Biology subjects

Plisnier, M.

Publications and source records attributed to Plisnier, M..

3 recordsLinked to original sources

bioRxiv2026

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 (R{superscript 2} = 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% {beta}-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 {beta}-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 O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/710150v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@1f5331aorg.highwire.dtl.DTLVardef@ee2234org.highwire.dtl.DTLVardef@798eaorg.highwire.dtl.DTLVardef@c84034_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2025

Definitive benchmarking of DDA and DIA for host cell protein analysis on the Orbitrap Astral in a regulatory-aligned framework

Host cell proteins (HCPs) are critical quality attributes in biotherapeutics and require accurate, protein-resolved quantification beyond the coverage limits of immunoassays. The Orbitrap Astral mass spectrometer was evaluated for label-free HCP analysis through a direct comparison of data-dependent acquisition (DDA, Top80) and data-independent acquisition (DIA, 4 m/z windows). Deterministic protein inference was implemented to ensure invariant protein grouping across datasets and software versions. Quantification was anchored using a whole-proteome stable isotope-labeled HCP standard, with identification error controlled through empirical false discovery proportion estimation. Both acquisition modes quantified total HCP content with high linearity (R2 > 0.99) and total error within {+/-}30% acceptance limits across a seven-point spike-in series. DIA achieved greater analytical depth, identifying 45% more proteins and 68% more peptides than DDA, with substantially reduced missingness. Protein-level fold-change behavior was evaluated using hierarchical Bayesian regression, with slopes centered near unity for DIA and systematic compression observed for DDA. Abundance-stratified resampling analysis demonstrated trueness and precision across the dynamic range and defined lower limits of quantification of approximately 0.6 ppm for DIA and 1.6 ppm for DDA. Both acquisition strategies accurately report global HCP content, while DIA provides improved coverage, fold-change fidelity, and sensitivity for low-abundance impurities. This study demonstrates that untargeted MS-based HCP measurements can be analytically qualified for use in regulated biopharmaceutical settings.

biochemistry↗

bioRxiv2024

Comparative analysis of MS/MS search algorithms in label-free shotgun proteomics for monitoring host-cell proteins using trapped ion mobility and ddaPASEF

Host cell proteins (HCPs) are critical quality attributes that can impact the safety, efficacy, and quality of biotherapeutics. Label-free shotgun proteomics is a vital approach for HCP monitoring, yet the choice of tandem mass spectrometry (MS/MS) search algorithms directly influences identification depth and quantification reliability. In this study, six prominent MS/MS search tools--Mascot, MaxQuant, SpectroMine, FragPipe, Byos, and PEAKS--were systematically benchmarked for their performance on complex samples spiked with isotopically labeled proteins from Chinese hamster ovary cells, using trapped ion mobility spectrometry and parallel accumulation-serial fragmentation in data-dependent acquisition mode. Key performance metrics, including peptide and protein identifications, data extraction precision, fold-change (FC) accuracy, linearity, and measurement trueness, were evaluated. A Bayesian modeling framework with Hamiltonian Monte Carlo sampling was employed to robustly estimate FC means and variances, alongside local false discovery rates through posterior probability calibration. Bayesian decision theory, implemented via expected utility maximization, was used to balance accuracy against posterior uncertainty, providing a probabilistic assessment of each tools performance. Through this cumulative analysis, variability across tools was observed: some excelled in identification sensitivity and protein coverage, others in quantitative accuracy with minimal bias, and a few offered balanced performance across metrics. This study establishes a rigorous, data-driven framework for tool benchmarking, delivering insights for selecting MS/MS tools suited to HCP monitoring in biopharmaceutical development. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=190 SRC="FIGDIR/small/621185v2_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@294a82org.highwire.dtl.DTLVardef@dabdb6org.highwire.dtl.DTLVardef@dd2446org.highwire.dtl.DTLVardef@7889c0_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