International Journal of Medicinal Chemistry

International Journal of Medicinal Chemistry / 2014 / Article

Research Article | Open Access

Volume 2014 |Article ID 586437 | 5 pages | https://doi.org/10.1155/2014/586437

Experimental Design-Based Response Surface Methodology Optimization for Synthesis of β-Mercapto Carbonyl Derivatives as Antimycobacterial Drugs Catalyzed by Calcium Pyrophosphate

Academic Editor: Bernard Pirotte
Received29 Aug 2013
Revised18 Dec 2013
Accepted28 Jan 2014
Published06 Mar 2014

Abstract

A simple protocol for the efficient preparation of β-mercapto carbonyl derivatives as antimycobacterial drugs has been achieved via Thia-Michael reaction between chalcones derivatives and thiols in the presence of calcium pyrophosphate as a heterogeneous catalyst under mild reaction conditions. The central composite design was used to design an experimental program to provide data to model the effects of various factors on reaction yield . The variables chosen were catalyst weight , reaction time , and solvent volume . The mathematical relationship of reaction yield on the three significant independent variables can be approximated by a nonlinear polynomial model. Predicted values were found to be in good agreement with experimental values. The optimum reaction conditions for reaction model (chalcone and thiophenol) obtained by response surface were applied to other substrates. This procedure provides several advantages such as high yield, clean product formation, and short reaction time.

1. Introduction

Tuberculosis is the second most common cause of death from infectious disease. Roughly one-third of the world’s population has been infected with Mycobacterium tuberculosis, and new infections occur at a rate of one per second. In 2007 there were an estimated 13.7 million chronic active cases, and in 2010 there were 8.8 million new cases and 1.45 million deaths. 0.35 million of these deaths occur in those coinfected with HIV [1].

The needs of newly developed antimycobacterial drugs are required for the control of tuberculosis in the present time. In the discovery of new antimycobacterial drugs, the emergence of multidrug-resistant and extensively drug-resistant strains of Mycobacterium has encouraged the researchers to intensify the efforts to discover novel drugs [2].

Recently, the Czech authors showed that the 1,3-diphenyl-3-arylsulfenylpropan-1-one derivatives presented an antimycobacterial activity [3].

Thia-Michael reaction is a convenient route for synthesis of these β-mercapto carbonyl derivatives [4]. Traditionally, this reaction is catalyzed by strong bases [5] such as alkali metal alkoxides, hydroxides, and amines. However the use of either strongly acidic or basic conditions frequently leads to the formation of undesirable side products owing to competing reactions, such as polymerization, self-condensation, and rearrangements. The development of solid basic catalysts which could replace the liquid bases currently used in industrial processes is a major field of today’s catalysis research [6].

In this paper we report a mild and convenient method for synthesis of β-mercapto carbonyl derivatives as antimycobacterial drugs catalyzed by calcium pyrophosphate using central composite experimental design [7].

2. Materials and Methods

2.1. Preparation and Characterization of Catalyst

The calcium pyrophosphate (CP) was prepared by the dehydration of CaHPO4 at 750°C for 2 h. The powder was dried for 2 hours at 100°C before use in order to eliminate possible water molecules adsorbed on the surface of the sample which may affect catalytic activity [8]. Analysis of CP catalyst by X-ray diffraction showed a single phase of β-Ca2P2O7. The analysis of morphology for this catalyst by scanning electron micrograph showed that the particle size of CP powder is less than 1 μm.

2.2. General Procedure

The general procedure for synthesis of β-mercapto carbonyl derivatives is reported in Figure 1 as follows: to a flask containing an equimolar mixture (1 mmol) of chalcones, derivatives as Michael acceptors 1 and thiols as Michael donors 2 in methanol catalyzed by CP were added, and the mixture was stirred at room temperature until completion of the reaction, as monitored by thin layer chromatography.

The catalyst was filtered and washed with dichloromethane, and the filtrate was concentrated under reduced pressure. The crude product was purified by crystallization. The product was analyzed by 1H, 13C NMR, and IR spectrometry.

1,3-Diphenyl-3-phenylsulfenylpropan-1-one (3a). White solid, mp 116–118°C; Rf (10% AcOEt/hexane) 0.36; υmax (KBr)1680 cm−1;δH (400 MHz CDCl3) 3.5 (H, CH2, dd,  Hz,  Hz); 3.7 (H, CH2, dd,  Hz,  Hz); 4.98 (H, CH, t,  Hz); 7.2–7.6 (13H, arom, m); 7.9 (2H, arom, d, ); δC (100 MHz CDCl3) 44.7; 48.3; 127.4; 127.6; 127.9; 128.1; 128.5; 128.6; 128.9; 132.8; 133.3; 134.6; 136.8; 141.3; 197.9; MS (m/z): 318 (M+); 206; 109.

