Enabler for process analytical technology implementation in Pichia pastoris fermentation: Fluorescence-based soft sensors for rapid quantitation of product titer

Rapid total sialic acid monitoring during cell culture process using a machine learning model based soft sensor

Total sialic acid content (TSA) in biotherapeutic proteins is often a critical quality attribute as it impacts the drug efficacy. Traditional wet chemical assays to quantify TSA in biotherapeutic proteins during cell culture typically takes several hours or longer due to the complexity of the assay which involves isolation of sialic acid from the protein of interest, followed by sample preparation and chromatographic based separation for analysis. Here, we developed a machine learning model-based technology to rapidly predict TSA during cell culture by using typically measured process parameters. The technology features a user interface, where the users only have to upload cell culture process parameters as input variables and TSA values are instantly displayed on a dashboard platform based on the model predictions. In this study, multiple machine learning algorithms were assessed on our dataset, with the Random Forest model being identified as the most promising model. Feature importance analysis from the Random Forest model revealed that attributes like viable cell density (VCD), glutamate, ammonium, phosphate, and basal medium type are critical for predictions. Notably, while the model demonstrated strong predictability by Day 14 of observation, challenges remain in forecasting TSA values at the edges of the calibration range. This research not only emphasizes the transformative power of machine learning and soft sensors in bioprocessing but also introduces a rapid and efficient tool for sialic acid prediction, signaling significant advancements in bioprocessing. Future endeavors may focus on data augmentation to further enhance model precision and exploration of process control capabilities.

Automation of Dead End Filtration: An Enabler for Continuous Processing of Biotherapeutics

Dead end filtration is a critical unit operation that is used for primary and secondary clarification during manufacturing of both microbial and mammalian cell based biotherapeutics. Dead end filtration is conventionally done in batch mode and requires filter pre-sizing using extensive scouting studies, along with filter over-sizing before deployment to handle potential variability. However, continuous manufacturing processes require consistent use of dead-end filtration over weeks or months, with potential unpredictable variations in feed stream attributes, which is a challenge currently facing the industry. In this work, a dead-end filtration skid is designed for continuous depth filtration, incorporating multiple small-sized filters along with turbidity, and pressure sensors with immediate switching to a fresh filter whenever turbidity or pressure breakthrough above a pre-determined cut-off is detected in real time. The skid has been successfully tested for manufacturing of granulocyte colony stimulating factor from Escherichia coli, human serum albumin from Pichia pastoris, and a monoclonal antibody therapeutic from CHO cells. The proposed skid can be directly applied for any dead-end filtration application with minimal prior scouting studies or sizing calculations for scale-up. It is a useful solution for continuous processing trains where the nature of the feed, such as its turbidity or host cell proteins content, may change over long continuous campaigns, rendering previous sizing calculations inaccurate. The skid also allows significant cost savings by eliminating the sizing safety factor of 1.5-2x which is generally added before filter deployment at manufacturing scale.

Real-time dissolved carbon dioxide monitoring II: Surface aeration intensification for efficient CO2 removal in shake flasks and mini-bioreactors leads to superior growth and recombinant protein yields

Mass transfer is known to play a critical role in bioprocess performance and henceforth monitoring dissolved O2 (DO) and dissolved CO2 (dCO2 ) is of paramount importance. At bioreactor level these parameters can be monitored online and can be controlled by sparging air/oxygen or stirrer speed. However, traditional small-scale systems such as shake flasks lack real time monitoring and also employ only surface aeration with additional diffusion limitations imposed by the culture plug. Here we present implementation of intensifying surface aeration by sparging air in the headspace of the reaction vessel and real-time monitoring of DO and dCO2 in the bioprocesses to evaluate the impact of intensified surface aeration. We observed that sparging air in the headspace allowed us to keep dCO2 at low level, which significantly improved not only biomass growth but also protein yield. We expect that implementing such controlled smart shake flasks can minimize the process development gap which currently exists in shake flask level and bioreactor level results.

Real-time dissolved carbon dioxide monitoring I: Application of a novel in situ sensor for CO2 monitoring and control

Dissolved carbon dioxide (dCO2 ) is a well-known critical parameter in bioprocesses due to its significant impact on cell metabolism and on product quality attributes. Processes run at small-scale faces many challenges due to limited options for modular sensors for online monitoring and control. Traditional sensors are bulky, costly, and invasive in nature and do not fit in small-scale systems. In this study, we present the implementation of a novel, rate-based technique for real-time monitoring of dCO2 in bioprocesses. A silicone sampling probe that allows the diffusion of CO2 through its wall was inserted inside a shake flask/bioreactor and then flushed with air to remove the CO2 that had diffused into the probe from the culture broth (sensor was calibrated using air as zero-point calibration). The gas inside the probe was then allowed to recirculate through gas-impermeable tubing to a CO2 monitor. We have shown that by measuring the initial diffusion rate of CO2 into the sampling probe we were able to determine the partial pressure of the dCO2 in the culture. This technique can be readily automated, and measurements can be made in minutes. Demonstration experiments conducted with baker's yeast and Yarrowia lipolytica yeast cells in both shake flasks and mini bioreactors showed that it can monitor dCO2 in real-time. Using the proposed sensor, we successfully implemented a dCO2 -based control scheme, which resulted in significant improvement in process performance.