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Evaluation framework for systems models.

Sietse Braakman1, Pras Pathmanathan2, Helen Moore3

  • 1Application Engineering, MathWorks Inc, Natick, Massachusetts, USA.

CPT: Pharmacometrics & Systems Pharmacology
|December 18, 2021
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Summary
This summary is machine-generated.

Evaluating quantitative systems pharmacology (QSP) models is crucial for drug development. This study presents a framework for appropriate model evaluation using qualitative and quantitative methods to ensure reliable predictions.

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Area of Science:

  • Pharmacology
  • Computational Biology
  • Systems Biology

Background:

  • Drug development increasingly relies on mechanistic systems models for predictions.
  • Assessing the predictive capability of quantitative systems pharmacology (QSP) models is becoming critical.
  • Existing QSP model development frameworks exist, but there is a need for enhanced evaluation methodologies.

Purpose of the Study:

  • To introduce a framework for QSP model evaluation focusing on appropriate qualitative and quantitative methods.
  • To provide guidance and references for applying various model evaluation techniques in QSP.
  • To illustrate the application of these methods in QSP model assessment and case studies.

Main Methods:

  • Sensitivity analysis
  • Identifiability analysis
  • Model validation
  • Uncertainty quantification

Main Results:

  • Demonstration of applying established evaluation methods to QSP models.
  • Proposal of specific methods for QSP model evaluation through case studies.
  • Examples of misleading outcomes from inappropriate analysis in QSP modeling.

Conclusions:

  • A robust framework for QSP model evaluation is essential for reliable drug development predictions.
  • The proposed framework integrates qualitative and quantitative methods for comprehensive model assessment.
  • Adoption of these evaluation methods can improve the trustworthiness and utility of QSP models in pharmaceutical research.