Abstract: Full factorial design of experiment for medium optimization was employed for lipstatin production by Streptomyces toxytricini in shake flask study. The full factorial DOE was very much effective in screening of nutritional parameters within the stipulated time frame in a limited number of experiments. A maximum lipstatin production was achieved 3.290 g/l with the following optimized factors: soya flour 35g/l and soya oil 25g/l. Validation experiments were also carried out to verify the adequacy and the accuracy of the model. The results also give a scope for large scale fermentation of lipstatin production. [Luthra, U., Kumar, H., Kulshreshtha, N., Tripathi, A., Trivedi, A., Khadpekar, S., Chaturvedi, A. and Dubey, R.C. Medium optimization for the production of lipstatin by Streptomyces toxytricini using full factorial design of experiment. Nat Sci 2013;1

[ Luthra

A Dubey

A Khadpekar

A Trivedi

Aditi Trivedi

Amitabh Chaturvedi

Archana Tripathi

H Kulshreshtha

Harish Kumar

N Tripathi

Nishtha Kulshreshtha

R C Dubey

S Chaturvedi

Sneha Khadpekar

U Kumar

Umesh Luthra

CiteSeerX

Nature and Science 2013;11(7)                                                    http://www.sciencepub.net/nature  
 
http://www.sciencepub.net/nature                                                                            naturesciencej@gmail.com 73
Medium optimization for the production of lipstatin by Streptomyces toxytricini using full factorial design of 
experiment 
 
Umesh Luthra, Harish Kumar, Nishtha Kulshreshtha, Archana Tripathi, Aditi Trivedi, Sneha Khadpekar, Amitabh 
Chaturvedi and R.C. Dubey
1 
 
Ipca Laboratories Ltd., Biotech R&D, Kandivali, Mumbai- 400067, Maharashtra, India 
1
Department of Botany & Microbiology, Gurukul Kangri University 
Haridwar-249404, Uttarakhand, India 
*Corresponding author: E-mail: umeshluthra@gmail.com; Tel.: +91-9892231361, +91-9167759646 
 
Abstract: Full factorial design of experiment for medium optimization was employed for lipstatin production by 
Streptomyces toxytricini in shake flask study. The full factorial DOE was very much effective in screening of 
nutritional parameters within the stipulated time frame in a limited number of experiments. A maximum lipstatin 
production was achieved 3.290 g/l with the following optimized factors: soya flour 35g/l and soya oil 25g/l. 
Validation experiments were also carried out to verify the adequacy and the accuracy of the model. The results also 
give a scope for large scale fermentation of lipstatin production. 
 [Luthra, U., Kumar, H., Kulshreshtha, N., Tripathi, A., Trivedi, A., Khadpekar, S., Chaturvedi, A. and Dubey, R.C. 
Medium optimization for the production of lipstatin by Streptomyces toxytricini using full factorial design of 
experiment. Nat Sci 2013;11(7):73-76]. (ISSN: 1545-0740). http://www.sciencepub.net/nature. 12 
 
