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Bioanalytical method development and validation often overlap. Assay development requires continuous assessments with distinct requirements from the validation process. Hence, the statistical and regulatory requirements for method development and validation are unique. Understanding these differences is critical for selecting statistical considerations for each phase. Utilizing best statistical practices in assay development and validation ensures fulfillment of regulatory requirements and establishment of bioanalytical method performance. 

Bioanalytical assay development focuses on defining and developing an idea into a robust method, delivering accurate results. Most bioanalytical assays are categorized into qualitative, semi-quantitative, and fully quantitative methods. These methods involve in vitro assay development during early drug discovery, followed by in vivo testing, including animals and human subjects.

After a robust assay development, immunoassay services focus on assay validation. Depending on the information obtained during assay development, researchers prepare a validation protocol. This protocol includes tests to confirm assay parameters that are necessary for the design and testing of the protocol and to confirm whether it meets the performance criteria. Some biological assay validation parameters include accuracy, linearity, precision, and specificity. Irrespective of assay development or validation, statistical analysis is a crucial component of experimental studies. The current article discusses best practices employed by assay development services for statistical analysis in assay qualification and validation.

Statistical analysis for biological assay validation 

A bioanalytical assay doesn’t fail in the optimization or development phase. If an assay fails to fulfill predefined criteria during method development, it is either re-optimized or rejected for the intended applications. However, a bioanalytical assay can be rejected in the validation phase. If a particular assay fails to fulfill the predefined acceptance criteria, it requires further development. After identifying and resolving the root cause of failure, the assay is re-optimized to meet satisfactory performance. Confirming whether a bioanalytical assay fulfills predefined acceptance criteria requires statistical analysis. 

Most quantitative assays require assessing assay results against a standard curve. This requires calibration. Although statistical principles are the same for all assays, those systems requiring quantitative approaches present unique challenges. Calibration standards are needed while quantifying the amount of a target analyte in a study sample. Samples with known analyte amounts are run, and the results are plotted against a reference standard to generate a curve. Sometimes, a linear fit calibration curve is generated. However, this is not the case for all samples.

Most biological assays do not show a linear fit across the entire value range. In such cases, researchers should evaluate the linear range and generate a standard curve for this particular range using a graphical plot. Multiple linear fit approaches are available, but the least squares technique is often sufficient. Besides, data transformation may also be needed to generate a linear fit. However, for instance, assays without a linear graphical output throughout its analytical range and showing a sigmoidal relationship will require a standard curve that is best on the equation of four-parameter logistic regression. Additionally, researchers can verify the goodness of fit by applying common regression diagnostics to a linear equation. 

In Conclusion

Statistical analysis is crucial for instilling confidence in assay development and validation approaches.