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Original article
10 (
1_suppl
); S881-S893
doi:
10.1016/j.arabjc.2012.12.024

Synthesis, antimicrobial evaluation and QSAR studies of propionic acid derivatives

, , ,
 Faculty of Pharmaceutical Sciences, Maharshi Dayanand University, Rohtak-124001, India

⁎Corresponding author. Mobile: +91 9416649342. naru2000us@yahoo.com (Balasubramanian Narasimhan)

© The Author(s) 2013
Disclaimer:
This article was originally published by Elsevier and was migrated to Scientific Scholar after the change of Publisher.

Peer review under responsibility of King Saud University.

Abstract

A series of Schiff bases (1–17) and esters (18–24) of propionic acid was synthesized in appreciable yield and characterized by physicochemical as well as spectral means. The synthesized compounds were evaluated in vitro for their antimicrobial activity against Gram-positive bacteria Staphylococcus aureus, Bacillus subtilis, Gram negative bacterium Escherichia coli and fungal strains Candida albicans and Aspergillus niger by tube dilution method. Results of antimicrobial screening indicated that besides having good antibacterial activity, the synthesized compounds also displayed appreciable antifungal activity and compound 10 emerged as the most active antifungal agent (pMICca and pMICan = 1.93). The results of QSAR studies demonstrated that antibacterial, antifungal and overall antimicrobial activities of synthesized propionic acid derivatives were governed by the topological parameters, Kier’s alpha first order shape index (κα1) and valence first order molecular connectivity index (1χv).

Keywords

Propionic acid derivatives
QSAR
Antibacterial
Antifungal
1

1 Introduction

Despite tremendous progress in medicinal chemistry, the threat due to infectious diseases has dramatically increased in developing countries due to non availability of desired medicines and emergence of widespread microbial resistance (Kumar et al., 2010).

The antimicrobial potential of simple organic acids viz. sorbic acid (Narasimhan et al., 2003), cinnamic acid (Narasimhan et al., 2004), anacardic acid (Narasimhan et al., 2006a), veratric acid (Narasimhan et al., 2009), myristic acid (Narasimhan et al., 2006b), caprylic acid (Chaudhary et al., 2008), anthranilic acid (Mahiwal et al., 2012) and dodecanoic acid (Sarova et al., 2011) is well established in the literature. In light of the above we have planned to explore the antimicrobial potential of propionic acid in the present study. Literature reports reveal that propionic acid and its derivatives possess a wide spectrum of biological activities like antioxidant (Dracheva et al., 2009), antitumour, analgesic, antimicrobial (Avetisyan et al., 2010) and antidiabetic activities (Berzosa et al., 2011).

Quantitative structure–activity relationships (QSAR) attempt to find relationships between the molecular properties of molecules and the biological responses they elicit when applied to a biological system. QSAR models allow the biological properties of virtual structures to be predicted, and a more informed choice of target to be selected for synthesis. The use of computational approaches for the estimation of the activity of various molecules as drug candidates prior to their synthesis can save resources and accelerate drug discovery procedure (Narang et al., 2012a).

Schiff bases are considered to be among the most important group of compounds in medicinal chemistry due to their preparative accessibility, structural variety and wide biological profile (Sigroha et al., 2012). Keeping this observation in mind and in continuation of our study in the field of antimicrobial evaluation and QSAR studies (Kumar et al., 2010, 2012; Judge et al., 2012; Narang et al., 2012a,b), we hereby report the synthesis, antimicrobial evaluation and QSAR studies of propionic acid derivatives.

2

2 Results and discussion

2.1

2.1 Chemistry

A series of Schiff bases (1–17) and esters (18–24) was synthesized using synthetic procedures as outlined in Scheme 1. Esters were synthesized by a reaction of propionic acid with different alcohols in the presence of sulphuric acid. Propionic acid hydrazide was synthesized by the reaction of ethyl propionate with hydrazine hydrate which on reaction with corresponding aldehydes yielded Schiff bases of propionic acid. All the compounds were obtained in appreciable yield and their physicochemical characteristics are presented in Table 1. The formation of target compounds was ascertained on the basis of results of elemental analysis in addition to their consistent IR and NMR spectral characteristics. IR spectra of compounds 1, 10, 17 and 21 are given in Figs. 1–4.

