that, those thoughts will be connected, I mean, we actually are witnessing that’s already is the thoughts are unconnected. AI and machine learning away already comes into the old all different fields. And then especially for the medical field for the patients have right now AI is already applying for is being applied for the imaging analysis, like they can actually detect a very like, you know, subtle difference in the X ray, same x ray. Of course, this can this will not be able to replay complete replay or completely replace the radiologist. But radiologists can use the AI as an aid for their decision maker, and then know the data analysis. And then there is a there are the the AI machine learning algorithms, the researchers used machine learning algorithms to based only only based on the patient record medical record, they can predict the date, this patient will get to this disease with a certain percentage, so they actually can predict the disease a lot earlier than the actual disease happens. So and then also the company like ours, we are predicting using the AI to predict the drug response inside the body before they actually inject it, they got injected. So all these kind of things is already happening here. And then I can imagine that also the drug discovery as a really big part two coming up the new drug, the candidates, AI actually is shrink down and then find out the new drugs on here. So all of this kind of application is already being happened. And then within 10 years, it will be just like really, really, really be nationals right now investors are like, Oh, machine learning AI for this, this is very new AI for pets, it’s new, but it’s going to be a very general idea in general term, and then kind of general concept within 10 years, I think, especially for the medical field, I can say. And I hope because these, the power of AI or machine learning is that they can digest all different data. And as long as they are trained properly. So the trainer, of course, it’s a human over this kind of what are the branches for this simulator, and then what they can what is once it is properly trained, what they can do is that they can do a great job to digest all different information right now, you know, we all know that more and more. We are overwhelmed with all different information from everywhere. The problem though the issue here is that the key here is how we can choose and then the the mix and combine the information properly for the certain proposed. That’s what AI can do better than human. Yeah. So

Andy Halko 43:35
So what are the some of the big roadblocks that an organization like yours faces, I guess in going to market and achieving growth and success? Like what are the challenges that you typically face?

Sungwon Lim 43:51
Yeah, well, challenges of course, every startup has all different challenges. So this so does our company. Going into the market with a new technology like new brand new technology is definitely not easy. We are working with our veterinarian closest they are our customers and then also the to be the salesperson who who actually add advertise our service to their clients, which are the patients, parents, Pet Pet owners. And then so working with this people in when we are providing our service for free was a lot easier, frankly, because they donate their patients sample for research. And then right now we this year early this year, we started to go into the market and we are right now we are at the commercial stage, we are selling our service with a paid service. So it’s definitely not easy from starting from the free to paid service. I think everyone understands this point that but you know more and more They have doctors are medical doctors and they, for the pet I know, you know, pet owners and medical doctors, they are moved by the data, and then the clinical validation, all these kind of things. That’s why we published our paper and working with all the other institution to do the clinical studies, all these kind of continuously working, and then improve and share data, present our data and conferences, all these kind of things. So it’s it’s it’s been slow our sales, we have very ambitious goal ambitious goal. And it’s been, it’s been slow, but we are every month we are, we see that the sales is has been increasing. And I mean, it’s it’s a difficult problem always to sales and commercialization is difficult going to the market. But we are very confident about our technology. There are many doctors who understand that point. So the conversion rate is more than 30%, starting from lead free to the comfort, the paid conversion, more than 30%, which is pretty good. Of course, we need to increase that more. But yeah, that’s a challenge. But it we are ajoint this kind of the new challenge.

Tony Zayas 46:14
Just to go back to your comment about, you know, 10 years from now, machine learning AI being involved in this type of stuff for you know, biotech, and, you know, precision medicine, all that, given that fact that, you know, this is the direction everything is headed. How do you as you know, co founder looking at your business, how do you plan out for the future, to ensure that you guys can stay ahead of the curve? Given there’s going to be a lot more competition coming in the space? Like what does that look like? How do you prepare and plan for that?