3-(4-Chlorophenyl)-1-phenyl-3-phenylsulfenylpropan-1-one (3b). White solid; mp 64–67°C; Rf (10% AcOEt/hexane) 0.35; υmax (KBr) 1730  cm−1; δH (400 MHz CDCl3) 3.6 (2H, CH2, m); 4.97 (H, CH, t,  Hz); 7.2–7.5 (12H, arom, m); 7.9 (2H, arom, d,  Hz); δC (100 MHz CDCl3) 44.2; 47.8; 122.3; 122.7; 128.1; 128.3; 128.8; 129.1; 129.3; 132.9; 133.4; 133.6; 134.2; 136.3; 143.8; 148.2; 196.1; MS (m/z): 353 (M+); 241.91; 109.

3-(4-Methoxyphenyl)-1-phenyl-3-phenylsulfenyl-propan-1-one (3c). White solid; 83–85°C; Rf (10% AcOEt/hexane) 0.26; IR (KBr, ν cm−1): 1730  cm−1; 1H NMR (400 MHz, CDCl3) δ: 3.62 (2H, dd,  Hz,  Hz,  Hz, CH2), 3.76 (3H, s, OCH3); 4,92 (H, dd, = 6,2 Hz,  Hz, CH), 6.78 (2H, m,  Hz, arom), 7.1–8.05 (12H, m, arom); 13C NMR (100 MHz, CDCl3) δ: 197.19, 158.77, 144.7, 136.78, 134.48, 133.13, 129.1, 128.63, 128.09, 127.47, 119.82, 114.46, 113.86, 55.33, 47.67, 44.86; MS (m/z): 348 (M+), 239, 238, 237, 109.

3-(2-Aminophenylsulfenyl)-1,3-diphenylpropan-1-one (3d). White solid; mp 126–128°C; Rf (10% AcOEt/hexane) 0.20; υmax (KBr) 1710; 3410  cm−1; δH (400 MHz CDCl3) 3.5 (H, CH2, dd,  Hz,  Hz); 3.7 (H, CH2, dd,  Hz,  Hz); 4.3 (2H, NH2, bs); 4,73 (H, CH, t,  Hz); 6.53–7.54 (12H, arom, m); 7.9 (2H, arom, d,  Hz); δC (100 MHz, CDCl3) 44.1; 47.1; 114.9; 115.3; 115.7; 118.0; 118.2; 127.3; 127.6; 128.1; 128.4; 128.6; 130.6; 131.6; 133.3; 136.8; 137.7; 141.7; 148.7; 149.5; 197.2; MS (m/z): 333 (M+); 207; 126.

3-(2-Aminophenylsulfenyl)-3-(4-chlorophenyl)-1-phenylpropan-1-one (3e). White solid; mp 78–81°C; Rf (10% AcOEt/hexane) 0.33; υmax (KBr) 1700; 3470  cm−1; δH (400 MHz CDCl3) 3.4 (H, CH2, dd,  Hz,  Hz); 3.6 (H, CH2, dd,  Hz,  Hz); 4.1 (2H, NH2, bs); 4.9 (1H, CH, t,  Hz); 6.5–7,5 (11H, arom, m); 7.9 (2H, arom, d,  Hz); δC (100 MHz CDCl3) 43.4; 45.8; 115.2; 118.3; 118.8; 122.2; 122.5; 128.1; 128.7; 129.1; 131.1; 131.6; 133.6; 134.1; 136.8; 137.5; 148.6; 149.4; 196. MS (m/z) 367 (M+); 242; 111.

3-(2-Aminophenylsulfenyl)-3-(4-methoxyphenyl)-1-phenyl-propan-1-one (3f). White solid; 108–111°C; Rf: (10% AcOEt/hexane) 0.24; IR (KBr, ν cm−1): 3420, 1730  cm−1; 1H NMR (400 MHz, CDCl3) δ: 3.52 (2H, dd,  Hz,  Hz,  Hz, CH2), 3.76 (3H, s, OCH3), 4.2 (2H, s, NH2), 4,8 (H, dd,  Hz,  Hz, CH), 6.48–7.76 (11H, m, arom), 7.95 (2H, d,  Hz, arom); 13C NMR (100 MHz, CDCl3) δ: 19736, 158.74, 149.5, 137.69, 136.33, 135.10, 133.25, 131.13, 130.62, 129.71, 129.72, 128.10, 127.49, 127.19, 125.32, 123.01, 118.07, 114.88, 114.09, 113.77, 55.30, 46.32, 44.32; MS (m/z): 363 (M+), 239, 238, 237, 125.

2.3. Statistical Analysis

A central composite rotatable design for independent variables was employed to design the experiments in which the variance of the predicted response, , at some points of independent variables, , is only a function of the distance from the point to the design centre.

The design of experiment is intended to reduce the number of experiments and to arrange the experiments with various combinations of independent variables. In the rotatable design, the standard error, which depends on the coordinates of the point on the response surface at which is evaluated and on the coefficients β, is the same for all points that have the same distance from the central point.

These designs consist of a factorial (coded to the usual ±1 notation) augmented by axial points (±α, 0, 0), (0, ±α, 0), (0, 0, ±α) and 2 centre points (0, 0, 0). The value of α for rotatability depends on the number of points in the factorial portion of the design, which is given in where is the number of points in the cube portion of the design (, is the number of factors). Since there are three factors, the number is equal to 8 points, while α is equal to 1.682 according to (1). In this study, the response was reaction yield. Each response was used to develop an empirical model that correlated the response to the reaction conditions variables for synthesis of an antimycobacterial drug, using a second-degree polynomial equation as given by where is the constant coefficient, is the linear coefficients, is the cross-product coefficients, and is the quadratic coefficients. The software Statgraphics-Plus was used for the experimental design, data analysis, model building, and graph plotting.