Keywords: Medium optimization, Lipstatin, Streptomyces toxytricini 
 
1. Introduction 
Lipstatin, a very potent inhibitor of 
pancreatic lipase is produced by Streptomyces 
toxytricini. Lipstatin contains a beta-lactone structure 
that probably accounts for the irreversible lipase 
inhibition. Lipstatin is of considerable importance as 
key intermediate for the preparation of 
tetrahydrolipstatin (THL, Orlistat), which is useful in 
the prophylaxis and treatment of diseases associated 
with obesity. The lipophilic β-lactone irreversibly 
inactivates lipase by covalent modification of the 
serine residue of its catalytic triad. The applications of 
lipstatin have promoted the commercial production of 
these compounds. Hence, the investigations on the 
improvement of the productions of these products are 
of commercial importance (Weibel et al., 1987). 
As medium composition can significantly 
affect the formation, concentration and yield of a 
fermentation end product, designing an appropriate 
fermentation medium is of crucial importance. Thus 
affecting the overall process economics therefore it is 
important to consider the optimization of fermentation 
medium in order to maximize the profits from 
fermentation process. There are many challenges 
associated with optimization of fermentation process; 
it is laborious, expensive, open ended and time 
consuming process involving many experiments 
(Stanbury et al., 1997; Panda et al., 2007). However, 
there are conventional methods for optimizing 
medium composition via sequential manipulation of 
single parameter; but these often fail to identify the 
optimal conditions for the bioprocess, because 
interactions between different factors are neglected. 
So, effective problem solving methods are preferred 
(Xiong et al., 2008; Hong et al., 2009). A factorial 
design has eliminated the drawbacks of classical 
methods and has proved to be powerful and useful for 
the optimization of the target metabolite production. It 
can also be used to evaluate the relative significance 
of several variables simultaneously (Li et al., 2008; 
Hong et al., 2009). 
The aim of this work was to apply the full 
factorial design, followed by the path of steepest 
ascent to optimize the culture medium composition for 
lipstatin production by S. toxytricini. The major 
variables affecting the performance of the culture in 
terms of lipstatin production were also investigated.  
2. Material and Methods  
Microorganism 
The mutated strain (combined mutation of 
U.V and nitrosoguanidine) of S. toxytricini used in the 
study was stored in 20% glycerol at – 80 
O
C. The 
culture was maintained at  4 
O
C on yeast extract-malt 
extract-glucose (YMG) agar slants (Luthra et al., 
2013). 
Medium 
Lab inoculum medium adjusted to pH 6.8 
was composed of (g/l): yeast extract 4; malt extract 
10; dextrose 4. Seed medium adjusted to pH 6.8 was 
composed of (g/l): soyaflour 10; glycerol 10; yeast 
extract 5. The components of non-optimized 
fermentation medium were: glycerol, soyaflour, 
soyalecithin, soyaoil, polypropylene glycol. The pH of 
the medium was adjusted to 6.8 and the concentrations 
of components were adjusted according to the 
experimental design. 
Nature and Science 2013;11(7)                                                    http://www.sciencepub.net/nature  
 
http://www.sciencepub.net/nature                                                                            naturesciencej@gmail.com 74
Lab and seed media were sterilized by 
autoclaving for 30 min and fermentation media for 45 
min respectively at 121
O
C. After sterilization, the pH 
of lab, seed and fermentation medium was 6.6. 
Culture conditions 
Fermentation was performed in three stages: 
lab growth, seed growth and lipstatin production. For 
the lab growth stage, mycelium from a slant culture 
was inoculated into 250 ml conical flask containing 35 
ml of lab medium and grown at 28 
O
C with 220 rpm 
on a shaker incubator for 24 h. Then, 3% (v/v) lab 
culture was inoculated into seed medium. The strain 
was incubated at 28 
O
C with 220 rpm for 36 h. Then, 
10% (v/v) seed cultures were inoculated into the 
fermentation medium. The strain was incubated at 28 
O
C with 220 rpm for 12 days (Luthra et al., 2013). 
Analytical methods 
Lipstatin activity in the culture broth was 
determined by HPLC. The culture broth of 5.0 gm was 
taken in 50 ml volumetric flask with 30 ml acetone 
and sonicated for 10 minutes and the volume was 
made up with acetonitrile. The resulting extracted 
solution was injected into the HPLC (Waters 2496) 
having C-18 column (Hypersil ODS, 5u C18 (150mm 
X 4.6 mm) for the estimation of lipstatin. 
Concentrations of lipstatin were calculated by 
comparison of peak areas with those standard lipstatin 
and subsequently lipstatin activity was calculated. 
Biomass was measured in terms of percentage 
mycelial volume (%) (Ferreira et al., 2005). 
Experimental design and data analysis 
Various treatments in a series of experiments 
were developed or generated according to several 
standard texts and software on designing experiments 
(Box et al., 1987; Montgomery, 1997; Long-Shan et 
al., 2002). In preliminary experiments, various carbon 
and nitrogen sources were evaluated for their 
suitability to sustain good lipstatin production by S. 
toxytricini. On the basis of literature study, glycerol 
and sorbitol were selected as carbon source; soyaflour 
and corn steep liquor along with cotton seed meal as a 
nitrogen source. Oil source chosen for the study were 
soya oil and groundnut oil and brij 35 and soyalecithin 
as an emulsifier.  
The categorical factorial design of 
experiment was employed to select the best carbon, 
nitrogen, emulsifier and oil source. 
 
