Modeling and Optimization of Recombinant Tocilizumab Production From Pichia pastoris Using Response Surface Methodology and Artificial Neural Network

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Abstract
This study has demonstrated the optimization of the defined medium that significantly enhanced the production of recombinant monoclonal antibody (mAb) Tocilizumab (TCZ) as full‐length and Fab fragment from Pichia pastoris. Out of the four tested defined media, FM22 was found to be suitable for the growth of recombinant strains and antibody yield. Among the various carbon and nitrogen sources tested, mannitol and glycine, respectively, were found to be suitable for the enhanced production of full‐length TCZ. Similarly, sorbitol and ammonium sulfate were found to be suitable carbon and nitrogen sources, respectively, for enhanced production of Fab. The medium components that significantly influenced the production of TCZ were found to be mannitol, glycine, histidine, and K2SO4 and sorbitol, ammonium sulfate, KH2PO4, and CaSO4.2H2O for full‐length and Fab, respectively, using a two‐level factorial Plackett‐Burman design. The screened medium components were optimized using response surface methodology (Box‐Behnken Design). Artificial neural network (ANN) models combined with genetic algorithms (GA) further improved predictions and showed a remarkable impact on mAb production in P. pastoris. Under the optimal levels of medium components, the full‐length TCZ and Fab were determined to be 0.35 mg/L and 0.42 g/L, respectively, in the shake‐flask culture. The yield of full‐length TCZ and Fab in batch reactor (2‐L culture) was found to be 0.44 mg/L and 0.45 g/L, respectively, at the optimal levels of the medium components. The overall increased yields were observed to be 3.8 and 2.9‐folds of full‐length TCZ and Fab, respectively.
Funding Information
  • Indian Council of Medical Research

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