scholarly journals Developing an effective scale-down model for a suspension adapted HEK293T-derived lentiviral vector stable producer cell line

Author(s):  
Hamza Patel ◽  
Peter Archibald ◽  
Cindy Jung ◽  
Blair Roszell ◽  
Farlan Veraitch ◽  
...  

Lentiviral vectors (LVV) represent an important tool for vaccine development and other therapeutic modalities. However, inefficiencies in LVV manufacturing processes, such as the inability to achieve high cell densities with HEK293T cell lines in a fed batch process, have resulted in poor upstream yields. Optimisation of cell culture conditions is needed to improve upstream yields, which can be expedited by high-throughput screening (HTP). In this work, we describe the use of the 24 deep square well (24-DSW) microwell platform to develop a scale-down mimic of GSK’s established stable suspension LVV production process model at 2 L bioreactor scale. We found that matched mixing time was an effective basis for scale-translation between the stirred tank reactor (STR) and microwells. The growth kinetics and LVV productivity profile in the microwell were reproducible and comparable to the 2 L bioreactor process model. In both vessels, a 6-fold increase in cell density was achieved at the harvest time point and high cell viability (i.e. > 90 %) was also maintained throughout the entirety of the cultures. The 24-DSW model, therefore, is an effective scale-down model for larger-scale stirred-tank bioreactor culture and provides an important tool for rapid, high-throughput optimization of the LVV production process.

2020 ◽  
Author(s):  
Xinzhe Zhu ◽  
Chi-Hung Ho ◽  
Xiaonan Wang

<p><a></a><a>The production process of many active pharmaceutical ingredients such as sitagliptin could cause severe environmental problems due to the use of toxic chemical materials and production infrastructure, energy consumption and wastes treatment. The environmental impacts of sitagliptin production process were estimated with life cycle assessment (LCA) method, which suggested that the use of chemical materials provided the major environmental impacts. Both methods of Eco-indicator 99 and ReCiPe endpoints confirmed that chemical feedstock accounted 83% and 70% of life-cycle impact, respectively. Among all the chemical materials used in the sitagliptin production process, </a><a>trifluoroacetic anhydride </a>was identified as the largest influential factor in most impact categories according to the results of ReCiPe midpoints method. Therefore, high-throughput screening was performed to seek for green chemical substitutes to replace the target chemical (i.e. trifluoroacetic anhydride) by the following three steps. Firstly, thirty most similar chemicals were obtained from two million candidate alternatives in PubChem database based on their molecular descriptors. Thereafter, deep learning neural network models were developed to predict life-cycle impact according to the chemicals in Ecoinvent v3.5 database with known LCA values and corresponding molecular descriptors. Finally, 1,2-ethanediyl ester was proved to be one of the potential greener substitutes after the LCA data of these similar chemicals were predicted using the well-trained machine learning models. The case study demonstrated the applicability of the novel framework to screen green chemical substitutes and optimize the pharmaceutical manufacturing process.</p>


2020 ◽  
Author(s):  
Xinzhe Zhu ◽  
Chi-Hung Ho ◽  
Xiaonan Wang

<p><a></a><a>The production process of many active pharmaceutical ingredients such as sitagliptin could cause severe environmental problems due to the use of toxic chemical materials and production infrastructure, energy consumption and wastes treatment. The environmental impacts of sitagliptin production process were estimated with life cycle assessment (LCA) method, which suggested that the use of chemical materials provided the major environmental impacts. Both methods of Eco-indicator 99 and ReCiPe endpoints confirmed that chemical feedstock accounted 83% and 70% of life-cycle impact, respectively. Among all the chemical materials used in the sitagliptin production process, </a><a>trifluoroacetic anhydride </a>was identified as the largest influential factor in most impact categories according to the results of ReCiPe midpoints method. Therefore, high-throughput screening was performed to seek for green chemical substitutes to replace the target chemical (i.e. trifluoroacetic anhydride) by the following three steps. Firstly, thirty most similar chemicals were obtained from two million candidate alternatives in PubChem database based on their molecular descriptors. Thereafter, deep learning neural network models were developed to predict life-cycle impact according to the chemicals in Ecoinvent v3.5 database with known LCA values and corresponding molecular descriptors. Finally, 1,2-ethanediyl ester was proved to be one of the potential greener substitutes after the LCA data of these similar chemicals were predicted using the well-trained machine learning models. The case study demonstrated the applicability of the novel framework to screen green chemical substitutes and optimize the pharmaceutical manufacturing process.</p>


2004 ◽  
Vol 76 (9) ◽  
pp. 1234-1234 ◽  
Author(s):  
B. Knorr ◽  
H. Schlieker ◽  
H.-P. Hohmann ◽  
D. Weuster-Botz

Author(s):  
Bárbara B. Sousa ◽  
Marcos F. Q. Sousa ◽  
Marta C. Marques ◽  
João D. Seixas ◽  
José A. Brito ◽  
...  

2014 ◽  
Vol 118 (6) ◽  
pp. 702-709 ◽  
Author(s):  
Heiner Giese ◽  
Paulien Kruithof ◽  
Kristina Meier ◽  
Michaela Sieben ◽  
Elena Antonov ◽  
...  

Micromachines ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 853
Author(s):  
Spyridon Achinas ◽  
Jorn-Ids Heins ◽  
Janneke Krooneman ◽  
Gerrit Jan Willem Euverink

Many articles have been published on scale-down concepts as well as additive manufacturing techniques. However, information is scarce when miniaturization and 3D printing are applied in the fabrication of bioreactor systems. Therefore, garnering information for the interfaces between miniaturization and 3D printing becomes important and essential. The first goal is to examine the miniaturization aspects concerning bioreactor screening systems. The second goal is to review successful modalities of 3D printing and its applications in bioreactor manufacturing. This paper intends to provide information on anaerobic digestion process intensification by fusion of miniaturization technique and 3D printing technology. In particular, it gives a perspective on the challenges of 3D printing and the options of miniature bioreactor systems for process high-throughput screening.


2021 ◽  
Author(s):  
Alex Olivares-Molina ◽  
Brenda Parker

Brown macroalgae are an attractive third-generation feedstock of natural products, in order to design green chemistry-compliant processes and reduce the use of organic solvents in bioactive product extraction, aqueous two-phase systems (ATPS) was applied. This research aimed to develop a high-throughput screening (HTS) to recover polyphenols from Ascophyllum nodosum using ATPS. In total, 384 different 2-phase systems were assessed using an automated liquid-handling system to evaluate polyphenol recovery using a model system of phloroglucinol to establish an optimal 2-phase system for polyphenol partitioning. Various ratios of PEG:potassium phosphate solutions were explored to evaluate partitioning of polyphenols via a scale-down approach. Scale-down selected system showed a recovery of phloroglucinol of 62.9±12.0%, this system was used for scale-up trials. Scale-up studies confirmed that the HTS method was able to recover polyphenols with a 54.8±14.2% in the phloroglucinol model system. When the optimised ATPS system was tested with a polyphenol extract, 93.62±8.24% recovery was observed. When ATPS was applied to a fucoidan and alginate biorefinery residue, 88.40±4.59% polyphenol was recovered. These findings confirm that ATPS is a valuable addition to the bioprocess toolkit for sustainable extraction of natural products from macroalgae in a multiproduct biorefinery approach.


Planta Medica ◽  
2012 ◽  
Vol 78 (11) ◽  
Author(s):  
L Hingorani ◽  
NP Seeram ◽  
B Ebersole

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