Illuminating the Dark Host Cell Proteome: A host cell protein coverage method for LC-MS impurity assays

Area of Science:

• Biopharmaceutical Analysis
• Analytical Chemistry
• Proteomics

Background:

• Host cell protein (HCP) analysis is critical for biopharmaceutical safety.
• Liquid chromatography-mass spectrometry (LC-MS) is increasingly used for HCP analysis.
• Systematic evaluation of LC-MS HCP coverage, especially for undetectable proteins, is poorly defined.

Purpose of the Study:

• To develop a transparent and generalizable in silico pipeline (DARK-COV) for assessing theoretical HCP coverage in LC-MS assays.
• To identify potential analytical blind spots in biopharmaceutical impurity characterization.
• To provide a method-specific approach for quantifying theoretical coverage and comparing predictions with experimental observations.

Main Methods:

• Developed DARK-COV pipeline combining physicochemical filtering of in silico-digested proteomes with assay-specific LC-MS parameters.
• Classified proteins yielding fewer than two detectable peptides as Dark Host Cell Proteins.
• Benchmarked pipeline against a large-scale experimental dataset (>15,000 LC-MS analyses) from HCPedia™.
• Applied DARK-COV to CHO, E. coli, HEK, and Sf9 expression systems.

Main Results:

• DARK-COV predicts theoretical undetectability for only 1.0-5.2% of proteins across tested expression systems under idealized conditions.
• Strong concordance was observed between DARK-COV predictions and experimental LC-MS data (>15,000 datasets).
• The majority of predicted Dark Host Cell Proteins are low-molecular-weight proteins with limited relevance to biopharmaceutical impurity risk.

Conclusions:

• The DARK-COV pipeline offers a reproducible, method-specific approach for quantifying theoretical HCP coverage in LC-MS assays.
• Identifies analytical blind spots, supporting risk-based evaluation of undetected HCPs.
• Provides a rational foundation for regulatory justification of LC-MS-based HCP impurity analyses (e.g., USP Chapter 1132.1).