Bioanalysis uses several bioanalytical methods to determine study analytes in biological samples. But to be sure that the produced results are valid, the study should be performed using validated bioanalytical methods. It becomes even more crucial with method transfer, as the receiving laboratory should already be equipped with validated protocols. Several regulatory agencies have released their guidelines for ideal bioanalytical assay validation. However, there are some misconceptions when it comes while conducting assay validation. Following are seven of the most common myths related to bioanalytical assay validation.
Both micro and macromolecules require similar reference standards.
Reference standards form the core of stock solutions, which are used in the preparation of quality controls, calibration standards, and other spiked samples. These spiked samples validate the bioanalytical method performance, and hence reliable knowledge of reference standard purity, identity, and stability parameters are essential for estimating the analyte of interest. As the process through which micro and macromolecules are produced differ significantly, macromolecules are comparatively not well characterized. They are often heterogeneous and pose variability in purity among batches and preparations. Hence, method validation should employ appropriate reference standards for macromolecules.
Selectivity and specificity are the same.
It is generally observed that scientists interchangeably use specificity and selectivity in bioanalytical method validation. But it is critical to understand that both specificity and selectivity are two discrete terms. Selectivity measures the extent, while specificity is absolute. It can be said that specificity is the measure of the upper end of selectivity.
Selective and sensitive LC-MS/MS systems can guarantee high selectivity.
In the LC-MS/MS system, ion formation is a crucial step in analyte detection. However, successful ion formation is often affected by several undetectable factors of the incurred sample matrix. Hence, even with highly selective and sensitive systems, the matrix effect may hinder its capability of delivering high selectivity.
Quality control and the incurred sample matrix behave similarly.
Matrix complexity imposed by incurred samples is one of the main challenges in bioanalytical method development. Although often both the matrices for quality control and incurred samples are the same, they might not necessarily behave the same. It may be because of several reasons, such as the quality controls do not contain drug metabolites and drug isomers.
Re-injection of failed analytical batches.
Sponsors generally carry out the re-injection of failed batches. But it is against good practices to bring failed analytical batches to acceptance. For high levels of batch failure, FDA recommends investigating the reason and getting it resolved before commencing sample analysis. Ideally, predetermined criteria should be in place for the evaluation of failed analytical batches.
Parallelism and quality control dilution linearity are interchangeable.
Parallelism is a concept that demonstrates sample dilution response and standard concentration-response curve is parallel to each other. As the notion involves the term dilution, it is at times confused with quality control dilution linearity. But it is critical to differentiate both the terms as parallelism uses incurred samples and the latter doesn’t.
Quality control and incurred sample stability will be the same.
While short-term and long-term stability assessments of quality control help understand analyte stability, they will never entirely mimic the conditions observed in incurred samples. Hence, during bioanalytical method validation, sponsors should accumulate scientific proof of the differences observed between incurred samples and quality controls.
Bioanalytical methods are heavily exploited in the drug development domain. They are used in PK-PD studies, BABE analysis, and toxicology studies in drug development for generating crucial study data. The success of all bioanalytical assays directly depends on the quality of data they provide. Hence, bioanalytical assays must be sufficiently robust for supporting the needs of the drug development process.