Hansch analysis of substituted benzoic acid benzylidene/furan-2-yl-methylene hydrazides as antimicrobial agents

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European Journal of Medicinal Chemistry 44 (2009) 1853–1863

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European Journal of Medicinal Chemistry

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Original article

Hansch analysis of substituted benzoic acid benzylidene/furan-2-yl-methylenehydrazides as antimicrobial agents

Pradeep Kumar a, Balasubramanian Narasimhan b,*, Deepika Sharma a, Vikramjeet Judge a,Rakesh Narang a

a Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar 125001, Indiab Faculty of Pharmaceutical Sciences, Maharshi Dayanand University, Rohtak 124001, India

a r t i c l e i n f o

Article history:Received 24 July 2008Received in revised form25 October 2008Accepted 31 October 2008Available online 5 November 2008

Keywords:Substituted hydrazidesAntibacterial activityAntifungal activityQSAR

* Corresponding author. Tel.: þ91-1262-272535; faE-mail address: [email protected] (B. Naras

0223-5234/$ – see front matter � 2008 Elsevier Masdoi:10.1016/j.ejmech.2008.10.034

a b s t r a c t

A series of substituted hydrazide derivatives have been synthesized and screened for their in vitroantimicrobial activities against five representative microorganisms. The results of antimicrobial studyindicated that the presence of electron withdrawing groups on the benzoic acid moiety improvedantimicrobial activity. Further, the presence of heterocyclic ring furan does not improve the antimicrobialactivity of substituted hydrazides. To understand the relationship between physicochemical parametersand antimicrobial activity of substituted hydrazide derivatives, QSAR investigation was performed by thedevelopment of one-target and multi-target models. The multi-target model was found to be effective indescribing the antimicrobial activity of substituted hydrazides in comparison to the one-target models.Further, it indicated the importance of the topological parameter, valence third order molecularconnectivity index (3cv) and the electronic parameter, energy of highest occupied molecular orbital(HOMO) in describing the antimicrobial activity of substituted hydrazides.

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1. Introduction

The development of antimicrobial agents (antibacterials andantifungals) to treat infections has been one of the most notablemedical achievements of the past century. The advances in medicalcare are threatened, however, by a natural phenomenon known as‘‘antimicrobial resistance.’’ The increased use of antibacterial andantifungal agents in recent years has resulted in the development ofresistance to these drugs with important implications formorbidity, mortality and health care costs. In spite of a largenumber of antibiotics and chemotherapeutics available for medicaluse, the antimicrobial resistance created a substantial need of newclass of antimicrobial agents in the last decades [1–3]. Hydrazide-hydrazones form a class of compounds possessing a wide range ofbiological activities viz. antimicrobial [4], antimycobacterial [5],antitumour [6], anti-inflammatory [7], trypanocidal [8], leishma-nicidal [9], anti-HIV [10], inhibitor of anthrax lethal factor [11],antidiabetic [12] and antimalarial agents [13].

Quantitative structure–activity relationship (QSAR) is a meth-odology used to correlate biological property of molecule withmolecular descriptors derived from chemical structures. It isa mathematical model of statistically validated correlation betweenthe chemical structures and their activity profile [14].

x: þ91-1262-274133.imhan).

son SAS. All rights reserved.

In view of the above and in continuation of our ongoing researchprogram in the field of synthesis, antimicrobial and QSAR studies ofmedicinally important compounds [15–25], in the present study wehereby report the synthesis, antimicrobial and QSAR evaluation ofsubstituted benzoic acid benzylidene/furan-2-yl-methylene hydrazides.

2. Chemistry

The synthesis of compounds 1–26 followed the general pathwayelicited in Scheme 1. The substituted benzoic acid was treated withthionyl chloride to get the corresponding acid chloride which inturn was reacted with hydrazine hydrate to get the correspondingacid hydrazide. The treatment of hydrazide with differentsubstituted aldehydes gave the corresponding substituted benzoicacid benzylidene/furan-2-yl-methylene hydrazides (1–26). Thephysicochemical characteristics of synthesized compounds arepresented in Supplementary data. The structures of compounds 1–26 were found in agreement with the assigned molecular structureconfirmed by their consistent IR and NMR spectra.

3. Results and discussion

3.1. Antimicrobial activity

The antimicrobial activity of the synthesized compounds wasdetermined by tube dilution method [26]. In case of Staphylococcus

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R1R2

R3

R4 R5

COOH + SOCl2

R1R2

R3

R4 R5

COCl + NH2NH2

R1R2

R3

R4 R5

CONHNH2 + OHC

R1R2

R3

R4 R5

CONHN=CH XX3 h

2h

30min

Scheme 1. Scheme for syntheses of substituted benzoic acid benzylidene/furan-2-yl-methylene hydrazides.

P. Kumar et al. / European Journal of Medicinal Chemistry 44 (2009) 1853–18631854

aureus, compounds 17 and 25 were found to be most active withpMIC value of 2.65 and 2.67 respectively. Against Bacillus subtilis,compounds 12 and 17 emerged as the most potential candidates. Inthe case of Escherichia coli, the dinitro compound (18) andcompound with chloro and nitro substituents (25) were found to beactive. Compound 18 emerged as most active one against Candidaalbicans. Against Aspergillus niger compounds 18 and 22 emerged aseffective antifungal agents. The results of antimicrobial studies arepresented in Table 1 (pMIC values for ot-QSAR model development)and Table 2 (average pMIC values for mt-QSAR model development).

3.2. QSAR studies

3.2.1. Development of one-target QSAR modelIn order to identify substituent effect on antimicrobial activity,

quantitative structure–activity relationship (QSAR) studies of titlecompounds were performed. Biological activity data (MIC) werecalibrated to their logarithmic values (pMIC) in micromoles and arelisted in Table 1. The compounds were analyzed by physicochem-ical-based QSAR (Hansch) approach using different physicochem-ical parameters [27–33] (Supplementary data) as independentand pMIC values as dependent variables. These QSAR descriptorsof substituted hydrazides were calculated using the molecularpackage TSAR 3.3 for Windows [34] and the values of selecteddescriptors used in the regression analysis are presented in Table 3.

