Dr. Kevin Bateman talks innovation, dried blood spots, and the LCS model!: this mp3 audio file was automatically transcribed by Sonix with the best speech-to-text algorithms. This transcript may contain errors.
Chad:
Welcome to the Molecular Moments podcast in today's episode, we sit down with our guest, Dr. Kevin Bateman, scientific associate vice president at Merck. Kevin is a veteran of the bioanalytical industry. He's known for innovating on a level many of us never have the opportunity to do. He's always thinking outside the box, making bold moves to progress our industry forward. He brings unique insight, experience and excellence to everything he does. He's the guy I look to when I wonder, what are we going to be doing in our labs in five or 10 years? I hope you'll enjoy our conversation. We're talking science as scientists do. So, without further ado, here's another Can't-Miss episode of molecular moments. Welcome to the podcast, Kevin. I'm delighted to have you join me today. And can we start with you just giving a few highlights from your career?
Kevin:
Yeah, great. Great to be here, Chad. Really appreciate the invitation to come and have a conversation with you. Yeah, some of the highlights is spending some time with you at various conferences. Show reality. Yeah, this is my 25th year at Merck, right? And so yeah, and that's that's a long time at one company. And I'm pretty lucky to have survived those many years in the farm industry, right? Because there's been a lot of changes over the years. So lucky enough to start my career in Canada and then relocate down here to the U.S. So I'm at the West Point site just outside of Philly, and I know it's been it's been a fun, fun journey and the journey continues. So access to technology that never would have seen before, you know, when you think back 25 years and how it's constantly evolving. So for me, the highlights are the change, the constant change and the push to get better over the years.
Chad:
So Kevin, tell me about some of the different responsibilities that you've had at Merck over that twenty five year career.
Kevin:
Yeah, it's been an interesting journey, starting. Obviously, when you first start, you're in the lab, right? You're in the wet lab doing experiments at the bench and as time goes by and you learn more and gain responsibilities. So in my first 13 years at Merck, it was in the lab focused on it was an analytical lab. So we did everything we did P.K., we did did we did biomarker work and my job was to make sure we had the right capabilities to do that. I work with chemistry, I work with pharmacology, I worked biology. And then when I moved down here to the U.S., I moved into the regulated space, working on clinical trials and worked on method development and running a method development group for LC-MS assays, for small molecules and then moving into peptides and proteins. And around that time I said, OK, doing protein mass spectrometry is an area that Merck needs to expand. So I I stepped out of that role and took on a role to build a protein mass spec group, just me and a postdoc to start and over the next several years built it up to like almost a dozen people around.
Chad:
When was that? And I'm curious trying to track against where, you know, when the industry started making that transition, saying, Hey, we need to we need to be looking at large molecules with LC CMS.
Kevin:
Yeah, it was around 2012, right? So a decade ago it started that transition for Merck. And I got to say for Merck, you know, we're traditionally a small molecule pharma company. And so biologics was not a big part of our portfolio. Vaccines always was, but like monoclonal antibodies was not, you know, as big at Merck as for other companies. So probably a little bit behind in terms of what other companies were doing using LC-MS for proteins. But I think we've done a pretty good job of catching up and bleeding in some cases. So yeah, and then thread all the way through that. Going back 20 years was just how do we collect data that we couldn't get in traditional approaches? And so and so I'm alluding to small volume sampling. And to me, that's that started when I was an early discovery and we were doing animal studies in mice. And mice are really small and you can't get a lot of sample from them. And so how do we how do we do studies with mice and use really small volumes? So that's what we worked on, and I started using dried blood spots in animal studies. Way back, you know, 20 years ago. And then over the years, I kept pushing on that technology as a thread throughout my career and taking it all the way to the clinic and then trying to advance the technology, you know, because cutting your finger and dripping blood on a piece of paper is not really a high performance analytical technique. And so looking at other options and volumetric sampling and devices, and we continue in that space then trying to push it into even. Other applications beyond traditional drug level monitoring.
