Unlocking scientific data with Mike Tarselli at TetraScience



    TetraScience's mission isn't too different from Qualio's: they believe in the power of grabbing and unlocking cloud-based data to save patient lives.

    We invited Chief Scientific & Knowledge Officer Mike Tarselli onto From Lab To Launch to discuss how TetraScience's IoT platform opens up a new world of possibilities for scientific R&D, QA/QC and manufacturing.


    About Mike
    Mike holds chemistry degrees from UMass Amherst and UNC Chapel Hill. He joined TetraScience as Chief Scientific Officer in 2020 after a string of scientific director and advisory roles.


    View previous From Lab To Launch episodes


    Apply to be on the show



    More of a visual person?

    Access the complete transcript of our chat with Mike below.


    Thanks for joining us today, Mike. Welcome to the show. 

    Hello, Kelly. Thank you very much for having me. 

    So Chief Knowledge Officer seems to be an emerging role. Can you tell us more about that role and a little bit about your background?

    Sure. I'd love to. Thank you.  I'll rewind back a little bit to say, because there's a lot of weird words in there and an ampersand, which I actually asked for. So  scientific was my very first role here at TetraScience.
    I'll say that in its previous evolution the company, TetraScience, was handling a different kind of business and was looking more in the tech side for being a cloud platform for relaying instrumental measurements.
    But our real Tetra journey with this cloud-based data platform has started really in earnest in 2019 to 2020. And I was brought in somewhere in the middle of 2020 to be  Chief Scientific Officer.

    And of course you say, "why does a data platform need one of those?" And the answer is: if you're going to serve biopharma scientists and you're going to say, "hey, what do you need to do with your data? Where does it need to go? And how does it need to be integrated into discovery and development workflows so you can actually get things done?" then you're going to need a scientist to look at that.  
    So with this sort of title, I worked on our external integrations. I worked on our product marketing message. I worked on our initial stabs at our quality system, which has evolved leaps and bounds thanks to my very talented team now in the past two and a half years.

    And then in mid-2022, actually late 2022, I was very humbled and honored to be called  initial Chief Knowledge Officer as well, which means that my remit now expands past just, "hey, can you get scientists to work with our platform and tell us their use cases"? To now more holistically, "how do we train people for this new world?"

    We talk about GAMP 5 Second Edition,  CSA procedures versus CSV. We talk about getting people into being data science literate upfront. We talk about science as not just being, "hey, can you pour fluids together or pipette cells on a lab bench"?, but, "can you also look at the data at the back end and figure out whether your conclusions are statistically relevant?"

    So how are you gonna train people for that? 

    How are you gonna train people at the interface of science and tech? We have a  house term we call Sciborgs  - S-C-I Borgs, get it? - because you gotta be at that interface like a bioinformatician, right? Or a chemical matician or a systems biologist. If you can't get both of those sides, it's going to be hard for you to interact in this new world, we think. So my Chief Knowledge Officer role allows me to expand into training and then into how we know what we know. 

    Nice. Yeah, we're on a similar mission here at Qualio with, bringing data sets together and those kinds of things.
    'Cause again, from a quality systems perspective, you're right,  so much data to be had out there, but if we can't see it and get it and touch it and handle it  process it, if it's still sitting on a spreadsheet somewhere or it's in the lab on an instrument somewhere, and nobody's really looking at that from a bigger picture perspective.
    It's amazing to me  we've gotten so far as an industry and yet it feels like baby steps from a data perspective.

    Yeah, it's an excellent point.  Think about your academic training  if you did a postdoc or an early internship, what you were doing ' for data', quote unquote.

    And if you can't see, I'm making air quotes. And you would print out a piece of paper or maybe you'd handwrite in a lab notebook  maybe you'd take a carbon copy or get an actual screenshot from something and send it to yourself and move it on a USB stick, right? This is craziness.

    And you'd say, "didn't that go away in 1995?" And the answer is no. Maybe every couple of weeks we still have a company that will come to TetraScience and say, "hey, help me. My entire manufacturing cGMP suite is still on paper. We don't yet have an MES or a LIM selected. We really need to go that direction.

