Optimizing recombinant antibody fragment production: A comparison of artificial intelligence and statistical modeling.

Maximizing the recombinant protein yield necessitates optimizing the production medium. This can be done using a variety of methods, including the conventional "one-factor-at-a-time" approach and more recent statistical and mathematical methods such as artificial neural network (ANN), genetic algorithm, etc. Every approach has advantages and disadvantages of its own, yet even when a technique has flaws, it is nevertheless used to get the best results.

Genome-scale metabolic model-based engineering of Escherichia coli enhances recombinant single-chain antibody fragment production.

Purpose: Escherichia coli is an attractive and cost-effective cell factory for producing recombinant proteins such as single-chain variable fragments (scFvs). AntiEpEX-scFv is a small antibody fragment that has received considerable attention for its ability to target the epithelial cell adhesion molecule (EpCAM), a cancer-associated biomarker of solid tumors. Due to its metabolic burden, scFv recombinant expression causes a remarkable decrease in the maximum specific growth rate of the scFv-producing strain.

Machine learning modeling for solubility prediction of recombinant antibody fragment in four different E. coli strains.

The solubility of proteins is usually a necessity for their functioning. Recently an emergence of machine learning approaches as trained alternatives to statistical models has been evidenced for empirical modeling and optimization. Here, soluble production of anti-EpCAM extracellular domain (EpEx) single chain variable fragment (scFv) antibody was modeled and optimized as a function of four literature based numerical factors (post-induction temperature, post-induction time, cell density of induction time, and inducer concentration) and one categorical variable using artificial neural network (ANN) and response surface methodology (RSM).

RSM-based Model to Predict Optimum Fermentation Conditions for Soluble Expression of the Antibody Fragment Derived from 4D5MOC-B Humanized Mab in SHuffle™ T7 .

Overexpression of the EpCAM in epithelial-derived neoplasms makes this receptor a promising target in antibody-based therapy. Due to the lack of N-glycosylation, () seems to be the most appropriate choice for the expression of antibody fragments. However, developing a robust and cost-effective process that produces consistent therapeutic proteins from inclusion bodies is a major challenge.

Statistical optimization of culture conditions for expression of recombinant humanized anti-EpCAM single-chain antibody using response surface methodology.

Background And Purpose: The epithelial cell adhesion molecule (EpCAM), is one of the first cancer- associated markers discovered. Its overexpression in cancer stem cells, epithelial tumors, and circulating tumor cells makes this molecule interesting for targeted cancer therapy. So, in recent years scFv fragments have been developed for EpCAM targeting.

Solubility assessment of single-chain antibody fragment against epithelial cell adhesion molecule extracellular domain in four Escherichia coli strains.

Background: Overexpression of the EpCAM (epithelial cell adhesion molecule) in malignancies makes it an attractive target for passive immunotherapy in a wide range of carcinomas. In comparison with full-length antibodies, due to the small size, the scFvs (single-chain variable fragments) are more suitable for recombinant expression in E. coli (Escherichia coli).