Parallelism matrix effects

In their broadest application, CRMs are used as controls to verify in a direct comparison the accuracy of the results of a particular measurement parallel with this verification, traceability may be demonstrated. Under conditions demonstrated to be equal for sample and CRM, agreement of results, e.g. as defined above, is proof. Since such possibilities for a direct comparison between samples and a CRM are rare, the user s claims for accuracy and traceability have to be made by inference. Naturally, the use of several CRMs of similar matrix but different analyte content will strengthen the user s inference. Even so, the user stiU has to assess and account for all uncertainties in this comparison of results. These imcertainty calculations must include beyond the common analytical uncertainty budget (i) a component that reflects material matrix effects, (2) a component that reflects differences in the amount of substance determined, (3) the uncertainty of the certified or reference value(s) used, and 4) the uncertainty of the comparison itself AU this information certainly supports the assertion of accuracy in relation to the CRM. However, the requirement of the imbroken chain of comparisons wiU not be formally fulfilled. 

The phenomenon of discrimination induced by matrix effects is quite disadvantageous in qualitative analysis, but in the quantification procedure its influence is devastating. Several techniques applying MS or MS-MS, however, can help to overcome this crucial influence. The most common way out of this dilemma is the application of column separation to minimise or even exclude matrix effects entailed with the disadvantage of long-lasting separations. In parallel, the difference between the time needed for the performance of LC examinations and the time needed for FIA becomes obvious and will be pointed out later on. 

It is important that a linear curve is repeatable from day to day. However, linear ranges may be different for different matrices. The reason for this is a possible effect of interferences inherent to the matrix. A test for general matrix effects can be performed by means of standard additions or the method of analyte additions. For a set of samples, obtained by adding different concentrations of analyte to a certain matrix, the slope of the calibration curve is compared with the slope of the usual calibration function. A lack of significance (curves are parallel) means that there is no matrix effect [21,75]. 

Because the catabolic and metabolic pathways of biotech drugs are often poorly defined and sufficiently sensitive comparator assays are lacking, additional matrix effect tests by parallelism should be conducted with actual study samples. These are often performed on subject samples with aberrant PK profiles. A pool from several time points with sufficient analyte concentration of that subject is serially diluted. The observed concentration times the dilution factors should be within... 

Physical and chemical effects can be combined for identification as sample matrix effects. Matrix effects alter the slope of calibration curves, while spectral interferences cause parallel shifts in the calibration curve. The water-methanol data set contains matrix effects stemming from chemical interferences. As already noted in Section 5.2, using the univariate calibration defined in Equation 5.4 requires an interference-free wavelength. Going to multivariate models can correct for spectral interferences and some matrix effects. The standard addition method described in Section 5.7 can be used in some cases to correct for matrix effects. Severe matrix effects can cause nonlinear responses requiring a nonlinear modeling method. 

If the slopes of both curves do not differ significantly [t(b) < t s with d.f. = ns + rca — 4], matrix effects are not present and a standard-solution-based calibration line may be used. It is noted that, for calibration lines having a very small residual standard deviation (Sy), matrix interferences have often been detected based on the statistical significance while the lines are nearly parallel. The contribution of the error of this small matrix effect is often negligible compared to the total measurement error. Therefore, it is strongly recommended to perform a visual interpretation of the parallelism of the lines in conjunction with this t-test. 

Treatments C and E (the bilayer tablets that contain the IR component) had a steeper amount absorbed profile as compared to the parallel matrix tablets (Treatments B and D). This effect was more pronounced under fasting conditions. Only with Treatment E (KKK bilayer tablets) did the hypothetical in vivo dissolution profiles surpass the 100% absorption, both under fasting and non-fasting conditions. For Treatment C, this occurred only under fasting conditions and for Treatment D only under non-fasting conditions. 

Figure 16.8. Parallelism plot. At high dilution factors matrix effect are minimized. The ideal curve is a flat horizontal line, where the measured concentration, after correction for dilution, is independent of the dilution factor.

Coefficients of variation for intraassay and interassay calibration data are shown in Table 16.2. Matrix effects were studied by dilution with buffer of two real plasma samples. Both parallelism tests (Table 16.3) show that matrix effects are not significant for this method, since the observed-calculated results show apparently random scatter 100%... 

Interferences due to matrix effects can be detected by comparing the slopes of the curves for the spike sample and the pure standard solutions. In the absence of interferences both slopes should be parallel. In effect, the method is equivalent to preparing the standard calibration curve with exact matrix matching. To apply this... 

Value of g the required precision value a can be found in the certificate of the CRM itself a can be the uncertainty of the certified value when the same method as the certification method is used by the analyst. It can also come from the individual set of measurement values of one of the methods used in the interlaboratory certification study. In such a case, all individual data and the methods must be available from the certificate or the certification report. Such information is given in some CRM reports of BCR and is illustrated in Annex 3.1. The a can also be stated in a written standard as a minimal or target precision value to be obtained. Finally, a can also simply come from the laboratory itself which applied another method previously or with another instrument or from another laboratory experienced with the method, or it may be requested by a customer. Care must be taken in extrapolating a simply from another element or substance which is analysed in parallel using a multi-elemental or multiresidue methods, as these may not be comparable at all because of matrix effects etc. 

A useful tool to test matrix effect is parallelism. Test samples from a clinical trial and/or samples from various control batches, with known amounts of analytes added, are diluted with control samples containing no analyte, and these are used as standard calibrator preparations. Various dilutions (e.g., 2-, 4-, 6-, 8-, 10-, and 20-fold) are prepared and analyzed against the standard calibrators. The dose-response curves of the diluted samples are compared to those of the standard calibrators. A parallel line of the test (or spiked) sample shows that the compound present in the sample has the same antigen-antibody binding response as the analyte and, therefore, is very probably the analyte itself. If the line is not parallel to the standard curve line, and the concentrations at higher dilutions agree with one another, the matrix effect is nonspecific and could be overcome by dilution. 

