The Bioinformatics CRO Podcast
Episode 41 with Damian Kao
Transcript of Episode 41: Damian Kao
Grant: [00:00:00] Welcome to The Bioinformatics CRO Podcast. I’m Grant Belgard and joining me today is Damian Kao. Damian is the Chief Operating Officer at Basepaws. Welcome.
Damian: [00:00:10] Thanks, Grant. Happy to be here.
Grant: [00:00:11] Happy to have you. So, can you tell us about what Basepaws is? What do you do there?
Damian: [00:00:15] Yeah. So, Basepaws started out as a kind of a 23andMe for cats type of business. I think a good analogy would be Embark for dogs. However, I think in recent years we’ve been kind of pivoting into more of a pet health care angle. So, the goal of Basepaws is really to utilize as much genomics data as we can for our pets to profile their health and try to predict health outcomes, basically. So, preventive medicine in some sense. Right? So, being able to predict whether your cat or dog will have a disease before it happens, so we can save you that huge veterinary bill that you might have down the line.
Grant: [00:00:56] And you mentioned dog. Are you guys moving into dogs?
Damian: [00:00:59] Dog is a very competitive market, as you may know. Embark actually recently just got a lot of funding from SoftBank. I think they’re valued at 700 million dollars now. So, that’s obviously a big competitor. We are thinking about other animals. It’s a bit intimidating to go into the dog space so we might try to go in there with some other products. So, for example, we just came out with a dental/oral microbiome product, actually. Where we give you a risk score on whether your cat will develop periodontal disease or some other health issues. So, we might try to enter the dog market with that type of product.
Grant: [00:01:39] How actionable are those reports? So, if you’re a cat owner and you find your cat is in the 90th percentile of risk for periodontal disease, do you brush their teeth? The way you’re supposed to, right. I don’t think most cat owners do routinely brush their cat’s teeth.
Damian: [00:01:55] Most pet owners in general don’t brush their cat’s teeth, right? Or their dog’s teeth. It’s, as you may imagine, very difficult to brush them.
Grant: Getting your hands anywhere near their mouth is this just asking for trouble I think.
Damian: [00:02:16] Yeah, I think the goal is to at least let the cat owners around the world know that this is an issue. Periodontal disease in cats is a huge issue and huge vet bill. So, there are a series of products recommended by veterinary council that is not always active brushing. Maybe you can give them chews or water additives or certain types of food that might prevent those types of problems. And that’s what we are advising people to do right now.
Grant: [00:02:37] And what kind of dynamic range do you have? You know, so a cat at either extreme, what kind of difference in rescue you’re looking at?
Damian: [00:02:45] So, let me talk a little bit about the bioinformatics of that, I guess. Our product is really just a swab that we provide to our customers. They swab their cat’s mouth, they send it back to us. And from the very beginning, we’ve noticed that after sequencing the DNA up to 10 to 15 percent of the DNA is not cats. So, what could that be? It’s just whatever is in the cat’s mouth. That could actually be residual food. That could be the oral microbiome flora. That has always been really exciting and interesting for us. And we didn’t really act upon it until maybe a couple of months or a half a year ago. But what we did find is we have a cohort of at least 30000 cats. So, 30000 oral microbiome samples. And then we have good phenotype data that tells us whether the cat is on a certain type of diet, whether they’re indoor or outdoor cats or whether they have any systemic diseases. So, this became a really interesting thing for us, because now we can try to look at a microbiome profile and correlate it with all this phenotypic data that we’ve gathered. Dental disease is obviously the most direct thing that we can look at.
[00:03:53] So, in our cohort of cats, we have hundreds of cats with periodontal disease and cats with other oral issues. And we’ve found that certain populations of microbes in combination seems to be correlated to these disease states. We don’t do 16S-rRNA seq. We are doing the WGS. So, we are looking at everything that’s in the mouth. So, we do see fungus. We do see bacteria. We do see some archaea. And we actually see a lot of residual food things, too. We pick up on plants, maybe a spider that the cat ate while it was outside. So, we do pick up on those type of things, too.
Grant: [00:04:30] Do you have a longitudinal component to your data? So you can certainly imagine if a cat has active periodontal disease, that their oral microbiome probably looks pretty different at that point. It would be super interesting if you had good predictive power years in advance to say like this cat is at high risk.