Synthetic route followed for the synthesis of propionic acid derivatives.
Scheme 1 Synthetic route followed for the synthesis of propionic acid derivatives.
Table 1 Physicochemical properties of synthesized propionic acid derivatives.
Comp. M. formula M. wt. m.p./b.p.a (°C) Rf value⁎ % Yield
1. C10H11N3O3 221 173–175 0.52b 65.23
2. C11H14N2O2 206 107–110 0.40b 80.64
3. C10H11ClN2O 210 121–123 0.83b 75.27
4. C11H14N2O2 206 53–55 0.40b 83.71
5. C10H12N2O 176 78–80 0.60b 77.65
6. C11H14N2O3 222 140–143 0.21b 85.76
7. C12H16N2O3 236 177–180 0.34b 76.93
8. C11H12N2O2 204 233–235 0.30b 66.03
9. C12H17N3O 219 222–225 0.22b 90.12
10. C13H18N2O4 266 157–160 0.22b 75.20
11. C11H14N2O2 206 141–143 0.23b 89.01
12. C10H11ClN2O 210 113–115 0.81b 80.36
13. C10H12N2O2 192 197–200 0.43b 73.02
14. C10H11ClN2O 210 163–165 0.36b 77.43
15. C10H11BrN2O 255 190–192 0.53b 85.21
16. C11H14N2O 190 123–125 0.36b 80.92
17. C12H14N2O 202 139–142 0.41b 70.31
18. C7H14O2 130 123–125a 0.36c 75.34
19. C10H12O2 164 143–145a 0.51c 76.56
20. C6H12O2 116 93–95a 0.55c 83.23
21. C8H16O2 144 117–120a 0.27c 67.60
22. C9H16O2 156 123–125a 0.39c 85.34
23. C10H20O3 188 130–132a 0.77c 78.12
24. C7H14O2 130 138–139a 0.65c 90.45
⁎ TLC mobile Phase.
a b.p.
b Benzene.
c Hexane:acetone (1:1).
IR spectra of compound 1.
Figure 1 IR spectra of compound 1.
IR spectra of compound 10.
Figure 2 IR spectra of compound 10.
IR spectra of compound 17.
Figure 3 IR spectra of compound 17.
IR spectra of compound 21.
Figure 4 IR spectra of compound 21.

2.2

2.2 Antimicrobial activity

The synthesized compounds (1–24) were screened in vitro for their antimicrobial potential against Gram-positive bacteria Staphylococcus aureus, Bacillus subtilis, Gram negative bacterium Escherichia coli and fungal strains Candida albicans and Aspergillus niger by tube dilution method (Cappucino and Sherman, 1999) using norfloxacin and fluconazole as reference drugs for antibacterial and antifungal activities, respectively. Double strength nutrient broth I.P. and Sabouraud dextrose broth I.P. (Pharmacopoeia of India, 2007) have been employed as media for the growth of bacterial and fungal strains, respectively. The results of antimicrobial activity are presented in Table 2.

Table 2 Antimicrobial activity of synthesized synthesized propionic acid derivatives.
Comp. Minimum inhibitory concentration (MIC, μ Mol/ml)
pMICsa pMICbs pMICec pMICca pMICan pMICab pMICaf pMICam
1. 1.25 1.25 0.95 1.55 1.55 1.15 1.55 1.31
2. 1.22 1.22 1.22 1.52 1.52 1.22 1.52 1.34
3. 1.23 1.53 1.23 1.53 1.23 1.33 1.38 1.35
4. 1.22 1.52 1.22 1.52 1.82 1.32 1.67 1.46
5. 1.15 1.45 1.15 1.15 1.45 1.25 1.30 1.27
6. 1.25 1.55 1.55 1.55 1.85 1.45 1.70 1.55
7. 1.28 1.58 1.28 1.88 1.58 1.38 1.73 1.52
8. 1.21 1.51 1.21 1.51 1.81 1.31 1.66 1.45
9. 0.94 1.24 0.94 1.54 1.85 1.04 1.70 1.30
10. 1.33 1.33 1.33 1.93 1.93 1.33 1.93 1.57
11. 1.22 1.22 1.22 1.52 1.52 1.22 1.52 1.34
12. 1.23 1.23 1.23 1.53 1.83 1.23 1.68 1.41
13. 1.19 1.19 1.19 1.49 1.49 1.19 1.49 1.31
14. 1.23 1.53 1.53 1.83 1.53 1.43 1.68 1.53
15. 1.61 1.61 1.61 1.61 1.91 1.61 1.76 1.67
16. 1.18 1.48 1.48 1.48 1.48 1.38 1.48 1.42
17. 1.21 1.51 1.51 1.51 1.51 1.41 1.51 1.45
18. 1.02 1.02 1.02 1.32 1.02 1.02 1.17 1.08
19. 1.42 1.72 1.12 1.42 1.72 1.42 1.57 1.48
20. 1.27 1.27 1.27 1.27 0.97 1.27 1.12 1.21
21. 1.06 1.36 1.06 1.06 1.06 1.16 1.06 1.12
22. 1.40 1.40 1.10 1.40 1.40 1.30 1.40 1.34
23. 1.18 1.48 1.48 1.78 1.48 1.38 1.63 1.48
24. 1.02 1.32 1.32 1.62 1.32 1.22 1.47 1.32
S.D. 0.14 0.17 0.19 0.20 0.28 0.13 0.21 0.14
Std 2.61a 2.61a 2.61a 2.64b 2.64b – – –

pMICsa, pMICbs, pMICec, pMICca and pMICan = −log MIC in μ Mol/ml against different microorganisms. i.e. Staphylococcus aureus, Bacillus subtilis, Escherichia coli, Candida albicans and Aspergillus niger, respectively.

a Norfloxacin.
b Fluconazole.

Results of antibacterial study indicated that compound 15 was the most active antibacterial agent against S. aureus and E. coli having pMICsa and pMICec values of 1.61. Against B. subtilis compound 19 emerged as potential antibacterial candidate (pMICbs = 1.72). Besides having good antibacterial activity, the synthesized compounds also displayed appreciable antifungal activity and compound 10 emerged as the most active antifungal agent against C. albicans and A. niger (pMICca and pMICan = 1.93) which may be taken as a lead compound for the development of novel antifungal agents.

