Literature DB >> 30027813

Rapid Microbiology Screening in Pharmaceutical Workflows.

C Surrette1, B Scherer1, A Corwin1, G Grossmann2, A M Kaushik3, K Hsieh3, P Zhang3, J C Liao4, P K Wong5, T H Wang3, C M Puleo1.   

Abstract

Recently advances in miniaturization and automation have been utilized to rapidly decrease the time to result for microbiology testing in the clinic. These advances have been made due to the limitations of conventional culture-based microbiology methods, including agar plate and microbroth dilution, which have long turnaround times and require physicians to treat patients empirically with antibiotics before test results are available. Currently, there exist similar limitations in pharmaceutical sterility and bioburden testing, where the long turnaround times associated with standard microbiology testing drive costly inefficiencies in workflows. These include the time lag associated with sterility screening within drug production lines and the warehousing cost and time delays within supply chains during product testing. Herein, we demonstrate a proof-of-concept combination of a rapid microfluidic assay and an efficient cell filtration process that enables a path toward integrating rapid tests directly into pharmaceutical microbiological screening workflows. We demonstrate separation and detection of Escherichia coli directly captured and analyzed from a mammalian (i.e., CHO) cell culture with a 3.0 h incubation. The demonstration is performed using a membrane filtration module that is compatible with sampling from bioreactors, enabling in-line sampling and process monitoring.

Entities:  

Keywords:  bacteria; bioburden; cell processing; cell therapy; microfluidic; pathogen; pharmaceutical; rapid microbiology; screening; sterility

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Year:  2018        PMID: 30027813      PMCID: PMC6363118          DOI: 10.1177/2472630318779758

Source DB:  PubMed          Journal:  SLAS Technol        ISSN: 2472-6303            Impact factor:   3.047


  2 in total

Review 1.  Sterility Testing for Cellular Therapies: What Is the Role of the Clinical Microbiology Laboratory?

Authors:  James E T Gebo; Anna F Lau
Journal:  J Clin Microbiol       Date:  2020-06-24       Impact factor: 5.948

2.  Raman spectra-based deep learning: A tool to identify microbial contamination.

Authors:  Murali K Maruthamuthu; Amir Hossein Raffiee; Denilson Mendes De Oliveira; Arezoo M Ardekani; Mohit S Verma
Journal:  Microbiologyopen       Date:  2020-10-16       Impact factor: 3.139

  2 in total

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