    We're still in a CSV world. We need desperately to modernize and digitize. What can you do for us?" They wanna leap, they wanna go two or three technological levels up in one go, they wanna go past ELM and LIM. They wanna go past the sort of clunky point-to-point integrations world of the 2010s and they wanna go right to cloud data and hey, great, we're here to help you.

    That's awesome. So yeah, TetraScience's mission there: solving humanity's grand challenges by accelerating and improving scientific outcomes.
    And as you said,  in the industry along into 2023, that's a big, bold mission. What are some good examples of how that's happening?

    Great question.  Mission statements are inherently big and bold. They've gotta be, or else you're not going to achieve what you set out to do. So in ours, we wanna solve humanity's great challenges, but we're gonna start the place we know best, which is biopharma workflows, especially those in discovery, development, manufacturing. At this time, 2023, not a forward-looking statement.

    We are not playing in clinicals or patient data or patient safety. Completely eschewing that.  That actually less complicates our space. It makes it a little easier to handle, right? Yeah, definitely. No PHI, no PII, at least limited if nothing else. Because of that, we are able to take that mission and say, can we... we actually just asked this in a survey of 500 life sciences execs who were very kind and responded to us with great data.

    Things emerged in there that I've never heard before. If you don't have a cloud system handling your data, you are seven times more likely to repeat experiments. That's nuts. That is nuts. And they would they say they, you don't even have to truncate the drug development and discovery process, from that current 10 years, $4 billion to, they're not looking for two years or five years.

    They're looking to shave off months to a year. That's something we can do, right? We can immediately have a direct impact both on those companies' bottom lines, if biopharma wants to save some biobucks in order to get their development going faster, and also to help accelerate things to patients. Every two or three months something goes in is another cohort of patients that could have been saved if that was approved earlier.

    So you think that data management as a strategy doesn't have that much impact, but it does, being able to access in a fair way, being able to get at it and accelerate it, interrogate it, and then make decisions upon it to release therapeutics faster is the way to go.

    So that's what we're here to do. We do one thing and we do it well, and that's, take data outta systems, put it somewhere you can find it, and then put it where it needs to go in your internal systems. 

    Nice. Nice. It is the beginning of the new year. What are some of your projections for this space in this year and maybe into the next five to ten years?

    Sure. PWC, a much larger consulting organization, has a very broad range and said that this is gonna be the year of digitization in this space. They said this is gonna be the year where everybody wakes up and says, we're in a post-COVID pandemic world. We are awash in data as you probably know this already, but we're going to take all the data that's ever been done in human history around 50, I think it's exabyte, no, sorry, zetabytes now. And we're going to literally triple that in the next five years. Who's gonna handle all that? Wow. Somebody's gotta and biopharma by the way, and healthcare generally generates about a third of that.

    So this means anybody who's doing data and integration work and storage work and computational work right now is gonna literally see their work three to five x in terms of both impact and amount in the next three to five years. This means you gotta get ahead of that curve. So this is the year where every pharma's gonna appoint, if they don't have it already, a Chief Information Security Officer, a Chief Data Officer, probably multiple CIOs, right?

    And they're gonna start really looking at how to take every single workflow that can be automated and trying to automate it. This is from robotics on the discovery side to looking at paperless trials and animal list testing in the development side to looking at high-end manufacturing and some VR and AR and things in the manufacturing realm.

    All of these are things which unsurprisingly generates diverse data. You gotta put it somewhere. Definitely. So we're hoping that somewhere is TetraScience's scientific data platform.

    Definitely. And thank goodness that we are moving into the CSA realm, 'cause holy cow, could you imagine applying CSV principles to all of this? It would shut it down. It would bring it to its knees. 

    Admittedly, when I first started I inherited what I could from my predecessors in these roles. And we had a couple of procedures, SOPs and policies and we had and being honest, an IQ OQ PQ trajectory we just did. That was what was de rigeur in 2020.

    So that's what we went forward with. And we quickly found ourselves in a morass. We said, okay. So we installed this, we qualify it, and now somebody wants us to pass data through that's theirs and do us a validation. But how do we do that? I don't know. Validation isn't our responsibility.