2.3

2.3 Structure activity relationship

  1. Results of antimicrobial screening indicated that esters of propionic acid were poor antimicrobial agents than Schiff bases except compound 19 (synthesized using benzyl alcohol) which was found to be the most active against B. subtilis.

  2. Presence of electron withdrawing group (p-Br, compound 15) improved the antibacterial activity of the synthesized compounds against S. aureus and E. coli, whereas the presence of electron releasing trimethoxy group (3,4,5-trimethoxy, compound 10) improved the antifungal activity of the synthesized compounds against C. albicans and A. niger.

  3. Schiff base synthesized using cinnamaldehyde (17) was found to be a less active antimicrobial agent, which indicates that benzylidene portion is necessary for the antimicrobial activity of the synthesized compounds as its replacement by phenylallylidine moiety (compound 17) decreases the antimicrobial activity.

  4. From these results we may conclude that different structural requirements are required for a compound to be effective against different targets. This is similar to the results of Sortino et al. (Sortino et al., 2007).

  5. The abovementioned findings are summarized in Fig. 5.

Structural requirements for the antimicrobial activity of synthesized propionic acid derivatives.
Figure 5 Structural requirements for the antimicrobial activity of synthesized propionic acid derivatives.

2.4

2.4 QSAR studies

2.4.1

2.4.1 Development of multi-target QSAR models (mt-QSAR)

In the present study, we have performed the quantitative structure–activity relationship study by conventional Hansch’s analysis using the linear free energy relationship model (LFER) described by Hansch and Fujita (Hansch and Fujita, 1964). In this approach, structural features of drug molecules are quantified in terms of different parameters and these structural features are correlated with quantified biological activity through equation using regression analysis. Biological activity data determined as MIC values were first transformed into pMIC values (i.e. –log MIC) and used as dependent variables in QSAR study.

The different molecular descriptors selected for the present study are listed in Table 3. Molecular descriptors (independent variables) like log of octanol–water partition coefficient (log P), molar refractivity (MR), Kier’s molecular connectivity (0χ, 0χv, 1χ, 1χv, 2χ, 2χv) and shape (κ1, κα1, κα2, κα3) topological indices, Randic topological index (R), Balaban topological index (J), Wiener topological index (W), Total energy (Te), energies of highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO), dipole moment (μ) and electronic energy (Ele.E) (Hansch et al., 1973; Kier and Hall, 1976; Randic 1975, 1993; Balaban 1982; Wiener 1947) were calculated for propionic acid derivatives and the values of selected descriptors are presented in Table 4.

Table 3 QSAR descriptors used in the study.
S. no. QSAR descriptor Type
1 Log P Lipophilic
2 Zero order molecular connectivity index (0χ) Topological
3 First order molecular connectivity index (1χ) Topological
4 Second order molecular connectivity index (2χ) Topological
5 Valence zero order molecular connectivity index (0χv) Topological
6 Valence first order molecular connectivity index (1χv) Topological
7 Valence second order molecular connectivity index (2χv) Topological
8 Kier’s alpha first order shape indice (κα1) Topological
9 Kier’s alpha second order shape index (κα2) Topological
10 Kier’s first order shape indice (κ1) Topological
11 Randic topological index Topological
12 Balaban topological index Topological
13 Wiener’s topological index Topological
14 Kier’s second order shape index (κ2) Topological
15 Ionization potential Electronic
16 Dipole moment (μ) Electronic
17 Energy of highest occupied molecular orbital (HOMO) Electronic
18 Energy of lowest unoccupied molecular orbital (LUMO) Electronic
19 Total energy (Te) Electronic
20 Nuclear Energy (Nu. E) Electronic
21 Molar refractivity (MR) Steric
Table 4 Values of selected descriptors used in QSAR studies of propionic acid derivatives.
Comp. Log P MR 1χv κα1 J W Te LUMO HOMO μ
1 2.09 59.52 4.70 12.55 1.97 512.00 −3009.58 −1.23 −9.48 5.04
2 1.89 58.66 4.72 11.95 2.05 420.00 −2654.42 −0.26 −8.67 3.79
3 2.66 57.00 4.71 11.29 1.98 351.00 −2538.71 −0.50 −9.02 2.95
4 1.89 58.66 4.72 11.95 1.92 434.00 −2654.59 −0.35 −8.94 3.53
5 2.14 52.20 4.20 10.00 1.86 296.00 −2178.57 −0.13 −8.84 4.27
6 1.60 60.36 4.86 12.91 2.00 510.00 −2975.02 −0.19 −8.47 6.49
7 1.95 65.10 5.45 13.90 2.04 606.00 −3130.80 −0.16 −8.44 6.59
8 1.82 58.79 4.63 11.66 1.92 448.00 −2626.81 −0.77 −9.11 7.08
9 1.93 65.91 5.22 12.95 1.97 533.00 −2710.42 −0.14 −8.17 4.38
10 1.38 71.59 5.78 15.86 2.23 776.00 −3605.90 −0.30 −8.62 6.06
11 1.89 58.66 4.72 11.95 1.92 448.00 −2654.45 −0.10 −8.53 3.18
12 2.66 57.00 4.70 11.29 1.89 358.00 −2538.97 −0.43 −9.40 1.71
13 1.86 53.89 4.34 10.96 1.98 351.00 −2499.18 −0.27 −8.71 5.60
14 2.66 57.00 4.70 11.29 1.90 365.00 −2538.68 −0.40 −8.89 5.09
15 2.93 59.82 5.11 11.47 1.90 365.00 −2518.18 −0.47 −8.95 5.09
16 2.61 57.24 4.61 11.00 1.90 365.00 −2334.59 −0.17 −9.10 5.35
17 2.55 62.44 4.86 11.73 1.79 476.00 −2461.86 −0.63 −8.70 4.98
18 1.71 35.78 3.32 8.63 3.06 104.00 −1731.27 1.20 −11.19 1.97
19 2.27 46.65 4.02 8.94 1.90 226.00 −2086.67 0.22 −9.69 1.65
20 1.25 31.21 2.86 7.63 3.10 71.00 −1575.46 1.24 −11.10 1.66
21 2.03 40.46 3.82 9.63 3.03 146.00 −1887.13 1.20 −11.12 1.78
22 2.08 43.05 4.52 8.72 1.95 168.00 −2015.38 1.24 −10.76 1.62
23 1.74 51.64 5.07 12.59 2.91 346.00 −2519.55 1.18 −10.89 0.32
24 1.70 35.91 3.46 8.63 2.88 110.00 −1731.41 1.20 −11.15 1.84

