 Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  You may not further distribute the material or use it for any profit-making activity or commercial gain  You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

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 (melting temperature of the protein determined with DSF) and C (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.

Light chain amyloidosis (AL) is a systemic disease in which abnormally proliferating plasma cells secrete large amounts of mutated antibody light chains (LCs) that eventually form fibrils. The fibrils are deposited in various organs, most often in the heart and kidney, and impair their function. The prognosis for patients diagnosed with AL is generally poor. The disease is set apart from other amyloidoses by the huge number of patient‐specific mutations in the disease‐causing and fibril‐forming protein. The molecular mechanisms that drive the aggregation of mutated LCs into fibrils have been enigmatic, which hindered the development of efficient diagnostics and therapies. In this review, we summarize our current knowledge on AL amyloidosis and discuss open issues.

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 ⁰C and 25 ⁰C, while widely-used stability-indicating parameters like protein melting and aggregation onset temperatures failed to provide accurate predictive formulation rankings.