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Article

Discovery of Novel 1,2,4-Oxadiazole Derivatives as Potent Caspase-3 Activator for Cancer Treatment

Pharmacy College, Uttar Pradesh University of Medical Sciences, Saifai, Etawah 206130, India
Submission received: 15 December 2020 / Revised: 24 February 2021 / Accepted: 1 March 2021 / Published: 8 March 2021
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications 2021)

Abstract

:
In the present study, a quantitative structure–activity relationship (QSAR) and docking studies were accomplished on a series of 1,2,4-oxadiazoles. The results of QSARs are reliable and have high predictive ability for both the internal (q2 = 0.610) and external (pred_r2 = 0.553) datasets with least standard error (SE; i.e., 0.130) and four principal components, which signifies the reliability of the generated model. Molecular docking was also reported by the GOLD docking program, which showed that the hydrogen bonding may be responsible for the activity, and may be further increased upon adding high electronegative substitutions.

1. Introduction

Cancer is the second leading cause of mortality globally and becomes the principal cause of mortality in developing countries [1,2,3]. Recently, several anticancer drugs have become available on the market, acting through different mechanisms; however, the majority of them are associated with serious side effects [4,5]. The lack of targeting ability of these drugs is responsible for their side effects [6]. Large numbers of heterocyclic compounds have been reported for potential anticancer activities [7]. These heterocyclic-based anticancer agents are either under investigation or marketed as potent anticancer agents [8,9,10,11]. Oxadiazole is an important five-membered heterocyclic compound, having one oxygen atom and two nitrogen atoms. Recently, several 1,2,4-oxadiazole derivatives have been shown to possess anticancer activity [12,13].
Nowadays, researchers are focusing on various groups of molecules that are involved in the apoptosis inducing cytotoxicity. Caspases, a group of cysteine proteases, are the executioners of apoptosis. These caspases cleave their substrates after aspartic acid residues. Initiator caspases (caspase 2/8/9/10) and effector caspases (caspase 3/6/7) are the two classes of caspase. Recently, the activation of caspase-3 mediated apoptosis becomes an interesting therapeutic strategy for cancer therapy. Zhang et al. reported a series of 3-Aryl-5-aryl-1,2,4-oxadiazoles as a novel apoptosis inducer through caspase-3 activation. The compounds’ activities have been reported against breast and colorectal cancer cell lines [14].
The quantitative structure–activity relationship (QSARs) is an attempt to correlate the structural features of the compounds quantitatively with their biological activities. Researchers reported thousands of QSAR studies in the search for novel anticancer agents [15,16,17,18,19].
In the search for new anticancer agents, our research group previously reported QSAR studies of 1,2,4-oxadiazole derivatives describing the key structure features responsible for anticancer activities [20,21,22]. In the continuation of our previous work, herein we report the two-dimensional QSAR (2D-QSAR) and molecular docking studies’ outcomes.
The 2D-QSAR studies were done using Step Wise k Nearest Neighbor Molecular Field Analysis [(SW) kNN MFA] using V-Life Molecular Design Software Version 3.0 (V-Life Molecular Design). The docking studies were also performed using GOLD software.

2. Material and Methods

2.1. Dataset

A dataset of twenty eight 3-aryl-5-aryl-1,2,4-oxadiazoles derivatives has been taken for present QSAR study (Table 1). Compounds have high structural diversity with ample range of biological activity [14,23].

2.2. 2D QSAR

2D QSAR studies were performed via Step Wise k Nearest Neighbor Molecular Field Analysis [(SW) kNN MFA] method using V-Life Molecular Design Software Version 3.0 (V-Life Molecular Design) [24,25,26].
The 2D QSAR studies were performed by dividing compounds in the training and test dataset which resulted several QSAR equations. Unicolumn statistics was done to divide training and test data compounds. Twenty-two compounds were positioned in the training set and 6 compounds (4b, 4d, 4e, 10a, 10d and 11h) in the test set.

