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A rapid, simple and sensitive LC-MS/MS method for lenvatinib quantification in human plasma for therapeutic drug monitoring

Zanchetta M.
•
Iacuzzi V.
•
Posocco B.
altro
Toffoli G.
2021
  • journal article

Periodico
PLOS ONE
Abstract
Lenvatinib (LENVA) is an oral antineoplastic drug used for the treatment of hepatocellular carcinoma and thyroid carcinoma. LENVA therapeutic drug monitoring (TDM) should be mandatory for a precision medicine to optimize the drug dosage. To this end, the development of a sensitive and robust quantification method to be applied in the clinical setting is essential. The aim of this work was to develop and validate a sensitive, rapid, and cost-effective LC-MS/MS method for the quantification of LENVA in human plasma. On this premise, sample preparation was based on a protein precipitation and the chromatographic separation was achieved on a Synergi Fusion RP C18 column in 4 min. The method was completely and successfully validated according to European Medicines Agency (EMA) and Food and Drug Administration (FDA) guidelines, with good linearity in the range of 0.50–2000 ng/mL (R≥0.9968). Coefficient of variation (CV) for intra- and inter-day precision was ≤11.3% and accuracy ranged from 96.3 to 109.0%, internal standard normalized matrix effect CV% was ≤2.8% and recovery was ≥95.6%. Successful results were obtained for sensitivity (signal to noise (S/N) ratio >21) and selectivity, dilution integrity (CV% ≤ 4.0% and accuracy 99.9–102%), and analyte stability under various handling and storage conditions both in matrix and solvents. This method was applied to quantify LENVA in patient’s plasma samples and covered the concentration range achievable in patients. In conclusion, a sensitive and robust quantification method was developed and validated to be applied in the clinical setting.
DOI
10.1371/journal.pone.0259137
Archivio
http://hdl.handle.net/11390/1217913
info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85117914199
https://ricerca.unityfvg.it/handle/11390/1217913
Diritti
open access
google-scholar
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