2020
DOI: 10.1002/btpr.3062
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A rational approach to improving titer in Escherichia coli‐based cell‐free protein synthesis reactions

Abstract: Cell-free protein synthesis (CFPS) is an established method for rapid recombinant protein production. Advantages like short synthesis times and an open reaction environment make CFPS a desirable platform for new and difficult-to-express products. Most recently, interest has grown in using the technology to make larger amounts of material. This has been driven through a variety of reasons from making site specific antibody drug conjugates, to emergency response, to the safe manufacture of toxic biological produ… Show more

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Cited by 11 publications
(18 citation statements)
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References 70 publications
(167 reference statements)
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“…However, concerning the optimization of CFPS yields, it must be mentioned that selected parameters could be specific for the target protein, and this represents a form of overfitting that can be solved only by performing more experiments using other target proteins [141]. Concerning the optimization of CFPS yields in a multidimensional experimental space, there are also examples where the multivariate statistical analysis was applied, requiring less computational resources [144][145][146]. For instance, multivariate statistical data analysis (MSDA) was applied to predict the yield of monoclonal antibodies (MAbs) using a scalable cell-free expression system developed by Sutro Biopharma [144].…”
Section: Utilizing Machine Learning In Cell-freementioning
confidence: 99%
“…However, concerning the optimization of CFPS yields, it must be mentioned that selected parameters could be specific for the target protein, and this represents a form of overfitting that can be solved only by performing more experiments using other target proteins [141]. Concerning the optimization of CFPS yields in a multidimensional experimental space, there are also examples where the multivariate statistical analysis was applied, requiring less computational resources [144][145][146]. For instance, multivariate statistical data analysis (MSDA) was applied to predict the yield of monoclonal antibodies (MAbs) using a scalable cell-free expression system developed by Sutro Biopharma [144].…”
Section: Utilizing Machine Learning In Cell-freementioning
confidence: 99%
“…This means that multiple products can be produced at the same time. There is no need to fundamentally change the other reaction components, namely, the cell extract and the concentrated reaction mixture, although some minor modifications to other process parameters may allow for dramatic improvements in titer [19].…”
Section: Figurementioning
confidence: 99%
“…The cell extract was prepared using BL21 Star TM (DE3) (Thermo Fisher Scientific, Paisley, UK) E. coli cells according to the protocol in Colant et al, 2020 [19]. When OD 600 ≈ 0.6 was achieved, 500 µL of 1M IPTG was used to induced T7 RNA polymerase expression.…”
Section: Extract Preparationmentioning
confidence: 99%
“…24,25 Additionally, the present work aims to provide a generic methodology that allows rapid process characterization and optimization to the manufacturing of other therapeutic proteins and biopolymers by developing data-based relationships between process parameters and process outputs such as yield, which are likely to be highly dependent on the cell extract type, target protein, construct design, and so forth. 26 Section 2 describes the methodology that was followed when applying the MSDA techniques, with Section 3 providing the methodology for operating the laboratory equipment and collecting data. Section 4 presents the results and provides a discussion and finally, Section 5 provides the conclusions and the authors' perspective on the use of MSDA techniques as tools for optimizing the performance of CFPS processes.…”
Section: Introductionmentioning
confidence: 99%
“…The specific case study for the analyses presented in this article is the scalable cell‐free synthesis of monoclonal antibodies (mAb), using the cell‐free lysate system developed by Sutro Biopharma 24,25 . Additionally, the present work aims to provide a generic methodology that allows rapid process characterization and optimization to the manufacturing of other therapeutic proteins and biopolymers by developing data‐based relationships between process parameters and process outputs such as yield, which are likely to be highly dependent on the cell extract type, target protein, construct design, and so forth 26 …”
Section: Introductionmentioning
confidence: 99%