A Novel Lack-of-Fit Assessment as a System Suitability Test for Potency Assays

Abstract

Bioassay data analysis is used to determine the potency of protein therapeutics. To properly determine potency, the experimental data need to be fitted to a model that adequately describes the observed dose-response relationship. Typical models include 4-parameter logistic curve fits (Healy (5)), 5-parameter logistic curve fits (Prentice (6)) or parallel line analysis (Finney (7)). Lack-of-Fit (LOF) assessment can be used as a measure of potency assay system suitability to ensure appropriate closeness of the chosen model fit to the experimental data. We present a novel LOF approach that overcomes the shortcomings of previously described LOF tests, such as the conventional ANOVA F-test and the LOF sum of squares test. Simulation studies and examples are used to assess the performance of the new LOF test. The results show that the described LOF approach can effectively reject poorly fitted data while retaining well-fitted data, and has advantages in potency assay applications where instrument-to-instrument variability in absolute readout is expected.