Brandy (00:01.966)

Hello and welcome to Catalyst. Today I have Lee Bowman with me. He is the Chief Operating Officer at Metaphage. They are a leader in genetic medicine innovation. As Metaphage's first employee, Lee has been instrumental in driving growth by forging strategic partnership and leading initiatives to maximize the impact of the MS DNA platform for patients.

Most recently overseeing the integration of AI and ML models into the MSDNA production process, enhancing efficiency and scalability in bio manufacturing. Lee has a background in strategic operations and leadership. His career spans molecular biology, software development industries, and he's thriving at the intersection of biotechnology and digital transformation.

driving impactful solutions for a rapidly evolving industry. Lee, how'd you like that introduction?

Lee Bowman (01:00.582)

That's quite the introduction. Thank you very much, Brandy. Thank you. I'm really happy to be here. Thanks for bringing me on today. I'm looking forward to it.

Brandy (01:11.866)

we're excited to have you on. Let's just go ahead and jump right into this. We'd love to have you share just your journey to becoming CEO of Metaphage, with your background in molecular biology, software development. How did this all prepare you for the role that you have now?

Lee Bowman (01:30.918)

It actually all starts at the University of Waterloo. So, Metaphage spun out of the University of Waterloo. I was a student there. My background was bioinformatics. So, was studying there and then working there thereafter with our founder who was developing the technology at that time along with our CTO now. And once they had...

filed the patent for this technology. They started the company and as you mentioned, I joined as the earliest employee and the rest is ongoing history. In terms of the in between the invention of the technology and the founding of the company, I had worked in the software industry. So that element of it has helped with the digital transformation aspect, the opportunities I had at University of Waterloo, not just in my studies, but also

plenty of opportunity to work in lab and understand that maybe the lab isn't the best place for me, but I still love the technology and being able to enable and empower the team we have to continue to innovate and develop the technologies they have is essentially my dream job. So I'm pretty happy where I am right now. In between that, working in software was product ownership, scrum master, essentially

All of it comes down to moving between the people developing the technology and innovating and the end users, be it users of a piece of software or the patients for a therapeutic product. So I've been able to find some analogies in all of it. And it's exciting right now for where technology is right now, both on the biotechnology front and in AI, ML software, rapid software development.

coalescing right now at very interesting time.

Brandy (03:24.116)

Absolutely. So this company essentially just rolled right out of university.

Lee Bowman (03:29.986)

Yeah, so University of Waterloo is really interesting in that it has an IP policy where its inventors are the owners of the IP. So we were able to move directly into a company and begin working from there. Shortly after the patent was granted, we moved into JLABS, the accelerator center here in Toronto. And we've grown from there. So all of our R &D, our manufacturing, and overall operations are based out of there.

Brandy (03:59.33)

Now that's really incredible. I think, you know, it would be really important, especially for those who are listening who are unfamiliar with mini string DNA, MS DNA technology. Can you talk through that a little bit and why it's a game changer in genetic medicine?

Lee Bowman (04:16.184)

Happy to. It's mini-string DNA is the foundational technology that metaphage is built on. we, mini-string DNA and its production process are core to its innovation. So DNA plasmids are typically circular. Mini-string DNA is linear with covalently closed ends. And it's novel in the way it's produced, but also its structure. So it's modular. We can swap genetic construct in and out very quickly.

has enhancer elements that improve its expression, its efficiency, and the lack of a bacterial backbone is an important aspect. So that's an immunogenic component that can cause some issues in using it in a therapeutic context and also as a starting material in other applications. This allows it to be safer in a patient context but also operate more effectively and efficiently as a starting material.

And lastly, being produced in E. coli, which dovetails well to the bio manufacturing aspect, is we can produce it really quickly. E. coli is a well established microbial system for production and we've been able to build on that. It allows us to generate it quickly in large quantities and furthermore, the quality that you get using that platform, it really does stand out against competing solutions today.

Brandy (05:34.486)

Yeah, that's really incredible. I'm just thinking about just some of the challenges, right, of what you're producing and how technology comes into play. Can you kind of talk through that a little bit?

Lee Bowman (05:51.43)

Certainly. We've been fortunate enough to get a hold of, some fantastic talent on our process development, and on the other side is some fantastic equipment that have allowed us to scale up the upstream and the downstream. As far as the AI ML element, it's the upstream that we're focusing on at the moment. And it's a case where we can rapidly produce the material and generate an enormous amount of data with each of these production runs that we're

to build on and leverage in producing more optimized runs as we go forward.