Table 1: The categorical full factorial design for 
screening variables in lipstatin production 
Factors Code High level (+1) Low level   (-1) Effect 
Carbon X1 Glycerol Sorbitol 0.45 
Oil X2 Groundnut Oil SoyaOil -0.23 
Nitrogen X3 Soyaflour CSL + CSM 0.76 
Emulsifier X4 Brij 35 Soyalecithin 0.43 
Abbreviation: CSL: Corn steep liquor; CSM: Cotton 
seed meal 
 The results revealed that glycerol, soyaflour, 
soyaoil, soyalecithin were best carbon, nitrogen, oil 
and emulsifier source respectively for the production 
of lipstatin. These components were chosen for further 
optimization. 
 
Table 2: The full factorial design for screening 
variables in lipstatin production 
Factors (g/l) Code 
Low 
level(-1) 
High 
level (+1) 
Effect Coef 
Glycerol A 22.5 35 -0.01 -0.01 
Soyaoil B 10 20 0.06 0.032 
Soyaflour C 15 35 0.08 0.038 
Soyalecithin D 15 25 -0.06 -0.028 
Abbreviation: Coef: coefficient 
 
Table 3: The full factorial design variables (in 
coded levels) with lipstatin as response. 
Run 
Variable levels 
Lipstatin (mg/ml) 
A B C D 
1 -1 -1 -1 -1 2.371 
2 1 -1 -1 -1 2.406 
3 -1 1 -1 -1 2.614 
4 1 1 -1 -1 2.571 
5 -1 -1 1 -1 2.527 
6 1 -1 1 -1 2.573 
7 -1 1 1 -1 2.561 
8 1 1 1 -1 2.537 
9 -1 -1 -1 1 2.526 
10 1 -1 -1 1 2.298 
11 -1 1 -1 1 2.422 
12 1 1 -1 1 2.423 
13 -1 -1 1 1 2.449 
14 1 -1 1 1 2.528 
15 -1 1 1 1 2.512 
16 1 1 1 1 2.551 
 
Full factorial design 
 Full factorial design was employed for 
screening the most significant parameters affecting 
lipstatin production by S. toxytricini. Each 
independent variable was tested at two levels, high 
and low, which are denoted by (+) and (-), 
respectively. The experimental design with the name, 
symbol code, and actual level of the variables is 
shown in Table 1, whereas Table 2 shows the detail of 
the design. Lipstatin production was carried out in 
duplication and the average value was taken as the 
response.  
Path of the steepest ascent experiment 
 To get the optimum response, we used the 
technique of steepest ascent. The  experiments were 
adopted to determine a suitable direction by increasing 
or decreasing the concentrations of variables 
according to the results of factorial experiment 
(Gheshlaghi et al., 2005; Hong et al., 2009). 
Statistical analysis 
 Design expert 8.0 software was used for the 
experimental designs and subsequent regressional 
analysis of the experimental data. Stastistical analysis 
Nature and Science 2013;11(7)                                                    http://www.sciencepub.net/nature  
 
http://www.sciencepub.net/nature                                                                            naturesciencej@gmail.com 75
of the model was performed to evaluate the analysis of 
the variance (ANOVA). Optimization procedure is 
facilitated by construction of an equation that 
describes the experimental results as a function of the 
factor level. A polynomial equation can be constructed 
in the case of a factorial design where the co-efficient 
in the equation are related to the effects and 
interactions of the factors. Statistical analysis of the 
model was performed to evaluate the analysis of the 
variance (ANOVA). 
 
Result and discussion 
Optimization by full factorial design 
 The importance of the four components, 
namely, glycerol, soyaoil, soyaflour, soyalecithin for 
lipstatin production was investigated by full factorial 
design. Table 1 shows the effect of these components 
on the response and significant levels. 
 Based on the statistical analysis, the effects 
of soyaflour were (+) 0.08 had confidence level above 
90%. So, it was identified as influencing lipstatin 
production component significantly. Next to 
soyaflour, soyaoil has an effect of (+) 0.06. Others had 
less effect, so they were considered as insignificant. 
 In the Pareto chart (Fig. 1), the maximal 
effect was presented in the upper portion and then 
progress down to the minimal effect. In addition, it 
directly shows that the most important factor 
determining lipstatin production was soya flour. 
Design-Expert® Software
R1
A: Glycerol
B: Soya Oil
C: Soya flour
D: Soya lecithin
Positive Effects 
Negative Effects 
Pareto Chart
t-
V
a
lu
e
 o
f 
|E
ff
e
ct
|
Rank
0.00
2.14
4.29
6.43
8.58
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Bonferroni Limit 8.57517
t-Value Limit 3.18245
C
B
BCD D
AC
BC ABDACD BD
ABC
CD AD
 
