Digital twins are becoming essential tools for scientists in biopharma, offering a way to understand and optimize bioprocesses virtually. Unlike static models, digital twins are dynamic, integrating real-time data from sensors, historical runs, and predictive algorithms to simulate process behavior.
What Are Digital Twins?
A digital twin is a virtual replica of a physical process or system. It allows scientists to experiment, monitor, and predict outcomes in a risk-free environment. For example, in upstream cell culture, a digital twin can simulate how changes in nutrient feed affect cell growth and product quality.
How Digital Twins Differ from Traditional Models
Traditional models are often static and rely on historical data. Digital twins, on the other hand, are continuously updated with real-time data, providing a live view of the process. This capability enables predictive modeling and scenario testing without disrupting ongoing operations.
Real-World Applications in Biopharma
- Process Optimization: Digital twins enable scientists to test and optimize process parameters like aeration and pH without running additional lab experiments.
- Risk Mitigation: They simulate “what-if” scenarios to predict and prevent potential process failures.
- Tech Transfer and Scale-Up: Digital twins ensure that processes are efficiently scaled from laboratory to production.
Actionable Takeaways for Scientists
- Start Small: Begin by applying digital twins to a single process step, such as bioreactor operations. Focus on areas with clear data availability to maximize early benefits.
- Leverage Scenario Testing: Use digital twins to test different feeding strategies or scale-up conditions without risking real-world operations. This minimizes disruptions while improving process understanding.
- Integrate with Real-Time Data: Connect your digital twin to live data from sensors and control systems. This ensures that the twin reflects actual process conditions, making it a reliable tool for decision-making.
- Optimize Scale-Up: Use digital twins to predict and mitigate challenges during scale-up, such as changes in shear forces or oxygen transfer rates.
- Collaborate with IT Teams: Partner with IT and data teams to ensure seamless integration of digital twin technology into your existing systems.
By leveraging digital twins, scientists can accelerate development, reduce costs, and improve process robustness.