Abstract
As biomarkers grow in relevance for both the design and support of therapeutics and the clinical trials associated with them, there is an ever increasing need for accurate quantitation of these biochemical entities in biological matrices. While quantifying many biotherapeutics via ligand binding assay platforms can be fairly straightforward, biomarkers present some unique challenges that must be taken into account during assay development, validation and subsequent sample analysis. These challenges can be especially confounded by the relationship between two ligand binding assay tools: The regression curve and quality control samples. Due diligence must be performed to develop an assay that takes into account matrix vs. buffer effects and endogenous biomarker presence. Lack of diligence in these areas can lead to less than reliable results, thus potentially rendering the intended use of the assay moot.
Highlights
Biomarkers play an important role in the development of therapeutics
While quantifying many biotherapeutics via ligand binding assay platforms can be fairly straightforward, biomarkers present some unique challenges that must be taken into account during assay development, validation and subsequent sample analysis
Due diligence must be performed to develop an assay that takes into account matrix vs. buffer effects and endogenous biomarker presence
Summary
By up-regulating or downregulating in response to disease states or pharmacological intervention, biochemical biomarkers are important indicators of disease progression and drug efficacy (Frank and Hargreaves, 2003) They have been used to show proof of mechanism for drug efficacy, as safety indicators in response to drug dosing and even as screening criteria for potential patient enrollment in clinical trials (Colburn and Lee, 2003; Chau et al, 2008). Due to this underlying importance, the need for accurate quantitation of biomarkers in various biological matrices requires an understanding of how they differ biochemically from therapeutics, and what the optimal method of quantitation might be for each individual analyte of interest. The intended use of the assay, whether fit-for-purpose in early development or fully quantitative in support of clinical trials, will drive the need for accuracy and reproducibility in results
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