4-(1-Aryl-5-chloro-2-oxo-1,2-dihydro-indol-3-ylideneamino)-N-substituted benzene sulfonamides: Synthesis, antimicrobial, anticancer evaluation and QSAR studies

2.1 Chemistry

The synthesis of 4-(1-aryl-5-chloro-2-oxo-1,2-dihydro-indol-3-ylideneamino)-N-substituted benzene sulfonamide derivatives (1–20) was accomplished (Scheme 1). The physicochemical characteristics of the synthesized compounds are presented in Table 1.

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Scheme 1

Synthetic scheme of the synthesis of 4-(1-aryl-5-chloro-2-oxo-1,2-dihydro-indol-3-ylideneamino)-N-substituted benzenesulfonamide derivatives (1–20).

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The synthesized compounds were characterized by IR and ¹H NMR spectroscopy and the results are in accordance with the assigned molecular structures. IR stretching band ranging from 1688 to 1681 cm⁻¹ (C = O str., Ar–C = O) confirmed the acylation of isatin. IR stretching band at 1656–1650 cm⁻¹ (C = N str.) confirmed the formation of a Schiff base. Further, peak of NH in plane bending at 1516–1491 cm⁻¹, O = S = O str. at 1186–1142 cm⁻¹ confirmed the presence of sulfonamide moiety in the synthesized compounds.

In the ¹H NMR spectra the signals of the respective protons of the synthesized compounds were confirmed based on their chemical shifts, multiplicities and coupling constants. These spectra showed a singlet at 4.01–4.76 ppm, which corresponds to the SO₂NH protons and multipletes at 6.55–8.74 ppm, showed aromatic protons.

2.2 Antimicrobial activity

The antimicrobial activity of the synthesized compounds was determined by tube dilution method (Cappucino and Sherman, 1999) and the results are given in Table 2. Compound 19 demonstrated potent antibacterial activity against Staphylococcus aureus (pMIC_sa = 2.01 μM). In the case of Bacillus subtilis compounds 9 and 19 emerged as most effective antibacterial agents with pMIC_bs values of 1.98 and 2.01 μM, respectively. Compounds 9 and 19 (pMIC_ec values 1.98 and 2.01 μM, respectively) also emerged as the most active candidates among the synthesized compounds against Gram-negative bacterium Escherichia coli. In the case of antifungal activity against Candida albicans, compound 19 emerged as the most active candidate among the synthesized compounds (pMIC_ca values 2.31 μM) and against Aspergillus niger compounds, 9 and 19 (pMIC_an values 1.98 and 2.01 μM, respectively) emerged as most active antifungal agents.

The antimicrobial results of the standard drugs, norfloxacin and fluconazole were almost similar against all the tested microorganisms. In the present study however, the standard drugs were more potent than the synthesized compounds.

In general, the results of MBC/MFC studies revealed that the synthesized compounds were bacteriostatic and fungistatic in action as their MFC and MBC values were (ranging from >0.08 to >0.10 μM/ml) 3-fold higher than their MIC values (a drug is considered to be bacteriosatic/fungistatic when its MFC and MBC values are 3-fold higher than its MIC value (Emami et al., 2004).

2.3 Anticancer activity

The anticancer activity (IC₅₀) of the synthesized isatin derivatives was determined against mouse leukemic monocyte macrophage (RAW 264.7) and colon cancer (HCT116) cell lines using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay (Mosmann, 1983). The anticancer activity results are presented in Table 1. In general all the synthesized compounds were more active than the standard drug carboplastin (IC₅₀ = >100 μM) but less active than the standard drug 5-FU (IC₅₀ = 4.6 μM) against both the cell lines (HCT116 and RAW 264.7). Compounds, 4-(1-Benzoyl-5-chloro-2-oxoindolin-3-ylideneamino)-N-(pyrimidin-2-yl)benzene sulfonamide (3) and 4-(5-chloro-1-(2-chlorobenzoyl)-2-oxoindolin-3-ylideneamino)-N-(4,6-dimethoxypyrimidin-2-yl)benzene sulfonamide (16) were found to be most potent with IC₅₀ values of 17.38 μM against HCT116 and 8.16 μM against RAW 264.7, respectively.

