Advances in Physical Chemistry

Advances in Physical Chemistry / 2017 / Article

Research Article | Open Access

Volume 2017 |Article ID 7629056 | 14 pages | https://doi.org/10.1155/2017/7629056

Organic Compounds Based on (E)-N-Aryl-2-ethene-sulfonamide as Microtubule Targeted Agents in Prostate Cancer: QSAR Study

Academic Editor: Jan Skov Pedersen
Received25 Oct 2016
Revised08 Jan 2017
Accepted05 Feb 2017
Published18 Apr 2017

Abstract

(E)-N-Aryl-2-ethene-sulfonamide and its derivatives are potent anticancer agents; these compounds inhibit cancer cells proliferation. A study of quantitative structure-activity relationship (QSAR) has been applied on 40 compounds based on (E)-N-Aryl-2-ethene-sulfonamide, in order to predict their anticancer biological activity. The principal components analysis is used for minimizing the base matrix and the multiple linear regression (MLR) and multiple nonlinear regression have been used to design the relationships between the molecular descriptor and anticancer properties of the sulfonamide derivatives. The validation of the models MLR and MNLR has been done by dividing the dataset into training and test set, the external validation of multiple correlation coefficients was RpIC50 = 0.81 for MLR and RpIC50 = 0.91 for MNLR. The artificial neural network (ANN) showed a correlation coefficient close to 0.96, which concluded that this latter model is more effective and much better than the other models. This obtained model (ANN) has been confirmed by two methods of LOO cross-validation and scrambling (or Y-randomization). The high correlation between experimental and predicted activity values was observed, indicating the validation and the good quality of the derived QSAR model.

1. Introduction

Cancer is a major public health problem. The number of new cancer cases in 2012 was estimated to be at 14, 1 million and 8, 2 million deaths. It was estimated in 2008 that more than 70% of the fatalities due to cancer originated from developing countries. The frequency of cancers may go up by 50%, with 15 million new cases per year in 2020 [1, 2].

It is previewed that in 2030 the number of the fatalities due to cancer will increase up to 13.1 million. Even though its incidence is increasing in most regions of the world, the incidence rate is the highest in the more developed regions, but its fatality is relatively higher in the developing countries because of the inaccessibility to treatment and the absence of preliminary detection [3, 4].

One in two men and one in three women are affected by cancer. The distribution of cancers by age shows the trend since the beginning of registration; namely, there are a high number of cancer cases among women, taking into account the fact that the onset of cancer in women is at the age of 39 years, while for men it is at the age of 49 years. The number of cancer cases decreases from the age of 65 years in women whereas it begins to increase in men in this age [5].

Scientific advances of recent years now make it possible to decipher the genetic code of cancer and to understand how this disease is related to the mechanisms of life itself. For this reason, the cancer disease will probably never be completely eradicated, and basic research is redirected to the pharmaceutical industry that provides a plethora of increasingly targeted molecules, ever more efficient and always more expensive, so as to combine them in the manufacture of medicines.

Many sulfonamide derivatives have been reported to finally show in the pharmaceutical industry significant antitumor properties. The sulfonamides were the first widely used medications and they are systematically used as preventive and chemotherapeutic agents against various diseases. Over 30 medicines containing this feature are used clinically, including the antihypertensive bosentan, antibacterial, antiprotozoal, antifungal, anti-inflammatory, and nonpeptide antagonists of vasopressin receptors [68].

Sulfonamides are compounds which have a general structure represented in Figure 1. After the discovery of sulfanilamide, thousands of chemical changes were studied and the best therapeutic results were obtained from compounds where hydrogen ring (SO2NH2) has been replaced by heterocyclic [9, 10]. To date, more than twenty thousand sulfanilamide derivatives were synthesized. These syntheses have resulted in the discovery of new compounds having pharmacological properties that vary in said main structure; R, R1 may be hydrogen, alkyl, aryl, or heteroaryl, and so forth [11, 12].

Chemically, for its anticancer activity, sulfonamide exists in a variety of pharmacological targets. Drug discovery is a long and complex process. This discipline can occur at different levels of the process of drug discovery. Among the techniques of chemoinformatics, we can mention QSAR techniques of finding a correlation between biological activity measured for a panel of compounds and some molecular descriptors. Quantitative structure-activity relationship (QSAR) methodology is an essential tool in medicinal chemistry. The modern QSAR appeared since the year 1960 but the first correlation investigations of biological activity with physicochemical properties began nearly 60 years before the important work of Overton and Meyer linking aquatic toxicity for partitioning of lipids in water. In 1962 came the seminal work of Corwin Hansch and colleagues, which arose great interest in predicting biological activities. Since 2011, the QSAR studies have begun to grow, with more than 1,400 publications per year [13]. QSAR techniques are based on the concept postulating that similar structures have similar properties and that the more the molecules are different, the harder it is to correlate the physicochemical properties and biological activity, whereas the opposite is easier [14]. The application of the quantitative structure-activity relationship (QSAR) technique for the purpose of molecular modeling and drug design has provided a potential approach in the field of computational chemistry. This tool deals with the determination of the quantitative correlation between molecular structures and their activities employing various chemometric tools. The prime importance of the QSAR technique lies in its ability to determine the essential structural requirements of the molecules for exhibiting definite responses and to predict the activity of untested molecules followed by the design of virtual libraries [11]. On the other hand, QSAR studies were reported to pick out important structural features answerable for the anticancer activity [12]. The quantitative structure-activity relationships (QSAR) are assuredly a significant factor in coeval drug design. Consequently, it is quite evident why a great number of users of QSAR [13, 14] are located in industrial research units. So, classical QSAR and 3D-QSAR are highly active areas of research in the drug design [15, 16]. The basis for different quantitative structure-activity relationship (QSAR) methods is the “description” of the molecular structures by means of numbers. Right now, there are a large number of molecular descriptors that can be used in QSAR studies [1719].

