ABSTRACT
There are many sources of analytical variability in ligand binding assays (LBA). One strategy to reduce variability has been duplicate analyses. With recent advances in LBA technologies, it is conceivable that singlet analysis is possible. We retrospectively evaluated singlet analysis using Gyrolab data. Relative precision of duplicates compared to singlets was evaluated using 60 datasets from toxicokinetic (TK) or pharmacokinetic (PK) studies which contained over 23,000 replicate pairs composed of standards, quality control (QC), and animal samples measured with 23 different bioanalytical assays. The comparison was first done with standard curve and QCs followed by PK parameters (i.e., Cmax and AUC). Statistical analyses were performed on combined duplicate versus singlets using a concordance correlation coefficient (CCC), a measurement used to assess agreement. Variance component analyses were conducted on PK estimates to assess the relative analytical and biological variability. Overall, 97.5% of replicate pairs had a %CV of <11% and 50% of the results had a %CV of ≤1.38%. There was no observable bias in concentration comparing the first replicate with the second (CCC of 0.99746 and accuracy value of 1). The comparison of AUC and Cmax showed no observable difference between singlet and duplicate (CCC for AUC and Cmax >0.99999). Analysis of variance indicated an AUC inter-subject variability 35.3-fold greater than replicate variability and 8.5-fold greater for Cmax. Running replicates from the same sample will not significantly reduce variation or change PK parameters. These analyses indicated the majority of variance was inter-subject and supported the use of a singlet strategy.
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ACKNOWLEDGMENTS
The authors would like to acknowledge Boris Gorovits’s scientific review of this publication and appreciate the contributions that Greg Sjogren made to generating some of the data used in this retrospective analysis.
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Clark, T.H., Yates, P.D., Chunyk, A.G. et al. Feasibility of Singlet Analysis for Ligand Binding Assays: a Retrospective Examination of Data Generated Using the Gyrolab Platform. AAPS J 18, 1300–1308 (2016). https://doi.org/10.1208/s12248-016-9944-8
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DOI: https://doi.org/10.1208/s12248-016-9944-8