Biomarker Discovery and Validation Using Large Molecule Bioanalysis

Large Molecule Bioanalysis

The importance of biomarkers in drug development has increased exponentially over the last few decades. As more biomarkers enter drug discovery and clinical trials, robust quality assurance becomes critical for establishing the reliability of bioanalytical techniques used in biomarker evaluation. Novel biomarkers can advance and revolutionize the development and application of therapeutics. However, the successful implementation of biomarkers depends on robust validation processes that include method validation and technology integration in the development of biomarker candidates.  

Large molecule bioanalysis includes analyzing large molecule therapeutic in biological matrices. Plasma or serum is one of the most common biological matrices for both large and small-molecule bioanalysis. Large molecules such as antibody-drug conjugates, monoclonal antibodies proteins, and peptides have proven effective in treating different diseases. As more large molecule drug products enter the market, large molecule bioanalysis solutions offered by laboratories are increasingly becoming more common. These bioanalytical studies include large molecule biomarker assays such as qPCR services employed for exploratory and regulated purposes. The current article explores biomarker discovery and validation using large molecule bioanalysis

Biomarker discovery and validation

The development of biomarkers has multiple benefits, such as diagnosing, screening, and monitoring disease activity or evaluating therapeutic responses. These biomarkers are known surrogate endpoints employed during the early phase of drug testing. These novel applications have resulted in an appreciated space for biomarker discovery efforts and a development pipeline for accelerating biomarker discovery and validation. The biomarker development workflow includes discovery, qualification, optimization, verification, validation, and commercialization. 

Importantly, adequate distinction is needed between bioanalytical validation and qualification. Bioanalytical validation establishes assay performance, characteristics, and optimal conditions for generating accurate and reproducible data. On the other hand, qualification refers to correlating a biomarker with clinical endpoints and biological processes. However, both qualification and validation are intertwined and require accurate integration for developing robust biomarkers suitable for the intended application. Hence, biomarker development and validation often follow a fit-for-purpose approach. 

Must Read: Good Clinical Practices in the Bioanalytical Laboratory

Biomarker validation is different from routine laboratory validation and pharmacokinetic validation. Biomarker validation requires a novel approach different from routine bioanalytical method validation used in drug analysis. This novel approach, a fit-for-purpose method for biomarker method development and validation, follows the idea that validation should be designed to fulfill the intended applications of a biomarker study. 

Fit-for-purpose method validation should establish assay reliability for the intended purpose, with the extent of method validation increasing from the initial effort proposed during exploratory studies to more rigorous validation based on evidence as a biomarker moves to the clinical phase of drug development. Fit-for-purpose assay validation includes distinct stages of method validation protocols, including prevalidation and exploratory validation. Hence, biomarker method validation is a continuous process involving iteration and refinement of assay parameters where the extent of method validation depends on the intended application of the biomarker assay. The extent and rigor of method validation increase at each validation step and focus on cross-validation, method robustness, and documentation control. 

In Conclusion

Biomarkers are crucial for advancing drug discovery and development. However, adequate method development and validation initiatives are critical for reliable and reproducible results



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