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Method validation is a concept that has been ingrained (or should have been ingrained) in the minds of most analytical chemists. Any new method that is created has to be proven reliable and provide some level of performance under well-defined conditions.
Method validation is a concept that has been ingrained (or should have been ingrained) in the minds of most analytical chemists. Any new method that is created has to be proven reliable and provide some level of performance under well-defined conditions. There are many guidelines provided by companies and governing bodies about the proper steps to take in validating a new method. When evaluating a new method to be reported in the literature, any reviewer worth his salt will impose some reasonable standards that the authors must meet to prove their approach is reliable. Precision, accuracy, linearity, and limits of detection and quantification are the usual suspects. Some analysts may even go as far as evaluating robustness and sample or reagent stability to better define operational limits for their method. Here, I want to emphasize that an evaluation of matrix effects, especially for liquid chromatography–electrospray ionization mass spectrometry (LC–ESI-MS) methods designed to determine analytes in biological fluids, should be a foremost thought in the analytical chemist’s mind.
Matrix effects are a real concern in LC–ESI-MS because ESI is a competitive ionization process (1,2). Species in electrospray droplets compete for a limited number of charge-bearing sites on the droplet surface, and those species that are more surface active or are in much higher concentration are good at beating out less surface active or less abundant molecules. In the end, the abundance of observed ions is proportional to the concentration of species on the surface of the ESI droplet. In biological fluids, there are many components (such as salts and lipids) that, if not well resolved from the analyte of interest, can outcompete and suppress analyte ion signal, and result in a matrix effect. In other words, the response of the analyte ion in the presence of the matrix is less than what was expected for the analyte ion generated from a pure standard solution (response enhancements are also possible). If the method is not calibrated to account for matrix effects that change the ion signal from that which might be expected from a pure sample solution, then the determined concentration for the analyte in the sample will be grossly underestimated (for ion signal suppression) or overestimated (for ion signal enhancement).
The magnitude of matrix interferences observed in an LC–ESI-MS analysis depends both on the physicochemical nature of the analyte and the mode of LC used. Matrix components present in an injected sample can be of high abundance and can be eluted throughout the duration of the chromatographic run.
Let’s consider reversed-phase LC. Samples with high salt content (such as urine) will create a great deal of interferences around the dead time of the run. This is why analytes of interest should be retained with a retention factor of two or greater (in some cases k’ > 1 will suffice), to avoid the unpredictable effects that high abundances of salts can have on ion generation in ESI. It is worth noting that if hydrophilic interaction chromatography (HILIC) mode separations are being used, these high abundance salts can be retained and eluted at different points in time in the chromatogram.
Similarly, samples with high lipid content (such as plasma or serum) can create interferences in the middle to latter half of a reversed-phase LC run, because this is where more hydrophobic components will be eluted. In addition to being present in high abundance in the sample, lipids are also quite surface active, so they compete very effectively for limited sites at the surface of ESI droplets and cause ion suppression.
Different biological fluids have very different profiles of matrix components that need to be taken into account. On top of this, the physicochemical nature of the analyte will control where it will be eluted in the chromatographic run, and consequently, which matrix components might be present along with the analyte in the ESI droplets produced.
The best way to evaluate the magnitude of matrix interferences is to compare the slope of a calibration curve obtained when the analyte is present in its native matrix (such as a biological fluid) to that obtained when the analyte is prepared in a pure solution (3,4). If the slope obtained in the native matrix is significantly greater than or less than the slope obtained from the pure solution analysis, then ion enhancement or ion suppression effects, respectively, are present. A plot can be generated that directly compares these slopes and enables one to best ascertain the magnitude of the effect. To address the problem, one might adjust the sample preparation to better remove the interferences or adjust the chromatography to move the analyte away from the interferences, but these steps can be challenging, time consuming, or impractical. A more common approach is to make sure that the calibration standards are prepared in a representative pooled or surrogate matrix that will mimic and manifest the same response variations for the analytes of interest as those that will be observed for the real sample. This is one reason why a standard addition quantitative analysis procedure can be useful: All calibration and analysis is performed in the exact matrix of interest. Yet, if sample volumes are limited, then a suitable surrogate matrix must be found. This might be a pooled matrix, free of the analyte of interest, or a synthetic combination of components that is demonstrated to mimic the matrix in question. Much more can be said about these strategies, and we will do so in future posts. For now, I point you to a couple of nice references, which can help you better understand and evaluate matrix effects in LC–ESI-MS analysis (5,6). Remember, this is an important point to consider in your method validation efforts.
(1) P. Kebarle and L. Tang, Anal. Chem. 65, 3654–3668(1993)
(2) C.A. Enke, Anal. Chem. 69, 4885–4893 (1997).
(3) H.P. Nguyen, L. Li, I.S. Nethrapalli, N. Guo, C.D. Toran-Allerand, D.E. Harrison, C.M. Astle, and K.A. Schug, J. Sep. Sci. 34, 1781–1787 (2011).
(4) B.K. Matuszewski, J. Chromatogr. B 830, 293–300 (2006).
(5) R. Bakhtiar and T.K. Majumdar, J. Pharmacol. Toxicol. Methods 55, 227–243 (2007).
(6) E. Rogatsky and D. Stein, J. Am. Soc. Mass Spectrom. 16, 1757–1759 (2005).
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