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A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes
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Recommendations for Systematic Statistical Computation of Immunogenicity Cut Points.

Viswanath Devanarayan1, Wendell C Smith2, Rocco L Brunelle2

  • 1Abbvie, Inc, North Chicago, Illinois, USA.

The AAPS Journal
|July 23, 2017
PubMed
Summary

This study simplifies statistical analysis for anti-drug antibody (ADA) testing in biotherapeutics. It offers a refined approach for reliable cut point determination in immunogenicity assessments.

Keywords:
anti-drug antibodycut pointsimmunogenicityoutliersvalidation

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Area of Science:

  • Biopharmaceutical Development
  • Immunology
  • Analytical Chemistry

Background:

  • Immunogenicity assessment is crucial for biotherapeutics and biosimilars.
  • Anti-drug antibody (ADA) detection and characterization are key to risk evaluation.
  • Standardized three-tiered testing (screening, confirmation, titer assessment) is common for ADA analysis.

Purpose of the Study:

  • To refine statistical approaches for determining immunogenicity cut points.
  • To address challenges posed by evolving biotherapeutics and analytical technologies.
  • To provide a simplified, reliable statistical process for cut point evaluation.

Main Methods:

  • Review and refinement of statistical methods for immunogenicity cut point determination.
  • Development of a simplified statistical analysis process and flow-scheme.
  • Application to screening, confirmatory, and titering cut point evaluations.

Main Results:

  • A refined statistical approach is presented for immunogenicity cut point determination.
  • A simplified flow-scheme is proposed for practical application in biotherapeutic development.
  • The approach aims to provide reliable estimates for screening, confirmatory, and titering cut points.

Conclusions:

  • The proposed simplified statistical method enhances the reliability of immunogenicity cut point evaluations.
  • This approach is applicable to a wide range of biotherapeutic projects.
  • It supports robust risk-based evaluation of immunogenicity in drug development.