Biological Sample Matrix Variables

Drug Discovery Toxicology From Target Assessment to Translational Biomarkers, First Edition. Edited by Yvonne Will, J. Eric McDtiffie, Andrew J. Olaharski, and Brandon D. Jefly. 

BEST PRACTICES IN PRECLINICAL BIOMARKER SAMPLE COLLECTIONS 

Tube type and additives, for example, anticoagulants (EDTA, sodium citrate, heparin), clot activators, separator gels, RNase free, PAXgene tubes 

Tube fill—correct ratio of blood to anticoagulant 

Arterial, central venous, peripheral venous, capillary 

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Here again two cases can be distinguished. With in vitro measurements there is no great difference in practical techniques as compared to other samples (see Chap. 5), aside from the complexity of the sample matrix. In contrast to other fields of application, however, in biological systems the danger always exists that the sampling process may influence the physiological equilibrium. The consequence of such a disturbance is that no matter how accurately an analysis is carried out, the actual variable of interest is not measured. This problem is reduced with in situ or in vivo measurements however, here problems of sterilization and electrode calibration come up. 

On the other hand, factor analysis involves other manipulations of the eigen vectors and aims to gain insight into the structure of a multidimensional data set. The use of this technique was first proposed in biological structure-activity relationship (i. e., SAR) and illustrated with an analysis of the activities of 21 di-phenylaminopropanol derivatives in 11 biological tests [116-119, 289]. This method has been more commonly used to determine the intrinsic dimensionality of certain experimentally determined chemical properties which are the number of fundamental factors required to account for the variance. One of the best FA techniques is the Q-mode, which is based on grouping a multivariate data set based on the data structure defined by the similarity between samples [1, 313-316]. It is devoted exclusively to the interpretation of the inter-object relationships in a data set, rather than to the inter-variable (or covariance) relationships explored with R-mode factor analysis. The measure of similarity used is the cosine theta matrix, i. e., the matrix whose elements are the cosine of the angles between all sample pairs [1,313-316]. 

Due to the intrinsic variability of biological matrices, guidelines require to prove that in several independent sources of the same matrix FDA requires a minimum of six, EC requires a minimum of 20 representative blank samples. 

In this study, mice received Alcalase or a test amylase at several doses in detergent matrix by either the i.n. route or the i.t. route. For the i.t. study, the mice were given four weekly doses and blood was collected 5 days after the last dose. Enzyme-specific IgE was measured in each serum sample by the RBL 2H3 cell 3H-serotonin release assay. It is difficult to quantify precisely the difference in potency, because of the flat dose-response curves seen for both Alcalase and amylase in the i.t./IgE study. This difficulty is further compounded by the variability seen in the titres of anti-Alcalase IgE seen from study to study (e.g. compare Alcalase titres in Figure 8.1C and 8.2E over the same doses administered). This variability may be due to differences in the biological responses in the animals and/or differences in the sensitivity of the RBL assay from study to study. 

Furthermore, given the large quantity of multivariate data available, it was necessary to reduce the number of variables. Thus, if two any descriptors had a high Pearson correlation coefficient (r > 0.8), one of the two was randomly excluded from the matrix, since theoretically they describe the same property to be modeled (biological response). Therefore it is sufficient to use only one of them as an independent variable in a predictive model (Ferreira, 2002). Moreover those descriptors that showed the same values for most of the samples were eliminated too. 

However, there are few reports on chitosan/bentonite nanocomposites (Yang Chen, 2007 Zhang et al., 2009 Wan Ngah et al., 2010). The physical properties and biological response of chitosan strongly depend on the starting materials and nanocomposite preparation conditions. In the present study chitosan/day nanocomposites were prepared using two kinds of clay and different chitosan/day ratios, to evaluate how these variables affect the dispersion of clay particles into the chitosan matrix. The samples obtained were characterized by infrared spectroscopy, x-ray diffraction, and mechanical (tensile) properties. 

The recovery of an analyte in an assay is defined by the FDA in a strictly operational way as the detector response obtained Ifom an amount of the analyte added to and extracted from the biological matrix, compared to the detector response obtained for the true concentration of the pure authentic standard. Recovery pertains to the extraction efficiency of an analytical method within the limits of variability. Recovery of the analyte need not be 100 %, but the extent of recovery of an analyte and of the internal standard should be consistent, precise, and reproducible. Recovery experiments should be performed by comparing the analytical results for extracted samples at three concentrations (low, medium, and high) with unextracted standards that represent 100 % recovery (FDA 2001). In terms of the symbols used in Section 8.4, the recovery is thus defined as the ratio (R /R"), and is equivalent to determination of F provided diat no suppression or enhancement effects give rise to differences between R and R" and that the proportional systematic errors and 1 are negligible. The FDA definition of recovery also corresponds to that of the PE ( process efficiency ) parameter (Matuszewski 2003) discussed in Section 5.3.6a, since the former (FDA 2001) measures a combination of extraction efficiency and matrix effects (if any). 

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