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Journal of Drug Delivery and Therapeutics

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Open Access Full Text Article                                                                                     Research Article

2D QSAR of Novel hybrid motifs of 4-nitroimidazole-piperazinyl tagged 1,2,3-triazoles for Anti-cancer Activity against Breast Cancer cell line MCF-7

Pranay Raikwar ¹, Arun Kumar Rai ²*, Shabnam Yadav ¹, Nitin Singh Rajput ¹, Shivraj Pandey ¹, Megha Mishra ¹, Pradeep Kumar Singour ¹

¹ Department of Pharmaceutical Chemistry, VNS Group of Institutions- Faculty of Pharmacy, Neelbud, Bhopal (M.P.) 462044.

² Department of Pharmaceutical Chemistry, Corporate Institute of PharmacyPatel Nagar Raisen Road Bhopal, Madhya Pradesh, India (462022)

 

 

INTRODUCTION:

Cancer is a perplexing and terrifying illness, or group of illnesses. For over 200 million years, multicellular organisms have suffered from cancer; evidence of cancer in contemporary humans' progenitors dates back more than a million years. In contrast to many environmental disorders, parasites, and infectious diseases, cancer is not largely brought on by an outside force. Human cells that have, in a sense, lost control and been recruited and partially changed into pathogenic organisms or the building blocks of tumors are its agents of destruction. In Explaining Cancer (2018), Anya Plutynski states “Cancer can be compared to a car breaking down, an infectious disease, a process of natural selection, a process of development, or even the expansion of an ecological community”^1,2.

One kind of cancer that manifests differently in women is breast cancer (BC)³. The United States reported 268,670 new cases of BC in 2018. Worldwide, BC is a prevalent malignancy that impacts women⁴. BC may be divided into three types based on molecular and histological evidence: BC expressing human epidermal receptor 2 (HER2+), BC expressing hormone receptors (estrogen receptor (ER+) or progesterone receptor (PR+)), and triple-negative breast cancer (TNBC).The BC molecular features should serve as the foundation for the therapeutic strategies. Furthermore, the TNBC was classified into six groups: immunomodulatory (IM), basal-like 1 (BL-1), basal-like 2 (BL-2), mesenchymal (M), mesenchymal stem cell-like (MSL), and luminal androgen receptor (LAR)⁵. It's unclear exactly how breast cancer begins^6,7. The most common type of BC, BC expressing HR, accounts for 60–70% of BC occurrences in affluent nations and only affects premenopausal women. Consequently, the most popular therapeutic strategy is hormonotherapy⁸.

In India, breast cancer has surpassed cervical cancer as the leading cause of cancer-related deaths among women. Researchers throughout the world are under pressure to create new therapies more quickly due to the rising incidence of breast cancer and the emergence of drug resistance to current anticancer medications^9-11. The structure-based drug designing technique, which has emerged as a potent tool to improve the drug discovery processes, can be used to meet this demand in lead identification and optimization¹². Additionally, a number of researchers investigated the potential of plant compounds like resveratrol, isoflavones, and indoles against cancer.

The development of contemporary medications for the treatment of cancer is greatly aided by the use of natural plant products. In light of this, maslinic acid, a member of the class of triterpenes (oleananes), was the subject of a thorough structure-activity connection investigation¹³. It is one of the significant anticancer drugs for which no three-dimensional quantitative structure–activity relationship (3D-QSAR) analysis has yet been published in order to identify the major structural regulating areas and various active and inactive locations¹⁴. In order to identify the critical regulatory elements governing the toxicity and anticancer activity of maslinic acid, a 3D-QSAR research was conducted on this natural series. A common pharmacophore was created because there is currently no structural information available for maslinic acid in its target-bound state¹⁵.

This creates a pharmacophore template that mimics the bioactive conformation by using the molecular field-based similarity technique for the conformational search. To gain a deeper understanding of the structure-activity relationship (SAR), activity-atlas models were also created¹⁶. The active compounds' positive and negative electrostatic areas were also made visible using 2D-QSAR. As a result, more potent and effective maslinic acid analogs were created. This 2D-QSAR method had a distinct effect and was a useful predictive tool, mostly in pharmaceutical design.Nevertheless, obtaining a high-quality 3D-QSAR is a difficult undertaking. This is because accurate alignments for every compound with the least amount of noise are required, as well as high-quality and trustworthy biological data^17,18.