Chad:
Yeah, so let me rewind you back to the beginning there with the DHBs. I think a lot of people would actually say that the the paper you put out and maybe two thousand four or something like that was kind of a, I'll call it, a seminal DBS paper for bio analysis, right? We know that dried blood spots had been around and in use for four wheel pricks for for a neonatal. I think going back to early seventies, something like that. But what as an innovator to to move your company into that line of thinking and then to publish that sort of seminal paper that's that's going to be challenged, quite frankly, on the on the utility of it. Tell me about that experience and how you as an individual drove that forward because it really did start something in our industry.
Kevin:
So it's interesting how life experiences impact what you do. And so for me, I'll blame my daughter, right? So around that time when I first started Merck, I had my my wife had our first daughter and
Chad:
Their wife, also Kevin.
Kevin:
She was involved. Right. Yeah, right. So but in Canada, the public health nurse would come to your house. And part of that visit was doing a heel prick and collecting a blood sample for testing for inborn errors of metabolism. It just routine health monitoring. Right. And this was when I was still doing my PhD before I started at Merck, right. And then when I joined Merck, I started learning about doing PK studies in animals and again back to the mice. And it was like back then you had to use one mouse for every time point. And this was expensive, especially for using knockout mice. And so I remember in a project meeting, you know, this was the big topic, and it's like, why can't we just use a drop of blood? And I'm like, Hmm, why can't we just use a drop of blood? And I remembered the heel prick and it's like, OK, dried blood spots and that sort of, you know, those events coming together, the need to improve how we do the analytical, the experience of seeing dried blood sample collected and saying, OK. And I hired a summer intern and gave him the project to work on. And that's you know, that that was who who was on the paper. So undergrad student and myself working on dried blood spots and just putting it out there and say, here's a technique that you know is going to help reduce animal usage and could be applied to just routine screening. It's interesting it took a long time after that for it to be, you know, looked into and it's had its ups and downs. But I think what we've seen through COVID and the pandemic is the need to decentralize clinical trials and use remote clinical trials. So techniques like at home sample collection of blood samples is this becoming more and more mainstream and to think it started 20 years ago with a mouse PK study?
Chad:
Right? Yeah, that's that's pretty interesting. And I recently had a conversation with Neil Spooner, of course, who you know well and is and has worked in that space and done a lot to advance that space. And he also referenced your your paper when when we talked to him and which was which was cool. And I said, Hey, we're talking to Kevin soon. So. So it's nice. We make those connections and one of the things and we'll talk a lot about other innovations and other things. You've done it at Merck with with new technology. But one of the things that came through that I was thinking about is when you work at Merck and you're innovating and you're doing new things and you have the competitive space you have to work in and you also have that sort of noncompetitive and you're a scientist, you want to share those results. How do you make that balance at Merck is a is a I'm going to call you a serial innovator in what you've done. You want to share it all, but you can't share it. All right. How does
Kevin:
That work? Yeah, the question why? Why not just keep it a secret at Merck? Because it is an advantage for Merck, right? And exactly is coming up with something new and innovative and then making it available for everyone else is potentially giving up a competitive advantage, right? Yeah. And we do have those discussions internally about, Hey, you know, is this something that we should keep to ourselves? But I think my attitude and sort of the attitude is, well, if we thought it up, someone else could probably think it up, too. And putting it out there makes it generally available versus someone else might want to patent that approach and block us from using it. So Merck's attitude is, you know, we don't want to compete on tools. We want to compete on molecules, right? And so put the tools out there and let the best molecule win from, you know, from a patient perspective. So, you know, so I always taking that attitude to heart, it's like, Hey, if this is an. Approach. It's more likely to be accepted if more people use it, especially if you're going to take a new approach all the way into clinical application and have data go to a regulatory agency with a new approach. You want money companies coming with that same type of data because if it's more like more likely to be accepted,
Chad:
One of the things you do is you are frequently asked to speak on new technology. And I've seen, gosh, I don't know. Maybe it doesn't. It doesn't talk from you where it's like, Hey, Kevin reviews 10 different technologies and dried blood spots or in patient centric sampling. 10 new mass spec innovations. And tell me first sort of How do you find time? I've often thought, Geez, how does Kevin does he? How does he read all these papers and and get all this information and pull this all together? Because what I do is I just look at Kevin's publications and presentations go, Hey, look at all these cool things that are going on.