Brandy (06:29.676)

Yeah, no, that's great. mean, what's, I guess, kind of what's the ultimate vision for Metaphage? Like what different applications are on the horizon for MSDNA technology?

Lee Bowman (06:30.077)

down there.

Lee Bowman (06:44.216)

Mini-string DNA was initially developed for non-viral gene therapy, and that still remains a primary focus for metaphage in developing these redosable non-viral gene therapies for patients in need. Benefit of mini-string DNA is it can handle a very large genetic payload in contrast to viral methods that are typically used in gene therapy. But unexpectedly and fortuitously, it turns out it has applications in other verticals in areas such as gene editing,

acting as a starting material for viral vector production and mRNA production as well. So those are other verticals that we're exploring as well and working with different pharma and institutional partners to expand and see how others might be able to make use of this technology. in the meantime, we get better at making it and leveraging it as a tool for therapeutic products for patients in need.

Brandy (07:38.798)

And are you, do you all have a manufacturing facility in Toronto? What is this? What does that look like?

Lee Bowman (07:43.93)

We do. So our facility is based in Toronto, the same, we're all local in one small facility called J Labs in the Mars building in the Discovery District in Toronto. It's been an excellent location for us to grow as a company. We have a full process development lab and then downstream production lab as well. With the scale we've been able to achieve with the systems we have in place, hasn't necessitated the growth.

We will get to that point, but right now we're fortunate that the system is operating as well as it is and we're able to satisfy both our internal needs for research and also the external needs for those of our pharma partners that are working with the technology and pushing it to its limits.

Brandy (08:30.712)

Yeah, and as you're, you know, I know that your primary focus right now is the integration of AI and ML and you're working with Katalyze, correct?

Lee Bowman (08:41.072)

That's right. That's right. Now, actually, we had a meeting this morning. It's been fantastic working with them. They're focusing on the upstream production process. So this is the fermentation in our bioreactors. As I mentioned, this equipment generates an enormous amount of data of many, different variables at different time points throughout the entire process. All of this data is sitting there, and it seems like a waste for it not to go to use. So leveraging the ML

algorithms that the Katalyzed team is helping us develop, it's really helping us reduce our optimization time. our base process of fermentation, it's relatively standardized, but with each novel construct. So you're putting a new gene, say, in ministring DNA that you need to produce. Maybe longer length, maybe different GC content. Different elements of it can affect the how

different aspects of the production process variables you need to toy with in order to get the optimized production at the right set of variables before you move into the full production run. This allows us to start feeding that in more quickly and get some of those.

settings to evaluate initially. So instead of having to do a number of iterations manually and some guesswork, some evaluation with our team, it can reduce that time. So ultimately our full production time gets a lot faster. That'll be in the short term. In the long term, the model ingests more and more data on a broader scale, we begin to surface different areas that we might be able to leverage, areas to optimize that can improve our yield.

rate of production, the efficiency of it, and ultimately the quality of the product. So it's really this pilot is just the beginning of something that will be, I think, much bigger and impactful for the organization.

Brandy (10:38.582)

Yeah, it's incredible. I mean, I think about all of the hurdles, especially for smaller biotech companies just launching. I feel like this embrace of this type of technology will help them tremendously. Do you have some insights into that? Just kind of thinking about biomed from like a bigger, wider lens.

Lee Bowman (11:04.326)

Yeah, I'll admit this pilot project is really phase one and it's something where we have clean, actionable data that can move directly into a model. I mean the exciting stuff, the sort of things that brought me into bioinformatics initially, way back in my undergrad days it got me excited and still have me excited today. It's the reason I'm working in this field is what can these algorithms, these models, these new technologies

paired with the sheer volume of data structured and unstructured that's growing every day, when those come together, what can it mean for drug development and the design and the rapid iteration and prototyping of therapeutic products can help patients more quickly and just make them more effective? What are the in-silical models that we can then develop at the molecular, the cellular, at the digital twin of a patient? If you really want to go to that level, this is

like 30,000, 40,000 feet, but it's really exciting where it can go. essentially the technology is catching up with imagination. And that's probably the most exciting part of it for me and I think pretty much everyone else in this field is hearing where it's all kind of coming together. And honestly, my imagination is starting to have trouble keeping up with where it might go, which is really cool.

