Key elements of bioanalytical method validation for macromolecules

The Third American Association of Pharmaceutical Scientists/US Food and Drug Administration (FDA) Bioanalytical Workshop, which was held May 1 and 2, 2006, in Arlington, VA, addressed bioanalytical assays that are being used for the quantification of therapeutic candidates in support of pharmacokinetic evaluations. One of the main goals of this workshop was to discuss best practices used in bioanalysis regardless of the size of the therapeutic candidates. Since the last bioanalytical workshop, technological advancements in the field and in the statistical understanding of the validation issues have generated a variety of interpretations to clarify and understand the practicality of using the current FDA guidance for assaying macromolecular therapeutics. This article addresses some of the key elements that are essential to the validation of macromolecular therapeutics using ligand binding assays. Because of the nature of ligand binding assays, attempts have been made within the scientific community to use statistical approaches to interpret the acceptance criteria that are aligned with the prestudy validation and in-study validation (sample analysis) processes. We discuss, among other topics, using the total error criterion or confidence interval approaches for acceptance of assays and using anchor calibrators to fit the nonlinear regression models

Appropriate calibration curve fitting in ligand binding assays

Calibration curves for ligand binding assays are generally characterized by a nonlinear relationship between the mean response and the analyte concentration. Typically, the response exhibits a sigmoidal relationship with concentration. The currently accepted reference model for these calibration curves is the 4-parameter logistic (4-PL) model, which optimizes accuracy and precision over the maximum usable calibration range. Incorporation of weighting into the model requires additional effort but generally results in improved calibration curve performance. For calibration curves with some asymmetry, introduction of a fifth parameter (5-PL) may further improve the goodness of fit of the experimental data to the algorithm. Alternative models should be used with caution and with knowledge of the accuracy and precision performance of the model across the entire calibration range, but particularly at upper and lower analyte concentration areas, where the 4-and 5-PL algorithms generally outperform alternative models. Several assay design parameters, such as placement of calibrator concentrations across the selected range and assay layout on multiwell plates, should be considered, to enable optimal application of the 4- or 5-PL model. The fit of the experimental data to the model should be evaluated by assessment of agreement of nominal and model-predicted data for calibrators