Damian: [00:04:48] Yeah. So, there are a series of studies that we’re actually conducting right now with various clinics all over the country that are gathering these samples for us. So, we’re working with dental specialty clinics that are gathering samples before and after examination, for example, and maybe also follow up weeks or months after examination. It’s through these samples that we want to start looking at what the predictive power really could be.
But the longitudinal question is really interesting for us, too, because we are seeing signal not just for oral or dental issues. We are seeing interesting signals for some systemic diseases, too. And this is reported in literature somewhat. For example, Ckd, chronic kidney disease in cats. There does seem to be some link between that and periodontal disease. And in our data set, we are seeing a signal coming out for chronic kidney disease via our microbiome dataset. We are also seeing some signal for other autoimmune diseases or even allergies, which is kind of interesting. It’s hard to kind of tease all of these things apart, though, right? So, getting good high resolution phenotypic data, I think is really our next big thing.
Grant: [00:06:01] How careful do you have to be about how you ask those questions, right? Because you can imagine if you’re asking about behavioral traits, the same pet owner may describe their cat’s behavior in very different ways.
Damian: [00:06:13] There is definitely an art to asking questions from our customers, and that’s something that we’ve had a lot of trial and error on. I think the best thing to do is to ask them the same question in multiple ways, many times, spread across multiple time points. And that’s really how you can increase the confidence of those answers. That’s something we’re building into our account system right now. The whole idea is we would have a question bank in our backend where we might ask the same question 10 different times, and we would present those questions to the customer at various times. And hopefully they will be consistent in their responses.
Grant: [00:06:51] Have you ever looked at what’s predictive of inconsistency, right? Do you have certain respondents who are just consistently inconsistent?
Damian: [00:07:00] No, we have not looked at that. But that is actually really interesting. Yeah, we should definitely do that.
Grant: [00:07:07] It would be super crazy if you found something different about the cats, right? Maybe certain cat breeds are associated, you know. But this brings up an interesting point, right? Most of the people we have on the podcast work for companies that are essentially B2B or they’re just drug development companies. What are your experiences with running a B2C Science company? Because generally, in tech, people always talk about B2C’s having a lot more challenges.
Damian: [00:07:35] I have never worked at a B2B, so I can’t really make that comparison. However, I could say that B2C has definitely been a huge learning experience for me. So, I would say that you have to balance satisfying your customers and maintaining the scientific integrity of your work. And that’s always difficult because they’re not always in sync.
Grant: [00:07:58] People always want more information. Right? Like what kind of wine do I like based on my DNA kind of thing.
Damian: [00:08:04] Yeah. Because it’s very easy to kind of stretch the science to accommodate what people want to see. And there’s a limit to that, right? It’s always a push and pull, to be honest. So, at Basepaws I’m the COO. I run kind of the science and the tech and the lab operations. And then we have the other half of the company, which is run by Anna, which deals with customers and acquisition and marketing and all that. And I find that it’s a great push and pull between me and her, because I tend to always think I need to be crazy rigorous. We can’t show anything. But she’s always like, if it’s interesting, as long as we explain it well, we should be able to show to our customers. Because we have to trust that they’re smart enough to understand not to take this at face value. So, I think there’s always this push and pull between kind of the marketing side of things, that B2C side of things, and the science side of things. And that’s been a really good experience for me personally, I think.
Grant: [00:08:59] What have been the biggest surprises for you in your Basepaws journey?
Damian: [00:09:04] I think part of the reason why I decided to not stay in academia and go industry was… There are multiple reasons why. I’m sure most graduating PhDs or postdocs will understand the reasons why. But I guess the biggest epiphany I found was that I enjoy building things rather than answering questions. And I think that’s kind of the biggest epiphany I had doing Basepaws. Building up a Lab, setting up these processes, seeing things happen and producing a product is extremely enjoyable.
Grant: [00:09:36] What elements of that do you think you are prepared for through your education and training? And what did you really have to pick up on the job?