A correlation analysis was performed on all the descriptors,depending on the intercorrelation among the independentdescriptors and also their individual correlation with antimicrobialactivity. The correlation matrix elicited in Table 4 indicates thecorrelation of antibacterial activity of substituted hydrazides withS. aureus. The correlation matrix depicted in Table 4 indicated thatthere is a high autocorrelation (r> 0.7) observed between themolecular descriptors except with log P. The high and low auto-correlations were observed between k1 and 0c (r¼ 0.999) andlog P and 1c (r¼�0.005) respectively.

The antibacterial activity of substituted hydrazides against S.aureus is best described using the molecular descriptor, 3cv

(r¼ 0.910, Eq. (1), Table 5).

3.2.1.1. QSAR model for antibacterial activity against S. aureus.

pMICsa ¼ 3:3813cv þ 0:485 (1)

n¼ 26, r¼ 0.910, r2¼ 0.828, q2¼ 0.795, s¼ 0.126, F¼ 116.18.

Here and thereafter, n – number of data points, r – multiplecorrelation coefficient, q2 – cross-validated r2 obtained by leave oneout (LOO) technique, s – standard error of the estimate and F –Fischer statistics.

The topological index, 3cv, signifies the degree of branching,connectivity of atoms and the unsaturation in the molecule whichaccounts for variation in activity [35]. The positive coefficient of 3cv

in Eq. (1) indicates that there is a positive correlation between theantibacterial activity of substituted hydrazides and 3cv. This isevidenced by the antibacterial activity data of substituted hydra-zides (Table 1) and their 3cv values (Table 3). Compounds 17 and 25with maximum 3cv values of 0.64 and 0.64 respectively havemaximum antibacterial activity against S. aureus (Compound 17,pMICsa¼ 2.65; compound 25, pMICsa¼ 2.67). Similarly thecompound 1 having minimum 3cv value has minimum antibacterialactivity (compound 1, 3cv¼ 0.32, pMICsa¼ 1.61).

Squared correlation coefficient (r2) of 0.828 in Eq. (1) explains82.8% variance in antibacterial activity against S. aureus. Eq. (1)also indicated the statistical significance of >99.9% with F value of116.18. Similarly the cross-validation of obtained Eq. (1) wassubsequently checked by employing ‘‘leave one out’’ (LOO)method. The q2 value of Eq. (1) (q2¼ 0.795) qualifies it to bea valid model according to the recommendations of Golbraikh andTrophsa [36].

In order to confirm our results we have predicted the antibac-terial activity of substituted hydrazides against S. aureus using Eq.(1). The comparison of observed and predicted values (Supple-mentary data) demonstrated that they are close to each otherevidenced by the low residual activity values. Further, it is sup-ported by the plot of pMICsa observed vs pMICsa predicted (Fig. 1).

To determine the existence of systemic error in the modeldevelopment we have plotted pMICsa observed against pMICsa

residual values (Fig. 2). The propagation of residuals on both sidesof zero indicates that there is no systemic error in the developmentof QSAR model [37]. The QSAR models elicited in Eqs. (2)–(5) weredeveloped to predict the antimicrobial activity of substitutedhydrazide derivatives against B. subtilis, E. coli, C. albicans andA. niger.

3.2.1.2. QSAR model for antibacterial activity against B. subtilis.

pMICbs ¼ 1:8393cv þ 1:478 (2)

n¼ 26, r¼ 0.843, r2¼ 0.712, q2¼ 0.648, s¼ 0.096, F¼ 59.38.

1

R1,R4,R5=H; R2,R3= OCH3;

X = O 14 R1,R2,R4,R5=H; R3=NO2; X =

NO2

2

R1,R3,R5=H;R2,R4=NO2;

X = O 15 R1,R2,R4,R5=H; R3=Cl; X =

NO2

3

R1,R3,R5=H;R2,R4=NO2;

X = OMe

16 R1,R4,R5=H; R2,R3= OCH3; X =

NO2

4

R1,R2,R4,R5=H;R3=CH3;

X = OMe

17 R2,R3,R5=H; R1=Cl; R4=NO2; X =

NO2

5

R1,R2,R4,R5=H; R3=NH2 ;

X =OMe

18 R1,R3,R5=H;R2,R4=NO2; X =

OMe

OMe

6

R1,R2,R4,R5=H; R3=NO2;

X =OMe

19 R1,R4,R5=H; R2,R3= OCH3; X =

OMe

OMe

7

R1,R4,R5=H; R2,R3= OCH3;

X = OMe

20 R1,R2,R4,R5=H;R3=CH3; X =

OMe

OMe

8

R1,R2,R4,R5=H;R3=CH3;

X = O 21 R1,R2,R4,R5=H; R3=NH2 ; X =

OMe

OMe

9

R1,R3,R5=H;R2,R4=NO2;

X =

NO2

22 R2,R3,R4,R5=H;R1=Br; X =

OMe

OMe

10

R1,R2,R4,R5=H;R3=CH3;

X =

NO2

23 R1,R3,R4,R5=H; R2=NO2; X =

OMe

OMe

11

R1,R2,R4,R5=H; R3=NH2 ;

X =

NO2

24 R1,R2,R4,R5=H; R3=NO2; X =

OMe

OMe

12

R2,R3,R4,R5=H;R1=Br;

X =

NO2

X =

NO2

25 R2,R3,R5=H; R1=Cl; R4=NO2; X =

OMe

OMe

13 R1,R3,R4,R5=H; R2=NO2; 26 R1,R2,R4,R5=H; R3=Cl; X =

OMe

OMe

Scheme 1. (continued).

P. Kumar et al. / European Journal of Medicinal Chemistry 44 (2009) 1853–1863 1855

3.2.1.3. QSAR model for antibacterial activity against E. coli.

pMICec ¼ 0:227ka3 þ 1:527 (3)

n¼ 26, r¼ 0.899, r2¼ 0.809, q2¼ 0.762, s¼ 0.044, F¼ 101.86.

