Special Episode: Zoe Yu Tung Law - New Wave Biotech
Welcome to the Protein Production Technology International podcast. We explore the latest advancements in alternative proteins from cultured meats to plant based proteins. We talk to experts and innovators who are working towards a more sustainable, efficient and kind protein system. Join us as we dive into the exciting possibilities of the alternative protein industry.
Nick Bradley:Right. I am delighted to be joined by Zoe from NewWave Biotech. They're a UK based company developing software to optimize biomanufacturing processes. Their target or one of their targets is the alternative protein industry, but their software can also be applied to biochemicals and biomaterials. The goal is to streamline research and development to get sustainable products to market faster and cheaper and all important goals.
Nick Bradley:So Zoe, what's the genesis of NewWave Biotech, and, what was your inspiration for setting the company up?
Zoe:Hi, Nick. Yeah. Thanks for inviting me and also for such a great introduction. I should really, get you to do my introductions and the people. Yeah.
Zoe:So, our genesis really, me me and my cofounder, Ollie, we met through a sustainability venture builder called Carbon 13, and it's really, basically love violin for nerds, who wants to something to do with sustainability. And what we you know, we're both really excited about what synbio can do and particularly in the operating world because, you know, food is sustenance, but it's also our culture. And, you know, it it really, but how we produce food today is so unsustainable. It accounts for such a huge part of emissions, and we want to do something about it. And, so we thought, okay.
Zoe:What can we do, to do with synbio and alt proteins? Nido is a biologist, and we're not gonna go get a PhD now. So, well, so then we basically went and talked to as many companies in this area and basically realized a big issue that most people faced was really cost, unsurprisingly. And so we did our own cost analysis and then figured that actually, at the moment, 90% of, Syn Bio companies, well, technologies don't get to scale. And a big part of it is because experimentation is incredibly expensive and slow.
Zoe:So, experiments are between 10 to $100,000 per experiment, and it takes about 3 to 10 years to get from lab to market. So loads of people just run out of money before they even get there. Now, my background is in consulting. So for the last 10 years, it's mostly been helping large corporates launch new products, new services, mostly emerging tech and data related products into market. And my cofounder, Ollie, they're a software developer with a background in data engineering, and they also used to be the lead developer at, a biotech company called LabGenius, where they helped take their, r and d from Excel to AI.
Zoe:So then we thought, okay. There's probably a much better way of doing this r and d. And, so that's how we came up with our product, which is, allows basically allows our customers to virtually experiment with thousands and thousands of, processes without the physical experiments. And so when they do do the experiments, they can focus already on the highest potential ones by purity, by yield, by cost, and also by sustainability.
Nick Bradley:I mean, we're gonna come on to the the specifics of the technology in a moment, but, alternatives proteins have been going through a pretty bumpy ride over the past 18 months or so. You launched just over a year ago, I think. What have been your biggest challenges in getting the company off the ground?
Zoe:I think, for us, I think, the first part was really to, basically get someone to believe in us because, you know, these 2 people just come out of nowhere, and they still wanna do something. And so, really, within 3 months of deciding on what we're gonna do, we launched our MVP, and that's really what really changed things for us because it went from people who said they're interested to someone who's actually got something that we can play with that is interesting. And, you know, and, actually, 2 months after that, we signed our 1st client, which is really great. I think, it AltProgenes is really having a tough time because, as the economy slows, obviously, investors want their money back quicker. And, so a lot of our customers are having a tough time.
Zoe:For us, that's probably you know, because we're a SaaS company, it's we're probably hit less. And if anything, it means that, there's more people willing to try our software because they know this is a problem.
Nick Bradley:Yeah. Yeah. Well, I know the investment community has been pretty, diligent, shall we say, with their, funding over the past year or so, but, it did fantastic to, get up and running. Now what are your observations of the old protein market overall?
Zoe:I think there's, the old protein market, I think, is, pretty incredible in terms of, like, how quickly it's evolved or how much, plant based foods have really become mainstream. I think there's the issue right now is really getting the taste and, price parity, really, and, well, and mouthfeel and things like that. And, and because research really shows that there are so many people who have tried vegetarian food, and actually the biggest market out there really is the flexitarians, right now. But a lot of people try, plant based foods, and then after that, they refer and they don't go back and buy it the second time. And that's really the main issue that we're seeing.
Zoe:But I'm feeling really excited about because, you know, the clients we work with, they work with really exciting new ingredients that can really change this. It's really about these novel ingredients, whether it's, say, you know, whether it's novel, well, basically being able to have the same kind of, like, animal fats, proteins, let's say, collagen, things like that, that really changes how food tastes and how it feels. And obviously from our side, really working on the cost side, I think the next five years is gonna things are gonna change dramatically because of these innovations.
