Method Validation and Application of Protein Biomarkers: Basic Similarities and Differences from Biotherapeutics

Abstract

Protein drug development and biomarkers share common bioanalytical technologies that are applied for different purposes. A fit-for-purpose approach should be used for biomarker assays at various stages of novel biomarker development and their application to drug development. Biomarker quantifications can be absolute or relative, depending upon the characteristics of the standard curve, which include the reference standard, substituted matrix and parallelism. Appropriate method-validation experiments should be carried out on sample collection, relative accuracy and precision, range finding, parallelism, selectivity, specificity and stability in order to meet the need for exploratory or advanced application that is specified for a study. The interaction of a biotherapeutic with the target ligand or inter-related biomarkers should be taken into consideration for method platform choice and validation. Direct adoption of commercial diagnostic kits can produce confounding data. Therefore, kit comparison, modification and appropriate validation experiments are often carried out to meet the specific purpose for drug development. Multiplex assays and physicochemical methods can complement the single-analyte ligand-binding assay for protein drugs and biomarkers.

The Use of Immunohistochemistry for Biomarker Assessment—Can It Compete with Other Technologies?

“…The current theory in biomarker development is that they must be ''fit for purpose,'' that there be an evaluation of science around the target, and that the best method for its assessment is used (Lee 2009;. However, trying to understand why so few predictive biomarkers, the keystone of personalized medicine, have been developed suggests a biomedical quandary.…”

“…In addition, the American Association of Pharmaceutical Scientists and the United States Clinical Ligand Society have identified four general classes of biomarker assays (Figure 1) (Lee et al 2006). Note that the standard method of defining biomarker expression provided by pathologists examining a glass slide is considered a ''qualitative assay''-an assay that generates discrete, discontinuous data in either ordinal (e.g., low, medium and high, scores 1-5) or nominal (yes/no, positive/ negative) formats (Lee 2009;Cummings, Ward, and Dive 2010). Even the most sophisticated image analysis now performed on histologic sections qualifies as ''quasi-quantitative''-an assay that generates numeric data along a continuous scale in terms of a characteristic of the test sample (e.g., percent of area of tumor expressing an immunohistochemical stain) .…”

Calculations for Adjusting Endogenous Biomarker Levels During Analytical Recovery Assessments for Ligand-Binding Assay Bioanalytical Method Validation

“…Physical means have been proposed to deal with matrix effects, including increasing the MRD and use of stripped or immunodepleted matrix. Matrix effects and means to address issues with endogenous biomarker levels have been discussed in detail elsewhere (1,3,20,23).…”

“…Specifics about %AR calculations were not substantially discussed in the recently released 2013 FDA BMV draft guidance, nor in the Crystal City V workshop report (18). Nevertheless, influential publications support use of the subtraction method (e.g., [19][20][21]. Moreover, recent white papers on biomarker BMV by the Global CRO Council and by the Global Bioanalysis Consortium Harmonization Team state that the subtraction method should be used for adjusting endogenous biomarker content in selectivity determinations (22,23).…”

Multivariate Statistical Analysis of Diverse Strains of Yersinia pestis by Comparative Proteomics

“…The Extended Data Analysis (EDA) module of DeCyder serves as an alternative analysis tool which can provide multivariate statistics, such as principal component analysis (PCA) [19][20][21][22], hierarchical clustering [23][24][25][26], K-means clustering [27], and biomarker selection [28][29][30], and can be evaluated by scientists with less statistical expertise. Further, it is possible to generate a set of protein biomarkers or classifiers, which can be used to designate unknown samples [31,32], based solely on protein expression patterns. Although the sole use of pattern expression has proven problematic for bacterial identification and is not as rapid as PCR or MALDI-MS for bacterial detection, the results obtained from this approach may serve to complement these other detection techniques by further characterizing the particular bacteria under study.…”