Total error assays

The protein-to-protein variation observed with the various protein assay methods makes it obvious why the largest source of error for protein assays is the choice of protein for the standard curve. If the sample contained IgG as the major protein and BSA was used for the standard curve, the estimated total protein concentration of the sample will be inaccurate. Whether the concentration was underestimated or overestimated depends upon which total protein assay method was used. If the Coomassie... 

The relative uncertainty of measurements at or just exceeding the LoD may be large, and often a quantitative result is not reported. The lower limit for reporting quantitative results, the limit of quantitation (LoQ), relates to the total error being considered acceptable for an assay. From a precision profile for the assay and an evaluation of the bias in the low range, LoQ may be determined in relation to specifications of the method. For example, a laboratory may specify that the total error (e.g., expressed here as Bias -i- 2 SD) of an assay is lower than 45% (corresponding to a bias of 15% and a CV of 15%) of the measurement concentration. " In this case, the LoQ is the lowest assay value at which this specification is fulfilled. LoQ constitutes the lowest limit of the reportable range for quantitative results of an assay. 

Chapter 5 discusses in depth the statistical considerations related to LB A development and validation. In addition to the most appropriate algorithms for describing the nonlinear calibration curves typically found in LBAs, the authors also provide further insight into the performance characteristics to be evaluated during assay validation, including the concepts of total error in prestudy validation and the use of the 4-6-X rule. The decision rules at the prestudy validation and routine assay implementation stages are also discussed in some detail in Chapter 5. 

The primary performance measures of a ligand-binding assay are bias/trueness and precision. These measures along with the total error are then used to derive and evaluate several other performance characteristics such as sensitivity (LLOQ), dynamic range, and dilutional linearity. Estimation of the primary performance measures (bias, precision, and total error) requires relevant data to be generated from a number of independent runs (also termed as experiments or assay s). Within each run, a number of concentration levels of the analyte of interest are tested with two or more replicates at each level. The primary performance measures are estimated independently at each level of the analyte concentration. This is carried out within the framework of the analysis of variance (ANOVA) model with the experimental runs included as a random effect [23]. Additional terms such as analyst, instmment, etc., may be included in this model depending on the design of the experiment. This ANOVA model allows us to estimate the overall mean of the calculated concentrations and the relevant variance components such as the within-run variance and the between-run variance. 

To evaluate assay performance, a set of VS prepared independently of the standards is used. At least five concentrations VS should be prepared, including the target LLOQ and ULOQ, low QC ( 3 times of the LLOQ), mid-QC, and high QC (—75 % of the ULOQ). Accuracy and precision can be evaluated from the total error (the sum of bias and precision) of VS data from the qualification runs in a similar way as for macromolecular protein drugs [17]. Given biological variability and other factors in biomarker research, more lenient acceptance criteria may be used for biomarker PD than for PK studies. Still, it should be recognized that accuracy and precision data of VS in buffer provide only a relative quantification, which may be quite different from measurements in the authentic matrix. 

The acceptance criteria for a novel biomarker can initially be determined by assay performance in prestudy method validation. Data obtained from the in-study validation using subject samples can then be used to refine the initial acceptance criteria set during the prestudy validation. For example, an assay with 50% total error may still be acceptable for detecting a twofold treatment effect observed in a clinical trial. Setting acceptance criteria a priori may not be appropriate (or even possible) in an exploratory application of novel biomarkers, since the values seen in the incurred samples may not be what is expected or predicted. 

For each in-study run, the standard curve must satisfy criteria described in the standard-curve section however, run acceptance is based primarily on the performance of the QC samples. When using total error for ligand binding assays of macromolecules, the run acceptance criteria recommended in the precision and accuracy section requires that at least four of six (67%) QC results must be within 30% of their nominal values, with at least 50% of the values for each QC level satisfying the 30% limit. The recommended 4-6-30 rule imposes limits simultaneously on the allowable random error (imprecision) and systematic error (mean bias). If the application of an assay requires a QC target acceptance limit different than the 30% deviation from the nominal value, then prestudy acceptance criteria for precision and accuracy should be adjusted so that the limit for the sum of the interbatch imprecision and absolute mean RE is equal to the revised QC acceptance limit. 