Damian: [00:09:44] The advice I can give to any students going into their PhD is learn transferable skills. You’re not there to learn a very specific lab technique that only five labs in the world can do. You’re there to learn how to think. You’re there to learn how to pick up a new skill when it’s presented to you. So, learn those type of skills, don’t memorize concepts, don’t learn some niche technique. I think bioinformatics is very much a field where you don’t learn specific techniques because there aren’t really any standardized techniques for bioinformatics. It’s kind of a Wild West still in some sense. I feel like you really have to understand algorithms very well, data structures, etc. I think all of these things that you have to learn through your bioinformatics, PhD helps you in industry. It definitely helped me, sitting in my lab knowing how to analyze the data that comes out of the pipelines I set up. What I had to learn on a job is really more management skills I would say. In the lab I manage a couple of technicians. I manage the tech team. So, how you get all these different people to kind of share your vision and execute on that vision, that’s very difficult. It’s not something you learned during your PhD. And I had to learn that on the job.
Grant: [00:10:59] Let’s talk about you. So, when you were a kid, did you wanted to be a scientist?
Damian: [00:11:05] I was a very unremarkable student. As a kid, I would say in university, I changed my majors a lot. I didn’t really know that I wanted to be a scientist. I actually liked graphic design. I was a graphic designer in high school.
Grant: [00:11:17] Have you ever used that skill? Has that been one of those transferable skills that came in handy?
Damian: [00:11:21] Actually, the current Basepaws logo, I designed it all and I coded it all. So, there were some transferable skills there. I did film studies for a little bit in college, and I decided to go in genetics because I also studied a lot programming in high school. So, I made my own websites that type of thing. Back then, in the late 90s, that’s what a lot of computer geeks did. And of course, I did that. So, those programming skills led to my interest in genetics, because there are those obvious parallels between programming and genetics. After learning more about genetics, you realize those parallels don’t really apply that much. But I think that’s what kind of made me want to become a scientist: through computer science.
Grant: [00:12:04] You ended up landing on genetics at UC Davis. What did you do after that?
Damian: [00:12:09] I actually did not think about going into bioinformatics. I wanted to do lab work. So, I was a lab technician for a couple of years, working on drosophila. I did a lot of molecular work, did a lot of injecting stuff into Drosophila eggs to make transgenic lines and all these things. After a while honestly, I was a bit lost for a little bit, just didn’t know what to do. At some point, I decided that I needed a change of scenery. So, I said, I should do a PhD. Let’s go to another country and do it. So, I went to U.K. and did my PhD there.
Grant: [00:12:43] What attracted you to the UK?
Damian: [00:12:44] It was a different country. That was a main reason. You know, I felt like I’d been in California for so long. I feel like when you’re in one place for too long, you lose opportunity to kind of reinvent yourself because you’re surrounded by all the things that you know. Going to the UK allowed me to kind of reinvent myself, I guess, to maybe see myself in different lights. And it was kind of there that I developed that state of mind where I wanted to do a PhD. I want to do all of these things. I was able to, I guess, be more aggressive about my goals in some sense.
Grant: [00:13:16] And what are your thoughts on the British PhD training system as compared to the American system?
Damian: [00:13:23] I mean, there’s pros and cons to both. I think the biggest pro for me on a very practical level is that you’re done in four years, five years, max. After that, it’s really looked down upon if you’re not done. The universities I think lose funding if they have PhD students for longer than a certain amount of time. So, they really are incentivized to get them finished. So, that’s practically that’s one of the biggest con. And then personally, the UK is extremely strong in bioinformatics, as you probably know. So, my supervisor and a couple other PI’s around the UK would yearly set up a genomics conference that I would be a part of where I get to meet all the other great bioinformaticians there. And that was a real, really good opportunity for me to connect with others and learn as much as I can about the entire field.
Grant: [00:14:10] And you must have liked it a lot, because after you finished your PhD, you stayed.
Damian: [00:14:14] Yeah, I stayed for a couple of years. Yeah. After the PhD, I thought about staying academia. I worked on some genome assemblies. I worked on some transcriptomic stuff. So, doing a postdoc in Oxford was very eye-opening to me because there are a lot of really, really smart people. And you just learn so many things, new things and interesting things every day. I was in a zoology department where you got to look at other people’s research and a variety of animals and biology that’s out there. I think that’s one of the biggest reasons why I stay around for a couple of years.
Grant: [00:14:49] Why did you leave?
Damian: [00:14:51] I left because I decided I didn’t want to stay in academia and if I didn’t want to stay in academia, I might as well go home. And I feel like the startup culture in the US, especially California, is just more vibrant.
Grant: [00:15:06] And when you returned to the U.S., you started HHMI, right?
