Optimizing cell density of Pichia pastoris for production of recombinant hepatitis B surface antigen via employing short-period continuous operation

Accurate and cost-effective prediction of HBsAg titer in industrial scale fermentation process of recombinant Pichia pastoris by using neural network based soft sensor

In the current work, the attempt was made to apply best-fitted artificial neural network (ANN) architecture and the respective training process for predicting final titer of hepatitis B surface antigen (HBsAg), produced intracellularly by recombinant Pichia pastoris Mut⁺ in the commercial scale. For this purpose, in large-scale fed-batch fermentation, using methanol for HBsAg induction and cell growth, three parameters of average specific growth rate, biomass yield, and dry biomass concentration-in the definite integral form with respect to fermentation time-were selected as input vectors; the final concentration of HBsAg was selected for the ANN output. Used dataset consists of 38 runs from previous batches; feed-forward ANN 3:5:1 with training algorithm of backpropagation based on a Bayesian regularization was trained and tested with a high degree of accuracy. Implementing the verified ANN for predicting the HBsAg titer of the five new fermentation runs, excluded from the dataset, in the full-scale production, the coefficient of regression and root-mean-square error were found to be 0.969299 and 2.716774, respectively. These results suggest that this verified soft sensor could be an excellent alternative for the current relatively expensive and time-intensive analytical techniques such as enzyme-linked immunosorbent assay in the biopharmaceutical industry.

Application of recurrent neural network for online prediction of cell density of recombinant Pichia pastoris producing HBsAg

Artificial neural networking (ANN) seems to be a promising soft sensor for implementing current approaches of quality by design (QbD) and process analytical technologies (PAT) in the biopharmaceutical industry. In this study, we aimed to implement best-fitted ANN architecture for online prediction of the biomass amount of recombinant Pichia pastoris (P. pastoris) - expressing intracellular hepatitis B surface antigen (HBsAg) - during the fed-batch fermentation process using methanol as a sole carbon source. For this purpose, at the induction phase of methanol fed-batch fermentation, carbon evolution rate (CER), dissolved oxygen (DO), and methanol feed rate were selected as input vectors and total wet cell weight (WCW) was considered as output vector for the ANN. The obtained results indicated that after training recurrent ANN with data sets of four fed-batch runs, this toolbox could predict the WCW of the next fed-batch fermentation process at each specified time point with high accuracy. The R-squared and root-mean-square error between actual and predicted values were found to be 0.9985 and 13.73, respectively. This verified toolbox could have major importance in the biopharmaceutical industry since recombinant P. pastoris is widely used for the large-scale production of HBsAg.

Integration of size-exclusion chromatography and ultracentrifugation for purification of recombinant hepatitis B surface antigen: An alternative method for immunoaffinity chromatography

In purification process of recombinant hepatitis B surface antigen (rHBsAg), immunoaffinity chromatography (IAF) is one of the most important and effective steps in rHBsAg purification. However, the buffer composition and the interaction of ligands-rHBsAg often lead to disassembly, deformation, and clumping of a portion of these virus-like particles (VLPs). Besides, the expensive media, variable biospecific ligand density and the possibility of product contamination are other reported drawbacks of using IAF which makes the production process of rHBsAg more challenging. This study investigated the possibility of substituting IAF with purification methods of size-exclusion chromatography (SEC) and ultracentrifugation. In the SEC, the efficacy of rHBsAg purification was examined by four different media in which Toyopearl HW 65S resin demonstrated the best results. By integrating Toyopearl HW 65S resin - with a bed height of 51 cm - and ultracentrifugation process at 47,000 rpm for 48 hr, 95% of protein impurities were removed. Compared to the IAF in rHBsAg production, the purified sample contained a higher percentage of multimeric rHBsAg particles without any noticeable monomer and aggregate forms. The result of this study indicates that the proposed integrated system could be an efficient mild purification alternative for conventional IAF.