From the results of our previous QSAR studies (Narang et al., 2012a; Judge et al., 2012; Kumar et al., 2010; Kumar et al., 2009), we observed that multi-target QSAR (mt-QSAR) models gave better results than one-target QSAR (ot-QSAR) models in describing the antimicrobial activity. So, in light of our past experiences, we decided to develop multi-target QSAR models directly to describe the antimicrobial activity of synthesized propionic acid derivatives in the present study.

According to ot-QSAR models one should use five different equations to predict the activity of a new compound against five microbial species. The ot-QSAR models, which are found in most of the literature, become impractical to use when we have to predict each compound’s results for more than one target. In these cases we have to develop one ot-QSAR model for each target. In opposition to ot-QSAR, the mt-QSAR model is a single equation that considers the nature of molecular descriptors which are common and essential for describing antibacterial and antifungal activities (Prado-Prado et al., 2008; Gonzalez-Diaz and Prado-Prado, 2008; Cruz-Monteagudo et al., 2007; Gonzalez-Diaz et al., 2007).

In order to develop mt-QSAR models, initially we have calculated the average antibacterial, antifungal and antimicrobial activity values of propionic acid derivatives which are presented in Table 2. The standard drugs norfloxacin and fluconazole were not included in the model generation because of dissimilarity in structure with synthesized compounds.

In the present study, a data set of 24 compounds was subjected to single/multiple linear free energy regression analysis for model generation. Compounds 9, 15, 19 and 21 were designated as outliers and were not included in the data set for QSAR model generation. In multivariate statistics, it is common to define three types of outliers (Furusjo et al., 2006).

  1. X/Y relation outliers are substances for which the relationship between the descriptors (X variables) and the dependent variables (Y variables) is not the same as in the (rest of the) training data.

  2. X outliers are substances whose molecular descriptors do not lie in the same range as in the (rest of the) training data.

  3. Y outliers are only defined for training or test samples. They are substances for which the reference value of response is invalid.

There was no difference in the activity (Table 2) as well as the molecular descriptor range (Table 3) of these outliers (9, 15, 19 and 21) when compared to other propionic acid derivatives indicating the fact that these outliers belong to the category of Y outliers (Substances for which the reference value of response is invalid).

Preliminary analysis was carried out in terms of correlation analysis. A correlation matrix constructed for antifungal activity is presented in Table 5. In general, high collinearity (r > 0.5) was observed between different parameters. The high interrelationship was observed between W and κα1 (r = 0.971) and low interrelationship was observed between κα1 and log P (r = −0.003). The correlation of antibacterial, antifungal and antimicrobial activities with calculated molecular descriptors is given in Table 6.

Table 5 Correlation matrix for the antifungal activity of synthesized propionic acid derivatives.
Log P MR 1χv κα1 J W LUMO HOMO μ pMICaf
Log P 1.000 0.290 0.243 −0.003 −0.605 0.039 −0.455 0.315 0.048 0.043
MR 1.000 0.921 0.920 −0.729 0.957 −0.810 0.874 0.680 0.799
1χv 1.000 0.915 −0.607 0.893 −0.568 0.679 0.481 0.847
κα1 1.000 −0.427 0.971 −0.608 0.691 0.573 0.863
J 1.000 −0.557 0.798 −0.823 −0.582 −0.431
W 1.000 −0.716 0.786 0.698 0.828
LUMO 1.000 −0.839 −0.709 −0.499
HOMO 1.000 0.761 0.555
μ 1.000 0.498
pMICaf 1.000
Table 6 Correlation of different molecular descriptors with antimicrobial activity of propionic acid derivatives.
Descriptor pMICab pMICaf pMICam
Cos E 0.343 0.576 0.543
Log P 0.239 0.043 0.150
MR 0.466 0.799 0.748
0χ 0.397 0.827 0.730
0χv 0.459 0.858 0.781
1χ 0.425 0.818 0.739
1χv 0.537 0.847 0.815
2χ 0.377 0.761 0.678
2χv 0.469 0.751 0.719
3χ 0.031 0.375 0.254
3χv −0.169 −0.006 −0.091
κ1 0.399 0.844 0.742
κ2 0.434 0.752 0.702
κ3 0.124 0.131 0.147
κ2α1 0.417 0.863 0.763
κ2α2 0.432 0.715 0.677
κ2α3 0.092 0.063 0.087
R 0.425 0.818 0.739
J −0.352 −0.431 −0.456
W 0.413 0.828 0.739
Te −0.371 −0.852 −0.733
Ee −0.367 −0.835 −0.719
Ne 0.365 0.829 0.715
SA 0.493 0.864 0.804
IP −0.390 −0.555 −0.554
LUMO −0.203 −0.499 −0.422
HOMO 0.390 0.555 0.554
μ 0.350 0.498 0.497

