2011
DOI: 10.1002/elsc.201000207
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Enhanced extraction of 3‐demethylated colchicine from fermentation broth of Bacillus megaterium: Optimization of process parameters by statistical experimental design

Abstract: Biotransformation of colchicine into its pharmacologically active regiospecific derivative 3‐demethylated colchicine (3‐DMC) mediated by microbial monooxygenases is an economic and promising strategy for the production of this inexpensive and potent anti‐cancer drug. Response surface methodology (RSM), with central composite design (CCD) model, was employed with three extraction variables (temperature, pH, and process time) for optimization; which most significantly affected recovery of 3‐DMC. The validity of … Show more

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Cited by 21 publications
(21 citation statements)
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“…Statistical optimizations such as RSM are studies where participating parameters are varied simultaneously for optimization experiments (Greasham and Inamine, 1986; Greasham and Herber, 1997; Zhang et al, 2000; Margarita et al, 2005). RSM can collectively optimize all the affecting parameters to eliminate the limitations with single-factor optimization process (Gough et al, 1996; Dubey et al, 2011; Kanmani et al, 2013). We therefore, attempted to decipher and model glucose and NaCl content of the growth medium, using R. toruloides.…”
Section: Introductionmentioning
confidence: 99%
“…Statistical optimizations such as RSM are studies where participating parameters are varied simultaneously for optimization experiments (Greasham and Inamine, 1986; Greasham and Herber, 1997; Zhang et al, 2000; Margarita et al, 2005). RSM can collectively optimize all the affecting parameters to eliminate the limitations with single-factor optimization process (Gough et al, 1996; Dubey et al, 2011; Kanmani et al, 2013). We therefore, attempted to decipher and model glucose and NaCl content of the growth medium, using R. toruloides.…”
Section: Introductionmentioning
confidence: 99%
“…In such a practice interaction effect between the variables is not taken into consideration. Nowadays, RSM is a good statistical experimental design tool that has proved its worth and it is commonly used in optimisation experiments of similar processes (Alam, Muyibi, and Toramae 2007;Dubey, Jawed, and Haque 2011). RSM technique has been employed (Shieh, Liao, and Lee 2003;Yuan et al 2008) to provide a fast and efficient way to understand and identify the significant process variables, thereby saving time and maintaining key information of each component.…”
Section: Introductionmentioning
confidence: 99%
“…In order to meet the market demand, several high yielding techniques have been discovered in the past, and successfully implemented in various processes, like production of primary or secondary metabolites, biotransformation, oil extraction etc. (Dubey et al, 2008, 2011; Singh et al, 2009; Rajeswari et al, 2014). …”
Section: Introductionmentioning
confidence: 99%