Nick Bradley:Mhmm. Yeah. I've just been writing in our hybrid foods article, and there's a lot of discussion about alternative fats in there. Now can your solution be, utilized by other players in alternative proteins, presumably, as you're you're looking at fermentation as a potential market for your software, not just self cultivated meat?
Zoe:Yeah. So, actually, we're focused on precision fermentation. And so, for example, in our collaboration with, maltis, we work with them, and they work on cultivated meat. But because they create the ingredients for cultivated meat, via precision fermentation. So that's actually where we help them with that.
Zoe:We so and, beyond that, we also are, beyond alternative protein. We're also talking to customers that work in cosmetics, people who work in biochemicals, materials. We know that it really is you know, where wherever the bioeconomy is, they will need processing. And, actually, what we're doing, is applicable. It's just really a prioritization of where we wanna go first and where we go next.
Nick Bradley:It's, still early days, for your company, but what would you say have been your key milestone so far?
Zoe:Oh, wow. So, obviously, like I said before, the launching the MVP was a huge deal for us. Signing our first client is a huge deal. And then, and then, pretty soon after that, I think, actually getting our 1st investment from Big Idea Ventures, big shout out, really great people. They, yeah.
Zoe:That actually allowed us to get a team and also plus grant funding that meant that, you know, we've been able to grow really quickly, with relatively small amounts of initial funding. And what has been really exciting recently is, since beginning of this year, we've doubled our, customer, our kind of, like, customer base, but also we've really seen some larger companies being interested in this, which is really great. And, we recently even won a customer from a competitor, which is a big like, kind of a huge win for us, especially as it's set up. So yeah. Sorry.
Zoe:I know you said key, but I I put in with you.
Nick Bradley:No. Oh, that's fine. That's fine. That's fine. That's a lot of milestones in your 1st year.
Nick Bradley:So it's a good sign of good things to come. Now you mentioned there what where you are with investment and funding. I mean, our investment feature in this, this edition shows funds veering more towards solution providers, such as yourself, as opposed to the actual bakers of end food products. So what do you think that suggests about where the head industry is heading? Or maybe maybe not the industry, but the the investment, for for instance.
Zoe:I think, really, I think, you know, obviously, I will be biased in this fee, but, I think that actually this is very much needed because if you talk to any protein company and look at how they currently work, so much of the infrastructure, and the tools that have been built have been built for pharmaceuticals, which have completely different unit economics. And, actually, it's own and a lot of, innovations have well, a lot of the enabling tools are also quite outdated. So I think actually this shift is the, is the investor community realizing that, actually, for all proteins to thrive, actually, the infrastructure, and the supply chains and everything that underlies that industry also needs to be upgraded.
Nick Bradley:Yeah. Well, we're gonna move on to your technology now. I'm presuming it's extremely, complicated. You know, it's certainly gonna be complicated for me, so you'll have to go easy on me. Could you elaborate on specific AI algorithms used in your software?
Nick Bradley:I mean, are they tailored to different types of bioprocesses, or is there a more general approach?
Zoe:Right. So, what we, so what we do is, actually, and this is really what differentiates us from our competitors, is that we have a hybrid approach that mixes kind of more traditional mechanistic modeling with AI machine learning. So what that means is, essentially well, so, with mechanistic modeling, that's based on research and, kind of scientific principles of how you can model something, and that's something that's already being used by precious engineers today and proven, in academic papers. And that's what all the traditional tools use, but there's a gap between modeling and reality. And there are some things that are very hard to model, and that's where and with our software, we actually integrate that with machine learning.
Zoe:So then it actually means that as you put an experimental data, you as you can adjust to learn and get better predictions. So what that means is, when you first start, you don't even need any experimental data, and we'll be able to model what the predicted outcome would be. But then as you add more and more data and it gets more and more precise, and also our recommendation will continuously help you optimize, because, obviously, the recommendations change, as the system learns. So that's kind of where, how our software, design is and where it's different.
Nick Bradley:Mhmm. You you mentioned, previously that the cost, I think you said 10,000 to a $100,000 per experiment. You also mentioned 3 years to 10 no. 3 to 7 years, I think it was, from lab, to market, which seems like a a very long time. How does your software handle the challenges of scaling, production process from lab to commercial levels?
Nick Bradley:I mean, what validation methods do you use to ensure the software's accuracy and reliability?
Zoe:Yeah. So, yeah, for sure, like, scaling is a huge challenge. And where we help customers today is you can no matter how big or small your experiment size is, you can, you can model it on the software. So whether you want to model, 2 leisure or you wanna model a 500,000 leisure, kind of, batch, that's fine. You can do that.