Kevin:
So I don't do anything all that bad. Yeah, I just play right? Yeah. Yeah, no. Well, OK, I've been lucky, you know, opportunity, but you've got to be prepared, you know, the prepared mind past, you know, so being able to jump on new opportunities when you see them right has sort of been what I've been able to do. Part of it, too, is I have no hobbies or outside life, right? So like work has been my my hobby. My wife will back that up, right? And so, you know, I was lucky enough that she would stay home and raise the kids, and I was able to go and to work. And I think part of it, too, is the culture of when I first joined Merck at Merck Frost, you know, I finished my PhD. When you do your PhD, you're working all hours of the day. And then when I joined Merck Frost, it was the same thing. Everyone was there all hours of the day. So it's like, Oh, that's how you work, right? And there was the focus on, you know, how do we discover new drugs and get them onto market? Like that was the mindset and everyone, you know, focused on that mission. So anything you can do to improve that and add value was encouraged, right? So that's over my career. It's like, how do we improve what we do? I'm never satisfied to accept the status quo. Right? And so lucky enough that I've had management support to encourage me to constantly try to improve things. Not 100 percent goes perfectly well, and sometimes the status quo is good enough, and I'd rather try to push for something and be told it's not needed, then not push it. All right.
Chad:
Yeah. Excellent. So so let's think back again, maybe 10, 10, 12 years, or I don't know, you can go back as far as you want. Tell me a prediction you made or something you investigated. Spent a significant amount of time on that was that was absolutely the wrong call and one that was absolutely the right call.
Kevin:
Oh man. Ron, costs get embarrassing.
Chad:
Yeah, that's what I'm trying to do, so let's.
Kevin:
Yeah, I don't know. So I don't know that the wrong call. I think things that have taken a lot longer to come into routine use, we can go back to, you know, dried blood spots. It's been like 20 years and we're still not routinely using them in clinical development. And it's I think it's it's the learning along the way. It's like, OK. And for drive, let's party. We can measure a drug out of a dried blood spot. You know, that's great, you know? But the reality is, if you want to do it for peak, there's a lot of other information you need to know to do. And so when when did the person take the drug right and when was the sample collected? So while being able to measure the drug out of a dried blood spot sample is probably relatively routine, putting in the context of peak is not right. And I didn't realize that when I first started on that. And so that's why over the last, you know, five or more years, it's been trying to identify technologies that allowed us to put that drug measurement in the context of peak, so failed to understand the context of the whole experiment. You know, you start doing things like, Hey, I've delivered a solution for you and like, Well, actually, no, you haven't.
Chad:
Right? Yeah. Neil Neal and I talked about that a little bit with regards to dried blood spots because it it felt like for a while it was the tail wagging the dog right, the dog being the clinical engine and trying to move these studies forward. And here we are as bioanalytical scientists, exactly as you said, saying, Hey, we've got a solution and they're going, we're not sure we have a problem.
Kevin:
Yeah, well, yeah, it's it's finding the right use cases to right. It's not a panacea. When does this make sense? And I think that Merck, you know, some of the successes we've had are things like episodic disease states like migraine, right? And so asking someone to sit in the clinical site and wait until you have a migraine before we take a blood sample is not really practical, but sending someone home and saying, OK, when you have a migraine, take the medicine. And then a little while later, you know, take a blood sample because then we write, you know that that makes sense. But just doing it because you can. Does it make sense?
Chad:
I've recently during COVID had when you mentioned, you know, pricking your finger and dropping it on a card and whatnot. During COVID, I was working with a company and we were doing some, some blood spotting. And for the first time, I pricked my finger and dripped it on a card and it actually was difficult. And I don't want to like, you know, I kind of figured it out, but it took some tries and things like that. And that's where I think evolving these technologies is so important past prick your finger and it's just going to drip. It doesn't really work that way.
Kevin:
So, yeah, and that's why, yeah, we've focused on moved away from that and gone to more of a device based approach that has it all integrated to collect.
Chad:
So Kevin, as I mentioned and as you mentioned, you've worked on so much more than than dried blood spots and the patient centric sampling space. I want to hear about some of the really cool innovations that are going on right now. What I said in the intro that, hey, Kevin's the guy you ask when you wonder what what you're going to be doing in five or 10 years. So tell me about, I don't know, two, three, four, whatever, whatever the really cool things are that you're looking at that you're excited about for the future.
