新型 PETINIA 测定法与 HPLC-UV 法检测麦考酚酸浓度的比较。

Comparison of mycophenolic acid concentrations determined by a new PETINIA assay on the dimension EXL analyzer and a HPLC-UV method.

机构信息

Department of Pathology, University of Texas-Houston Medical School and Laboratory Services Memorial-Hermann Hospital at Texas Medical Center, United States.

出版信息

Clin Biochem. 2013 May;46(7-8):685-7. doi: 10.1016/j.clinbiochem.2012.11.025. Epub 2012 Dec 5.

DOI:10.1016/j.clinbiochem.2012.11.025

PMID:23219740

Abstract

OBJECTIVE

Mycophenolic acid requires routine therapeutic drug monitoring. We evaluated the suitability of a new PETINIA (particle enhanced turbidimetric inhibition immunoassay) assay on the Dimension EXL analyzer for monitoring of mycophenolic acid by comparing values obtained by this assay with a HPLC-UV method.

DESIGN AND METHODS

Mycophenolic acid concentrations determined in sera of 60 organ transplant recipients using high performance liquid chromatography combined with ultraviolet detection (HPLC-UV, reference method) and the new immunoassay on the Dimension RxL analyzer.

RESULTS

The within and between run precision of the new PETINIA assay was <10%. The assay was linear for a mycophenolic acid concentration up to 30μg/mL. When mycophenolic acid concentrations in 60 transplant recipients obtained by the HPLC-UV (x-axis) method were compared with corresponding values obtained by the PETINIA assay (y-axis), the following regression equation was obtained: y=1.1204 x+0.0881 (r=0.983, n=60).

CONCLUSIONS

If PETINIA assay is used for therapeutic drug monitoring of mycophenolic acid, caution must be exercised in interpreting serum mycophenolic acid level due to observed positive bias.

摘要

目的

麦考酚酸需要常规的治疗药物监测。我们评估了新的 PETINIA（颗粒增强浊度抑制免疫测定）法在 Dimension EXL 分析仪上用于监测麦考酚酸的适用性，通过将该测定法与 HPLC-UV 法获得的值进行比较。

设计和方法

使用高效液相色谱法结合紫外检测（HPLC-UV，参考方法）和 Dimension RxL 分析仪上的新免疫测定法，在 60 名器官移植受者的血清中测定麦考酚酸浓度。

结果

新的 PETINIA 测定法的批内和批间精密度均<10%。该测定法在麦考酚酸浓度高达 30μg/mL 时呈线性。当通过 HPLC-UV（x 轴）方法获得的 60 名移植受者的麦考酚酸浓度与通过 PETINIA 测定法（y 轴）获得的相应值进行比较时，得到以下回归方程：y=1.1204 x+0.0881（r=0.983，n=60）。