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Saul Marquez:
Hey everybody! And welcome back to the Outcomes Rocket. I’m super excited that you’ve joined us today because I have an amazing guest. His name is Amrit Chaudhuri. He’s the chief executive officer and co-founder of SmartLabs. Amrit spent his first eight years in the industry working with hundreds of R&D leaders and universities, biotechs, and large pharma companies to develop commercial products from discovery to market approval. In that time, he has gained major insights to help companies fast-track their advances from research to market. He founded Advanced Peptides in 2007, a BioPharma CRO, he holds patents in chemistry for global commercialized products, and he’s one of Inc. Magazine’s top 20 innovators in healthcare. Amrit, so excited you’re here with us.

Amrit Chaudhuri:
Saul, thank you so much for having me.

Saul Marquez:
It’s a pleasure. And look, I’m excited to cover this topic of the value chain in pharma because it’s certainly a huge opportunity for innovation and advancement. Before we do, though, tell us a little bit about you. What is it that inspires your work in healthcare?

Amrit Chaudhuri:
Good question. Um, you know, I think that I grew up in a household academics as parents. And my mother actually taught me really early that if you have the capabilities of doing something that can help people, it’s almost like a moral duty of yours to go and do that. And I think she did it right because it got drilled into me, and that’s kind of how I see the world.

Saul Marquez:
Well, kudos to your mom. Big shout out to Miss Chaudhuri. And look, right now, we have a big need in our market, right? So talk to us about what you guys are doing at SmartLabs and how it’s making a difference.

Amrit Chaudhuri:
True. Effectively, our company was launched in, this company was launched in 2015, and one of the co-founders and CEO, the goal of the organization was to create a dynamic resource set for the future of science. We saw in the marketplace, my co-founders and I saw that the way that science was changing and the pace of change was accelerating, and the diversity of resources necessary to just explore the science and then commercialize it was getting more and more diverse, more and more expanded, and the market was having a really hard time building the infrastructure, building the resource sets, building the SOPs and operational programs to adapt to things like gene editing, mRNA, cell therapies, a host of combinatorial and personalized medicine-based drugs that were being explored in the mid-2010s and that are now coming to market here in 2023. And so with that kind of early insight that, hey, the world is about to change, but the world isn’t changing fast enough, how do you go and propel that change by building the next set of tools to go and develop those therapies, cures, and treatments for patients? That was the initial idea behind the business. Um, and so we built an engineering infrastructure, operations, and resourcing company quite fast, with expertise in everything from animal research and clinical manufacturing all the way down to how do you structure information security around large data sets that are being hosted by multiple different companies in a co-located research center.

Saul Marquez:
Well, look. It’s certainly efficiencies of scale that are much needed and a huge shift from what large pharma could do versus what small, innovative, more agile companies could do. Tell us what you believe you guys are positioned to do for many of these companies.

Amrit Chaudhuri:
I mean, what we’re really talking about doing is creating the most dynamic infrastructure and resource set to support the science environment that scientists work out of and doing so in a way that’s nimble, agile, and on-demand. And so, think of, if you could build all the tools to build a drug in the same model that AWS delivers services, think of how that transforms the industry: the speed, the efficacy, the cost, the chance of success in outcomes. And so, that’s what we’re working on. How do you both create the tools and then create the access to those tools? And how do you make those tools not just iterative, you know, change, but disruptive? How do you actually go and break that system and rebuild it from the ground up using modern methodologies?

Saul Marquez:
No, for sure. It’s really fascinating. Out of all of the tool sets that you, actually, why don’t we just go there? Like, what are the tool sets, right? And talk to us about those toolsets. Give us kind of like the bird’s eye view, and then which one do you think is like the one that people just need to get on? The one that you guys do best.

