QSAR Study of 1, 2, 4-Triazole for their Anticancer Activity

 

Yogita A. Ladgaonkar*, Pooja Waskar

Department of Pharmaceutical Chemistry, Gahlot Institute of Pharmacy Koparkhairane,

Navi - Mumbai – 400070, Maharashtra, India.

*Corresponding Author E-mail: yogitakandekar9793@gmail.com

 

 

1. INTRODUCTION:

A large number of heterocyclic compounds containing the 1, 2, 4- triazole ring and N-C-S linkage of Triazole are especially responsible for biological activities i. e. antifungal, anti-tubercular, anti-inflammatory, anticonvulsant, antibacterial, antiviral and antitumor ate. Incorporating various substituents into the 1, 2, 4-triazole rings and its fusion with various heterocyclic systems yield compounds with enhanced biological activities. Triazole inhibits the 14 alpha-demethylase that prevents ergosterol production. The nitrogen in Triazole is bound with the heme ion of the CYP450 prosthetic group. The remaining antifungal azoles develop bonding interactions with the apoprotein, determining the drug's relative selectivity for specific apoprotein. Computational chemistry has made enormous advances recently, posing new hurdles to drug development through a statistical method.

 

In this paper, an attempt was made to develop a quantitative activity relationship (2D and 3D QSAR) on a 1, 2, 4-triazole. 2D QSAR was performed using multiple linear regression (MLR), principal component regression (PCR), and partial least squares regression (PLS) method. Among these three methods, the multiple linear regression (MLR) method has come out with a promising result compared to the other two methods. According to model 1 by MLR anticancer activity of 1, 2, 4-triazole derivatives were influenced by Electrostatic (Most +Ve and –Ve Potential Distance), Distance-based (Wiener Index) help in understanding the effect of the substituent at different positions of 1, 2, 4-triazole.

 

QSAR is a dependable method for establishing a quantitative relationship between biological activity and descriptors and representing the physicochemical properties of compounds in a sequence. It helps to determine the biological activity of the newly developed series that contributes to the drug discovery process. 3D-QSAR studies have been conducted on several compounds to predict their biological functions at specific targets. For instance, the CoMSIA approach has been used by Daoui and his colleagues to develop a 3D-QSAR model of several compounds derived from Magnolia officinalis targeting EGFR tyrosine kinase in non-small cell lung cancer line^3,4. In addition, another study has identified a compound derived from imidazole as a lead compound in targeting breast cancer cells. The current work is to search for novel 1, 2, and 4-triazole that tend to be used as an anticancer drug. KNN-MFA, GA- kNN-MFA, like many 3D QSAR approaches, requires a sufficient alignment of a collection of molecules.

 

2. MATERIALS AND METHOD:

2.1 Methodology:

The anticancer activity of 1, 2, 4 triazole moiety was taken from the reported work. A data set of 18 compounds for anticancer activity was used for the present QSAR study. The molar concentrations of the compounds required to produce binding at the receptor site (in nm) converted to negative logarithm MIC values for undertaking the QSAR study. The biological activity data (IC50 in nm) were converted to their molar units and then further to negative logarithmic scale (pIC50) and subsequently used as the dependent variable for the QSAR analysis. Table 1 shows the structure of 20 such compounds along with their biological activity values ^{5, 6.} The 2D and 3D QSAR was carried out on the software namely: V-life MDS (Molecular Design Suite). All the structures were constructed using the 2D draw application provided as a tool of the main MDS window. The 2D structures were converted to 3D structures by sending them to MDS. Energy minimization and geometry optimization were conducted using the Merck Molecular Force Field (MMFF) method with Root Mean Square (RMS) gradient set to 0.01Kcal/mol A° and iteration limit to 10000. The 2D descriptors (physicochemical and alignment independent) were calculated for the optimized compounds on the QSAR worksheet. The invariable descriptors (the constant descriptors for all the molecules) were removed, as they do not contribute to QSAR.

 

2.2 A brief review of 1, 2, 4-triazole:

The medicinal importance, synthesis, and use of 1, 2, 4 triazole as synthetic tools in organic chemistry. The 1, 2, 4 triazole functionality is much more widespread in pharmaceuticals. 1, 2, 4 triazole has been the subject of pharmaceutical interest due to its potent biological activities such as antihypertensive, anticancer, anti-inflammatory, and antiviral agents^7,8,9. Several 18 derivatives having anticancer activity were considered in the present study. Biological activity expressed in terms of IC50 was converted into pIC50 (pIC50= log 1/IC50).

