8 research outputs found

    Plant cell culture platforms for production of bioscavengers for biodefense

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    There is a critical need for flexible, rapid, cost effective biomanufacturing platforms for medical countermeasures. Our team has developed plant cell culture-based manufacturing platforms for production of recombinant protein bioscavengers against organophosphate (OP) nerve agents and anthrax toxins using both stable transgenic cell cultures for known chemical and biological threats, as well as transient production for rapid response to new and/or unanticipated threats. Plant cells offer several advantages over other hosts for production of medical countermeasures, particularly their ability to produce complex biologics and perform post-translational modification, inherent biosafety since they don\u27t harbor or propagate mammalian viruses thereby simplifying and/or eliminating viral clearance steps required for mammalian production systems. Plant cells are robust, have minimal nutrient requirements (grow in simple, chemically defined media containing sucrose, salts and plant hormones), and are relatively insensitive to changes in environmental conditions. These characteristics, robustness of upstream cultivation/use and reduced downstream purification requirements, make plant cells an ideal choice for field-deployable production of medical countermeasures. Here we present results for the production of functional recombinant butyrylcholinesterase (BChE), an OP nerve agent bioscavenger, in transgenic rice cell suspension cultures in different bioreactor configurations, and transient production of a bioscavenger against an anthrax toxin in N. benthamiana cell cultures. Techno-economic models for scaled-up versions of these plant cell culture production systems will also be presented

    Analysis of Variability of Functionals of Recombinant Protein Production Trajectories Based on Limited Data

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    Making statistical inference on quantities defining various characteristics of a temporally measured biochemical process and analyzing its variability across different experimental conditions is a core challenge in various branches of science. This problem is particularly difficult when the amount of data that can be collected is limited in terms of both the number of replicates and the number of time points per process trajectory. We propose a method for analyzing the variability of smooth functionals of the growth or production trajectories associated with such processes across different experimental conditions. Our modeling approach is based on a spline representation of the mean trajectories. We also develop a bootstrap-based inference procedure for the parameters while accounting for possible multiple comparisons. This methodology is applied to study two types of quantities—the “time to harvest” and “maximal productivity”—in the context of an experiment on the production of recombinant proteins. We complement the findings with extensive numerical experiments comparing the effectiveness of different types of bootstrap procedures for various tests of hypotheses. These numerical experiments convincingly demonstrate that the proposed method yields reliable inference on complex characteristics of the processes even in a data-limited environment where more traditional methods for statistical inference are typically not reliable

    Analysis of Variability of Functionals of Recombinant Protein Production Trajectories Based on Limited Data.

    No full text
    Making statistical inference on quantities defining various characteristics of a temporally measured biochemical process and analyzing its variability across different experimental conditions is a core challenge in various branches of science. This problem is particularly difficult when the amount of data that can be collected is limited in terms of both the number of replicates and the number of time points per process trajectory. We propose a method for analyzing the variability of smooth functionals of the growth or production trajectories associated with such processes across different experimental conditions. Our modeling approach is based on a spline representation of the mean trajectories. We also develop a bootstrap-based inference procedure for the parameters while accounting for possible multiple comparisons. This methodology is applied to study two types of quantities-the "time to harvest" and "maximal productivity"-in the context of an experiment on the production of recombinant proteins. We complement the findings with extensive numerical experiments comparing the effectiveness of different types of bootstrap procedures for various tests of hypotheses. These numerical experiments convincingly demonstrate that the proposed method yields reliable inference on complex characteristics of the processes even in a data-limited environment where more traditional methods for statistical inference are typically not reliable
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