Damian: [00:15:10] Yeah. So, through some work that I did in Oxford, I became a consultant at HHMI where I worked on some single cell transcriptomic projects and some genome assembly projects, and that was a really cool experience for me, because Janelia is just a great place to work. They basically built this entire compound where you can live and everyone just loves science and does science. It’s great.
Grant: [00:15:37] Bit of a scientific monastery, right? Isolated and such.
Damian: [00:15:40] Yeah. Monastery. That’s a really good word for it. Yeah.
Grant: [00:15:44] What was your thinking in leaving there? Was it basically you really wanted to do the startup thing? And what’s the story there? How did you and Anna meet?
Damian: [00:15:52] Yeah, I really wanted to do start up things. So, I was in California remote working for HHMI. I just felt like doing the same type of analysis on the same data sets is just going to be boring for me. And I really wanted to get into the startup world and see how that works. My mom was entrepreneur in Taiwan and she’s a successful businesswoman there. And I’ve always wanted to see what that was like. I think at the end of day, I wanted to work for myself. Didn’t want to work for someone else. So, my wife did PhD with me at the same place. We came to California together. We both are kind of into this whole startup scene. So, we put out some feelers.
Grant: [00:16:32] Is she also American or?
Damian: [00:16:35] She’s actually a Bulgarian. She did her PhD in the same lab, got together there, got married in the UK. So, we put out some feelers in the startup world. I had some NGS experience. Anna was in need of someone with that experience. So we met at a coffee shop one day, talked about our respective skills and our interests, and I thought that we were a perfect fit to do this company together. So, I joined and I set up the lab, did all the pipelines, and we went from there.
Grant: [00:17:10] What were your biggest challenges when you got started with Basepaws?
Damian: [00:17:13] I think something that carries over from academia and into the first few years of industry is imposter syndrome, I think a lot of people have that. The first year at Basepaws you know, I’m the scientist. I know the science. However, the business side was not something I’d experienced. And so whenever I had to make a business decision, I would always second guess myself. And I think it stems from that imposter syndrome. But I think at the end of the day, what I learned is that business decisions are like any scientific decision. You get a lot of data, you analyze it and you make a decision. There’s nothing special about it. So, getting over that imposter syndrome and having more confidence in the decisions that you’re making. Yeah, I think that’s what I learned.
Grant: [00:18:01] Do you think you’ll ever leave the startup world?
Damian: [00:18:04] Well, I just had a kid who’s one year old, so it’s a Covid baby. If I ever leave the startup world, it will be because it’s becoming too crazy and I can’t spend enough time with my kid. That’s probably going to be the reason.
Grant: [00:18:17] It is tough. What advice would you have for people considering doing the startup thing? Like we have a number of clients who started their company after, spending a long career in big pharma, where everything was taken care of for them. And they would focus on their one area and they were the expert on that. But, they could access any kind of expertise they wanted just by going down the hall. And obviously that’s not the case at the start up. Right? So, what advice would you have for people like that considering making the jump?
Damian: [00:18:51] I think hugely depends on what your business is. I can tell you from a kind of B2C point of view that scaling up is very, very hard, especially in a biological context. It’s very easy to get an assay or a product or a test to work a couple of times, but to get it working consistently for thousands or tens of thousands of times, that’s extremely hard. So depending on what industry you’re in, if you’re in an industry where you have to do that thousands and tens of thousands of times, you have to think about that. And you have to think about the long term cost of what you’re doing, because it’s also very easy to over-optimize and over-engineer in the beginning, buy all this fancy equipment that you just never use because there’s simpler solutions out there. So, I would say worry about scaling up if that’s the industry you’re in.
Grant: [00:19:44] It brings in a very interesting point. The funding climate right now for human therapeutics is quite hot. How well does that translate to pet health?
Damian: [00:19:56] Pet health is actually one of the industries that grew a lot last year during Covid and is steadily growing, and because of that, there are actually plenty of investors interested. I think the problem that stops an investor from actually putting in the money at the end, though, is that there haven’t really been any big exits that they can see in this sector. So, I feel like maybe that’s what’s holding it back a little bit. So, I think there’s a lot of money pouring in. And because most of these investors are looking for a relatively quick turnarounds, they’re a little bit more hesitant to put their money in.