 

2.2 Structures used for QSAR study:

The 1, 2, 4 triazole functionality is much more widespread in pharmaceuticals. 1, 2, 4 triazole has been the subject of pharmaceutical interest due to its potent biological activities such as antihypertensive, anticancer, anti-inflammatory and antiviral agents. A number of 18 derivatives having anticancer activity were considered in the pzresent study. Biological activity expressed in terms of IC50 was converted into pIC50 (pIC50= log 1/IC50).

 

 

Compound No.	R	Compound No.	R
1		11
2		12
3		13
4		14
5		15
6		16
7		17
8		18
9		19
10		20

 

Table No.1: Observation Table of Observed and Predicted Value of Synthesized Compound

Comp. No	Actual Value	Predicted Value	Residual Value	IC50	PIC50
1.	61.21	61.13	0.03	1.329	0.123
2.	60.07	50.40	1.67	1.354	0.131
3.	74.33	80.24	-5.91	1.094	0.039
4.	45.81	43.61	2.20	1.776	0.249
5.	72.62	66.81	5.81	1.120	0.049
6.	48.28	50.36	-2.08	1.685	0.226
7.	81.93	69.24	12.69	0.993	-0.0030
8.	92.20	82.05	10.15	0.882	-0.0545
9.	63.11	65.13	-2.62	1.368	0.136
10.	84.03	77.96	6.07	0.968	-0.0141
11.	95.00	84.64	10.36	0.856	-0.0675
12.	86.00	79.88	6.12	2.198	0.342
13.	37.00	43.02	-6.02	1.535	0.183
14.	53.00	50.38	2.62	1.291	0.1109
15.	63.00	73.54	-10.54	1.660	0.220
16.	49.00	45.27	5.73	1.162	0.0652
17.	70.00	65.73	4.27	1.849	0.266
18.	44.00	37.60	6.4	1.312	0.117
19.	62.00	53.31	10.69	1.271	0.1041
20	64.00	58.18	5.82	2.198	0.342

 

3. 2 D QSAR ANALYSIS:

3.1 Creation of training and test set:

The sphere exclusion method was adopted for the division of training and test data sets comprising twenty molecules respectively, with dissimilarity value of compounds where the dissimilarity value gives the sphere exclusion radius. To assess the similarity of the distribution pattern of the molecules in the generated sets, statistical parameters (concerning the biological activity), i.e., mean, maximum, mini- mum and standard deviation were calculated for the training and test sets. The first 12 compounds, were used as a test set while the remaining molecules were used as the training set.

 

3.2 The results were as follows by multiple linear regression analysis:

Multiple Regression:

Training Set Size = 14

Test Set Size = 6

 

Selected Descriptors:

XK Most Hydrophobic Hydrophilic Distance

 

Coefficient:

-0.0827(±0.0229)

0.0179(±0.0085)

 

Constant:

-0.0009

 

Table no.2: Observation of k-NN MFA model using Multiple Regression selection method

n	14
Degree_of_freedom	11
r2	0.6676
q2	0.5738
F_test	11.0449
r2_se	0.0712
q2_se	0.0806
pred_r2	0.5116
pred_r2se	0.2604

 

3.3: Multiple Regression Analysis of 2D QSAR

 

3.4 Interpretation:

All points for the training and test set lie on or near the regression line hence confirming that there is not that much variance between the test and training set

 

4. 3D QSAR ANALYSIS

4.1 The results were as follows by variable selection method analysis:

kNN Method

Training Set Size = 14

Test Set Size = 6

Selected Descriptors:

E_198

S_684

S_594

 

Table no3: Observation of k-NN MFA model using the variable selection method

Statistics:

k Nearest Neighbour	2
n	14
Degree of freedom	10
q2	0.5754
q2_se	0.0742
pred_r2	0.4861
pred_r2se	0.0821

 

Descriptor Range:

E_198 -0.4190 -0.3520

S_684 -0.2273 -0.1274

S_594 -0.2705 -0.2336

 

4.2 Fitness Plot for the training and test set:

 

4.3 Interpretation:

All points for the training set and test set lie on or near the regression line hence confirming that there is not that much variance between the test and training set.

 

 

4.4 Radar plot of training set:

 

4.5 Radar plot of test set:

 

 

4.6 Interpretation:

The actual vs. predicted activity provides an idea about how well the model was trained and how well it predicts the activity of the external test set. From the plot, it can be seen that the model can predict the activity of the training set and the external test set providing confidence in the predictive ability of the model. The color indicates the actual values while the blue color indicates the predicted values and both don’t differ much so the model is validated.

 

 

4.7 Interpretation of 3D QSAR

3D QSAR was used to optimize electrostatic, steric and hydrophobic requirements. The values for generated grid points helped us to design potent NCEs. The ranges of data point values were based on the variation of the field's values at chosen points using the most active molecules of the data set and its nearest neighbor set. Points generated in 3D QSAR models are electronic E_198 (-0.4190 - 0.3520) and steric S_684 (-0.227-0.1274), S_594 (-0.2705 -0.2336

 

Fig No. 1 Grid points (E&S) generated in a 3D rectangular grid.

