Arginine as an eluent overcomes the hindrance of monoclonal antibody quantification by dextran sulfate in protein A affinity chromatography

The downstream bioprocess toolbox for therapeutic viral vectors

Viral vectors are poised to acquire a prominent position in modern medicine and biotechnology owing to their role as delivery agents for gene therapies, oncolytic agents, vaccine platforms, and a gateway to engineer cell therapies as well as plants and animals for sustainable agriculture. The success of viral vectors will critically depend on the availability of flexible and affordable biomanufacturing strategies that can meet the growing demand by clinics and biotech companies worldwide. In this context, a key role will be played by downstream process technology: while initially adapted from protein purification media, the purification toolbox for viral vectors is currently undergoing a rapid expansion to fit the unique biomolecular characteristics of these products. Innovation efforts are articulated on two fronts, namely (i) the discovery of affinity ligands that target adeno-associated virus, lentivirus, adenovirus, etc.; (ii) the development of adsorbents with innovative morphologies, such as membranes and 3D printed monoliths, that fit the size of viral vectors. Complementing these efforts are the design of novel process layouts that capitalize on novel ligands and adsorbents to ensure high yield and purity of the product while safeguarding its therapeutic efficacy and safety; and a growing panel of analytical methods that monitor the complex array of critical quality attributes of viral vectors and correlate them to the purification strategies. To help explore this complex and evolving environment, this study presents a comprehensive overview of the downstream bioprocess toolbox for viral vectors established in the last decade, and discusses present efforts and future directions contributing to the success of this promising class of biological medicines.

A genome-scale nutrient minimization forecast algorithm for controlling essential amino acid levels in CHO cell cultures

Mammalian cell culture processes rely heavily on empirical knowledge in which process control remains a challenge due to the limited characterization/understanding of cell metabolism and inability to predict the cell behaviors. This study facilitates control of Chinese hamster ovary (CHO) processes through a forecast-based feeding approach that predicts multiple essential amino acids levels in the culture from easily acquired viable cell density data. Multiple cell growth behavior forecast extrapolation approaches are considered with logistic curve fitting found to be the most effective. Next, the nutrient-minimized CHO genome-scale model is combined with the growth forecast model to generate essential amino acid forecast profiles of multiple CHO batch cultures. Comparison of the forecast with the measurements suggests that this algorithm can accurately predict the concentration of most essential amino acids from cell density measurement with error mitigated by incorporating off-line amino acids concentration measurements. Finally, the forecast algorithm is applied to CHO fed-batch cultures to support amino acid feeding control to control the concentration of essential amino acids below 1-2 mM for lysine, leucine, and valine as a model over a 9-day fed batch culture while maintaining comparable growth behavior to an empirical-based culture. In turn, glycine production was elevated, alanine reduced and lactate production slightly lower in control cultures due to metabolic shifts in branched-chain amino acid degradation. With the advantage of requiring minimal measurement inputs while providing valuable and in-advance information of the system based on growth measurements, this genome model-based amino acid forecast algorithm represent a powerful and cost-effective tool to facilitate enhanced control over CHO and other mammalian cell-based bioprocesses.