Synthesis, antimicrobial, anticancer evaluation and QSAR studies of N′-substituted benzylidene/2-hydroxynaphthalen-1-ylmethylene/3-phenylallylidene/5-oxopentylidene -4-(2-oxo-2-(4H-1,2,4-triazol-4-yl) methylamino)benzohydrazides

2.1 Instrumentation

Starting materials were obtained from commercial sources and were used without further purification. Reaction progress was observed by thin layer chromatography making use of commercial silica gel plates (Merck), Silica gel F254 on aluminum sheets. Melting points were determined in open capillary tubes on a Sonar melting point apparatus. ¹H nuclear magnetic resonance (¹H NMR) spectra were determined by Bruker Avance II 400 NMR spectrometer in appropriate deuteriated solvents and are expressed in parts per million (δ, ppm) downfield from tetramethylsilane (internal standard). NMR data were given as multiplicity (s, singlet; d, doublet; t, triplet; m, multiplet) and number of protons. Infrared (IR) spectra were recorded on an Agilent Resolutions Pro FTIR spectrometer. Elemental analysis was performed on a Perkin–Elmer 2400 C, H and N analyzer. Mass spectra were recorded using Waters Micromass Q-Tof micro instrument.

2.2 General procedure for the synthesis of N′-substituted benzylidene/2-hydroxynaphthalen-1-ylmethylene/3-phenylallylidene/5-oxopentylidene-4-(2-oxo-2-(4H-1,2,4-triazol-4-yl)ethylamino) benzohydrazides

A mixture of 1,2,4-triazole (0.01 mol) and chloroacetyl chloride (0.01 mol) in absolute ethanol (20 ml) was refluxed for 8–10 h. The contents were cooled and poured into ice cold water. The separated product was filtered, washed with water, dried and recrystallized from ethanol to yield 2-chloro-1-(4H-1,2,4- triazol-4-yl) ethanone triazole chloride. The later was treated with p-amino benzoic acid (0.1 mol) in ether (50 ml) maintained at 0–10 °C temperature. The solution was stirred for 30 min, the precipitated anilide was separated by filtration, which was then treated with 5% hydrochloric acid, 4% sodium carbonate and water to remove residual aniline, and the resultant anilide was recrystallized with alcohol (Mahiwal et al., 2012). The above formed product (0.08 mol) was refluxed with ethanol (0.80 mol) in the presence of a few drops of concentrated sulfuric acid until completion of the reaction. The reaction mixture was then added to 200 ml ice cold water and the residual acid was removed by treatment with sodium bicarbonate. The ester formed was extracted with ether and its evaporation yielded the crude ester, which was recrystallized from alcohol (Narang et al., 2012a). Hydrazine-hydrate (99%) (0.015 mol) was added to ethanolic solution of ester (0.01 mol) synthesized above and refluxed for 8 h. The reaction mixture was then cooled and the precipitated acid hydrazide was filtered off, washed with water, dried and recrystallized from ethanol. The solution of acid hydrazide (0.01 mol) and appropriate aldehyde (0.01 mol) in ethanol was refluxed for 5–6 h. The precipitated title compounds were then filtered off, washed with water and recrystallized from ethanol (Narang et al., 2012a).

2.3 Evaluation of antimicrobial activity

The antimicrobial activity of the synthesized compounds was performed against Gram-positive bacteria: Staphylococcus aureus MTCC 2901, Bacillus subtilis MTCC 2063, Gram-negative bacterium: Escherichia coli MTCC 1652 and fungal strains: Candida albicans MTCC 227 and Aspergillus niger MTCC 8189 using 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 MIC.

2.4 Evaluation of anticancer activity

The anticancer activity of the synthesized compounds (1–17) was determined against human colon (HCT116) cancer cell line. The cells 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 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) (Sigma) assay, as described by Mosmann, but with minor modifications, 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 from which the concentration of test compounds required to kill 50% of cell population (IC₅₀) was determined (Mosmann, 1983).