The structural effects on variations in the antifungal activity of the propionic acid derivatives in terms of pMICaf were examined by regression analysis with molecular parameters as shown in Table 4. For 20 propionic acid derivatives, Eq. (1) was derived as that of the best quality using the topological parameter, Kier’s alpha first order shape index (κα1).

LR mt-QSAR model for antifungal activity

(1)
pMIC af = 0.0867 κ α 1 + 0.547 n = 20 r = 0.863 q 2 = 0.686 s = 0.101 F = 52.46 Here and thereafter, n – number of data points, r – correlation coefficient, q2 – cross validated r2 obtained by leave one out method, s – standard error of the estimate and F – Fischer statistics.

The coefficient of κα1 in Eq. (1) is positive which signifies that the antifungal activity of synthesized compounds will increase with an increase in the value of κα1. This is evidenced by the antifungal activity data of propionic acid derivatives (Table 2) and their κα1 values (Table 4). Compound 10 having the highest κα1 value (15.86, Table 4) has the maximum antifungal activity with a pMICaf value of 1.93 (Table 2) and compound 21 having a low κα1 value (9.63, Table 4) has the minimum antifungal activity with a pMICaf value 1.06 (Table 2).

In order to account for the variation in size contribution to shape from different atoms the radius of atom X relative to the covalent radius of a carbon sp3 hybrid atom is considered. The specific correction in computing 1κ is made by modifying the count of atoms, n, with a modifier, α, calculated as: αX = (rX/rcsp3)1.

where α represents a decrement or increment of n for a noncarbon sp3 element X. The modified kappa shape indices are given by (Kier and Hall, 1999): κ α 1 = ( n + α ) ( n + α - 1 ) 2 / ( 1 P i + α ) 2 κ α 2 = ( n + α - 1 ) ( n + α - 2 ) 2 / ( 2 P i + α ) 2 κ α 3 = ( n + α - 1 ) ( n + α - 3 ) 2 / ( 3 P i + α ) 2 n is odd κ α 3 = ( n + α - 3 ) ( n + α - 2 ) 2 / ( 3 P i + α ) 2 n is even The developed QSAR model (Eq. (1)) was cross validated by its high q2 value (q2 = 0.686) obtained by leave one out (LOO) method. The value of q2 more than 0.5 indicated that the developed model is a valid one (Golbraikh and Tropsha, 2002). Further the observed and predicted values are close to each other (Table 7), the mt-QSAR model for antifungal activity (Eq. (1)) is a valid one. The plot of predicted pMICaf against observed pMICaf (Fig. 6) also favours the developed model expressed by Eq. (1). Further, the plot of observed pMICaf vs residual pMICaf (Fig. 7) indicated that there was no systemic error in model development as the propagation of error was observed on both sides of zero (Kumar et al., 2007).

Table 7 Observed, predicted and residual antimicrobial activities of the synthesized propionic acid derivatives obtained by mt−QSAR models.
Comp. pMICab pMICaf pMICam
Obs. Pre. Res. Obs. Pre. Res. Obs. Pre. Res.
1 1.15 1.30 −0.15 1.55 1.64 −0.09 1.31 1.41 −0.10
2 1.22 1.30 −0.08 1.52 1.58 −0.06 1.34 1.41 −0.07
3 1.33 1.30 0.03 1.38 1.53 −0.15 1.35 1.41 −0.06
4 1.32 1.30 0.02 1.67 1.58 0.09 1.46 1.41 0.05
5 1.25 1.26 −0.01 1.30 1.41 −0.11 1.27 1.33 −0.06
6 1.45 1.32 0.13 1.70 1.67 0.03 1.55 1.43 0.12
7 1.38 1.36 0.02 1.73 1.75 −0.02 1.52 1.52 0.00
8 1.31 1.30 0.01 1.66 1.56 0.10 1.45 1.40 0.05
9 1.04 1.35 −0.31 1.70 1.67 0.03 1.30 1.49 −0.18
10 1.33 1.39 −0.06 1.93 1.92 0.01 1.57 1.57 0.00
11 1.22 1.30 −0.08 1.52 1.58 −0.06 1.34 1.41 −0.07
12 1.23 1.30 −0.07 1.68 1.53 0.15 1.41 1.41 0.00
13 1.19 1.27 −0.08 1.49 1.50 −0.01 1.31 1.35 −0.04
14 1.43 1.30 0.13 1.68 1.53 0.15 1.53 1.41 0.12
15 1.61 1.34 0.27 1.76 1.54 0.22 1.67 1.47 0.20
16 1.38 1.29 0.09 1.48 1.50 −0.02 1.42 1.39 0.03
17 1.41 1.32 0.09 1.51 1.56 −0.05 1.45 1.43 0.02
18 1.02 1.19 −0.17 1.17 1.30 −0.13 1.08 1.20 −0.12
19 1.42 1.25 0.17 1.57 1.32 0.25 1.48 1.31 0.17
20 1.27 1.15 0.12 1.12 1.21 −0.09 1.21 1.14 0.07
21 1.16 1.23 −0.07 1.06 1.38 −0.32 1.12 1.28 −0.16
22 1.30 1.29 0.01 1.40 1.30 0.10 1.34 1.38 −0.04
23 1.38 1.33 0.05 1.63 1.64 −0.01 1.48 1.46 0.02
24 1.22 1.20 0.02 1.47 1.30 0.17 1.32 1.23 0.09
Plot of predicted pMICaf values against observed pMICaf values for the linear regression model developed (Eq. 1).
Figure 6 Plot of predicted pMICaf values against observed pMICaf values for the linear regression model developed (Eq. 1).
Plot of residual pMICaf values against observed pMICaf values for the linear regression model developed (Eq. 1).
Figure 7 Plot of residual pMICaf values against observed pMICaf values for the linear regression model developed (Eq. 1).

