Looking Towards the Digital Lab: An Interview with TetraScience’s CEO

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Spin Wang
Spin Wang

Spin Wang is co-founder and CEO of TetraScience, which offers a data integration platform for life sciences, including bioprocess. On the Forbes 2018 list of 30 science entrepreneurs under 30, Wang has devoted his career to “improving science with software.”

TetraScience’s software collects and harmonizes data from multiple instruments, such as bioreactors. The data is centralized in a “data lake” on the cloud where it can be visualized and analyzed.

[Interview edited for clarity, context, and length.]

GEN: Why is data management important?

Wang: Data management is the backbone of analysis. Because bioprocessing is an end-to-end process, like growing or cooking something, you’re constantly monitoring parameters like the optimum conditions to grow your cells. With analytics, you can feed that data back into an algorithm, which can help you make plans or adjust parameters in real-time.

GEN: But isn’t data collection an easy process?

Wang: Data collection, even though it looks like a simple problem, hasn’t really been solved. You don’t want a point solution for one instrument—you want all the data aggregated. Cleaning data is a well-known barrier to analytics in any industry: we’ve focused on that.

We provide a vendor-neutral platform. Many vendors are involved, so we need to prepare, harmonize, and standardize data from all their instruments, as well as adding relevant metadata, such as the time a sample was collected. Our perspective is that it will take a neutral third-party; individual vendors don’t have the incentive to collaborate directly.

GEN: How popular is the digital lab today?

Wang: The overall trend we’re seeing is that a lot of the top 20-30 pharma companies have initiatives or a team focusing on the lab of the future. People are definitely paying attention, but it’s still an emerging topic. My thinking is that, in the next 3–4 years, you’ll see strategies materialize and people articulating the value proposition more quantitatively with tangible impacts on R&D.  Then there’s going to be a period when things are rolled out…

GEN: What other trends are you seeing?

Wang: One trend is a vendor-neutral data standard—that’s crucial, to be honest. If data is coming from different places in a different format, that’s hard. Another trend is moving Bioprocess 4.0 to the cloud with machine learning and analytics software built on top of that.

A third trend is life science companies demanding data to be more open—because they want easier, more effective analyses. They don’t just want to use the software provided for an instrument and then export it into Excel. I can see some forward-looking manufacturers leaping onto that.

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