Related Experiment Video
Updated: Jun 17, 2026
08:51
Preparation of Poly(pentafluorophenyl acrylate) Functionalized SiO2 Beads for Protein Purification
Published on: November 19, 2018
Correlation between protein-polydimethylsiloxane visible particle formation and computationally derived antibody
Kazunori Hirayama1, Kengo Arai1, Masakazu Fukuda1
1Formulation Development Department, Chugai Pharmaceutical Co., Ltd., 5-5-1 Ukima, Kita-ku, Tokyo 115-8543, Japan.
Journal of Pharmaceutical Sciences
|June 15, 2026
Summary
Researchers developed a computational method to predict protein aggregation risk in drug formulations. This approach identifies key protein surface features, aiding in the design of safer biologic drugs and preventing visible particle formation.
Area of Science:
- Biopharmaceutical development
- Computational chemistry
- Protein aggregation
Background:
- Proteinaceous visible particles (pVPs) are a concern in biologic drug formulations.
- Their formation is influenced by protein properties and formulation components like poloxamer 188 (PX188).
- Predicting and mitigating pVP formation is crucial for drug safety and efficacy.
Purpose of the Study:
- To develop a computational chemistry approach for predicting the risk of protein-polydimethylsiloxane visible particles (psVPs) in monoclonal antibody (mAb) formulations.
- To quantitatively compare psVP formation risk across different mAbs.
- To identify key protein surface characteristics associated with psVP formation.
Main Methods:
- Developed an accelerated assessment system using reduced PX188 concentration in siliconized polymer pre-filled syringes (PFS).
- Quantified computationally derived surface charge and hydrophobic patches on eight different mAbs.
- Assessed the correlation between these surface features and psVP formation risk.
Main Results:
- Established a rapid psVP risk assessment system for mAbs in PFS.
- Identified that charge patches contribute more significantly to psVP formation than hydrophobic patches.
- A composite feature combining charge and hydrophobic patch areas showed the highest correlation with psVP formation.
- Demonstrated that reducing headspace in PFS can mitigate psVP formation.
Conclusions:
- Computational analysis of surface charge and hydrophobic patches can predict psVP risk in mAb formulations.
- This approach supports sequence- and structure-based prediction of psVP risk.
- Findings aid in designing lower-risk mAbs and inform early decisions on mitigation strategies like headspace reduction.
