3D-QSAR and Pharmacophoric study on 2,6-Disubstituted Thiazolo [4,5-b] Pyridines as H₃ Receptor Antagonists

 

S. K. Jain*, S. K. Bharti, B.G.V.S. Jagan, Ajay K. Gupta

Drug Discovery and Research Laboratory, Department of Pharmacy (Formerly SLT Institute of Pharmaceutical Sciences), Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur (CG), India, 495009.

 

ABSTRACT:

Histamine H₃ receptor antagonist (H₃RA) is a promising therapeutic for CNS disorders including attention deficit hyperactivity disorder [ADHD], sleep disorders, epilepsy, schizophrenia and obesity. 2,6-Disubstituted thiazolo[4,5-b]pyridines reported for their H₃ receptor antagonistic activity were selected for three dimensional quantitative structure activity relationship (3D-QSAR) and pharmacophoric study in order to establish structure activity relationship quantitatively and essential structural features. In the current study, VLife Molecular Design Suite software (VlifeMDS) was used for QSAR and biophore studies. Pharmagist (web based server) was used for pharmacophoric study. Partial least square regression (PLSR) analysis showed r²= 0.7902, q²=0.6449 and pred_r²= 0.6650. In this model steric [S_138, S_826] and electrostatic descriptors [E_243, E_652] are involved to play an important role in eliciting biological activity. It showed good internal and external prediction. The contour plots provided further insight of the relationship between structural features of substituted thiazolo[4,5-b]pyridine derivatives and their activities which should be applicable to design newer potential H₃R inhibitors. In addition with these studies, pharmacophoric models were also produced using Molsign (VLifeMDS) and Pharmagist (web based server). The identified pharmacophore features are two aromatic and two hydrogen bond acceptor with Molsign whereas common pharmacophoric features with Pharmagist are two aromatic, two hydrophobic and four hydrogen bond acceptors. The present work may be useful for further lead optimization and designing of potent H₃ receptor antagonists.

 

 

 

INTRODUCTION: 

Histamine H₃ receptor (H₃R) expressed in the central nervous system (CNS) and to a lesser extent in the peripheral nervous systemis a G-protein coupled receptor modulates the release of neurotransmitters in the brain^1-9. Antagonism of the H₃R leads to the enhanced transmitter release (e.g., dopamine, norepinephrine, acetylcholine, serotonin, glutamate etc.), which is helpful in the various disease states, e.g. cognitive disorder^10-12, epilepsy^13-17, anti-psychotic¹⁸, attention deficit hyperactivity disorder (ADHD), schizophrenia^19,20, obesity²¹, alzheimer's disease^22,23, vigilance and sleep-wake disorders²⁴ and CNS disorders²⁵, shown in Fig.1.

 

 

 

Fig. 1: Importance of the H₃ receptor antagonists in various disorders

 

Non-imidazole derivatives show advantages related to the binding affinity, CNS penetration and reduced potential for inhibition of cytochrome P450 enzymes^26-35.2,6-Disubstituted thiazolo[4,5-b]pyridine derivatives³⁶ reported as H₃ receptor antagonist were picked, in order to develop quantitative structure activity relationship^37-52 (QSAR) model(s). Pharmacophore study was also performed using Molsign module of VLifeMDS⁵³ and Pharmagist (web based server) in order to find the structural features necessary for biological activity which can be used for designing of the newer analogues^54-56.

 

MATERIALS AND METHODS:

Three dimensional (3D) QSAR study was performed by using Molecular Design suite⁵³ (VLife MDS software) on HCL computer with Intel Pentium Dual Core processor and Windows XP operating system. For pharmacophore studies two approaches were applied (i) Molsign module of VLife MDS and (ii) a ligand based pharmacophore generation server Pharmagist.

 

Data set:

In the present study, 2,6-disubstituted thiazolo[4,5-b]pyridines taken from the literature³⁶ as H₃ receptor antagonists (Table 1) were chosen for QSAR and pharmacophore generation because the compounds display wide range of biological activity (~ 2 log values). The structures and biological activity [negative logarithm of inhibition constant values (pK_i)] of the selected data set are given in Table 1.

