Understanding unfolding and refolding of the antibody fragment (Fab). I. In-vitro study

December 15, 2020

Gani, K., Bhambure, R., Deulgaonkar, P., et al.

Biochemical Engineering Journal 2020, vol: 164 doi: 10.1016/j.bej.2020.107764

Abstract

In-vitro protein refolding is a major rate-limiting step in the large scale production of antibody fragments expressed using a microbial source like E. coli. This investigation is focused on understanding the in-vitro unfolding and refolding of the multi-domain protein involving inter-domain disulfide linkage, like antibody fragment (Fab). Solubilization behavior of the inclusion bodies and unfolding events of Fab fragment (Biosimilar rHu Ranibizumab) were studied using nano-differential scanning fluorimetry (nano-DSF). Fab unfolding behavior was studied by fitting experimental data with the two-state and three-state thermodynamic model. Based on the Fab unfolding understanding, a two-stage design of experiment (DoE) strategy was used for the optimization of the in-vitro refolding condition of a Fab fragment. Refolding yield of 56.03 ± 1.15 % was achieved using the optimized oxidative refolding conditions maintained by appropriate dilution factor and redox reagent ratio. Refolding kinetics of the rHu Ranibizumab was analyzed using a three-parameter kinetic model showing rate constant k1:7.05e-6 l/mg.min, k2:0.57 l/mg.min, and k3:310.19 l/mg.min. Based on observed refolding kinetics, it was concluded that the Fab refolding follows a three-state mechanism with the refolding intermediate/(s) formation from light and heavy chain of the Fab fragment as an overall rate-limiting step. The method described here is a useful tool to identify high-yield scalable refolding conditions for multi-domain proteins involving inter-domain disulfide bonds.

View Publication

Topics: Prometheus, nanoDSF, Biologics, Publications

Previous Article
How stability studies get your mAb candidate FDA-approved
How stability studies get your mAb candidate FDA-approved

Up next
Analyze self-interaction to predict viscosity and aggregation propensity at higher concentrations
Analyze self-interaction to predict viscosity and aggregation propensity at higher concentrations

Want to see more
biologics content?

Explore resources