Guru sat down with Mike Tarselli, CSO of TetraScience, an R&D Data Cloud company. In this interview, he sheds light on the data challenges of the biotech and pharma industry, the importance of data harmonization, the lab of the future, and much more
Journey of lab informatics leader
Mike was encouraged by the nurses in his family to become a doctor.
My family showed me how to take pulses and how to take temperatures and administer drugs. And they said, be a doctor, be a doctor, Mike recalls
So I got into college and trained as an emergency medical technician to eventually become a doctor.
But very soon, he realized that he did not care for the sight of blood. After passing out a few times, he started exploring other, less bloody career options.
Mike started thinking about alternative career options where he can still help people, but don't have to cut them open and treat their wounds. In the pursuit of a more suitable career, he decided to join a pharma company in Boston called Ariad pharmaceuticals where he did a couple of summer internships before switching to Millennium Pharmaceuticals, which later became Takeda Oncology. Through his journey, he fell in love with the field of medicinal chemistry. He pursued a Ph.D. in medicinal chemistry from the University of North Carolina at Chapel Hill, followed by a postdoc from Scripps Research. Mike then spent over a decade juggling between roles at CROs (contract research organizations), big pharma like Novartis, and the scientific society called SLAS (Society for Laboratory Automation and Screening). After gaining these diverse experiences, he joined Tetrascience as the Chief Scientific Officer.
Motivation to join Tetrascience
Mike realized that the pharma industry is moving towards automation and data integration on the cloud.
"During my tenure at SLAS, it was very clear to me that everyone was trying to move towards integration. And many senior pharma executives were saying that they have to fix their data problems. I kept hearing it over and over from many major vendors and many pharmaceutical companies, so my current role at Tetrascience was already sort of primed in my head." Mike tells us.
What is Tetrascience?
"We call ourselves digital plumbers. We build pipes for pharma companies. That's a fanciful way to say that we do data integration into the cloud: for instruments, for software, and data science connectors.
For example, if a pharma company has 50-100 years of a surfeit of data and wants to figure out a way to get that data usable, interpretable, understandable by the whole staff. It is not straightforward to do that. Their data is generally fragmented across multiple departments, databases, apps, thumb drives, and local storage.
So how do you get that all in one place? One way to do it is to say from this point on, we're going to start moving everything to one data lake, right? And Tetrascience helps with that data lake. And we can also help you connect all those instruments that are producing that data, get it into the lake, and then we can help harmonize it to a standardized open readable format.
Just like what a pipe would do for water where you don't necessarily notice the pipes under your house or your street. You turn on a spigot and water comes out. This is what we strive to do in biotech or pharma, you open your electronic lab notebook and just see data come pouring out.
Big Pharma v. Biostartup
Mike talks about the various differences between big pharma and biotech startups and if biotech startups can lead the biotech revolution. Even though biostartups are more agile in their approach and can become very successful, big pharma still has the majority of the market share, revenue from existing drugs, and enough resources to acquire these smaller companies.
Garage Biotech
With small footprint instruments that require very little space to set up, Mike puts forth the possibility of setting up a lab in your own garage and doing real science. Coming to the question of regulating a "garage biotech", he points out that if we can have regulatory guidelines for the production of cannabis, tobacco, and beer, theoretically we can have these guidelines for garage biotechs as well.
Cloud Biotech
Mike talks about the advantages of a "cloud-first" mentality by pointing out the rapid growth of Beam Therapeutics and Recursion Pharmaceuticals. Both of these companies embrace this mentality by ditching paper in favor of capturing data electronically and doubling down on automation. Mike also predicts that we are going to see more companies like these because they are poised to succeed right from the start and have greater potential for exponential growth in a short period of time.
Lab of the future
The term "lab of the future" doesn't mean much to Mike when the labs using this buzzword aren’t indicative of the ambitious implications associated with it. He shares his own vision of what a “lab of the future” might look like in our lifetime.
Human-machine interface
Can we make machines more humanized to accelerate their adoption into society? Mike tells us one of the main factors of resistance when it comes to better adoption of technology is that it doesn't come across as practical, approachable or helpful. Users have to follow strict guidelines to use it and that can make adoption harder.
Piecemeal innovation v. wholemeal change
Mike stresses how important it is to define your objective first before figuring out how to go about it. Once you know what you want to do, you will be able to find creative and innovative ways to do it.
Augmented reality (AR) in the lab
Even though people think AR is not advanced enough to use in the lab, that's not entirely true. Mike argues that augmented reality in the commercial sector has grown by leaps and bounds and continues to excel. Some challenges for using AR in the current lab setting might be the limitations of the operator, restrictive space that doesn't allow for free movement, and the risks of the AR equipment being damaged by chemicals or other equipment.
Value of clean data
The value of structuring your captured data is priceless. If a lab can maintain "data hygiene", this might reduce the number of wet-lab experiments performed in the lab. Like squeezing every drop of juice out of an orange, if one can capture everything while doing an experiment, you can get the most out of the data. This could negate the need to repeat it.
Power of automation
Automation has come a long way. Mike points out that operators were needed simply to make phone calls back in the 40s and 50s. What once required a person to physically connect switchboards is now automated with the touch of a button. It would be prudent to continue tapping into the potential of automation and implement its power into our labs.
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