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Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
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Bioassay case study applying the maximin D-optimal design algorithm to the four-parameter logistic model.

Todd Coffey1

  • 1Seattle Genetics, Inc., 21823 30th Dr SE, Bothell, WA, USA.

Pharmaceutical Statistics
|August 4, 2015
PubMed
Summary
This summary is machine-generated.

Selecting optimal concentrations for cell-based potency assays enhances robustness. This study uses a

Keywords:
cytotoxicityexperimental designnonlinear modeloptimalitysigmoidal curve

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Area of Science:

  • Biopharmaceutical characterization and analytical method development.

Background:

  • Cell-based potency assays are crucial for biopharmaceutical characterization.
  • These assays exhibit higher inherent variability compared to other analytical methods, posing development challenges.

Purpose of the Study:

  • To identify optimal concentration points on a dose-response curve to improve the robustness of cell-based potency assays.
  • To address the inherent variability challenges in biopharmaceutical characterization.

Main Methods:

  • Application of the maximin D-optimal design concept to the four-parameter logistic (4 PL) model.
  • Derivation and computation of the maximin D-optimal design for a challenging bioassay.
  • Utilizing dose-response curves representative of typical assay variation.

Main Results:

  • The 'best worst case' design selected specific concentration points.
  • These selected points demonstrated adequate fitting across various four-parameter logistic (4 PL) curve shapes.
  • The design led to demonstrably improved assay robustness.

Conclusions:

  • The maximin D-optimal design is an effective strategy for selecting concentration points in cell-based potency assays.
  • This approach enhances assay robustness by accounting for potential assay variation.
  • Optimized assay design is critical for reliable biopharmaceutical characterization.