Brandy (12:10.243)

Yeah.

Brandy (12:27.244)

It is cool. mean, it definitely takes the imagination to a place of personalization, right? Of medicine, really. I mean, it seems like you can get to a place where you can truly treat individuals and their individual needs by leveraging technology.

Lee Bowman (12:49.708)

It's personalized medicine is definitely growing in pace and that's an opportunity for us to engage in as well, particularly in the gene editing space. If you're looking for CAR T applications, but also where we're focusing is more broad gene therapy treatments that can deal with an entire sub population that has a particular genetic disease that they're dealing with. We can address that mutation that's creating an issue for them, deliver a healthy gene on a cadence that works best for them.

the degree of expression that they need, and we're able to develop that very quickly. We can prototype it quickly. We've done one construct, and within about six weeks, we can have a new one ready to go for a partner or ourselves to evaluate internally with the models available. So it's a much faster path is what this represents. So that's really exciting for us also. And then if the designs themselves that we're incorporating into these newly iterated products are being informed.

by some of the new stuff on the horizon and actively in use right now. It represents some great opportunities for patients that maybe have otherwise been overlooked just because of the sheer cost involved in developing of therapies for relatively small patient population.

Brandy (14:01.507)

Yeah.

Brandy (14:05.902)

Yeah, it doesn't make sense, right? For a lot of companies to pursue if there's not a huge population of patients who are dealing with a certain disease. Do you see any limitations, I guess, kind of thinking about the acceleration of this field in particular, the technology, the innovation? Are there...

limitations, I guess, just in the overall industry that's kind of holding the acceleration back a bit.

Lee Bowman (14:41.456)

Good question.

Lee Bowman (14:45.178)

I would say it's just time. mean, you could say money and talent and personnel, but we have a lot of talent there and it's becoming a little easier to engage them as well. is funding available for kind of pilot projects like this. So I really think it's just, we're priming the engine. So it's getting in the hands of researchers that previously wouldn't have access to this type of technology and this work and they can begin playing with it. And honestly, my advice for anyone isn't, wait until you have three days of well, three years of well-structured data.

that you can then move into a project, just start a pilot. Start figuring out what questions you need to ask. Ingest some data. Maybe it isn't a huge data set that can have something immediately actionable, but you can start having those conversations between your product development, your process development, your drug development team, and an AI ML team that is familiar with the models. And you can begin finding that common language towards a common objective. Because you're speaking different languages initially, and you need to figure out how to work together effectively.

Brandy (15:19.886)

Yeah.

Brandy (15:42.018)

Yeah.

Lee Bowman (15:44.952)

and just get going, get started. And if we have enough teams working together, sort of priming the pump on this technology, I think that's what will have the catalytic effect that really moves things forward. you'll also be working with, what I'm most excited is we're working in upstream development right now, but we'll have other members of our team involved in this aspect of it. And we'll begin to see like, okay, well, we're generating data in this space now, maybe in a completely different area of the company.

maybe more on the drug development side than on the process development. And that can get their wheels turning as to like, well, this is being generated. Could it be leveraged maybe to use this way? Or I've identified another data set externally that maybe could be used in this way. And it gets the wheels turning in different ways going, OK, I have an idea. And it's an opportunity to spark ideas, to spark innovation that maybe wouldn't have happened otherwise if you used far too structured an approach and

and used a ready aim fire as opposed to a ready fire aim approach. But that said, not to say you shouldn't do effective planning going forward, but don't wait too long to be 100 % ready. Just start exploring, having conversations, coming up with ideas together.

Brandy (16:59.168)

Yeah, I think that that is a really exciting aspect is just in a setting that things are traditionally siloed and people are speaking different languages. People are involved in different processes and they may not be thinking about the other aspects, but I do feel like this provides an opportunity to open up that conversation and provide that transparency that maybe wasn't provided otherwise.

Lee Bowman (17:24.944)

Certainly, yeah. And I mean, if you're approaching it in exactly that way. So recognize that part of the integration process is you've got a pretty common objective, finding that common language to work together. it's been fun working with the Katalyze team as well. It's been educating from both sides. So this is how we do our process in producing the material. And this is what's important to us. is why looking at this, it results in this kind of yield. This is what we've identified and what we see.