3.2.1.4. QSAR model for antifungal activity against C. albicans.

pMICca ¼ 1:046Bþ 0:042 (4)

n¼ 26, r¼ 0.815, r2¼ 0.663, q2¼ 0.613, s¼ 0.034, F¼ 47.41.

3.2.1.5. QSAR model for antifungal activity against A. niger.

pMICan ¼ 0:162HOMOþ 3:269 (5)

n¼ 26, r¼ 0.614, r2¼ 0.377, q2¼ 0.278, s¼ 0.107, F¼ 14.52.

As in the case of S. aureus Eq. (2) describes the importance ofvalence third order molecular connectivity index (3cv) in demon-strating antibacterial activity of substituted hydrazide derivativesagainst B. subtilis. Here also similar to S. aureus, positive correlationhas been observed with 3cv and antibacterial activity againstB. subtilis.

The model depicted in Eq. (3) describes the statistically signifi-cant relationship between Kiers alpha shape topological index ka3

in describing the antibacterial activity of substituted hydrazidederivatives against E. coli. In this case also the positive relationshipwas observed between ka3 and antibacterial activity against E. coli.

The model described in Eq. (4) indicated the importance oftopological parameter, Balaban topological index (r¼ 0.815, Eq. (4))in demonstrating antifungal activity of substituted hydrazidederivatives against C. albicans. The model described in Eq. (5)

Table 1Antimicrobial activity of substituted hydrazide derivatives for the development ofone-target QSAR model.

Comp pMICsa pMICbs pMICec pMICca pMICan

1 1.61 1.98 2.30 1.64 1.872 1.99 2.29 2.47 1.69 1.693 2.20 2.42 2.64 1.74 1.804 2.24 2.32 2.54 1.63 1.885 1.63 2.24 2.54 1.63 1.806 1.68 2.25 2.56 1.62 1.987 2.17 2.58 2.58 1.68 1.688 1.89 2.19 2.28 1.56 1.909 2.34 2.48 2.63 1.76 1.7610 2.14 2.40 2.47 1.59 1.8511 1.98 2.29 2.56 1.66 1.8012 2.35 2.65 2.49 1.75 1.7513 1.99 2.30 2.61 1.70 1.7014 1.99 2.09 2.61 1.70 1.7015 2.59 2.59 2.59 1.69 1.6916 2.05 2.33 2.63 1.66 1.7217 2.65 2.65 2.65 1.75 1.5918 2.38 2.51 2.68 1.78 2.0819 2.07 2.34 2.64 1.74 2.0420 2.17 2.58 2.58 1.68 1.7521 1.94 2.29 2.49 1.68 1.9022 2.52 2.35 2.63 1.76 2.0623 2.05 2.28 2.63 1.64 1.7524 1.99 2.34 2.63 1.72 1.7525 2.67 2.62 2.67 1.76 1.9526 2.61 2.61 2.61 1.71 2.01SD 0.30 0.18 0.10 0.06 0.13Std. 2.61a 2.61a 2.61a 2.64b 2.64b

a Norfloxacin.b Fluconazole.


indicated the importance of electronic parameter, energy of highestoccupied molecular orbital (HOMO) (r¼ 0.614, Eq. (5)) in demon-strating antifungal activity of substituted hydrazide derivativesagainst A. niger. This is evidenced by the high HOMO values (�8.70,�8.35, �8.38, Table 3) of compounds 18, 19 and 22 having highactivity values (2.08, 2.04 and 2.06, Table 1).

Table 2Antimicrobial activity of substituted hydrazide derivatives for the development ofmulti-target QSAR model.

Comp pMICb pMICf pMICam

1 1.96 1.76 1.882 2.25 1.69 2.033 2.42 1.77 2.164 2.37 1.76 2.125 2.14 1.72 1.976 2.16 1.80 2.027 2.44 1.68 2.148 2.12 1.73 1.969 2.48 1.76 2.1910 2.34 1.72 2.0911 2.28 1.73 2.0612 2.50 1.75 2.2013 2.30 1.70 2.0614 2.23 1.70 2.0215 2.59 1.69 2.2316 2.34 1.69 2.0817 2.65 1.67 2.2618 2.52 1.93 2.2919 2.35 1.89 2.1720 2.44 1.72 2.1521 2.24 1.79 2.0622 2.50 1.91 2.2623 2.32 1.70 2.0724 2.32 1.74 2.0925 2.65 1.86 2.3326 2.61 1.86 2.31

The electronic parameter HOMO, which denotes the energy ofhighest occupied molecular orbital directly relates to the electronaffinity and characterizes the sensibility of the molecule towards anattack by electrophile [38]. The contribution of HOMO in describingantifungal activity may be attributed to the interaction ofsubstituted hydrazide derivatives with electrophilic amino acidresidue of fungi [39].

Similar to Eq. (1) the high q2 values (q2> 0.5) obtained by leaveone out technique and the observance of low residual values(Supplementary data) indicated the validity and predictability ofEqs. (2)–(4). Further their predictability was supported by the plotof their predicted pMIC against observed pMIC (Figs. 3–5).

The cross-validation of the models was also done by leave oneout (LOO) technique. The cross-validated correlation coefficient(q2> 0.5) values obtained for the best QSAR models indicated theirreliability in predicting the antimicrobial activity of substitutedhydrazide derivatives. In the case of A. niger the q2 value is less than0.5 which shows that the developed model is an invalid one. Butone should not forget the recommendations of Golbraikh andTropsha [36] who have recently reported that the only way toestimate the true predictive power of a model is to test theirability to predict accurately the biological activities of compounds.As the observed and predicted values are close to each other(Supplementary data), the QSAR model for A. niger (Eq. (5)) is avalid one.

It is important to note that Eqs. (1)–(5) are derived using theentire data set and no outliers were found during model develop-ment. Even though the sample size and the ‘Rule of Thumb’ allowedus to go for development of tetra-parametric model in multiplelinear regression analysis, the high multico-linearity among theparameters restricted us for mono-parametric model. The ‘rule ofthumb’ gives information about the number of parameters to beselected for regression analysis in QSAR based on the number ofcompounds [21]. According to this rule for QSAR model develop-ment one should select one parameter for a five-compound data set.