2.4 Structure–activity relationship

1. Results of the antimicrobial and anticancer screening indicated that phenyl nucleus attached to benzenesulfonamide moiety increased the antimicrobial potential, whereas its replacement with pyrimidine nucleus improved the anticancer potential of the synthesized isatin derivatives.

2. The high antimicrobial activity of compound 19 indicated that the presence of electron withdrawing group (2-chloro) on benzoyl portion and electron donating group (4-isopropoxy) on benzamide portion increased the antimicrobial activity of the synthesized isatin derivatives. The role of electron releasing groups in enhancing the antimicrobial activity of isatin derivatives is further supported by the study of Pandeya et al. (1999).

3. The anticancer activity results indicated that the presence of unsubstituted pyrimidine ring attached to benzenesulfonamide moiety (3) increased the anticancer potential of the synthesized compounds against HCT116 cancer cell lines.

4. The anticancer activity results also indicated that the presence of electron releasing methoxy group on the pyrimidine ring attached to benzene sulfonamide moiety (16) improved the anticancer activity of synthesized compounds against RAW 264.7 cancer cell lines which indicated that this substitution is beneficial for the binding with the receptor ANP. The role of electron releasing groups in improving anticancer activity is supported by the studies of Mologni et al. (2010).

5. From the above mentioned antimicrobial and anticancer activity results, it can be concluded that different structural requirements are necessary for a compound to be active against different microbial and cancer targets. This is in accordance with the findings of Sortino et al. (2007) and Yogeeswari et al. (2005).

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Figure 1

Structure–activity relationship for antimicrobial and anticancer activity of synthesized isatin derivatives.

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2.5 QSAR studies

In order to identify the substituent effect on the antimicrobial activity, quantitative structure–activity relationship (QSAR) studies were undertaken using the linear free energy relationship model (LFER) described by Hansch and Fujita (1964)). Biological activity data determined as MIC values were first transformed into pMIC values (i.e. −log MIC) and used as a dependent variable in the QSAR study. The different molecular descriptors (independent variables) like log of octanol–water partition coefficient (log P), molar refractivity (MR), Kier’s molecular connectivity (⁰χ, ⁰χ^v, ¹χ, ¹χ^v, ²χ, ²χ^v) and shape (κ₁, κ₂, κ₃, κ_1, κ_2, κ₃) 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), nuclear repulsion energy (Nu.E) and electronic energy (Ele.E) were calculated for isatin derivatives and values of selected descriptors are presented in Table 3 (Hansch et al., 1973; Kier and Hall, 1976; Randic, 1975, 1993; Balaban, 1982; Wiener, 1947). Units of the energies and dipole were electron volts (eV), and atomic units (a.u.), respectively (Dai et al., 1999).

In the present study, a data set of 20 isatin derivatives was subjected to linear free energy regression analysis for model generation. During the regression analysis studies it was observed that the response values of compounds 1, 3, 7, 11, 16 and 17 were outside the limits of response values of other synthesized isatin derivatives. Thus compounds 1, 3, 7, 11, 16 and 17 were designated as outliers and were not involved 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. Briefly, a substance is an X outlier if the molecular descriptors for this substance do not lie in the same range as 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.

Preliminary analysis was carried out in terms of correlation analysis. A correlation matrix constructed for antifungal activity against A. niger is presented in Table 4. In general, high colinearity (r > 0.5) was observed between different parameters. The high interrelationship was observed between topological parameter, Randic index (R) and steric parameter, molar refractivity (MR, r = 0.991) and low interrelationship was observed between electronic parameter, HOMO and topological parameter, valance zero order molecular connectivity index (⁰χ^v, r = -0.002). The correlations of different molecular descriptors with antibacterial and antifungal activity against different microorganisms are presented in Table 5.

The correlation matrix (Table 4) indicated the predominance of topological parameter, valance zero order molecular connectivity index (⁰χ^v) in describing the antifungal activity of the synthesized compounds against A. niger. Thus, QSAR model for antifungal activity against A. niger was developed using valance zero order molecular connectivity index (⁰χ^v).