Brandy (17:43.598)

you

Lee Bowman (17:54.798)

And then working with the Katalyze team, OK, these are models we develop. And what we do is we'll work with this size of data set. The data needs to be prepared in this sort of way. We need to be able to align different data sets in this manner. And by doing that, we can generate something. So you begin to educate one another on what each other needs and how one another's process works. And by doing that as the foundation, the conversations really begin flowing.

and the ideas come there, the integration is faster, there's less delay and when you do have questions or hiccups that do come up, inevitably they're resolved a lot more quickly and I think there's just a common respect that's developed as you're beginning to learn something that's really sometimes outside of your area of expertise. It's really been a lot of fun and we're looking at the Katalyze team as sort of an extension of Metaphage in that sense because

Well, as a small company, bringing in an entire development team that would be able to do this stuff, it doesn't make sense for a company our size and use of capital. But by leveraging some of the funding that's available in non-dilutive form, paired with the expertise that you can source pretty easily now from a lot of different places in your area of focus, we're in a great shape. It's exciting to see how it all comes together.

Brandy (19:02.862)

Hmm.

Brandy (19:17.858)

Yeah. And I think it's obviously in your setting and at Metaphase, this has been incredibly beneficial, but for a lot of folks that not necessarily like in a traditional startup sense, but companies who are small and who are innovating, but they oftentimes reach a place where it maybe is cost prohibitive for them to continue. And I think that this allows

innovation to develop in a really unique way that it hasn't traditionally done in this setting.

Lee Bowman (19:52.432)

Certainly. Yeah, I mean, as a small company, maybe the hardest thing and the most important thing is figuring out what to say no to, what to move forward with. I mean, I always look at, so when you're doing a company like this, anyone worth their salt doing this kind of work is dealing with the double-edged sword of both passion comes with curiosity.

Brandy (20:03.33)

Yeah.

Lee Bowman (20:19.398)

And if you can't have one without the other and an academic setting, there's so many areas to explore and it's exciting and you want to answer all that questions that doesn't go away. We still feel that we still want to know like, what if we did that? What happened there? But you need to add some guardrails to that curiosity, to that passion, to direct that focus for the health of the company, to get to get something to patients as quickly as possible. And perhaps that was even bigger challenge for us as a platform.

Brandy (20:29.464)

Yeah.

Lee Bowman (20:49.03)

So our product, MiniString DNA, you can develop a gene therapy for a myriad of different applications, therapeutic targets, patient populations. But you need to make some strategic decisions on that as to what is the best use of the capital we have in hand, the personnel and resources and knowledge we have available to us, and the ability to action it and move it to market through different partners that we have on hand.

All of that, you do have to constrain it. It doesn't mean we don't get excited about all the other really cool, shiny stuff. And that's where partnerships come into play. And as our production yields have increased, it's allowed us to do that a bit more. engaging with pharma partners, biotech partners, manufacturers to evaluate our technology for specific applications they're working on. now that we're able to generate enough material for both our needs and increasing number of external needs at larger scale.

Brandy (21:24.845)

Yeah.

Lee Bowman (21:45.766)

We're able to support that innovation by getting the material out there into the hands of people that can take that and run with it. Large Pharma is going to get to the clinic much faster than Mediphaage can. And we're excited to help them get there. And we're going to take close notes as they do it because we're excited to see what we feel is the next generation in gene therapy technology getting to patients. It's not easy. all the help we can have and all the ways we can work together is certainly

Brandy (21:57.486)

Sure.

Brandy (22:16.066)

Yeah, I know it's truly incredible. I'm thinking back to something that you said previously and just, you know, talking about the talent, right? That there's no shortage of talent. as we start to move towards leveraging more technology, especially AI, ML, do you feel like that talent need is shifting a bit? The types of talent that need to be considered is changing?

Lee Bowman (22:44.403)

that's, that's a big question.

Brandy (22:47.787)

I know.

Lee Bowman (22:51.45)

So I would say the technical talent that we're generating on the AI ML space, it's growing, it's getting more refined. There's a lot to keep on top of as well, of course. That's one aspect of it. And then there's the parties that can bridge that. I think we're getting better at bridging that between the life sciences and these areas. And that's something that's been ongoing for 20, 30 years now. But one area that's

I think we're not paying enough attention to is the ingrained industrial knowledge from industry that's being lost with retirement and it's not being transferred fast enough. And it's really important to continue engaging these people that have been there, done that. mean, some of our greatest resources in our organization are exactly those people. They're still just as curious and passionate as we are.