2.3. Molecular Docking Analysis

Molecular docking was employed to locate the appropriate binding orientations and conformations of these 1,2,4-oxadiazoles interacting with caspase-3 using the docking program GOLD version 3.2. Ten docked conformers were produced for each 1,2,4-oxadiazole derivative. The conformation with the lowest docking energy in the most populated cluster is selected as the possible “active” conformation against the 1RE1 active site. In the present study, 28 compounds were successfully docked into the 1RE1 site.
The X-ray crystal structure (pdb: 1RE1) of caspase-3 was obtained from the Protein Data Bank. Initially, for protein preparation, water molecules were removed, hydrogen atoms added and AMBER7FF99 charges to the protein were applied. The ligands were docked inside a cubic GRID box (within 5 A° surrounding to the cocrystallized ligand) centered at the midpoint between the Cys205 and Gly238. Ten docking runs were performed for each compound in the dataset. In most cases the chosen pose was the top ranked solution.

3. Results and Discussion

3.1. 2D QSAR Results

The results of the unicolumn statistics are summarized in Table 2, which showed that the test is interpolative i.e., both test and training dataset contain compounds of high structure diversity with variation in biological activity. The test and the training set contained a diverse set of compounds with low, moderate and high biological activity.
Finally, the following model was selected.
pEC50 = 0.243 * IP − 0.139 * BC + 0.155 * DM + 0.008 * PSA + 0.0005
The obtained model showed a high correlation coefficient (r = 0.862) between descriptors including ionization potential (IP), bromine count (BC), dipole moment (DM), polar surface area (PSA) and anticancer activities. The squared correlation coefficient (r2) of 0.743, explains 74.29% of the variance in biological activity. The obtained model is statistical significant with F values F(4,21) = 11.561. The obtained model showed both good internal and external predictive ability with cross-validated squared correlation coefficient for internal dataset (q2) value 0.610 and for external dataset (pred_r2) value 0.553 with a standard error (SE) of 0.130 (Table 3).
In the model, the contribution of the descriptors is presented in the contribution chart (Figure 1), signifying the positive contribution of the ionization potential (IP), dipole moment (DM) and polar surface area (PSA) towards the biological activity. The addition of substitution that increases the polarity of the compounds results in increased anticancer activity. The negative contribution of the bromine count signifies the lower number of bromine encouraging biological activities.
The correlation between the experimental and predicted activity of the compounds is shown in Table 4 and represented in Figure 2.

3.2. GOLD Docking Studies

All 28, 1,2,4-oxadiazoles derivatives were docked into the binding site of caspase-3 and the energy scores of the activators are also shown in Table 4. A precise correlation was observed in between docking scores and pIC50 values.
A complete overview of GOLD docking is presented in Figure 3 and Figure 4.
The docking results revealed that most active compound 4m is properly located at the binding site of the Cys205 and Gly238 amino acid residues and numerous interactions occur between it and the binding region of the enzyme. The four key hydrogen bond interactions occur: (1) between the NH of Gly238 and the O of the oxadiazole ring; (2) between the NH of Gly238 and the N of the oxadiazole ring; (3) between the NH of Cys285 and the N of the oxadiazole ring; (4) between the NH of THR288 and the N of the pyridine ring residue (Figure 3). The hydrogen bonding distances observed were 1.549 Å (O···H-NH-Gly238), 2.712 Å (N···H-NH-Gly238), 2.429 Å (N···H-NH-Cys285) and 2.092 Å between the N of the pyridine ring and NH of THR288 (N···H-NH-THR288).
Akin to compound 4m, compound 10b was also docked at the same binding pockets having Cys205 and Gly238 amino acid residues (Figure 4). The result showed the formation of two hydrogen bonds: (1) between the NH of Cys205 and the O of the oxadiazole ring (O···H-NH-Cys205), having 2.145 Å bond distance; (2) between the NH of Cys205 and the N of the oxadiazole ring (N···H-NH-Cys205) with 2.614 Å bond length.
The docking results revealed that the hydrogen bonding may be responsible for biological activity, which may be further increase upon adding more electronegative substitutions. The correlation between the dock score and the experimental activity is shown graphically in Figure 5, which shows a linear correlation between the dock score and biological activity.
The results of the QSAR analysis clearly show that upon increasing the polarity in terms of theionization potential (IP), dipole moment (DM) and polar surface area (PSA), biological activity will also be enhanced. The docking results also support the QSAR outcomes.

