Synergic kinetic and physiological control to improve the efficiency of Komagataella phaffii recombinant protein production bioprocesses.

The yeast Komagataella phaffii (Pichia pastoris) is currently considered a versatile and highly efficient host for recombinant protein production (RPP). Interestingly, the regulated application of specific stress factors as part of bioprocess engineering strategies has proven potential for increasing the production of recombinant products. This study aims to evaluate the impact of controlled oxygen-limiting conditions on the performance of K. phaffii bioprocesses for RPP in combination with the specific growth rate (µ) in fed-batch cultivations. In this work, Candida rugosa lipase 1 (Crl1) production, regulated by the constitutive GAP promoter, growing at different nominal µ (0.030, 0.065, 0.100 and 0.120 h-1 ) under both normoxic and hypoxic conditions in carbon-limiting fed-batch cultures is analysed. Hypoxic fermentations were controlled at a target respiratory quotient (RQ) of 1.4, with excellent performance, using an innovative automated control based on the stirring rate as the manipulated variable developed during this study. The results conclude that oxygen limitation positively affects bioprocess efficiency under all growing conditions compared. The shift from respiratory to respiro-fermentative metabolism increases bioprocess productivity by up to twofold for the specific growth rates evaluated. Moreover, the specific product generation rate (qp ) increases linearly with µ, regardless of oxygen availability. Furthermore, this hypoxic boosting effect was also observed in the production of Candida antarctica lipase B (CalB) and pro-Rhizopus oryzae lipase (proRol), thus proving the synergic effect of kinetic and physiological stress control. Finally, the Crl1 production scale-up was conducted successfully, confirming the strategy's scalability and the robustness of the results obtained at the bench-scale level.

CPV of the Future: AI-Powered Continued Process Verification for Bioreactor Processes.

According to the standard guidelines by the FDA, process validation in biopharma manufacturing encompasses a life cycle consisting of three stages: process design (PD), process qualification (PQ), and continued process verification (CPV). The validity and efficiency of the analytics methods employed during the CPV require extensive knowledge of the process. However, for new processes and new drugs, such knowledge is often not available from Process performance qualification and Validation (PPQV). In this work, the suitability of methods based on machine learning/artificial intelligence (ML/AI) for the CPV applied in bioprocess monitoring and cell physiological control of the yeast Pichia pastoris (Komagataella phaffii) was studied with limited historical data. In particular, the production of recombinant Candida rugosa lipase 1 (Crl1) under hypoxic conditions in fed-batch cultures was considered as a case study. Supervised and unsupervised machine learning models using data from fed-batch bioprocesses with different gene dosage clones under normoxic and hypoxic conditions were evaluated. Firstly, a multivariate anomaly detection (isolation forest) model was applied to the batch phase of the bioprocess. Secondly, a supervised random forest model for prediction of required operator's control actions during the semiautomated fed-batch phase under hypoxic conditions was assessed to maintain the respiratory quotient (RQ) within the desired range for maximizing the specific production rate (qP ). The performance of these models was tested on historical data using independent evaluation of the process by the process control engineer (subject matter expert-SME), and on real-time data in the case of manual action prediction, where the model was implemented to guide the control of the bioprocess. The work presented here constitutes a proof-of-concept that multivariate analytics methods, based on machine learning, can be a valuable tool for real-time monitoring and control of biopharma manufacturing bioprocesses to improve its efficiency and to assure product quality.

Innovative Bioprocess Strategies Combining Physiological Control and Strain Engineering of Pichia pastoris to Improve Recombinant Protein Production.

The combination of strain and bioprocess engineering strategies should be considered to obtain the highest levels of recombinant protein production (RPP) while assuring product quality and process reproducibility of heterologous products. In this work, two complementary approaches were investigated to improve bioprocess efficiency based on the yeast P. pastoris. Firstly, the performance of two Candida rugosa lipase 1 producer clones with different gene dosage under the regulation of the constitutive P GAP were compared in chemostat cultures with different oxygen-limiting conditions. Secondly, hypoxic conditions in carbon-limited fed-batch cultures were applied by means of a physiological control based on the respiratory quotient (RQ). Stirring rate was selected to maintain RQ between 1.4 and 1.6, since it was found to be the most favorable in chemostat. As the major outcome, between 2-fold and 4-fold higher specific production rate (q P ) values were observed when comparing multicopy clone (MCC) and single-copy clone (SCC), both in chemostat and fed-batch. Additionally, when applying oxygen limitation, between 1.5-fold and 3-fold higher q P values were obtained compared with normoxic conditions. Thus, notable increases of up to 9-fold in the production rates were reached. Furthermore, transcriptional analysis of certain key genes related to RPP and central carbon metabolism were performed. Results seem to indicate the presence of a limitation in post-transcriptional protein processing steps and a possible transcription attenuation of the target gene in the strains with high gene dosage. The entire approach, including both strain and bioprocess engineering, represents a relevant novelty involving physiological control in Pichia cell factory and is of crucial interest in bioprocess optimization, boosting RPP, allowing bioproducts to be economically competitive in the market, and helping develop the bioeconomy.