 

Table 1: General Structure of 2-[4-(piperidin-1-yl)piperidin-1-yl]-6-substituted thiazolo[4,5-b]pyridines and their biological activities (Data set of 22 molecules)

 

 

S. No.	Compound	R	pKi	PLSR 3D QSAR
				Calculated/ Predicted pKi	Residual
1	12a	H-	6.856	6.868	-0.012
2	12b	H2N-	7.102	7.011*	0.091
3	12c	NC-	7.443	7.386	0.057
4	12d	Cl-	6.903	7.166*	-0.263
5	12e		7.508	7.511	-0.003
6	12f		7.552	7.559	-0.007
7	12g		7.638	7.728	-0.090
8	12h		7.481	7.278	0.203
9	12i		6.853	6.853	0.000
10	12j		7.229	7.214	0.015
11	12k		7.2	7.613	-0.413
12	12l		7.886	7.684	0.202
13	12m		7.376	7.361	0.015
14	12n		7.795	7.549	0.246
15	12o		7.455	7.506	-0.051
16	12p		8.397	8.430	-0.033
17	12q		6.876	7.283	-0.407
18	12r		7.376	7.577*	-0.201
19	12s		7.795	7.677	0.118
20	12t		8	8.380*	-0.380
21	12u		7.602	7.441	0.161
22	12v		7.18	7.302*	-0.122

*Test set

 

Molecular Modeling:

Structure of the selected data set were sketched using 2D Draw application and converted to 3D structure by exporting to QSAR window. Energy minimization was carried out using Merck Molecular Force Field (MMFF) method using Modified Qeq charge. Template based alignment method was used for alignment of the molecules (fig.2). After alignment, the molecular fields (steric and electrostatic interaction energies) were computed in the space around the molecule using a methyl probe of charge +1. These fields provide a description of how each molecule will tend to bind in the active site. Total 1232 descriptors (3D) were calculated (electrostatic = 616, steric = 616).

 

Fig. 2: Alignment of all the molecules on the common template

 

Creation of Training and Test sets for QSAR study:

Data set was divided into training set (used for generation of QSAR model) and test set (not used in QSAR model development, used for prediction) using random selection method. Data set following the Unicolumn statistics was used for statistical analysis.

 

Development of QSAR models:

Partial least squares regression (PLSR) was used for QSAR model generation. PLSR is a multivariate statistical data analysis method thatyieldssuitable, robust equations even when the number of columns hugely surpasses the number of rows. Stepwise variable selection method was used in both studies⁵⁷.

 

Pharmacophore study:

Pharmacophore study was performed using two different approaches. In the first approach, Molsign was used for generation of biophoric features (pharmacophores). Different combinations of pharmacophore features, tolerance limit and maximum distance allowed were used. In the second approach, pharmacophore features were generated by using Pharmagist. Data was uploaded in the form of a zip folder to the server and default settings were used.

 

Table 2: Best 3D QSAR model generated by PLSR

Parameters	3D PLSR Model	Limitation
Training Set Size (n)	17	-
Test set size	5 (Comp. 12b, 12d, 12r, 12t, 12v)	-
Optimum Components	2	-
Degree of freedom	14	-
r2	0.7902	> 0.7
r2_se	0.1954	Smaller is better
q2	0.6449	> 0.5
q2_se	0.2542	Smaller is better
Pred_r2	0.6650	> 0.5
Pred_r2se	0.2631	Smaller is better
RMSEC	0.1773	Smaller is better
F test	26.37	Higher is better
Best Rand R^2	0.68378	Low as compared to r2
Best Rand Q^2	0.31895	Low as compared to q2
Alpha Rand R^2	0.00100	Smaller is better
Alpha Rand Q^2	0.05000	Smaller is better

 