Initial Test of Matrix Effects Assay matrices are typically the most troublesome component in LBA. There should be careful consideration of this variable during method validation. Two types of tests are used to address the two major concerns surrounding matrix effects. These are performed in consideration of (1) whether there is a matrix difference between the standards and anticipated study samples that impacts the relative accuracy of an assay and (2) whether there are inter-individual or disease-specific differences in matrix in the target patient population. Two types of tests are used to evaluate such matrix effects spike recovery, where known amounts of analyte are mixed ( spiked ) into characterized matrix, and parallelism in patient samples. However, limited availability of patient samples may prevent the latter testing during the method feasibility phase. 

Parallelism and Dilutional Linearity to Evaluate Matrix Effects... 

For a majority of biomarker assays, standard calibrators are prepared in an analyte-free alternative matrix instead of the de facto sample matrix of patient samples. For such methods, it is crucial to demonstrate that the concentration response relationship in the sample matrix is similar to that of the alternate matrix. Spike-recovery experiments with the reference standard may be inadequate to evaluate the matrix effect, as the reference standard may not fully represent the endogenous analyte. Instead, parallelism experiments are performed through serial dilutions of a high-concentration sample with the calibrator matrix. Multiple individual matrix lots (>3 lots) should be tested to compare lot-to-lot consistency. In the instance that the limited amounts of sample are available, apooled matrix strategy can be used with caution as discussed by Lee et al. [15]. The samples can be serially diluted with the standard matrix (standard... 

Some workers will interpret this as the ability to reproduce calibration curves for a particular assay that are parallel to each other when plotted graphically whether linear or nonlinear, as opposed to a demonstration of analyte recovery without bias, when sample matrix is diluted to assay samples whose concentrations lie above the analytical range of the method. Parallelism and other potential matrix effects should be investigated separately. 

Linearity has been described by some workers in a way which, by the current authors, would be interpreted as matrix parallelism, whereas others will use the term to describe the extent to which a calibration curve is linear in nonligand-binding assays. For the purpose of this chapter, the term linearity or dilution linearity is used to describe the results of experiments conducted using spiked samples to demonstrate the potential for high-concentration samples to be able to be diluted into the analytical range and read with acceptable accuracy and precision. It is often used to give an indication that matrix effects will not cause a problem upon sample dilution in circumstances where incurred or volunteer samples are not available with concentrations of analyte sufficiently high to conduct parallelism experiments. 

Chemical matrix effects were encountered in the HVAA determination. When a constant amount of chromium was added to different petroleum samples, the atomization response varied considerably from that obtained in THF alone (Table 8.II). Since almost parallel effects were noted after ashing, the effect of Fe, Ni, and V was studied. Excesses of vanadium had a significant effect at levels which often occur in crude oils (10 to > 100 ppm). Once vanadium was introduced in the atomizer, the chromium signal was consistently suppressed until the atomization furnace was baked at maximum temperature to remove residual vanadium. 

Standard additions of BTEXs to industrial wastewaters showed no matrix effect. The angular coefficient of the straight line obtained by four additions in the 0.1-2 ppm range was practically identical to that of a similar calibration plot using triply-distilled water (18,954 vs 18,883), the two lines being parallel (Figure 16.3). 

Another aspect that is common to matrix interferences (direct contrihutions of matrix components to the signal measured for analyte and/or SIS) and matrix effects (suppression or enhancement of ionization) is that of the consequences of the presence of metabolites or other types of degradates when analyzing incurred analytical samples. Such interferences are in principle absent from the control matrix used for matrix matched calibrators, QC samples etc. Thus use of re-analysis of incurred samples to evaluate and consequent matrix effects was discussed in Section 9.4.7b, and applies equally to matrix interferences arising from presence of metabolites. Variations of metabolite levels among samples (e.g. from different time points in a pharmacokinetic study), which can lead to parallel variations in the extent of both matrix effects and matrix interferences, are an example of how some problems can arise unexpectedly despite prior precantions. 

The standard addition method [35] represents a combination of calibration with the aid of both external and internal standards. In ion chromatography, it is used predominantly for the analysis of samples with difficult matrices. Matrix problems may lead to an increase in nonprecision and/or express themselves as constant or proportional systematic deviations of the analytical results. Matrix influence can be identified via calculation of the recovery function. In constant systematic deviation, the error is independent of the analyte component. Such a deviation will cause a parallel shift of the calibration line. A possible origin for this deviation might be a codetection of a matrix component. In proportional systematic deviations, the error depends on the concentration of the analyte component. This type of deviation results in a change of the slope of the calibration line. Deviations of this kind can be caused by individual sample preparation steps such as sample digestion and sample extraction, and also by matrix effects. Systematic deviations can be identified by standard addition and/or calculation of the recovery function. 

Standard addition calibration is more robust and reliable than conventional external calibration, but is more time consuming and costly if it is applied separately for each sample. A major advantage of standard addition is the correction of multiplicative matrix effects, for example alteration of nebuli-sation efficiency. The intensities of all samples (and spiked samples) change by the same factor, which leads to an altered cahbration slope. However, for additive effects, such as interferences caused by the matrix, the calibration line is shifted parallel and the intercept changes, which results in biased analyte concentrations. In some cases, this bias can be avoided (or indeed identified) by choosing another isotope and comparing the results for each. Standard addition has no inherent compensation for instrumental drift in the ICP-MS system. However, reduction of the drift, whieh limits the applicability of standard addition for ICP-MS, has been achieved by applying a chemometric method (a bracket approach, where the spiked sample is measured between two different measurements of the sample). ... 

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