Sungwon Lim 46:52
Yeah, that’s a yeah, thanks for asking that question. And that’s what I like, every day, I think about that, how we can be constantly be the number one precision medicine company in the pet industry, and then also in the human side later. So I can only if I can only focus on how our company can sustain. So the biggest challenge for the Precision Medicine company like our system to make a good prediction, what is happening inside the body from the outside of body, the bigger good prediction is that we need a large number of clinical data. So to do that, that’s, again, that’s why we are starting with pets. All the very known closes to share the data and the patient data and the patient clinical samples under the constant form signed. And then these kind of things if we once we, once we secure this all of the veterinary on purchase network, there are only it’s surprisingly, there are you know, 12 million dogs and cats diagnosed with cancer but only a 450 board certified veterinarian oncologist in the US. So that’s why they’re super super busy. But that means that it’s a kind of like, you know, easy market acquisition easier compared relatively easier micro acquisition, we once we secure all these kind of 450 people secure it and then continuously receiving all this kind of new patients sample and data, I think we can sustain our you know, data addition, the speed of data collection is definitely we will be superior. And then we already are shared, we already shared all this kind of the clinical data, all these kind of things with our doctors, they are convinced by our tech technology and and the research outcomes. So once we have this kind of maintain this network very solid, it’s going to be adding a new technology. And then all our new technology will be developed from with our pets, our dogs and cats. So right now we are selling our block the cancer dog, the loss cancer service, and then it will be translated to cats around the end of this year. And then after that, we’re going to move on to the other types of cancer, for example, like bone cancer, and then once it is developed in dogs, it will be translated to the cats and then later into the human. So that’s all the new technology is being generated from this pet cancer space. So once we secure this and then once we have a good amount of cash count from this pet industry, I think we can we can have our cell funpay you know self sustaining, financially self sustaining, and also that can be used for the human to cancer application development. So, the key thing here is that kept a good network and a good relationship with our veterinarian, oncologist and veterinary doctors, and then expand, you know, to other pet markets are we are planning to go into the European market 2023. And then we already set up our lab in South Korea and Japan. So all these kind of things will be very exciting. And then yeah, but it’s a, you know, it’s a never add the question and never ended the answer to this question never ended, I need to continue to think about how we can do better and better.

Tony Zayas 50:44
That’s great. It sounds like something that you’re always thinking about. So appreciate sharing. Thank you.

Andy Halko 50:52
So what aspects do you think are different from the human market in the pet market? You know, I have, you know, three dogs and a cat, and I know what they think, but they can’t speak to me yet. So I mean, I assume, obviously, your end patients can’t give you a ton of direct feedback, maybe their pet parents can. But you know, besides that, what what are other differences in working with pets like pricing, you know, who you need to convince? Whether you can get the data about the, you know, quality of life after treatment? You know, what are these differences that you think are gonna you’re gonna have to face with pets over humans?

Sungwon Lim 51:36
That’s a great question. Um, so yeah, I mean, it you, you nail down the, the differences actually. And then that’s why we see, we, we usually say that, like, our pet cancer patients are very similar to the human pediatric cancer patients, so they don’t speak and they express just like crying and, and just like all these kind of things, that’s why we need to be a lot more careful about like listening to and listening to what they are trying to say. And then the cancer itself was very similar to the pediatric cancer and then the dog and cat cancers, those are very similar to other than that for the pricing point. So for example, our company is a b2b company, we sell our service to the veterinary clinics, especially veterinary specialty hospitals, and then very narrow specialty hospitals, they add their own margin to the pet owners, so we can control that margin. For our companies, we are we cannot do this service, yet to the direct to consumer, because they pay patients need to come to the hospital, and they need to get the biopsy, the sampling, and the doctors or nursing staff, they need to do that procedure. So they need to come to the hospital. And then the decision makers should be the mainly through the oncologists, of course, they are discussing where the pet owners, but so that’s why we need to do the b2b. So we cannot control the enterprise. But we always try our best to convince the doctors that we minimize your additional burden. It actually it’s a very simple, once they take out the patient sample is done all the other like downstream process, we take care of the shipping, and we take care of the old sample processing, and then send out the data, send down the records, and then explain about the how we read the report all these kind of things. So really, we have been doing our giving our best airport these days, to convince the doctors that minimize the margin for the pet owners, we really want to see that more, you know, as many pet owners use our, the service as many as possible. So some we have some kind of success there. They only had at some medium or margin because they are convinced that this is very, very essential service for their clients. But you know, older, like big hospitals, we’re still negotiating. That’s kind of differences, I think, the price point. And then, you know, and then of course, the insurance company. There are insurance companies out there, but statistically there were only like 11, 9 to 11% of the pet owners are using the pet insurance yet because they don’t cover the big disease like cancer or infectious disease. So that’s why but more and more insurance companies are starting to do the cancer cooperation. So I think the pricing and the payer, this kind of ecosystem will be changed in the next five years.

