Quantification of target analytes in various biofluids using a postcolumn infused-internal standard method combined with matrix normalization factors in liquid chromatography-electrospray ionization mass spectrometry

Liquid chromatography-electrospray ionization mass spectrometry (LC-ESI-MS) has become one of the most widely used methods in pharmaceutical laboratories. Although LC-ESI-MS provides high sensitivity and high specificity for quantifying target analytes in complicated biofluids, the associated severe matrix effects (MEs) generally result in large quantification errors. Here, we propose a novel strategy for correcting MEs in various biofluids using a postcolumn infused-internal standard (PCI-IS) method in combination with matrix normalization factors (MNFs). We used the MNFs to normalize the encountered MEs in various biofluids to the MEs encountered in standard solutions. The use of a postcolumn infused-internal standard also corrects the MEs for individual samples. When using the PCI-IS method in combination with MNFs, the calibration curve generated from standard solutions can be applied to quantify the target analytes in various biofluids. We applied this new approach to quantify etoposide and etoposide catechol in plasma and CSF. The accuracy of the test results showed that over 93% of the data have quantification errors less than 20% and that 99% of the data have quantification errors less than 30%. The successful application of this method to evaluate real clinical samples revealed that our proposed MNFs in combination with the PCI-IS method largely simplifies the entire method development and validation processes, saves a great deal of time and cost without sacrificing quantification accuracy, and provides a simple means of quantifying target analytes in various biofluids. This method will be particularly useful in fields in which the same target analytes need to be quantified in various types of matrices, including bioanalysis, forensic toxicology, environmental studies, and food safety control.