A quality evaluation strategy for residual host cell proteins based on orthogonal analysis

Accurate quantification of host cell proteins (HCPs) is essential for the safety and quality control of biopharmaceuticals. Although enzyme-linked immunosorbent assay (ELISA) remains the current industry gold standard, heterogeneity among the polyclonal antibody repertoires of different commercial kits often results in substantial method-dependent bias. This study aimed to establish a novel strategy for the quality evaluation of HCP detection kits for biologics. Two Chinese hamster ovary cell-derived recombinant protein drug substances with different HCP burdens were utilized as model samples to functionally compare nine mainstream commercial kits. A quantitative metric, adjacent dilution-gradient back-calculation recovery, was introduced to comprehensively assess dilution linearity, sensitivity, and resistance to matrix interference. To eliminate evaluation bias, an unsupervised machine learning workflow utilizing hierarchical clustering analysis (HCA) based on a 10-dimensional functional feature matrix was implemented for objective kit stratification. Functional assessment showed clear stratification among the nine kits. Kit A displayed the most robust analytical performance, with adjacent dilution-gradient back-calculation recovery strictly maintained within 80%-120% over dilution windows ranging from 16-fold to 128-fold, thereby overcoming the hook effect and false-negative risks, as mathematically validated by its unique branching under the HCA model. IMBS-MS/MS analysis demonstrated that although the mainstream kits evaluated (A and I) both achieved overall antibody coverage above 80%, only Kit A showed notable concordance between high coverage and superior functional performance. High antibody coverage is a necessary but insufficient condition for dependable ELISA performance. Pursuit of overall coverage alone cannot comprehensively reflect the quantitative reliability of a kit. Therefore, we recommend an orthogonal evaluation framework that combines functional verification, with priority given to robust dilution linearity, coupled with HCP coverage analysis during bioprocess development and quality control, to ensure scientifically sound and compliant impurity monitoring.