Although the male adrenal cortex can manufacture estrogen, estrogen hormone is a steroid molecule that performs important modulatory roles in physiological processes, particularly in females^19,20. The ovaries and placenta cells are the primary sources of estrogen, which is produced through the interaction and activation of the estrogen receptors ER α and ERβ, which belong to the nuclear receptor superfamily of transcription factors and have highly conserved DNA- and ligand-binding domains^21–24. The female reproductive tract, breast, liver, bone, brain, skin, colon, and salivary gland are among the various tissues that contain estrogen receptors^25–27. Consequently, ER α controls a number of intricate physiological functions in humans.

However, through estrogen signaling, ER contributes to the development of cancers. Tumor surface ER α protein has the ability to bind to estrogen (17b-estradiol, E2) and subsequently contribute to the development of cancers through estrogen signaling. Consequently, the hormone-responsive genes that support DNA synthesis and cell division are activated¹⁹. The reproductive system (endometriosis, breast, ovarian, and prostate cancer), bone, lung cancer, cardiovascular disease, gastrointestinal disease, urogenital tract disease, neurodegenerative disorders, and cutaneous melanoma are among the tumors where this promotes the growth and spread of cancer cells²⁴. Compared to 10% in healthy tissues, ER α is overextended in 50–80% of breast cancer tissues. Consequently, it has been determined that estrogen has a major role in the development of ER α positive breast cancer, which accounts for approximately 70–80% of all cases of breast cancer^28-30. As a selective estrogen receptor modulator (SERM), 4-hydroxytamoxifen (4-OHT) increases the growth rate of the human breast cancer cell line MCF-7 in response to estrogens and decreases it in response to antiestrogens³¹. demonstrates the function of ERs in the various cancer cell types employed in this investigation. While the involvement of ERβ is still debatable, it can be observed that Er α α has promising roles in the development of breast cancer.

Conversely, ER and ERβ have a major impact on prostate cancer, with ER α using tumor growth-promoting actions and ERβ using tumor growth-suppressive effects^24,36,37. By lowering fibrosis and immune response, ERβ inhibits the development of liver cancer. The role of ER carcinoma cells in the liver when ER α α is still debatable and encourages hepatocellular proliferation cooperates with ERβ.

Among the most significant organic compounds that are commonly found in molecules of interest in medicinal chemistry are heterocyclic compounds⁴⁴ and heterocyclic scaffolds⁴⁵. Additionally, heterocycles that include nitrogen are crucial to life science because they exhibit a variety of biological functions, including representative alkaloids and other nitrogen. Serotonin⁴⁶, thiamine, popularly known as vitamin B1⁴⁷, atropine⁴⁸, the infamous morphine⁴⁹, and the majority of vitamins, nucleic acid, enzymes, co-enzymes, hormones, and alkaloids all contain N-based heterocycles as scaffolds.

These facts reveal and highlight the critical importance of heterocycles in contemporary drug design and drug discovery. Nitrogen heterocyclic molecules have always been appealing candidates to synthetic organic chemists due to their various biological activities.

A well-known idea in drug design and research, molecular hybridization of heterocyclic motifs relies on the combination of pharmacophoric moieties of several bioactive compounds. When compared to the parent medications, they create a new hybrid system with better activities⁵⁰.

 One of the most thoroughly researched substances in both the natural and synthetic worlds are those that contain nitroimidazole^51–53. Because of its many biological and pharmacological characteristics, imidazoles have garnered a lot of attention recently^54,55. In addition to their clinically demonstrated anticoagulant and antithrombotic properties, several triazoles have demonstrated antibacterial and anticancer properties^56,57. This technique may also produce molecules with altered selectivity profiles, distinct or multiple modes of action, and less undesirable side effects.

