Reprogramming T Cells And Reducing Manufacturing Costs With RegCell’s Michael McCullar, Ph.D.

In this episode of “Better Biopharma,” host Tyler Menichiello speaks with Michael McCullar, Ph.D., CEO at RegCell, a cell-therapy company developing regulatory T cell (Treg) therapies for autoimmune diseases. They discuss RegCell’s origins and its disease-agnostic approach to product development, as well as the ongoing efforts to improve the commercial viability of cell therapies by reducing manufacturing costs through automation and point-of-care models.

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Transcript

SPEAKER_00 0:17

Hello and welcome back to Better Biopharma, the official podcast of bioprocess online. I'm your host, Tyler Menechello, and today I'm joined by Dr. Michael McCullough, CEO of Regcel, an immunotherapy company working to reprogram T cells to treat autoimmune diseases.

unknown 0:32

Dr.

SPEAKER_00 0:32

McCullough, thank you so much for joining me today. Thank you. It's great to be here. So for our audience, why don't we just I mentioned a little bit about Regcel, but I'd love for you to give us a bit of background about uh the history of Regcel, what you guys are working on, your lead assets, and um yeah, we'll get into the conversation here on reprogramming T cells. Great.

SPEAKER_02 0:51

Well, our company was actually founded by uh Professor Shemal Sagaguchi, who you may recall or be aware of that, was awarded or at least shared the Nobel Prize of Physiology and Medicine in October. And it's a great honor to have him part of the company. We spend quite a bit of time with him still. He's highly engaged thinking about the platform and how do we really translate this discovery into clinical applications. We're very much focused on launching clinical trials this year. And his view has always been that loss of tolerance is a hallmark of bodyimmune disorders and that T regs or regulatory T cells, they play an indispensable non-redent function to maintain tolerance. So when they lose their suppressive features or are unable to maintain sufficient numbers, that's a hallmark of bodyimmune disorders. So he was really wondering could we leverage regulatory T cells to restore tolerance in a curative way? But no one knew how to do that. So what we learned how to do was take a bad T cell, a T cell that's disease-causing, and learn how to recapitulate these essential T Reg ebject features that are involved in T Reg importing and persistence and functional durability, and then recapitulate those into these bad disease-causing T cells to develop a T-reg phenotype that's very stable, but can also specifically target autoreactive antigians. But that enables us to imagine using these cell therapies to really target disease-causing immune cells, but preserving healthy normal immune cells.

SPEAKER_00 2:15

Whereabouts in development are you in your most asset right now?

SPEAKER_02 2:19

So we have finished effectively, we're IND ready in Japan. We could launch a proof of mechanism trial here in the next couple of months, to validate the science, and then we were able to secure a very meaningful access to non-luther capital, about$40 million to advance the platform into another disease area that we think is a major medical need. It's kind of a fast-to-market concept. And we should launch that trial, we hope, by the end of this year.

unknown 2:44

Yeah.

SPEAKER_00 2:45

That's awesome. Congratulations, preemptively. I hope you guys get there sooner than later.

SPEAKER_02 2:51

Yeah. Yeah. We're very happy. We're very grateful. It's been a great journey in the last couple of years. We made quite a bit of progress. That's awesome.

SPEAKER_00 2:58

And when we talked, Mike, you said you said that Reichcel takes a disease agnostic approach to treating treating indications. I'm curious if you could tell us a little bit more about that, that philosophy, how that shapes the company and the development of your assets here. Where you kind of, you know, if you could really go towards any autoimmune therapies, how do you really narrow that down into the state?

SPEAKER_02 3:22

No, that's a great question. That's a fabulous question. Yeah. So I mean, what we do is one challenge with autoimmune disorders and really having precision medicine is that many of these disorders are caused by auto reactive antigens that we don't know what they are, right? They're undefined. So if you don't know what they are, it's very difficult to target them. That's really one of the reasons why Simon Sensei wanted to start with auto-reactive T cells, because if you can convert those to a T reg and you can conserve the TCR profile, then the T regs can actually fingerprint the exact same autoantigen profile that the disease-causing T cells are targeting. So what we were able to do is take the broad profile of autoreactive T cells and particularly program all of those so the resulting T regs are actually polyclonal, but they're actually enriched to target multiple or even unknown autoreactive antigens, which is a major difference compared to the types of cell therapies. Got it.

SPEAKER_00 4:18

Got it. And refresh my memory, what's the lead indication you're in right now?

