Statistical Analysis of Metal Chelating Activity of Centella asiatica ...

Hindawi Publishing CorporationBiotechnology Research InternationalVolume 2013 Article ID 137851 5 pageshttpdxdoiorg1011552013137851

Research ArticleStatistical Analysis of Metal Chelating Activity ofCentella asiatica and Erythroxylum cuneatum UsingResponse Surface Methodology

R J Mohd Salim12 M I Adenan12 A Amid3 M H Jauri1 and A S Sued1

1 Natural Product Division Drug Discovery Centre (DDC) Forest Research Institute Malaysia (FRIM) Jalan KepongSelangor Darul Ehsan 52109 Kepong Malaysia

2Malaysian Institute of Pharmaceuticals and Nutraceuticals Ministry of Science Technology and InnovationUSM 10 Persiaran Bukit Jambul 11900 Bukit Jambul Malaysia

3 Department of Biotechnology Engineering International Islamic University Malaysia GombakPO Box 10 50728 Kuala Lumpur Malaysia

Correspondence should be addressed to R J Mohd Salim roshanjahnfrimgovmy

Received 24 October 2012 Accepted 20 December 2012

Academic Editor Triantafyllos Roukas

Copyright copy 2013 R J Mohd Salim et al This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

Thepurpose of the study is to evaluate the relationship between the extraction parameters and themetal chelating activity ofCentellaasiatica (CA) and Erythroxylum cuneatum (EC)The response surfacemethodology was used to optimize the extraction parametersof methanolic extract of CA and EC with respect to the metal chelating activity For CA Run 17 gave optimum chelating activitywith IC

50= 093mgmL at an extraction temperature of 25∘C speed of agitation at 200 rpm ratio of plant material to solvent at

1 g 45mL and extraction time at 15 hour As for EC Run 13 with 60∘C 200 rpm 1 g 35mL and 1 hour had metal chelating activityat IC50= 03817mgmL Both optimized extracts were further partitioned using a solvent system to evaluate the fraction responsible

for the chelating activity of the plants The hexane fraction of CA showed potential activity with chelating activity at IC50= 0090

and the ethyl acetate fraction of EC had IC50= 0120mgmL The study showed that the response surface methodology helped to

reduce the extraction time temperature and agitation and subsequently improve the chelating activity of the plants in comparisonto the conventional method

1 IntroductionThe knowledge and practice of traditional medicine areuniversal amongst the respected ethnic groups in each coun-try In Malaysia the benefits of herbal medicine are beingconveyed down from one generation to another Latif et al[1] state that there are four sources of traditional Malaysianmedicine namely Malay village medicine (including OrangAsli medicine) Chinese medicine (introduced from China)Indian medicine (introduced from India) and other formsof traditional medicine (including those introduced by theJavanese Sumatrans Arabs Persians Europeans etc)

Centella asiatica (CA) also locally known as pegaga isa crawling plant usually growing wildly in a humid climatearound the globe Its wide medicinal benefits include wound

healing enhancing memory treating mental weariness [2]anti-inflammatory property [3] anticancer activity [4]antilipid peroxidativity [5] and free radical scavenger [6]

Erythroxylum cuneatum forma cuneatum (Miq) Kurz(EC) is a genus of tropical flowering plants in the familyof Erythroxylaceae [7] While CAs are being well studiedfor their various medicinal fortunes Erythroxylum cuneatum(EC) on the other hand has a very limited report on itsmedicinal value In Terengganu the leaves are pounded andapplied on the forehead of women after miscarriage InBunguran Indonesia leaves are reported to be used in Sajur(vegetable soup) [8] It is used in Thai traditional medicinefor antifever purposes as well as an anti-inflammatory agent[9]

2 Biotechnology Research International

Table 1 Parameters to be optimized using response surfacemethod-ology for CA and EC

