02423nas a2200253   4500008004100000022001400041245009200055210006900147260000800216300001100224490000800235520158900243653002201832653002301854653002301877653002001900653003701920100001801957700002001975700002601995700001702021700001802038856011302056       2020                        eng d  a1369-703X00aUnderstanding unfolding and refolding of the antibody fragment (Fab). I. In-vitro study0 aUnderstanding unfolding and refolding of the antibody fragment F  cDEC  a1077640 v1643 a<p>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 k(1) :7.05e(-6) l/mg.min, k(2) :0.57 l/mg.min, and k(3) :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.</p>
10aAntibody fragment10aIn-vitro refolding10aRefolding kinetics10arHu Ranibizumab10aTwo-state and Three-state models1 aGani, Kayanat1 aBhambure, Rahul1 aDeulgaonkar, Prashant1 aMehta, Deepa1 aKamble, Manoj  uhttps://library.ncl.res.in/content/understanding-unfolding-and-refolding-antibody-fragment-fab-i-vitro-study02210nas a2200265   4500008004100000022001400041245011100055210006900166260000800235300001400243490000700257520132700264653001501591653003001606653002601636653001401662653001501676100002601691700002001717700002501737700001801762700001901780700001701799856012801816       2022                        eng d  a0268-257500aMechanistic modeling of continuous capture step purification of biosimilar monoclonal antibody therapeutic0 aMechanistic modeling of continuous capture step purification of   cSEP  a2404-24190 v973 a<p>
	{BACKGROUND Continuous multicolumn Protein A chromatography offers various advantages for capture stage purification of monoclonal antibody therapeutics, like higher productivity and resin capacity utilization, lower buffer consumption, small footprint, etc. Due to the complexity of the continuous process, experimental optimization is time-consuming and cost-intensive. This investigation proposes a hybrid process development approach integrating experimental and mechanistic modeling for time- and cost-effective development and optimization of continuous Protein A affinity chromatography. RESULTS Productivity and capacity utilization of the continuous CaptureSMB process under varying operating conditions were predicted using the Chromatography Analysis and Design Toolkit (CADET) framework and validated with experimental results. Effects of critical process parameters like feed concentration (c(0)), loading breakthrough (s) and residence time (RT) on productivity and capacity utilization were evaluated. Model predictions were validated using the experimental results proving the reliability and feasibility of the modeling approach. At 15.00 +/- 0.20 mg mL(-1) feed model mAb concentration, the model-based approach predicted the best performance giving 27.56 g L-1 h(-1) productivity (RT = 2 min</p>
10aCaptureSMB10acontinuous chromatography10aMathematical modeling10aProtein A10asimulation1 aDeulgaonkar, Prashant1 aBhambure, Rahul1 aPrasad, Bhaskarjyoti1 aMishra, Ashok1 aTiwari, Sanjay1 aMody, Rustom  uhttps://library.ncl.res.in/content/mechanistic-modeling-continuous-capture-step-purification-biosimilar-monoclonal-antibody