🔍 A hybrid model approach is recommended by researchers. It combines knowledge-based and data-driven methods.
💡 Hybrid models enhance understanding while simplifying processes. They facilitate cost-effective, modular insights into system behaviors.
🔬 This blended approach can better support experienced biopharmaceutical developers in achieving effective production processes.
Introduction:
This article discusses the evolving role of digital twins in biopharmaceutical processes, highlighting the benefits of employing knowledge-based models as opposed to traditional data-centric approaches. Digital twins serve as essential tools for process optimization, but their effectiveness can vary based on the modeling methodology used.
- Digital twins in biopharma primarily utilize empirical models based on statistical methods, which can simplify modeling but may not handle complex production processes effectively.
- The challenges of empirical models include the necessity of extensive experimentation, particularly as the complexity of process parameters increases, leading to issues related to the ‘curse of dimensionality.’
- Knowledge-centric models, based on first principles and equations, offer a deeper understanding of the underlying systems and support knowledge transfer across projects, but require significant expertise.
- A hybrid approach combining mechanistic frameworks with statistical methods is advocated as a more effective strategy, benefitting from both methodologies’ strengths.
- This hybrid model can enhance the utility and cost-effectiveness of digital twins in biopharma, facilitating better understanding of key system behaviors.
Conclusion:
The article emphasizes the importance of selecting suitable modeling approaches for digital twins in biopharmaceutical development. It concludes that while both empirical and knowledge-centric models have their merits, a hybrid approach may provide a more balanced solution, enhancing the effectiveness and adaptability of digital twins within complex biopharma processes.
