<p>An assessment of parallelism is critical for biomarker assays to confirm whether the assay recognizes the endogenous analyte&#xa0;similarly to the calibrator, the suitability of a surrogate calibrator matrix and the potential need for a minimal required dilution. While&#xa0;the importance of parallelism has been raised in numerous publications there remains a lack of detail on how to conduct and interpret&#xa0;parallelism experiments, as well as some confusion between parallelism, dilution linearity, and spike recovery experiments. This best&#xa0;practice paper provides a detailed discussion of the reasons for conducting parallelism, as well as recommendations for when to&#xa0;conduct parallelism experiments, the number of samples needed, the selection of appropriate surrogate matrices, the interpretation of&#xa0;parallelism data, including graphical and statistical methods, and parallelism results reporting. It emphasizes the need for continuous&#xa0;evaluation of parallelism throughout the assay life cycle to ensure reliable measurement of the desired analyte within the context of&#xa0;use. Finally, a number of short case studies are provided to illustrate the application and interpretation of parallelism.</p>

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Best practices in the application of parallelism for biomarker assay validation

  • Lindsay King,
  • John Allinson,
  • Lakshmi Amaravadi,
  • Robert Kernstock,
  • Fabio Garofolo,
  • Michele Gunsior,
  • Barry Jones,
  • Joel Mathews,
  • Robert Neely,
  • Robert Nelson,
  • Marc-Olivier Pepin,
  • Honglue Shen,
  • Lauren Stevenson,
  • Troy Voelker

摘要

An assessment of parallelism is critical for biomarker assays to confirm whether the assay recognizes the endogenous analyte similarly to the calibrator, the suitability of a surrogate calibrator matrix and the potential need for a minimal required dilution. While the importance of parallelism has been raised in numerous publications there remains a lack of detail on how to conduct and interpret parallelism experiments, as well as some confusion between parallelism, dilution linearity, and spike recovery experiments. This best practice paper provides a detailed discussion of the reasons for conducting parallelism, as well as recommendations for when to conduct parallelism experiments, the number of samples needed, the selection of appropriate surrogate matrices, the interpretation of parallelism data, including graphical and statistical methods, and parallelism results reporting. It emphasizes the need for continuous evaluation of parallelism throughout the assay life cycle to ensure reliable measurement of the desired analyte within the context of use. Finally, a number of short case studies are provided to illustrate the application and interpretation of parallelism.