| Literature DB >> 29159147 |
Ali Mesgari-Shadi1,2, Mohammad Hossein Sarrafzadeh1.
Abstract
Introduction: Single chain variable fragment (scFv) antibodies are reduced forms of the whole antibodies that could be regarded as an alternative tool for diagnostic and therapeutic purposes. The optimization of processes and environmental conditions is necessary to increase the production yields and enhance the productivity. This can result in a cost-effective process and respond to the high demand for these antibodies.Entities:
Keywords: Antibody; Escherichia coli; Inducer; Optimization; Osmotic condition; scFv
Year: 2017 PMID: 29159147 PMCID: PMC5684511 DOI: 10.15171/bi.2017.23
Source DB: PubMed Journal: Bioimpacts ISSN: 2228-5652
Placket-Burman design for screening of the effective factors on scFv production among 10 variablesa
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| 1 | 35 | 250 | 1/10 | 24 | 0.26 | 0.24 | 0.1 | 0.5 | 0.2 | 0.2 | 0.57 | 0.32 |
| 2 | 30 | 200 | 1/10 | 24 | 2.6 | 0.024 | 0.1 | 0.5 | 0.5 | 0.5 | 0.29 | 0.20 |
| 3 | 35 | 250 | 1/5 | 24 | 2.6 | 0.024 | 0.1 | 0.1 | 0.2 | 0.5 | 0.49 | 0.14 |
| 4 | 35 | 200 | 1/10 | 24 | 2.6 | 0.24 | 1 | 0.1 | 0.5 | 0.2 | 0.95 | 0.10 |
| 5 | 35 | 200 | 1/5 | 12 | 2.6 | 0.024 | 1 | 0.5 | 0.2 | 0.2 | 1.66 | 0.15 |
| 6 | 30 | 250 | 1/10 | 12 | 2.6 | 0.24 | 1 | 0.5 | 0.2 | 0.5 | 0.04 | 0.05 |
| 7 | 30 | 200 | 1/10 | 12 | 0.26 | 0.024 | 0.1 | 0.1 | 0.2 | 0.2 | 1.61 | 1.82 |
| 8 | 35 | 250 | 1/10 | 12 | 0.26 | 0.024 | 1 | 0.1 | 0.5 | 0.5 | 0.42 | 0.59 |
| 9 | 30 | 250 | 1/5 | 24 | 0.26 | 0.024 | 1 | 0.5 | 0.5 | 0.2 | 0.52 | 1.03 |
| 10 | 35 | 200 | 1/5 | 12 | 0.26 | 0.24 | 0.1 | 0.5 | 0.5 | 0.5 | 0.33 | 0.36 |
| 11 | 30 | 250 | 1/5 | 12 | 2.6 | 0.24 | 0.1 | 0.1 | 0.5 | 0.2 | 0.08 | 0.27 |
| 12 | 30 | 200 | 1/5 | 24 | 0.26 | 0.24 | 1 | 0.1 | 0.2 | 0.5 | 0.13 | 1.40 |
a 12 runs were performed and the responses were the concentrations of scFv in the periplasmic and culture media (mg/L of the culture media) respectively denoted by R1 and R2.
Figure 1
Figure 2
Figure 3Full factorial design with center points
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| 1 | 1 | 0.4 | 0.4 | 2.8 |
| 2 | 1 | 0.6 | 0.4 | 1.7 |
| 3 | 1 | 0.4 | 0.6 | 1.8 |
| 4 | 1 | 0.6 | 0.6 | 1.2 |
| 5 | 1 | 0.5 | 0.5 | 2.5 |
| 6 | 1 | 0.5 | 0.5 | 2.4 |
| 7 | 2 | 0.4 | 0.4 | 2.5 |
| 8 | 2 | 0.6 | 0.4 | 1.8 |
| 9 | 2 | 0.4 | 0.6 | 1.9 |
| 10 | 2 | 0.6 | 0.6 | 1.5 |
| 11 | 2 | 0.5 | 0.5 | 2.5 |
| 12 | 2 | 0.5 | 0.5 | 2.1 |
| 13* | 3 | 0.331821 | 0.500000 | 2.5 |
| 14* | 3 | 0.668179 | 0.500000 | 1.6 |
| 15* | 3 | 0.500000 | 0.331821 | 2.5 |
| 16* | 3 | 0.500000 | 0.668179 | 1.5 |
Axial points (*) were added to modify the design (b). All factors are in uncoded units.
Main effects in the full factorial design
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| X1 (Sucrose molarity) | 0.002 |
| X2 (Sorbitol molarity) | 0.004 |
| X1.X2 | 0.098 |
| Center point | 0.006 |
| Block | 0.881 |
The center point effect is significant (P <0.05) then there is a curvature effect.
Figure 4
Figure 5ANOVA for the proposed quadratic model
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| Blocks | 2 | 2.79 | 0.114 |
| Regression | 4 | 23.19 | 0.000 |
| Linear | 2 | 6.65 | 0.017 |
| X1 | 1 | 8.99 | 0.015 |
| X2 | 1 | 10.85 | 0.009 |
| Square | 2 | 8.79 | 0.008 |
| X1.X1 | 1 | 12.18 | 0.007 |
| X2.X2 | 1 | 14.13 | 0.004 |
| Residual error | 9 | ||
| Lack of fit | 7 | 0.75 | 0.675 |
| Pure error | 2 | ||
| Total | 15 | ||
| R2 | 92% |
Figure 6
Figure 7
Figure 8