Amrit Chaudhuri:
So it’s kind of crazy. So, our business is basically split into two really different things. We’ve invented the ability to build any kind of lab on demand and then change that lab from one type of lab to another. So go back to like being at a university, having a chemistry lab, and then being in a medical school and having an animal research lab where mice are being held or whatnot, and then going to a biology lab or a tissue culture room or whatever it is. These are all different types of infrastructure. So today, people have to figure out the kinds of resources they need. They need to build those, and once built at a specific capacity and a specific configuration, that’s it. That’s all you get. And in order to make a change, you kind of have to demo that and rebuild it. We have invented a way and a system we call the Universal Lab framework that lets us build labs like Legos and not like, you know, temporary, you know, a rickety, you know, labs that are, you know, kind of held together by duct tape. I’m talking about enterprise scale environments, meeting the highest level of standards for, you know, pharma companies, institutions all the way down to startups that utilize our platform. And so it’s like, rebuilding what a lab is, is part of what we do, the same way that Tesla reinvented what a car is based on a completely different paradigm of how you can design and build a car. And then because we did that and because we’ve standardized that, it actually has let us do a secondary thing, which is how do you actually digitize science? How do you digitize the operations and the coordination and communication that’s required across all these different disciplines necessary to work on the science and to commercialize it? So we built like a salesforce for science that lets us operate scaled research institutes with like machine learning, centralized cloud management, scheduling, and resourcing of resources across teams and between different departments. And doing so allows us not just labor efficacy but a response rate to how you solve problems; that’s unbelievable. We’ve taken an average time of response in this industry from 48 hours to 18 minutes.

Saul Marquez:
Massive. That kind of a transformation is huge. So then the hardwork came first, you guys got modular, you figured out a way to give people flexibility, and then the software came.

Amrit Chaudhuri:
It actually did.

Saul Marquez:
You started with the software?

Amrit Chaudhuri:
We started, the original company was like, How do you build a massive operational engine with tremendous expertise across, like basically building an open source pharma company? Everything you’d see at a Novartis or Pfizer and all these different departments except for the project itself, rebuilding that in a plug-and-play framework. What we realized is that, two years into the business, you couldn’t do that if you had a bunch of static infrastructure. And so imagine if you said, oh, I’m going to put this one server in my data center, and I can never touch that server again, like it doesn’t work. You’ve got to be able to go and update and change and modernize. You know, five years ago, from an IBM blade server to an Intel Xenon server. Imagine if you break down a data center to do that.

Saul Marquez:
That’s insane.

Amrit Chaudhuri:
Like and rebuild a data center. That’s how it works. Like, literally, how our industry works is insane. They spend $100 million, $200 million on a resource that they’ll mothball in three years and then maybe not use for seven years and maybe go and be like, should we be using that in year eight? Like, it’s unbelievable. And the time to get that resource is years. So if you discovered a cure, the early indication of a discovery of a cure to something like a cell therapy for cancer or for Parkinson’s we were working on with partners today, it’s actually in clinical trials. We were able to take a pharma company’s project and from Q4 of 2019, the ability to go and expand that project, the first chance they were going to get to do so was Q3 of 2023. That’s how long it takes to build scaled infrastructure. And we were able to take that project and spin up 18 projects like that by Q2 of 2020. So imagine just accelerating Parkinson’s research and cancer research by three years in the marketplace. Like, it’s just profound.

Saul Marquez:
Man, that’s insane.

Amrit Chaudhuri:
That’s just never been possible before.

Saul Marquez:
So like, let me dig into this a little bit more because it’s fascinating. And so, like, you know, a lot of businesses like just kind of, I’m going to take a pivot here and talk about business for one second. And many early-stage businesses fail because they don’t have their SOPs in place, they don’t have their established ways of doing things, processes, and systems. Is this kind of like that, or like differentiate it for me?

Amrit Chaudhuri:
Sure. Are you talking about from a client perspective? Are you talking about for ourselves?

Saul Marquez:
You know, like for a client? I’m thinkin, for the clients that are using your stuff.

Amrit Chaudhuri:
So this is the crazy part. Um, you would imagine that small companies, and it’s true, don’t have anything in place. They’re branding, right? Or they’re scaling for the first time. They’re building the first set of resources they have. So there’s a lot of execution risk. There’s a lot of delay. There’s a lot of you have to develop the solutions to then start working. Like everything happens, and then you get to start working in the scientific space. And so what we’re enabling using our model, we call it a managed research center. It’s this on-demand data center-like way of accessing this resource. Is that a project team, whether it’s a startup or a large company? But let’s use a startup as an example. If you give a startup of like ten people, $100 million, which is not outlandish in this industry of doing research and said, go like you have, you’re onto something, go as fast as possible and like try to make this real. What we are enabling that team to have is overnight, they can have an identical set of resources to Pfizer. Like, imagine that. Like it takes a decade of success for an organization to get to that mid-sized biotech, you know, 500 employees, 1000 employees, and finally, they start having scaled resources or the coordination or the efficacy of resource that you get at, you know, a major institution or enterprise company, but we’re enabling anyone of any scale to have access to that on demand.

