Indian Journal of BiotechnologyVol 2, October 2003, pp 564-570

Optimization of Medium and Cultural Conditions for NeomycinProduction using Response Surface Methodology

K Adinarayana'>, P Ellaiah1, B Srinivasulu', J Lakshmi Narayana" and K V V S N Bapi Raju''Pharmaceutical Biotechnology Division, Department of Pharmaceutical Sciences

Andhra University, Visakhapatnam 530 003, India

2Department of Statistics, Andhra University, Visakhapatnam 530003, India

Received 10 September 2002; accepted 6March 2003

Optimization of medium ingredients and cultural conditions for maximum neomycin production was carried outusing a new species, Streptomyces marinensis. The C source (maltose), the N source (sodium glutamate) and thecultural conditions such as pH, temperature and agitation (rpm) were selected for optimization. Full factorialcomposite experimental design and response surface methodologies were used in the design of experiments and in theanalysis of results. The optimum values for the tested variables for maximum neomycin production were: maltose,51.67 g r', sodium glutamate, 12.36 g rl,pH, 7.48, temperature, 30.6°C and agitation of the shake flask 174 rpm. Themaximum neomycin production was 7815 mg r'. This method was efficient; only 36 experiments were necessary toassess these conditions, and model adequacy was very satisfactory, as the coefficient of determination was 0.9448.

Keywords: optimization; central composite design; response surface methodology; neomycin production

IntroductionNeomycin is one of the important aminoglycoside

antibiotics widely used in the pharmaceuticalpreparations for local applications. It is effectiveagainst Gram-positive, Gram-negative and acid-fastbacteria. Also it has wide applications in veterinarypractice, assay of deoxyribonuclease, storage ofpetroleum fuels in tanks and to increase the flow oflatex from rubber tree plants. Such an importantantibiotic was first produced by Streptomyces fradiaeisolated by Waksman & Lechevalier (1949). Later, anew species Streptomyces marinensis was identifiedas producer of neomycin (Ellaiah, 1974;Sambamurthy & Ellaiah, 1974). In the preliminarystudies, in the development of production medium,maltose and sodium glutamate were found to beimportant nutrients in enhancing the neomycinformation (unpublished data). Also, the culturalconditions such as pH, temperature and agitation (rpmof shake flask) played an important role in theformation of neomycin. However, no systematicstudy, to achieve optimum medium composition andprocess conditions, has been reported for theproduction of neomycin.

*Author for correspondence:Tel: 91-891-2505159, Fax: 91-891-2755547E-mail: adikunamneni@rediffmail.com

The aim of the present study includes the statisticaloptimization of medium components (maltose andsodium glutamate) and cultural parameters (PH,temperature and rpm of shake flask), that have beenpredicted to playa very significant role in enhancingthe production of neomycin antibiotic. Hence, the useof experimental factorial design (Fannin et al, 1981)and of the response surface methodology (Deshayes,1980; Mathews et al, 1981) already successfullyapplied in other fields. Recently, it has beensuccessfully applied for optimization of the media andcultural conditions in many cultivation processes forthe production of primary and secondary metabolites(Chen & Liu, 1996; Haltrich et at, 1994; Houng et al,1989; Mehmet et at, 1999; Liu et at, 1999; Prapulla etat, 1992; Ramanamurthy et at, 1999; Shirai et al,2001). It is well suited to the study of the main andinteraction effects of the factors on the production ofneomycin. Thus a 25.1 full factorial central compositedesign and response resurface methodology (RSM)was used in this study (Box et at, 1978; Akhnarova &Kafarov, 1982; Cochran & Cox, 1957; Yee & Blanch,1993; Kenneth et al, 1995; Box & Wilson, ]95 LKhuri & Cornell, 1987),

The classical method for the optimization ofmedium and cultural conditions involves one variableat a time, keeping the others at fixed levels. Beingsingle dimensional, this is laborious and time-

ADINARA YANA et al.: NEOMYCIN PRODUCTION USING RESPONSE SURFACE METHODOLOGY 565

consuming method, often does not guaranteedetermination of optimal conditions (Box et al, 1978;Akhnazavova & Kafarov, 1982; Cochran & Cox,1957). Hence, it is proposed to adopt the experimentalfactorial design (Fannin et al, 1981) and responsesurface methodology for the optimization of themedium components and cultural conditions. It wasalready successfully employed in other fields(Deshayes, 1980; Mathews et al, 1981).

