Use of Enzymes in the Downstream Processing of Biopharmaceuticals
Peter Grunwald in Pharmaceutical Biocatalysis, 2019
The majority of the enzymes used in these categories are hydrolases, which fall within the Enzyme Commission (EC) enzyme class 31 (Schomburg et al., 2014). This class of enzymes catalyzes the cleavage of hydrolytic bonds such as C–O, C–C, and C–N in a biomolecule to yield two smaller molecules. In the following sections, an outline of enzyme-assisted process steps within each of these categories is presented (Table 2.1). The focus is on operations that have gained industry acceptance and/or are routinely used in bioprocess engineering research.
Harnessing the potential of machine learning for advancing “Quality by Design” in biomanufacturing
Published in mAbs, 2022
Ian Walsh, Matthew Myint, Terry Nguyen-Khuong, Ying Swan Ho, Say Kong Ng, Meiyappan Lakshmanan
While ML models offer potential over conventional MVDA in identifying significant CPPs within an allowable range that affect CQAs with good accuracy, one notable limitation is the large data requirement for a model to be well-trained and able to produce desirable predictions on unseen data. Generation of large amounts of biomanufacturing data is highly challenging, as each bioprocessing campaign is quite expensive. Companies must invest in automated samplers, digitization, and high-throughput technologies to generate large amounts of data with minimal human effort. Moreover, substantial resources and investment are required to store the historical data in an organized manner so that it can be both expanded and continuously used to improve the model predictions successively. In order to achieve this goal, pharmaceutical companies are now investing in both digitization technologies and big data management services such as cloud data storage and Internet of Things (IoT).66 While investing in data accumulation over a period of time can reap benefits for individual players, establishing a consortium among both public and private biomanufacturing data generators could accelerate the pace at which data are generated and could benefit the wider community. Such efforts require the pharmaceutical companies to work together while still protecting their sensitive information. Academia must also play a role to release datasets for the public to use freely.
Stability of a high-concentration monoclonal antibody solution produced by liquid–liquid phase separation
Published in mAbs, 2021
Jack E. Bramham, Stephanie A. Davies, Adrian Podmore, Alexander P. Golovanov
Since the approval of the first recombinant protein therapeutic in 1982, biopharmaceutical proteins, including monoclonal antibodies (mAbs), have developed into critical treatments for a wide range of diseases.1–3 For the prolonged treatment of chronic conditions, such as arthritis and other autoimmune diseases, subcutaneous injection by the patient represents an attractive administration strategy for mAbs.4,5 Due to the limited volume (<2 mL) possible for injection into subcutaneous tissue,6 such strategies require high-concentration protein formulations, with protein concentrations typically greater than 100 mg/mL.7 Using high-concentrations solutions may also be beneficial during bioprocessing and manufacturing. However, achieving such high concentrations and stabilizing the final formulation against degradation remains challenging.8
Biomanufacturing evolution from conventional to intensified processes for productivity improvement: a case study
Published in mAbs, 2020
Jianlin Xu, Xuankuo Xu, Chao Huang, James Angelo, Christopher L. Oliveira, Mengmeng Xu, Xia Xu, Deniz Temel, Julia Ding, Sanchayita Ghose, Michael C. Borys, Zheng Jian Li
In comparison to the magnitude of upstream improvements in fed-batch titers, the overall batch downstream yield has increased, to a lesser extent, from approximately 35% to 70% (although 80% has been reported) in commercial manufacturing for the past decades.1 One common step for mAb capture is Protein A chromatography. Protein A loading capacity has been improved from <40 to 70–80 g/Lresin owing to advances in resin manufacturing technologies.28,29 The polishing chromatography steps have evolved from bind-elute to flow-through mode for streamlined operation and significantly improved throughput.30 Nonetheless, with a substantial increase in upstream titers, the batch downstream operation is still a major productivity bottleneck in bioprocessing, and accounts for a substantial portion of the overall biomanufacturing cost.6,8 Due mainly to its limited loading capacity and high cost, Protein A resins present a throughput challenge to batch downstream operation, especially when upstream titers exceed approximately 10 g/L.28
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