2.5 QSAR studies

The structures of triazole derivatives (1–17) were first pre-optimized with the Molecular Mechanics Force Field (MM⁺) procedure included in Hyperchem 6.03 (Hyperchem, 1993) and the resulting geometries were further refined by means of the semi empirical method PM3 (Parametric Method-3). We selected a gradient norm limit of 0.01 kcal/Å for the geometry optimization. The lowest energy structure of each molecule was used to calculate physicochemical properties using TSAR (2000). Further, the regression analysis was performed using the SPSS for Windows (1999).

2.6 Cross-validation

The predictive powers of the equations were validated by leave one out (LOO) cross-validation method (Schaper, 1999), where a model is built with N−1 compounds and N^th compound is predicted. Each compound is left out of the model derivation and predicted in turn. An indication of the performance is obtained from cross-validated (or predictive q²) method which is defined as $$q^2=1-\mathrm{\Sigma }{\left({\text{Y}}_{\text{predicted}-}{\text{Y}}_{\text{actual}}\right)}^2/\left({\text{Y}}_{\text{actual}-}{\text{Y}}_{\text{mean}}\right)$$ where Y_predicted, Y_actual and Y_mean are the predicted, actual and mean values of target property (pMIC), respectively. Σ (Y_predicted − Y_actual)² is the predictive residual error sum of squares.

3.1 Chemistry

A series of N′-substituted benzylidene/2-hydroxynaphthalen-1-ylmethylene/3-phenylallylidene/5-oxopentylidene-4-(2-oxo-2-(4H-1,2,4-triazol-4-yl)ethylamino)benzo hydrazides (1–17) was synthesized as outlined in Scheme 1. At first, 1,2,4 triazole was reacted with chloroacetyl chloride and the resultant product was treated with p-amino benzoic acid to yield 4-(2-oxo-2-(4H-1,2,4-triazol-4-yl)ethylamino)benzoic acid which was then esterified using ethanol. The later on treatment with hydrazine hydrate yielded hydrazide which on reaction with corresponding aldehydes yielded the title products, N′-substituted benzylidene/2-hydroxynaphthalen-1-ylmethylene/3-phenylallylidene/5-oxopentylidene -4-(2-oxo-2-(4H-1,2,4-triazol-4-yl)ethylamino)benzohydrazides (1–17). The physicochemical characteristics of the synthesized compounds are presented in Table 1. The structures of the synthesized compounds were characterized by elemental analysis in addition to IR, NMR and Mass spectroscopy and the spectral data were found in agreement with the assigned molecular structures.

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Scheme 1

Scheme for the synthesis of 1,2,4-triazole derivatives.

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3.2 Antimicrobial activity

The synthesized compounds were evaluated for their in vitro antibacterial activity against S. aureus, B. subtilis, E. coli and antifungal activity against C. albicans and A. niger by tube dilution method using norfloxacin (antibacterial) and fluconazole (antifungal) as reference standards and the results are presented in Table 2.

Among the synthesized compounds, 8 and 15 were found to be effective against S. aureus (pMIC_ca = 1.55 μM/mL) and C. albicans (pMIC_ca = 1.85 μM/mL), compound 15 emerged as the most potent antibacterial agent (pMIC_bs = 1.55 μM/mL) against for B. subtilis. In the case of Gram negative bacterium E. coli, compound 8 was found to be the most potent with pMIC_ec values of 1.85 μM/mL (Table 2).

Antimicrobial screening revealed that the synthesized compounds were found to be most potent against A. niger among the panel of bacterial and fungal strains tested and compound 14 having electron releasing hydroxyl and methoxy groups was found to be very effective against A. niger (pMIC_an = 2.10 μM/mL) and was having antifungal potential comparable to the standard drug fluconazole (pMIC_an = 2.64 μM/mL). Thus, compound 14 may serve as an important lead in the field of antifungal research.

3.3 Anticancer studies

The in vitro anticancer activity of the synthesized 1,2,4 triazole acid derivatives was determined against human colorectal cancer (HCT116) cancer cell line using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay [23] and the results are presented in Table 1.