A number of structural modification techniques have been used to rationally plan new synthetic prototypes with the goal of creating new compounds with optimized pharmacodynamic and pharmacokinetic properties. These techniques include investigating the fragments of bioactive substances (Fragment-Based Drug Design)⁵⁸, active metabolites of drugs^59, bioisosterism⁶⁰, selective optimization of drug side effects⁶¹, and drug latentiation.

Because of their broad pharmacological profiles, which include antibacterial^64,65 and anticancer ^66,67 properties, and their easy synthesis using click chemistry^62,63, 1,2,3-triazoles have a significant position in drug discovery. Potential structural characteristics of bioactive triazoles include their excellent selectivity, stability against metabolic degradation, and hydrogen bonding capacity, which may be advantageous when binding biomolecular targets⁶⁸. It has been shown that various kinds of chemical bridges at the C-4 of the 1,2,3-triazole core remove planarity, which may improve drug-ability and make it easier for molecules to attach to potential receptors by induced fit.

Our team has been synthesizing several derivatives of novel 5-substituted piperazinyl-4-nitroimidazole derivatives and assessing their anti-HIV efficacy for the past fifteen years^69-71. Because they circulate freely throughout the body and are irreversibly bound by nucleophilic covalent binding to proteins in low oxygen settings, nitroimidazoles are a popular class of compounds that have been thoroughly explored as molecular probes⁷². 4-Derivatives of nitroimidazoles have been investigated as radiopharmaceuticals for tumor hypoxia probe, therapeutic therapy, antifertility potential, and anti-tubercular profile^73,74. The synthesis of the highly effective hybridization system that our group reported has been the subject of other investigations⁷⁵.

MATERIAL AND METHODS: 

The Novel hybrid motifs of 4-nitroimidazole-piperazinyl tagged 1,2,3-triazoles derivatives chemicals produced by were the materials employed in this investigation. In Table 1, the dependent variable was the Inhibition Concentration (IC50)⁷⁶.

 

Table 1: Structure and biological activity of 4-nitroimidazole-piperazinyl tagged 1,2,3-triazoles 

S. No.	Compounds	Substitution R	MCF-7 Cell Actual IC50 (µM)	Biological Activity
1.	COMPOUND 2	-	0.068	7.167491087
2.	COMPOUND  3	-	4.2	5.37675071
3.*	COMPOUND  4	-	0.085	7.070581074
4.	COMPOUND 5	--	0.084	7.075720714
5.	COMPOUND 9a	H	0.055	7.259637311
6.	COMPOUND 9b	p-Br	0.079	7.102372909
7.	COMPOUND 9c	p-F	0.024	7.619788758
8.	COMPOUND 9d	m-NO2	0.04	7.397940009
9.	COMPOUND 9e	m-Me	0.079	7.102372909
10.	COMPOUND 9f	m—Br	0.053	7.27572413
11.	COMPOUND 9g	m-F	0.002	8.698970004
12.	COMPOUND 9h	o-NO2	0.048	7.318758763
13.*	COMPOUND 9i	o-Br	0.106	6.974694135
14.	COMPOUND 9j	o-F	0.151	6.821023053
15.	COMPOUND 9k	p-Me	0.005	8.301029996
16.	COMPOUND 10a	H	0.112	6.950781977
17.	COMPOUND 10b	p-Br	0.372	6.42945706
18.	COMPOUND 10c	m-OME	0.585	6.232844134
19.	COMPOUND 11a	p-Br	2.12	5.673664139
20.	COMPOUND 11b	o-SMe	0.136	6.866461092
21.*	COMPOUND 11c	o-OEt	0.399	6.399027104
22.	COMPOUND 11d	H	0.312	6.879426069
23.	COMPOUND 11e	p-Me	0.219	6.659555885
24.	COMPOUND 11f	p-Ome	0.251	6.600326279
25.	COMPOUND 11g	o-Me	0.183	6.73754891
26.	COMPOUND 11h	o-Et	0.088	7.055517328
27.*	COMPOUND 11i	o-NO2	0.185	6.732828272
28.	COMPOUND 11j	p-Cl	0.279	6.554395797
29.	COMPOUND 11k	P-NO2	0.671	6.17327748
30.	COMPOUND 11l	m-OMe	0.198	6.70333481
31.	COMPOUND 11m	m-Me	0.161	6.793174124
32.	COMPOUND 11n	m-Cl	0.155	6.809668302
33.	COMPOUND 11o	Tetralin	0.131	6.882728704
34.	COMPOUND 11p	Benzdioxole	0.052	7.283996656
35.	COMPOUND 11q	Naphthalene	0.27	6.568636236