    Can we admit to that? Is that a thing we can do? So we quickly pivoted upon the publishing of the new GAMP regulations to a full CSA and to saying, look, there's so much going on.  I'm gonna ballpark this. In a given biopharma lab, they probably have somewhere between 50 and a hundred different kinds of instruments going.

    Kinds, flow cytometers, mass specs, HPOCs, particle sizers. You gotta have data coming in from all of those all the time, and you have to verify that each of those connections operating in spec and that the platform's handling them when they come in, that no data's being duplicated or overwritten, and that you have a full audit trail.

    Oh, and that those are publishing to where they're supposed to go all in real time or near real time. How are you gonna do that with a CSV approach? You can't have a human with a piece of paper looking at each independent workflow you think you might do. There's just gonna be an explosion of options, and you're not gonna be able to have PQ on all of that.

    So the better way is to go CSA with that. 

    Definitely. That's a tough sell, although, I say that, it's a tough time in the economy right now. And a lot of founders, we have a lot of founders in our audience. And curious for any thoughts or advice you'd give to those who might be facing some headwinds, right now?

    Admittedly, I'll be honest, I'm a scientist by training. I'm not an economist and I'm not a titan of industry. I'm doing my best to learn and adapt my roles as we're going. But we as an executive team here at TetraScience are definitely taking the long run approach, right?

    We know what we need to do to deliver for our customers. We're going to be looking at absolutely every spend, every investment, every everything very carefully. 'Cause we wanna be good stewards of our capital. And so we're going to be in it for the long haul. We don't throw those lavish startup parties.

    We don't give crazy FANG company type salaries out at a whim. We can't, we have to be fiscally prudent. We have to be conservative with our management strategy, but we have to keep our eye on our broad mission, right? Which is to accelerate and improve human life.

    You gotta be around and you gotta get outta the headwinds of the economy section to do that. So we are in buckle down, look for efficiencies, automate everything mode this year and  that's gonna be our sort of canvas to play on. . 

    Yep. Yep. That all sounds very familiar to me here. To pivot a little bit, if you could go back to the start of your career, what would you tell yourself based on what you know now? 

    Which career should I start from?

    I maybe don't look all that old on camera if you see this on YouTube in the future, but I'm in my 27th year of working world. And that goes all the way from getting trash outside of dumpsters for my old apartment complex to washing dishes in our dining commons to taking part in data entry studies in college and working as a paramedic for a little while.

    Working as a hospital assistant, then working in labs as an intern, and then graduating into my informatics roles maybe about 10, 15 years ago. But it's been a very interesting and varied career. So first thing I'd say is to younger me is don't aim at the outliers.

    What I mean by that is you might want to win a Nobel Prize, or you might wanna be a billionaire or to have your own island in Greece, but those are not the usual outcomes for 99.9% of the population. They aren't things you get, so the best you should do is to adopt a set of values, adopt a set of working norms, try to get better every year, but if you say at the end of your life that if you haven't won a Nobel Prize, then your life's worthless, that's probably not a great idea.

    Second thing I'd say is, appreciate the bumps more than the ups. You actually learn an awful lot in downturns. You learn a lot if you get fired, you learn a lot if you botch a big project or if you fail. The old saw goes that good judgment comes from experience and experience comes from bad judgment.

    I'm not gonna say I've always made the right decision every time I've tried, but things have not always gone my way. And I've actually learned a lot more in the things that I've failed at than I have in the things that have gone amazingly. And then the third thing I just say is: look at what's most important.

    You don't actually have to, again, have tons of money or five cars or seven houses or fly around the world or be a big thought leader. Having really committed good relationships with friends and colleagues, having enough, without having too much, being gracious for what you have and knowing that you're getting better every year is well enough reward for me.

    That's what I tell myself. 

    Awesome. Awesome. Those are all good advice for sure. Another fun question that I always like to ask: if I walked into Barnes and Noble, where would I find you? What section would you be in?

    You did introduce this podcast with a science nerds motif, so you're gonna also find me in the scientific biographies.

    That's a starting point. I love learning about how people before me went and how Salk was doing with his polio vaccines, or how Pasteur was doing when he was looking at the sewer systems under Paris. That's really neat stuff to me. But I also love trivia and puzzles.