During sample analysis before assessing the QC samples for acceptance, the standard curve must be deemed appropriate by predetermined criteria. Only after the curve is accepted may the assessment of QC samples continue. QC sample results determine whether the assay run is valid. Acceptance criteria can be based on 4-6-20 rule or on Total Error and should be predicated on the criteria used in both the development and the prestudy validation phase. Overall, the immunoassay is a highly sensitive assay that can be used to quantify protein and peptide drugs in a biological matrix, often routinely in the pg/mL range. 

In spite of its widespread acceptance and use, there are definite limitations in this method of total T4 assay. There is variability in the efficiency of extraction of the T4 by the ethanol (Bll) and recently an ethanol-extractable substance was discovered which binds sufficient T to cause significant error in the CPBA technique (15). These factors among others, are probably associated with the underestimation of serum T4 by CPBA, particularly in patients with thyrotoxicosis (B4, F4, G7, KIO). 

The Cobas Fara 11 provides on-line sample pre-dilution or pre-treatment, enabling total automation of assays such as serum, proteins and urine chemistries. Pre-diluted samples can also he pre-incubated before transfer to the analyser. These on-line features minimize labour and errors while ensuring result integrity. 

A systematical approach of sample preparation methods and optimisation of the quality aspects of sample preparation may enhance the efficiency of total analytical methods. This approach may also enhance the quality and knowledge of the methods developed, which actually enhances the quality of individual sample analyses. Unfortunately, in bioanalysis, systematical optimisation of sample preparation procedures is not common practice. Attention to systematical optimisation of assay methods has always been mainly on instrumental analyses problems, such as minimising detection limits and maximising resolution in HPLC. Optimisation of sample extraction has often been performed intuitively by trial and error. Only a few publications deal with systematical optimisation of liquid-liquid extraction of drugs from biological fluids [3,4,5]. 

The selection of a protein standard is potentially the greatest source of error in any protein assay. Of course, the best choice for a standard is a highly purified version of the predominate protein found in the samples. This is not always possible nor always necessary. In some cases, all that is needed is a rough estimate of the total protein concentration in the sample. For example, in the early stages of purifying a protein, identifying which fractions contain the most protein may be all that is required. If a highly purified version of the protein of interest is not available or it is too expensive to use as the standard, the alternative is to choose a protein that will produce a very similar color response curve with the selected protein assay method. 

For greatest accuracy of the estimates of the total protein concentration in unknown samples, it is essential to include a standard curve in each run. This is particularly true for the protein assay methods that produce nonlinear standard curves (e.g., Lowry method, Coomassie dye-binding method). The decision about the number of standards used to define the standard curve and the number of replicates to be done on each standard depends upon the degree of nonlinearity in the standard curve and the degree of accuracy required of the results. In general, fewer points are needed to construct a standard curve if the color response curve is linear. For assays done in test tubes, duplicates are sufficient however, triplicates are recommended for assays performed in microtiter plates due to the increased error associated with microtiter plates and microtiter plate readers. 

In a preliminary experiment, weanling male Wistar rats were depleted of zinc by feeding a low zinc basal diet (0.6 yg/g zinc) for two weeks and then repleted by adding 12 yg/g zinc as zinc sulphate. The analysis of the zinc content of the different tissues at weekly intervals for four weeks revealed that the body weight and the total femur zinc were the parameters of choice because the responses were linear with duration of feeding. Moreover, the relative errors of the slopes of the regression lines were minimal (5). The results of this experiment also showed that since depletion did not reduce the variability in these parameters, it was not essential for the assay. 

As noted previously, fluctuations in concentration estimates about the true value arise from multiple, independent, random errors. Each of the independent errors (erf) is statistically additive, such that the total assay error cr = Ecrf. Similarly, the CV% of the assay will be the square root of the sum of the squared CV%... 

Artefactual increases of as much as 50% in total thyroxine, estimated by a competitive protein-binding assay, and of as much as 30% in triiodothyronine resin uptake are probably due to rapid and continuing lipolytic hydrolysis of triglycerides after blood has been drawn (126). Thyroid function tests should therefore always be performed on blood samples taken before (or a sufficient time after) heparin treatment (127). An increase in serum-free thyroxine concentrations has also been reported after low molecular weight heparin, by up to 171% in specimens taken 2-6 hours after injection. When specimens were obtained 10 hours after injection, the effects were smaller, but with concentrations still up to 40% above normal the results can still cause errors of interpretation (128). 

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