IN-SILICO STUDY OF ARYL SULPHONAMIDES AS 5-HT6 SEROTONIN LIGAND: A 2D QSAR STUDY USING TOPOLOGICAL DESCRIPTORSHTML Full Text
IN-SILICO STUDY OF ARYL SULPHONAMIDES AS 5-HT6 SEROTONIN LIGAND: A 2D QSAR STUDY USING TOPOLOGICAL DESCRIPTORS
Pawan Kumar, Priti Singh, Rajesh Kumar Singh and Mohd. Adil Khan *
Department of Chemistry, M. L. K. P. G. College, Balrampur, Uttar Pradesh, India.
ABSTRACT: Serotonin plays a crucial role in various cognitive and behavioral functions. The serotonin signaling is mediated by binding of serotonin to specific receptors on the cell surface. Serotonin 5-HT6 receptors turned out to be promising biological targets for the modulation of central nervous system dysfunctions. Several classes of serotonin 5-HT6 receptor ligands have been discovered. Among them, many aryl sulphonamides derivatives as 5-HT6 antagonists were reported to have better affinity towards the receptor. In the present study, a quantitative structure-activity relationship (QSAR) study of forty derivatives of aryl sulphonamide and sulfone based 5-HT6antagonists has been made with the help of topological parameters. The descriptors that have been used are valence connectivity indices of order 0, 1 & 2 and shape indices of order 1, 2 & 3. The biological activities of compounds have been taken from the literature. The study indicates that the valence connectivity index (order-0) appears an important descriptor for the QSAR study of aryl sulphonamides which alone gives a QSAR model with reliable predictive power. The best combination of descriptors obtained for this study is valence connectivity index (order-0), shape index (order-1) and shape index (order-2). The predicted activities obtained from this QSAR model are very close to observed activities. This QSAR model can be used to find the activity of any new derivative of aryl sulphonamides.
Keywords: Serotonin ligands, Aryl sulphonamide, Valence connectivity indices, Shape indices
INTRODUCTION: Serotonin, or 5-hydroxy-tryptamine (5-HT), is an important neurotransmitter 1, 2. It plays a crucial role in various cognitive and behavioral functions such as learning, mood, stress, pain, sleep, aggression, depression, anxiety, cognition, sexual behavior etc 3, 4. Improper serotonergic signaling leads to mental disorders such as schizophrenia, depression, suicidal behavior, infantile autism, obsessive-compulsive disorder etc 5, 6.
The binding of serotonin to specific receptors on the cell surface mediates serotonin signaling 7-9. Drugs targeting serotonin receptors are useful in treating various disorders, making drug development involving serotonin receptors an important area of research 10. In previous decades, serotonin 5-HT6 receptor turned out to be promising biological targets for the modulation of central nervous system dysfunctions.
5-HT6 serotonin receptor is the family member of G-protein coupled receptors (GPCRs) 11, 12 which is present in various brain regions. Blockade of their function enhances the cognitive process, which sufficiently demonstrates the therapeutic usefulness of this receptor for CNS-mediated disorders such as schizophrenia, Alzheimer’s disease, obesity, eating disorders etc. 13–21. Worldwide research efforts have discovered several classes of serotonin 5-HT6 receptor ligands with good affinity and selectivity. Many aryl sulphonamides as 5-HT6 antagonists reported better affinity towards the receptor 22. Literature survey depicts various highly active aryl sulphonamides based 5-HT6 antagonists 23. Understanding common structural features of these aryl sulphonamides responsible for affinity is helpful for designing novel entities. In the research work of Velingkar and Chindhe, the pharmacophore generation and atom-based 3D-QSAR analysis of earlier reported aryl sulphonamide and sulfone based 5-HT6 antagonists were studied using the PHASE program to get insight into their structural requirements responsible for high affinity 24.