4. Conclusions

In conclusion, the current QSAR studies established a reliable QSAR model with high predictive ability with q2 = 0.610, r2 = 0.743 and low standard error (SE) = 0.130 and four principal components. The predicted value of the external test set (pred_r2) was also high (i.e., 0.553). The developed model was reliable, which indicated the importance of substitution in 1,2,4-oxadiazoles at their respective positions to improve anticancer activity. The positive contribution of ionization potential (IP), dipole moment (DM) and polar surface area (PSA) is conducive for biological activity, and further addition of these substitutions increases anticancer activity, while the negative contribution of the bromine count signifies the lower number of bromine encouraging the biological activities. The docking results explore the binding mode between the ligands and the receptor.

Funding

This research received no external funding.

Data Availability Statement

Data available in article and raw data are available from the corresponding authors upon request.

Acknowledgments

We would like to thank G. Narahari Sastry, former head of the department, Molecular Modeling Group, IICT Hyderabad, India, for providing access to computational resources and for their valuable help during the modeling studies.

Conflicts of Interest

The authors declare that they have no conflict of interest.

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Figure 1. Contribution chart of descriptors in 2D QSAR model.
Figure 1. Contribution chart of descriptors in 2D QSAR model.
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Figure 2. Correlation of experimental and predicted activity in 2D QSAR model.
Figure 2. Correlation of experimental and predicted activity in 2D QSAR model.
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Figure 3. Overlay of docked highest potent oxadiazole compound (4m) at the active site of 1RE1.
Figure 3. Overlay of docked highest potent oxadiazole compound (4m) at the active site of 1RE1.
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Figure 4. Overlay of docked least potent oxadiazole compound (10b) at the active site of 1RE1.
Figure 4. Overlay of docked least potent oxadiazole compound (10b) at the active site of 1RE1.
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Figure 5. Correlation between the experimental activities and dock score in GOLD docking.
Figure 5. Correlation between the experimental activities and dock score in GOLD docking.
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Table 1. 1,2,4-Oxadiazole analogues and their experimental caspase-3 activator activity.
Table 1. 1,2,4-Oxadiazole analogues and their experimental caspase-3 activator activity.
Chemistry 03 00027 i001
S. No.CompoundAr1Ar2Experimental Activity pEC50 (nM) (DLD1)
11d Chemistry 03 00027 i002 Chemistry 03 00027 i0033.357
24a Chemistry 03 00027 i004 Chemistry 03 00027 i0053.102
34b Chemistry 03 00027 i006 Chemistry 03 00027 i0073.119
44c Chemistry 03 00027 i008 Chemistry 03 00027 i0093.367
54d Chemistry 03 00027 i010 Chemistry 03 00027 i0112.839
64e Chemistry 03 00027 i012 Chemistry 03 00027 i0132.848
74g Chemistry 03 00027 i014 Chemistry 03 00027 i0153.553
84h Chemistry 03 00027 i016 Chemistry 03 00027 i0173.432
94i Chemistry 03 00027 i018 Chemistry 03 00027 i0193.252
104j Chemistry 03 00027 i020 Chemistry 03 00027 i0213.420