CA ECTemperature (∘C)(1198831) 25 30 35 55 60 65

Speed (rpm)(1198832) 100 150 200 150 200 250

Ratio (g mL)(1198833) 1 35 1 40 1 45 1 30 1 35 1 40

Time (min)(1198834) 30 60 90 30 60 90

Neurodegenerative disease (ND) results from the deteri-oration of neurons which functionalize the intellectual andcognition ability of a human body [10] Zecca et al [11]reported that iron may engage in a mechanism involvingmany neurodegenerative disorders It was deduced that asthe brain ages iron accumulates in regions that are affectedby Alzheimer and Parkinson diseases diseases categorizedunder ND Thus it is the interest of the research to study theability of CA and EC to chelate the metal iron and furtheroptimize the extraction process of the plants with respect totheir chelating activity

The extraction of plant material for example bioactivecompounds can be affected by more than one factor suchas particle size extraction solvent temperature and time[12] Response surface methodology is a software tool usedto study the interaction that may occur between variablefactors [13] This statistical experimental design is a powerfultool that enables the extraction process conducted effectivelyby verifying the effects of operational factors and theirinteractions [14] The traditional empirical methods onlystudy a single factor at a time and fail to acknowledge theinteraction that they have between each other [15]

2 Materials and Methods

21 Materials Centella asiatica (CA) was purchased fromlocalmarket Pasar Borong Selayang Selangor and Erythrox-ylum cuneatum (EC) was collected from FRIMrsquos compoundMethanol was purchased from Fisher Scientific ethanol fromJ Kollin Corporation Germany and hexane ethyl acetateand n-butanol from Merck USA All chemicals and solventsused were of analytical grade Iron (II) sulfate heptahy-drate (FeSO

4) was a product of Aldrich USA 441015840-[3-(2-

pyridinyl)-124-triazine-56-diyl]bis also known as ferrozinefrom Aldrich USA

22 Methods

221 Response Surface Methodology (RSM) RSM was usedto optimize the conditions for extraction of CA and EC togive the optimum metal chelating activity A face-centeredcube design (FCD) in RSM consisting of 30 experimentalruns including six replications at the center point was chosen

to evaluate the combined effect of the independent variablesThree levels were adopted and coded to low center andhigh levels The experiments were performed in randomorder to minimize the effects of unexplained variabilityin the observed responses due to systematic errors [15]The independent variables were temperature (∘C) speed ofrotation (rpm) ratio of raw material to solvent (g mL)and time of extraction (h) while the response is the metalchelating activity reported in 1IC

50 As the software was

meant to display the response at maximum the inverse IC50

(1IC50) was reported in this study so that the IC

50will be

displayed at its optimum activityThe total of 30 runs designed by Design Expert by com-

bining the parameters for extraction was shown in Table 1The figures for each parameter were deduced from prelimi-nary experiment Each run was performed in triplicate

222 Extraction Process A constant weight of 2 g plants wasused for all the 30 runs while adjusting accordingly to theratio of methanol solvent that was needed in each run asoutlined byDesignExpert softwareTheplantswere extractedin incubator shaker according to the combination parametersas given by each run The extracts were then separated fromthe filtrate and the methanol solvent was removed usingrotary evaporator at 40∘C and at a reduced pressure Theextracts from each run were then subjected to the metalchelating activity

223 Metal Chelating Activity The assay was initiated byadding 250120583L of 25mM FeSO

4to 500120583L sample solutions

CA and EC crude extracts were prepared in a series ofconcentrations This mixture was vortexed briefly for 10seconds before adding 250120583L of 6mM ferrozineThemixturewas vortexed again briefly for 10 seconds and allowed toequilibrate for 10min at room temperature The absorbanceof the mixture (formation of the ferrous iron-ferrozinecomplex) was measured at 562 nm [16] Sample solutionswith appropriate dilutions were used as blanks The abilityof extracts to chelate ferrous ion was calculated relative tothe control (consisting of iron and ferrozine only) using thefollowing formula [17]

Chelating effect

=(Absorbance of control minus Absorbance of sample)

Absorbance of control

times 100

(1)

224 Partitioning Process The crude methanolic extractswere weighed to be 50 g and were suspended in water andthen subjected to liquid-liquid partition by adding hexaneethyl acetate and n-butanol successively The residual partthat was suspended in water which was the water residuefraction [18] and the hexane ethyl acetate and n-butanolfraction were collected and subjected tometal chelating assayas described above

