Various stability indicating techniques find application in the early stage development of novel therapeutic protein candidates. Some of these techniques are used to select formulation conditions that provide high protein physical stability. Such approach is highly dependent on the reliability of the stability indicating technique used. In this work, we present a formulation case study in which we evaluate the ability of differential scanning fluorimetry (DSF) and isothermal chemical denaturation (ICD) to predict the physical stability of a model monoclonal antibody during accelerated stability studies. First, we show that a thermal denaturation technique like DSF can provide misleading physical stability rankings due to buffer specific pH shifts during heating. Next, we demonstrate how isothermal chemical denaturation can be used to tackle the above-mentioned challenge. Subsequently, we show that the concentration dependence of the Gibbs free energy of unfolding determined by ICD provides better predictions for the protein physical stability in comparison to the often-used T-m (melting temperature of the protein determined with DSF) and C-m (concentration of denaturant needed to unfold 50% of the protein determined with ICD). Finally, we give a suggestion for a rational approach which includes a combination of DSF and ICD to obtain accurate and reliable protein physical stability ranking in different formulations.

Antibody drugs should exhibit not only high-binding affinity for their target antigens but also favorable physicochemical drug-like properties. Such drug-like biophysical properties are essential for the successful development of antibody drug products. The traditional approaches used in antibody drug development require significant experimentation to produce, optimize, and characterize many candidates. Therefore, it is attractive to integrate new methods that can optimize the process of selecting antibodies with both desired target-binding and drug-like biophysical properties. Here, we summarize a selection of techniques that can complement the conventional toolbox used to de-risk antibody drug development. These techniques can be integrated at different stages of the antibody development process to reduce the frequency of physicochemical liabilities in antibody libraries during initial discovery and to co-optimize multiple antibody features during early-stage antibody engineering and affinity maturation. Moreover, we highlight biophysical and computational approaches that can be used to predict physical degradation pathways relevant for long-term storage and in-use stability to reduce the need for extensive experimentation.