Anticancer study indicated that the synthesized compounds exhibited good anticancer potential. Compounds 1, 3, 7, 8, 9, 10 and 13 were found to be more potent than standard drug carboplatin (IC₅₀ ⩾ 100 μM) but none of the synthesized compounds exhibited anticancer potential better than that of 5-FU (IC₅₀ = 4.6 μM). Compound 7 (IC₅₀ = 14.90 μM) was found to be most potent anticancer agent against human colorectal cancer (HCT116) cell line and may be taken as a new lead for the development of novel anticancer agents. From the results of anticancer and antimicrobial screening, it can be concluded that electron-releasing group on benzylidene moiety improved the antimicrobial as well as anticancer potential of the synthesized 1,2,4-triazole derivatives.

3.4 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 values of selected molecular descriptors used in the QSAR study are presented in Table 3.

During the regression analysis studies compounds 2, 7, 8 and 14 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 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.

Since there was no difference in the activity (Table 2) as well as the molecular descriptor range (Table 3) of the outliers when compared to other triazole derivatives indicated the fact that these outliers belong to the category of Y outliers (Substances for which the reference value of response is invalid).

Our earlier studies (Kumar et al., 2012; Judge et al., 2012a,b; Narang et al., 2012a,b) indicated that the multi-target QSAR (mt-QSAR) models are better than one-target QSAR (ot-QSAR) models in describing the antimicrobial activity. Therefore, in the present study muti-target QSAR models were developed to describe the antimicrobial activity of the synthesized N′-benzylidene-4-(2-oxo-2-(4H-1,2,4-triazol-4-yl)ethylamino)benzohydrazides.

According to the ot-QSAR models one should use five different equations with different errors to predict the activity of a new compound against the five microbial species. The ot-QSAR models, which are almost in the whole literature, become impractical to use when each compound’s result must be predicted for more than one target. In this case, one ot-QSAR for each target needs to be developed. However, very recently the interest has increased in the development of multi-target QSAR (mt- QSAR) models.

In opposition to ot-QSAR, the mt-QSAR model is a single equation that considers the nature of molecular descriptors that are common and essential for describing the antibacterial and antifungal activities (Gonzalez-Diaz and Prado-Prado, 2008; Cruz-Monteagudo et al., 2007; Gonzalez-Diaz et al., 2007; Prado-Prado et al., 2008). In light of the above, we attempted to develop three different mt-QSAR models viz. mt-QSAR model for describing antibacterial activity of synthesized compounds against S. aureus, B. subtilis and E. coli, mt-QSAR model for describing antifungal activity of synthesized compounds against C. albicans and A. niger as well as a common mt-QSAR model for describing the antimicrobial (overall antibacterial and antifungal) activity of the synthesized compounds against all of the above mentioned microorganisms.

In order to develop mt-QSAR models, initially we calculated the average antibacterial, antifungal and antimicrobial activities of 1,2,4-triazole derivatives which are presented in Table 2. Preliminary analysis was carried out in terms of correlation analysis. A correlation matrix constructed for antibacterial activity is presented in Table 4. In general, high colinearity (r > 0.5) was observed between different parameters. The high interrelationship was observed between Kier’s first order shape index (κ₁) and W (r = 0.973), and low interrelationship was observed between Balaban index (J) and dipole moment (μ) (r = 0.006, Table 4).

The antibacterial activity of 1,2,4-triazole derivatives is best described by the electronic parameter, dipole moment (μ) (r = 0.592, Table 4, Eq. (1)).

3.4.2

3.4.2 MLR mt-QSAR model for antibacterial activity

(2)

$$\begin{array}{cc}\hfill & {\text{pMIC}}_{\text{ab}}=-{0.438}^3{\chi }^v+0.034\mu +1.636\hfill \\ \hfill & n=13r=0.855q^2=0.640s=0.047F=13.53\hfill \end{array}$$

The molecular connectivity index, an adjacency based topological index proposed by Randic is denoted by χ and is defined as sum over all the edges (ij) as per following: $$\chi ={\displaystyle \sum _{i=l}^n}{\left(\mathit{Vi}\mathit{Vj}\right)}^{-1/2}$$ where V_i and V_j 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 that accounts for variation in activity (Gupta et al., 2003).

The QSAR model expressed by Eq. (2) was cross validated by its high q² value (q² = 0.640) obtained by 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). As the observed and predicted values are close to each other (Table 5), the mt-QSAR model for antibacterial activity (Eq. (2)) is valid. The plot of predicted pMIC_ab against observed pMIC_ab (Fig. 1) also favors the developed model expressed by Eq. (2). Further, the plot of observed pMIC_ab vs residual pMIC_ab (Fig. 2) 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 5 Observed, predicted and residual antimicrobial activities of the synthesized 1,2,4-triazole derivatives.