Brandy (23:29.251)

Yeah.

Lee Bowman (23:48.614)

but they've seen it, they've had the pitfalls. They know the mistakes they've made. They're just like, yeah, I made this one. You don't need to do it. You go make your own mistakes and tell me about it. And that's one of their greatest joys is passing that on. And it's really critical that we continue to engage however we can. We just don't lose that knowledge. And yeah, our systems are great at capturing that, but there's nuances that will be lost if we don't start working together more.

So that's still figuring out how exactly to do that most effectively, but it's absolutely critical. And it will serve the next generation of innovators in an enormous amount.

Brandy (24:32.29)

Yeah, absolutely. I've heard that from quite a few people actually, just that the people who have been in industry for a long time and have been there, done that. there's no technology solution that can capture that completely, entirely.

Lee Bowman (24:50.212)

No, no, like, yeah, there's there's details about like your fermentation run and things they've seen, but there's also bigger picture economic impacts that their industry has dealt with over the years. So as a small company, they can guide you on that, or they can see like, okay, there was a shift in technology happening at that. Maybe we're seeing a pattern here. This is how we handled it.

this is what we shouldn't have done. And you can have these conversations and you know what, whether that is what happens, it's a case where it's more knowledge you can build off of, it's additional preparation. You think things through, the bigger picture and the more people you have sitting at the table that can provide you that guidance, the stronger you'll be going forward. So we're grateful for the mentors we've got, that's for sure.

Brandy (25:15.074)

Yeah.

Brandy (25:34.796)

Yeah, absolutely. Yeah, so, know, Lee, as we're as we're kind of winding this conversation down, just providing some insight for those in biotech digital transformation space looking to make an impact. What advice would you give them based on your experience?

Lee Bowman (25:55.27)

I think it goes back, just get started. Go do it. It's not going to be easy. If you're looking at the digital transformation aspect, what I said before, just go do a pilot. There's talent out there, there's non-dilutive funding available in droves that honestly the funding bodies are just trying to figure out how best to use it. Give them an idea. Help them do it, help them help you.

Because it can be often quite straightforward to make a case once you have even a prototype level process that is replicable. You can start moving there on the bio manufacturing and drug discovery. If you're in the thick of that and you have something that you feel really can go for it, dive in, do a pilot. Even if you don't feel your data set is large enough, start that conversation, get it going, and it'll help inform how you collect your data, how you process it, what you look at, what you're capturing as you go. That'll make you a stronger organization in the long term.

So yeah, I would say just get started. Know that it's gonna be a challenging road. It's harder than it's been in a while. Funding is not as ample as it used to be. It's easy for people to fund later stage de-risked assets right now, but I think they're coming around to realize that.

Brandy (27:08.75)

Not as easy to come by. Yeah.

Lee Bowman (27:20.484)

If we don't fund the early stage stuff now, there won't be any late stage to fund later on. And Pharma has been great for this. They tend to take a long view on this. They recognize that these smaller biotechnology and therapeutics companies are operating as innovation engines. And counterpoint is these smaller companies look to large Pharma as they're enabling and supporting to help validate their technology, to help to work together to get to the clinic.

which would be otherwise really difficult and very costly for smaller companies. And it's been a boon for us as well. So we love these partnerships. And it's an exciting time to be here. It's a slog, but it's 100 % worth it.

Brandy (28:05.75)

Yeah, it means you have to learn by doing, right? So it's just getting in there. If you've got an idea, go for it. It's the only way that you'll be able to push forward. So Lee, thank you so much for joining our program today. It was wonderful.

Lee Bowman (28:15.814)

Absolutely.

Lee Bowman (28:24.23)

Thank you.

Brandy (28:27.768)

to have you, was a wonderful conversation and we look forward to having you on again. Maybe in a couple months when we can kind of see where things are going. I feel like things are changing that fast.

Lee Bowman (28:38.31)

They absolutely are. I'm curious what we'll be talking about then.

Brandy (28:41.934)

Wonderful. Thank you, Lee.

Lee Bowman (28:46.139)

Thank you, Brandy.