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Svilenov, Hristo ORCID logoORCID: https://orcid.org/0000-0001-5863-9569 und Winter, Gerhard (April 2019): The ReFOLD assay for protein formulation studies and prediction of protein aggregation during long-term storage. In: European Journal of Pharmaceutics and Biopharmaceutics, Bd. 137: S. 131-139 [PDF, 2MB]

Abstract

The formulation of novel therapeutic proteins is a challenging task which aims at finding formulation conditions that will minimize protein degradation during long-term storage. One particularly important and difficult-to-predict protein degradation pathway is the so-called non-native aggregation. The qualitative and quantitative prediction of the latter has been a subject of extensive research over the past two decades. An increasing body of evidence shows that the widely-used short-term biophysical techniques cannot accurately rank formulation conditions in order of their effect on the aggregation during long-term storage of some therapeutic proteins, e.g. monoclonal antibodies. Here we suggest a novel approach for the selection of formulation conditions that will suppress the formation of protein aggregates during long-term storage. We postulate that conditions (i.e. pH, buffer type, ionic strength) that reduce the isothermal aggregation of various denaturant-induced partially folded protein species will be conditions that impede protein aggregation during long-term storage. To test our hypothesis, we developed an isothermal microdialysis-based unfolding/refolding assay, named ReFOLD, which we use to induce moderate aggregation of partially folded proteins. Next, we assessed the relative monomer yield after isothermal unfolding/refolding of two monoclonal antibodies, each formulated in 12 different conditions. Using the proposed approach, we were able to accurately rank the formulations in order of their effect on the amount of protein aggregates detected after storage for 12 months at 4 °C and 25 °C, while widely-used stability-indicating parameters like protein melting and aggregation onset temperatures failed to provide accurate predictive formulation rankings.

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