Kevin:
Yeah, there's a few. So one is just speed related to analysis, right? And so last few years, we've invested in technology around really rapid mass spec analysis. So you can imagine, you know, and we've I've looked at this many different techniques over the years, but most recently partnering with science on acoustic ejection mass spectrometry. So pretty much a second sample into a mass spectrometer, you know, way faster than we could have done previously. So what does that speed mean and how does that change our approach to experiments? And we're still in early days of doing this and we've applied it, you know, in other parts of Merck for high throughput screening where it makes sense, you know, you want to screen a library and you have right, maybe you're looking at an enzymatic reaction where you put in a substrate and get a product and perfect, you know, mass shift perfect for mass spec. But we've also looked at doing PK studies and what does it look like and can we do quantitation? And we've shown that an OK speed, maybe not the driver there for running PKA, but there's a lot of experiments that you can do and a lot of experiments you don't have to do because there's no chromatography, so you don't have to worry about changes in chromatography over time. The sample prep becomes much more straightforward.
Kevin:
Right, it just a balance between do you get the sensitivity that you need to meet the criteria? But it also opens up completely. I would not new but old ways of doing quantitation that we've not thought about things like Standard Edition, where you divide your unknown sample up into multiple quarts and spike in known amount of drug into each of those and then run them. We don't do that because it takes a lot more analysis. Time to do that. And now with speed, we have the potential to think about new ways of doing quantitation and how do we leverage the data sets where we could analyze samples multiple times and really understand the variability associated with them. So instead of using arbitrary guidance related, you know, cut offs on my accuracy and precision, we could actually truly measure the precision of our unknown samples and use that which then goes into how we model the data to and putting error bounds on the the model. So you have to think about completely new ways of of generating data and how that would impact our interpretation of those results. And really, at the end of the day, it's a better understanding of our drugs that we're developing. And if we can have data sets that help that, then that's what we should we should aim for.
Chad:
So, yeah, that's really interesting. I guess I hadn't thought about it because initially when I think about the the technique I think of, OK, we're not having internal standards, and so we're losing a little bit of control. So we have to get the technique really precise. But with Standard Edition, you could run multiple samples faster than you run one traditionally with less prep and then you build the precision into the actual instrument analysis side of it. Then you got to shift that that paradigm of thinking and what? What do you when, when? So let's say you're ready. You know, the next step beyond high throughput screening is probably to look at early, early discovery studies to get some quantitation. And then and then, you know, nonclinical and then moving slowly towards clinical and ind enabling studies. What's your approach to moving people's thinking and ultimately the FDA's thinking on and really over? If that moved forward, you'd really have to overhaul the four six four six 15 four six 20 thinking of acceptance criteria in studies. So how do you
Kevin:
Well, how does one even start the data? Right? Yeah, right. Because that data talks and BS walks right is sort of my view of the world, right? And so let's get the data. Let's let the data speak for itself, right? We're a science driven, you know, organization where science driven industry. And so if the data is compelling, maybe that's an approach that is worth implementing. Yeah. And so we have early on we can start with animal studies and show the value, right? And so this is me just spouting off right. I have to. Yeah, well, that's what you're here for. Yeah. And and to me, that's I prefer the challenge, right? Hey, this is we've not done this like and I haven't thought through all of, you know, the variables associated with it. And then that's sort of my approach to things. I jump in both feet and hopefully don't drown and find people that are like minded yet skeptical, right? Which is always why I've enjoyed talking to you because you're like, OK, that's good. But what about this? What about that? Right. And I love those conversations, right? I'd rather have a good scientific debate with someone that is like, Hmm, that's interesting, but you haven't thought it through because that's where I learned versus people that like, OK, go ahead. I don't have anything to say or no, right? Like, if you're going to push back on something I want to try, you better have like suggestions on how to prove it right. So I think I'm going to go on a sidebar for you like Merck, you know, over the years of sends people on a lot of training courses and things like that. And I only remember one and one thing that I learned was about communication, and it's called the LCS model, right? Anytime someone has a suggestion or is putting out an idea, you should say one thing that you like about it. One concern you have about it and one suggestion for improving it. Right. And so if you can't do all those three, then you probably shouldn't be talking.