Topological indices are numerical quantifiers of molecular topology and are sensitive to bonding pattern, symmetry, content of heteroatom as well as degree of complexity of atomic neighborhoods (Lather and Madan, 2005). The valence zero order molecular connectivity topological index (⁰χ^v) represents the molecules with branched structure. In this case, the positive correlation was observed between ⁰χ^v and antifungal activity against A. niger. Therefore, the antifungal activity of synthesized compounds against A. niger will increase with increase in their ⁰χ^v values. This is clearly evident from Table 3 that compound 19 having highest ⁰χ^v value of 25.26 is having highest antifungal activity against A. niger (pMIC_an = 2.01, Table 2).

The QSAR model expressed by Eq. (1) was cross validated by its high q² value (q² = 0.945) obtained with leave one out (LOO) method. The value of q² greater than 0.5 is the basic requirement for qualifying a QSAR model to be a valid one (Golbraikh and Tropsha, 2002). The comparison of observed and predicted antifungal activities is presented in Table 6. It can be seen from the results that the observed and predicted antifungal activities against A. niger lie close to each other as evidenced by their low residual values Table 6. The plots of observed, predicted and residual pMIC_an values were also developed to check the statistical validity of QSAR models. The plot of predicted pMIC_an against observed pMIC_an (Fig. 2) also favors the model expressed by Eq. (1). Further, the plot of observed pMIC_an vs residual pMIC_an (Fig. 3) 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).

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Figure 2

Plot of observed pMIC_an against predicted pMIC_an obtained by Eq. (1).

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Figure 3

Plot of observed pMIC_an against residual pMIC_an obtained by Eq. (1).

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Lipophilic parameter, log P was the most dominating parameter in describing the antibacterial activity of the synthesized compounds against B. subtilis and E. coli.

In order to improve the value of correlation coefficient, we coupled log P with dipole moment (μ), which resulted in best QSAR model for antibacterial activity of synthesized compounds against B. subtilis (Eq. (3)).

As in case of antibacterial activity against B. subtilis, the antibacterial activity of the synthesized compounds against E. coli (Eq. (4)) was also positively correlated with log P. Hence, antibacterial activity of synthesized compounds against both bacterial strains will increase with increase in their log P values.

Antibacterial activity of synthesized compounds against S. aureus was best described by topological parameter, valance third order molecular connectivity index (³χ^v) which was positively correlated with antibacterial activity (Eq. (5)).

Progress in the use of quantitative structure–activity relationship (QSAR) methods has shown the importance of the hydrophobic or lipophilic nature of biologically active molecules. The lipophilicity modifies the penetration of bioactive molecules through the apolar cell membranes. This property is usually characterized by the partition coefficient (log P), which is essentially determined from distribution studies of the compound between an immiscible polar and non-polar solvent pair.

This quantitative descriptor of lipophilicity (log P) is one of the key determinants of pharmacokinetic properties. Knowing the exact values for this parameter, it is possible to predict the inhibitory activity of the drugs (Podunavac-Kuzmanovic et al., 2008).

log P is the logarithm of the ratio of the concentrations of the un-ionized solute in two solvents, which is calculated according to the following equation, where o is octanol and w is un-ionized water. $${\text{log P}}_{o/w}=\text{log}([{\text{solute}}_o]/[{\text{solute}}_w])$$ The hydrophobic effect is the major driving force for the binding of drugs to their receptor targets in pharmacodynamics, and is based on the log P contribution of each atom. Each atom in a molecule contributes to the log P by the amount of its atomic parameter multiplied by the degree of exposure to the surrounding solvent (Park et al., 2008)

It is important to note that no significant correlation was observed between the antifungal activity of synthesized compounds against C. albicans and their physicochemical parameters. Further, high residual activity values observed in case of outliers (compounds 1, 3, 7, 11, 16 and 17) justified their removal before the development of QSAR models.