Biotechnology Research International 3

Table 2 Face centered central composite design setting with theindependent variables and their responses in CA

Run number 1198831119883211988331198834

Y1IC50

IC50

1 30 150 40 60 172 058142 30 150 40 60 1429 069983 35 100 35 30 52 019234 30 150 40 60 155 064525 30 150 40 90 196 051026 35 200 35 30 24 041677 30 150 40 60 13426 074488 30 150 35 60 084 119059 30 150 40 30 157 0636910 25 100 45 30 23 0434811 35 200 45 90 2703 0370012 35 100 45 30 201 0497513 25 100 35 30 46 0217414 35 150 40 60 456 0219315 30 200 40 60 1399 0714816 30 150 45 60 094 1063817 25 200 45 90 10753 0093018 35 200 35 90 198 0505119 30 150 40 60 149 0671120 35 100 35 90 084 1190521 30 150 40 60 149 0671122 25 200 45 30 3 0333323 35 200 45 30 029 3448324 25 150 40 60 798 0125325 25 100 45 90 349 0286526 25 100 35 90 112 0892927 25 200 35 30 4167 0240028 30 100 45 90 0098 10204129 35 100 45 90 093 1075330 25 200 35 90 435 02299

3 Results and Discussion

31 Optimization of Extraction with respect to Metal ChelatingActivity The optimum 1IC

50value for CA (referred to in

Table 2) was 10753mgmL (IC50= 0093mgmL) obtained

in the combined interaction of the independent parameter atRun 17 with 25∘C 200 rpm 1 g 45mL ratio and for durationof 15 hour

Table 3 summarized the experimental results for ECThe optimum 1IC

50value of 26196mgmL (IC

50=

03817mgmL) was obtained in Run 13 with temperatureof 60∘C agitation at 200 rpm and ratio of raw material tosolvent 1 g 35mL ratio for extraction duration of 1 hour

32 Multiple Regression Analysis The statistical model wasdeveloped by applying multiple regression analysis methodson using the experimental data for themetal chelating activity

Table 3 Face centered central composite design setting with theindependent variables and their responses in EC

Run number 1198831119883211988331198834

Y1IC50

IC50

1 60 200 35 60 24 041672 55 150 40 30 13 076923 55 250 30 90 14 071434 60 150 35 60 21 047625 60 200 30 60 15 066676 65 150 30 90 076 131587 65 150 30 30 118 084758 60 200 40 60 159 062899 60 200 35 60 26 0384610 55 250 30 30 0909 1100111 65 150 40 30 11 0909112 55 150 30 90 159 0628913 60 200 35 60 262 0381714 65 150 40 90 07 1428615 60 200 35 60 257 0389116 55 250 40 90 139 0719417 60 200 35 90 157 0636918 65 200 35 60 168 0595219 60 200 35 60 256 0390620 55 200 35 60 189 0529121 60 200 35 30 155 0645222 60 250 35 60 21 0476223 65 250 30 90 0833 1200524 65 250 30 30 125 0800025 60 200 35 60 256 0390626 55 150 30 30 128 0781327 65 250 40 30 136 0735328 55 150 40 90 166 0602429 65 250 40 90 0906 1103830 55 250 40 30 11 09091

which is given in (2) for CA and in (3) for EC The responsefunction (119910) measured the 1IC

50value of themetal chelating

activity of the crude extracts CA and EC This value wasrelated to the variables (119860 119861 119862 119863) by a second-degreepolynomial using (2) and (3) which is displayed in termsof coded factors The coefficients of the polynomial wererepresented by a constant term 119860 119861 119862 and 119863 (lineareffects) 1198602 1198612 1198622 and 1198632 (quadratic effects) and 119860119861119860119862 119860119863 119861119862 119861119863 and 119862119863 (interaction effects) The analysisof variance (ANOVA) tables were generated and the effectand regression coefficients of individual linear quadraticand interaction terms were determined The significancesof all terms in the polynomial were judged statistically bycomputing the 119865-value at a probability (119875) of 0001 001 or005 In this case 119860 119861 1198602 1198612 1198622 119860119861 119860119862 119860119863 119861119862 119861119863and 119862119863 are significant model terms On the other handvalues greater than 01000 indicate that the model terms are