Chad:
All right, I like that. Yeah. Yeah, it's good. It's good to know how the training budget is spent. Kevin Kevin learned one thing in twenty five years
Kevin:
They gave up on sending me for training.
Chad:
I suppose, probably. But, but yeah, I think that kind of illustrates a point. Also, you're not you're not concerned about saying what's on your mind, right? And I think people recognize, you know, where you're serious and where you're making a point, but still somewhat serious. And I think that's actually a. Communication skill that you have, so maybe you learned that in one of the courses and didn't realize it, but
Kevin:
We all need a bit of prodding.
Chad:
No, right? I feel like we've lost that, that opportunity that we get in conferences, face to face and sitting down. And quite frankly, it's the sitting at the bar after a long day discussing the interesting presentations where that, you know, for people who aren't scientists, it might be listening. That's what we do at those conferences, right? We talk about more science when we have a beer or a glass of wine and
Kevin:
Heading that way. Really?
Chad:
Yeah. Yeah. Kind of kind of. But but yeah, but that's that's what we love. So good. A good colleague of mine. I'll be giving her a hard time. But she she she recently said, El is really boring, Chad. I said, I said, Thank you very much. That's my almost 30 year career is LC ms. So but yeah, but it's it's all good.
Kevin:
There are aspects of it that are pretty boring, but I think that I think that's a testament to the success of the technique, right? It's like it's become so routine and accepted that it's become boring. Then we are successful, right? But I think there's lots of other opportunity to make it exciting, right? But still need to be worked on.
Chad:
Yeah. Yeah, completely agree. So what else tell me about another innovation that we can we can discuss?
Kevin:
Yeah. So one that's tied to high throughput mass back is. And then even even in other areas, too, is the rise in data science and and big data and using large data sets to drive better decisions. And I think of it from an analytical perspective of might. Ideally, the goal is give me a structure and I'll tell you the method before I even inject a sample, right? It's like you think of all the methods that have been developed and validated and documented over the years, and we never leverage any of that data, which is kind of sad in itself. But imagine if we were able to capture that data and then use a structure to say OK in my huge database of previous methods, what's as close to this molecule? You know, give me a suggested sample prep, a suggested chromatography method and also, you know, predict the fragmentation and tell me what the method is going to be. We can't do that today, but there's no reason why we can't start building those, those models. Now there's already software for predicting chromatography out there. But you know, I think from a bioanalytical perspective, yeah, it's it's an untapped area that, you know, potentially we could leverage, you know, we're starting to look at and just mining the literature and using natural language processing to see if we can't pull out all the papers in the literature and look at the standard things that we talk about, you know, accuracy and precision and that are written into papers because there's been so many published that meet FDA criteria. Can we mine those build us a database that then is not to replace the analyst, it's to provide them a toolbox that would accelerate the process of of method development.
Kevin:
So that's one related to that, too, because now I ventured into ligand binding land as well, away from mass spectrometry and collaborated with people in that group, part of their early process of developing a ligand. Binding assays, especially for a therapeutic antibody, is is to screen the type of antibodies that you have. Mm hmm. Sure. And so initially, you might have 20 or 30 that you do a standard screening with. That is, you know, pretty routine these days, but you get down to maybe, you know, five or six that you want to follow up on and then you have to label them and do pairwise comparisons and that that becomes a very big experiment. And so we've worked at automating that and using some data tools to explore a much bigger base of bigger experimental space. So assay formats, pairwise comparison, interference checking. I have a paper that is just going through internal clearance that sort of describes that approach, and we've been working with Purdue University and some of their data scientists to work on the the data processing, and we're developing and making more data sets to allow us to because the goal is to get a good pair of reagents to use and then take those into a full method development where we use things like D. But if you don't have a good pair to start with, then your assay becomes much harder to develop and validate.
Chad:
So yeah, and it's expensive to pick multiple pairs. Yeah, and it's time consuming to have to go back if you don't pick right the first time. And so, yeah, I think. These are the kinds of tools that we're going to need to really to really accelerate what we can do. So that's yeah, that's super cool. Yeah, I'm excited. Honestly, I'm excited for that paper. It's it's, you know, it's funny. You mentioned, hey, you know, the mass spec guy. I'm now looking at ligand binding things, and I think that's I think that's yeah, I think I'm the same. Yeah, exactly, exactly. So, you know, it's it's an evolution in our industry, I think. I think there's crossover everywhere where we used to have those kind of stricter lines of I'm a mass spec or I'm a binding ass or I'm a small molecule on a large molecule person. So it's fun fun to have us all working together, maybe a little more than we used to. Yeah.
