Validation of pharmacokinetic model for quizartinib quantified by UPLC-MS/MS in patients with FLT3-ITD negative newly diagnosed acute myeloid leukemia
Year: 2025
Type: article
Abstract: PURPOSE: Quizartinib pharmacokinetics in FLT3-ITD negative acute myeloid leukemia (AML) remain largely unexplored. This study aims to validate a population pharmacokinetics model (popPK) for quizartinib in plasma samples of FLT3-ITD negative AML patients. To do so, an ultra-performance liquid chromatography coupled with tandem mass spectrometry (UPLC-MS/MS) method has been developed and validated for the quantification of quizartinib. METHODS: Plasma samples were collected from FLT3-ITD negative newly diagnosed AML patients undergoing quizartinib therapy at induction in the QUIWI phase II clinical trial [NCT04107727, PETHEMA group] between March 2020 and February 2022. The UPLC-MS/MS method was developed and validated. A previously described popPK model was validated using external validation techniques and implemented using the software NONMEM v7.5. RESULTS: The developed UPLC-MS/MS method demonstrated high accuracy and precision with a linear range of 6 to 200 ng/mL, with relative standard deviation between 3-11 and accuracy of 88-97% from nominal values. The external validation of the quizartinib popPK model showed minimal bias at the population level (MdPE: -9.86%; ME: 0.50 ng/mL, p = 0.964), but moderate imprecision (MdAPE: 32.28%) and suboptimal accuracy (F20: 24.5%; F30: 43.4%). Individual predictions improved performance, with negligible bias (MdPE: -0.50%), acceptable precision (MdAPE: 11.27%), and F20 (64.2%) and F30 (77.4%) exceeding predefined thresholds. Visual predictive checks confirmed adequate prediction of median concentrations, though some deviations occurred at extremes. CONCLUSION: This study presents a replicable UPLC-MS/MS method for the determination of quizartinib in plasma. The validated popPK model can be used to optimize dosing strategies in future clinical studies.
Authors: Antonio Solana-Altabella, Carles Iniesta-Navalón, Maria Chovi-Trull, Rebeca Rodriguez-Veiga, Marina Lopez-Nogueroles, David Martínez-Cuadrón, Mayte Gil-Candel, Laura Torres-Miñana, Evelyn Acuña-Cruz, Isabel Cano-Ferri, Blanca Boluda, Irene Navarro-Vicente, Pilar Lloret-Madrid, Eva Barragán, Jose Vicente Gil, Mario Rodenas-Rovira, Juan Eduardo Megías-Vericat, Jorge Labrador, José Esteban Peris-Ribera, Jose Luis Poveda-Andrés, Pau Montesinos
Language: English
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,090
Awards: G1756900807, G3225460733, G7115633492
OpenAlex ID: W4413847267