Table 4 Fit statistics for the response of 1IC50 value of CA

Standard deviation 014 R-squared 09746Mean 149 Adjusted R-squared 09509CV 940 Predicted R-squared 08608PRESS 162 Adequate precision 27272

Table 5 Fit statistics for the response of 1IC50 value of EC


not significant The regression coefficients were then used tomake statistical calculation to generate contour maps fromthe regression models

IC50= +179 minus 116 lowast 119883

1+ 058 lowast 119883

2+ 0051 lowast 119883

3

+ 014 lowast 1198834+ 420 lowast 119883

1

2minus 132 lowast 119883

2

2

minus 118 lowast 1198833

2031 lowast 119883

4


1lowast 1198832

minus 061 lowast 1198831lowast 1198833minus 057 lowast 119883

1lowast 1198834

+ 043 lowast 1198832lowast 1198833+ 110 lowast 119883

2lowast 1198834

+ 115 lowast 1198833lowast 1198834

(2)

IC50= +229 minus 015 lowast 119883


2+ 0022 lowast 119883

3

minus 0012 lowast 1198834minus 025 lowast 119883

2

2+ 0063 lowast 119883

2

2

+ 0022 lowast 1198833


4

2+ 010 lowast 119883

1lowast 1198832


1lowast 1198834

+ 0026 lowast 1198832lowast 1198833+ 3750 expminus003 lowast 119883

2lowast 1198834

minus 0010 lowast 1198833lowast 1198834

(3)

33 Fit Statistics for the Response Some characteristics of theconstructed model can be explained by details in Table 4 andTable 5 The statistical analysis indicates that the proposedmodel was adequate possessing no significant lack of fitand with satisfactory values of the 119877-squared The quality offit of the polynomial model equation was expressed by thecoefficient of determination (1198772 adjusted 1198772 and adequateprecision) 1198772 is a measure of the amount of variation aroundthe mean explained by the model and equal to 09569 (CA)and 09028 (EC) The closer the value of 119877-squared is tothe unity the better the empirical model fits the actual dataThe smaller the value of 119877-squared is the less relevant thedependent variables in themodel have to explain the behaviorvariation [18] and [19] The adjusted-1198772 is adjusted for thenumber of terms in the model It decreases as the numberof terms in the model increases if those additional terms donot add value to the model Adequate precision is a signal-to-noise ratio It compares the range of the predicted values

at the design points to the average prediction error Ratiosgreater than four indicate adequate model discriminationAs for CA it was 21064 whereas for EC it was 9404 Thestandard deviation of 066 (CA) and 026 (EC) indicatesthat the model designed was acceptable with a minimumdeviation Coefficient of variation (CV) is the standarddeviation expressed as a percentage of the mean which is2534 (CA) and 1612 (EC) CVdescribes the extent towhichthe data were dispersed The small values of CV give betterreproducibility In general a high CV indicates that variationin the mean value is high and does not satisfactorily developan adequate response model [20]

The predicted residual error sum of squares (PRESS) is ameasure of model fitness to each point in the design whichgave an amount of 4629 (CA) and 1612 (EC)

4 Conclusion

The metal chelating activity of CA and EC was optimizedusing statistical analysis to improve the chelating activity ofthe both plants by varying the parameters for the extractionIt shows that the extraction parameters had been optimized(IC50= 0093mgmL at extraction temperature of 25∘C

speed of agitation at 200 rpm ratio of plantmaterial to solventat 1 g 45mL and extraction time at 15 hour) As for EC Run13 with extraction temperature at 60∘C speed of agitationat 200 rpm ratio of plant material to solvent at 1 g 35mLand extraction time at 1 hour had metal chelating activity atIC50= 03817mgmL

Acknowledgment

The authors would like to thank FRIM and MOSTI forproviding fund for the research

References

[1] M Latiff ldquoGenetic resources of medicinal plants in Malaysiardquoin Genetic Resources of Under-Utilised Plants in MalaysiaProceedings of the NationalWorkshop on Plant Genetic ResourcesHeld in Subang Jaya Malaysia A H Zakri Ed MalaysianNational Committee on Plant Genetic Resources KualaLumpur Malaysia 1989