The multi-colinearity occurs when two independent variablesare correlated with each other. One should note that the change insigns of the coefficients, a change in the values of previous coeffi-cient, change of significant variable into insignificant one or anincrease in standard error of the estimate on addition of an addi-tional parameter to the model are indications of high interrela-tionship among descriptors [21].

Generally for QSAR studies, the biological activities ofcompounds should span 2–3 orders of magnitude. But in thepresent study the range of antimicrobial activities of the synthe-sized compounds is within one order of magnitude. But it isimportant to note that the predictability of the QSAR modelsdeveloped in the present study is high evidenced by the lowresidual values (Supplementary data) This is in accordance withresults suggested by Bajaj et al. [40], who stated that the reliabilityof the QSAR model lies in its predictive ability even though theactivity data are in the narrow range. Further, recent literaturereveals that the QSAR has been applied to describe the relationshipbetween narrow range of biological activity and physicochemicalproperties of the molecules [41,42]. When biological activity datalie in the narrow range, the presence of minimum standard devi-ation of the biological activity justifies its use in QSAR studies[19,22]. The minimum standard deviation (Table 1) observed in theantimicrobial activity data justifies its use in QSAR studies.

3.2.2. Development of multi-target modelAccording to the above ot-QSAR (one-target QSAR) models one

should use five different equations with different errors to predictthe activity of a new compound against the five microbial species.The ot-QSAR models, which are almost in all the literature, becomeunpractical or at less complicated to use when we have to predict

Table 3Values of selected descriptors used in the regression analysis.

Comp log P MR 0c HOMO 1c 1cv 2c 2cv 3c 3cv ka1 ka2 ka3

1 1.87 73.06 1.56 �8.64 9.74 5.83 7.97 3.78 0.94 0.32 14.68 7.17 3.892 2.28 74.79 1.62 �9.12 10.47 5.78 9.53 3.97 1.61 0.43 15.86 6.96 4.243 3.08 88.86 1.60 �8.94 11.90 6.82 10.68 4.72 1.81 0.52 18.56 8.36 5.104 3.64 79.25 1.49 �8.51 9.69 6.23 8.22 4.34 1.10 0.47 14.51 7.04 4.325 2.39 78.91 1.49 �8.45 9.69 6.02 8.22 4.09 1.10 0.39 14.47 7.01 4.306 3.13 81.53 1.51 �8.80 10.60 6.32 9.12 4.28 1.31 0.41 16.04 7.61 4.597 3.39 85.71 1.56 �8.69 10.63 6.76 8.94 4.68 1.23 0.52 16.43 7.90 4.538 2.84 65.18 1.48 �8.63 8.25 5.19 7.08 3.58 0.90 0.37 11.83 5.62 3.419 3.29 89.72 1.64 �9.91 12.27 6.79 11.42 4.80 2.11 0.57 19.15 8.27 5.1610 3.85 80.11 1.55 �9.37 10.06 6.21 8.96 4.42 1.40 0.51 15.10 6.92 4.3411 2.60 79.77 1.55 �8.90 10.06 5.99 8.96 4.17 1.40 0.44 15.06 6.90 4.3212 4.17 82.69 1.59 �9.42 10.08 6.71 8.87 4.90 1.31 0.62 15.57 7.26 4.3213 3.33 82.39 1.57 �9.69 10.97 6.29 9.88 4.36 1.61 0.45 16.63 7.50 4.6214 3.33 82.39 1.56 �9.73 10.97 6.29 9.86 4.36 1.61 0.45 16.63 7.50 4.6215 3.90 79.87 1.55 �9.48 10.06 6.30 8.96 4.53 1.40 0.54 15.38 7.13 4.4916 2.87 88.00 1.60 �9.17 11.55 6.85 9.85 4.62 1.44 0.46 17.97 8.47 4.8117 3.85 87.20 1.61 �9.69 11.38 6.81 10.40 4.91 1.81 0.64 17.90 7.89 4.7718 2.83 95.32 1.65 �8.70 12.85 7.35 11.40 5.07 1.94 0.57 20.49 9.21 5.3319 2.42 93.60 1.60 �8.35 12.12 7.40 9.83 4.88 1.27 0.47 19.31 9.46 4.9820 3.39 85.71 1.57 �8.35 10.63 6.76 8.94 4.68 1.23 0.52 16.43 7.90 4.5321 2.14 85.37 1.57 �8.29 10.63 6.55 8.94 4.44 1.23 0.44 16.39 7.87 4.5122 3.71 88.29 1.61 �8.38 10.65 7.26 8.85 5.17 1.14 0.63 16.90 8.25 4.5223 2.87 88.00 1.59 �8.56 11.55 6.85 9.85 4.62 1.44 0.46 17.97 8.47 4.8124 2.87 88.00 1.58 �8.59 11.55 6.85 9.84 4.62 1.44 0.46 17.97 8.47 4.8125 3.39 92.80 1.64 �8.56 11.96 7.36 10.38 5.18 1.64 0.64 19.24 8.84 4.9426 3.44 85.48 1.57 �8.42 10.63 6.86 8.94 4.79 1.23 0.55 16.72 8.11 4.68


each compound results for more than one target. In these cases wehave to develop one ot-QSAR for each target. However, veryrecently the interest has been increased in the development ofmulti-target QSAR (mt-QSAR) models. In opposition to ot-QSAR,the mt-QSAR model is a single equation that considers the nature ofmolecular descriptors which are common and essential fordescribing the antimicrobial activity [43–47].

In the present study we have attempted to develop threedifferent types of mt-QSAR (multi-target QSAR) models viz. mt-QSAR model for describing antibacterial activity of synthesizedcompounds against S. aureus, B. subtilis and E. coli, mt-QSAR modelfor describing antifungal activity of synthesized compounds againstC. albicans and A. niger as well a common mt-QSAR model fordescribing the antimicrobial activity of substituted hydrazidederivatives against all the aforementioned microorganisms.

