The present study describes a fast high performance liquid chromatography-diode array detection analytical methodology for quantification of tacrolimus, everolimus and cyclosporine A in whole blood samples, with minimum sample preparation steps. A short isocratic chromatographic elution was coupled with second-order calibration using multivariate curve resolution to stablish a smart and green methodology. Due to presence of matrix effect, a sample-added calibration strategy was used for quantification purposes. The serious issues related to background drift, chromatographic shifts and co-elution of non-calibrated blood components, were resolved by a proper background correction and multivariate curve resolution-alternating least squares (MCR/ALS) methods The main features of this study were based on the fact that the acquired data matrices were handled intelligently and all features of the concerned target analytes were taken into account. Satisfactory resolution and quantification results in the presence of matrix interferences were achieved and the second-order advantage was fully exploited. The average recoveries in therapeutic concentration ranges were 102 ± 10%, 99 ± 11% and 104 ± 12% for TAC, EVR and CsA, with average relative prediction errors of less than 7%. Considering the advantages of the present strategy, such as increased selectivity, sensitivity and sufficiency of lower limit of quantification through multivariate advantage, simplicity of sample treatment steps, a fast elution pattern and also a low-cost instrumentation compared with LC–MS/MS, the proposed method has the significant merits as an alternative for simultaneous therapeutic monitoring of immunosuppressants.

本研究描述了一种快速高效液相色谱-二极管阵列检测分析方法，用于以最少的样品制备步骤定量全血样品中他克莫司，依维莫司和环孢素A的含量。短暂的等度色谱洗脱与使用多元曲线分辨率的二阶校正相结合，从而建立了一种明智且绿色的方法。由于存在基质效应，因此添加了样品的校准策略用于定量。与本底漂移，色谱平移和未校准血液成分的共洗脱有关的严重问题，通过适当的背景校正和多变量曲线分辨率-交替最小二乘法（MCR / ALS）方法进行了解析。本研究的主要特征是基于以下事实：对采集的数据矩阵进行了智能处理，并采用了相关目标分析物的所有特征考虑在内。在存在基质干扰的情况下获得了令人满意的分辨率和定量结果，并且充分利用了二阶优势。TAC，EVR和CsA在治疗浓度范围内的平均回收率分别为102±10％，99±11％和104±12％，平均相对预测误差小于7％。考虑到本战略的优势，例如通过多元优势提高了选择性，灵敏度和定量下限的充分性，