John Simboli 0:00
Today I'm speaking with Yochi Slonim, co-founder and CEO of Anima Biotech, headquartered in Boston with R&D in Tel Aviv. Welcome to BioBoss, Yochi.
Yochi Slonim 0:11
Hi, John, thank you for inviting me.
John Simboli 0:14
What led you to your role as co-founder and CEO of Anima Biotech?
Yochi Slonim 0:19
People that have known me, they know that I actually come from software. I was a software guy, I created three or four software companies. A couple of those actually got to be very big companies, one of them became worth more than four and a half billion dollars and employed over 10,000 people, eventually acquired by HP, became the software division of HP. And after that, I did a couple of more software companies. So how did I end up in biotech? That's a question that even people that haven't seen me for a decade, all of a sudden, they see me in biotech, they say, how did this come by?
Yochi Slonim 0:57
So actually, the story is that I was the first investor in Anima Biotech, I was running a software accelerator at the time. And my co-founder, he just came to me with an idea in an area that I really didn't know anything about. And he kind of told me that, and I thought to myself, this is big; this is really big, you know, I'm not doing stuff like that. I ended up investing without knowing anything about biology, then I was infected by that biology thing. I started to train myself in biology, I became really deep into that. I went, and I studied biology, trained myself for over a decade in biology. And sooner than later, I became the CEO of that company. That's what happened.
John Simboli 1:45
Did your software friends forgive you?
Yochi Slonim 1:48
Actually, Anima is half software. That's the interesting part about the company. It's a biology company, but one-half of it is software. That makes the company very different from many other companies that are just pure biotechs.
John Simboli 2:04
How did you decide you wanted to lead a biotech company?
Yochi Slonim 2:10
The truth is that really what happened after I invested is that the company was really going into an academic phase. I invested at the wrong time, obviously, because the company was not ready to be a company, it was ready to be an academic project. And we ended up being an incubated company at the University of Pennsylvania in the biochemistry lab. And the technology was developed there almost for seven or eight years. And then it started to work. And we reached out and did more academic collaborations, like 17 of those. And I started to understand that this is really, something with a big potential to become a next-generation drug discovery platform. And the idea of discovering drugs was something that I thought, at my stage in my career, this is really a substantial thing, this is something that could change the lives of people. I did software companies that brought quite a lot of money to investors, but I don't think that they changed the world in any way. And that thing was something that I really felt is going to be really big. And I want to do that, because it can have an impact on the world. And the idea that this company has software at its core alongside biology was also connecting me to it. I had something in common with that thing to get started. So this is how I actually decided to lead that company,
John Simboli 3:40
After you were an investor and as you began to see the potential for the company, did you at any point think, this is going to be a tremendous amount of time, now, I'm going to be putting into this, investing my own time into this as it goes forward. Now that I have a better sense of the landscape, maybe I should just take this idea, this company, this new company, and sell it to someone, to a large technology company. Did you go through that process? Or did you know pretty clearly that, no, I have to take this, hold this, develop this?
Yochi Slonim 4:09
I just felt that I want to do this. I've started companies and I sold a couple of companies before, and I wasn't in any urgency to quickly sell another company, I was actually thinking about let's build a sustainable, substantial company around the technology because this could be life-changing. And I was really thinking about this with a lot of excitement that I want to do that. And if it's going to take 10 years or 20 years, I mean, I'm up for it. That was actually something that I didn't think, it's going to be a short runway to sell some company to somebody.
John Simboli 4:50
And it sounds like, based on what you just told me, that the potential to change the world on a large scale. and on a narrow scale to really change the lives of individual patients, that sounds like that a pull for you as well, right?
Yochi Slonim 5:06
This is something that I really want to do, because of the potential of it to make an impact on people's lives. In software, I must say, you don't make, I mean, you could make if you are inventing Facebook, you know, or Instagram, yes, you are making an impact on people lives, I don't know if it's a positive impact. So the impact has to be positive. And what could be more positive than finding drugs for diseases? I mean, to me that made a lot of sense.
John Simboli 5:33
What was it about Anima Biotech that made you think, I can do this with this company in a way that I couldn't do any other places? Did you go through much sifting to get to the point where you're saying, this is the one I want to be a part of?
