Search for: All records

ABSTRACT Recombinant adeno associated virus (rAAV) vectors have become popular delivery vehicles for in vivo gene therapies, but demand for rAAVs continues to outpace supply. Platform processes for rAAV production are being developed by many manufacturers, and transient chemical transfection of human embryonic kidney 293 (HEK293) cells is currently the most popular approach. However, the cutting edge nature of rAAV process development encourages manufacturers to keep cell culture media formulations, plasmid sequences, and other details proprietary, which creates hurdles for small companies and academic labs seeking to innovate in this space. To address this problem, we leveraged the resources of an academic‐industry consortium (Advanced Mammalian Biomanufacturing Innovation Center, AMBIC) to develop an rAAV production system based on transient transfection of suspension HEK293 cells adapted to an in‐house, chemically defined medium. We found that balancing iron and calcium levels in the medium were crucial for maintaining transfection efficiency and minimizing cell aggregation, respectively. A design of experiments approach was used to optimize the transient transfection process for batch rAAV production, and PEI:DNA ratio and cell density at transfection were the parameters with the strongest effects on vector genome (VG) titer. When the optimized transient process was transferred between two university sites, VG titers were within a twofold range. Analytical characterization showed that purified rAAV from the AMBIC process had comparable viral protein molecular weights versus vector derived from commercial processes, but differences in transducing unit (TU) titer were observed between vector preps. The developed media formulation, transient transfection process, and analytics for VG titer, capsid identity, and TU titer constitute a set of workflows that can be adopted by others to study fundamental problems that could improve product yield and quality in the nascent field of rAAV manufacturing. 

AbstractThe pharmaceutical industry employs various strategies to improve cell productivity. These strategies include process intensification, culture media improvement, clonal selection, media supplementation and genetic engineering of cells. However, improved cell productivity has inherent risk of impacting product quality attributes (PQA). PQAs may affect the products’ efficacy via stability, bioavailability, or in vivo bioactivity. Variations in manufacturing process may introduce heterogeneity in the products by altering the type and extent of N-glycosylation, which is a PQA of therapeutic proteins. We investigated the effect of different cell densities representing increasing process intensification in a perfusion cell culture on the production of an IgG1-κ monoclonal antibody from a CHO-K1 cell line. This antibody is glycosylated both on light chain and heavy chain. Our results showed that the contents of glycosylation of IgG1-κ mAb increased in G0F and fucosylated type glycans as a group, whereas sialylated type glycans decreased, for the mAb whole protein. Overall, significant differences were observed in amounts of G0F, G1F, G0, G2FS1, and G2FS2 type glycans across all process intensification levels. G2FS2 and G2 type N-glycans were predominantly quantifiable from light chain rather than heavy chain. It may be concluded that there is a potential impact to product quality attributes of therapeutic proteins during process intensification via perfusion cell culture that needs to be assessed. Since during perfusion cell culture the product is collected throughout the duration of the process, lot allocation needs careful attention to process parameters, as PQAs are affected by the critical process parameters (CPPs). Key points• Molecular integrity may suffer with increasing process intensity.• Galactosylated and sialylated N-glycans may decrease.• Perfusion culture appears to maintain protein charge structure. 

Efficaciously assessing product quality remains time- and resource-intensive. Online Process Analytical Technologies (PATs), encompassing real-time monitoring tools and soft-sensor models, are indispensable for understanding process effects and real-time product quality. This research study evaluated three modeling approaches for predicting CHO cell growth and production, metabolites (extracellular, nucleotide sugar donors (NSD) and glycan profiles): Mechanistic based on first principle Michaelis-Menten kinetics (MMK), data-driven orthogonal partial least square (OPLS) and neural network machine learning (NN). Our experimental design involved galactose-fed batch cultures. MMK excelled in predicting growth and production, demonstrating its reliability in these aspects and reducing the data burden by requiring fewer inputs. However, it was less precise in simulating glycan profiles and intracellular metabolite trends. In contrast, NN and OPLS performed better for predicting precise glycan compositions but displayed shortcomings in accurately predicting growth and production. We utilized time in the training set to address NN and OPLS extrapolation challenges. OPLS and NN models demanded more extensive inputs with similar intracellular metabolite trend prediction. However, there was a significant reduction in time required to develop these two models. The guidance presented here can provide valuable insight into rapid development and application of soft-sensor models with PATs for ipurposes. Therefore, we examined three model typesmproving real-time product CHO therapeutic product quality. Coupled with emerging -omics technologies, NN and OPLS will benefit from massive data availability, and we foresee more robust prediction models that can be advantageous to kinetic or partial-kinetic (hybrid) models. 

Abstract A new biomanufacturing platform combining intracellular metabolic engineering of the oleaginous yeastYarrowia lipolyticaand extracellular bioreaction engineering provides efficient bioconversion of plant oils/animal fats into high‐value products. However, predicting the hydrodynamics and mass transfer parameters is difficult due to the high agitation and sparging required to create dispersed oil droplets in an aqueous medium for efficient yeast fermentation. In the current study, commercial computational fluid dynamic (CFD) solver Ansys CFX coupled with the MUSIG model first predicts two‐phase system (oil/water and air/water) mixing dynamics and their particle size distributions. Then, a three‐phase model (oil, air, and water) utilizing dispersed air bubbles and a polydispersed oil phase was implemented to explore fermenter mixing, gas dispersion efficiency, and volumetric mass transfer coefficient estimations (k_La). The study analyzed the effect of the impeller type, agitation speed, and power input on the tank's flow field and revealed that upward‐pumping pitched blade impellers (PBI) in the top two positions (compared to Rushton‐type) provided advantageous oil phase homogeneity and similar estimatedk_Lavalues with reduced power. These results show good agreement with the experimental mixing andk_Ladata.