Kevin:
And then the thing that ties it all together is that we have to consider, too, is automation and small volume dispensing technologies than we've used some. But we've recently invested in more automation and dispensing technologies to try to miniaturize assays just to really to enable exploring many more experimental parameters, right? Because reagent precious, you know, if you don't want to waste them in using maybe traditional large volume approaches so you can miniaturize a lot of the experiments you do then allows you to do a lot more with the same amount of reagent and explore a bigger space. So you know, we haven't, you know, 96 well, plate standard format. But now, you know, looking at 384 well plate formats and miniaturizing assay volumes, even for traditional assays like metabolic stability, PK, there's, you know, certain others in the industry have gone to 384 well plate, but coupling 384 well plate with high content data capture on high res based systems and data mining and measuring all those together is sort of an area that you know for me is of interest. And pushing forward is like, OK, combining all of those and not just keeping them as individual experiments, but thinking about how do we leverage all that data to to make better decisions about right, not only the analytical aspect, but help drive programs along as well?
Chad:
Yeah, and that's that's ultimately what we're trying to do. Another area I'm curious for your opinion on is where we're at with intact protein analysis by mass spec. And also, I'm curious, I just saw the news release that Waters had acquired. I think I think the company's mega Dalton that was Dave Klemmer and Martin Gerald's Company and. And so that's even looking at intact viruses and capsids. And so what do you think is the future there in that that space, that really big molecule analysis?
Kevin:
Yeah, I was going to come down to what the use case is to write because, you know, we've done some work just like other industry peers on doing piqué of intact molecules in the pre-clinical space. It's it's a challenge, right? Because yeah, the molecule molecules are big. They don't behave well from a chromatography perspective. The mass spec part of it is is complex because of the big ion envelope and in resolving power. And but it's doable and it gets down to what's the question you're trying to answer at the end of the day, maybe early in discovery, you're trying to figure out maybe it's you're worried about clipping and you have like whether it's maybe an ADC or you have a protein construct that has a peptide attached to an FC chain of an antibody, right? You know, you worry about, does it get chewed up? Does it stay intact? Then using intact type of analysis, it's going to really help understand what you're measuring because if you die just down to the peptides, you don't know if something had changed in vivo that that you're missing, right? The virus stuff is pretty cool. You know, Merck obviously works in the vaccine space and is not in my area, but talking to the people that work there, like viral capsid, full or empty.
Chad:
Yeah. Simple question, right?
Kevin:
Simple question, right? Complex analytical challenge. So things like, yeah, yeah. Detection mass spectrometry is helping answer those types of questions. So yeah, it's pretty cool. Just again, expanding the types of applications that mass spectrometry can be applied to. So pushing into higher molecular weight, intact molecules from from analysis perspective, but then applying native mass spectrometry to understand biologically relevant, you know, folding or unfolding and or binding of target molecules to a protein. So all of these are, you know, helping us understand better the biology of what's going on. So coupling that with things like cryo-EM and combining the data sets to get a better insights into the structure of those proteins or that they're going to help because that's. Biggest challenge in pharma is understanding biology, right at the end of the day. Why did drugs fail? They failed mainly for lack of efficacy or for safety, and both of those are directly linked to our understanding of of human biology. So better, we can do it to help us understand human biology better and more successful, we will be at bringing efficacious and safe treatments for patients.
Chad:
Yeah, I'm just super excited by all this, Kevin. I really am. I think that's why we do it. That's why he said, work is your hobby and reading, you know, reading papers and things like that because we love it. That's why we stay late at conferences and go to the bar and talk science instead of instead of baseball. Although sometimes it's baseball, sometimes other sports hockey. Yeah, hockey with the Canadiens, of course. But but I was thinking actually just of I always talk baseball with Brad Ackerman, of course, because he's also a Tigers fan, but just to throw another industry guy out there that I, you know, see as a great innovator and probably somebody I should do do a podcast with. But anyways, I wanted to ask you one of the things that you mentioned going through here was working with interns with with the new scientists and and things like that. I was thinking as we're going through this conversation, we're sort of the first generation you and I and other people who started their career in the nineties, kind of the mass spec applying bioanalytical chemistry. And now we have this new generation and I see across different meetings promoting young scientists and and making sure that group comes along and has the same opportunities we did. So tell me what you're doing and to try and make that happen for people and leave, I guess I'll say leave a legacy, although I think you're going to be around for quite a while. I hope unless you want to want a lottery ticket or something and looking to retire soon, but I don't see that happen.
