DSC Derived (Ea & ΔG) Energetics and Aggregation Predictions for mAbs

Area of Science:

• Biophysical Chemistry
• Protein Therapeutics
• Formulation Science

Background:

• Monoclonal antibodies (mAbs) are crucial biotherapeutics, but their stability during storage is a significant concern.
• Aggregation, particularly high molecular weight (HMW) species formation, can impact mAb efficacy and safety.
• Understanding the energetics of mAb unfolding is key to predicting and mitigating aggregation.

Purpose of the Study:

• To calculate the Arrhenius energy of activation of unfolding (Ea) and Gibbs free energy of unfolding (ΔG) for mAbs using differential scanning calorimetry (DSC).
• To investigate the relationship between DSC-derived energetic parameters and mAb aggregation.
• To develop predictive models for mAb aggregation based on energetic landscapes and surface properties.

Main Methods:

• Differential scanning calorimetry (DSC) was employed to measure melting temperatures (Tm) and heat capacity changes (ΔCp) for 4 mAbs across various scan rates (60-200 °C/hr).
• Kinetic unfolding energy (Ea) was calculated from DSC-derived ΔTm changes at different scan rates.
• Thermodynamic unfolding energy (ΔG) was derived from Tm, ΔCp, and ΔH measurements, extrapolated to equilibrium conditions (0 °C/hr).
• Statistical multivariate analysis integrated kinetic (Ea) and thermodynamic (ΔG) energies with in-silico surface properties.

Main Results:

• DSC-derived Ea trends correlated with observed aggregate formation, enabling prediction of %HMW formation after 9-month storage at 5 °C and 40 °C.
• Full energetic landscapes of mAb unfolding and aggregation were constructed by combining kinetic (Ea) and thermodynamic (ΔG) data.
• Multivariate analysis identified significant parameters influencing aggregation, leading to the development of predictive models for mAb formulations.

Conclusions:

• DSC-derived unfolding energetics (Ea and ΔG) provide valuable insights into mAb stability and aggregation propensity.
• The developed predictive models can guide the formulation of stable mAb therapeutics, minimizing aggregation during storage.
• This approach offers a robust strategy for assessing and enhancing the long-term stability of monoclonal antibody formulations.