    So you'd find me in the jokes section and looking for the 17 best ways to complete Crossword X. I might be buying a puzzle on the wall cause I also love puzzles in my free time. And I just also love looking at broad-based non-fiction. How to do X better. Whether that's how to build a business better, how to build a garden better, how to do lighting better.

    I love those self-improvement things that say if you just did this 10 minute thing, you'd probably be doing better in your life. So probably those sections. 

    Nice. Yeah, I think we'd have a lot of overlap there. That would be fun. 

    No. Tell me yours. You should. Where's your sections?

    Oh my gosh. Yeah, definitely the science type sections. I love fantasy, sci-fi type books. I think I have read everything Michael Crichton ever wrote five times. Not as much his pseudonym books.

    And the other one I always loved was Robin Cook. Cause he was a medical doctor. My degree is in biology and chemistry as well. That's my background. So I love all  the biology and the science nerds sorts of things, which is part of what I love about this industry.

    And I love what you were saying about... my goal certainly wasn't to grow up and get rich and win a Nobel Prize. I just, honestly, I needed a good job that paid well that could support my horse habit 'cause I have horses and that's a whole other conversation, but yeah. But it's been incredibly rewarding to be 25 years into my career and getting to see some of the drugs that have made it into the market or medical devices.

    'Cause I didn't wanna be a medical doctor kind of situation, but I'm fascinated by all of those things. And so to have found this industry... I didn't even really know it existed. When I graduated from college, things said I wanted to go into either medical research or be a teacher.

    That's what you think you're gonna do with a biology degree, right? And here we are. Yeah it's fun and I, again, I particularly love now here at Qualio, where I used to just help, I had a consulting business, so I had three or four different clients at any given time.

    We have over 500 customers, so I get to see across what everybody's doing and it's just amazing. It's amazing to be a part of it. 
    Platform economics, it's a real thing. Watching what happens around the world and knowing that, you're looking at a dashboard for somebody's risk-based profiling who lives in Sri Lanka or Australia or South America is craziness.

    And I love it. That's one of the aspects of my job I really love. 

    And it's fascinating too as well, because in our situations, then we have the opportunity to learn from those different  customers and locations and help bring that broader perspective across. 'Cause it just benefits everyone to be like, "hey, I've seen this before!" and obviously, you've gotta protect some confidentiality and all that kind of stuff.

    But you can be like, "I've seen this before. Here's some ideas on how it got resolved last time I saw it." And people can collaborate on those things and it just makes everything better. 

    So where can folks go to connect with you follow along with TetraScience progress?

    So I am a LinkedIn guy. I love to post there to talk about different papers I've read, different standards and regulations, different things and cool places our company pops up and who we've helped success stories. So please, I'm the only Tarselli in science in LinkedIn, and that's T-A-R-S-E-L-L-I. 

    If you can't find me, then I've done a very bad job. For TetraScience proper, we have obviously a Twitter engagement: @TetraScience. We can be found, I believe on Instagram and LinkedIn as well. We have several posts that highlight our partner network, which is where other companies that are in our space, so not biopharmaceutical companies, but rather more on the software devices and hardware side congregate with us and say, "hey, we see this future evolving as well and we wanna be part of it". We actually look at these companies as being part of our extended canvas of what we can offer.

    And so they get together with us, they sign letters of intent with us. We get integration agreements together and we start literally building APIs together or exchanging code. And then I'd say we have a very good web presence.

    You can find obviously our corporate website and that's fine, but a better one for most people on here because it's a quality audience, is looking on the resources tab of our website and looking for our GxP and our 21 CFR Part 11 white papers. We do self-audits. We do a standard package, CSA-based. I should credit my team. They're the ones who pushed me into this world, which is good. And then we have a ton of open developer documentation at developers.tetrascience.com. You can see all of our API calls, all of the services we handle, all of the Python-based  integrations we support, where we're going next.

    We try to be very open and transparent so it's all out there in the world. 

    Awesome. Alright, Mike. Thanks so much for your time today. It's been a real pleasure. 

    Thank you so much, Kelly. It's been wonderful being here as a guest. Appreciate it.