 

 

RESULT AND DISCUSSION:

In the above results it was observed that most of the active molecules have electrostatic and steric field values at E_198 (-0.4190 - 0.3520) and steric S_684 (-0.227-0.1274), S_594 (-0.2705 -0.2336) contributed negatively for the activity. So increasing electronegativity of the substituent group may enhance the anticancer activity.

In the present 2D QSAR study all proposed models were statistically significant. However multiple regression analysis could considered as best. According to the model, the anticancer activity of Triazole derivatives was influenced by electrostatic (most +ve and –ve potential distance) and dipole moment, which helps in understanding the effect of the substituent at different positions of Triazole.

 

3D QSAR was used to optimize electrostatic, steric requirements around 1,2,4 triazolo[3,4-b][1,3,4]-thiadiazole. The obtained model shows that steric interactions play a major role in determining biological activity. The statistical model is good concerning r2, q2, and pred_r2. It uses two steric and one electrostatic field descriptors to evaluate the activity of new molecules. From the fitness and radar plot, it can be seen that the model is able to predict the anticancer activity of the training set as well as the test set providing confidence in the predictive ability of the mode.

 

REFERENCES:

1.      Tejaswi T. Kamble, Anagha S. Patil, S. K. Mohite, C. S. magdum, QSAR study of 1, 2, 4 triazole for their anticancer activity, International Journal of Institutional Pharmacy and Life Sciences. 2016; 189-199.  

2.      Shailaja P. Desai, S.K. Mohite, Saad Alobid, M.G. Saralaya, Ashwini S Patil, Kuntal Das, Moneer E. Almadani, Syed Arif Hussain, Bader Hussain Alamer, Ebtesam Abdulrahman Jibreel, Ali Ibrahim Almoteer, Syed Mohammed Basheeruddin Asdaq, 3D QSAR study on substituted 1, 2, 4 triazole derivatives as anticancer agents by kNN MFA approach. 2023; 101836: 1-5.        

3.      Yurugi S, Miyake A, Fushimi T, Imamiya E, Matsumura H, Imai Y. Studies on the synthesis of N-heterocyclic compounds. 3. Hypocholesterolemic 1, 2, 4- oxadiazole derivatives. Chem Pharm Bull. 1973; 21: 1641-1650.

4.      Vesna Dimova, Nada Perisic Janjic QSAR study by 1,2,4 triazoles using several physicochemical descriptors, Macedonian Journal of Chemistry and Engineering. 2009; 28(1): 79-89.       

5.      Soukaina Bouamrane, Ayoub Khaldan, Hamid Maghat, Mohammed Aziz Ajana, Mohammed Bouachrine, and Tahar Lakhlifi. Quantitative Structure-Activity Relationships and Molecular Docking studies of 1.2.4 triazole derivatives as antifungal activity. Academic Scientific Journals.  2020; 10: 33-48.      

6.      Dilmaghani, K.A., Nasuhi Pur, F., Nezhad, M.H. Synthesis and antibacterial evaluation of new thione substituted 1, 2, 4-triazole Schiff bases as novel antimicrobial agents. Iran. J. Pharm. Res. 2015; 14 (3): 693–699.      

7.      Singh, R.J., Singh, D.K. Syntheses, characterization, and biological activity of some 1, 2, 4-triazole derivatives. EJ. Chem. 2009; 6(3): 796–800.        

8.      Kunal G. Raut. Development of antitubercular agents using pharmacophore optimization by molecular modeling studies, Pune University.  2015: 44-61.          

9.      Mayura A. Kale and Kirtee G. Baheti, G-QSAR studies of novel 1, 2, 4 triazolo [3, 4-b] - 1, 3, 4-thiadiazole derivatives. Pelagia Research Library.  2015; 6(9): 15-20.           

10.   Malleshappa N. Noolvi, H. M. Patel, Navdeep Singh Sethi, 3D-QSAR Studies on a series of 1, 3, 4-thiadiazole-2-arylhydrazone derivatives as antitrypanosomal agents the k- nearest neighbor molecular field analysis approach, Digest Journal of Nanomaterials and  Biostructures.  5(2).

 

 

 

Received on 14.08.2024      Revised on 11.01.2025 Accepted on 15.04.2025      Published on 19.06.2025 Available online from June 23, 2025 Asian J. Research Chem.2025; 18(3):117-122. DOI: 10.52711/0974-4150.2025.00019 ©A and V Publications All Right Reserved
This work is licensed under a Creative Commons Attribution-Non Commercial-Share Alike 4.0 International License. Creative Commons License.