Yochi Slonim 5:48
Sometimes when you think about things backwards, you can find good answers and explanations, but to be honest here, I just thought about this and I felt that this is big. I was excited by that idea. I can even go back in time, to the moment that I heard what the idea is, from my co-founder, and the way that it was described to me, I still describe it to some other people. And you know, it's like, when you start with the question, what is a drug? Most people, you know, that's the second question. The question is, what is a disease? And I actually had to agree when I was asked this question, that I don't know, What is a disease? What does that mean? And actually, when you think about it, most of the diseases that we know of are caused by something that is going wrong with a protein. It could be either mutated, that's called genetic diseases, or it could be too much of it, it's called in the technical language overexpression of a protein, or a missing protein or too little of it. And the thing is, one protein, and there are 100,000 of them, but one protein that goes wrong, and you can find yourself with a disease that has no cure, or no treatment. And the way the proteins are made is ending up, eventually, as like, golf balls, they are collapsing into those structures. And they're running in the cells doing their own things. The drugs are really trying to find them. And to bind to them, these are the molecules, the small molecule drugs, they are like pills that you take. Think about them as like, they are finding those proteins and neutralizing them, but nobody sees that process. Nobody understands how proteins are really made. And they're made by machines that are called ribosomes, inside the cells, they're small cellular machines.
Yochi Slonim 7:56
The idea behind Anima that made me so excited about is that you could visualize that process with light pulses. So if you transect into cells, you put inside the cells, what is called tRNAs; tRNAs are molecules that are involved in the production of proteins. And you label them with fluorescent colors—one red, one green. When a pair like this ends up sitting in one of those ribosomes, the machines that are making proteins, you get a light pulse out of the cells. And to cut the long story short, with this kind of technology that was developed in the University of Pennsylvania, as I said, we are able to visualize, with light pulses, the process by which the ribosomes are making the protein so you can actually visualize it. And when I was thinking about it, I was saying to myself, you know, what, you're visualizing the process of life happening. That's kind of exciting. Those images that we create, by the way, they look like the Milky Way at night, they are glowing with light out of the cells. And that light is the light of the protein being made. And now comes the big idea. If you could run a library of molecules through an automated system and check the impact on the light. So compounds, molecules that would reduce or increase the light, they are doing this because they are increasing or decreasing the making of the protein. And now you’ve got it. Now you’ve got a way to look at the protein being made, visualize it and try the effect of molecules on that process. So then I thought to myself, I'm a software guy. I mean, I could imagine a system that would automate this whole thing, run millions of experiments like this, running molecules, watching the protein being made and watching the effect of the molecule on the making of the protein. And that sounds like an engineering project to me at the time. I was not in biology And I said, I just need a biologist alongside myself to bring the biology. And that was my CFO; she is running today all the all the R&D of the company, basically. But I brought many software people into the company. And we built that idea that, on one hand, we are running the biology, fully automated, what is called screenings; screening of molecules to try to see what molecule actually has the activity inside those cells to affect the making of the protein, either increase it or decrease it. And then we are actually, with the software, analyzing all that with AI software.
Yochi Slonim 10:44
Now, I need to say one word, which is a very important word that I didn't say so far. mRNA. Everybody knows today, what is mRNA because of the vaccines. But our company is all about small molecule mRNA drugs, because what I didn't say is those ribosomes, what are they doing? They are assembling the mRNAs into proteins. And with our technology, we can see that, can visualize that process. So really, when the drugs are actually doing something, they're affecting what happens to the mRNA. And therefore, these are mRNA drugs in small molecules, we visualize that process, we generate millions of images that are showing that, as I said before, they look like the Milky Way at night, there are quite interesting images to watch. But then we use AI software to analyze them and figure out how those molecules are actually doing that. And all these coming together, biology together with software, is what makes also Anima very unique in that space of mRNA small molecule drugs, because we are the only company even pursuing what is called a phenotypic screening approach, on one hand, and then coupling it with the AI software to actually get to the mechanism of action of those drugs.
John Simboli 12:06
When people say, maybe people you haven't seen in a while, say, what are you up to? What do you do? How do you answer that question?