Saul Marquez:
Fantastic. Yeah, and that’s amazing, right? And I did want to double-click on that because I want people to appreciate what the resource is. Uh, you know, so as you’ve been putting this together, you did mention, you know, all right, we built the software and then realized, Oh, wow, things are static. We need them more agile, more mobile. You built the hardware piece and the offering. What would you say is the setback, I guess, that you’ve experienced thus far? I think we learn more from setbacks than wins as entrepreneurs, right, and leaders.

Amrit Chaudhuri:
I mean, I think that when we figured out that we needed to go and actually fix the hardware of this industry, the actual infrastructure itself, that was both the setback because we were toiling away for two years working on a problem that we couldn’t figure out why we weren’t making traction on, right? What ended up happening from a business perspective was, because we had very static infrastructure, if you, Saul, were at a drug company and you came to me and said, I have a chemistry project, I need space for 30 chemists and 15 fume hoods, I don’t have exactly that type of thing, 15 fume hoods in a space with space for 30 people to work, then I can’t solve your problem. And it turns out there are thousands of unique requirements, right? Every team has a unique requirement of the kind of environment, the kind of resource, the kind of infrastructure, and then the scale of it, and then the type of operational support or resource support necessary. So if you could, you could have the best, you know, stable of experts, you could have the best SOPs, you could have the best digital platform, but unless I can go and solve the actual highly specific infrastructure you need, then I’m not actually solving your problem. And it means that for a marketplace, if there are a thousand clients, I can service like 50 of them because I have the configuration for 50 of those thousand clients. It’s not a sustainable business model. The market’s not large enough to be able to go and sustain that business. So we didn’t realize that, and in fact, nobody did before us in this industry. So when we were like, man, is this infrastructure stuff only a problem for us? We went around to the large pharma companies where we had connectivity from our careers, and we were like, so how do you do this? How do you address these issues? Is it just purely economy of scale? And they’re like, we have, we don’t know how to actually address this. Do you know something? And literally everybody we went to, and we were like, oh my God.

Saul Marquez:
That’s the problem.

Amrit Chaudhuri:
That is the problem we should be solving. That’s the actual problem. And that’s how we basically rebuilt the company in 2017.

Saul Marquez:
I mean, like when you made that realization, like how did you feel? Like at that point, you’re like, what went on in your head, man? You’re like.

Amrit Chaudhuri:
I mean, joy, frustration, and like, it’s so like, joy because like, oh, we’re not completely stupid, our business is failing, right? Or like, we were like, hey, do we have a business model that doesn’t work? Like, do we just spend like career risk raising money? We have investors that we care about, want to be good food series of their dollars. Um, did we just waste, you know, we leave our jobs as CEOs of other companies, like literally three CEOs, and go and build this, and then we just waste our, you know, push our careers sideways? Um, so getting some validation that like, okay, there’s a mechanistic issue here. There’s like a fundamentals issue here that wasn’t tracked and followed, but now that we know it, we can actually go and fix it. Um, that was, that was, you know, relief, let’s call it, in that situation. But you know what’s crazy is that like, after you explain it today, like looking backwards, I can talk more eloquently about this because back then you were just still grasping at straws of how do you describe the problem? Um, you know, looking backwards, if I tell you that labs are all different, they’re extremely expensive, they’re static, and they’re non-interchangeable, and that’s a problem. Today, you’re like, yeah, that makes sense. But like, that has been the given way this entire world has worked for all of history. And one of like the things that we had to realize were like, how can that be? Why is it such a problem today? Why wasn’t this such a problem 40 years ago? And it turns out it goes back to that early statement I made. There wasn’t as much diversity of science. In fact, drugs until the 1980s were all small molecule chemicals. And so you are using from like the 1800s to the 1980s an iterative approach to improving a single, streamlined process. It’d be like, you know, using boats, you’re getting better and better at using boats, the boats are advancing. And you can say, okay, it used to be a sailboat, now it’s a motorboat, now it’s a tanker, that’s how this works. And all of a sudden, being like, well, now there’s a plane, and now there’s a train, and now there’s a car. What do you do? You can be the best boating company in the world, shipping company in the world, but that’s a different problem space that you’re going to have to go and be like, well, I guess he can take our shipbuilder and have him build a plane.

Saul Marquez:
Yeah

Amrit Chaudhuri:
Like, okay, like, so that’s what’s happening. The diversity of science since the 80s and really in the last 20 years have exploded so much that all of a sudden, having the same set of resources just stamped out. No longer…

Saul Marquez:
It’s a great story, Amrit. Have you guys done that? Like, you know, I’m just envisioning like this, you know, that the, how the economy works, the one that Ray Dalio did.

Amrit Chaudhuri:
Oh, yeah. Yeah, yeah.