3. Results and Discussion

Conjugate addition between chalcone (R1=H) and thiophenol (R=Ph) was chosen as model substrates to determine suitable reaction conditions for synthesis of 1,3-diphenyl-3-phenylsulfenylpropan-1-one catalyzed by CP in methanol at room temperature.

Preliminary experiments were carried out to screen the appropriate parameters and to determine the experimental domain. From these experiments, the effects of catalyst weight (), reaction time (), and solvent volume () are investigated on reaction yield as response. The parameter levels and coded values were given in Table 1.


Natural variableUnitCoded variables , , and
−10+1

Catalyst weightmg40120200
Reaction timemin203040
Solvent volumemL123

The experimental design matrix and the corresponding experimental parameters and response value were shown in Table 2.


OrderCoded units of variablesReaction yield

01+1+1+196
02+1+1−182
03+1−1+174
04+1−1−168
05−1+1+178
06−1+1−169
07−1−1+165
08−1−1−157
09−1.6820061
10+1.6820094
110−1.682056
120+1.682093
1300−1.68277
1400+1.68292
1500094
1600093

The 10 coefficients of this design are easily calculated by the least squares method. The significance of effects can be estimated by comparing the distribution of the experimental values () to a critical value (). According to the results showed in Table 3.


Source of variationCoefficient Sum of squares Mean square Significance test

93.91671
07.79820830.50410830.50434.87*
09.02301111.88011111.88046.68*
04.55650283.53410283.53411.90*
−06.66290411.26910411.26917.27*
01.37500015.12510015.12500.63NS
00.37500001.12510001.12500.05NS
−07.72350552.63110552.63123.20*
01.12500010.12510010.12500.43NS
−04.18800162.48510162.48506.82*

Significant at a level of 5%; NS: not significant.

In this case, the linear terms (, , and ) and the squared terms (, , and ) were significant model terms whereas the interaction terms (, , and ) were insignificant to the response. The final empirical model in terms of coded factors after excluding the insignificant terms for reaction yield is shown in Positive sign in front of the terms indicates synergistic effect, whereas negative sign indicates antagonistic effect.

The analysis of variance (ANOVA) for model of the reaction yield is listed in Table 4. From this ANOVA, the value of the Model was 13.6956 (>F0.01) implying that the model was significant.


Source of variationSum of squares Mean square Significance test

Regression2936.0229326.224613.6956*
Residue142.918623.8196

Total3078.9415

Significant at a level of 1%; NS: not significant.

The quality of the model developed was evaluated based on the correlation coefficient value (). The value for (3) was 0.9765. The value obtained was relatively high, indicating that there was a good agreement between the experimental and the predicted values from the model.

The value for (3) was 0.9536. This indicated that 95.36% of the total variation in the reaction yield was attributed to the experimental variables studied. From the statistical results obtained, it was shown that the above model was adequate to predict the reaction yield within the range of variables studied.

The investigation of (3) showed that, if , , and , the value predicted from the results using response surface model is 95%. The experimental checking in this point, that is, under the optimum reaction conditions such as catalyst  mg, reaction  min, and solvent  mL with high reaction yield 94% of product 3a, confirms this result.

To determine the scope and limitation for synthesis of β-mercapto carbonyl derivatives, the optimum reaction conditions of product 3a were applied to other substrates as shown in Table 5.


Products RYield %

3aH–Ph94
3bCl–Ph96
3cOMe–Ph77
3dH–2-NH2-Ph93
3eCl–2-NH2-Ph95
3fOMe–2-NH2-Ph92

In this study, the Thia-Michael reaction between several chalcones and thiols has been used for synthesis of β-mercapto carbonyl derivatives.

The yields obtained of β-mercapto carbonyl derivatives are very high and exceed 92% in short reaction time, except for product (3c). The products of undesirable side reactions resulting from 1, 2 addition, polymerization, and bis-addition are not observed. The use of CP catalyst is particularly interesting since it is regenerated by calcinations at 750°C during 15 min, and after five successive recoveries, sulfanyl products were obtained with same yield.

4. Conclusion

In conclusion, the optimization of reaction conditions for synthesis of β-mercapto carbonyl derivatives catalyzed by calcium pyrophosphate has been studied using central composite design. This work-up procedure provides a clean, fast, high yielded, environmentally friendly, and simple route to synthesis of antimycobacterial drugs.

Conflict of Interests

The authors declare that they have no conflict of interests regarding the publication of this paper.

Acknowledgments

The authors are grateful to Mr. Hamid Dhimane and Mr. Marc David of Laboratory of Chemistry and Biochemistry and Pharmacology Toxicology, University René Descartes Paris, France, for their help with data analysis.

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Copyright © 2014 Younes Abrouki et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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