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Lactate-Based Model Predictive Control Strategy of Cell Growth for Cell Therapy Applications.

Kathleen Van Beylen1,2, Ali Youssef1, Alberto Peña Fernández1

  • 1Department of Biosystems, Division Animal and Human Health Engineering, M3-BIORES: Measure, Model & Manage Bioresponses Laboratory, KU Leuven, Kasteelpark Arenberg 30, 3001 Heverlee, Belgium.

Bioengineering (Basel, Switzerland)
|July 24, 2020
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Summary

This study optimizes bioprocess feeding strategies by using lactate measurements to control cell growth. Personalized feeding reduces costs and improves efficiency in cell culture processes.

Keywords:
advanced therapy medicinal productsbio-processcell growthlactatemodel predictive control

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Area of Science:

  • Biotechnology
  • Cell Culture
  • Process Control

Background:

  • Overfeeding in bioprocesses leads to unnecessary costs.
  • Personalized feeding strategies can enhance efficiency.
  • Lactate measurements indicate cell growth during culture.

Purpose of the Study:

  • To develop a personalized feeding strategy for bioprocesses.
  • To utilize lactate measurements for adaptive feeding control.
  • To reduce overfeeding costs in cell culture.

Main Methods:

  • Model predictive control (MPC) was employed.
  • Lactate measurements were used as a proxy for cell growth.
  • Individualized MPC controllers were developed for human progenitor cells from three donors.

Main Results:

  • Process models achieved 99.80% ± 0.02% accuracy.
  • Simulations to reproduce experimental runs reached 98.64% ± 0.10% accuracy.
  • The adaptive feeding strategy effectively controlled cell growth.

Conclusions:

  • A promising framework for controlling cell growth via adaptive feeding strategies was established.
  • Lactate-based control offers an efficient method for bioprocess optimization.
  • Personalized feeding strategies based on real-time measurements reduce operational costs.