SPEAKER_02 4:23

Well, we will do a proof of mechanism trial. I think we we kind of, you know, I spent quite a, I don't know how many years in quantitative analysis and pharma. And we so what people were asking at one point with investors and other parties, you know, there's a lot of discussion to get them comfortable with the science. Science is a little bit complicated for many people. So getting them to understand the approach of why it's important and why T Rex play a dispensable role in the ability to have anti-specific tolerance. But once we can articulate the science, then the question obviously becomes, well, how are you going to do this in the clinic? So when we would be asked that question, my brain is automatically thinking, well, you're asking me really 10 questions at once. And we're trying to answer complex questions about the pathology, right? For example, where do impaired T Rex play a major role in driving pathology? Then we need to answer questions about can we find in this mixture of diseases areas where there's a major gap in efficacy? Because we need to solve major problems, right? For patients. And then we need to, one thing early companies sometimes overlook is the complexity and endpoints, right? So as a younger company, we have to have unambiguous signals, meaning that we can't have indications with complex endpoints, right? Things that are hard to interpret, things that you can't access. So we have to be very mindful of that. We're also interested in market access. Can we get this to patients, for example? Then we have to manage things about competitive intensity. Can we enroll a trial? That's another complicated question. So we didn't say much about that. Then we did a lot of homework, right? Internally trying to rationalize these things and building AI tools to help us rationalize these. We're looking for those things. And yet we discovered an area of medical need amongst a disorder, a group of disorders called audium and liver disorders. And the liver energy is really a classic loss of tolerance organ. And these have been overlooked. So it was very clear to us that these diseases are driven by impaired T-reg function. And there's a very huge, obvious and very clear lack of efficacy for these patients. Don't have a lot of options. And the endpoints are very easy to interpret. So here's a spot we feel we could really have a very quick reading in terms of proving the validity of our platform in an area where we think there's a fast to market. We can control a lot of the KOLs, we can communicate with them. So it's a space that a small company could quickly develop, but we think it was a category-leading position in autumnal liver disorders. That was really how we secured the first grant to enable us to have a very capital-efficient way to get it to a clinical proof of concept in this space. Yeah.

SPEAKER_00 6:47

Thank you, Michael. Um, and not to put you in the hot seat here, but I'm curious. You know, you mentioned a couple of factors there from endpoints to clinical impact. Do you weigh any of those have more heavily than others?

SPEAKER_02 6:59

Or is that kind of is it kind of Yeah, well, yeah, that's a good question. The first thing we have to do is make sure we get the science right. For example, if we miss the other stuff and the science isn't right, you know, that doesn't matter. So most of it and what we do is we leverage our privileged sites. You know, I would speak with our founders and our science team and just in a casual way, asking them, you know, when T-Rex, for example, we everyone will agree, I think, that they're indispensable and non-redent cell type to maintain tolerance, right? But when they lose their function, either they lose their suppressive features, they can't maintain enough numbers, you know, what's happening to them? And then they would tell, well, Mike, it's no big deal, blah, blah, blah, blah. And so we would, you know, use those that this wouldn't go for months, right? But then we could take these concepts, make them into strings of vectors, and then use pretty basic tools to, and then we can mine a lot of data we have access to. Then we look for these loss of tolerance features, right? These profiles. And then we could start to look at that and looking for clustering around disease areas. At least that gives us a better signal that we think that broken T regs are playing a role in pathology. So we could get more comfort about the biology. Then we can start to rank order other things. But I think the need is the most important thing because I mean we have to really look, we're very focused on delivering quality health care for patients. So patients matter the most, right? When we get the science right, then we have to be mindful of patients. Can we really impact their disease? Can we find patients who are overlooked or underserved? Because that to me becomes the most important thing. And then the rest of it has to fall into place as well. But so we're looking for really signals and features of loss of tolerance. Then we're looking for areas where there are very limited options for patients. We don't care as much about population size. We think value is really delivered or created by how what quantum quality solutions you provide to patients.

SPEAKER_00 8:47

Yeah, I think a lot of your peers would agree with you that the patient comes first, and from that and the clinical impact, you can really everything else becomes secondary.

SPEAKER_02 8:56

Yeah, I think so. Too nice, I think, you know, when I first started doing this. You know, there's a lot. Even when I worked for big Japanese companies, I think they're they get that too. But lots of people, I think, would work bottoms up and looking for epidemiology. But that I don't think that makes the the biggest impact on patients, is what you can do to treat problems. I mean, every good company is to solve a problem, right? And it doesn't always have to be the value isn't driven by the numbers of patients you treat. It's driven by the quality of your innovation and what kind of problems you can solve.