* test set compounds

 

 

 

Instrumentation:

An Asus laptop with an Intel® Core i5 processor, 8GB of RAM, and a 500GB SDD was utilized in this study. The package Chemoffice application Version 16.0 for Windows was used to calculate all of the compounds' physiochemical parameters (Table 1), and the semi-empirical Chem3D was used to finish geometry optimization. The statistical application Valstat version 16 for Windows was then used for analysis.

Data Set:

The physicochemical properties of novel hybrid motifs of 4-nitroimidazole-piperazinyl tagged 1,2,3-triazole derivatives were linked to their inhibitory action using QSAR analysis. The data series were taken from a study released by Sadeekah O.W. Saber et. al., 2023 There are 35 in the selected series. The structure and diversity of activities in both sets are thereby maintained for the purpose of creating QSAR models. The biological activities were expressed using the inhibitory concentration (IC50) in micromolar concentrations. For correlational purposes, the indicated IC50 values were first converted to their molar units and then to the free energy-related negative logarithmic state, or Log (1/IC50) or pIC50.

Descriptors calculation

Five steps are involved in the development of the QSAR model. The first step consists of determining the series of pyrazole-benzimidazole compounds to be studied and the IC50 value found in the lab experiment. The second stage is to select a set of descriptors that are likely related to the biological activity of interest in order to optimize for the most stable skeleton structure of the series of pyrazole-benzimidazole compounds. The next step is to compute descriptors using the optimized structure. In the fourth step, a mathematical equation model representing the relationship between the biological activity and the chosen descriptors is created using statistical analysis with VALSTAT Software for Windows. Validating the QSAR models is the final stage.

All of the pyrazole-benzimidazole derivative structures were displayed using Chem Draw 16.0. The molecular mechanics (MM2) technique was used to look for lower energy conformations in each molecule. Molecular orbital property accompanying name (MOPAC) was used to reoptimize the compounds with the lowest energy. To avoid the compounds' local stable conformations, the geometry of each molecule was optimized utilizing a range of starting points. When determining the molecular descriptors, the conformation with the lowest energy was considered⁷⁷. The QSAR study's thermodynamic, electronic, steric, and molecular descriptors were computed using ChemOffice 2001 displayed in Table 2.

 

 