Table 3: Validation Parameters for model generated using PSLR

Parameters	3D PLSR Model			Limitation
Q2F1	0.665			Q2> 0.5
Q2F2	0.608			Q2> 0.5
Q2F3	0.631			Q2> 0.5
Q2Rm	0.4766			Q2> 0.5
Standard deviation (Sd)	0.1758			Smaller is better
Standard error of estimate (SEE)	0.2103			Smaller is better
RMSEP	0.2353			Smaller is better
Correlation Coefficient (R) between actual and predicted activities	0.9638 (R² = 0.9289)			R2> 0.6
t-Value	6.26 [t  > t3 (.995) = 5.840]  there is slope and relationship exists between observed and predicted activity
ANOVA
		df	MS	F-value
Linear regression explained (SSE)	1.086	1	1.086	MSexplained / MSunexplained = 39.20 [F > F1,3 (.99) = 34.1] Linear relationship exists between observed and predicted activity
Residual (SSU)	0.083	3	0.0277
Total (SST)	1.169	4

pK_i= - 0.0252 E_243 + 0.1126 S_138 + 4.1890 E_652 - 967.287 S_826-42.1267

 

RESULTS AND DISCUSSION:

QSAR and pharmacophoric studies were performed on a data set of 2,6-disubstituted thiazolopyridines for their H₃ receptor antagonistic activity. In order to validate the QSAR models externally, data set was divided into training set (75%) and test set (25%) randomly.

 

Interpretation of 3D QSAR models:

3D QSAR model with most significant value of pred_r² is described as under using PLSR. Result and validation parameters for this model are presented in Table 2 and 3.

 

This model shows q² and pred_r² of 0.6449 and 0.6650 respectively suggesting that it has good internal and external predictive ability (~64% and 66%). The 3D field points depicted by this model are steric (S_138and S_826) and electrostatic [E_243, E_652] in nature (fig. 2). Steric contribution in the developed QSAR model is 58% and electrostatic contribution is 42% suggesting that the model is dominated by steric contribution.

S_138 with its positive coefficient value and position towards substitution site-6 (R-group) suggest the need of a bulky group at this site, while less bulky group will result in decrease in biological activity. This observation is supported by activities of compounds 12e, 12f, 12h, 12p, 12t etc., in which R is occupied by relatively bulky groups. This descriptor has major contribution in biological activity (~ 40%) in the developed model. On the other hand S_826 (~ 18% contribution in biological activity), being located away, suggests that less bulky functional group may be useful.

 

The electrostatic descriptor E_243 has negative coefficient value (~ 22% contribution in biological activity) indicates negative electrostatic potential is favorable for increase in biological activity and hence a more electronegative substituent group is preferred in that region. This observation is supported by activities of compounds 12c, 12g, 12l, 12n etc. The electrostatic descriptor E_652 has positive coefficient value (~ 20% contribution in biological activity) indicates positive electrostatic potential is favorable for increase in biological activity and hence a less electronegative substituent group is preferred in that region. 3D contribution plot and a graph between observed and predicted activities of data set are described in Fig.3 and Fig.4.

 

 

Fig. 3: Contour 3D-plot for the developed model by PLSR

 

Fig. 4: Contribution chart for the developed model by PLSR

 

A QSAR model is considered acceptable when it predicts the activity of the test set compounds which were not included in the training set (i.e. not used for model development). The QSAR model developed from the training set molecules has good predictive power because the predicted activities for the test set molecules shows very good correlation with their actual activities (correlation coefficient, R = 0.9638; R²= 0.9289) as shown in Fig. 5. Q² metrics (Q²F₁, Q²F₂ and Q²F₃) were calculated⁵⁸ for evaluating the prediction ability of external set (test set) and found to be > 0.5. The root mean squared error in prediction (RMSEP) which is an optimal parameter for evaluating the predictive ability of the model was found to be 0.2353 (Smaller is better). Standard deviation (Sd) and Standard error of estimate (SEE) were also calculated and found to be 0.1758 and 0.2103 respectively (Smaller is better).