Fig. 1: Pareto chart of four factors and their 
interaction standard effects on lipstatin production. 
The most important term was soya flour  
 A normal plot is a plot of the absolute value 
of the effect estimates against their normal % 
probabilities. It is useful to distinguish between 
positive (upper right) and negative (lower left) effects 
of the variables and their interactions. The normal plot 
(Fig.2) shows that the variables D (Soya lecithin) and 
A (Glycerol) has negative effect while B (Soya oil) 
and C (Soya flour) has the positive effect on lipstatin 
production. Also, the interactive effect of DCB, AC, 
ABD, ACD, CD and ABCD variables has the positive 
effect whereas the variables CD, ACD., ABD, AC, 
and BCD has negative effect on lipstatin. 
 
Design-Expert® Software
R1
Shapiro-Wilk test
W-value = 0.993
p-value = 0.841
A: Glycerol
B: Soya Oil
C: Soya flour
D: Soya lecithin
Positive Effects 
Negative Effects 
Normal Plot
N
o
rm
a
l 
%
 P
ro
b
a
b
ili
ty
Standardized Effect
-0.06 -0.02 0.01 0.04 0.08
1
5
10
20
30
50
70
80
90
95
99
B
C
D
AC
AD
BC
BD
CD
ABC
ABD
ACD
BCD
 
Fig. 2: Normal Plot for distinguishing between 
positive and negative effects of variables.  
 
Analysis through ANOVA 
Data shown in Table 3 were analyzed using 
Design experiment 8.0 software. The P-value was 
used to identify the effect of each factor on lipstatin 
production. P-value less than 0.05 indicate that the 
model terms are significant. The Model F-value of 
2.81 implies there is a 8.57% chance that a "Model F-
Value" could occur due to noise. In this case C i.e. 
Soya flour is significant model terms. Values greater 
than 0.1000 indicate the model terms are not 
significant. A negative "Pred R-Squared" implies that 
the overall mean is a better predictor of the response 
than the current model. "Adeq Precision" measures the 
signal to noise ratio. A ratio greater than 4 is desirable. 
The ratio of 4.509 indicates an adequate signal. This 
model can be used to navigate the design space. 
 
Table 4: Analysis of variance table 
 
Factors SS DF MS F P 
A – Glycerol 0.001 1 0.001   
B – Soyaoil 0.016 1 0.016 4.02 0.08 
C – Soyaflour 0.023 1 0.023 5.62 0.0451 
D – Soyalecithin 0.013 1 0.013 3.1 0.1161 
BC 0.007 1 0.007 0.21 0.1161 
BD 0.006 1 0.006 0.27  
CD 0.001 1 0.001 0.61  
BCD 0.014 1 0.014 3.44 0.1006 
Model 0.08 7 0.011 2.81 0.0857 
 
Abbreviation: DF: Degree of freedom; SS: sum of 
squares, MS: mean square 
 R-Squared  0.7107   
Adj R-Squared 0.4575 
Pred R-Squared    -0.1573 
Adeq Precision 4.509 
 
Optimization by the path of steepest ascent 
experiment 
Based on the factorial design, the Soya flour 
and Soya oil effects were positive. Thus, increasing 
their concentration should result in a higher 
production of lipstatin. The center point of the 
Nature and Science 2013;11(7)                                                    http://www.sciencepub.net/nature  
 
http://www.sciencepub.net/nature                                                                            naturesciencej@gmail.com 76
factorial design has been considered as the origin of 
the path. The titer of lipstatin production was 
obtained. These experiments (Table 4) showed the 
maximum production of lipstatin. This was obtained 
when the parameters were 35 g/l Soya flour and 25 g/l 
Soya oil. It suggested that this point is the nearest 
region of the maximum lipstatin response. 
 
