Helping Biotech Thrive: Insights From a Bench Scientist Turned Benchling Advocate
About Lauren Kossy
Lauren Kossy, Sales Development Representative at Benchling, spent the first part of her career in the pharmaceutical industry, researching treatments for infectious disease and cancer through in vitro and in vivo pharmacology. From microbiology to automation engineering, she helped execute complex experimental workflows and collaborated with a wide range of teams. Each team had multiple different data management systems, which made collaboration challenging and impacted productivity. This firsthand experience has allowed Lauren to deeply understand the pain points of Benchling customers and what our technology can mean both for individual scientists and for biotech companies as a whole. Here’s what she has to say.
Q: First off, tell me a little bit about your academic and professional background. Why did you want to work in life sciences?
I’ve always loved biology as a broad subject; I was introduced to infectious disease specifically in college and really enjoyed the lab work I did. Studying living things has always spurred my interest, whether that be ecology, evolutionary biology, or as minuscule as microbiology.
So, I now have a bachelor’s degree in biology and pharmacology experience in drug discovery at two large pharmaceutical companies: Novartis (specifically, the Genomics Institute of the Novartis Research Foundation, then the Novartis Institutes for BioMedical Research, where I focused on infectious diseases) and Bristol-Myers Squibb (where I focused on immuno-oncology). As an associate research scientist and then research scientist, I participated in a range of in vivo and in vitro studies—including, for example, testing proteasome inhibition treatments for kinetoplastid infections and evaluating therapeutic treatments using Syngeneic mouse tumor models.
Q: What was your favorite part of working at a large pharmaceutical company?
Being at a large organization meant a lot of cross-collaboration, which I loved! I was always working with different groups of varying specialties and backgrounds within the drug discovery and development pipeline. I worked with teams in microbiology, medicinal chemistry, bioinformatics, laboratory animal sciences, and automation engineering, for example. It was awesome to learn from the best of the best in the pharmaceutical industry, and all that collaboration was supported by plenty of resources. There were few limits, it seemed.
Q: What was the biggest challenge you or your teams faced?
In some ways, the biggest challenge was also collaboration! Organizing all these complex studies meant a whole lot of scheduling and preparation with technicians and other team members, as well as with other cross-functional teams we’d work with. Some tests were day-long, some week-long, some months-long, and keeping every detail organized with all those colleagues and teams was just a huge challenge.
One thing I also found especially challenging was digging through years and years of data in collaboration tools, such as SharePoint. There was so much great historical data available, but the data lived within different systems for each project team and those systems were often hard to navigate and comprehend because of all the clutter and lack of organization. This easily affected the progress of the studies I worked on; it was so time-consuming.
Q: What kind of data management systems did you use for your primary tasks in the lab? For instance, systems for data capture, data analysis, or project management?
Oh, so many. We used a kind of e-workbook at both Novartis and Bristol-Myers Squibb, as well as Excel for study design and in vivo data capture, plus another software for tracking animals individually. We used SharePoint and Excel for project management, Excel and a couple other tools for data analysis, and we communicated using SharePoint, Excel, and email.
Q: How did you and your colleagues like using these systems? Were there any problems you ran into?
Some systems were well-liked, but some were stereotypically known as “scientists’ worst nightmares”! The software we used as an e-workbook was slow and clunky, and we had problems inputting different file formats and sharing with colleagues—even just opening and closing the software was a hassle. SharePoint was fine, but it wasn’t at all organized, and the material in there was disconnected from our other work. It didn’t talk to any other systems. Excel and email got the job done, but it was informal and we struggled to use them with a bunch of different data formats.
Q: How did those systems affect your daily work?
I think they had a big impact. We were constantly having to repeat in vivo studies. And, in general, everything took so much time, since we spent so long parsing all that data in all those different systems.
It affected the companies as a whole, too. Having to repeat studies slowed the project teams’ progress on compound testing. Repeating studies also meant we had to use additional resources—like fresh compounds, fresh strains of bacteria, fresh antibodies, and additional lab animals—which, if we’d done it right the first time, wouldn’t have been necessary. Plus, because all those different systems were siloed from one another and from other teams at the company, a lot of data was siloed. And siloed data could often cause misinterpretation of a study’s findings.
Q: So, what drew you from working at the lab bench to working at Benchling?
Really, it had a lot to do with wanting a change around all these slow-moving, old-school workflows and work environments. I immediately saw value in Benchling’s platform; I was actually surprised and taken aback that a system like this exists! There’s just the one interface with multiple applications and completely centralized data. Many scientists have no idea that a system like this is even an option.
Q: What is your current role at Benchling? How is it similar or different to what you were doing in the past?
My main job right now is talking about science! Both as it relates to research and the systems being used. Benchling has a faster-paced work environment than I’d been used to, with new processes and information coming out constantly. In many ways, Benchling reminds me of my time in drug discovery. Even though the company is not doing research directly, it’s still very informed — possibly even more informed than some pharmaceutical companies — on current drug discovery efforts.
Q: What capabilities do you think Benchling unlocks for scientists?
It’s easy to use, for one. And I think it takes a huge burden off their shoulders when it comes to centralizing everything they’re generating at the bench. It also gives them more confidence with their data. Scientists work so hard on a daily basis; Benchling gives them the support they need to be more efficient and accurate.
Q: What value have you seen Benchling bring to scientists at enterprise companies?
Better collaboration! Number one. Also, data standardization. This allows scientists and directors to have more confidence and control over not only their experimental data, but their research as a whole. It helps projects move faster—and there’s better visibility into the lab work for leadership and C-suite executives. A unified platform like this also, frankly, saves money. It leads to fewer repeated studies and fewer user and system mistakes that require additional resources.
Overall, I think it makes so much sense. These days, pharmaceutical organizations are under extreme pressure to deliver outstanding data findings every year. Although legacy data management systems are so ingrained in pharmaceutical culture, workflows are getting more complex. The pressure to deliver positive results is getting higher. Having a bunch of data management systems means the margin of error for all these studies becomes so much larger. Companies really need a firm backbone of accurate data representation to deliver the results they want—at the bench and at the enterprise level.
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