Predictive tools for stabilization of therapeutic proteins

Area of Science:

• Biochemistry
• Computational Biology
• Pharmaceutical Sciences

Background:

• Monoclonal antibodies (mAbs) are a rapidly expanding class of pharmaceuticals.
• Protein aggregation is a significant challenge during mAb manufacturing and storage due to high concentrations.
• Identifying and mitigating aggregation-prone regions is crucial for therapeutic protein stability.

Purpose of the Study:

• To introduce and validate a novel in silico technology, spatial aggregation propensity (SAP), for identifying protein aggregation hot-spots.
• To demonstrate the utility of SAP in enhancing the stability of therapeutic proteins.
• To propose SAP as a screening tool for assessing the developability of protein-based drug candidates.

Main Methods:

• Utilized molecular simulations to develop the spatial aggregation propensity (SAP) technology.
• SAP identifies aggregation-prone regions by analyzing the dynamic exposure of spatially-adjacent hydrophobic amino acids.
• Validated SAP by introducing single mutations in monoclonal antibodies (mAbs) to reduce high-SAP scores in identified aggregation-prone regions.

Main Results:

• The SAP method successfully identified aggregation-prone regions in proteins.
• Monoclonal antibodies (mAbs) engineered with reduced SAP scores in specific regions exhibited enhanced stability compared to wild-type proteins.
• The study validated the effectiveness of the SAP method for mapping and mitigating protein aggregation.

Conclusions:

• The spatial aggregation propensity (SAP) technology provides an effective in silico approach for identifying and predicting protein aggregation.
• SAP-based protein engineering can significantly improve the stability of therapeutic proteins, including monoclonal antibodies.
• This technology offers a valuable tool for protein stabilization and serves as a rational screening method to assess the developability of protein therapeutics during drug discovery and development.