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Reducing Structural Nonidentifiabilities in Upstream Bioprocess Models Using Profile-Likelihood.

Heiko Babel1, Ola Omar1, Albert Paul1

  • 1Boehringer Ingelheim Pharma GmbH & Co.KG, Biopharmaceuticals Germany, Biberach an der Riß, Germany.

Biotechnology and Bioengineering
|January 18, 2025
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Summary
This summary is machine-generated.

Profile likelihood analysis enhances biopharmaceutical process models by improving parameter certainty, even with limited data. This method reduces uncertainty in process development, optimization, and scale-up, minimizing risks in predictions.

Keywords:
biopharmaceuticalsmodellingparameter identifiabilityprofile‐likelihood

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Area of Science:

  • Biopharmaceutical Process Development
  • Computational Modeling
  • Systems Biology

Background:

  • Process models are vital for biopharmaceutical upstream development, aiding optimization, scale-up, and reducing experimental work.
  • Parametric unstructured models are promising due to minimal data requirements, but parameter estimate certainty is critical.
  • Uncertainty in parameter estimates impacts model predictions and increases associated risks, necessitating robust estimation methods.

Purpose of the Study:

  • To apply profile likelihood for determining parameter identifiability in a biopharmaceutical upstream process model.
  • To investigate the impact of data quantity on parameter identifiability.
  • To utilize likelihood profiles for identifying and implementing structural model improvements.

Main Methods:

  • Application of profile likelihood analysis to assess parameter identifiability.
  • Investigation of the effect of varying data amounts on model identifiability.
  • Analysis of likelihood profiles for non-identifiable parameters to guide model structural changes.

Main Results:

  • Increased data amount was found to reduce non-identifiability in the upstream process model.
  • Likelihood profiles revealed structural model changes that effectively addressed most parameter non-identifiabilities.
  • Only one out of 21 parameters remained non-identifiable after model adjustments.

Conclusions:

  • Profile likelihood is a highly suitable method for determining parameter confidence intervals in upstream process models.
  • The method provides reliable parameter estimates for nonlinear models, even with limited data.
  • This study presents the first application of profile likelihood to a complete upstream process model, demonstrating its efficacy.