SPEAKER_00 9:24

Yeah. Ain't that the truth? I think we're just born to be problem solvers, right? I think that's the condition, right? Sure.

SPEAKER_02 9:31

That's what that's what companies do. I mean, if you can't solve a problem, then you know you shouldn't have a company, right? So but we're very clear. I mean, that we we really take a very kind of a step back when I joined the company was really asking that question. And it became obvious to us that, for example, we've been using the same drugs for 35 years to treat autoimmune disorders, right? And none of these drugs really are curing patients. And sometimes they take them for a life, the duration of the rest of their life and without a curative potential. Or these drugs are very effective, but they still are really kind of blunt instruments that broadly suppress immune systems. Even the things that we think of as the most contemporaneous drugs still often will have a box warning for serious infections, increased malignancies, increases mortality. And fundamentally, it's because uh these aged steroids, for example, are still widely used. They aren't able to distinguish a good immune cell from a bad immune cell. So they will suppress all of the immune cells, right? So here we believe we wanted to restore tolerance, but you wanted to do it through it in a specific mechanism, right? And that's why we think these T rigs we reprogrammed from these cognitive T cells are able to distinguish and deplete a bad immune cell while preserving healthy immune cells. So I think this challenges that patients experience with serious infections, increased rates of cardiac toxicities or even malignancies, we can get away from those, right? Yeah.

SPEAKER_00 10:49

Yeah, and you I mean, you hear it all the time, the curative potential of these cell cell therapies, cell and gene therapy. I mean, it's for sure. It's an amazing time to witness it. I'm very happy to be having conversations with people like yourself about it. And it's it's almost surreal that we're here talking about how we could take and reprogram cells to treat previously. Um, you know, just kind of, I don't know if masking is the right word, but like you said, just inhibiting the immune system as opposed to just fixing it.

SPEAKER_02 11:15

Yeah, well, yeah. I mean, and it's it was what we call it anti-anagespecific, right? So if you talk to Shimon Sensei, that's what he speaks of a lot. And that really gets to the point of, you know, we don't want to, I mean, here's a way it really happens is that all of us in our bodies are going to have a population of auto-eractive T cells that mistakenly target our own antigens foreign, right? And they could amount to immune response against those tissues. And if it goes unchecked, that could be very damaging, right? But in a healthy, intact immune system, regulatory T cells play an indispensable role in suppressing those bad immune cells, right? Blocking them from attacking our own cells. It's when they get impaired that these bad T cells will become enriched. Right. So what we a T Rech will be able to do, if we could take these bad T cells, we program them to adopt these T Rech features, they maintain the same TCR profile, so they respond to the exact same antigens the bad immune cell is responding to. And we can suppress those, but make other immune cells maintain they're intact. They're not impaired, or they're not inhibited. So we can have a really effective way to suppress these bad immune cells but keep the healthy immune cells intact. Other agent steroids, even we think of as the targeted agents for autoimmune disorders, still aren't able to do that. But they're still broadly suppressing the immune system.

SPEAKER_00 12:31

Yeah. Thanks, Mike. Um, and speaking of cell therapies, I want to take a step back and look at them more broadly because I know in our in our briefing call we spoke about kind of the biggest challenges that cell therapies are facing. And you know, people last year saw that a lot of a lot of companies, I mean, the first one that comes to mind is like Takeda shuttering their cell therapy division. And so it's no it's no question that cell therapies are up against some significant challenges, uphill challenges here, one of which is improving commercial viability, um, along with reducing the cost of their manufacturing. And so I wanted to dig into both of those points with you. We could start, we'll start very broadly with improving commercial viability, of which I'm sure the the manufacturing component is a part. Um what what do you think are the hurdles to clear in that pursuit to improve?

