Identifying, evaluating, and controlling bioanalytical risks resulting from nonuniform matrix ion...

Matrix ion suppression/enhancement is a well-observed and discussed phenomenon in electrospray ionization mass spectrometry. Nonuniform matrix ion suppression/enhancement across different types of samples in an analytical run is widely believed to be well compensated for by using a stable isotope-labeled internal standard (SIL-IS) in bioanalysis using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). Therefore, the risk of nonuniform matrix ion suppression/enhancement is usually deemed low when an SIL-IS is used. Here, we have identified, evaluated, and proposed solutions to control bioanalytical risks from nonuniform matrix ion suppression/enhancement even with an SIL-IS through a case study using omeprazole. Two lots of human blank urine were tested, and ion enhancement of about 500% for omeprazole was observed in one lot but not in the other. When a quadratic regression model had to be used, the assay failed the industry acceptance criteria due to unacceptable positive bias for the middle and high quality control (QC) samples. The failure was attributed to different extents of matrix ion enhancement between the standards (STDs) and QCs, which resulted in the misaligned results from the regression model. It was concluded that, for the same amount of drug, nonuniform ion enhancement for different types of samples (STD or QC) resulted in different ion intensities, therefore leading to different response behaviors (linear or nonlinear) at the mass spectrometer detector. A simplified mathematical model was used to evaluate the risk when unmatched response models occurred for different types of samples. A diagnostic factor Q (Q = X(ULOQ)(-A/B)) was proposed to monitor the risks, where X(ULOQ) is the upper limit of quantitation of the assay, A is the quadratic slope of the curve, and B is the linear slope of the curve. The potential maximum errors were estimated on the basis of the mathematical model for different scenarios, and Q values were given to control the risks under these conditions for bioanalysis using LC-MS/MS.