Deconvoluting Biophysical Factors that Influence Long-Term Aggregation Rates of High-Concentration Monoclonal...

Area of Science:

• Biopharmaceutical development
• Protein aggregation kinetics
• Machine learning in drug discovery

Background:

• Protein aggregation is a major challenge in developing therapeutic proteins, impacting manufacturing, storage, and patient safety.
• Predicting aggregation under various conditions is crucial for identifying stable drug candidates.
• Monoclonal antibodies (MAbs) are a significant class of protein therapeutics prone to aggregation.

Purpose of the Study:

• To systematically evaluate factors influencing monoclonal antibody (MAb) aggregation rates across diverse solution conditions and temperatures.
• To compare the predictive power of accelerated stability studies (30-50 °C) versus refrigerated storage conditions for MAb aggregation.
• To apply machine learning models to identify key attributes governing high-concentration MAb aggregation.

Main Methods:

• Studied the stability of four MAbs under varying pH, ionic strength, MAb concentrations, and incubation temperatures.
• Analyzed results based on conformational stability and net protein-protein interactions (self-interactions).
• Utilized interpretable machine learning models to quantify aggregation phenomena, focusing on refrigerated conditions.

Main Results:

• Aggregation rates exhibited non-Arrhenius behavior, meaning they did not follow a simple temperature-dependent pattern.
• Accelerated stability studies (30-50 °C) provided misleading stability rankings compared to refrigerated conditions.
• Net valence (a measure of protein charge interactions) was the most significant predictor of MAb aggregation at refrigerated storage.

Conclusions:

• Standard accelerated stability testing may not accurately reflect MAb stability under typical refrigerated storage.
• Refrigerated storage conditions are critical for accurate assessment of MAb aggregation propensity.
• Net valence is a key parameter for predicting MAb aggregation and ensuring stability in pharmaceutical formulations.