The antibacterial activity of propionic acid derivatives is best described by the topological parameter, valence first order molecular connectivity index (1χv, Table 6, Eq. (2)). Hence, the QSAR model for antibacterial activity was developed using 1χv.

LR mt-QSAR model for antibacterial activity

(2)
pMIC ab = 0.0836 1 χ v + 0.909 n = 20 r = 0.536 q 2 = 0.0174 s = 0.0914 F = 7.28 Antibacterial activity of synthesized propionic acid derivatives is positively correlated with valence first order molecular connectivity index (1χv) which means that antibacterial potential of synthesized compounds will increase with increase in 1χv values (Tables 2 and 4).

A less significant (poor) correlation was observed between calculated molecular descriptors and antibacterial activity of the synthesized propionic acid derivatives as evidenced by low q2 value (0.0174, Eq. (2)).

In the case of antimicrobial activity also valence first order molecular connectivity index (1χv, Table 5) emerged as the most determinant parameter (Eq. (3)).

LR mt-QSAR model for antimicrobial activity

(3)
pMIC am = 0.148 1 χ v + 0.714 n = 20 r = 0.815 q 2 = 0.554 s = 0.0730 F = 35.55 Antimicrobial activity of synthesized propionic acid derivatives is also positively correlated with valence first order molecular connectivity index (1χv) which means that antimicrobial potential of synthesized compounds will increase with increase in 1χv values (Tables 2 and 4).

The molecular connectivity index, an adjacency based topological index proposed by Randic is denoted by χ and is defined as the sum over all the edges (ij) as per the following χ = ∑ i = 1 n ( V i V j ) - 1 / 2 where Vi and Vj are the degrees of adjacent vertices i and j and n is the number of vertices in a hydrogen suppressed molecular structure (Lather and Madan, 2005). The topological index, χ signifies the degree of branching, connectivity of atoms and unsaturation in the molecule which accounts for variation in activity (Gupta et al., 2003).

The validity and predictability of the QSAR model (Eq. (3)) is evidenced by its high q2 the low residual antimicrobial activity values (Table 7). The plot of predicted pMICam against observed pMICam (Fig. 8) also favours the developed model expressed by Eq. (3). Further, the plot of observed pMICam vs residual pMICam (Fig. 9) indicated that there was no systemic error in model development as the propagation of error was observed on both sides of zero (Kumar et al., 2007). The high residual activity value in the case of compounds 9, 15, 19 and 21 justifies their removal as outliers.

Plot of predicted pMICam values against observed pMICam values for the linear regression model developed (Eq. 3).
Figure 8 Plot of predicted pMICam values against observed pMICam values for the linear regression model developed (Eq. 3).
Plot of residual pMICam values against observed pMICam values for the linear regression model developed (Eq. 3).
Figure 9 Plot of residual pMICam values against observed pMICam values for the linear regression model developed (Eq. 3).

It was observed from mt-QSAR models [Eqs. 1–3] that the antibacterial, antifungal and overall antimicrobial activities of synthesized propionic acid derivatives were governed by the topological parameters, Kier’s alpha first order shape index (κα1) and valence first order molecular connectivity index (1χv).

Generally for QSAR studies, the biological activities of compounds should span 2–3 orders of magnitude. But in the present study the range of antimicrobial activities of the synthesized compounds is within one order of magnitude. This is similar to the results obtained by Bajaj et al. (Bajaj et al., 2005) who stated that the reliability of the QSAR model lies in its predictive ability even though the activity data are in the narrow range. The low residual values observed in Table 7 justify the QSAR studies with the synthesized propionic acid derivatives. When biological activity data lie in the narrow range, the presence of a minimum standard deviation of the biological activity justifies its use in QSAR studies (Narasimhan et al., 2007). The minimum standard deviation (Table 2) observed in the antimicrobial activity data justifies its use in QSAR studies.

