1. A Data Driven Strategy and Case Study for Implementation of Singlicate Analysis in Ligand Binding Assays Used for PK Quantitation.
- Author
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Qu, Qiang, Liu, Susana, You, Zhiping, Steeno, Gregory S., Dyleski, Lisa A., Mu, Xue, Wang, Ying, and Baltrukonis, Daniel
- Abstract
Duplicate analysis has been a conventional practice in the industry for ligand-binding assays (LBA), particularly for plate-based platforms like Enzyme-linked immunosorbent assay (ELISA) and Meso Scale Discovery (MSD) assays. Recent whitepapers and guidance have opened a door to exploring the implementation of single-well (singlicate) analysis approach for LBAs. Although the bioanalytical industry has actively investigated the suitability of singlicate analysis, applications in supporting regulated LBA bioanalysis are limited. The primary reason for this limitation is the absence of appropriate strategy to facilitate the transition from duplicate to singlicate analysis. In this paper we present the first case study with our data-driven approach to implement singlicate analysis in a clinical pharmacokinetics (PK) plate based LBA assay with ISR data. The central aspect of this strategy is a head-to-head comparison with Precision and Accuracy assessment in both duplicate and singlicate formats as the initial stage of assay validation. Subsequently, statistical analysis is conducted to evaluate method variability in both precision and accuracy. The results of our study indicated that there was no impactful difference between duplicate vs singlicate, affirming the suitability of singlicate analysis for the remaining steps of PK assay validation. The validation results obtained through singlicate analysis demonstrated acceptable assay performance characteristics across all validation parameters, aligning with regulatory guidance. The validated PK assay in singlicate has been employed to support a Phase I study. The appropriateness of singlicate analyses is further supported by initial Incurred Sample Reanalysis (ISR) data in which 90.1% of ISR samples fall within the acceptable criteria. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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