Saul Marquez:
Type of context, man, I feel like is valuable. Have you guys done that or no?

Amrit Chaudhuri:
No, I don’t think so. Like, we talk about this, and we talk about the diversity. Um, you know, it’s the problem space that we’re solving for. Um, but I don’t think that we’ve, we’ve gone and proliferated, so to speak.

Saul Marquez:
There’s value there, I think, maybe something to unpack for sure. Um, look, love it. And kudos to you, and look, I admire you and your founders’, uh, just courage and willingness to stick with it, right? And find the true problem in the haystack and say, all right, this is it. Like we got no other option but to solve it. Um, like, what was the, like, how are you solving it then? Like, what? What is it that you guys do that helps you do it?

Amrit Chaudhuri:
I mean, this is going to be the craziest, like, uh, so I love analogies, right? So you think about a.

Saul Marquez:
Good analogy, by the way, yeah.

Amrit Chaudhuri:
Thank you. Um, imagine how we used to build computers before a motherboard existed, like in the 70s and 80s. You would literally, like, go and identify the problem you’re trying to solve, and then you’d custom design a board and a computer and the software and the operations for that specific application. And that’s what Computers …, you know, in the 70s, when Sears Roebuck had a point of sale software and computer system, or you had IBM building custom computerized systems for groups that were super expensive, very large scale, not reusable for other functions, right? If IBM built you an accounting computer, you couldn’t just take that and do CAD on it, right? And so what happened was that all of a sudden, an IBM was actually the inventor of this, they said, wow, we need to go and get better at making a bunch of different computers, but let’s componentize it. Let’s go and figure out how we can build one motherboard, as they call it, literally the motherboard, you know, that everything interfaces.

Saul Marquez:
Is that where it comes from, the motherboard?

Amrit Chaudhuri:
The motherboard, right? That’s literally, it’s, yeah, IBM invented it and so, plugging in different, not just like different types of components, but then different capacities of those components. How much RAM do you need? Do I have to build a custom computer specifically for 8 at the time, megabytes of RAM or 16 or whatever it is? Um, and so we took a step back and realized that the process in which labs are designed and developed is super fragmented, from like the owner of who needs to use it to the people who make the decisions around the actual operations and facilities management and design of it, to the external firms of engineering firms and architecture firms that then take a requirement set. Like you given me a set of requirements, we’re going to build blueprints that we believe solve those requirements as a one-off. And every lab is designed as a one-off. In the world, every single lab is designed as a one-off. And so we’re sitting there and we’re like, you have to be able to systematize that. You have to be able to go and create a component-based, you know, a systems engineering-focused solution to what is a lab. And then we spent a couple of years and did a couple million dollars. Like a lot of research into it. It was not cheap.

Saul Marquez:
Sure it wasn’t.

Amrit Chaudhuri:
Because like you’re like unlike prototyping something that’s this big or prototyping a building totally like it’s huge. And so, you know, we’re now we make jokes internally. It’s like an iPhone, we’re on version eight, so we’re on we’re in version eight of our physical labs, and it’s just it’s wild.

Saul Marquez:
I think that’s so cool, Amrit. Look, thank you for sharing that, and for everybody listening. The opportunity is here. And if you’re doing things the old way and you’re not taking advantage of solutions like Amrit and his team are up to at biolabs, you’re missing out. There’s huge economies of scale. There’s huge opportunities. Amrit, we’re here at the end. I wish we had more time. What closing thought would you leave our listeners with and what’s the best place they could connect with you and and just follow the work that you guys are up to?

Amrit Chaudhuri:
Yeah, absolutely. Well, www.SmartLabs.com is the best place to go learn about us. Also happy to connect on LinkedIn, all of our management team. And we have remarkable leadership here. I make a joke that I’m the least qualified person there, right? We have people who’ve run divisions of pharma companies or have helped build the forefront organizations and instrumentation in the industry or who have, you know, built 9,000,000ft² of labs around the world, like remarkable people, um, and mostly just all friendly, right? All friendly, all trying to go and make an impact. And so we love talking to people and talking about what we do and learning about what they do.

Saul Marquez:
Amazing. Thank you for that. Folks, take Amrit up for the invitation to collaborate to connect. Uh, opportunities ahead are big. And remember, if you want to get on that Outcomes Rocket, you got to take action, you can’t stop at listening. So Amrit, thank you for taking action with me today by being on this interview. Can’t thank you enough.

Amrit Chaudhuri:
No. This is so much fun, Saul. Thank you again.

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