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Published on: July 20, 2016
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A sensitive LC-MS/MS method for quantifying clofarabine triphosphate concentrations in human peripheral blood
Xiaowei Tu1, Youming Lu1, Dafang Zhong1
1Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 501 Haike Road, Shanghai 201203, PR China.
Summary
A new LC-MS/MS method accurately quantifies clofarabine triphosphate in human cells. This validated technique measures the active metabolite in peripheral blood mononuclear cells (PBMCs) from cancer patients.
Area of Science:
- Pharmacology
- Analytical Chemistry
- Biochemistry
Background:
- Clofarabine triphosphate is the active intracellular metabolite of clofarabine.
- Accurate quantification of clofarabine triphosphate is crucial for understanding its pharmacokinetics and efficacy.
- Existing methods may lack the sensitivity or specificity required for cellular analysis.
Purpose of the Study:
- To develop and validate a rapid, sensitive, and selective LC-MS/MS method.
- To quantify clofarabine triphosphate concentrations in human peripheral blood mononuclear cells (PBMCs).
- To assess the stability of clofarabine triphosphate in biological samples.
Main Methods:
- Liquid chromatography-tandem mass spectrometry (LC-MS/MS) with negative electrospray ionization.
- Sample preparation involving Ficoll gradient centrifugation for PBMC isolation.
- Chromatographic separation on a CN column with an isocratic mobile phase.
- Validation across concentration ranges of 1.25-100 ng/10(7) cells.
Main Results:
- The developed LC-MS/MS method demonstrated high sensitivity and selectivity.
- The method exhibited acceptable accuracy and precision within the validated range.
- Clofarabine triphosphate showed stability under various conditions (bench-top, auto-sampler, freeze-thaw).
Conclusions:
- A robust and validated LC-MS/MS method for clofarabine triphosphate quantification in PBMCs has been established.
- This method is suitable for clinical research and therapeutic drug monitoring in cancer patients treated with clofarabine.
- The stability data supports the reliability of measurements in biological matrices.