Interest has been created in understanding the other structural features of these compounds responsible for their high affinity towards 5-HT6 receptor, which can help potent design of inhibitors of this receptor. In this paper, Topological descriptors have been used for the development of QSAR models for the forty derivatives of aryl sulphonamide and sulfone based 5-HT6 antagonists. The descriptors used are valence connectivity indices of order 0, 1 & 2 and shape indices of order 1, 2 & 3. Topological parameters gained much importance in recent years, and QSAR studies of different compounds have been made using these parameters 25-31. In the present work, the predicted activities obtained from developed QSAR models using these topological parameters were found close to reported observed activities.
MATERIAL AND METHOD: Forty derivatives of aryl sulphonamide and sulfone based 5-HT6 antagonists that have been taken from literature, 24 are used as study material. These are listed in Table 1 along with their observed biological activity in terms of pKi (nM). The geometry optimization of all the compounds has been done with the help of CAChe Pro software developed by Fujitsu Corporation of Japan, using the DFT 32-34 Method. Evaluation of the values of descriptors has been done with the help of the same software. The QSAR models have been developed by multi-linear regression (MLR) analysis with the help of the Project Leader program associated with CAChe Pro. The descriptors that have been used are described below.
Valence Connectivity Indices 35, 36: It is calculated from the hydrogen-suppressed molecular graph and defined as follows,
Valence connectivity for the k-th atom in the molecular graph,
Zk= the total number of electrons in the k-th atom,
Zvk= the number of valence electrons in the k-th atom,
Hk= the number of hydrogen atoms directly attached to the kth non-hydrogen atom,
m = 0 - atomic valence connectivity indices (called order-0),
m = 1 - one bond path valence connectivity indices (called order-1),
m = 2 - two bond fragment valence connectivity indices (called order-2).
Shape Indices 37, 38: The first order shape index (1κ or κ1) is given by,
1K = A (A-1)2 / (1P) 2
Where, iP = Length of paths of bond length i in the hydrogen suppressed molecule and A is the number of non-hydrogen atoms in the molecule. The second-order kappa shape index (2κ or κ2) is given by
2K = (A-1) (A-2)2 / (2P) 2
The third-order kappa shape index (3κ or κ3) is given by
3K = (A-1) (A-3)3 / (3P) 2 If “A” is odd
3K = (A-3) (A-2)2 / (3P) 2 If “A” is even
TABLE 1: ARYL SULPHONAMIDES AS 5-HT6 SEROTONIN LIGAND WITH THEIR EXPERIMENTAL pKi
*Test Set Compounds
RESULT AND DISCUSSION: QSAR models were generated to determine the effect of structural features of aryl sulphonamides on their 5-HT6 antagonist activity. Forty derivatives of aryl sulphonamides are given in Table 1 along with their observed activity in terms of pKi. The forty compounds were randomly divided into training set (thirty compounds) and a test set (ten compounds). The values of six descriptors of compounds, which have been calculated, are given in Table 2. For developing QSAR models multi-linear regression (MLR) analysis has been performed. Different combinations of six descriptors were used in the MLR analysis. In the development of QSAR models six descriptors were taken as independent variables and pKi as the dependent variable of the training set (thirty compounds). The validity of QSAR model was judged by statistical parameters such as correlation coefficient, cross-validation coefficient, standard error etc.
TABLE 2: VALUES OF DESCRIPTORS AND EXPERIMENTAL pKi OF ARYL SULPHONAMIDES
*Test Set Compounds
Where; 0χ = Valence connectivity index (order-0), 1χ = Valence connectivity index (order-1), 2χ = Valence connectivity index (order-2), κ1 = Shape index (order-1), κ2 = Shape index (order-2), κ3 = Shape index (order-3).
From MLR analysis a QSAR model with good predictive power was obtained by using only one descriptor. It means the activity of 5-HT6 serotonin ligand can be predicted by a mono-parametric regression equation using descriptor valence connectivity index (order-0). This QSAR model is given by following regression equation.
