Assay measuring impact

A battery of different biochemical quantitative assays was applied to many different tissues and species. DNA damage and lipid peroxidation assays measure the direct impact of genotoxics and oxidant pollutants [16,17] whereas alteration of GSH levels in liver is a marker for oxidative stress [18]. Mercury and other heavy metals are known to induce metallothionein levels in different tissues although this effect is variable in different species and organs [19-22]. 

Precision is a measure of the agreement between replicate assays and is usually expressed as the coefficient of variation (CV). A CV of 15% or less is desired although, like accuracy, some leniency in this criterion is made for samples at very low concentrations. Also, the regulatory agencies give some consideration to the combined impact of accuracy and precision. For example, a method that has a recovery of less than 70% but a CV of less than 10% might be viewed more favorably than a method with a 90% recovery and a CV of 20%. 

As in the case in the analysis of food samples, the introduction of relatively inexpensive MS detectors for GC has had a substantial impact on the determination of methylxanthines by GC. For example, in 1990, Benchekroun published a paper in which a GC-MS method for the quantitation of tri-, di-, and monmethylxanthines and uric acid from hepatocyte incubation media was described.55 The method described allows for the measurement of the concentration of 14 methylxanthines and methyluric acid metabolites of methylxanthines. In other studies, GC-MS has also been used. Two examples from the recent literature are studies by Simek and Lartigue-Mattei, respectively.58 57 In the first case, GC-MS using an ion trap detector was used to provide confirmatory data to support a microbore HPLC technique. TMS derivatives of the compounds of interest were formed and separated on a 25 m DB-% column directly coupled to the ion trap detector. In the second example, allopurinol, oxypurinol, hypoxanthine, and xanthine were assayed simultaneously using GC-MS. 

Figure 3.3 Impact of passive permeability on the efflux ratio (ER). Passive permeability (x-axis) was measured in a PAMPA assay [19]. Efflux ratios were derived from permeability measurements in a Caco-2 monolayer assay [44] and are expressed as the basolateral to apical/ apical to basolateral permeability ratios. The loading concentration was 5 XM in the PAMPA assay and 10 xM in the Caco assay. LC-MS/MS readout was used for both assays. The y-axis...

Low permeability can itself be the cause of apparent discrepancies between biochemical and cell-based assays and may or may not have physiological relevance. Independent of the solubility limitation mentioned above, the selection of an appropriate loading concentration in cell-based permeability assays impacts on the assay outcome and depends on what information one wants to extract from the measurement loading at high concentration (i.e., 100 pM) will essentially cancel the effect of active transports unless passive diffusion is low. When high loading concentrations are used, poor recovery and bioanalytics are usually not an issue. 

Data generated from metabolic clearance measurements using liver microsomes can lead to an overestimation of the tme in vivo clearance if the free versus bound fraction is not considered. A useful follow-up assay is therefore plasma protein binding measurement. The impact of cytochrome P-450 inhibition on metabolic clearance of the parent (and thus exposure) is more complicated and it remains rather difficult to make quantitative predictions from in vitro data alone. The reason is that there are generally multiple clearance pathways involved and genetic polymorphism needs to be considered as well. 

In the same way, o-nitrophenyl octyl ether (o-NPOE) was immobilized on polycarbonate (PC) filters and the apparent permeability measured after 5 h incubation time was correlated to log Pnpoe for a series of reference compounds (log Pnpoe ranging from —1 to 3.6) [90]. Lipophilicity values in the alkane/water system were also determined using PAMPA with hexadecane-PC coated filters [89]. In this case, a correlation was found between intrinsic permeability (log Pq, permeability corrected for ionization and for unstirred water layer contribution, which particularly affects permeability of lipophilic compounds) and log P ik. However, log Pq is obtained from the knowledge of the pJC, value(s) and the permeability pH profile and therefore requires the full permeability pH profile to be measured for each compound, which negatively impacts the assay throughput. 

In most food applications, the analyst is working with rather complex matrices. This raises the question of how to quantitatively extract a representative fraction of the enzyme to be assayed. In the best-case scenario, all or a representative fraction of the active target enzyme will be obtained in a solution devoid of other components that may hinder the assay (compounds that may affect either the enzyme itself or some other aspect of the assay). The objective of many assays is to measure the total amount of enzyme activity associated with a particular sample this determination depends on the quantitative extraction of the target enzyme. In all cases, it is essential that the enzyme preparation be clearly defined when reporting the amount of enzyme associated with a given product, because this step is likely to have a major impact on measured activities. Furthermore, it should not be assumed that an enzyme preparation protocol optimized for a particular sample or product is necessarily optimal for a different sample. 

Ion suppression is so far mainly considered in the context of sensitivity and the lower limit of quantification of an assay. But it has to be emphasized that short term variations in ion yields—particularly due to matrix components—can compromise the accuracy of analyses Whenever the variation of ion yield has a differential impact on target analyte and internal standard, accuracy is compromised. This means that the reliability of LC-MS/MS analyses critically depends on (1) how similar the impact of ion suppression or ion enhancement on target analyte and internal standard compound is and on (2) how similar the matrices of calibrator samples and actual patients samples are with respect to the modulation of ionization efficacy. This problem can be of relevance for an entire measuring series—if systematic differences in the ionization modulation properties of calibration materials and actual patients samples are present—or it may non-systematically affect individual patients samples as well. 

For all analytical methods the quality of the results ultimately relates back to the chemical purity of the very best available SRM and to the linearity of the correlation curve for the experimentally measured property vs. the SRM concentration. For substances that are naturally chiral there is the additional very serious concern about enantiomeric purity. The determination of an enantiomer whether for an enantiomeric purity test, or for an enantiomeric ratio or excess test in the study of a partial racemic mixture, is one of the more difficult analytical problems. To actually report the enantiomeric purity of an enantiomer as better than 99% is truly beyond the capability of current analytical methodology [31], for after all few substances ever have a chemical purity that is guaranteed to be greater than 99%. So, as mentioned earlier, one has to accept the fact that the results are measured relative to an enantiopurity of an SRM that is defined to be 100%. This limitation of course impacts on the true meaning of a calculated enantioexcess, and to a much lesser degree perhaps, in assays of chiral substances extracted from plant materials using calibration data that were obtained for synthetic SRM s. 

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