Materials and Methods

MicroorganismAn UV-NTG mutant strain of S. marinensis,

producer of neomycin was used in the present study(Srinivasulu et al, 2002; Adinarayana et al, 2003).This microorganism was isolated from sea water ofBay of Bengal, in the Department of PharmaceuticalSciences, Andhra University, Visakhapatnam, India(Ellaiah, 1974; Sambamurthy & Ellaiah, 1974). It wasmaintained on jowar starch agar slants at 4°C andsubcultured every 4 weeks.

Inoculum PreparationInoculum was prepared by transferring the 5 ml

spore suspension prepared from 7-day-old slantculture, into 250 rn1 Erlenmeyer flasks containing 45ml of sterile inoculum medium. The composition ofthe inoculum medium was: soluble starch, 25 g r', ,com steep liquor, 10 g r'. (NHhS04, 5 g r', NaC!, 5 gr' and CaC03, 5 g r' with pH, 7. The flasks were kepton a rotary shaker at 220 rpm at 30°C. After 48 hrs ofincubation, the whole broth was centrifuged at 2000rpm for 10 min.. The cell pellet was washedthoroughly with 20 g r' KCl and saline solutions.Finally, the cell mass was resuspended in sterile salinesolution. This was used as inoculum for the shakeflask experiments.

Shake Flask ExperimentsFive ml of 48 hrs inoculum consisting of 25 mg dry

cell wt was added to 45 rn1production medium in 250ml Erlenmeyer flasks. The composition of syntheticbasal medium employed in the production mediumwas: K2HPO", 0.10 g r ': MgS04 7H20, 0.5 g r',ZnS04 7H20, 0.005 g r'. FeS04 7H20, 0.005 g rl andCaCh 2H20, 0.04 g r' with pH 7. The range andlevels of the test variables are given in Table 1. Theconcentrations of maltose and sodium glutamate inthe production medium and the conditions of otherparameters such as pH, temperature and agitationwere varied according to the experimental design

shown in Table 2. The pH values of the medium wereadjusted before sterilization by adding 2N KOH and2N phosphoric acid. Five ml of samples werewithdrawn from each flask after six days ofcultivation and centrifuged at 2000 rpm for 10 min.

Table 1- Experimental range and levels of the independentvariables

Variables Range and levels-2

Maltose (M) (g r1) 10Sodium glutamate (G)( g rl) 1pH (PH) 6.0Temperature (T)(°C) 20rpm (R) 0

-I 0 +1 +2

25 40 55 706 12 17 22

7.0 8.0 9.0 10.025 30 35 4070 140 210 280

Table 2--Central composite design consisting of 36 experimentsfor the study of five experimental factors in coded units along

with observed responses

Runno.

Coefficientsassessed by

M G pH T R

1 -1 -1 -1 -1 -12 1 -1 -1 -1 13 -1 1 -1 -1 14 1 1 -1 -1 -15 -1 -1 1 -1 16 1 -I I -1 -I7 -1 1 1 -1 -I8 1 1 1 -1 19 -I -1 -1 1 110 I -1 -1 1 -1Jl -1 1 -1 1 -I12 1 1 -1 1 I13 -1 -1 1 1 -114 1 -1 1 1 115 -1 1 1 1 116 1 1 J 1 -117 --2 0 0 0 018 2 0 0 0 019 0 -2 0 0 0W 0 2 0 0 021 0 0 -2 0 022 0 0 2 0 023 0 0 0 -2 0~ 0 0 0 2 025 0 0 0 0 -2~ 0 0 0 0 2n 0 0 0 0 028 0 0 0 0 029 0 0 0 0 0~ 0 0 0 0 031 0 0 0 0 0n 0 0 0 0 033 0 0 0 0 0~ 0 0 0 0 035 0 0 0 0 036 0 0 0 0 0

(M: Maltose; G: Glucose; T: Temperature; R: Rotation per min)

~c~._ VJo t:0...__ 0'" 0..boc_

566 INDIAN J BIOTECHNOL, OCTOBER 2003

The clear supernatant broth was used to determine theantibiotic yield. All the experiments were carried outin triplicate and average values were reported.

