The continued use of animal serum as an important component in biotechnology manufacturing processes has raised questions regarding both the reliability of geographic origin and possible adulteration of product. The International Serum Industry Association (ISIA) has implemented a traceability certification program designed to demonstrate traceability from slaughterhouse or abattoir to the end-user. This is based on an audit performed by an independent, approved third-party auditor according to an approved audit plan, using a detailed audit checklist. Recent advances have led to the development of a complementary testing program to determine geographic origin of material. The methodology described in this paper differentiates fetal bovine serum from newborn calf serum on the basis of biochemical composition…
BioProcessing Journal Posts
Glycosylation drives protein quality and therapeutic benefits in protein-based therapies. Recently, there has been a push in the pharmaceutical industry to improve the consistency and quality of the glycan patterns on therapeutic proteins like monoclonal antibodies. Post-translational modification begins in the endoplasmic reticulum but is finished in the Golgi where more complex glycans are added. In this study, the addition of lipids via a novel mechanism provided by the medium supplement, Cell-Ess®, improves the consistency in glycan patterns so that they are more reproducible between product batches. The effect of media supplementation with Cell-Ess on the variation of glycan patterns was measured in two different media formulations across two separate experiments. Supplementation with Cell-Ess resulted in a statistically significant reduction in the variation of glycoforms when measured by the standard error of the mean. In addition, to improved consistency, there were increased higher glycoforms or galactosylation. There was also significantly more total galactosylation and significantly fewer lower glycoforms for antibodies produced by CHO cells supplemented with Cell-Ess. These data taken together suggest that the addition of lipids via Cell-Ess results in a more functional Golgi and an associated improvement of protein quality and consistency…
Biopharmaceutical manufacturing process risks can be described as a network of processes that may include some combination of unit operations, equipment, instruments, control systems, procedures, and personnel practices. The system’s risks can be modelled by a system risk structure (SRS) that describes how threats originate and flow through the network to result in negative consequences (risks). The SRS is a quality risk management (QRM) tool a team of subject matter experts can use to prospectively identify and evaluate a wide variety of risks over the product’s entire development and manufacturing lifecycle. Based on the understanding developed from an SRS analysis, control strategies can be developed by modifying or adding new processes to mitigate the threats, thus reducing the likelihood of the risk consequence being realized. The SRS tool extends the ICH Q9 QRM approach described in a series of articles. Two examples are used to demonstrate how an SRS can be assembled and then used to prospectively identify, understand, and reduce significant risks by controlling the source and flow of threats within the systems described…
I am not particularly spiritual. But as the shadow of the moon began to approach on August 21, 2017, I started to think about the limits of my knowledge of the solar system, galaxies, and beyond, compared to my focus for decades on smaller objects, such as molecules, cells, and tissues. A week later, Hurricane Harvey developed rapidly and lingered over southeast Texas, dumping over 50 inches of rain in some locations, causing widespread flooding, releasing sewage and toxic chemicals into the water, and triggering outbreaks of mold. Hurricane Irma, on its path through the Caribbean and up the western coast of Florida, devastated vegetation, buildings, and power lines, with intense winds that once peaked at 185 mph…
Because the Lambda MINIFOR bioreactor provides good mixing of cell culture, nutrients, and gases without any damaging hydrodynamic forces by using a bio-mimicking “fish-tail“ disc stirrer, it can be successfully applied for the cultivation of bacteria and yeast, insect, plant, and mammalian cells. However, reports on its application in mouse hybridoma cell culture using protein-free media is non-existent in the scientific literature. Therefore, this study describes preliminary findings of the Lambda MINIFOR bioreactor suitability in mouse hybridoma cell culture and antibody production using the SP2/O-Ag14-CB.Hep-1 mouse hybridoma cell and the PFHM-II protein-free medium as models. Results verified 2.45 × 106 viable cells/mL as the highest cell concentration, 86% as maximum cell viability, 0.0156/h as the exponential growth rate, 44 h as cell population doubling time, a stable phenotype measured by limiting dilution after 2.5 months, no antibiotic and antifoam requirements, 71.4% of IgG SDS-PAGE purity in the cell culture harvested supernatant, 38.68 ± 22.29 µg/mL, 39.23 ± 10.66 pg/cell/day, up to 99.5% of purity (sample measured by SDS-PAGE and SE-HPLC) after an IgG capture step based on protein A-Sepharose, a low pH incubation, and size-exclusion chromatography, no molecule aggregation, specificity for the CKTCTT epitope (located in the HBsAg “a” determinant), an IgG affinity constant equal to 1.11 × 1010 M-1, and < 78 pg mouse DNA/mg of IgG. In conclusion, this study corroborated a cumulative CB.Hep-1 mAb production of 1.77 g/15 days and validated the usefulness of the Lambda MINIFOR bioreactor in mouse hybridoma cell culture in protein-free media for research applications...
Automation in bioprocessing was a keynote topic at the ISBioTech 4th Annual Fall Meeting (December 12–14, 2016) in Virginia Beach, VA. Automation is becoming increasingly critical as biomanufacturers seek to improve their production efficiency and critical risk analysis, and reduce errors. But despite recent improvements and innovations, the actual integration of devices, software, sensors, and production equipment remains a challenge. In BioPlan Associates’ recent analysis of capacity and production, we found that nearly 20% of the biopharma industry sees increasing productivity and efficiency as the #1 critical issue the industry needs to focus on today. And over two-thirds expect better control of their processes. An obvious way to achieve these goals is through automation…
The same sensor instrumentation can now be used in process development laboratories, clinical trials, pilot plants, and large-scale manufacturing — thus simplifying product development, scale-up, and regulatory record-keeping…
In a world already awash with technology, life sciences companies are racing to add more automation and data sources, while ironically often spending less time focused on process improvements. In some cases, these two opposing actions can still produce positive results by: (a) reducing manual labor to minimize data translation errors; (b) adding sensors to gather a new kind of data about a protein or a process; or (c) implementing high-throughput techniques for biopharmaceutical development. But what about those situations where collecting new data is not so positive? Does it really make sense to run experiments without the full benefits of accessing accumulated data or gathering new data? Or to proceed without the insights gained from a colleague down the hall or at another site working on a related project? The difficulty in realizing these potential data analytics benefits often arises because more sensors tend to produce large, complex datasets with multivariate interactions. Further, the inherently complex nature of these datasets makes extraction of meaningful and relevant information a challenging task. This is where a streamlined data analytics methodology can help by providing the foundation to realize the benefits from all of this new data. This article illustrates how a comprehensive data analytics methodology can be used to develop insight into life sciences lab and production data, leading to improved operations. The focus is on sharing lessons learned from recent pharmaceutical case studies to illustrate how to drive innovation through use of a data analytics methodology. These case studies provide detailed, data-driven examples illustrating how to utilize a data analytics methodology to uncover important issues related to pharmaceutical development…