Automatic generation of a digital twin for continuous antibody capture

  • 0Division of Chemical Engineering, Department of Process and Life Science Engineering, Lund University, Lund, Sweden.

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Summary

This summary is machine-generated.

We developed an automated procedure to create and calibrate digital twins for monoclonal antibody purification. This digitalization enhances process efficiency and sustainability, reducing costs and accelerating drug discovery.

Area Of Science

  • Biotechnology
  • Chemical Engineering
  • Process Automation

Background

  • Digitalization offers enhanced efficiency and sustainability in monoclonal antibody downstream processing.
  • Digital twins provide process insights and optimization but are challenging to create.
  • Automated generation and calibration of digital twins are needed for wider adoption.

Purpose Of The Study

  • To develop an automated procedure for generating and calibrating digital twins for chromatography.
  • To demonstrate the utility of digital twins for process optimization in monoclonal antibody purification.
  • To reduce the time and effort required for digital twin acquisition.

Main Methods

  • Automatic generation of a digital twin based on chromatography setup configuration.
  • Automated execution of a calibration procedure involving experiments and simulations.
  • Application of the digital twin for optimizing yield, resin utilization, and column capacity variations.

Main Results

  • Experimental validation showed a 0.6% difference in yield and resin utilization compared to simulation.
  • Optimization for reduced column capacity increased resin utilization by 1.2% with 99.8% yield.
  • The automated procedure efficiently generated and calibrated a functional digital twin.

Conclusions

  • The developed procedure enables efficient digital twin generation and calibration for monoclonal antibody downstream processing.
  • Integrating digitalization through digital twins can lower production costs and improve sustainability.
  • This work represents a significant step towards automated and optimized biopharmaceutical manufacturing.