3

3 Experimental

Starting materials were obtained from Himedia Chemicals and were used without further purification. Melting points were determined in open glass capillaries on a sonar melting point apparatus and are uncorrected. Reaction progress was monitored by thin layer chromatography on silica gel sheets (Merck silica gel –G). 1H nuclear magnetic resonance (1H NMR) spectra were recorded on Bruker Avance II 400 NMR spectrometer (400 MHz) at 298 K, in appropriate deuterated solvents. Chemical shifts were reported as δ (ppm) relative to tetramethylsilane (TMS) as internal standard. Infrared (IR) spectra were recorded as KBr pellet on Perkin Elmer FTIR spectrometer. The wave number is given in cm−1. Elemental analysis was performed on a Perkin–Elmer 2400 C, H, N analyser.

3.1

3.1 General procedure for the synthesis of esters of propionic acid

A mixture of propionic acid (0.08 mol) and different alcohols (0.74 mol) was refluxed in the presence of sulphuric acid till the completion of reaction. Once the reaction had been completed, the reaction mixture was added to 200 ml ice cold water and the ester formed was extracted with ether (50 ml). The ether layer was separated which on evaporation yielded the ester derivatives of propionic acid.

3.2

3.2 General procedure for the synthesis of Schiff bases of propionic acid

Hydrazine-hydrate (99%, 0.015 mol) was added to ethanolic solution of ethyl propionate (0.01 mol) and refluxed for 3–4 h. The reaction mixture was then cooled and the precipitate was filtered off, washed with water, dried and recrystallized from ethanol. A solution of different aldehydes (0.05 mol) in ethanol was added to a solution of propionic acid hydrazide (synthesized above, 0.05 mol) in 50 mL ethanol and refluxed for 5 h. Then the reaction mixture was allowed to cool at room temperature and the precipitated hydrazone was filtered, dried and recrystallized from ethanol.

Compound 1. Mp (°C) 173–175; Yield – 65.23%; (KBr pellets)cm−1: 1632 (C⚌O str., 20 amide), 1519 (NO2 str.), 832 (C–N str., –NO2), 1075 (N–N str., –NHN⚌CH), 3086 (C–H str., phenyl); 1H NMR(DMSO): 7.120–8.385 (m, 4H, ArH), 8.416 (s, 1H, N⚌CH), 2.51 (m, 2H, CH2 of C2H5), 1.190 (t, 3H, CH3 of C2H5); Analysis Calculated for C10H11N3O3: C, 54.29; H, 5.01; N, 19.00; Found; C, 54.33; H, 5.05; N, 19.04; Compound 9: Mp (°C) 222–225; Yield – 90–12%; (KBr pellets)cm−1: 2909 (CH str.(asym), R–CH3), 1597 (C⚌O str., 20 amide), 1062 (N–N str., –NHN⚌CH), 1518 (C⚌C skeletal str., phenyl); 1H NMR(DMSO): 6.751–7.700 (m, 4H, ArH), 8.506 (s, 1H, N⚌CH), 2.507 (m, 2H, CH2 of C2H5), 2.994 (s, 6H, –N(CH3)2); Analysis Calculated for C12H17N3O: C, 65.73; H, 7.81; N, 19.16; Found; C, 65.76; H, 7.78; N, 19.19; Compound 10: Mp (°C) 158–160; Yield – 75–20%; (KBr pellets)cm−1: 2934 (CH str.(asym), R–CH3), 2839 (CH str. (sym), R–CH3), 1694 (C⚌O str., 20 amide), 1621 (C⚌N str., N⚌CH), 1125 (N–N str., –NHN⚌CH), 1504 (C⚌C skeletal str., phenyl), 2934 (C–H str., OCH3), 1230(C–O–C str., OCH3); 1H NMR(DMSO): 7.227(m, 2H, ArH), 8.65(s, 1H, N⚌CH), 2.512(m, 2H, CH2 of C2H5), 3.855(s, 9H, Ar–OCH3); Analysis Calculated for C13H18N2O4: C, 58.63; H, 6.81; N, 10.52; Found; C, 58.59; H, 6.84; N, 10.55; Compound 11: Mp (°C) 141–143; Yield – 89.01%; (KBr pellets)cm−1: 1601 (C⚌N str., N⚌CH), 1023 (N–N str., –NHN⚌CH), 1506 (C⚌C skeletal str., phenyl), 1249 (C–O–O str., OCH3); 1H NMR(DMSO): 7.041–7.832(m, 4H, ArH), 8.633(s, 1H, N⚌CH), 2.51(m, 2H, CH2 of C2H5), 3.830(s, 3H, Ar –OCH3); Analysis Calculated for C11H14N2O2: C, 64.06; H, 6.84; N, 13.58; Found; C, 64.10; H, 6.92; N, 13.61; Compound 17: Mp (°C) 139–141; Yield – 70.31%; (KBr pellets)cm−1: 2923 (CH str. (asym), R–CH3), 1628 (C⚌O str., 20 amide), 1586 (C⚌N str., N⚌CH), 1073 (N–N str.,–NHN⚌CH), 3038 (C–H str., phenyl nucleus), 1486 (C⚌C skeletal str., phenyl); 1H NMR(DMSO): 7.790–8.380 (m, 5H, ArH), 8.628 (s, 1H, N⚌CH), 2.523 (m, 2H, CH2 of C2H5); Analysis Calculated for C12H14N2O: C, 71.26; H, 6.98; N, 13.85; Found; C, 71.21; H, 6.95; N, 13.88; Compound 21: Mp (°C) 117–120; Yield – 67.60%; (KBr pellets)cm−1: 2956 (CH str. (asym), R–CH3), 1721 (C⚌O str., of ester).; 1H NMR(DMSO): 1.072 (t, 3H, CH3 of propionate), 2.292 (m, 2H, CH2 of propionate), 1.012 (d, 6H, CH3 of isopentyl), 1.716 (m, 1H, CH of isopentyl), 1.503 (m, 2H(C2) of isopentyl), 4.064 (t, 2H, CH2(C1) of isopentyl); Analysis Calculated for C8H16O2: C, 66.63; H, 11.18; Found; C, 66.69; H, 66.73; Compound 22: Bp (°C) 123–125; Yield – 85.34%; (KBr pellets)cm−1: 2929 (CH str.(asym), R–CH3), 1716 (C⚌O str., of ester), 1065 (ring str., cyclohexane); 1H NMR(DMSO): 1.148 (t, 3H, CH3 of propionate), 2.305(m, 2H, CH2 of propionate), 1.181–2.280 (m, 6H, cyclohexane); Analysis Calculated for C9H16O2: C, 69.19; H, 10.32; Found; C, 69.24; H, 10.35; Compound 23: Bp (°C) 130–132; Yield – 78.12%; (KBr pellets)cm−1: 2924 (CH str.(asym), R–CH3), 1681 (C⚌O str., of ester), 1012(C–O str. and O–H in plane bending); 1H NMR(DMSO): 1.087 (t, 3H, CH3 of propionate), 2.294 (m, 2H, CH2 of propionate), 4.003 (t, 2H, CH2(C1) of heptyl), 1.553 (m, 2H(C2) of heptyl), 1.253 (m, 6H(C3, C4, C5) of heptyl), 1.530 (m, CH2(C6) of heptyl), 3.386 (m, 2H, CH2(C7) of heptyl), 2.219 (t, 1H, OH); Analysis Calculated for C10H20O3: C, 63.80; H, 10.71; Found; C, 63.84; H, 10.68; Compound 24: Bp (°C) 138–140; Yield – 90.45%; (KBr pellets)cm−1: 2933 (CH str. (asym), R-CH3), 1721 (C⚌O str., of ester), 2874 (CH str., (sym), R–CH3); 1H NMR(DMSO): 1.217 (t, 3H, CH3 of propionate), 2.305 (m, 2H, CH2 of propionate), 0.977 (t, 3H, CH3 of butyl), 1.334 (m, 2H., CH2(C3) of butyl), 1.570 (m, 2H, CH2(C2) of butyl), 4.004 (t, 2H, CH2(C1) of butyl); Analysis Calculated for C7H14O2: C, 64.58; H, 10.84; Found; C, 64.62; H, 10.81.