[2] M T Thomas R Kurup A J Johnson et al ldquoElite geno-typeschemotypes with high contents of madecassoside andasiaticoside from sixty accessions of Centella asiatica of southIndia and the Andaman Islands for cultivation and utility incosmetic and herbal drug applicationsrdquo Industrial Crops andProducts vol 32 no 3 pp 545ndash550 2010

[3] L Suguna P Sivakumar and G Chandrakasan ldquoEffects ofCentella asiatica extract on dermal wound healing in ratsrdquoIndian Journal of Experimental Biology vol 34 no 12 pp 1208ndash1211 1996

[4] T D Babu G Kuttan and J Padikkala ldquoCytotoxic and anti-tumour properties of certain taxa of Umbelliferae with specialreference to Centella asiatica (L) Urbanrdquo Journal of Ethnophar-macology vol 48 no 1 pp 53ndash57 1995

[5] S S Katare and M S Ganachari ldquoEffect of Centella asiatica onhypoxia induced convulsions and lithium-pilocarpine induced


status epilepticus and antilipid peroxidation activityrdquo IndianJournal of Pharmacology vol 33 article 128 2001

[6] G Jayashree G K Muraleedhara S Sudarslal and V B JacobldquoAnti-oxidant activity ofCentella asiatica on lymphoma-bearingmicerdquo Fitoterapia vol 74 no 5 pp 431ndash434 2003

[7] R J M Salim Optimisation of extraction of Centella asiaticaand Erythroxylum cuneatum and their evaluation as a neuropro-tective agent [Masterrsquos thesis] International Islamic UniversityMalaysia Selangor Malaysia 2010

[8] A S Sued Kesan ekstrak Centella asiatica Linnaeus dan Ery-throxylum cuneatumForma Cuneatum (Miquel) Kurz ke atassymptom tarikan pada tikus ketagihan morfina dan proteinserummereka [Masterrsquos thesis] Universiti KebangsaanMalaysiaSelangor Malaysia 2009

[9] T Kanchanapoom A Sirikatitham H Otsuka and S Ruchi-rawat ldquoCuneatoside a new megastigmane diglycoside fromErythroxylum cuneatum Blumerdquo Journal of Asian Natural Prod-ucts Research vol 8 no 8 pp 747ndash751 2006

[10] J F Emard J P Thouez and D Gauvreau ldquoNeurodecgnerativediseases and risk factors a literature reviewrdquo Social Science andMedicine vol 40 no 6 pp 847ndash858 1995

[11] L Zecca M B H Youdim P Riederer J R Connor and R RCrichton ldquoIron brain ageing and neurodegenerative disordersrdquoNature Reviews Neuroscience vol 5 no 11 pp 863ndash873 2004

[12] B Yang X Liu and Y Gao ldquoExtraction optimization ofbioactive compounds (crocin geniposide and total phenoliccompounds) from Gardenia (Gardenia jasminoides Ellis) fruitswith response surface methodologyrdquo Innovative Food Scienceand Emerging Technologies vol 10 no 4 pp 610ndash615 2009

[13] J H Kwon J M R Belanger and J R J Pare ldquoOptimization ofmicrowave assisted extraction (MAP) for ginseng componentsby response surface methodologyrdquo Journal of Agricultural andFood Chemistry vol 51 pp 1807ndash1810 2003

[14] W Huang Z Li H Niu D Li and J Zhang ldquoOptimization ofoperating parameters for supercritical carbon dioxide extrac-tion of lycopene by response surface methodologyrdquo Journal ofFood Engineering vol 89 no 3 pp 298ndash302 2008

[15] C S Ku and S PMun ldquoOptimization of the extraction of antho-cyanin from Bokbunja (Rubus coreanus Miq) marc producedduring traditional wine processing and characterization of theextractsrdquo Bioresource Technology vol 99 no 17 pp 8325ndash83302008

