Digital Twins: Innovative Modelling Techniques for Secure Research
“What we have developed and what I would like to introduce you to today,” said Bär, “is the digital twin that we have set up with our in-house software.” Digital twins are virtual representations of a discovery process that span its lifecycle and update from real-time data to assist in decision-making. They provide holistic process models to break the correlations between wet-lab experiments and process knowledge. “This accelerates the learning curve and allows us to learn even more about the process in a shorter timeframe,” continued Bär.
Digital twins as models provide a means of viewing the status of the physical object being investigated. They are encoded with prior knowledge, and their purpose is to reduce the number of experiments being carried out by improving model reliability. As explained by Bär, the current approach is based on the quantity and execution of the experiments performed, with work carried out in critical path development enabling a much faster time to market.
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With the knowledge boost associated with new research and development, the process timeline can be reduced to under three years. The capture of knowledge into new models enables risk mitigation, which is central to commercial strategy. One case study provided by Bär came from the microbial world, where data-driven machine learning or artificial intelligence networks are applied to accelerate research processes.
Modelling Approaches with Boehringer Ingelheim Engineering Practices
“Let’s shed some light on engineering practices,” continued Bär. Boehringer Ingelheim have a lot of experts coming from different areas, often from backgrounds with different programming languages. “We had to break this Tower of Babel challenge,” said Bär, “and do it in a user-friendly fashion.” With a broad IT and Engineering backbone, setting up a user-friendly front end with a back end available to host various process models is often the best approach. A software platform was developed to easily leverage the full power of integrated process models to rapidly create the best biopharmaceutical manufacturing processes.
“It’s one thing to control your outputs, but in addition you should take into account your restrictions.”
Some of the processes involved with the digital twin platform include process model repository, process optimisation, process simulation and process robustness assessment. Results from preliminary experiments indicated a substantial productivity increase through machine learning and integrated process modelling. There was a demonstrative >10% titre increase in single unit operation, while Boehringer’s Integrated Process Model was capable of optimising multiple unit operations simultaneously. “By interconnecting these three steps, we found a way to optimise our capacities and raise our output by 50%,” continued Bär. “It’s one thing to control your inputs, but in addition you should take into account your restrictions.”
Digital Twins and Hybrid Models
The hybrid model includes approaches for mammalian applications, which Bär summarised as ‘one platform, one aim’. “It’s one thing to control and understand your inputs and how you could define their better process setting,” he said. “But in addition, it’s also important that you take into account what your restrictions are.” He added that he was confident there would not be a single model that would capture the entire production process. Instead, it depends on how much data is involved and the individual aims of a particular project. And with this various mathematical model types have to contribute to cover the complete process development and manufacturing steps and phases.
“Hopefully,” Bär concluded, “I could convince you that we have a very nice hosting platform.” Boehringer’s in-house process modelling hosting platform is applied to various use cases, with demonstrative applicability for various model formats, be they microbial or mammalian. Additionally, there is upstream and downstream potential on top of different modelling techniques and their hybridization. “We can apply this in development to reduce the effort and time that we need to bring our processes to the market, and also the process for our customers.” The use of process models and digital twins for process development and commercial optimisation suggests untapped potential in two key areas: the reduction of timelines and the subsequent increase of productivity and profitability.
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