In order to develop mt-QSAR models initially we have calculatedthe average antibacterial activity [pMICb¼ (pMICsaþ pMICbsþpMICec)/3], antifungal activity [pMICf¼ (pMICcaþ pMICan)/2] andantimicrobial activity values [pMICam¼ (pMICsaþ pMICbsþpMICecþ pMICcaþ pMICan)/5] of substituted hydrazide derivatives

Table 4Correlation matrix for pMICsa with molecular descriptors.

pMICsa log P MR 0c 0cv 1c 1cv 2c

pMICsa 1.000 0.692 0.495 0.381 0.522 0.356 0.585 0.399log P 1.000 0.173 0.029 0.143 �0.005 0.262 0.107MR 1.000 0.883 0.952 0.910 0.964 0.7860c 1.000 0.790 0.994 0.781 0.9710cv 1.000 0.835 0.989 0.6471c 1.000 0.819 0.9411cv 1.000 0.6572c 1.0002cv

3c3cv

k1

k2

k3

ka1

ka2

ka3

which are presented in Table 2. These average activity valueswere correlated with the molecular descriptors of synthesizedcompounds (Table 5). The data depicted in Table 5 indicated thatthe pMICb, pMICf and pMICam are equally correlated with themolecular descriptors as compared to ot-MIC values i.e. pMICsa,pMICbs, pMICec, pMICca and pMICan.

The mt-QSAR model for antibacterial activity signified theimportance of topological parameter valence third order molecularconnectivity index 3cv in describing the antibacterial activity ofsubstituted hydrazides (Eq. (6)).

3.2.2.1. mt-QSAR model for antibacterial activity.

pMICb ¼ 1:9923cv þ 1:386 (6)

n¼ 26, r¼ 0.935, r2¼ 0.875, q2¼ 0.846, s¼ 0.062, F¼ 167.35.The mt-QSAR model for antifungal activity of substituted hydra-

zides indicated the importance of topological parameter, valence zeroorder molecular connectivity index, 0cv and electronic parameter,energy of highest occupied molecular orbital, HOMO (Eq. (7)).

2cv 3c 3cv k1 k2 k3 ka1 ka2 ka3

0.759 0.404 0.910 0.372 0.297 0.289 0.426 0.363 0.4380.500 0.193 0.728 0.018 �0.047 0.107 0.029 �0.034 0.1490.902 0.570 0.637 0.893 0.932 0.779 0.936 0.951 0.9360.711 0.834 0.491 0.999 0.964 0.929 0.982 0.874 0.9530.906 0.387 0.630 0.804 0.874 0.596 0.887 0.964 0.8250.726 0.772 0.467 0.997 0.986 0.907 0.993 0.918 0.9570.953 0.418 0.710 0.793 0.852 0.618 0.872 0.935 0.8390.639 0.941 0.507 0.963 0.878 0.957 0.914 0.737 0.9131.000 0.476 0.888 0.716 0.727 0.582 0.786 0.799 0.785

1.000 0.488 0.816 0.666 0.891 0.731 0.471 0.7611.000 0.482 0.412 0.407 0.532 0.461 0.551

1.000 0.973 0.925 0.987 0.889 0.9571.000 0.869 0.983 0.959 0.952

1.000 0.864 0.711 0.9381.000 0.945 0.952

1.000 0.8871.000

Table 5Correlation of molecular descriptors with antimicrobial activity.

Mol. descriptor pMICsa pMICbs pMICec pMICca pMICan pMICb pMICf pMICam

log P 0.692 0.652 0.329 0.232 �0.187 0.690 �0.084 0.595MR 0.495 0.498 0.875 0.751 0.212 0.625 0.479 0.6950c 0.381 0.343 0.800 0.749 0.041 0.489 0.323 0.5360cv 0.522 0.488 0.761 0.745 0.338 0.613 0.592 0.7131c 0.356 0.324 0.806 0.738 0.094 0.469 0.367 0.5301cv 0.585 0.555 0.807 0.764 0.295 0.683 0.560 0.7662c 0.399 0.358 0.766 0.726 �0.087 0.499 0.197 0.5122cv 0.759 0.717 0.801 0.802 0.202 0.839 0.488 0.8873c 0.404 0.362 0.630 0.628 �0.247 0.478 0.013 0.4443cv 0.910 0.844 0.614 0.738 0.048 0.935 0.322 0.926k1 0.372 0.336 0.804 0.745 0.058 0.482 0.337 0.533k2 0.297 0.281 0.811 0.694 0.161 0.421 0.411 0.498k3 0.289 0.299 0.812 0.606 �0.089 0.425 0.150 0.432ka1 0.426 0.384 0.814 0.779 0.142 0.532 0.426 0.601ka2 0.363 0.336 0.777 0.704 0.299 0.472 0.541 0.575ka3 0.438 0.437 0.900 0.722 0.092 0.575 0.359 0.622R 0.356 0.324 0.806 0.738 0.094 0.469 0.367 0.530B 0.535 0.420 0.542 0.815 0.032 0.554 0.338 0.596W 0.341 0.307 0.795 0.709 0.106 0.452 0.367 0.514Te �0.385 �0.305 �0.719 �0.764 0.014 �0.463 �0.278 �0.501LUMO �0.216 �0.127 �0.427 �0.451 0.249 �0.248 0.055 �0.219HOMO �0.181 �0.125 �0.110 �0.171 0.614 �0.171 0.497 �0.029


3.2.2.2. mt-QSAR model for antifungal activity.

pMICf ¼ 0:0310cv þ 0:056HOMOþ 1:879 (7)

n¼ 26, r¼ 0.697, r2¼ 0.486, q2¼ 0.353, s¼ 0.056, F¼ 10.86.Further, the mt-QSAR model for antimicrobial activity of

substituted hydrazides demonstrated the involvement of topolog-ical parameter, valence third order molecular connectivity index,3cv and electronic parameter, energy of highest occupied molecularorbital, HOMO in describing the antimicrobial activity (Eq. (8)).

