Development and validation of an assay to analyze atazanavir in human hair via liquid chromatography/tandem mass spectrometry.

Rapid communications in mass spectrometry : RCM(2018)

引用 12|浏览16
暂无评分
摘要
RATIONALE:Assays to quantify antiretrovirals in hair samples are increasingly used to monitor adherence and exposure in both HIV prevention and treatment studies. Atazanavir (ATV) is a protease inhibitor used in combination antiretroviral therapy (ART). We developed and validated a liquid chromatography/tandem mass spectrometry (LC/MS/MS)-based method to quantify ATV in human hair, per the NIH Division of AIDS Clinical Pharmacology Quality Assurance (CPQA) program and the FDA bioanalytical method validation guidelines. METHODS:ATV was extracted from hair using optimized methods and the extracts were injected onto a BDS C-18 column (5 μm, 4.6 × 100 mm), followed by isocratic elution via a mobile phase composed of 55% acetonitrile, 45% water, 0.15% acetic acid, and 4 mM ammonium acetate, at a flow rate of 0.8 mL/min prior to analysis by MS/MS. Levels were quantified using positive electrospray ionization by multiple reaction monitoring (MRM) for the transitions MH+ m/z 705.3 to m/z 168.0 and MH+ m/z 710.2 to m/z 168.0 for ATV and ATV-d5 (internal standard), respectively. RESULTS:Our assay demonstrated a linear standard curve (r = 0.99) over the concentration range of 0.0500 ng ATV/mg hair to 20.0 ng/mg hair. The inter- and intraday accuracy of ATV quality control (QC) samples was -1.33 to 4.00% and precision (% coefficient of variation (%CV)) was 1.75 to 6.31%. The %CV for ATV levels in hair samples from highly adherent patients (incurred samples) was less than 10%. No significant endogenous peaks or crosstalk were observed in the specificity test with other HIV drugs. The overall extraction efficiency of ATV from incurred hair samples was greater than 95%. CONCLUSIONS:This highly sensitive, highly specific and validated assay can be considered for therapeutic drug monitoring for HIV-infected patients on ATV-based ART.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要