Yochi Slonim 12:16
So actually, that's something that is happening to me, as I told you, actually. When they hear what I do for a living, it's hard for them to imagine, how did I end up doing that? Because it's kind of with some discontinuity in my career path. As I was very well known as a software guy, and all of a sudden I am in biotech. Now I am already 12 years in biotech. So that's by itself, a long runway, enough, for people to forget I was in software. But when they ask me, what do I do for a living, I usually say something like “I run a company that has a technology for the discovery of drugs for many diseases that are not treatable today. And have you heard about mRNA?” And most people say, Yes, I've heard about mRNA, just recently, I know that word. And I say, OK, these are vaccines, but we are using mRNA biology, in order to discover drugs for diseases. That's something that most people actually are getting. And then I say, and these are specifically what are called small molecule drugs, these are the pills that you take orally. And we have a technology that enables us to identify molecules. When you give us a disease, and you give us a protein that is associated with that disease, we can identify molecules that would actually control the making of that protein, either decrease it, or increase it, or correct mutations in it by going after the mRNA, that intermediate step between the DNA and the protein. And we also use AI as part of our technology platform in order to actually discover the best ones, and to explain how they work, to discover the mechanism of action. Now, most people, by the way, are familiar right now with mRNA as something that is happening in drugs, in vaccines, whatever. And most people have heard about AI. And now AI is coming to drug discovery. So Anima is really at the intersection of mRNA and AI. So it's actually becoming easy to connect with some people that have no idea about drugs and diseases, because they heard about mRNA, they heard about AI. So it turns, all of a sudden, quite interesting and exciting for them, you know, mRNA, AI and it leads the conversation into how do you do that? You know, have you done this? Have you found, already, a drug?
Yochi Slonim 14:53
So the answer to that is we've identified some still early stage; they are preclinical. We haven't demonstrated this in human beings. But we've actually identified in our own drug discovery programs very promising drugs for fibrotic diseases. Lung fibrosis is a disease that we're working on; cancer, we are working on with our technology. And then there is the aspect of the partnerships that we've done with Lilly and Takeda. So there are big pharmas that are betting on our approach. And we've done very big deals with them. So when people are asking me that, I'm usually bringing up that aspect of we've got the mRNA, we’ve got the AI. And then there are big pharma companies that are working alongside with us on using this technology to go after the diseases they're interested in. And then there are the diseases that we are pursuing drugs for individually.
Now that so many of us are working from our home offices, I’ve talked to several CEOs and founders who have said, “Well, my kids see that I’m on the phone, zoom call, all day long.” And then they’re a little disappointed and they say “Dad or Mom, is that all you do? Do you just talk on the phone all day?” It’s sort of another way to look at that question, “What do you do?” So when people say, “OK, that’s all very interesting, what you just told me, Yochi, but what do you do?”
Yochi Slonim 15:08
I learned that the best thing that works is to bring in people that you don't need to follow up on and tell them, you do your job; when you have a problem come to me. Then you are the problem solver and most of the time, when they come to you with a problem, you listen to it and say, figure it out, you know. So to do the job of everybody is definitely not what a CEO should do. The CEO should work on the strategy, should work on communicating, and exciting people about it. You should be presenting the company to anybody outside and convince partners to work with you, investors to invest in you, the media to know about you. And to put up a clear vision and break it out into the pieces of a work plan, but at a high level, and then bring the right people to actually execute on that component of the plan. And this includes, by the way, in biotech specifically.
Yochi Slonim 17:04
Sometimes you rely on external people for a lot of the biology. You know, CROs is something that in software doesn't exist, you build your own stuff. But anyway, you have to build a very robust system that relies on somebody else to do their job. And I developed a management style that is very, very delegating. I'm not following up on the work, and still, the work gets done. And it's done in, I would say, a way that is clockwork for Anima. And this is something that I've learned and changed my management style over the years because it wasn't like that before.
John Simboli 17:47
To make a system like that work, I would think you'd have to be a pretty good talent scout to bring on board the kinds of people who thrive in that kind of work environment where you can trust them that they know what they're doing. How do you spot those people?