Table 2: Descriptors for training and test set compounds of series

S.NO	Henry's Law Constant	Mol Refractivity	Exact Mass	Mass	Mol Weight	Partition Coefficient	LogP	LogS	PKa
1	4.149	5.957299709	217.0851266	217.228	217.228	1.651100039	2.60282	-3.36199	0
2	4.149	6.73429966	294.9956392	296.124	296.124	2.326448202	3.39452	-4.1582	0
3	4.149	9.013700485	317.185175	317.393	317.393	3.054520607	3.5992	-4.48971	9.03019
4	4.149	10.20230103	355.2008251	355.442	355.442	4.067719936	4.12525	-4.82478	7.65804
5	4.149	13.97099972	488.2648223	488.596	488.596	4.600320339	5.16512	-7.05665	7.54281
6	4.149	14.74800014	566.1753349	567.492	567.492	5.463320732	5.96058	-7.8762	7.54152
7	4.149	13.98649979	506.2554005	506.5864032	506.5864032	4.743320465	5.32637	-7.28205	7.54332
8	4.149	14.5824995	533.2499005	533.593	533.593	4.343319893	5.05945	-7.4615	7.53104
9	4.149	14.43480015	502.2804724	502.623	502.623	5.099320412	5.60228	-7.41656	7.54227
10	4.149	14.74800014	566.1753349	567.492	567.492	5.463320732	5.96058	-7.87423	7.53708
11	4.149	13.98649979	506.2554005	506.5864032	506.5864032	4.743320465	5.32637	-7.27844	7.53794
12	4.149	14.5824995	533.2499005	533.593	533.593	4.263319969	5.05945	-7.45935	7.47959
13	4.149	14.74800014	566.1753349	567.492	567.492	5.463320732	5.96058	-7.87469	7.50477
14	4.149	13.98649979	506.2554005	506.5864032	506.5864032	4.743320465	5.32637	-7.27989	7.48475
15	4.149	14.43480015	502.2804724	502.623	502.623	5.099320412	5.60228	-7.41794	7.54303
16	4.149	14.47049999	516.2597369	516.606	516.606	4.217520237	4.78923	-6.98457	7.41852
17	4.149	15.24749947	594.1702495	595.502	595.502	5.156120777	5.58469	-7.79803	7.41912
18	4.149	15.08740044	546.2703016	546.632	546.632	4.437420845	4.69171	-6.99903	7.41652
19	4.149	14.28419971	552.1596848	553.465	553.465	6.086218357	5.82578	-7.77518	7.47903
20	4.149	14.77729988	520.236893	520.656	520.656	5.692176342	5.79707	-7.77285	7.47775
21	4.149	15.24109936	534.2525431	534.683	534.683	6.221176147	6.13665	-8.04249	7.47594
22	4.149	13.50719929	474.2491722	474.569	474.569	5.051320076	5.03032	-6.9576	7.48437
23	4.149	13.97099972	488.2648223	488.596	488.596	5.550319195	5.46748	-7.30925	7.48664
24	4.149	14.12409973	504.2597369	504.595	504.595	5.279327393	4.9328	-7.00997	7.50118
25	4.149	13.97099972	488.2648223	488.596	488.596	5.550319195	5.2541	-7.15653	7.49842
26	4.149	14.43480015	502.2804724	502.623	502.623	6.079319954	5.72377	-7.51987	7.4889
27	4.149	14.11869907	519.2342505	519.566	519.566	5.162673473	4.92465	-7.33855	7.21096
28	4.149	13.99859905	508.2101999	509.011	509.011	5.936218262	5.6523	-7.65374	7.47595
29	4.149	14.11869907	519.2342505	519.566	519.566	5.162673473	4.92465	-7.35822	7.44963
30	4.149	14.12409973	504.2597369	504.595	504.595	5.279327393	4.9328	-7.0175	7.43033
31	4.149	13.97099972	488.2648223	488.596	488.596	5.550319195	5.46748	-7.31969	7.47759
32	4.149	13.99859905	508.2101999	509.011	509.011	5.936218262	5.6523	-7.65175	7.43716
33	4.149	15.18500042	528.2961224	528.661	528.661	6.623319149	6.26172	-8.19125	7.47969
34	4.149	14.09979916	518.2390015	518.578	518.578	5.262735844	4.78137	-7.11108	7.44531
35	4.149	15.19519901	524.2648223	524.629	524.629	6.225319386	6.29714	-8.35474	7.46446

 

 

Statistical analysis:

The estimated descriptors for each enzyme inhibitory activity were gathered using a data matrix (D) with a dimension of (n × m). The number of molecules in each data set and the number of computed descriptors for each molecule are denoted by the numbers n and m, respectively. Initially, the descriptors were checked for constant or almost constant values, and those that were discovered were removed from the initial data matrix. Next, the relationship between the activity data and the descriptors was determined. To do the correlation study and identify the primary factors impacting the activity, the statistical tool VALSTAT was utilized.

Model development and validation:

QSAR models yielded multiple linear regression (MLR) analysis. The stepwise selection of variables, which combines forward selection and backward elimination techniques, was used to pick the most relevant subset of descriptors. Regression analysis was performed with the VALSTAT program. 

External validation was used to validate the QSAR model. This method is used to calculate the activity of each compound in the test set. The calculated and observed activities were used to construct the cross-validation coefficient q2. One way to gauge a model's stability and predictive accuracy is to look at its cross-validation coefficient, or q2. The square of the cross-validation coefficient (q2) should be at least 0.5 for a reliable model.

