A speaker at a recent industry event suggested that pharma needs to be ‘bolder’ in its approach to manufacturing, by employing Industry 4.0 technologies.
At the same event, Patrick Hyett, Pharma 4.0 director at GSK, explained how his company is approaching the digitisation challenge. The company first heard from other industries, and how they were adopting digital technologies, before investing in how it could be applied to its own processes.
The experiment paid off, with Hyett explaining that savings were made in the hundreds of millions of dollars by adopting efficiencies discovered at a project site in Stevenage, UK.
When in-PharmaTechnologist (IPT) spoke to Lawrence Ganti (LG), CEO of Life Sciences at Innoplexus, he remained optimistic about pharma’s adoption of new technology. However, he outlined how one part of Industry 4.0, artificial intelligence, could be applied to manufacturing in the industry and what the benefits could be.
IPT: How AI might be able to make manufacturing more efficient?
LG: Manufacturing is one area of the pharmaceutical industry that has always had large amounts of data. Improving efficiencies in biotech manufacturing and process development is one of the most impactful ways to improve gross margins. With margins already high in the biotech manufacturing, finding additional efficiencies requires analysing complex data sets and the inter-relationships between the various data sets. One way that AI can contribute would be around the ability to use it to identify patterns and relationships between data and then applying models to predict the impact of making seemingly minor changes.
IPT: How does manufacturing innovation in pharma compare with other industries?
LG: The one area of pharma which has been closer to keeping up to date with the use of technology is manufacturing. While most other functions in pharma are slow to adopt, manufacturing has generally been more progressive in the adoption of new technologies. Having said that, pharma still lags behind in terms of using new technology for innovation. We are seeing some companies, such as Novartis, that are taking a real approach to testing many new technologies.
IPT: Could AI improve the safety/security of processing?
LG: I don’t see a direct impact on safety or security per se. However, having the ability to see patterns quickly from large data sets is where AI can help. Specifically, AI can map, understand patterns, and bring in thousands upon thousands of data sets together and say, “Here are connections, and here is how these three data sets are linked.” Then, pharma companies can create insights out of that and generate hypotheses much faster. It’s about the ‘“aha moment’” and the ability to discover what you may not know.
IPT: What other areas could AI streamline?
LG: One of the real areas where AI can impact is in drug development. This is an area with a significant amount of disparate data. Companies spend upwards of 80% of their time collecting, cleaning, and aggregating data sets. Most data in this area is unstructured and while machines need data to be structured, AI can speed up and facilitate the connecting of unstructured data.