Kevin:
Yeah. So you mean, like, how are we attracting new talent and
Chad:
Yeah, attracting new talent, getting them into bio analysis, bringing them along so they have the same curiosity and desire to to work long hours because they love bio analysis. Not because not because the company is making them do it.
Kevin:
I think one one of the things that energizes me and that I've missed through the pandemic is is actually going out to conferences and meeting and talking to students, you know, in the poster sessions and they're there. There might be their first conference and they're so super excited about that. To me, that's like super energizing for me because sometimes we forget about what we do is is actually, you know, a pretty solid mission in life, right? We're helping advance human health and sometimes we're so close to it. We forget and take for granted what we do and we lose some of the excitement. So yeah, going and meeting students and talking to them, you know, luckily at Merck, we have pretty solid intern program or we hire interns. We also have a postdoc program as well. And so that's good. We just got a new postdoc, approved one of the guys that report to me, we're going to have a postdoc come in and work on the high throughput mass spec project, right? So anyone out there listening that wants to do a postdoc look me up because fantastic. It'll be a good one. But we also have recruiting with with universities, and we have a research center developed with Purdue University where we do a lot of a lot of projects.
Kevin:
Yeah, but then how do you once they're here at Merck, how do we keep them energized and excited? Yeah, it's a constant challenge because, yeah, we're dinosaurs, right? And I have two daughters, twenty five and twenty three. And so talking to them and their attitude towards work and life and work life balance is pretty indicative of, you know, people that we hire here. And it is a different attitude, right? It's like, Yeah, it is. Yeah, I, you know, work to live versus live, to work, right? Mm hmm. Yeah, I'm I'm a lived to work guy. But a lot of people prefer to work life balance and come in and and do really good work when they're here. But then they have a life outside of work, and I completely respect that. So that's that's, you know, that's just the balance of life and that's just not me, but I fully get and understand how like my daughter is like the clock hits five. She's done. She does a great job and she's an engineer and does a great job before that. But her life outside of work is equally or more important to her than her work life, right? And so, yeah,
Chad:
And and I respect that as well, right? So live to work versus, you know, love to work, right? I mean, I think it's kind of a I think those are those are tied together. And whether it's generational or something else, yeah, I don't really. But they probably haven't figured out more than we do. Yeah, I
Kevin:
Know for me, like just walking on walking down the hall and talking to the scientist is I find one way of getting them energized and like, because I don't know, I have crazy ideas all the time and I'm like, Hey, what if we did this? What do you think? Right? And yeah, so it's kind of fun having those conversations with new scientists and because they're not like, Oh, that just Bateman going off again, they don't know that yet. We haven't learned. Right?
Chad:
Yeah, yeah. Right. Well, and you have to you probably have to be careful that if you put an idea in their head and just say this or that, that that doesn't necessarily mean run in the lab and spend a week like doing it, like we're just too scientific. Oh, you want that, OK? Especially if they're in somebody else's team and can do work to help you out?
Kevin:
Yeah, yeah. I think that's great for me.
Chad:
Yeah, yeah. Yeah, that's fantastic. What, Kevin, you mentioned hockey, right? You're Canadian. It's almost you're almost born in your DNA. What other passions do you have outside of outside of bio analysis and mass spectrometry and innovation?
Kevin:
I got a great dog.
Chad:
Yeah. Well, that's good to know.
Kevin:
So yeah, what? So I guess you're asking what I would do outside of work, right?
Chad:
Yeah. What do you do outside of work? Or maybe you don't?