RESULT AND DISCUSSION:

Molar Refractivity, Mass, Partition Coefficient, LogP, LogS, Polar Surface Area, Sum of Degree, Sum of Val. Degree, and Wiener Index are among the descriptors employed in this investigation. The descriptors were obtained from the structural characteristics of each compound after the geometry optimization process. The descriptors of the series of pyrazole-benzimidazole compounds were obtained using the semi-empirical PM3 technique. This method can be used to analyze a variety of pyrazole-benzimidazole derivatives because they are organic compounds composed of the atoms C, H, and N. Regression analysis was used in the statistical study to identify the QSAR models and their statistical properties.

Multiple linear regressions and other statistical analysis were performed on every molecule in the training set. Descriptors were selected for the model based on their correlation coefficient; those with an interred correlation coefficient of less than 0.6 were taken into account. Numerous models were generated by multiple linear regression (MLR) analysis. The model's predictive power was evaluated using a variety of statistical measures, such as the correlation coefficient, regression coefficient (r2), Fischer statistical value (F), and standard error. All of these statistical features were computed, according to the VALSTAT.

The first regression analysis was performed on each training molecule, resulting in a regression model. The best QSAR model has a large F, low p-value, r2 and q2 values near 1, and P<0.001. The best QSAR model created using the multiple linear regression (MLR) technique is represented by the following equation:

optimized model no. 1 results are.......

BA= [6.33613(±0.565209)] +Exact [-0.348746( ± 0.185605)] +Mass [-0.169603( ± 0.173082)] +Mol [0.149827(±0.0646368)]

n=34,r=0.451694,r^2=0.204027,r^2adj=0.12443,variance=0.245301,std=0.495279,QF=0.911999, PE=0.0910055, F=2.56325, FIT=0.180592, LOF=11.6695, AIC=0.29865standard Fmax value at 95% confidence=5.58871

Henry’s law Constant, Partition Coefficient, and Mass, Molecular weight,  descriptors with r² = 0.204027 and std = 0.495279 were used for developing Model 1. Std was higher and r² was lower. Thus, we created Model 2 and 3 by substituting the Mass descriptor for the logP descriptor, and we discovered   Model 2 Value a r2 value of 0.192457 and a standard deviation of 0.572264. and Model 3 value is r2 value of 0.17021 and a standard deviation of0.580094.

optimizedmodel no. 2 results are.......

BA= [6.4112(±0.632438)] +Law [0.0885432( ± 0.0949709)] +Exact [-0.347976( ± 0.172289)] +Mol [0.144589(±0.0840339)]  

n=35, r=0.438699, r^2=0.192457, r^2adj=0.114308, variance=0.327487, std=0.572264, QF=0.766603, PE=0.0909998, F=2.46269, FIT=0.16791, LOF=15.8626, AIC=0.411999standard Fmax value at 95% confidence=5.53414

optimizedmodel no. 3 results are.......

BA=[6.73489(±0.634665)] +Constant[0.000266736(±0.00219479)] +Exact [-0.248804(±0.16037)] +Mol [0.180689(±0.0785019)]  

n=35, r=0.412565, r^2=0.17021, r^2adj=0.0899073, variance=0.336509, std=0.580094, QF=0.711203, PE=0.0935068, F=2.11961, FIT=0.144519, LOF=16.2996, AIC=0.42335standard Fmax value at 95% confidence=5.53414

We looked into creating several models utilizing different descriptors, and we found that Models 1, 2, and 3 had the descriptors Molar Refractivity along with Partition Coefficient, Henary law constant, Mass, and Exact mass. We found that model 1 is better than the other two models, with a r2 value of 0.204027, a standard deviation of 0.495279, and one outlier. MDB and MCF-7 inhibitor activity are positively correlated. The positive correlation of MR indicates that higher molecular weight or bulky group compounds are essential for enhanced MCF-7 cell inhibitory action. An electrophilic group would increase activity, according to the Molar Refractivity negative correlation.Furthermore, a correlation was found in the Partition Coefficient, indicating that less polar groups are more active. Table-3 and Figure-1 show the predicted pIC50 and biological activity values of the training and test sets of series using model-1. Figure-2 show the Residual value (RV= pIC50 – Actual Biological Activity).

