4.6 Article

Application of in-sample calibration curve methodology for regulated bioanalysis: Critical considerations in method development, validation and sample analysis

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ELSEVIER
DOI: 10.1016/j.jpba.2019.112844

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In-sample calibration curve (ISCC); LC-MS/MS; Quantitative; Regulated bioanalysis; Validation

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Traditionally, for a liquid chromatography tandem mass spectrometry (LC-MS/MS) bioanalytical assay, an external calibration curve is required to achieve accurate quantitation of an analyte. Recently, a novel in-sample calibration curves (ISCC) methodology that can achieve quick and accurate LC-MS/MS bioanalysis without the use of an external calibration curve was reported. The ISCC methodology utilizes the presence of multiple naturally occurring isotopologues of a stable isotopically labeled analyte to construct an in-sample calibration curve for the quantification. This methodology has great potential in many applications, for example biomarker measurement, quantitative proteomics and clinical diagnosis. Here, we assessed the feasibility of applying this ISCC-LC-MS/MS methodology in regulated bioanalysis using BMS-984478, a drug candidate, as the model compound. We also proposed method validation procedures/processes for this new approach for industry peers' consideration and feedback. A LC-MS/MS method using the ISCC strategy was successfully developed and validated for the quantitative analysis of BMS-984478 in human plasma over the range of 1.33-993.42 ng/mL. The validated ISCC-LC-MS/MS method was compared with a previously validated method using the conventional external calibration curve approach, and the two methods showed equivalent performance. Critical considerations and practical approaches in method development, validation and sample analysis were also discussed. Our work demonstrated that the ISCC-LC-MS/MS methodology is a promising approach for regulated LC-MS/MS bioanalysis. ISCC-LC-MS/MS methodology has its unique advantages and has great potential to be widely applied for various quantitative applications, and may even change the landscape of quantitative analysis. (C) 2019 Elsevier B.V. All rights reserved.

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