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Published on: February 18, 2014
Parallelism: considerations for the development, validation and implementation of PK and biomarker ligand-binding
Lauren F Stevenson1, Shobha Purushothama
1Biogen Idec, 14 Cambridge Center, Cambridge, MA 02142, USA.
This article explores parallelism assessments in pharmacokinetic (PK) and biomarker assays, addressing why, when, and how to conduct them. It highlights current practices and outstanding questions, emphasizing ongoing dialogue for best practices.
Area of Science:
- Pharmacology and Biomarker Assay Development
- Analytical Chemistry and Method Validation
Background:
- Parallelism assessment is crucial for validating pharmacokinetic (PK) and biomarker assays.
- Standardized methodologies and acceptance criteria are lacking, leading to variability in assay validation.
Purpose of the Study:
- To discuss fundamental questions regarding the rationale, timing, and methodology of parallelism assessments.
- To review current industry practices for PK and biomarker assays concerning sample selection and pooling.
- To identify outstanding questions and propose a path forward for establishing best practices.
Main Methods:
- Discussion of scientific rationale for different parallelism testing approaches.
- Review of current practices in sample selection and pooling for PK and biomarker assays.
- Identification of key areas requiring further investigation and consensus.
Main Results:
- Current practices for sample selection and pooling in PK and biomarker assays are diverse.
- Significant outstanding questions remain regarding the implementation and acceptance criteria for parallelism testing.
- The need for continued dialogue between industry and regulators is evident.
Conclusions:
- Ongoing collaboration and data sharing are essential for developing standardized best practices in parallelism testing.
- Further research and regulatory discussion are required to address the remaining questions in assay validation.
- Establishing clear guidelines will improve the reliability and interpretability of PK and biomarker data.
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