 

 

Table 3: The Actual and predicted pIC50 values of the training set of series by using model-1

S.No	Compound No.	Predicted pIC50	Actual Biological Activity	Residual Value
1.	COMPOUND 2	5.52393	7.167491087	-1.64356
2.	COMPOUND  3	6.99796	5.37675071	1.621209
3.	COMPOUND  4	8.19896	7.070581074	1.128379
4.	COMPOUND 5	6.9808	7.075720714	-0.09492
5.	COMPOUND 9a	7.1358	7.259637311	-0.12384
6.	COMPOUND 9b	6.89882	7.102372909	-0.20355
7.	COMPOUND 9c	7.06426	7.619788758	-0.55553
8.	COMPOUND 9d	7.26196	7.397940009	-0.13598
9.	COMPOUND 9e	7.00167	7.102372909	-0.1007
10.	COMPOUND 9f	6.89148	7.27572413	-0.38424
11.	COMPOUND 9g	7.0117	8.698970004	-1.68727
12.	COMPOUND 9h	7.29712	7.318758763	-0.02164
13.	COMPOUND 9i	6.89818	6.974694135	-0.07651
14.	COMPOUND 9j	7.09358	6.821023053	0.272557
15.	COMPOUND 9k	6.95579	8.301029996	-1.34524
16.	COMPOUND 10a	7.35716	6.950781977	0.406378
17.	COMPOUND 10b	7.08861	6.42945706	0.659153
18.	COMPOUND 10c	7.4807	6.232844134	1.247856
19.	COMPOUND 11a	6.73711	5.673664139	1.063446
20.	COMPOUND 11b	6.81799	6.866461092	-0.04847
21.	COMPOUND 11c	6.702	6.399027104	0.302973
22.	COMPOUND 11d	6.93344	6.879426069	0.054014
23.	COMPOUND 11e	6.80588	6.659555885	0.146324
24.	COMPOUND 11f	6.91907	6.600326279	0.318744
25.	COMPOUND 11g	6.80403	6.73754891	0.066481
26.	COMPOUND 11h	6.62007	7.055517328	-0.43545
27.	COMPOUND 11i	6.91297	6.732828272	0.180142
28.	COMPOUND 11j	6.6763	6.554395797	0.121904
29.	COMPOUND 11k	6.96582	6.17327748	0.792543
30.	COMPOUND 11l	6.90521	6.70333481	0.201875
31.	COMPOUND 11m	6.79827	6.793174124	0.005096
32.	COMPOUND 11n	6.64654	6.809668302	-0.16313
33.	COMPOUND 11o	6.47422	6.882728704	-0.40851
34.	COMPOUND 11p	6.89301	7.283996656	-0.39099
35.	COMPOUND 11q	6.67894	6.568636236	0.110304

Compound 11 was outlier.

Figure 1:Corelation between Experimental IC50 value and Predicted pIC50 value.

Figure 2: Graph of Residul value

 

 

CONCLUSION

For the MCF-7 cell inhibitory action of 4-nitroimidazole-piperazinyl tagged 1,2,3-triazoles compounds, we created a QSAR model. It is possible to determine that the MCF-7 inhibitory activity of 4-nitroimidazole-piperazinyl tagged 1,2,3-triazoles derivatives is significantly influenced by the thermodynamic and electrical nature of the substituents. According to the proposed QSAR model, the maximum Molar Refractivity, Partition coefficient, Henary law constant, and molecular weight should be taken into consideration while designing novel compounds for their potential MCF-7 inhibitory action. Novel compounds with potent MCF-7 cell inhibitory action can be further explored using this QSAR approach.

Acknowledgement: The principal of the VNS Group of Institute Faculty of Pharmacy in Bhopal is acknowledged by the authors for providing the facilities needed to carry out this study.

Conflict of Interest: The authors declare no conflict of interest.

Funding Source: The authors received no financial support for this research.

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