他克莫司
化学
色谱法
红细胞压积
蛋白质沉淀
全血
串联质谱法
质谱法
移植
外科
医学
内科学
作者
Camille Tron,Marie-José Ferrand-Sorre,Julie Querzerho-Raguideau,Jonathan Chemouny,Pauline Houssel‐Debry,Marie‐Clémence Verdier,Éric Bellissant,Florian Lemaître
标识
DOI:10.1016/j.jchromb.2020.122521
摘要
Volumetric absorptive microsampling (VAMS) is an innovative alternative strategy to venipuncture for monitoring tacrolimus levels in transplant recipients. In this study, we aimed to validate a new high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) method for quantifying tacrolimus in blood collected by VAMS. Tacrolimus was extracted from dried blood tips in an original process involving sonication, protein precipitation and salting out. The assay was validated in accordance with EMA and IATDMCT guidelines. For clinical validation, the tacrolimus concentrations measured in liquid venous whole blood (with the reference method) were compared with those measured in capillary whole blood collected simultaneously with VAMS by a nurse. The assay was then used to monitor tacrolimus exposure in transplant recipients. The method was linear, sensitive and fast. Within-day and between-day precisions and overall bias were within ±15%. No significant hematocrit effect was observed. The matrix effect was negligible and recovery exceeded 80% for every concentration and hematocrit levels. Tacrolimus was stable in blood collected by VAMS for 1 week at room temperature, 48 h at 60 °C and 4 °C and 1 month at −80 °C. Clinical validation (n = 42 paired samples) demonstrated a strong correlation between the two methods (r = 0.97 Pearson correlation). Bland-Altman analysis revealed that more than 90% of the differences between VAMS and liquid blood paired concentrations were within the ±20% acceptable range. The method had a satisfactory analytical performance and fulfilled clinical requirements. This minimally invasive VAMS-based assay appears reliable for the determination of tacrolimus levels in blood from transplanted patients.
科研通智能强力驱动
Strongly Powered by AbleSci AI