114k Chemistry 03 00027 i022 Chemistry 03 00027 i0233.387
124l Chemistry 03 00027 i024 Chemistry 03 00027 i0253.409
134m Chemistry 03 00027 i026 Chemistry 03 00027 i0273.620
144n Chemistry 03 00027 i028 Chemistry 03 00027 i0293.538
154o Chemistry 03 00027 i030 Chemistry 03 00027 i0312.879
1610a Chemistry 03 00027 i032 Chemistry 03 00027 i0333.081
1710b Chemistry 03 00027 i034 Chemistry 03 00027 i0352.827
1810d Chemistry 03 00027 i036 Chemistry 03 00027 i0372.971
1910e Chemistry 03 00027 i038 Chemistry 03 00027 i0393.319
2010f Chemistry 03 00027 i040 Chemistry 03 00027 i0413.076
2110g Chemistry 03 00027 i042 Chemistry 03 00027 i0433.155
2210h Chemistry 03 00027 i044 Chemistry 03 00027 i0453.237
2311a Chemistry 03 00027 i046 Chemistry 03 00027 i0473.229
2411b Chemistry 03 00027 i048 Chemistry 03 00027 i0493.236
2511c Chemistry 03 00027 i050 Chemistry 03 00027 i0512.959
2611d Chemistry 03 00027 i052 Chemistry 03 00027 i0532.959
2711e Chemistry 03 00027 i054 Chemistry 03 00027 i0553.149
2811f Chemistry 03 00027 i056 Chemistry 03 00027 i0572.921
Table 2. Unicolumn statistical data of training and test set in 2D quantitative structure–activity relationship (QSAR) models.
Table 2. Unicolumn statistical data of training and test set in 2D quantitative structure–activity relationship (QSAR) models.
AverageMaximaMinimaStd. Deviation
Training set3.1743.6202.8270.228
Test set3.2343.5532.8480.264
Table 3. Parameters value for the best 2D QSAR model generated.
Table 3. Parameters value for the best 2D QSAR model generated.
Modelr r2q2SE (r2 se)Pred_r2F-ValueDescriptors
10.8620.7430.6100.1300.55311.561IP, BC, DM, PSA
Table 4. Experimental, predicted and residual activities of the compounds obtained in 2D QSAR and GOLD score.
Table 4. Experimental, predicted and residual activities of the compounds obtained in 2D QSAR and GOLD score.
2D QSARDocking
Comp. No.Experimental
pEC50
[(SW) kNN MFA] Predicted
pEC50
[(SW) kNN MFA] ResidualGOLD Docking
1d3.3573.2910.06550.771
4a3.1023.155−0.05248.324
4b3.1193.1060.01347.105
4c3.3673.3000.06751.672
4d2.8392.912−0.07452.859
4e2.8483.102−0.25550.846
4g3.5533.2690.28450.357
4h3.4323.3060.12650.212
4i3.2523.382−0.13050.304
4j3.4203.1060.31556.319
4k3.3873.415−0.02850.294
4l3.4093.3610.04851.432
4m3.6203.620−0.000249.303
4n3.5383.5320.00550.930
4o2.8793.025−0.14650.968
10a3.0812.8990.18249.383
10b2.8273.045−0.21849.680
10d2.9713.128−0.15750.205
10e3.3193.2330.08651.265
10f3.0763.162−0.08649.302
10g3.1553.253−0.09849.203
10h3.2373.295−0.05849.839
11a3.2293.1020.12750.212
11b3.2363.2340.00349.423
11c2.9593.007−0.04945.962
11d2.9593.007−0.04947.860
11e3.1492.9270.22249.377
11f2.9212.8790.04250.891
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Vaidya, A. Discovery of Novel 1,2,4-Oxadiazole Derivatives as Potent Caspase-3 Activator for Cancer Treatment. Chemistry 2021, 3, 373-381. https://0-doi-org.brum.beds.ac.uk/10.3390/chemistry3010027

AMA Style

Vaidya A. Discovery of Novel 1,2,4-Oxadiazole Derivatives as Potent Caspase-3 Activator for Cancer Treatment. Chemistry. 2021; 3(1):373-381. https://0-doi-org.brum.beds.ac.uk/10.3390/chemistry3010027

Chicago/Turabian Style

Vaidya, Ankur. 2021. "Discovery of Novel 1,2,4-Oxadiazole Derivatives as Potent Caspase-3 Activator for Cancer Treatment" Chemistry 3, no. 1: 373-381. https://0-doi-org.brum.beds.ac.uk/10.3390/chemistry3010027

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