The cloud handles disparate file types for pharmaceutical research significantly better than traditional R&D ecosystems, according to TetraScience Inc., which specializes in lab-targeted data clouds.

That superior file handling is just one of the benefits to full cloud for the pharma vertical, the startup claims.

“We would argue they [pharma] haven’t done enough fast enough and that they need to get there faster in order to deliver patient value and efficiencies to their businesses,” said Mike Tarselli (pictured, left), chief scientific officer at TetraScience. The company operates a research and development data cloud for life sciences and reckons labs are missing out if they don’t go full cloud as soon as possible.

More advantages include that cloud proffers more scalable storage, plus it also allows for searching across tiers, according to Tarselli. Cost-reducing tiered storage is when data is hierarchically placed based on business value, he added.

Tarselli and Michelle Bradbury (pictured, right), vice president of product at TertraScience, spoke with Natalie Erlich, host of theCUBE, SiliconANGLE Media’s livestreaming studio, during the AWS Startup Showcase: The Next Big Things in AI, Security & Life Sciences. They discussed why they believe cloud must co-join with pharma R&D. (* Disclosure below.)

Data scales

“You’re just going to see this absolute data explosion,” Tarselli said.

What he’s referring to is the increase in data that will need triaging, manipulating and searching. It will be a result of a shift in pharma R&D to automation of labs along with new, personalized drugs and health care, among other advances. Artificial Intelligence applications are thought to be ripe for pharma.

“You’ll see more things that leverage larger amounts of data,” Bradbury added.

It’s diverse data types, though, that will benefit the most, the duo think.

“One thing that the data cloud handles very well [is] to be able to parse through, harmonize and bring together your data,” Bradbury stated. “It can [then] be leveraged for things like AI and machine learning at large-scale.”

The problem right now is that there’s snowballing data all over the place, and it’s unwieldly as is.

“What cloud offers, specifically, is a better way to store, more scalable storage, better ability to even tier your storage while still making it searchable, maintainable, and offer a lot of flexibility to the pharma companies,” Bradbury said.

Fully automated labs need cloud

AI and ML are areas that will propel automation in pharma R&D. Mass parallel compute, combined with “giant data clusters” will become common as this occurs, according to Tarselli and Bradbury.

“I can look up everything the rest of my colleagues have ever done on this particular project with a single click of a button in a simple term set in natural language,” is how Tarselli envisages it. That’s not how things are now, where R&D work, such as in vaccines, a hot item, has historically taken years to accomplish.

“I click three buttons, configure it, boom,” he said.

Tarselli is talking about sharing lab results across platforms — something hard to do today. A bench chemist should be able to set up an automation cascade, according to Tarselli.

“Data liquidity that enables you to pass results around outside of your division and outside of even your company to others who are able to see [it]” is how pharma must progress, he said, adding: “It should be fairly easy to achieve if all that data is ingested the right way.”

In other words, with cloud at scale. TetraScience’s R&D Data Cloud is currently available on the AWS Marketplace.

The ‘exabyte era’

Increased sequencing, more microscopy, plus capturing individual samples are just three of the data power users the duo believe will become commonplace. And that mix becomes even greater when one morphs-in analytics.

Personalized medicine, where one captures and retains information about specific patients, or even cells, produces far more data than has been found traditionally.

“On-prem, you will be buying disk drives and out of physical materials before you’re going to outstrip the data,” Tarselli said.

Personalized drugs is one area the team is bullish about.

“We’re seeing customers that want to, within a matter of three, four hours, get to a personalized drug for patients,” Bradbury explained.

Leveraging the data on a per-patient basis requires a combination of compute and data store. “It really becomes this marriage of getting a huge amount of data and getting the mass compute,” she added.

The aforementioned automation of labs is another area the group has been getting experienced with. In that case, internet of things can play too: A client has been using robotics arms.

“So, it’s a huge mix of being able to ingest IoT data, send experiment data to them, understand sampling, getting the results back, and really automating that whole process. If you can get the data together but you can’t compute it, if you can compute it but you can’t get it together, it all needs to come together. Otherwise it just doesn’t work,” Bradbury concluded.

Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of the AWS Startup Showcase: The Next Big Things in AI, Security & Life Sciences. (* Disclosure: TetraScience Inc. sponsored this segment of theCUBE. Neither TetraScience nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE