Prospective ICH Q2(R2)-aligned total-error validation of label-free untargeted proteomics for host cell protein quantification in biotherapeutics

Untargeted proteomics enables quantitative determination of host cell proteins (HCPs) in biotherapeutics, yet no workflow has been validated under ICH Q2(R2) for regulated quality control. We report a prospective validation of label-free untargeted proteomics for HCP quantification using a total-error (TE) approach. A stable isotope-labeled whole-proteome standard was spiked into NISTmAb at seven levels (20–80 ng). Four independent assays (198 injections) supported hierarchical replication and one-way random-effects ANOVA variance decomposition with Welch–Satterthwaite adjustment. Dual entrapment analysis demonstrated empirical peptide-level false discovery proportions below 1% at q = 0.01. Deterministic parsimony inference ensured invariant protein-group definition. Weighted least-squares regression (R² = 0.993) identified stable proportional compression with recoveries of 81–85%. Repeatability dominated the variance structure (median CV 2.7%); intermediate precision total SD ranged from 0.69% to 3.81% over the validated range. Accuracy profiles integrating empirical bias with a log– log variance model showed 95% β-expectation and 95/95 content tolerance intervals fully contained within ±30%, with a lower limit of quantification (LLOQ) of 20 ng. Abundance-stratified TE analysis revealed concentration-dependent calibration heterogeneity masked by aggregate-level estimation; stratum-specific β-expectation intervals within ±35% defined an abundance-aware LLOQ of 3.6 ppm (P95 = 3.87 ppm). Robustness under independent search software (FragPipe, CCC = 0.998, LoA ±9%) and cross-platform acquisition (Astral, CCC = 0.980, LoA ±18%) remained within predefined ±30% agreement limits. System suitability criteria were derived empirically from validation performance. This is the first prospective ICH Q2(R2)-aligned validation of untargeted proteomics for HCP quantification, with a statistical framework applicable to other high-dimensional analytical methods requiring regulatory qualification. Graphical Abstract