This paper reviews the importance of maintaining low temperature storage and handling (i.e., cold chain) for animal serum through all stages of processing, from finished product to the actual end-user. This cold chain extends from serum manufacture through the irradiation process, during shipment back to the supplier post-irradiation, as well as storage at supplier, irradiation, and end-user facilities. Anecdotal experience and theoretical considerations emphasize the point that maintenance of the cold chain is necessary for preserving the performance of serum for cell culture and other applications…
Category: <span>Manufacturing</span>
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…
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…
Plantibody purification is not as efficient as antibody purification from serum, ascites, or mammalian cell cultures. It is characterized by the application of inefficient plantibody solid-liquid extraction systems, low plantibody recovery, and short lifetimes of expensive chromatography matrices. To overcome it, several protocols of liquid-liquid aqueous two-phase extraction (ATPE) combined with affinity chromatography were previously studied to purify the CB.Hep-1 monoclonal antibody, which showed an unexpectedly high recovery. However, a study of ATPE combined with several affinity chromatography matrices to purify plantibodies has not been reported so far. Therefore, a combination of the best ATPE protocol with five specific affinity chromatography matrices to purify a plantibody for vaccine manufacturing is described in this study. Positive outcomes from plantibody recovery (%), specific activity (%), yield (mg purified IgG/L of leaf extract), and productivity (mg purified IgG/L of leaf extract/h) were achieved. Plantibody purity did not show statistical differences among all samples (> 97%, p < 0.05), and protein A leakage was thousands of times smaller than toxic protein A for non-human primates. In summary, the combination of ATPE (10% PEG 4000/15% K2PO4, pH 5.5) with two specific affinity resins were well-suited for large-scale plantibody purification from tobacco plant leaves...
One of the objectives of the upcoming ICH 12: Pharmaceutical Lifecycle Management guidance is to manage product development and manufacturing process information in order to establish and maintain appropriate change control over the entire product lifecycle. The 2014 ICH Q12 Concept Paper also stresses ICH Q12’s role in connecting ICH Q8 through ICH Q11 into a complete lifecycle approach to assure product quality and continuous improvement of manufacturing operations. However, neither the Concept Paper or subsequent public discussion and presentations appear to identify the ICH Q8 design space as a central mechanism for collecting and maintaining product and process information…