3.2.2.3. mt-QSAR model for antimicrobial activity.

pMICam ¼ 1:3673cv þ 0:042HOMOþ 1:819 (8)

n¼ 26, r¼ 0.943, r2¼ 0.890, q2¼ 0.854, s¼ 0.039, F¼ 93.01.The predictability of Eqs. (6)–(8) was verified by calculating the

antibacterial, antifungal and antimicrobial activities for substituted

Observed pMICsa

2.82.62.42.22.01.81.6

Pred

icted

p

MIC

sa

2.8

2.6

2.4

2.2

2.0

1.8

1.6

1.4

Fig. 1. Plot of predicted pMICsa activity values against the experimental pMICsa valuesfor the linear regression developed model by Eq. (1).

hydrazides respectively. The low residual activity values indicatedthe high predictability of above mentioned equations which is clearfrom the plot of their observed pMIC and predicted pMIC (Figs.6–8). The propagation of activity values on both sides of zero, in theplot of observed pMICam against residual pMICam, indicated thatthere was no systemic error in the development of mt-QSARmodels (Fig. 9).

It is important to note that residual activity values in the case ofmt-QSAR model for antimicrobial activity are less when comparedto the residuals of one-target models as well the multi-targetmodels for antibacterial and antifungal activities (Table 6). So thismt-QSAR equation (Eq. (8)) can be used to predict the activity ofsubstituted hydrazides against different microbial species. Furtherit indicated the importance of topological parameter, valence thirdorder molecular connectivity index, 3cv and electronic parameter,energy of highest occupied molecular orbital, HOMO in describingthe antimicrobial activity of substituted hydrazides.

It is important to mention here that the development ofmulti-target models exhibited the following advantages: (a). Thereis an improvement in statistical parameters when compared to

Observed pMICsa

2.82.62.42.22.01.81.6

Pred

icted

p

MIC

sa

2.8

2.6

2.4

2.2

2.0

1.8

1.6

1.4

Fig. 2. Plot of residual pMICsa values against the experimental pMICsa values.

Observed pMICca

1.81.71.61.5

Pre

dic

te

d p

MIC

ca

1.8

1.7

1.6

1.5

Fig. 5. Plot of predicted pMICca activity values against the experimental pMICca valuesfor the linear regression developed model by Eq. (4).

Observed pMICbs

2.72.62.52.42.32.22.12.01.9

Pred

icted

p

MIC

bs

2.7

2.6

2.5

2.4

2.3

2.2

2.1

2.0

Fig. 3. Plot of predicted pMICbs activity values against the experimental pMICbs valuesfor the linear regression developed model by Eq. (2).


one-target models (improvement in r, q2, etc. cf. Eq. (8) with Eqs.(1)–(7)). (b). No change in the trend of molecular descriptors hasbeen observed i.e. the both 3cv and HOMO showed the samepositive correlation with antimicrobial activity as observed in thecase of one-target models. (c). There was a significant decrease inresidual values when compared to the residual values observed inone-target QSAR models i.e. mt-QSAR model has better predict-ability than the ot-QSAR model.

3.3. SAR studies

From the results of antimicrobial activity following structure–activity relationships can be derived.

(1) In general, all the compounds which emerged as most activecompounds have electron withdrawing substituents on thebenzoic acid moiety. The role of electron withdrawing group inimproving antimicrobial activities is supported by the studiesof Sharma et al. [48].

Observed pMICec

2.72.62.52.42.32.2

Pred

icted

p

MIC

ec

2.8

2.7

2.6

2.5

2.4

2.3

2.2

Fig. 4. Plot of predicted pMICec activity values against the experimental pMICec valuesfor the linear regression developed model by Eq. (3).

(2) In general, presence of electron withdrawing (NO2) substitu-ents on the benzylidene moiety favors the antimicrobialactivity of title compounds against organisms under test.

(3) For activity against S. aureus, on the benzylidene moiety bothelectron withdrawing (compound 17) and electron donating(compound 25) groups favored the antibacterial activity. Thecontrary result observed with the compound containing elec-tron withdrawing group 17 may be due the fact that it has 3cv

value of 0.64 which is equivalent to the most active compound25 (Table 3). In the case of B. subtilis, the presence of electronwithdrawing groups (compounds 12 and 17) on the benzyli-dene moiety favored the antibacterial activity. Whereas in thecase of E. coli, presence of electron donating groups on thebenzylidene moiety favored the antibacterial activity. Theabove results suggested that there must be different structuralrequirements, which are needed for compounds to be activeagainst different microorganisms [49].

From the results of antifungal activity, following conclusionscan be withdrawn.

Observed pMICb

2.72.62.52.42.32.22.12.01.9

Pre

dic

te

d p

MIC

b

2.7

2.6

2.5

2.4

2.3

2.2

2.1

2.0

Fig. 6. Plot of predicted pMICb activity values against the experimental pMICb valuesfor the linear regression developed model by Eq. (6).

Observed pMICf

2.01.91.81.71.6

Pred

icted

p

MIC

f

1.9

1.8

1.7

1.6

Fig. 7. Plot of predicted pMICf activity values against the experimental pMICf values forthe linear regression developed model by Eq. (7).

Observed pMICam

2.42.32.22.12.01.91.8

Resid

ual p

MIC

am

.1

0.0

-.1

Fig. 9. Plot of residual pMICam values against the experimental pMICam values.


(a) Influence of substituents on the benzoic acid moiety on theantifungal activity:

(i) For a compound to be active against C. albicans, it does notrequire the presence of halogen group on the benzoic acidmoiety.

(ii) Presence of halogen as well as electron withdrawing NO2

group on the benzoic acid moiety is essential for a compoundto be active against A. niger (compounds 18 and 22).

(iii) Among the halogens (Cl and Br), the presence of bromogroup on the benzoic acid moiety is responsible forimproving the antifungal activity of synthesized compounds.

(b) Influence of substituents on the benzylidene moiety on theantifungal activity:

(i) In contrast to antibacterial activity, the presence of electronwithdrawing NO2 group on the benzylidene moiety is notfavorable for the title compounds (18 and 22) to be activeagainst tested fungal strains.

(ii) Presence of methoxy groups at meta and para positionof benzylidene moiety favored the antifungal activity(Compounds 18 and 22).