Our objective is to highlight a fundamental and original research on molecules sulfonamide basic core, in order to develop the relationship between the structure and the activity of the active chemical substance and its derivatives.

In this study, principal component analysis (PCA), multiple linear regression (MLR) analysis, multiple nonlinear regression (MNLR) analysis, artificial neural network (ANN) calculations, Crosse validation, and scrambling or Y-randomization are applied to a series of (E)-N-Aryl-2-ethene-sulfonamide inhibitors in order to set up a 3D-QSAR model reliable to predict anticancer activity.

2. Materials and Methods

2.1. Experimental Data

In the present study, we chose 40 substitutes of (E)-N-Aryl-2-ethene-sulfonamide of which the anticancer activities are reported in the literature by Shiri et al. [20]. On the other hand and for the 3D-QSAR study, the reported values of IC50 have been converted into pIC50 by taking negative logarithm (pIC50 = log10 IC50) and subsequently used as the dependent variable for the 3D-QSAR model development. Figure 1 presents the basic structure of the flavonoids and Table 1 shows the studied compounds and their corresponding experimental activities and pIC50.


CompoundRR1pIC50

1HH5.00
24-ClH4.70
34-F4-Br5.00
44-F4-OCH35.00
54-OCH34-OCH35.30
64-OCH32,4-(OCH3)24.82
74-OCH32,6-(OCH3)26.43
84-OCH32,4,6-(OCH3)36.7
94-OCH33,4,5-(OCH3)34.46
102,4,6-(OCH3)34-OCH35.12
114-OCH32,6-(OCH3)2, 4-OH5.00
124-OCH32,4,6-F34.12
133-F, 4-OCH32,4,6-(OCH3)37.52
163-NH2, 4-OCH32,4,6-(OCH3)34.12
173-NO2, 4-OCH33,4,5-(OCH3)34.46
183-NH2, 4-OCH33,4,5-(OCH3)34.00
223-NO2, 4-F2,4,6-(OCH3)35.00
233-NH2, 4-F2,4,6-(OCH3)35.00
243,5-(NO2)2, 4-OCH32,4,6-(OCH3)35.00
253,5-(NH2)2, 4-OCH32,4,6-(OCH3)35.60
263-F, 4-OCH34-OCH35.30
273-F, 4-OCH32,3,4,5,6-F55.00
283-NO2, 4-OCH32,3,4,5,6-F54.00
293-NH2, 4-OCH32,3,4,5,6-F54.12
302,3,4,5,6-F53-NO2, 4-OCH34.00
312,3,4,5,6-F53-NH2, 4-OCH34.46
322,3,4,5,6-F52,3,4,5,6-F54.46

CompoundR2pIC50

33CH26.46
34CH(CH3)7.00
35C(CH3)26.70
36CH(C6H5)5.60
37CH(C6H44-F)5.60
38CH(C6H44-Br)5.12

CompoundR2pÏC50

39CH26.40
40CH(CH3)7.40
41C(CH3)27.15
42CH(C6H5)7.12
43CH(C6H44-F)7.12
44CH(C6H44-Cl)7.12
45CH(C6H44-Br)6.60

2.2. Computational Methods

The quantum method DFT (density functional theory) was used in this study to predict the various physicochemical properties, in order to search for the best correlation between themselves. 3D structures of the molecules were generated using the Gauss View 3.0, and all quantum calculations of all compounds have been carried out using the Gaussian 03. The optimization geometry and the physicochemical properties of the 40 compounds were predicted by employing the B3LYP function coupled with 6–31 G basis set [21, 22].

2.3. Calculation of the Molecular Descriptors

Before every modelization, it is necessary to calculate or to measure a big amount of different descriptors because the mechanisms which determine molecules’ activity or one of its properties are frequently bad-known. Thus, we must select among variable cells the ones that are the most pertinent to modelization. This selection is done using the regression linear multiple methods. The optimized molecules were used to calculate a certain number of electronic descriptors: dipolar moment (DM), orbital borders energy (, ), total energy , and repulsion energy (RE) [23].

ChemBio Office (2015) was used to calculate the following parameters: molecular weight (MW), partition coefficient (), the hydrogen bond acceptor (HA), and the hydrogen bond donor (HD) [2426].

ChemSketch program was used to calculate the following parameters: molar volume (MV (cm3)), molar refractivity (MR (cm3)), parachor (Pc (cm3)), density (g/cm3), refractive index, surface tension (dyne/cm), and polarizability (cm3) [27, 28].

2.4. Statistical Analysis

To present the structure-activity relationship for 40 studied molecules, 16 descriptors are calculated using the Gaussian 03, chemoffice 2012, and chemsketch. The procedure of the study is as follows.

The principal component analysis (PCA) [29] was generated using the software XLSTAT, version 2015 [30], to predict anticancer activities pIC50. It is a statistical method based on minimizing all the information encoded in the structures of the compounds. It is also very helpful to understand the distribution of the compounds. This is an essentially descriptive statistical method which aims to present, in a graphic form, the maximum of information contained in the data listed in Tables 1 and 2.


MDRep MWHAHDpolardentSurf tenparcMR Ind MV

1
2
3
4
5
6
7
8
9
10
11
12
13
16
17
18
22
23
24
25
26
27
28
29
30
31