Yochi Slonim 18:03
So at Anima I actually I've built a company by bringing in people that I worked with before in previous companies. My COO is doing, you know, all the budgeting and all the operational stuff. And I mean, we worked together in two previous companies. The head of software worked with me before. My big bet was on the CSO, because coming back, I mean, this was seven years ago, so we are working together for seven years now. But at the time, that was a big puzzle for me, you know, how to identify somebody to build all the biology part of the platform, and I had the vision of what the platform should be. I don't know, by the way, how it came up to me that technology could be used for something. I didn't know the word back then, I'm going back 12 years ago. Okay. So, again, I'm more than a decade in this, but still, I remember that, when I thought about it, and using technology like that to discover drugs. And it was called drug discovery platform, you know, and to bring the guy, in that case, it's a woman my CSO, that knows all the biology and can build all the assays and all the experiments and to really build that, that was a huge bet, but actually proved to be the right bet. She brought in everybody there. She's managing today, like, 80 people, in that R&D. By the way, we are running all the R&D in Tel Aviv, Israel, although Anima is a US company operating out of Boston with all the business side and the partnerships, but actually we do all the R&D in Tel Aviv, in Israel.
John Simboli 19:48
It must have taken a certain amount of courage to hire that person, hire that woman, and know that there was a lot on the line, that you were making the right choice.
Yochi Slonim 19:58
Well, bear in mind that the company was two people and an idea. So it was a recoverable mistake if at that stage you bring the wrong person. It was really, really early when we started that. And we've built it very, very fast from there, because we took the technology out of the university in late 2015, really. And we decided to build a lab in Israel and to take the technology, transfer it there. And we are six years on that path. But actually, the first three years, we just built the platform itself, we started to build the pipeline only three and a half years ago, and already with a couple of programs that are in preclinical stages, 18 programs in the pipeline in total, two partnerships with big pharma behind us. So it's a machine, it's a machine that is working really well to generate new programs, to advance them and to move them forward. And it has a lot to do with the people. But it has a lot to do with the technology itself.
Yochi Slonim 21:06
The platform of Anima is actually unique by being the only phenotypic screening platform in the space of mRNA small molecule drugs. Now, why is that important? For people that don't know what is phenotypic screening, I would say, actually, there are only two approaches to discover drugs that have been successful over the years. One is called phenotypic screening, which is really the idea of let's try a molecule in sequence from a library in the live disease model, in live cells, expressing that disease condition. And let's find molecules that work. The other approach is called a target-based approach, meaning if you already know what's causing the disease, you know, the protein, it's in vitro, you're trying to find molecules, sometimes you try to actually run them in the computer to predict molecules that will bind or attach to that protein. The thing with phenotypic screening, that was the oldest method actually, and in a way, it's an amazing method because if you somehow screen, like you go and search one by one, hundreds of 1000s, maybe a million molecules, and you found a few that actually are doing this, actually are transforming the cells from diseased to healthy. That's an amazing thing because you already have it. I mean, working in the live biology of the disease, you're not in vitro, you know, some experiment that’s isolated, and then you have the huge challenge of, will this work in the actual biology. But the problem is that the phenotypic screening approach has been, all along, something that was challenged by one major thing, you found the molecules, but then you don't know how they're working. To figure out how the molecule works, could be a bigger problem than to find it in the first place. And this is a big challenge that has remained unaddressed for the last 20 years with phenotypic screening.
Yochi Slonim 23:13
The thing with Anima's platform that makes it so unique in the space is that, yes, we are the only phenotypic screening approach. And we as a result of that can find drugs very quickly. And we kind of run in a year all the way to lead candidates that are already working in the live biology of the disease, and are making the change, are doing something to increase, to decrease the protein, to fix the translation or mutation problem in the protein. But the other side of it was to figure out how they're working. And this is where all the software comes into play. Because those images, the millions of images that typically come off the phenotypic screening systems that are like huge piles of data that nobody can understand anything from them. Other than the fact that the molecules work, you cannot see in the images, anything. We decided to use AI to analyze those mRNA images. And not only to analyze them, but the AI is used to elucidate the mechanism of action of the drug to actually understand how it is working. There is a huge difference in the effectiveness of the platform. And as a result of that, when I'm saying we are three years, you know, and produced programs and partnered twice, and all that stuff. The pace of things is accelerated by the nature of the platform, that on one hand can produce, fairly quickly, drug candidates that actually are working in the live biology and then on the back end of it, to figure out how they're working. And with these two things, you've got a very tangible thing in your hand, because you've got something that modifies a disease phenotype, right in the cells of the disease. And then you can actually explain the biology of how it's working. This is extremely tangible and exciting for partners, like Lilly or Takeda, or other pharmas, because they look at it and say, Okay, I can see this working, and I know why it's working. Give it to me, I will move it forward. I know the chemistry, I know how to do this stuff, especially because it's small molecules. So this is really exciting that you can partner very early with partners, like we did. And we are working on additional partnerships. And second, that you can move your own programs with a high success rate. Because those drug candidates are actually showing promise right from the start. They work in the live biology and you understand that biology with the AI stuff that I described before, so you can actually move them forward with better success rates than the usual. And this is what we've been doing.