 

The statistics t-Value and F-value were calculated and found to be 6.26 [t > t_{3 (.995)} = 5.840] and 39.20 [F > F_{1,3 (.99)} = 34.1] respectively which is more than the critical values indicating that there is slope and linear relationship exists between observed and predicted activity⁵⁹. The actual and predicted activities of all the compounds (training and test set) for 3D QSAR model are listed in Table 1.

 

 

(a)

 

(b)

Fig. 5: (a) Graph of observed vs. predicted activities for Training set (b) Graph of observed vs. predicted activities for Test set

 

Interpretation of Pharmacophore identification:

Based on the alignment of molecules presented in Table 1, structural feature based pharmacophore models were constructed for 2,6-disubstitutedthiazolopyridinesfor their H₃ receptor antagonistic activity. Different biophores were developed using different settings of Molsign (primary pharmacophore feature count, tolerance and maximum distance allowed) and those having the lowest root mean square deviation (RMSD) from each combination with relevant features are reported.

 

It can be observed from Table 4 that all the combinations contain two hydrogen bond acceptor and two aromatic features in common which may be helpful for effective H₃ receptor antagonistic activity. Alignment of all the molecules on common pharmacophore features for different combinations and positions of the pharmacophore features on the molecule 12p are shown in Fig.6.

 

The web server Pharmagist was also used for generation of pharmacophores. After performing pair-wise and multiple flexible alignments with the compounds, a pharmacophore model was generated (Figure 7a, 7b). The score obtained by alignment of all the molecules and the pharmacophore features calculated by the server are presented in Table 4. There were eight common features present in all molecules according to the alignment with best score (Aromatic, 2; hydrogen bond acceptors, 4; Hydrophobic, 2). The distance constraints between the features are represented in Figure 7c.

 

 

Fig. 6: Alignment of all the Molecules on the Common Pharmacophoric Features for combination A-biophore 2 (Color Scheme for pharmacophore features: Buff spheres - aromatic; green/light pink spheres -hydrogen bond acceptor)

 

 

Table 4: Pharmacophore features of the biophores for different combinations using both Molsign and Pharmagist (the values in parentheses indicate the percentage of molecules aligned on the common pharmacophore features)

S. No	Combination (Molsign)#	Primary pharmacophore feature count#	Tolerance (%)#	Maximum distance allowed (Ǻ)#	Total Number of biophores generated	Biophore with better RMSD/ score	Features	RMSD(Molsign)/Score (Pharmagist)
Molsign
1	A	3	30	10	2	Biophore-1	AroC, HAc, HAc, AroC	0.0981 (95.45%)
						Biophore-2	AroC, HAc, HAc, AroC	0.0325 (100%)
Pharmagist
2	-	-	-	-	1	1	Aromatic-2, Hydrophobic-2, Acceptors-4	63.00 (100%)

 

 

Fig. 7: (a) Alignment of all the molecules on common pharmacophore features (b) position of the pharmacophore features on the most active molecule (12p) (c) Distance constraints between common features of the pharmacophore obtained by Pharmagist (Color Scheme: Sky blue-Aromatic, Green- Hydrogen bond acceptor, Light grey-Hydrophobic)

 

CONCLUSION:

In the present work a series of 2-(1,4’-bipiperidin-1’-yl) thiazolopyridines was subjected to 3D QSAR and pharmacophore studies. The most significant 3D QSAR model predicts importance of more bulky substitution at position towards substitution site-6 (R-group) as suggested by the positive coefficient of steric field point S_138 around this position. Additionally, negative coefficient value of the electrostatic descriptor E_243, suggesting negative electrostatic potential is favorable for increase in biological activity and hence a more electronegative substituent group is preferred in that region.

 

The pharmacophores generated from both approaches i.e. Molsign and Pharmagist predict two hydrogen bond acceptor and two aromatic features as the common pharmacophore. Findings of the present study are expected to be of help in the development of new lead compounds and further efforts in this direction are in progress.

 

ACKNOWLEDGEMENTS:

The author’s express gratitude to the Head, Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur (CG) for supporting to perform the research works.

 

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Accepted on 22.08.2023           © RJPT All right reserved

Research J. Pharm. and Tech 2023; 16(10):4575-4582.