Observed pMICam

2.42.32.22.12.01.91.8

Pred

icted

p

MIC

am

2.4

2.3

2.2

2.1

2.0

1.9

1.8

Fig. 8. Plot of predicted pMICam activity values against the experimental pMICam

values for the linear regression developed model by Eq. (8).

(4) Even though in the present study we have attempted tosynthesize benzylidene and furan-2-yl-methylene hydrazidederivatives for their antimicrobial activity, the presence offuran does not significantly improve the antimicrobial activityof the synthesized compounds.

The results of QSAR studies indicated that there is a positivecorrelation of the topological parameter, valence third ordermolecular connectivity index (3cv) and the electronic parameter,energy of highest occupied molecular orbital (HOMO) with anti-microbial activity of substituted hydrazides. The topological index,3cv, signifies the degree of branching, connectivity of atoms andthe unsaturation in the molecule which accounts for variation inactivity [35]. This was evidenced by the high antimicrobial activityof compounds 18, 22 and 25 which have two electron donatinggroups in comparison to other compounds i.e. their presenceincreases the branching of the molecule as well as increases theelectron density which in turn increases the HOMO values of thecompounds and makes them to be the most effective ones againstthe organisms under test. The SAR results are summarized inFig. 10.

4. Conclusion

In conclusion, a series of substituted hydrazide derivatives havebeen synthesized and their in vitro antimicrobial activities wereevaluated against five representative microorganisms. The resultsof antimicrobial study indicated that the presence of electronwithdrawing groups on the benzoic acid moiety improved anti-microbial activity and the presence of both electron withdrawingand donating groups on benzylidene moiety improved antimi-crobial activity. Further, the presence of heterocyclic ring furandoes not improve the antimicrobial activity of substituted hydra-zides. To understand the relationship between physicochemicalparameters and antimicrobial activity of substituted hydrazidederivatives, QSAR investigation was performed by the develop-ment of one-target and multi-target models. The multi-targetmodel was found to be effective in describing the antimicrobialactivity of substituted hydrazides in comparison to the one-targetmodels. Further, it indicated the importance of the topologicalparameter, valence third order molecular connectivity index (3cv)and the electronic parameter, energy of highest occupied molec-ular orbital (HOMO) in describing the antimicrobial activity ofsubstituted hydrazides. These results are similar to the results

Table 6Comparison of residual values obtained by one-target and multi-target QSAR models.

Comp Residual activity

pMICsa pMICbs pMICec pMICb pMICca pMICan pMICf pMICam

1 0.04 �0.09 �0.11 L0.06 �0.04 0.00 0.02 L0.022 0.06 0.03 �0.02 0.02 �0.04 �0.10 L0.01 0.013 �0.05 �0.02 �0.04 L0.01 0.03 �0.02 0.00 0.004 0.18 �0.01 0.03 0.06 0.03 �0.01 0.01 0.025 �0.19 0.04 0.04 L0.03 0.03 �0.10 L0.02 L0.046 �0.19 0.02 �0.01 L0.04 0.00 0.14 0.05 0.007 �0.06 0.15 0.02 0.03 0.01 �0.18 L0.10 L0.028 0.16 0.03 �0.02 0.00 �0.03 0.03 0.05 L0.019 �0.06 �0.04 �0.07 L0.03 0.00 0.10 0.04 0.0110 �0.07 �0.01 �0.04 L0.06 �0.08 0.10 0.02 L0.0411 0.01 0.01 0.05 0.02 �0.01 �0.02 0.02 0.0112 �0.23 0.03 �0.02 L0.12 0.04 0.01 0.02 L0.0813 �0.03 �0.01 0.03 0.01 0.01 0.00 0.01 0.0214 �0.03 �0.22 0.03 L0.06 0.03 0.01 0.01 L0.0215 0.27 0.12 0.04 0.13 0.02 �0.04 L0.01 0.0616 0.01 0.01 0.01 0.04 �0.06 �0.06 L0.08 0.0117 0.08 0.04 0.04 0.04 0.02 �0.11 L0.06 0.0018 �0.04 �0.02 �0.06 L0.01 0.01 0.22 0.10 0.0519 0.01 0.00 �0.02 0.04 0.02 0.13 0.03 0.0620 �0.06 0.15 0.02 0.03 �0.01 �0.16 L0.08 L0.0321 �0.05 �0.01 �0.06 L0.03 �0.01 �0.02 0.00 L0.0222 �0.08 �0.28 0.08 L0.13 0.03 0.15 0.08 L0.0723 0.01 �0.04 0.01 0.02 �0.07 �0.13 L0.10 L0.0224 �0.05 0.02 0.01 0.02 0.03 �0.13 L0.06 0.0025 0.08 0.00 0.02 0.03 0.00 0.07 0.03 0.0226 0.27 0.12 0.02 0.13 0.02 0.11 0.05 0.09


observed in previous studies [17,20] where the antimicrobialactivity was highly correlated with topological parameters, espe-cially with molecular connective indices. The design of newchemical entities with high 3cv and HOMO values than thesubstituted hydrazides included in the present study may result innovel compounds with improved antibacterial and antifungalactivity as the aforementioned parameters are positively correlatedwith the antimicrobial activity.

5. Experimental

Starting materials were obtained from commercial sources andwere used without further purification. Solvents were dried bystandard procedures. Reaction progress was observed by thinlayer chromatography making use of commercial silica gel plates(Merck), Silica gel F254 on aluminum sheets. Melting points weredetermined in open capillary tubes on a Sonar melting pointapparatus and are uncorrected. 1H nuclear magnetic resonance(1H NMR) spectra were determined by Bruker Avance II 400 NMRspectrometer in appropriate deuterated solvents and areexpressed in parts per million (d, ppm) downfield from tetrame-thylsilane (internal standard) NMR data are given as multiplicity(s, singlet; d, doublet; t, triplet; m, multiplet) and number ofprotons. Infrared (IR) spectra were recorded on a Shimadzu FTIRspectrometer.