Kevin:
Yeah. Now there's a few things that I've
Chad:
Told me once you're lazy, but
Kevin:
Yeah, I'm a pretty lazy line to coach, you know? Bonbon guy now. So when the weather's better, I bike a lot and so my wife and I, we bike. We've done a lot of biking trips all over, you know, not all over the world, but, you know, in Europe and and that, you know, I was never a big biker growing up. But when I moved here, I had my old bike from Canada and I, you know, there's good trails around here. So I started biking and I said, OK, if I put a thousand miles on this bike, I'll buy myself a new bike. And so I put a thousand miles on it. So I went and bought a better bike and then I bike on that for a while and then we said, Oh, let's go you on vacation and we'll do a bike trip. And we went to Portugal. Actually, it was like tide. There was an IBSF meeting in Lisbon.
Chad:
All right. Yeah, I remember that.
Kevin:
And then right after that, we did a bike trip there and it was harder than I thought, but it was so satisfying. And so when I came back, I joined a bike club and started biking more. And yeah. And so, yeah, biking is my bike out on the road and doing whatever 30, 40, 50 miles is is a nice mental break.
Chad:
Yeah, yeah. Fantastic, fantastic. And it's a mental break, but also gives you a time to think and give you a white space to think about whatever whatever you want, probably including science.
Kevin:
So yeah, and then hiking with my dog, right? I got a yellow lab named Penny and my phone. I have lots of pictures of my phone, as you know, but most of them now are just pictures of my dog, one from hiking. And yeah, we go all over and I find a dog a very calming influence in my life, right? Like, I can have a bad day. I come home and my dog still jumps on me and licks my face, and it's happy to see me. My wife just happens to sometimes not always, but
Chad:
But she doesn't like your face. No. Well, Kevin, even yeah. And any any closing comments you want to share any science we missed or that we ought to at least look up or anything else you want to you want to share with everyone before we before we close this out and I've had a great time talking. Thank you.
Kevin:
Yeah, no one area that is mobility.
Chad:
Yes, please. Yeah, yeah.
Kevin:
Let's talk about it's been around a long time and there's good advances. So, you know, so full disclosure, I sit on the Scientific Advisory Board of Mobile Ion, and so they're making their technology commercially available. It's pretty cool, like high resolution e-mobility separations. Yeah, on the front end of a cutoff at the moment. But yeah, you can imagine it could be expanded to other. Yeah. And so again, making faster analysis with higher content is again, the data stream that data, right? And so to me, the science of being able to generate more data, more relevant data and then coupling it with data science is like the future of our industry, right? The regulated side of stuff is always going to be tough, but it's like, man, we do all that work to get plasma samples out of patients, and we measure one thing right for 40k. And we have there's so much lost opportunity. So the last one is again on the sampling and expanding beyond peak. We've done a lot of work over the last couple of years on multi-omics analysis of dried blood samples. We've done proteomics, but. Avalon makes some lipido mix whole exome sequencing RNA seq, and we're in the process of standing putting these techniques into a translational oncology study, so it's going to be pretty exciting to get those samples collected and see what we can learn about integrating large data sets in.
Kevin:
Right. Collect it remotely or in a clinical study. So I think the next few years of that are going to be pretty interesting. We don't know if what is going to come out of it. And part of that is overcoming a risk aversion of, well, if we don't know, we shouldn't do it attitude right? But we're not we're not going to improve if we if we don't take some initial steps to collecting data sets that we've never had before. Right. Some some, yeah. Yeah, you know, some may not add any value. I will fully admit that others, we're going to gain insights that we could have never thought of before. Sometimes you don't like to do things unless we have a clear vision for what they're going to do, but sometimes we just have to explore and see where we land.
Chad:
I love that explore and see where we land. I think that's a perfect place to to leave it. Kevin, thanks so much for all the insights this fantastic. I hope a lot of people take the time to listen. Certainly, this was an episode for the scientists among the crowd because we really dug deep into it, which was fantastic. I loved it. Thanks so much, Kevin. That's all for this episode of molecular moments. If you enjoyed today's episode, be sure to subscribe to Apple Podcasts, Spotify or on your favorite podcast app so you never miss a conversation. If you'd like to hang out with us outside of the podcast, we have many webinars and other presentations available for your enjoyment and education. Visit Bioanalytical to see what's coming up and how you can stay in touch. Thanks for listening to the Molecular Moments podcast.
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