Cardiovascular drugs are the most commonly prescribed medications. Some prior assays successfully detect cardiovascular drugs among multiple classes using a single sample. Here, we develop an assay to detect a broad range of cardiovascular drug classes to include commonly used cardiovascular drugs and evaluate the assay's analytical and statistical properties in a clinical setting. We describe a protocol for drug detection that encompasses 34 commonly prescribed cardiovascular drugs or drug metabolites with a single LC-MS/MS method using 100μL of serum or plasma. Drug classes monitored by this assay include: anticoagulants, angiotensin converting enzyme inhibitors (ACEI), angiotensin II receptor blockers (ARB), beta blockers, calcium channel blockers, diuretics, statins, and vasodilators, as well as digoxin, fenofibrate, and niacin. Analytical accuracy and precision for each drug were evaluated by repeating the assay on spiked samples at low, medium, and high concentrations. In 294 clinical samples obtained from hospitalized patients for whom medication administration was recorded, we evaluated the assay's statistical sensitivity, specificity, and accuracy. For the 34 drugs or drug metabolites, the assay was statistically sensitive (>0.90) for all drugs except captopril (0.25), isosorbide (0.81), and niacin (0.89). The assay was statistically specific for all drugs, with a minimum specificity of 0.94 (aspirin). To our knowledge, this method is the first method of simultaneous analysis of 34 cardiovascular drugs or drug metabolites from nine drug classes evaluated using clinical samples from hospitalized patients.
An LC-MS assay for the screening of cardiovascular medications in human samples.
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作者:Dias Eduardo, Hachey Brian, McNaughton Candace, Nian Hui, Yu Chang, Straka Brittany, Brown Nancy J, Caprioli Richard M
| 期刊: | Journal of Chromatography B-Analytical Technologies in the Biomedical and Life Sciences | 影响因子: | 2.800 |
| 时间: | 2013 | 起止号: | 2013 Oct 15; 937:44-53 |
| doi: | 10.1016/j.jchromb.2013.08.010 | ||
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