[16] T C P Dinis V M C Madeira and L M Almeida ldquoActionof phenolic derivatives (acetaminophen salicylate and 5-aminosalicylate) as inhibitors of membrane lipid peroxidationand as peroxyl radical scavengersrdquo Archives of Biochemistry andBiophysics vol 315 no 1 pp 161ndash169 1994

[17] YWu SW Cui J Tang andXGu ldquoOptimization of extractionprocess of crude polysaccharides from boat-fruited sterculiaseeds by response surface methodologyrdquo Food Chemistry vol105 no 4 pp 1599ndash1605 2007

[18] T Satake K Kamiya Y An T Oishi and J Yamamoto ldquoTheanti-thrombotic active constituents from Centella asiaticardquoBiological and Pharmaceutical Bulletin vol 30 no 5 pp 935ndash940 2007

[19] T M Little and F J Hills Agricultural Experimentation Designand Analysis John Wiley New York NY USA 1978

[20] W W Daniel Biostatistics A Foundation for Analysis in theHealth Sciences vol 503 Wiley New York NY USA 5thedition 1991

Submit your manuscripts athttpwwwhindawicom

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Zoology


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Advances in

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Hindawi Publishing Corporationhttpwwwhindawicom

Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational



Enzyme Research



Microbiology


Table 1 Parameters to be optimized using response surfacemethod-ology for CA and EC

CA ECTemperature (∘C)(1198831) 25 30 35 55 60 65

Speed (rpm)(1198832) 100 150 200 150 200 250

Ratio (g mL)(1198833) 1 35 1 40 1 45 1 30 1 35 1 40

Time (min)(1198834) 30 60 90 30 60 90

Neurodegenerative disease (ND) results from the deteri-oration of neurons which functionalize the intellectual andcognition ability of a human body [10] Zecca et al [11]reported that iron may engage in a mechanism involvingmany neurodegenerative disorders It was deduced that asthe brain ages iron accumulates in regions that are affectedby Alzheimer and Parkinson diseases diseases categorizedunder ND Thus it is the interest of the research to study theability of CA and EC to chelate the metal iron and furtheroptimize the extraction process of the plants with respect totheir chelating activity

The extraction of plant material for example bioactivecompounds can be affected by more than one factor suchas particle size extraction solvent temperature and time[12] Response surface methodology is a software tool usedto study the interaction that may occur between variablefactors [13] This statistical experimental design is a powerfultool that enables the extraction process conducted effectivelyby verifying the effects of operational factors and theirinteractions [14] The traditional empirical methods onlystudy a single factor at a time and fail to acknowledge theinteraction that they have between each other [15]

2 Materials and Methods

21 Materials Centella asiatica (CA) was purchased fromlocalmarket Pasar Borong Selayang Selangor and Erythrox-ylum cuneatum (EC) was collected from FRIMrsquos compoundMethanol was purchased from Fisher Scientific ethanol fromJ Kollin Corporation Germany and hexane ethyl acetateand n-butanol from Merck USA All chemicals and solventsused were of analytical grade Iron (II) sulfate heptahy-drate (FeSO

4) was a product of Aldrich USA 441015840-[3-(2-

pyridinyl)-124-triazine-56-diyl]bis also known as ferrozinefrom Aldrich USA

22 Methods

221 Response Surface Methodology (RSM) RSM was usedto optimize the conditions for extraction of CA and EC togive the optimum metal chelating activity A face-centeredcube design (FCD) in RSM consisting of 30 experimentalruns including six replications at the center point was chosen

to evaluate the combined effect of the independent variablesThree levels were adopted and coded to low center andhigh levels The experiments were performed in randomorder to minimize the effects of unexplained variabilityin the observed responses due to systematic errors [15]The independent variables were temperature (∘C) speed ofrotation (rpm) ratio of raw material to solvent (g mL)and time of extraction (h) while the response is the metalchelating activity reported in 1IC

50 As the software was

meant to display the response at maximum the inverse IC50

(1IC50) was reported in this study so that the IC

50will be

displayed at its optimum activityThe total of 30 runs designed by Design Expert by com-

bining the parameters for extraction was shown in Table 1The figures for each parameter were deduced from prelimi-nary experiment Each run was performed in triplicate