Zoe:And from that, you can actually see, okay, at what scale does my process work or not work? How does that change my techno economic analysis? Because that's already built in, and automated. But, also, we're currently working on a scale up, scale down, functionality that would actually enable you to better understand. Okay.
Zoe:I've done this small scale experiment. What is the implications of this when, when this scales up, as a large scale process? And similarly, okay, if I'm targeting this large scale for, process, then when we look back at the small scale experiments that I should do, actually, which are the key uncertainties, that I need to focus on. So that's, one of the things that we're also doing to help handle the scale up, scale down. In terms of validation, so there's a couple of different things we're doing.
Zoe:So, obviously, like, with, our base algorithms, that's already been proven. But, beyond that, we also work with our customers, to see their validation and see how well we actually predict the results. We were actually really really excited to hear from one of our customers that, they said this is a direct quote, by the way. It's not just me saying. Is, they said that, our results consistently gave them, predicted within plus or minus 5 to 10% accuracy of their experiment, which obviously is music to our ears.
Zoe:We are doing ongoing more and more validation to try and do that. We're also looking at different collaborations, whether it's with, different research organizations to see how we can increase that validation, as much as possible as well. So bit of a shout out to any of your listeners if, anyone is interested, please, you know, let me know.
Nick Bradley:You get that free you get that free advert in there. That's absolutely fine. And does can your software account for any unforeseen issues that may arise, during bioprocess optimization? Does it incorporate any feedback loops to learn and adapt from real world data?
Zoe:Yeah. For sure. So, for, like, now, like, actually, even before you do any experiments, you can, put in, your different, say, a process that you're interested in, and it'll tell you what the predicted results would be. So then say, actually, because of you know, say if it it can tell you, say, if it's doesn't expect there to be any yield. For some reason, you can see why.
Zoe:And also you can, in terms of, like, feedback loops, basically, when you upload your experimental data, to the system, then the system would automatically update all of the projections, and that includes the yield, the, the costs, but also your techno economic analysis. So then all of those, kind of scenarios are already put in there. And if you, then press optimize, in the system, then, essentially, it would then look at multiple ways. So there are 4 ways of, 4 different optimizations that you can do by cost, by yield, by purity, and, like, a more balanced one. So that that it will then take that learning you already have from the experimental data and go, actually, from that, this is what we think might be a better process to try.
Nick Bradley:Mhmm. And and and how does your, your AI solution differ from other tools used in bioprocess optimization, especially those offered by larger companies?
Zoe:Yeah. So I think there is, 2 key differentiations. One is that, we have designed this in well, one is, as I said before, you can actually upload your experimental data, and then so the system learns from, actual real life data rather than just having a static, equation based kind of, software that doesn't change, no matter how many experiments you do. The other, part of it is really the optimization part. So rather than so at the moment, if you use something like a super red designer, for example, you would have to put in all of the parameters and all of the different variables.
Zoe:And then if you want to do like, see how you might optimize it, then you would basically tweak each of these. Or the other way you can do it is actually hire, techno economic analysis consultant, and they do a TA for you, and they give you sensitivity analysis. But everything's very, very manual. What ours does is, you can push in, this is my target product, my target purity. This is the sales price I'm looking for.
Zoe:And then it would run through thousands and thousands and thousands of processes for you to then recommend, the right ones to go for. So that's very much, how it differs. I guess we didn't necessarily intended to be a big differentiation when we built it, but we built we also built our software in a way that means that even when you're quite early stage, we'll have some assumptions for you, but you can actually then adjust those, assumptions as you chain that you get more and more sophisticated in your r and d. And we've designed it so it's very easy to use and doesn't require a lot of training, which is, just from feedback that we've got from people. So I guess that wasn't that's kind of a differentiating factor, especially when it comes to, like, startups that have a smaller team as well.
Nick Bradley:Mhmm. You stole my next question from me there, and that was about the, technical expertise required to use it, and user training and support for your clients. But it sounds like you've got that covered. I mean, how do you plan to maintain the effectiveness of your platform as biotechnologies and research methods change, in the future?
Zoe:Well, this is one part I'm quite excited about, actually. So because I think, you know, like you said before, before, right, there's investment going to enabling technologies. We're seeing a lot of innovations across the bioeconomy, which is really exciting. And, actually, what we are looking to do as a start up actually is to do collaborations. So then if we can be the 1st people to model a particular process for, say, new biomanufacturing technologies, whether it's to do with purification or, whether it's, say, like, more continuous fermentation, things like that.