SPEAKER_02 13:21

T days uh well, a couple couple of you know, my early in my career, when my first jobs was in manufacturer, very early days, late 90s, uh I was in my just a kid, really. But cell therapy production. So when I was recruited to join Regel as a board member, then as a CEO, the first thing I wanted to do was, you know, given I had at least a reasonable appreciation for the sometimes complexity and challenges of manufacturing. So first thing that we did was to recruit world-class talent in that field. And what I learned from them was about automation, right? And that's why we were excited about our process because we don't have to use viral vectors, right? We don't have to do gene editing. So effectively, that takes a lot of complexity and cost out of our system, but it makes it more amenable to automation. So if you start to look more closely about what are the biggest drivers of cost, what two things jump out at us. One of them is the bill of material agents, obviously, right? Labor. Labor is hugely expensive on insult theory. But the other one that's sometimes overlooked is that uh paying for idle capacity within a uh facility, right? It's very expensive. So we thought, well, let's look at these major doing sensitivity testing about these cost drivers, what can we tackle first? So given that our process is more simple, then we could really drive down automation to minimize labor costs, right? We also can substantially have a reduced uh what we call bill of materials or reagent cost. And then and then if we can automate it, then we could actually get it into the point of care. So what we learned about the challenges, one is the cost, right? That's for sure. So if we can tackle that, then the question really is about getting it to patients, so accessibility. So then we wonder well, if we can automate our process, we can drive the cost down, but now we can put it into a point of care where it's closer to patients, and then you get rid of the concern so much about idle capacity. So driving down the cost, but then getting into centers where patients can actually get to access the treatment. We think those are two big levers that we can pull to make our process much more commercially viable.

SPEAKER_00 15:31

Yeah. Thank you. And this might sound naive, so forgive me, but um, you know, you mentioned that Regel's manufacturing process kind of lends itself more to automation than say a car T that you'd need a vector for, right? Now, is that something is that by design, or is that just so is that just the nature of your your therapeutic approach?

SPEAKER_02 15:57

No, I think it's a little bit of a serendipity, right? Yeah, I think uh we we also have other ideas with our, you know, we do have concepts about pipeline innovations, both vertically up, what can we do with our current process, but also horizontally about could we contemplate other ways to edit cells? What is really clever was it's a little bit of a serendipity, as I mentioned, because you know we we kind of over iterations in the years came across as epigenetic programming. We didn't really set out to do that. But what we learned was part of the question was what happens in the thymus, right? What happens in the thymus when tu regs are precursor cells and there's a really important transcription factor that's a really the hallmark of T re bodies, it's called FOXP3. So our scientists wondered in the thymus, right? Before T Rex become T-rex, what happens to them that turns on FOXP3, that commits them to becoming a T reg phenotype. And then we learned about certain non-coding regions in the FOXP3 gene that are reprogrammed in the thymus, right? And if we could copy those, then we could really turn these bad T cells into T-rex. Just so happens we're able to do that without using any editing, right? Any viral DNA. So it's a little bit of serendipity that we were able to reprogram these things. These are locking phenomena that commit them to becoming T-Rex. They happen to be able to do that without having edited cells. So that's basically kind of the basis of our intellectual property too, around the process and how we use it. So if that's the case, then we need to think about ways to exploit that at our advantage. And we can now look and see where the people ahead of us have had problems. So if we can use that advantage, then I think to solve some constraints that have prevented cell therapy from reaching its full potential. And that's what we want to do. So it so happens, it's kind of fit together for us.

SPEAKER_00 17:44

Yeah. Yeah, and I mean that's that's great serendipity for for Ragsell for sure.

SPEAKER_02 17:49

Luck, yeah. Well, luck comes to the prepared aid. I think we're a little bit fortuitous and some things tended to line up for us, but that's something that early on we wanted to get ahead of. I still think that I felt if we we want to have a good control and have a commercially viable process, if we're not able to make the sales the way we want, then we run a trials will be much more difficult. So we're happy to invest effort to sort that out ahead of time.

SPEAKER_00 18:14

Yeah. And and broadly speaking, I'm curious on your your thoughts about the lever. You mentioned reagents as a cost point there. Do you think that's do you I guess I'm this is gonna be a multi-layered question, so bear me bear with me here. Um on one hand, do you think that's to do with just sourcing of these reagents? Like and to that point, do you think this effort to onshore US manufacturing is going to help that? Or twofold, what would you say? What do you think is the way through that? Like how can we, how can the industry reduce the cost of these reagents? Like, where is the scale? Well, scale is part of it. Scale.