Table 5: Design and results of Steepest ascent 
experiment. 
Run 
Factors 
Response(g/l) 
X1 (g/l) X2 (g/l) 
1 25 15 2.513 
2 30 20 2.652 
3 35 25 3.290 
4 40 30 2.720 
5 45 35 2.510 
X1: Soyaflour; X2: Soyaoil 
 
Validation of final concentration 
To verify the final concentration, three sets of 
experiments were carried out with the final 
composition of soyaflour (35 g/l) and soyaoil (25 g/l). 
The mean of lipstatin production was 3.275 g/l. 
Conclusion 
Statistical optimization of fermentation 
medium components for the production of lipstatin by 
mutated Streptomyces toxytricini had been proved to 
be a valuable tool for lipstatin production. A 
maximum lipstatin production of 3.290 g/l was 
achieved with the following optimized factors: 35g/l 
soyaflour and 25g/l soyaoil. Validation experiments 
were also carried out to verify the adequacy and the 
accuracy of the model. The results also give a basis 
for further study with large scale fermentation for 
production of lipstatin. From all the studies carried 
out, the final medium composition to get the higher 
titer value of lipstatin- 
 
S.No. Component Concentration (g/l) 
1 Glycerol 22.5 
2 Soyaoil 25 
3 Soyaflour 35 
4 Soyalecithin 15 
5 Polypropylene glycol 0.5 
 
Acknowledgements:   
 The author (UL) thanks for Ipca Laboratories 
Ltd. for the financial support and provided lab to do 
complete research work. 
 
Corresponding Author: 
Dr. Umesh Luthra  
Ipca Laboratories Ltd., Biotech R&D, Kandivali, 
Mumbai- 400067, Maharashtra, India 
 
 
References 
1. Bibhu PP, Mohd A and Saleem J. Fermentation 
Process Optimization. Res. J. Microbiol., 2007; 2: 
201-208. 
2. Gheshlaghi R, Scharer JM, Moo-Young M and 
Douglas PL. Medium optimization for hen egg 
white lysozyme production by recombinant 
Aspergillus niger using statistical methods. 
Biotechnol Bioeng. 2005; 90: 754-760. 
3. Hong G, Mei L, Jintao L, Huanqin D, Xianlong L, 
Ying Z, Wenquan Z and Lixin Z. Medium 
optimization for the production of avermectin B1a 
by Streptomyces avermitilis 14-12A using response 
surface methodology. Bioresource Technol. 2009; 
100: 4012-4016. 
4. Li Y, Jiang H, Xu Y and Zhang X Optimization of 
nutrient components for enhanced phenazine-1-
caroxilic acid production by gacA-inactivated 
Pseudomonas sp. M18G using response surface 
method. Appl. Microbiol. Biotechnol. 2008; 77: 
1207-1217. 
5. Stanbury PF, Whitakar A and Hall SJ. Principles of 
Fermentation Technology. Aditya Books, New 
Delhi, 1997. 
6. Weibel EK, Hadvary P, Hochuli E, Kupfer E and 
Lengsfeld H  Lipstatin, an inhibitor of pancreatic 
lipase, produced by Streptomyces toxytricini I. 
producing organism, fermentation, isolation and 
biological activity. J. Antibiotics., 1987; 8: 1081- 
1085. 
7. Xiong ZQ, Tu XR and Tu GQ Optimization of 
medium composition for actinomycin X2 
production by Streptomyces spp JAU4234 using 
response surface methodology. J. Ind. Microbiol. 
Biotechnol. 2008; 35: 729-734. 
8. Box G, Hunter WG and Hunter JS Statistics for 
experiments: an introduction to design, data 
analysis, and model building. Wiley, 1978. 
9. Montgomery DC. Design and analysis of 
experiments, 4th ed. Wiley, 1997. 
10.  Long-Shan TL, Chieh-Chang P and Bo-Kun T 
The influence of medium design on lovastatin 
production and pellet formation with a high-
producing mutant of Aspergillus terreus in 
submerged cultures. Process Biochem. 2003; 
38(9): 1317-1326. 
11. Luthra U, Kumar H and Dubey RC. Mutagenesis 
of the Lipstatin producer S. toxytricini ATCC 
19813. J. Biotechnol. Lett. 2013; 4(1): 68-71. 
E-mail: umeshluthra@gmail.com  
 
5/5/2013 


Medium optimization for the production of lipstatin by Streptomyces toxytricini using full factorial design of experiment

http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=E33B2877620E1CFE72DFB70DD94BDB6B?doi=10.1.1.1043.5397&rep=rep1&type=pdf

Medium optimization for the production of lipstatin by Streptomyces toxytricini using full factorial design of experiment

Abstract

Similar works

Full text

Available Versions

CiteSeerX