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Multi-laboratory evaluation of immunoaffinity LC-MS-based glucagon-like peptide-1 assay.

Rika IshikawaKosuke SaitoHidehisa TachikiRyoya GodaKoji AraiHisao ShimizuTomohiro AndouKentaro TakaharaHitoshi UchiyamaShin-Ichiro NittaMasaaki KakehiKozo HayashiNaohiro KatagiriKeiko KojimaHisashi FujitaKazuhiro TsuchinagaYoshiro Saito
Published in: Bioanalysis (2023)
Background: Although the fit-for-purpose approach has been proposed for biomarker assay validation, practical data should be compiled to facilitate the predetermination of acceptance criteria. Methods: Immunoaffinity LC-MS was used to analyze glucagon-like peptide-1 as a model biomarker in six laboratories. Calibration curve, carryover, parallelism, precision, relative accuracy and processed sample stability were evaluated, and their robustness among laboratories was assessed. The rat glucagon-like peptide-1 concentrations in four blinded samples were also compared. Results: The obtained results and determined concentrations in the blinded samples at all laboratories were similar, with a few exceptions, and robust, despite the difference in optimization techniques among laboratories. Conclusion: The results provide insights into the predefinition of the acceptance criteria of immunoaffinity LC-MS-based biomarker assays.
Keyphrases
  • high throughput
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  • clinical trial
  • machine learning
  • artificial intelligence
  • data analysis