SPEAKER_02 18:54

There's a cool paper that I read not too long ago. Scale. Scale is a big constraint if you look at even what we think is that are the most successful cell therapies for cancer patients. I mean, surprisingly few of them are able to be dosed. So scale is a constraint. Uh they're very high quality reagents too, right? So they're not as if they're commodities. Some people you know require very complicated reagents and they're expensive to make. Scale will drive those down. I'm not sure about onshoring if that will lower the cost or not. But scale is part of it. So, you know, when we talk to some of my other friends about what their bill of materials is, it's kind of shocking, right? For one patient, the costs of the reagents are tend to be several fold or almost the order of magnitude lower because we don't have to do that. And so that's part of it. And then we also can imagine, you know, our process is quite flexible. We can treat a wide range of pay diseases through one manufacturing footprint. So we could consume a lot of autocapacity if we if we had to, or we could even scale for the cost of reages. So I think this flexibility in our process makes it really helpful too, right? It doesn't matter. We we've been able to reprogram autoreactive T cells for a very wide range of autoimmune disorders. So we think we could use it strategically wherever we we want to for the most part, right? So that I think it's around some other constraints. So scale is one, complexity is another one. But I don't know yet if I can answer your question about onshoring the if that's going to drive the cost down or not.

SPEAKER_00 20:28

Yeah. Yeah. And so when you say scale, I know that scaling is a perpetual issue in the cell therapy manufacturing space. What what do you see as the biggest barriers there to scaling? And I mean it sounds like Reg Cell lends itself to, again, because of you you have these the process is not reliant on viral vectors. It's it's more so like uh reagent dependent, and you it's kind of lends itself to automation. But more broadly speaking, um how do you think companies what do they run up against when it comes to well hard to say because I can't speak for all the companies.

SPEAKER_02 21:08

I've never really am not a commercial CMC person. But we know what we learn and we have people on our team who are that's what they do. Part of it labor too, right? So I think when you fully automate and close a system, then you could put a much higher density, right, within a system, within a footprint. So contain automated closed systems can be much more dense in terms of uh units per square foot, for example, right? You could scale up or how it doesn't matter. So that's a major part of it. The other part of the scale is demand, right? I mean, you can if you're only treating a thousand patients a year, that's not a whole lot of demand to be able to create it very dense and to get economies of scale to lower costs, right? So there's obviously mathematical formula that dictate the economies of scale. So it's hard to really get a great economy of scale with that a thousand, two thousand patients indication. Where if you could have an impact, maybe you're you're treating 10x that, then I think the economy of scale in terms of driving down the bill materials might be more obvious. So that's another way we think about scaling. So scaling is also an economy of scale to drive down price. But you know, we're just not, I don't think that we're still in the very early days, right, of this. And I when I first worked in manufacturing, I was making antibodies. This was long years ago. And people would say that's a bad idea, Mike. You know, those are too expensive, right? And they were, right? But as we became better and we, I think we had much broader utility for antibodies, you know, those constraints went away. And I think to some extent, as we matured, learn how to better utilize or what how we could make cell therapies more effective in clinical trials, for example. There's some innovations beyond just the science itself that need to occur to make, I think, us be able to utilize potential cell therapies more effectively. That I think will also lead to more efficiencies in terms of manufacturing.

SPEAKER_00 22:58

Yeah, that's a great point that uh good clinical readouts and commercial, therefore commercial success will incentivize this.

SPEAKER_02 23:07

I think so. Yeah, but think about this. Other thing is, you know, interesting observation, but you know, just the pace of our scientific innovation has been so rapid, right? How we discovered, make progress on science. I think the systems that we use to translate those innovations to patient care, that those are lagging still, right? We can't have it kept up. So I think when these different ways to really leverage the utility of cell therapy and geneotherapy, they catch up in terms of clinical trial designs, regulatory considerations, how we interpret the results, endpoints, those things. Maybe then we can fully utilize these approaches and that kind of a wider adoption utility should catch up in terms of driving down costs and the challenges for people to access these.

unknown 23:52

Yeah.

SPEAKER_00 23:53

Yeah, thank you, Mike. You mentioned point of care earlier. And I'm I want to dig into that a little more because I mean, point of care is something that I've been hearing about since I began covering the industry a couple of years ago now. And so uh I want to know from your POV, where are we now compared to where we were a couple of years ago? And where does it have yet to go? You know, how can the industry better support point of care? Um, how is Reg Cell kind of, I guess, contributing to that, to that front? Because you do you guys do or at least intend to use point of care in the future, correct? It's important, yeah, yeah. Yeah.

