Precision, accuracy, and data acceptance criteria in biopharmaceutical analysis

Area of Science:

• Analytical Chemistry
• Bioanalytical Science

Background:

• Accuracy and precision are critical for analytical method validation.
• Quality control (QC) monitoring is essential for ensuring the reliability of bioanalytical results.

Purpose of the Study:

• To evaluate various accuracy, precision, and quality control (QC) testing approaches for bioanalytical methods.
• To compare the stringency of different QC acceptance criteria.

Main Methods:

• Applied fixed interval bias and significance testing for accuracy.
• Utilized fixed interval percentage relative standard deviation (%RSD) and analysis of variance (ANOVA) for precision.
• Assessed QC acceptance criteria including +/- 20% fixed range, 99% confidence interval, multiple rules, and range charts.

Main Results:

• Significance testing identified three methods as unacceptable for accuracy, while fixed interval bias accepted all seven.
• Precision assessment using unconfounded %RSD or ANOVA effectively evaluated between- and within-run variations.
• The fixed interval bias method for accuracy assessment was found to be more liberal than significance testing.

Conclusions:

• Significance testing provides a more conservative and reliable assessment of accuracy compared to fixed interval bias.
• ANOVA is effective for assessing precision by separating sources of variation in confounded data.
• Robust QC strategies are vital for validating bioanalytical data integrity.