Grant: [00:20:30] Can you tell us about how you use bioinformatics at Basepaws?
Damian: [00:20:35] Yeah, so just a brief overview of what we do. I think we’re actually one of the few companies that use NGS for this type of a product. I mean, as many of you may know, 23andme and Embark and these other companies, they all use microarrays, which relies on an already good existing resource for that organism, like humans and dogs. When we first started Basepaws, a really good feline genome was produced actually a couple of years back. So, we were able to take advantage of that. However, in terms of whole genome data sets for cats, I think when we first started, there were less than 100 in NCBI. So, we had to sequence a lot of those things ourselves, build up our own reference panel, our own imputation panel.
Grant: [00:21:20] So, that you could create a crazy cat assembly. This one, if you have 30000 cats at this point.
Damian: [00:21:25] Yeah. I mean, that was a project that we were thinking of doing. However, there was a really interesting paper as it came out earlier this year where they were able to a haploid assembly of feline genome by sequencing a wildcat and domestic cat. And then the offspring. So, from those reads from the offspring, they were able to say, all of these reads are the domestic cat, all these reads are the wildcat. And then they did a pseudo haploid assembly using that method is really cool. So, that’s a really, really good genome. And I don’t know if we can beat that, to be honest. One interesting thing we are thinking of doing is to try to produce a haploid stem cell line, because that is something as possible to get an oocyte out of a cat and use strontium chloride to kind of activate it. And it can sometimes become a haploid stem cell line. And when you have that then you can sequence the genome and it’s a haploid genome.
[00:22:17] So, you don’t have to worry about heterozygosity or any of that. So, I Basepaws we do low-pass sequencing. We sequence at around 0.5-1x. And then using our imputation panel, we impute a lot of other markers. Usually, we end up with a couple of million markers at the end. And then using these markers, we use a machine learning algorithm. We just use the random forest-like algorithm really to assign haploid type segments to a known breed. And then we calculate a similarity of your cat to this breed. So, that’s what we do for our breed analysis portion of things. And for the health markers side of things, we have a multiplex amplicon panel that we’ve developed where we interrogate I think right now, 40 or so loci using this multiplex amplicon approach. And then we give you the status of whether there are heterozygotes, whether you have copy. How many copies you have, that type of thing. And we are expanding that panel to about 120 markers by the end of the year.
Grant: [00:23:19] What conditions have you found good predictive power for?
Damian: [00:23:23] So, this is the stuff I get excited about, right? Because I feel like when people talk about bioinformatics, they have this artificial separation thing, genotype and phenotype. I think the correct view of looking at it is it’s just all data. It’s all just some kind of dimension of the sample that you collect. And I think when you throw all that together into multi-omic analysis, that’s where the power comes in. So, that’s what we’re working on right now. So, like, you know, like I was saying that CKD, the chronic kidney disease signal that we’re seeing from the oral microbiome, we’re seeing a big signal from that. However, it overlaps with the periodontal disease signal. So, it’s hard to tease apart. Does this cat really have chronic kidney disease or does it just have periodontal disease? However, if we apply a layer of genomic data or some other phenotype that we get, we find that we can pry these apart a little bit more. So, we’re still trying to find the set of features that can best tease those things apart. But I think we’re getting close to some interesting set of features.
Grant: [00:24:20] And how translatable do you think your findings in cats will be to, for example, human health?
Damian: [00:24:26] So, there was a great review paper by Leslie Lyons, who is kind of the main person in the feline genetics field. She wrote a review talking about how if you compare the feline genomes to the human genome is actually one of the closest mammals that exists. I think it’s the most syntonic aligned genome compared to every other mammal out there. You know, if you look at something like genes involved in eye development, I think all of those genes are syntonic with the human block of genes. So, I think there’s a lot of translation potential by studying felines. And I think a lot of the known health markers in felines have almost a direct homologous variant in the human genome, too.
Grant: [00:25:08] Is that something that Basepaws is planning at, looking at in a systematic way?
Damian: [00:25:12] It’s one of my pipe dreams, to be honest. I mean, there’s so many things we can do, but I think maybe like five years down the line, whatever it is, let’s say we collect a ton of cat data. We collect a ton of dog data. You know, can we have a pan-mammalian database where we just like all the variants and use that to narrow down disease markers? So, in humans, you find 80 potential markers for diabetes. You get to narrow that down to 10 because you find homologous variants in these other animals. I think that’s a great usage of this data.