Analytical MethodsThe neomycin present in the fermented broth

samples was estimated by microbiological assaymethod using Staphylococcus epidermidis NCIM2493 as test organism. The standard neomycinsulphate (MIs Shanghai Pharmaceutical IndustryCorporation, China) was used to construct thestandard graph.

Experimental Design and Optimization by RSMResponse surface methodology consists of a group

of empirical techniques devoted to the evaluation ofrelations existing between a cluster of controlledexperimental factors and the measured responses,according to one or more selected criteria. A priorknowledge and understanding of the process and theprocess variables under investigation are necessaryfor achieving a more realistic model. Based on theresults obtained in preliminary experiments, maltose,sodium glutamate, pH, temperature and rpm of shake

. flask were found to be major variables in theneomycin production. Hence, these variables wereselected to find out the optimized conditions forhigher neomycin production using central compositedesign and response surface methodology.

The central values (zero level) chosen forexperimental design were maltose, 40 g 1'1; sodiumglutamate, 12 g 1'1; pH, 8; temperature, 30°C and rpm140. In developing the regression equation the testfactors were coded according to the equation.

... (1)

Where Xi is the coded value of the ilh independentvariable, Xi is the natural value of the ilh independentvariable, XXi is the natural value of the ilh independentvariable at the center point and L1Xi is the step changevalue.

Y=bo+ Lb;x; +LLbijx;x; bixi+ LbiiX2i+ e (2). j j

Where Y is the measured response, b.; intercept term,b; bij, and bij are, respectively the measures of theeffects of variables Xi, XiXj, and X/. The variable XiXjrepresents the first-order interactions between Xi andX, (i

ADINARA YANA et al.: NEOMYCIN PRODUCTION USING RESPONSE SURFACE METHODOLOGY 567

Source ofvariations

Table 4-Analysis of variance (ANOV A) for the quadratic model

Sum of Degrees of Mean F value Prob. (P)squares freedom square

Regressions 5.8709 20 0.2935Residual 0.3424 15 0.0228Total 6.2133 35

Root MSE = 0.1509; CV = 4.5%; R2 = 0.9448; R=0.9720; Adj.R2 = 0.8714

12.8587 0.0000039

variable (ANOV A) are given in Table 4. The fisher F-test [F(20,15) = S/IS/ = 12.8> Ft(20,15) =3.37 ] with avery low probability value (Pmodel > F = 0.000003)demonstrates a very high significance for theregression model (Akhnazarova & Kafarov, 1982;Khuri & Cornell, 1987). The goodness of fit of themodel was checked by the determination coefficient(R2). In this case, the value of the determinationcoefficient (R2 = 0.9448) indicates that only 5.52% ofthe total variations are not explained by the model.The value of the adjusted determination coefficient(Adj R2 =0.87) is also very high to advocate for a highsignificance of the model (Akhnazarova & Kafarov,1982; Khuri & Cornell, 1987). A higher value of thecorrelation coefficient, R (=0.97) justifies an excellentcorrelation between the independent variables (Box etai, 1978). At the same time a relatively lower value ofthe coefficient of variation (CV=4.5%) indicates abetter precision and reliability of the experimentscarried out (Akhnazarova & Kafarov, 1982; Box &Wilson, 1951).

The application of response surface methodology(Box et ai, 1978; Kenneth et ai, 1995; Khuri &Cornell, 1987) yielded the following regressionequation, which is an empirical relationship betweenthe logarithmic values of antibiotic yields and testvariables in coded unit:

Y=3.8583+0. 0468 *M+O.0759 *G-O.0237*pH+0.0079*T +0.1297*R+0.0119*M*G-0. 0115*M*pH+0. 005*M*T -O.0024*M*R-0.0606*G *pH+O.0052 *G*T +0.065*G*R-O.J092*pH*T+0.0313*pH*R-O.0132*T*R-0.0842 *M*M-O.2647*G *G-O.041 7*pH*pH-0.239*T*T-O. 1334*R *R ... (3)

where Y is the response, i.e. the antibioticconcentrations expressed in logarithmic and M, G,pH, T and R are the coded values of the test variablesmaltose, sodium glutamate, pH, temperature, rpm ofshake flask, respectively.

