Enabling Pharma 4.0 Using AI: Interview With Rajiv Anand, Founder of Quartic.ai

by Andrii Buvailo, PhD          Interview

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Tackling production inefficiencies, faulty products, and costly machine maintenance are just a few of the many issues that are hardly manageable with a legacy approach to pharmaceutical manufacturing. A combination of human know-how and artificial intelligence (AI), big data technologies, and advanced digital infrastructures (e.g., cloud systems, analytics-as-a-service, etc.) — better known as Pharma 4.0 — is transforming the entire Life Sciences sector. Pharma 4.0 is the new manufacturing era, focusing on therapeutics in a digital-first environment.
To get a better understanding of what technologies and processes are driving the transition to next-generation manufacturing in pharma and biotech, we have sat down with Rajiv Anand, founder and CEO of Quartic.ai (www.quartic.ai), and discussed the past, present, and bit of future of pharma 4.0.

Quartic.ai is a developer of an enterprise-scale AI platform – the Quartic Platform™ – that makes autonomous manufacturing a reality by enabling comprehensive real-time insights into the entire manufacturing process. 

 

Andrii: Can you share your journey into industrial life sciences? 

Rajiv: I have been engaged in the control and automation of process manufacturing, including life sciences manufacturing, for over 35 years. I have seen the industry evolve from basic instrumentation and control to sophisticated batch execution, advanced process controls, MES systems, SPC, informatics, and manual data analytics.

Rajiv AnandWhile there still are many instances of manual workflows and paper-based data records, the industry has become “data-rich” over the past two decades. This provides an excellent opportunity for advancement in informatics to improve all stages and all aspects of industrial life sciences for the benefit of the patient. But analysis of this data to create meaningful and valuable intelligence has mainly remained manual or human. This means that even when sophisticated and powerful analysis tools are used, it is primarily performed by humans. Analysis workflows for the same problem-solving are often repeated, and analysis generally has a narrow perspective – like a single process unit at a time. Almost all analysis is also retrospective in that we learn things from analysis after the fact and then look for corrections and improvements in the future. Informatics and analysis are, therefore, highly inefficient and not very useful in real-time. 

With the increasing move towards biologics and biomanufacturing, and as molecules become more complex, so is the complexity of the multivariate relationships in the data related to these molecules. Sophisticated analytical instrumentation such as spectroscopy and near-infrared analyzers are a lot more applicable and starting to become pervasive. 

Creating intelligence from this increasingly complex data with a “zoomed-out” view of the entire process, and in a real-time and predictive manner, so that auto-correction of the process is taking place all the time, is what’s required to move forward. But existing, legacy technology has hit its limit to accomplish this. 

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