PA1 = 0.199639 × 0χ + 5.03626
r2 = 0.81227, rCV2 = 0.765826, Std. Error = 0.0909, P Value = 0,
r2test= 0.676373, N training set = 30, N test set = 10, VC = 1
In the above regression equation, r2 is the correlation coefficient, rCV2 is the cross-validation coefficient, Std. Error is the standard error, Ntraining set is the number of compounds of the training set used to develop QSAR model, N test setis the number of compounds of test set and VC is variable count. In the above regression equation, the value of r2 is sufficiently higher than 0.5 which is the essential condition for the validity of a QSAR model. From the higher correlation coefficient (r2) and cross-validation coefficient (rCV2) for the above QSAR model, it is clear that the model has high predictive power. Also, the low standard error and P value for this regression support the predictive capacity of this QSAR model. External validation (r2test) for test set compounds also confirmed the predictive power. QSAR models with improved predictive power were obtained by combining two descriptors. The best bi-parametric QSAR model is obtained by using descriptors valence connectivity index (order-0) and shape index (order-3). This QSAR model is given by following regression equation.
PA2 = 0.224281 × 0χ - 0.189884 × κ3 + 5.55946
r2 = 0.832759, rCV2 = 0.73415, Std. Error = 0.0847, P Value = 0
r2test= 0.901906, N trainingset = 30, N testset = 10, VC = 2
From the higher values of correlation coefficient (r2) and cross-validation coefficient (rCV2) and lower standard error value and P value for the above QSAR model, it is clear that the model has higher predictive power. External validation (r2test) for test set compounds also confirmed the predictive power.
By the combination of three descriptors, QSAR models with more improved predictive power were obtained. The best QSAR model obtained using a combination of three descriptors is given by following the regression equation.
PA3 = 0.378442 × 0χ - 0.350304 × κ1 + 0.44247 × κ2 + 5.37878
r2 = 0.874764, rCV2 = 0.844204, Std. Error = 0.0715, P Value = 0
r2test= 0.737303, N trainingset = 30, N test set = 10, VC = 3
This QSAR model involves the valence connectivity index (order-0) as the first descriptor, the shape index (order-1) as second descriptor, and shape index (order-2) as third descriptor. The values of correlation coefficient (r2), cross-validation coefficient (rCV2) and other statistical parameters for the above QSAR model confirm that this QSAR model has excellent predictive power. External validation (r2test) for test set compounds also confirmed the predictive power.
The predicted activities obtained from the above three QSAR models for the training set and test set of compounds are listed in Table 3 with their observed activity.
TABLE 3: EXPERIMENTAL AND PREDICTED ACTIVITIES (pKi) OF FORTY ARYL SULPHONAMIDES
|S. no.||Observed Activity||Predicted Activity|
*Test set compounds
CONCLUSION: From the above study, it is clear that the valence connectivity index (order-0) appears as an important descriptor for the QSAR study of aryl sulphonamides which gives a QSAR model with reliable predictive ability. The best combination of descriptors obtained for this study is the valence connectivity index (order-0), shape index (order-1), and shape index (order-2). This QSAR model can be used to find the activity of any new derivative of aryl sulphonamides. The best QSAR model obtained indicates the positive contribution of valence connectivity index (order-0) and shape index (order-2), whereas the negative contribution of shape index (order-1). The positive contribution of these descriptors shows that an increase in the values of these parameters increases the value of pKi whereas the negative contribution decreases the value of pKi.
ACKNOWLEDGEMENT: All the authors are thankful to their respective departments for providing the facility for the study.
CONFLICT OF INTEREST: There is no conflict of interest.
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How to cite this article:
Kumar P, Singh P, Singh RK and Khan MA: In-silico study of aryl sulphonamides as 5-HT6 serotonin ligand: a 2D QSAR study using topological descriptors. Int J Pharm Sci & Res 2023; 14(6): 2848-55. doi: 10.13040/IJPSR.0975-8232.14(6).2848-55.
All © 2023 are reserved by International Journal of Pharmaceutical Sciences and Research. This Journal licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.
Pawan Kumar, Priti Singh, Rajesh Kumar Singh and Mohd. Adil Khan *
Department of Chemistry, M. L. K. P. G. College, Balrampur, Uttar Pradesh, India.
29 September 2022
17 November 2022
18 November 2022
01 June 2023