Generally for QSAR studies, the biological activities of compounds should span 2–3 orders of magnitude. But in the present study the range of antibacterial and antifungal activities of the synthesized compounds is within one order of magnitude. It is important to note that the predictability of the QSAR models developed in the present study is high evidenced by their low residual values. This is in accordance with results suggested by the 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. Further, recent literature reveals that the QSAR models have been applied to describe the relationship between narrow range of biological activity and physicochemical properties of the molecules (Narasimhan et al., 2007; Sharma et al., 2006; Hatya et al., 2006; Kumar et al., 2006). When biological activity data lie in the narrow range, the presence of minimum standard deviation of the biological activity justifies its use in QSAR studies (Kumar et al., 2007; Narasimhan et al., 2007). The minimum standard deviation (Table 2) observed in the antimicrobial activity data justifies its use in QSAR studies.

In summary, the antimicrobial activity of synthesized isatin derivatives against different microbial strains was governed by lipophilic parameter, log P and topological parameters valance zero and third order molecular connectivity indices (⁰χ^v and ³χ^v).

4.1 Chemistry

Starting materials were obtained from commercial sources and were used without further purification. Solvents were dried by standard procedures. Reaction progress was observed by thin layer chromatography. Melting points were determined in open capillary tubes on a Sonar melting point apparatus and were uncorrected. ¹H and ¹³C NMR spectra were determined by Bruker 500 MHz NMR spectrometer in appropriate deuterated solvents and are expressed in parts per million (δ, ppm) downfield from tetramethylsilane (internal standard). NMR data are given as multiplicity (s, singlet; d, doublet; t, triplet; m, multiplet) and number of protons. IR spectra were recorded on a Varian Resolutions Pro spectrophotometer in a KBr disk.

4.2 General procedure for the synthesis of 4-(1-aryl-5-chloro-2-oxo-1, 2-dihydro-indol-3-ylideneamino)-N-substituted benzenesulfonamide (1–20)

Thionyl chloride 32.8 g (0.3 mol) was added to different aromatic acids (0.25 mol) in a round bottomed flask. After addition, the mixture was refluxed for 2 h. The excess of thionyl chloride was removed by distillation. To the solution of acid chloride (1 mol) 0.1 mol of 5-chloroisatin was added and the mixture was refluxed for 1 h and 30 min. Then the reaction mixture was cooled and the resultant precipitate (N-acyl 5-chloroisatin) was collected, washed with hexane and recrystallized from ethyl acetate. A solution of 0.05 mol of different sulfonamides in warm ethanol was added to the corresponding solution of N-acyl 5-chloroisatin (0.05 mol) in the presence of small amount of glacial acetic acid. The mixture was refluxed for 4–6 h. Then the reaction mixture was allowed to cool at room temperature and the precipitate obtained was filtered, dried and recrystallized from ethanol.

4.3 Evaluation of antimicrobial activity

4.4 Anticancer evaluation

The anticancer activity of the synthesized compounds was determined against murine leukemia (RAW 264.7) and colon cancer (HCT116) cell lines. Cancer cell lines were purchased from the American Type Culture Collection (ATCC), Manassas, VA, USA. All cell lines were cultured in RPMI 1640 (Sigma) supplemented with 10% heat inactivated fetal bovine serum (FBS) (PAA Laboratories) and 1% penicillin/streptomycin (PAA Laboratories). Cultures were maintained in a humidified incubator at 37 °C in an atmosphere of 5% CO₂. Anticancer activity of the synthesized compounds at various concentrations was assessed using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) (Sigma) assay, as described by Mosmann (1983) but with minor modification, following 72 h of incubation. Assay plates were read using a spectrophotometer at 520 nm. Data generated were used to plot a dose–response curve of which the concentration of test compounds required to kill 50% of the cell population (IC₅₀) was determined. Anticancer activity was expressed as the mean IC₅₀ of three independent experiments.

4.5 QSAR studies

The structures of synthesized isatin derivatives were first pre-optimized with the Molecular Mechanics Force Field (MM⁺) procedure included in Hyperchem 6.0 (1993) and the resulting geometries were further refined by means of the semiempirical method PM3 (Parametric Method-3). We chose 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).