222 Extraction Process A constant weight of 2 g plants wasused for all the 30 runs while adjusting accordingly to theratio of methanol solvent that was needed in each run asoutlined byDesignExpert softwareTheplantswere extractedin incubator shaker according to the combination parametersas given by each run The extracts were then separated fromthe filtrate and the methanol solvent was removed usingrotary evaporator at 40∘C and at a reduced pressure Theextracts from each run were then subjected to the metalchelating activity

223 Metal Chelating Activity The assay was initiated byadding 250120583L of 25mM FeSO

4to 500120583L sample solutions

CA and EC crude extracts were prepared in a series ofconcentrations This mixture was vortexed briefly for 10seconds before adding 250120583L of 6mM ferrozineThemixturewas vortexed again briefly for 10 seconds and allowed toequilibrate for 10min at room temperature The absorbanceof the mixture (formation of the ferrous iron-ferrozinecomplex) was measured at 562 nm [16] Sample solutionswith appropriate dilutions were used as blanks The abilityof extracts to chelate ferrous ion was calculated relative tothe control (consisting of iron and ferrozine only) using thefollowing formula [17]

Chelating effect

=(Absorbance of control minus Absorbance of sample)

Absorbance of control

times 100

(1)

224 Partitioning Process The crude methanolic extractswere weighed to be 50 g and were suspended in water andthen subjected to liquid-liquid partition by adding hexaneethyl acetate and n-butanol successively The residual partthat was suspended in water which was the water residuefraction [18] and the hexane ethyl acetate and n-butanolfraction were collected and subjected tometal chelating assayas described above



Run number 1198831119883211988331198834

Y1IC50

IC50

1 30 150 40 60 172 058142 30 150 40 60 1429 069983 35 100 35 30 52 019234 30 150 40 60 155 064525 30 150 40 90 196 051026 35 200 35 30 24 041677 30 150 40 60 13426 074488 30 150 35 60 084 119059 30 150 40 30 157 0636910 25 100 45 30 23 0434811 35 200 45 90 2703 0370012 35 100 45 30 201 0497513 25 100 35 30 46 0217414 35 150 40 60 456 0219315 30 200 40 60 1399 0714816 30 150 45 60 094 1063817 25 200 45 90 10753 0093018 35 200 35 90 198 0505119 30 150 40 60 149 0671120 35 100 35 90 084 1190521 30 150 40 60 149 0671122 25 200 45 30 3 0333323 35 200 45 30 029 3448324 25 150 40 60 798 0125325 25 100 45 90 349 0286526 25 100 35 90 112 0892927 25 200 35 30 4167 0240028 30 100 45 90 0098 10204129 35 100 45 90 093 1075330 25 200 35 90 435 02299








50=




Run number 1198831119883211988331198834

Y1IC50

IC50

1 60 200 35 60 24 041672 55 150 40 30 13 076923 55 250 30 90 14 071434 60 150 35 60 21 047625 60 200 30 60 15 066676 65 150 30 90 076 131587 65 150 30 30 118 084758 60 200 40 60 159 062899 60 200 35 60 26 0384610 55 250 30 30 0909 1100111 65 150 40 30 11 0909112 55 150 30 90 159 0628913 60 200 35 60 262 0381714 65 150 40 90 07 1428615 60 200 35 60 257 0389116 55 250 40 90 139 0719417 60 200 35 90 157 0636918 65 200 35 60 168 0595219 60 200 35 60 256 0390620 55 200 35 60 189 0529121 60 200 35 30 155 0645222 60 250 35 60 21 0476223 65 250 30 90 0833 1200524 65 250 30 30 125 0800025 60 200 35 60 256 0390626 55 150 30 30 128 0781327 65 250 40 30 136 0735328 55 150 40 90 166 0602429 65 250 40 90 0906 1103830 55 250 40 30 11 09091










IC50= +179 minus 116 lowast 119883

1+ 058 lowast 119883

2+ 0051 lowast 119883

3

+ 014 lowast 1198834+ 420 lowast 119883

1


2

2


2031 lowast 119883

4


1lowast 1198832


1lowast 1198834


2lowast 1198834

+ 115 lowast 1198833lowast 1198834

(2)

