Life Sciences Embraces Industry 4.0
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The following article is an opinion piece written by Kevin Seaver. The views and opinions expressed in this article are those of the author and do not necessarily reflect the official position of Technology Networks.
As of one of the seemingly last adopters of technology, the life sciences industry has gone “all in” over the last few years. Digital solutions, once hard, expensive and time consuming to implement, were rare to find in a lab or manufacturing floor. Today, you will find everything from augmented reality to biometrics to AI assisting medicine makers from the laboratory to the manufacturing floor. The industry is now at a digital inflection point, akin to the internet revolution of the 1990s.
Think back to the 1950s–1990s, when airplane manufacturers had giant wind tunnels to test airframes. Today, they do it all with computational fluid dynamics modeling. The life sciences industry is now making the same transition to predictive modeling of conditions for production and purification of therapeutic molecules. Now certain aspects of process development can go in silico. With the use of mechanistic modeling, simulations based on information, such as physiochemical phenomena in chromatography, can be used to get the process right earlier, with less failures and repeat work throughout the development phases. The modeling done downstream can go on to become digital twins upstream. Although the use of in silico modeling and digital twins has only recently been adopted by some, it is quickly becoming a critical technology. Expanding pipelines and new modalities are putting increasing pressure on developers to get new therapies to patients faster. With this shift in industry trends, you can expect to see in silico solutions become more common.
When it comes to biomanufacturing, there are many digital tools available. Smaller factories tend to connect and control single units, while larger manufacturers have integrated their entire bioprocessing environment thanks to the evolution of automation from project to product. Creating a software product broke open the barriers to the majority in the industry who were hesitant to convert their equipment due to the cost and time it took for a customer solution. Now, customers can add digital functionality that has been tested and proven time and again.
Digital solutions range from controlling equipment to optimizing the manufacturing line. With automation infrastructure available, in addition to the tangible benefits of reduced scrap, labor and deviations, the data collected can be aggregated and managed, generating insights through various applications in ways that people and paper rarely could. Records can also be easily and quickly accessed and investigated for regulatory control and accelerated for batch review and release.
While the biomanufacturing process has been largely digitalized, data pools still tend to be spread over different systems and difficult to combine for analysis and process optimization. Think back to the early 1990s when it was still hard to get on the internet. Computers and data required a lot of effort and expertise to connect and make use of. Manual data transfer with floppy disks was still then the norm. Macs and PCs had incompatible formats. Printers were not plug-and-play. By early 2000s everything was different. It was all plug-and-play and web access was the standard operating procedure for business and consumer alike.
In the last ten years, solutions have vastly improved. Biomanufacturers now have the means to aggregate and integrate data in the cloud, and to apply machine learning and AI tools to find the causes of deviations and predict if batches are going to have problems in a few days.
Expediting data for regulatory control has been a large driver of adoption and innovation. Process analytical technologies, or PAT, an initiative from the US Food and Drug Administration, is a key component of 4.0. By providing guidelines for quality by design (the ICH Q series) over the past two decades, the ICH and member regulatory bodies have pushed the industry to improve their pharmaceutical manufacturing processes through the measurement of critical process parameters which affect critical quality attributes. PAT enables biomanufacturers to access higher levels of the Digital Plant Maturity Model, improving yield and product quality.
After two decades of envisioning the benefits of such digitalization in therapeutics, it's now happening – driven in large part by the cloud data revolution, and market need for speed, efficiency, flexibility and robust production processes.