Optimization and comparison of two microsampling approaches for LC-MS/MS analysis of a panel of immunosuppressants in blood samples

2021 
Abstract Immunosuppressive drugs have commonly been used to prevent transplant rejection, and recently, also in pharmacotherapy of patients with coronary stents. Due to low level of those drugs in biological fluids and their significant distribution into erythrocytes, sample preparation steps are critical for the determination and monitoring of these drugs in complex biomatrices, especially in whole blood. In this study, two modern and environmentally friendly microextraction strategies—namely, solid-phase microextraction (SPME) and dispersive liquid-liquid microextraction (DLLME)—are optimized and compared with respect to their extraction efficiencies for four immunosuppressants (tacrolimus, TAC; novolimus, NOV; everolimus, EVE; sirolimus, SIR) in serum and whole blood samples. Analyte separation was carried out using a Kinetex® C18 column (50 × 2.1 mm, 1.7 μm) thermostated at 55 °C, and analyses were performed in positive ion mode with a total analysis time of 6.5 min. The results revealed that, while the optimized SPME and DLLME protocols showed similar efficiency for the extraction of TAC, SIR, and EVE from biofluids, the DLLME protocol exhibited significantly better performance for the extraction of the novel immunosuppressive drug, NOV. Therefore, DLLME using ethanol (dispersive solvent) and chloroform (extraction solvent) in a ratio of 800/200 (v/v) was selected for further studies. The final optimized DLLME-LC-MS/MS conditions enabled a limit of quantitation of 1 ng/mL for TAC, 2.5 ng/mL for SIR and EVE, and 25 ng/mL for NOV. The results presented herein demonstrate that the proposed method can be successfully applied for the analysis of selected immunosuppressants in real samples during pharmacokinetic studies and therapeutic monitoring.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    38
    References
    1
    Citations
    NaN
    KQI
    []