John Simboli 25:58
Do you remember what it was you wanted to do when you were eight or nine? Does that interest in any way connect to what you're doing now?
Yochi Slonim 26:06
From the first moment that I got into my hand, a calculator, you know, the first calculators, they could do very little. I was excited by that and, and this was the beginning of my software journey, because I looked at that calculator, and I was asking myself, you know, maybe it was 10, maybe it wasn't eight or nine, I don't remember. And I was asking myself, could it do more than just, could I program it? Now, I didn't know the word program. But very quickly, there started to come out those calculators that actually you could program, you could program them in very, very primitive ways. And I was all into it. I became a hacker at the age of, maybe it was 12 already then, okay, I cannot tell you if it was nine, or eight, or 12. But this attracted me and I actually started, you know, at the age of 16, I was already programming, you know, an Apple II computer. And I was really excited by that. And somehow there was a kind of destiny to go on the software thing from the beginning. There was nothing to tell me that I will switch to biotech, that I can tell you. That happened for the reasons that I explained before, but the fact that this is software in biotech makes sense, somehow. Otherwise, it would be really like, I jumped to the other side of a river, and I don't even know how to swim. I knew how to swim. And I kind of swam my way into this. But right now, as I say, I swim quite well in this. And I feel quite confident, you know, in the biology part of it. But it seems like somehow there was a destiny in the whole thing.
John Simboli 28:02
When people hear the Anima Biotech story, what do they get right? What do they get wrong? How do you help them understand?
Yochi Slonim 28:11
So I would say that, it depends on who those people are. I mean, you asked me before about people that are not familiar with the whole industry. And that's the way to explain it. But I'm interacting most of my time, actually, with people that are fairly familiar. And the reason that they are talking to me is that they are fairly familiar with mRNA as an area of interest. And sometimes they are in AI, as I said before, but let's talk about pharma or investors, and they come to us and they say, what makes Anima different from the other companies that we are seeing right now in the space, which is mRNA, small molecule drugs? What are you doing that is different? And the way that I'm looking at this is I'm saying, first, there are different approaches, but really, this is a novel biology space. And nobody really understands yet about how mRNA biology plays out. So we are the only company that really doesn't make a bet on what works and what doesn't. Phenotypic screening, by the nature of the platform, means you are just looking to find molecules that work to modify the disease phenotype. At the later stage, you have to address the question of how they work. And that, we do, as I explained before. So really what people usually don't get right, I would say, is not generally about Anima, but about the whole space, is there are so many different approaches right now. Because people don't know what's working and what's not.
Yochi Slonim 29:46
For example, there are companies that are trying to target the RNA itself, with molecules that would bind to the RNA and do something to it, prevent it from being translated into a protein. or prevent it from being processed, or changing the way that is processed. There are companies that are going after specific mechanisms of action that they are betting on. And I'm saying betting on because it's not a very safe bet, because nobody knows still what will actually work. So this could be RNA binding proteins, something very technical, or it could be splicing factors. And there has been success, to a certain extent, in each of those. What makes Anima really different is that there is no bet; it's just about let's use something that works by phenotypically screening the molecules, and then let's figure out how it works. And the thing is, it's very exciting when you have this AI technology because the difference between having that part with the software and not having it is a 10 times improvement in the ability and the speed of figuring that out.