Zoe:You know, if you're a hardware manufacturer, right, like a novel, with a novel technique, it's quite hard to convince someone to use your technology and say, this is gonna scale. Trust us. And this is what it's going to be. So we feel that actually we're and especially with the ability to take in you know, like, with machine learning to learn from that, we feel that, this is really a good way, to be able to collaboratively kind of, like, codevelop with different new technology developers. And from there, really become the bioprocess software that has all the newest solutions, to help you explore what is possible for what you're trying to do.
Nick Bradley:Mhmm. And you mentioned that testimonial there from a from a client. I mean, how do you measure, you as a company measure the impact of your software in terms of reducing costs, accelerating development timelines, and promoting sustainability, I guess, in this field?
Zoe:Yep. So in terms of, like, where we are now as a company, because we're quite early stage, what we what is the easiest validation we can do is really about the accuracy
Nick Bradley:Mhmm.
Zoe:That we can do. And, and because I think the software has 2 parts. Right? One is how well can we predict something, and the second part is, how well does it optimize. And for the optimization part, we are, through different collaborations.
Zoe:We look at for the, a baseline. So when they first start working with us, how much are they spending on different things? How are they testing it? And then, and then as they use optimizer, then actually how much does that improve, and then using that to then kind of, do a benchmark of our effectiveness. And in terms of promoting sustainability, what we do is in our platform alongside the yield, the purity, and the cost, we also already predict the energy use, water, water use, and waste, for any process, which means that if you net here producing something, and you have 2 different processes, you can actually see right from the start what is the difference in, like, say, energy per kilogram of what you're producing.
Zoe:So then that also gives, our customers the transparency to be able to see actually you know, to help them with their decision making, but also in general by enabling the, the protein and different, like, bioproduction fields to get to market quicker. That in itself also has sustainability impact. So, yeah, that's kind of how we see it.
Nick Bradley:Now this is an industry where collaboration is, is rife. Are there any specific areas where you see opportunities for collaboration with, you know, academic institutions or other companies in the space to further advance your platform's capabilities? I know, for instance, we covered a story recently where you were partnering up with Multis, another company going places.
Zoe:Yeah. Multis' team is really great. So, Yeah. In terms of collaborations, absolutely. I mean, for us, we are taking a codevelopment approach, where we work with partners, kind of like strategic customers, and we go, okay.
Zoe:What what is your goals? Okay. That aligns with what we wanna do, and our development, priorities. So then we work with them to, help them with their r and d. And that also means as we develop things, we have immediate user feedback, immediate validation to go, is this helpful, to make sure that we develop things in a way that is actually useful for the end user rather than us throwing up a massive grand plan and going, okay.
Zoe:I've made you this. And, and with academic institutions, I think there's, 2 parts. So one is, we're very keen to work with different, universities, and we are already in discussion with you, to work together to enable their students to use our software for their design projects, but also work with leading academics, to basically get their, innovations, to help build out our software as well. So that's kind of how we are. In terms of our collaboration with Multis, it is one of those co development partnerships that we have where we essentially are helping accelerate their r and d by using our software.
Zoe:But at the same time through that, we get iteration, and we can then measure our effectiveness. So, yeah, that's kind of how that works at a minute.
Nick Bradley:So, Zoe, final question. What what are the ultimate aims for a company such as NewWave Biotech? I mean, where where do you want the company to be by 2030, for instance?
Zoe:Well, by 2030, we want to be the bioprocess, optimization company. We want to be able to say, because, you know, not only are we the company that most people use, but also we look at our clients and we can see that, oh, their products have gotten to the market super quickly, you know, rather than, say, 5 years it's taken them to. You know, that is what we're looking for. And on and because we our aim is all around sustainability and helping, buyer money buyer materials, basically, and bio produced, products to replace animal and petrochemical products. What we also want to see is be able to go, okay.
Zoe:We've enabled, this much, carbon emission reduction. That's really what we want to see.
Nick Bradley:Brilliant. Well, Zoe, thank you very much for spending some time with us. It's been absolutely fascinating hearing about your technology, and, of course, we're looking to meeting you. We're looking forward to meeting you in person in Chicago. In a few weeks, We will hear more, I'm sure, about that talent share.
Nick Bradley:I'd love to introduce you to a few people who would be very interested in your software. I'm sure Francisco Cadona at ScaleUpBio in Singapore. I'm sure he'd be very interested in it. And, Brian Jacobson, I think he's from the University of Minnesota. He'd also be very interested, I'm sure.
Nick Bradley:So Zoe, thank you very much for your time, and, good luck with everything.
Zoe:Thank you very much. Have a great day, and I'll speak to you soon.
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