Continuous processing based on artificial intelligence, robotics and data science hasn’t been adopted in the pharma and biotech manufacturing verticals yet, says a disruptor business.

While the data-science side of things, such as data collection, and the formatting of the figures to make the numbers useful has been approached by innovators, the smart manufacturing-oriented digital transformations aren’t there, according to John Vitalie (pictured), chief executive officer at AI firm Aizon.

“Leveraging machine learning and artificial intelligence in the manufacturing process” produces higher yields and predictability, and ultimately better outcomes for patients, though, Vitalie explained, so there should be a focus on it. Newer tech would also benefit, such as precision or personalized medicine, which needs small, or micro-level batch manufacturing.

The systems that Vitalie is referring to have been termed Pharma 4.0, which riffs off of the moniker Industry 4.0, or the Fourth Industrial Revolution: widely written about and where human intervention isn’t a part of the manufacturing process — machines perform all the roles, including analyzing process and self-monitoring. Vitalie’s firm specializes in this area using AWS cloud.

In anticipation of the AWS Startup Showcase: The Next Big Thing in Security, AI and Life Sciences event — set to kick off on June 16 — Lisa Martin, host of theCUBE, SiliconANGLE Media’s livestreaming studio, spoke with Vitalie for a special CUBE Conversation on how Aizon is disrupting scientific discovery and life sciences by automating pharma and bio manufacturing with AWS cloud. (* Disclosure below.)

How to get to Pharma 4.0

The science of artificial intelligence in life sciences is ripe for exploiting, the entrepreneur reckons. “If you look at manufacturing in other industries, it’s been widely adopted for a number of years,” he said. Vitalie uses the example of automaker Tesla, a company that has embraced AI, robotics and data science, advancing “the efficiency of manufacturing,” he added.

Addressing “pre-digital” is the key. “You’d be amazed to find the amount of Excel spreadsheets that are still used in manufacturing today,” Vitalie said. “That data is not accessible.” So, the first thing that has to happen is to get the data, harmonize it and make it useful. As it is now, data in the spreadsheet is generally only used by the operator or the user.

The second level toward the Pharma 4.0 goal is connecting machines: “The connected plant and data lakes; to begin to get more value and find more ways to improve the processes,” he stated.

Thirdly, one must add advanced analytics and AI. And the final part of the puzzle is creating adaptive manufacturing capabilities throughout the enterprise, which is “really, the ultimate vision and ultimate goal every manufacturer has,” Vitalie said. Adaptive manufacturing is the development, production and delivery of demand-driven products. Manufacturers, overall, historically have found it difficult to achieve because different systems must communicate with each other.

Aizon says its GxP AI platform, which stands for  good practice, can perform all of this — the breaking of data silos (to bring data in from multiple sources) and run AI and analytics across sites in cloud with AWS.

Pandemic wake-up

“COVID has been the greatest catalyst of all time for this industry,” Vitalie went on to say. “It was a wake-up call for a lot of people.”

It became apparent that capacity was underserved, he explained. Even though patient safety, compliance and overall quality is of the highest standards, the supply chain failed.

“The realization is that we do not have the capacity in any geography or with any company to meet the demands that we’re seeing today,” he said.

The demands on getting product to market and getting the supply chain right simply aren’t being met. That’s despite collaboration taking place in the vaccine production between companies.

Smart manufacturing would go a long way to solving the issues, according to Vitalie. “It’s time to get over the kind of cultural barriers, risk aversion and really come together to coalesce around a smart manufacturing strategy.”

Multi-variable analysis

Some of the more technical advantages to processing, based on Artificial Intelligence (AI), robotics and data science include the contribution of statistics, he explains:

Real-time multivariable analysis, where the contribution of various factors are taken into account statistically, as they happen, in order to figure out why an outcome occurred, is an example of where automation of process and AI can play a great part in pharma modernization, Vitalie believes.

Those predictions allow a manufacturing process to be adapted in real time: for example, an operator can be alerted to make production changes as necessary. Improvements in yield on batches is another benefit that would derive from increased implementation of data-driven analysis.

“We can predict what will happen,” Vitalie concluded.

Watch the complete video interview below, be sure to check out more of SiliconANGLE’s and theCUBE’s CUBE Conversations, and tune in to theCUBE’s live coverage of the AWS Startup Showcase: The Next Big Thing in Security, AI and Life Sciences event on June 16. (*Disclosure: Aizon sponsored this CUBE Conversation. Neither Aizon nor other sponsors have editorial control over the content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE