In silico method development for the reversed-phase liquid chromatography separation of proteins using chaotropic...

Area of Science:

• Biopharmaceutical Analysis
• Chromatography
• Protein Chemistry

Background:

• Biopharmaceutical development increasingly relies on protein and peptide therapeutics.
• Chromatographic and spectrometric hyphenation is crucial for biopharmaceutical analysis.
• Computer-assisted chromatographic modeling for large molecules lags behind small molecules due to conformational changes.

Purpose of the Study:

• To investigate the impact of chaotropic and denaturing mobile phases on computer-assisted chromatographic modeling of proteins.
• To improve the accuracy of retention models for large biomolecules in reversed-phase liquid chromatography (RPLC).

Main Methods:

• Utilized ACD/Labs software for linear and polynomial regression retention modeling.
• Tested eight model proteins (12–670 kDa) with varying mobile phase modifiers (trifluoroacetic acid, sodium perchlorate, guanidine hydrochloride).
• Varied gradient slope, column temperature, and mobile phase buffer composition.

Main Results:

• Substantially improved correlation between experimental and modeled outputs using strong chaotropic/denaturing mobile phases.
• Linear regression modeling showed high accuracy with chaotropic agents, similar to small molecules.
• Conventional trifluoroacetic acid concentrations at low temperatures necessitated polynomial regression, suggesting protein conformational changes.

Conclusions:

• Strong chaotropic agents enhance the reliability of computer-assisted chromatographic modeling for proteins.
• This approach can overcome conformational variability issues in protein RPLC.
• Enables development of new RPLC assays for protein-based therapeutics and vaccines.