"When I started looking at how AI was being brought into manufacturing, I felt that technology companies are coming at automation from a top down approach. There was a big gap between that strategy and the production floor with the machines, equipment and processing."

Rajiv Anand


January 13, 2021

What insights did you gather over the course of your career that helped inspire the creation of Quartic.ai?

I have lived with Industry 3.0 automation all my career. While automation evolved, control and automation is limited to a zoomed-in view of equipment and process. Existing automation hit its limitation when dealing with a zoomed-out, elevated view of production – this is why big data and AI are needed for the next generation of automation. When I started looking at how AI was being brought into manufacturing, I felt that technology companies are coming at automation from a top down approach. There was a big gap between that strategy and the production floor with the machines, equipment and processing. That is both in terms of understanding the nature of how these processes and machines work, and also the nature of the data that they generate. The people who can apply AI and ML best are the people who understand the manufacturing process. Much of the focus of tech development has been for people in IT and data science. This creates a big barrier to adoption for chemists, biologists or chemical engineers; it is unsustainable. Overcoming those two barriers is the inspiration behind Quartic.ai.

How will Quartic.ai’s partnership with Bright Path Labs expand its presence in the life sciences?

Bright Path Labs has a technology that brings speed to the commercialization of API production. Continuous manufacturing is a big need in the pharmaceutical sector and Bright Path Labs’ technology accelerates that process. In order to enable and commercialize the technology they needed modern automation. It became a natural fit for us to work together, as their method of manufacturing can be accelerated with use of machine learning and AI. In moving from one molecule to the other molecule in the same reactor it is a challenge to get to quality production quickly. They are using inline monitoring (ILM), which are analytic measurements, generally done offline. That is typical for a batch process. However, if you have a continuous process you need to do those measurements online. Quartic.ai is able to use online measurements to create closed loop control. This creates a semi autonomous control system. It has basic automation that is going to control the equipment but the basic automation is being guided by AI models, which will be trained for particular molecules. For each molecule, we know what the perfect set points will be to achieve a quality target quickly.

What long-term impact do you see the COVID-19 having on pharmaceutical manufacturing?

There has been a realization that these types of events can happen again and they can be very disruptive. The current situation requires speed of experimentation and speed of commercialization and secondly, requires agility. This requires a higher level of automation and a much better understanding of data. The second part is speed to market. We have all been contemplating ways to commercialize these new drugs through internal supply chains or contract supply chains very quickly. Quartic.ai is doing this with a technique called transfer learning, which enables us to successfully make something in one manufacturing facility, train the machine learning model on that manufacturing facility, let's say in North America, and then when I open an additional manufacturing facility in Taiwan, the process and product knowledge that I gained is captured into an algorithm and applied to the new process. I see commercialization tech transfer as perhaps the biggest and easiest path to achieving returns to the bottom line from AI and machine learning.

What are some applications of AI that are currently aspirational that might add a lot of value in the future?

The one at the top is autonomous bioreactors. We have proven these strategies using a digital twin. So, we simulated a bioreactor, which is very difficult. On that simulation, the recipe can be controlled automatically. We are now working with a couple of manufacturers of biologics who are all very interested in autonomous bioreactors.

The other big opportunity is in process analytics technology (PAT), which is an enabler of continuous manufacturing and existing batch manufacturing. CMO’s and large pharma manufacturers have invested a lot in this technology.


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