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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.

 
Inactivation of Adventitious Agents by UVC Irradiation in a Plant-Based Influenza Vaccine Production Process

by Todd L. Talarico, Kevin Williams, Timothy Yeh, Bruno Pancorbo, Mélanie Bérubé, Michael Murphy, and Michèle Dargis
Volume 16, Issue 1 (Spring 2017)

Biologics are often produced in or derived from matrices that harbor the potential for introduction of adventitious agents to the drug product. This potential is not strictly theoretical, as viruses such as hepatitis B virus (HBV), hepatitis C virus (HCV), human immunodeficiency virus (HIV), porcine circovirus (PCV), and minute virus of mice (MVM) have been detected in biological products in the past. From a regulatory and safety perspective, assurance that adventitious agents are not present in the drug product is a critical measure of product quality. Guidelines for assuring safety, with respect to adventitious agents in blood-derived products and products produced in mammalian cell culture, are addressed in specific guidances from the Food and Drug Administration (FDA) and the Committee for Proprietary Medicinal Products (CPMP). These guidance documents suggest that safety is best assured through screening donor material or production cell lines, by controlling animal-derived raw materials used during manufacture, incorporating viral removal and inactivation steps in the production process, and protecting the product from the environment during manufacture. Even though Medicago develops products that are produced in plants, a host that does not support the replication of viruses that infect mammals, various regulatory agencies have advised that the production process should contain one or more operations that remove or inactivate adventitious agents. Medicago has investigated multiple methodologies to accomplish this goal, and has found ultraviolet C (UVC) irradiation treatment to be effective for adventitious agent inactivation in the production process used to manufacture their quadrivalent influenza vaccine without detrimental impact to the product...

Citation:
Talarico TL, Williams K, Yeh T, Pancorbo B, Bérubé M, Murphy M, Dargis M. Inactivation of adventitious agents by UVC irradiation in a plant-based influenza vaccine production process. BioProcess J, 2017; 16(1): 15–24. https://doi.org/10.12665/J161.Talarico.

Posted online May 8, 2017.

 
Identification of Worst-Case Model Viruses for Low and High pH Inactivation

by Raymond Nims, S. Steve Zhou, and Mark Plavsic
Volume 16, Issue 1 (Spring 2017)

In this paper, we review the efficacy data for low and high pH inactivation of viruses in solutions (i.e., liquid inactivation) and discuss the mechanisms of action and the impact of temperature and treatment time, as these are the primary determinants of inactivation efficacy, besides pH, for different viruses. Only enveloped viruses were considered for low pH inactivation, as the literature concerning low pH inactivation of non-enveloped virus is not extensive and low pH is not considered to be an effective inactivation approach for most non-enveloped viruses. We conclude that for low pH treatment of enveloped viruses, and high pH treatment of both enveloped and non-enveloped viruses, an enteric flavivirus such as bovine viral diarrhea virus represents a worst-case model virus...

Citation:
Nims R, Zhou SS, Plavsic M. Identification of worst-case model viruses for low and high pH inactivation. BioProcess J, 2017; 16(1): 7–14. https://doi.org/10.12665/J161.Nims.

Posted online May 8, 2017.

 
Opinion
The Key to Meeting Future Challenges: Forcing Complexity into Simplicity by Making it Straightforward

by Mark F. Witcher, PhD
Volume 16, Issue 1 (Spring 2017)

Unless the trend of ever-rising product development costs is reversed, as demonstrated by Eroom’s law (look it up), the development of new biopharmaceutical products is doomed. In my opinion, the primary culprit is the industry’s inability to competently deal with complexity...

Citation:
Witcher MF. The key to meeting future challenges: forcing complexity into simplicity by making it straightforward. BioProcess J, 2017; 16(1): 5. https://doi.org/10.12665/J161.Witcher.T.

Posted online May 8, 2017.

 
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