Engineering MVM Resistance in CHO Cells
by Joaquina Mascarenhas, Trissa Borgschulte, and Henry George
Volume 16, Issue 1 (Spring 2017)
For decades, Chinese hamster ovary (CHO) cells have proven to be indispensable for the biopharmaceutical manufacturing industry, serving as cell factories that reliably produce grams per liter of recombinant proteins with the appropriate post-translational modifications and protein folding. However, one of the challenges of working with mammalian cells is that they are susceptible to viral contamination. Although the adoption of a wide range of risk mitigation strategies has made viral contamination a rare event, staggering costs and a shortage of life-saving medicines can result when these prevention strategies do fail, as demonstrated by a number of high-profile contamination events within the industry...
Citation:
Mascarenhas J, Borgschulte T, George H. Engineering MVM resistance in CHO cells. BioProcess J, 2017; 16(1): 65–8. https://doi.org/10.12665/J161.Mascarenhas.
Posted online May 8, 2017.
Mouse Hybridoma Cell Culture in a Protein-Free Medium Using a Bio-Mimicking Fish-Tail Disc Stirred Bioreactor
by Rodolfo Valdés, Hasel Aragón, Marcos González, Daily Hernández, Déborah Geada, David Goitizolo, Williams Ferro, Adelma Pérez, José García, Yordanka Masforrol, Pedro Aguilar, Gabriel Márquez, Maylín LaO, Tatiana González, Yodelis Calvo, Alexander Hernández, Grechen Menéndez, and Andrés Tamayo
Volume 16, Issue 1 (Spring 2017)
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...
Citation:
Valdés R, Aragón H, González M, Hernández D, Geada D, Goitizolo D et al. Mouse hybridoma cell culture in a protein-free medium using a bio-mimicking fish-tail disc stirred bioreactor. BioProcess J, 2017; 16(1): 51–64. https://doi.org/10.12665/J161.Valdes.
Posted online May 8, 2017.
Trends in Bioprocess Automation: Quality Control and Process Analytical Technology in High Demand
by Kathleen Estes and Eric S. Langer
Volume 16, Issue 1 (Spring 2017)
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...
Citation:
Estes K, Langer ES. Trends in bioprocess automation: quality control and process analytical technology in high demand. BioProcess J, 2017; 16(1): 46–9. https://doi.org/10.12665/J161.Estes.
Posted online May 8, 2017.
Instrument Technology Moves into Bioprocess Development Laboratories
by Ravi Shankar
Volume 16, Issue 1 (Spring 2017)
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...
Citation:
Shankar R. Instrument technology moves into bioprocess development laboratories. BioProcess J, 2017; 16(1): 42–5. https://doi.org/10.12665/J161.Shankar.
Posted online May 8, 2017.
Deriving Insight at the Speed of Thought: Advancing Medicine Production Through an Effective Data Analytics Methodology
by Lisa J. Graham, PhD, PE
Volume 16, Issue 1 (Spring 2017)
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...
Citation:
Graham LJ. Deriving insight at the speed of thought: advancing medicine production through an effective data analytics methodology. BioProcess J, 2017; 16(1): 32–41. https://doi.org/10.12665/J161.Graham.
Posted online May 8, 2017.
Quality Risk Management (QRM): Part III – An Approach for Understanding and Either Accepting or Mitigating the Results of a QRM Analysis
by Mark F. Witcher, PhD
Volume 16, Issue 1 (Spring 2017)
Accepting any identified and evaluated risk is “taking a smart risk.” The acceptance decision, before or after mitigation, is a complex and sometimes difficult choice that is based on the information generated during the ICH Q9 quality risk management (QRM) exercise along with many subjective viewpoints impacted by previous experience, knowledge, risk appetite, and bias. This paper provides an approach for understanding and making acceptance decisions centered around the risk-rating methods that define the severity (harm) and uncertainty (likelihood) of the risk’s consequence occurring. It also builds on concepts developed in the first two parts of this QRM series to provide an overall framework for identifying, evaluating, managing, and accepting a wide variety of biopharmaceutical development and manufacturing risks...
Citation:
Witcher MF. Quality risk management (QRM): part III – an approach for understanding and either accepting or mitigating the results of a QRM analysis. BioProcess J, 2017; 16(1): 25–31. https://doi.org/10.12665/J161.Witcher.Q.
Posted online May 8, 2017.
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