Comp.	pMICab			pMICaf			pMICam
	Obs.	Pre.	Res.	Obs.	Pre.	Res.	Obs.	Pre.	Res.
1	1.33	1.33	0.00	1.38	1.46	−0.08	1.35	1.39	−0.04
2	1.26	1.59	−0.33	1.16	1.75	−0.59	1.22	1.65	−0.43
3	1.59	1.58	0.01	1.49	1.61	−0.12	1.55	1.58	−0.03
4	1.39	1.40	−0.01	1.64	1.61	0.03	1.49	1.49	0.00
5	1.59	1.51	0.08	1.64	1.66	−0.02	1.61	1.57	0.04
6	1.48	1.46	0.02	1.78	1.76	0.02	1.60	1.59	0.01
7	1.46	1.41	0.05	1.91	1.65	0.26	1.64	1.51	0.13
8	1.55	1.60	−0.05	1.85	1.65	0.20	1.67	1.61	0.06
9	1.41	1.52	−0.11	1.51	1.70	−0.19	1.45	1.59	−0.14
10	1.48	1.48	0.00	1.78	1.73	0.05	1.60	1.58	0.02
11	1.50	1.52	−0.02	1.65	1.50	0.15	1.56	1.51	0.05
12	1.42	1.42	0.00	1.67	1.59	0.08	1.52	1.49	0.03
13	1.45	1.47	−0.02	1.90	1.84	0.06	1.63	1.62	0.01
14	1.60	1.51	0.09	1.95	1.70	0.25	1.74	1.58	0.16
15	1.55	1.55	0.00	1.70	1.71	−0.01	1.61	1.61	0.00
16	1.58	1.52	0.06	1.93	1.84	0.09	1.72	1.65	0.07
17	1.54	1.56	−0.02	1.89	1.95	−0.06	1.68	1.72	−0.04

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Figure 1

Plot of predicted pMIC_ab vs. observed pMIC_ab by Eq. (2).

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Figure 2

Plot of observed pMIC_ab vs. residual pMIC_ab by Eq. (2).

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Topological parameter, valence third order molecular connectivity index ( ${}^3{\chi }^v$ ) was the most effective in describing antifungal activity of the synthesized compounds (Eq. 3).

3.4.5

3.4.5 MLR mt-QSAR model for antimicrobial activity

(5)

$$\begin{array}{cc}\hfill & {\text{pMIC}}_{\text{am}}=-0.703{}^3{\chi }^v+0.017\mu +1.916\hfill \\ \hfill & n=13r=0.842q2=0.530s=0.058F=12.17\hfill \end{array}$$

Validity of developed QSAR models for antifungal and antimicrobial activities was confirmed by their high q² values (0.548 and 0.530, respectively) as well as low residual values (Table 5). The plot of predicted pMIC_am against observed pMIC_am (Fig. 3) also favors the developed model expressed by Eq. (5).

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Figure 3

Plot of observed pMIC_am against predicted pMIC_am by Eq. (5).

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Further, the plot of observed pMIC_am vs. residual pMIC_am (Fig. 4) 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 value (Table 5) observed in case of outliers (2, 7, 8 and 14) justified their removal as outliers.

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Figure 4

Plot of observed pMIC_am against residual pMIC_am by Eq. (5).

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In summary, the mt-QSAR models [Eqs. (1–5)] indicated that antimicrobial activity of the synthesized 1,2,4-triazole derivatives is governed by the topological parameter, valence third order molecular connectivity index $({}^3{\chi }^v$ ) and electronic parameter, dipole moment (μ).

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 was within one order of magnitude. This is in accordance with results suggested by Bajaj et al. (2005) who stated that the reliability of the QSAR model lies in its predictive ability even though the activity data are within a narrow range (Bajaj et al., 2005). 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 (Narasimhan et al., 2007). The minimum standard deviation (Table 2) observed in the antimicrobial activity data justifies its use in QSAR studies.