Grant: [00:25:41] How do you think about R&D and kind of building capacity versus having a sustainable revenue driven company? Because generally in the biotech space, most companies are pre revenue for a very long time. And obviously Basepaws already is very actively engaging with customers and has been for a long time. But at the same time, you’re doing a lot of internal R&D work. So, kind of what’s your framework for that?
Damian: [00:26:13] In terms of R&D I always separate in two buckets. One is maintaining or optimizing what we have currently. That means lowering costs for library preps, how we can normalize things better. And the second bucket is what new products we can get from that. In terms of new products, I think for the last one or two years, we’ve mainly been focused on the bioinformatics side of things because it’s cheaper. That’s really the reason we have a lot of data. Can we generate new products from that data? Which we have with the oral microbiome product, for example. But I think now we’re actually close to the end of our series, a funding. I want to start focusing more on kind of lab assays or products or tests that we can do. Something I’m kind of interested in doing is one of those epigenetic clock aging test type of things.
Grant: [00:27:02] Oh, that would be cool.
Damian: [00:27:03] Yeah. You know, one thing I’m kind of interested in figuring out, and there are a couple of papers on this already, is the DNA that you get from on saliva, does that correlate with the blood DNA that’s traditionally done in the epigenetic clock studies. There are a couple of papers looking at that and saying that it does correlate. So, maybe you can do all these epigenetic aging tests through the saliva DNA. That would be cool.
Speaker3: [00:27:26] Very cool. Very cool. How would you think about the kind of commercial path for in, you know, a cat saliva based epigenetic test of aging?
Damian: [00:27:36] I think biological aging is something that people are just interested in. And being able to gather that data and compare it to its real age can give you a lot of insight into longevity and a host of other interesting biological concepts. Longevity is something that me and my wife are personally interested in. So, I think a lot of people might be interested, too. And we’re always looking for products that are not the standard 23andMe ancestry or health marker type of tests. And this is just another one of them, because I feel like if we want to enter, especially a dog space or other animals, you need to have something different.
Grant: [00:28:12] How do you think about engaging with your community?
Damian: [00:28:14] So, the cat community is very different from, as you might imagine, other dog or human communities. People are a lot more obsessive about their cats, I would say, in a good way. I don’t want to suggest that that’s a bad thing. And I think they are a lot more curious about their pets than they are about themselves, actually. That’s one trend I’ve seen. I wonder about this in the human space, too. I would much rather get a DNA test for my kid than for myself because I think most people are like, oh, I know myself, I don’t need to know more. So, I think in the pet space, that kind of applies too. I would much rather find out more about my cat, my dog, who can’t really tell me what’s wrong, than about myself. I think maybe that’s one advantage we have over the human space in some sense.
Grant: [00:29:01] Great. Is there any advice you’d have for scientists looking at transitioning into the biotech startup world?
Damian: [00:29:09] I think as an academic scientist, I don’t want to paint the situation with a broad brush here. But I think the academic mindset, sometimes it’s like I have a choice. I can do really good science or I can have enjoyable personal life. You know, I think it’s a false choice. Personally, I think you can have both. When a scientist gets into an industry, they maintain that mindset a little bit. And I think industry sometimes will try to take advantage of that. So, I think any academic scientists going into industry should change their mindset. They should see their value, get over that imposter syndrome and know that you’re probably one of the few people who can answer or solve these types of problems, have that confidence. I think in academia, when you’re surrounded by a bunch of really intelligent people, it’s kind of hard to have that kind of confidence. I guess, don’t carry over your academic baggage into industry would be my best advice.
Grant: [00:30:05] Right. It’s like a lot of really smart people who enjoy shooting each other down, right?
Damian: [00:30:09] Exactly. Yeah. Yeah.
Grant: [00:30:12] Do you have any parting words for our listeners?
Damian: [00:30:15] I mean, since this is The Bioinformatics CRO Podcast, I would say that I’m excited about the future of this field. There’s a lot of interesting things happening. And I would encourage more people to join this industry because there is a lack of bioinformaticians. We’re hiring, by the way. So, apply for a job with us, if you’re interested.
Grant: [00:30:36] Thank you so much. It was great having you on.
Damian: [00:30:38] Yeah, no problem, Grant. Thank you.