3.3

3.3 Antimicrobial assay

The antimicrobial activity that was performed against Gram-positive bacteria: S. aureus, B. subtilis, the Gram-negative bacterium E. coli and fungal strains: C. albicans and A. niger using the tube dilution method (Cappucino and Sherman, 1999). Dilutions of test and standard compounds were prepared in double strength nutrient broth - I.P. (bacteria) or Sabouraud dextrose broth - I.P. (fungi) (Pharmacopoeia of India, 2007). The samples were incubated at 37 °C for 24 h (bacteria), at 25 °C for 7 d (A. niger) and at 37 °C for 48 h (C. albicans) and the results were recorded in terms of minimum inhibitory concentration (MIC).

3.4

3.4 QSAR studies

The structures of propionic acid derivatives were first pre-optimized with the Molecular Mechanics Force Field (MM+) procedure included in Hyperchem 6.03 (Hyperchem 6.0, 1993) and the resulting geometries were further refined by means of the semiempirical method PM3 (Parametric Method-3). We had chosen a gradient norm limit of 0.01 kcal/A° for the geometry optimization. The lowest energy structure was used for each molecule to calculate physicochemical properties using TSAR 3.3 software for Windows (TSAR 3D Version 3.3, 2000). Further, the regression analysis was performed using the SPSS software package (SPSS for Windows, 1999).

4

4 Conclusion

A series of Schiff bases (1–17) and esters (18–24) of propionic acid was synthesized and evaluated in vitro for their antimicrobial activity against Gram-positive bacteria S. aureus, B. subtilis, Gram negative bacterium E. coli and fungal strains C. albicans and A. niger by tube dilution method. Results of antimicrobial screening indicated that in general, esters of propionic acid were poor antimicrobial agents than Schiff bases. Schiff bases having electron withdrawing substituents on benzylidene moiety were found to be better antibacterial agents and those having electron donating substituents on benzylidene moiety were found to be better antifungal agents among which compound 10 emerged as the most potent antifungal agent and may be taken as a lead compound for the development of novel antifungal agents. QSAR studies were carried out in order to find out the relationship between antimicrobial activity of synthesized compounds and their structures (molecular descriptors) which demonstrated the importance of topological parameters, Kier’s alpha first order shape index (κα1) and valence first order molecular connectivity index (1χv) in determining the antimicrobial activity of synthesized propionic acid derivatives.

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