IC50= +229 minus 015 lowast 119883


2+ 0022 lowast 119883

3


2

2+ 0063 lowast 119883

2

2

+ 0022 lowast 1198833


4

2+ 010 lowast 119883

1lowast 1198832


1lowast 1198834


2lowast 1198834


(3)




4 Conclusion



Acknowledgment


References






























Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology



Run number 1198831119883211988331198834

Y1IC50

IC50

1 30 150 40 60 172 058142 30 150 40 60 1429 069983 35 100 35 30 52 019234 30 150 40 60 155 064525 30 150 40 90 196 051026 35 200 35 30 24 041677 30 150 40 60 13426 074488 30 150 35 60 084 119059 30 150 40 30 157 0636910 25 100 45 30 23 0434811 35 200 45 90 2703 0370012 35 100 45 30 201 0497513 25 100 35 30 46 0217414 35 150 40 60 456 0219315 30 200 40 60 1399 0714816 30 150 45 60 094 1063817 25 200 45 90 10753 0093018 35 200 35 90 198 0505119 30 150 40 60 149 0671120 35 100 35 90 084 1190521 30 150 40 60 149 0671122 25 200 45 30 3 0333323 35 200 45 30 029 3448324 25 150 40 60 798 0125325 25 100 45 90 349 0286526 25 100 35 90 112 0892927 25 200 35 30 4167 0240028 30 100 45 90 0098 10204129 35 100 45 90 093 1075330 25 200 35 90 435 02299








50=




Run number 1198831119883211988331198834

Y1IC50

IC50

1 60 200 35 60 24 041672 55 150 40 30 13 076923 55 250 30 90 14 071434 60 150 35 60 21 047625 60 200 30 60 15 066676 65 150 30 90 076 131587 65 150 30 30 118 084758 60 200 40 60 159 062899 60 200 35 60 26 0384610 55 250 30 30 0909 1100111 65 150 40 30 11 0909112 55 150 30 90 159 0628913 60 200 35 60 262 0381714 65 150 40 90 07 1428615 60 200 35 60 257 0389116 55 250 40 90 139 0719417 60 200 35 90 157 0636918 65 200 35 60 168 0595219 60 200 35 60 256 0390620 55 200 35 60 189 0529121 60 200 35 30 155 0645222 60 250 35 60 21 0476223 65 250 30 90 0833 1200524 65 250 30 30 125 0800025 60 200 35 60 256 0390626 55 150 30 30 128 0781327 65 250 40 30 136 0735328 55 150 40 90 166 0602429 65 250 40 90 0906 1103830 55 250 40 30 11 09091










IC50= +179 minus 116 lowast 119883

1+ 058 lowast 119883

2+ 0051 lowast 119883

3

+ 014 lowast 1198834+ 420 lowast 119883

1


2

2


2031 lowast 119883

4


1lowast 1198832


1lowast 1198834


2lowast 1198834

+ 115 lowast 1198833lowast 1198834

(2)

IC50= +229 minus 015 lowast 119883


2+ 0022 lowast 119883

3


2

2+ 0063 lowast 119883

2

2

+ 0022 lowast 1198833


4

2+ 010 lowast 119883

1lowast 1198832


1lowast 1198834


2lowast 1198834


(3)




4 Conclusion



Acknowledgment


References






























Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology







IC50= +179 minus 116 lowast 119883

1+ 058 lowast 119883

2+ 0051 lowast 119883

3

+ 014 lowast 1198834+ 420 lowast 119883

1


2

2


2031 lowast 119883

4


1lowast 1198832


1lowast 1198834


2lowast 1198834

+ 115 lowast 1198833lowast 1198834

(2)

IC50= +229 minus 015 lowast 119883


2+ 0022 lowast 119883

3


2

2+ 0063 lowast 119883

2

2

+ 0022 lowast 1198833


4

2+ 010 lowast 119883

1lowast 1198832


1lowast 1198834


2lowast 1198834


(3)




4 Conclusion



Acknowledgment


References






























Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology

























Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology

Statistical Analysis of Metal Chelating Activity of Centella asiatica ...

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