ONH

N

RR'

S. aureus - NO2, Cl

B. subtilis - NO2, Br, Cl

E. coli - NO2

A. niger - NO2, Br

Fig. 10. Structural requirements for the antimicrobial activity of substi

5.1. General procedure for the synthesis of substituted benzoic acidbenzylidene/furan-2-yl-methylene hydrazides (1–26)

Thionyl chloride 32.8 g (0.3 mol) was added to substitutedbenzoic acid (0.25 mol) in a round bottom flask. After addition, themixture was refluxed for 2 h. The excess of thionyl chloride wasremoved by distillation. To the solution of 0.05 mol of substitutedacid chloride in methanol is added 0.1 mol of hydrazine hydrate.The mixture was refluxed for 30 min. Then the reaction mixturewas cooled and the resultant precipitate (substituted benzoicacid hydrazide) was collected, washed with distilled water andrecrystallized from ethanol. A solution of 0.05 mol of substitutedaldehyde [substituted benzaldehyde (3–7, 9–26)/furan-2-aldehyde(1–2, 8)] in ethanol was added to a solution of corresponding0.05 mol of substituted benzoic acid hydrazides in 50 ml ethanol.The mixture was refluxed on a water bath for 3 h. Then the reactionmixture was allowed to cool at room temperature and the precip-itate obtained was filtered, dried and recrystallized from ethanol.

5.1.1. Compound 5Mp (�C) 158–161; Yield – 44.6%; 1H NMR (CDCl3): d 3.85 (s, 3H,

OCH3), 6.94–7.57 (m, 4H, CH of ArNH2), 7.74–8.77 (m, 4H, CH ofArOCH3); IR (KBr pellets, cm�1): 1625.9 (C]O str., secondaryamide), 3084.1 (CH str., aromatic), 2990.1 (CH, str., aliphatic), 1265.6(C–N str., aryl primary amine).

NO2, OCH3 - S. aureus

NO2 - B. subtilis

OCH3 - E. coli, C. albicans, A. niger

tuted benzoic acid benzylidene/furan-2-yl-methylene hydrazides.



CH3 of ArCH3), 7.0 (m, 2H, CH of C3 and C4 of furan), 7.44 (d, 1H, CHof C5 of furan), 7.26–7.76 (m, 4H, CH of ArCH3), 9.0 (s, 1H, NH); IR(KBr pellets, cm�1): 1027.4 (Ring breathing, 2-substituted furan),1630.5 (C]O str., secondary amide), 3055.6 (CH str., aromatic),2960.1 (CH, str., aliphatic), 948.5 (CH out of plane bending, 2-substituted furan).

5.1.3. Compound 14Mp (�C) 69–72; Yield – 49.7%; 1H NMR (CDCl3): d 7.27–8.22 (m,

4H, CH of mArNO2), 8.23–8.30 (m, 4H, CH of pArNO2), 8.70 (s, 1H,NH); IR (KBr pellets, cm�1): 1603.7 (C]O str., secondary amide),3077.2 (CH str., aromatic), 1348.0 (NO2 symmetric str., ArNO2),1525.1 (NO2 asymmetric str., ArNO2), 857.4 (C–N str., ArNO2).


OCH3), 6.90–7.23 (m, 3H, CH of Ar(OCH3)2), 7.25–7.54 (m, 3H, CH ofAr(NO2)2), 8.60 (s, 1H, NH); IR (KBr pellets, cm�1): 1622.6 (C]O str.,secondary amide), 3000.9 (CH str., aromatic), 1343.7 (NO2

symmetric str., ArNO2), 1508.0 (NO2 asymmetric str., ArNO2), 865.7(C–N str., ArNO2) 2960.3 (CH str., aliphatic).


CH of OCH3), 6.91–7.24 (m, 3H, CH of Ar(OCH3)2), 7.26–7.55 (m, 4H,CH of ArBr), 8.6 (s, 1H, NH); IR (KBr pellets, cm�1): 564.5 (C–Br str.,aromatic), 1598.3 (C]O str., secondary amide), 3002.1 (CH str.,aromatic), 2837.5 (CH symmetric, aliphatic OCH3).


CH of OCH3), 6.82–7.10 (m, 3H, CH of Ar(OCH3)2), 7.39–7.94 (m, 4H,CH of ArCl), 8.38 (s, 1H, NH); IR (KBr pellets, cm�1): 1640.6 (C]Ostr., secondary amide), 3036.2 (CH str., aromatic), 752.3 (C–Cl str.,monochlorinated aromatic compounds), 2959.1 (CH str., asym-metric, aliphatic CH3), 2839.6 (CH str., symmetric, aliphatic OCH3).

5.2. Evaluation of antimicrobial activity

The antimicrobial activity was performed against Gram-positivebacteria: S. aureus, B. subtilis, Gram-negative bacterium: E. coli andfungal strains: C. albicans and A. niger. The minimum inhibitoryconcentration (MIC) was determined by tube dilution method. Twofold dilutions of test and standard compounds were prepared indouble strength nutrient broth – I.P. (bacteria) or Sabourauddextrose broth – I.P. [50] (fungi). The samples were incubated at37� C (bacteria) for 24 h and at 37� C for 48 h (C. albicans) and 25� Cfor 7 d (A. niger) and the results were recorded. Growth of micro-organisms was determined visually. The lowest concentration atwhich there was no visible growth (turbidity) was taken as MIC.

5.3. QSAR studies

The details of molecular descriptors are available in literatureand therefore they are not discussed over here [27–33]. Thestructures of substituted hydrazide derivatives are first pre-opti-mized with the Molecular Mechanics Force Field (MMþ) procedureincluded in Hyperchem 6.03 [51], and the resulting geometries arefurther refined by means of the semi-empirical method PM3

(Parametric Method-3). We chose a gradient norm limit of0.01 kcal/Å for the geometry optimization. The lowest energystructure was used for each molecule to calculate physicochemicalproperties using TSAR 3.3 software for windows [34]. Further theregression analysis was performed using the SPSS software package

[52]. The predictive powers of the equation were validated by thedetermination of cross-validated r2 (q2) using leave one out (LOO)cross-validation method.

Appendix. Supplementary data

Supplementary data associated with this article can be found inthe online version, at doi:10.1016/j.ejmech.2008.10.034.

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