SPEAKER_02 24:27

Well, that means my first focus on trying to really make a commercially available process really was about controlling the cost of goods, like controlling the bill of materials, right? That was obvious to me. Then board members, Steve Canner, right, who came from Caribou, he said, Mike, you're missing the other part of the equation. It's it's not just the cost, and it's really getting it into outside of the major institutions. Many people patients don't live in big cities, they can't access these. So this automation concept, these closed-contained systems, like we see, are being pushed out by really more sophisticated uh uh suppliers of equipment, right? Those can be put into centers that don't need a huge GMP facility. And I think that's what we're talking about, you know, centers that aren't the major medical institutions in big cities. So if you can put it there, the question also has to be they have to be able to do aphoresis or access blood. But in these these conditions that don't require lympho depletion, for example, it's a little easier for you to imagine them having access to a small footprint that's able to automate a closed system, right, in a smaller organization. That I think is the the the next iteration or next advancement of what we think of as point of care access. The other one that we're working towards for us is is you know what a lot of the analytical work, if you can automate that real time in situ, that's another way for us to really think get around some of the constraints. I mean, the analytical work and process testing, you know, that's takes a lot and that's hard, I think, now uh in terms of logistics. So I think our view is it'll be easier for us to imagine in situ type of monitoring in process to make the automation a lot more simple, too.

SPEAKER_00 26:17

Mm-hmm. Yeah. I mean, analytics, I just can't escape the importance of analytics. How old are you cut it, right? Almost every conversation I have. Is that right? Analytics brings up.

SPEAKER_02 26:27

But but that's what we feel is that for good reason.

unknown 26:29

Yeah.

SPEAKER_02 26:30

No, you have to. I mean, they're they're not, there are not uh trivial processes, and yeah, we can't make that too simple. They're not small molecule drugs, right? They need to be tested and monitored. But ways to do that in situ, I think. I think that could really be a big lever in terms of getting the process a lot more approachable to be a point of care.

SPEAKER_00 26:50

So as we stand right now, analytics aside, and that in situ analytical component aside, do you think it's more of a equipment capability problem or just availability and like accessibility of the equipment that we currently have problem? Or is it a little bit of both?

SPEAKER_02 27:06

A little bit of both, a little bit some development on the technology, right? Um we're getting really much well monitoring cells, right? So my view is that's becomes a you know, data sets. We're not a data science company, of course, but we're gonna use data science to solve big problems and we're gonna use data science as tools to automate complexity. So I can imagine that the ability really to look at a cell in terms of image quality and image processing as we reprogram one from one cell type to another. That probably is something that we could automate, right? And getting rid of a lot of the in-process testing that we have to do. Well, obviously, we want to test the final product. There's no doubt that could be done, I think, in a more physical way, but automating, you know, how we access the progress and the of the completion of our process, right, could be done, I think, in an automated way in situ.

unknown 27:57

Yeah.

SPEAKER_00 27:58

I mean, I feel like more and more too much.

SPEAKER_02 28:00

I'm probably giving away something I shouldn't give away, but that's kind of what we think.

SPEAKER_00 28:03

Yeah, don't give away secret sauce here. Yeah, it's it's funny. I think more and more it's kind of being a biotech is part and partial with being a data company, right? Like maybe maybe.

SPEAKER_02 28:14

I think, you know, I think, well, you know, during COVID, when we we were just getting much done, and my my old boss said if you should get there's some data science stuff you should be taking courses on. And it wasn't to be a data scientist, but uh I think it just gives you my uh mid different view about how do we use it as a tool, right? We try to use it. You know, we take another step back and we don't want to build a huge organization, right? But to do that, you know, can you leverage technology to make us more effective? And that's the way we think of it. We're not going to create a huge data science platform, but we're going to use those as tools, really, to one, enhance our ability to make complicated decisions and the quality of our decisions, and then to improve our effectiveness by trying to make people make them more productive. And here you can imagine a very labor-intensive process in terms of CMC and the analytical part. That's a very labor intensive too. So can we use automation through data science to make that become more effective? That's good for everybody, right?

SPEAKER_01 29:13

Mm-hmm.

SPEAKER_00 29:14

Yeah, absolutely. Absolutely. Thank you, Mike. I really appreciate those thoughts. And of course, I appreciate you being here today. And uh, I don't want to hold you too long, but I do want to ask you the homework question of the show that I ask every guest. And that is, Mike, how do you think we can better biopharma?

SPEAKER_02 29:30

Well, for me, that was I think I pointed out a little bit earlier, right? I mean, it's just the the pace of our innovation is for me is accelerated so fast that the systems that we have in place to look at and measure and implement these innovations, how do they get to patients? Those types of systems and processes have to be updated, right? Otherwise, I think we're struggling a bit to be able to fully leverage those things.

SPEAKER_00 29:54

That's a great point. A great point to end on. Thank you so much, Mike. Literally on this episode of Better Biopharma, the official podcast of Bioprocess Online. We'll see you next time.