Andy Halko 54:53
And I generally think there’s like a trend of of pets seeing much more as family, you know, I remember years ago that debate of always like, wow, if they you know, there was some sort of disease and it cost $10,000 for my pet, would I be willing to your child, you’re always gonna say yes, your pet you question it. But today it feels like people are a little bit more, you know, in that space where money isn’t as much of an object to make sure that their pet is, is healthy.

Sungwon Lim 55:23
Exactly. Yeah. And that’s what I realized that what I what I surprised here, once I just like, jumped into this space, people, you know, we spent like, you know, the $10,000. So if, if, if my if my dog got cancer, for example, lymphoma, and if my dog is diagnosed with lymphoma, that’s what your doctors who will ask me like, are you going to go to the treatments? And if I say yes, that means that basically, it’s at least it’s gonna span, I need to spend around like, more than 10 $10,000 worth $15,000. But people spend that money just because exactly the same reason that you you mentioned, Andy, we see our dogs and cats as our family. And then interestingly, there are a human issue as companies started to include the pet as a family, do the pet issue, not like separately, the pedicures. They are the Family Insurance, that can they can take care of the this kind of veterinary costs. So it’s a big trend here. And then us is actually definitely you are is the number one company country in the world who have that kind of like, you know, the that kind of concept and then the family. Family concept. Yeah.

Tony Zayas 56:48
So we got a few minutes left here. Before we wrap up, this has been fascinating. Just real quick, where can our viewers find out more about you and IMPRIMED and pay attention what you guys are up to?

Sungwon Lim 57:02
Yeah, sure. Of course, you can learn more about our company and just type IMPRIMED in Google or INPRIMEDICINE.com. That’s our website, we, we included a plenty of good amount of information there. You can learn more. If you are a veterinarian, you can click the four baths. And then you can eat you are the pet owners, pet parents, you can click the or pet parents there. There are tabs and then information. Categorize, like properly for each audience. And also, yeah, you can you can you can find our company in the LinkedIn. And then we have Instagram to IMPRIMEDICINE. And also, if someone who wants to reach out to me personally, I definitely have a LinkedIn personal page so that you can just message me. And yeah, it’s great.

Andy Halko 57:59
So if you were able to go back in time, what advice would you give to your younger self?

Sungwon Lim 58:08
That’s a good question. I can see that. my younger, younger me, is definitely, I try to try to do the startup like go to the startup career and startup journey. I just want to let that younger me that whatever you are imagining the difficulties you’re imagining, you won’t be overwhelmed. I mean, it’s so it will be a lot more difficult about that than you imagined. But just be persistent. And trust your team. And then love your team. And trust your vision. And if you feel like any difficulties, just give at least like one more shot with the you know, with all your best. So it’s not the done like what you want it. There’s still another way to reach out to the end goal. So it can be a detour. things cannot be always straight. So it can be a detour, but you can learn a lot from the detour.

Tony Zayas 59:19
That’s awesome. Well, salam, thank you so much. This has been outstanding. We appreciate your time here and the conversation. Everybody I would recommend you guys check out what IMPRIMED is up to you visit their site, thumb on social. But thank you. We will be back next week with another episode and we look forward to more great guests. Thanks again. Someone have a great day.

Sungwon Lim 59:46
Thanks for having me. Thanks, Andy. Bye.

Tony Zayas 59:50
Take care.

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