Researchers at Clausthal University of Technology in Germany have found that using a digital twin approach during the freeze-drying process for biologics can increase productivity by up to 300%. This approach also reduces costs by 74% and lowers the global warming potential by 64%. By incorporating Process Analytical Technology and modeling, the digital twin accurately predicts temperature and drying endpoints with smaller errors than experiments. The optimization process takes only 14 days and offers scalability benefits.
Introduction
Lyophilization, also known as freeze-drying, is an important process in the biopharmaceutical industry for prolonging the shelf-life of biologics and reducing refrigeration requirements. However, the process development for lyophilization often requires extensive experimentation and can be time-consuming and costly. In order to streamline the process and improve outcomes, scientists at the Clausthal University of Technology in Germany have implemented a digital twin approach when designing the freeze-drying methodology for biologics. This approach has resulted in significant improvements in productivity, cost reduction, and a decrease in global warming potential compared to traditional trial-and-error design methods.
- A digital twin approach for designing the freeze-drying methodology for biologics has increased productivity by up to 300%.
- Costs have been reduced by 74% and global warming potential has decreased by 64% compared to trial-and-error design.
- Incorporating Process Analytical Technology (PAT) and modeling has allowed the digital twin to accurately predict the product’s temperature and drying endpoint.
- The digital twin has the ability to operate under “proven acceptable ranges” instead of fixed set points, optimizing conventional processes.
- The optimization process using the digital twin took 14 days and involved 540 mL of product, but scalability is a key benefit.
Conclusion:
The implementation of a digital twin approach in the design of freeze-drying methodology for biologics has shown significant improvements in productivity, cost reduction, and environmental impact. By incorporating Process Analytical Technology and modeling, the digital twin is able to accurately predict temperature and drying endpoint, resulting in smaller errors compared to experimentation. This approach not only streamlines the process development for lyophilization but also has the potential for scalability in the future.