The significant of each coefficient was determinedby student's t-test and p values (Table 5). The largerthe magnitude of the t- value and smaller the p-value,

Table 5 - Model coefficients estimated by multiples linearregression (significance of regression coefficients)

Factor Coefficient Computed p-valuet-value

Intercept 3.8583 (p.0471) 81.899 2.64 E-21M 0.0468(0.0308) 1.5186 0.1496G 0.0759 (0.0308) 2.4604 0.0264

pH -0.0237 (0.0308) -0.7678 0.4545T 0.0079 (0.0308) 0.2568 0.8007R 0.1297 (0.0308) 4.2039 0.0007

M*G 0.0119 (0.0377) 0.3137 0.758M*pH -0.0115 (0.0377) -0.3052 0.7644M*T 0.0050 (0.0377) 0.1319 0.8967M*R -0.0024 (0.0377) -0.06234 0.9511G*pH -0.0606(0.0377) -1.604 0.1295G*T 0.0052 (0.0377) 0.1369 0.8929G*R 0.0650 (0.0377) 1.722 0.1056

pH*T 0.1092 (0.0377) -2.8909 0.0111pH*R 0.0313 (0.0377) 0.8283 0.4204T*R -0.0132 (0.0377) -0.3489 0.7320M*M -0.0842 (0.0267) -3.1516 0.0065G*G -0.2647 (0.0267) -9.9094 5.63 E-08

pH*pH -0.0417 (0.0267) -1.5614 0.1392T*T -0.2390 (0.0267) -8.9469 2.12 E-07R*R -0.1334 (0.0267) -4.9935 0.00016

The number in the parentheses is the standard errorStandard error of mean = 0.151

the more significant is the corresponding coefficient(Akhnazarova & Kafarov, 1982; Khuri & Cornell,1987). This implies that the quadratic main effects ofmaltose and temperature (PM < 0.006 and PT < 2E-7)are more significant than their respective first ordereffects. whereas the first order main effects of bothsodium glutamate and rpm of shake flask are highlysignificant as is evident from their respective p-values(Po

568 INDIAN J BIOTECHNOL, OCTOBER 2003

Also, other investigators explained that the alkalineconditions favour the biosynthesis of neomycinwhereas in acidic conditions, the inhibition ofneomycin formation occurred, when studied with S.fradiae (Sebek, 1955).

The maltose influences neomycin production. Athigher concentration, it may inhibit the synthesis ofneomycin. Sodium glutamate is more significant atfirst order. However, the concentrations of maltoseand sodium glutamate are very significant in thesecond order level, meaning that they can act aslimiting nutrients and little variations in theirconcentration will alter either growth or productformation rate or both to a considerable extent. Thetemperature is more significant at quadratic level,indicating inhibition of product formation. Also, therpm is highly significant at first order and quadraticlevel, indicating the influence on antibioticproduction;

Contour plots of the response surface as a functionof two factors at a time, holding all other factors atfixed levels (zero, for instance), are more helpful inunderstanding both the main and the interactioneffects of these two factors. These plots can be easilyobtained by calculating from the model, the valuestaken by one factor where the second varies (from -2to +2, step 0.5 for instance) with constraint of a givenY value. The yield values for different concentrationsof the variables can also be predicated from therespective contour plots (Figs 1-5; Box et al, 1978;Box & Wilson, 1951; Khuri & Cornell, 1987). Themaximum predicted yield is indicated by the surfaceconfined in the smallest ellipse in the contourdiagram.