Yochi Slonim 31:04
It's known in the industry, and most pharmas, when they talk to us, or investors say, hey, that approach of the phenotypic screening will get you very quickly some very exciting molecules, we agree. But we also know from history, from experience, that it can take you up to five years to figure out how they're working. We don't want to wait that much. We don't want to make that bet. But when I tell them, listen, guys, in one year, for six different molecules in two different programs, we elucidated the mechanism of action, and actually got to the molecular targets in less than 12 months, for six programs, six molecules. So this is like 20 times faster and better than anything out there. And it's because of the AI that we actually couple to the phenotypic screening that makes all the difference. Otherwise, you are in the dark, really, you are, it's called fishing for targets in the dark. Like, where do you start? Millions of images, no clue why it's happening. All of a sudden, you've got this software doing that for you. So what people really get after they speak with us is that, one, it's completely differentiated from anybody out there simply for the fact that we are the only phenotypic screening approach in the whole space. Second, we are not making a bet on any mechanism, we just find molecules that work in the live biology, and then we map them back to how they work by using our software in the AI mRNA image analysis. That's what makes Anima different.
Yochi Slonim 32:39
Now they say, usually, they say, Oh, now I get it, you know, now I get it. And I understand why it could be a more productive, and more valuable approach to go into, because you could find any mechanism of action with that technology. And you are not really betting on anything, it's multiple shots on goal. Now imagine that you are Lilly or Takeda or any other pharma, you will get from Anima, maybe 12 different molecules that we generate within a year, very quickly, out of the screening, they all work in the live biology of the cells that they gave to us. And then we say, Okay, now we can tell you how they work. And you've got multiple shots on goal to progress these projects forward, you're not betting on one molecule that maybe is not even a mechanism of action that clinically will translate into any substantial effect. You're getting 12 of them. And we'll figure out the mechanism of action of all of those, and you choose. So it's giving them a very tangible asset to work with. For investors that are mapping the space, they realize really quickly that Anima is very different from anything else that they've seen. And then they need to actually go back and figure out how to put that into the approach that they are looking outside and seeing so many different approaches. So that's still challenging when the new space is formed like this and so fast.
John Simboli 34:12
Is there anything you would like to talk about in terms of how the field is developing, that's of particular interest to you? Not necessarily in the way that Anima going to pursue it, but is there anything that's remotely like when you first realized you wanted to program a calculator or when you first realized that you could unify that world and the world of biology? Are there aspects of biopharma and biotech right now that are really pulling you in? Just drawing you in?
Yochi Slonim 34:42
With all the technology I mean, you look at what technology can do today, last generation of iPhones, whatever. I mean, amazing stuff. And for the most part, we are still in the very, very beginning in understanding the real reasons that disease happens, and how to actually find treatments or drugs. And what excites me, really, is that for the first time ever, it seems that these worlds are merging together. And the power of technology that is not the traditional biology, not the traditional chemistry, but coming from a completely different world, software and AI, and this is coming into the mix, to try to build something that would accelerate our ability as humanity to actually deal with that challenge. Now, for us today, communication is not a problem. I mean, we are talking here, with video with audio, and we sit on two different parts of the world. I mean, we don't even question how that is possible. But you go to forums, and I've been doing this, of rare diseases and stuff, you see people and they are all talking to each other, maybe 1000 people. They share, unfortunately, something in common, which is a rare disease, and they are kind of helpless, and nobody knows, and nobody has any clue as to what to do, even though they know about this for, already, 30 years. Now, a case at hand, Parkinson's disease, which is something that, you know, the first drug was 40 years ago. What has been done over 40 years to improve it? Everybody knows somebody that has that disease; it's becoming more and more and more, you know, out there, and it's not making any progress. We are working, actually, in one of our programs on that. But this is what excites me about the ability of technology, coming from fields that are different from the traditional biotech, to make an impact and to accelerate our ability to actually find treatments for those diseases that have been resistant and kind of almost immune to everything that the traditional biotech has been able to offer. That is exciting. And I think this is a trend that is starting and the ability to process huge amounts of data. And to do that with technology that is coming from a different area is really promising and I think that this will change the world.
John Simboli 37:24
Thanks for speaking with me today, Yochi.
Yochi Slonim 37:27
Thank you, John, for inviting me. It was a pleasure.