The slope of each contour curve of each variable isalmost independent of the concentration of the other.This contour plot shows that the optimal maltoseconcentration is around 52 g r' (Fig. 1). Sodiumglutamate concentration (Figs 2 & 3) has nointeraction with temperature and agitation (rpm), andis evident from the relatively circular nature of thecontour curves and shows an optimal concentrationaround 12 g i'. Fig. 4 shows no interaction of pH withrpm. The optimum value of pH might be around 7.7.The rpm and temperature have not much interaction(Fig, 5). The optimum value of temperature wasaround 30°C and agitation was 175 rpm.

The neomycin synthesis is mainly influenced bysodium glutamate and rpm of shake flask. Themaltose and sodium glutamate are the key nutrientmaterials, which control the biosynthesis of antibiotic.

2

i.,~'"E~

·2

·2 ·1 0 2

MaHose

Fig, l-e-Contour plot of neomycin yield; the effect of maltose.sodium glutamate and their interaction on neomycin production.Other variables held at zero level

2

·1

·2

Sodium glutamate

Fig, 2--{:ontour plot of neomycin yield; the effect of sodiumglutamate, temperature and their interaction on neomycinproduction. Other variables held at zero level

·1

2

~ 0

·2

Sodium glutamate

Fig, 3--{:ontour plot of neomycin yield; the effect of sodiumglutamate, rpm and their interaction on neomycin production.Other variables held at zero level

At higher concentrations both may cause inhibition ofantibiotic synthesis. Other investigators, who studiedproduction of various antibiotics also suggested thisfact by carbon and nitrogen repression effects(Sathosh et al, 1976, Dulaney, 1948), The alkaline pHof the medium favours the neomycin production,

ADINARAYANA et al.: NEOMYCIN PRODUCTION USING RESPONSE SURFACE METHODOLOGY 569

E 0e-

_1~~

-2~

-2 -1 0 2

pH.

Fig. 4-Contour plot of neomycin yield; the effect of pH, rpm andtheir interaction on neomycin production. Other variables held atzero level

2

E 0e-

-2 -1

Temperature

Fig. 5-Contour plot of neomycin yield; the effect of temperature,rpm and their interaction on neomycin production. Other variablesheld at zero level

Several investigators also reported similar results withthe S. fradiae that pH of the medium is critical in theproduction of neomycin (Sebek, 1955). Thetemperature also plays major role in the neomycinsynthesis. At low and high temperatures, the cellmetabolism may change and cause reduced antibioticformation.

Each of the observed values is compared with thevalues predicted from the model (Table 5). Thecomparison of the residuals with the error variance s,2

(=0.0228) indicates that none of the individualresidual exceeds twice the square root of the residualvariance (Akhnazarova & Kafarov, 1982). All theabove considerations indicate an excellent adequacyof the regression model (Conley, 1984).

The optimum values of selected variables wereobtained by solving the regression equation (eqn. 2).The optimal values of the test variables in coded unitarea are as follows: M = 0.3336, G = 0.2717, pH = -0_5224, T = 0.1289 and R = 0.4823 with the

corresponding Y=3.9136. The natural values obtainedby putting the respective optimum values in equation(1) are: maltose, 51.67 g 1"; sodium glutamate, 12.36g 1"; pH, 7.48; temperature, 30.6°C and agitation,173.8 rpm. The model predicts that the maximumconcentration of antibiotic that can be obtained usingthe above optimum concentrations of the variables is3.9136. The verification of the results using theoptimized conditions was accomplished by carryingout shake flask experiments, which showed a higheryield of antibiotic of about 3.8929 (7815 mg 1").These experimental findings are in close agreementwith the model predictions.

ConclusionIn the present work, the authors have demonstrated

the use of a central composite factorial design bydetermining the conditions leading to the high yield ofantibiotic production. The methodology, if employedwill give success to any process, where an analysis ofthe effects and interactions of many experimentalfactors are required. Central composite experimentaldesign maximizes the amount of information that canbe obtained, while limiting the numbers of individualexperiments. Isoresponse curves are very helpful invisualizing main effects and interaction of the factors.Thus, smaller and lesser time consuming experimentaldesigns could generally suffice for the optimization ofmany fermentation processes.

AcknowledgementThe authors are thankful to University Grants

Commission, New Delhi, for providing financialsupport to carry out this work.

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