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Structural cytotoxicity relationship of 2-phenoxy(thiomethyl)pyridotriazolopyrimidines: Quantum chemical calculations and statistical analysis

  • Hatem A. Abuelizz , El Hassane Anouar , Nasser S. Al-Shakliah , Mohamed Marzouk and Rashad Al-Salahi EMAIL logo
Published/Copyright: June 30, 2020
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Open Chemistry
From the journal Open Chemistry Volume 18 Issue 1

Abstract

Previously, a series of pyridotriazolopyrimidines (1–6) were synthesized and fully described. The target compounds (1–6) were evaluated for their cytotoxicity against MCF-7, HepG2, WRL 68, and A549 (breast adenocarcinoma, hepatocellular carcinoma, embryonic liver, and pulmonary adenocarcinoma, respectively) cell lines using MTT assay. The tested compounds demonstrated cytotoxicity, but no significant activity. To elucidate the structure–cytotoxicity relation of the prepared pyridotriazolopyrimidines, several chemical descriptors were determined, including electronic, steric, and hydrophobic descriptors. These chemical descriptors were calculated in the polarizable continuum model (water as solvent) using density functional theory calculations at B3LYP/6-31+G(d,p). By employing simple linear regression (SLR) and multiple linear regression (MLR) analyses, the impact of the selected descriptors was assessed statistically. The obtained results clearly reveal that the cytotoxicity of pyridotriazolopyrimidines depends on their (i) basic skeleton and (ii) the type of the tested cell. Interestingly, SLR and MLR analyses show that the impact of the selected descriptors is strongly related to the tested cells and basic skeleton of the tested compounds. For instance, the cytotoxicity of subclasses 2a and 2c–2f against A459 shows strong correlation with ionization potential, hardness (η), and hydrophobicity (log P) with a correlation coefficient of 99.86% and a standard deviation of 0.53.

Keywords: pyridotriazolopyrimidines; DFT; cytotoxicity; MLR; SLR

1 Introduction

Cancer is a complex ailment and remains a major health concern despite intensive efforts to elucidate its biology and develop more efficacious antitumor agents. Notably, resistance often develops in patients treated with anticancer agents, resulting in disease progression and poor prognosis. Hence, several chemotherapeutic compounds, approved as adjuvant therapy for several types of cancers, have demonstrated inadequate response rates, approximately between 30% and 70%. Furthermore, resistance can occur as a consequence of decreased drug activity even prior to drug exposure (primary) or during/after the treatment course (acquired). The ever-growing resistance to anticancer agents is a leading cause of cancer-related mortality worldwide. Therefore, the discovery of new antitumor agents with minor side effects is a crucial task and a highly pursued aim in contemporary pharmaceutical chemistry [1,2,3,4,5].

Heterocyclic compounds including nucleic acids, novel substances, the plurality of medicine, and synthetic/natural dyes are extensively present in nature. Investigators have attempted to generate diverse heterocyclic structures bearing triazole, quinazoline, benzoquinazoline, pyridine, and pyrimidine moieties presenting numerous biological purposes, which remains an ongoing scientific challenge. The pyridine and pyrimidine platforms are used as precursors in agrochemicals and pharmaceuticals and occur in many bioactive important products such as niacin (antipellagra), isoniazid (antituberculosis), thiamine (vitamin B1), barbiturates (central nervous system depressant), zidovudine (antiretroviral medication), and antiviral and anticancer medications. In contrast, 1,2,4-triazoles are associated with various pharmacological activities, and a large number of predominant triazole drugs have been successfully developed and prevalently used in the treatment of various microbial infections such as fluconazole, posaconazole, and itraconazole (antifungal). Combining these three structure features in one molecule (pyridotriazolopyrimidine) has showed significant pharmacological efficiency as fungicidal, herbicidal, antidiabetic, and antioxidant agents [6,7,8,9,10,11].

Quantum chemical methods are considered efficient tools to determine molecular electronic properties of intermediate systems, correlating them with their biological activities. Several methods have been applied to explain the relation between the cytotoxicity of active compounds and their chemical descriptors [12,13,14]. For instance, a PM5 semi-empirical method was reported by Ishihara et al., who proved that the correlation is relatively good for tropolone compounds with a basic skeleton of similar dipole moment (µ), hydrophobicity (log P), hardness (η), electrophilicity (ω), and electronegativity (χ) [15]. Furthermore, the cell line type and the parent skeleton of the evaluated compounds (natural or synthesized) play a crucial role in the cytotoxicity profile. Density functional theory (DFT) methods along with statistical analyses is employed to rationalize and confirm the relationship between the cytotoxicity and their correlated structures. Further studies on 4-hydroxycoumarin and ganoderic acid compounds have been reported by Stanchev et al. and Yang et al., revealing that cytotoxicity correlated with log P, µ, volume (V), and the molecular orbital energies (EHOMO and ELUMO) [16,17] for 4-hydroxycoumarin compounds; HOMO energy, χ, electronic energy, log P, and molecular area (A) are dependable variables to distinguish between higher and lower active ganoderic acid compounds [15].

The cytotoxicity of the targets (1–6) was evaluated against four cancer cell lines, namely A549, HepG2, and MCF7 carcinoma cells and WRL 68 cells. Herein, we report the structure–cytotoxicity relationship of 2-phenoxy(thiomethyl)pyridotriazolopyrimidines (1–6). We employed the polarizable continuum model (PCM) at the B3LYP/6-31+G(d,p) level of theory to calculate the electronic and steric molecular descriptors of the target pyridotriazolopyrimidines and utilized simple linear regression (SLR) and multiple linear regression (MLR) analyses to determine the correlation between the cytotoxicity of pyridotriazolopyrimidines and the calculated descriptors.

2 Materials and methods

2.1 Cell culture and cytotoxicity assay

The cell lines, HepG2, A549, MCF-7, and WRL 68, were obtained from the American Type Culture Collection (ATCC; Rockville, MD, USA). The cells were cultured in two types of media: minimum essential medium and Roswell Park Memorial Institute 1640 medium, supplemented with 10% fetal bovine serum and 1% (v/v) l-glutamine. The cells were incubated in a CO2 incubator at 37°C, in a humidified atmosphere containing 5% CO2 and were subcultured once a week using trypsin/ethylenediaminetetraacetic acid (0.25%/0.02%, v/v) for cell detachment from the flasks. Cells at 60–80% confluency were later used for a 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay.

A cell viability test was conducted using the MTT assay [18]. Briefly, 2 × 104 cells/well were seeded in a 96-well plate and incubated with 5% CO2. After 12 h, the cells were treated with 200 µg/mL for 24 h. Next, 20 µL of the MTT solution was added to each well and incubated for another 4 h. The medium was discarded, and the crystalline deposits in the cells were dissolved in 100 µL of dimethyl sulfoxide. The absorbance of the colored formazan crystals was measured at 520 nm using a microplate reader (Tecan, Austria), and the results were presented as mean values ± standard deviation (SD; n = 3).

2.2 DFT calculations

By employing the exchange–correlation hybrid functional B3LYP combined with 6-31+G(d,p) double-ζ Pople-type basis, the geometry optimization and frequency calculations of pyridotriazolopyrimidine derivatives were performed, with polarized and diffuse functions taken into consideration [19]. The absence of imaginary frequencies confirms that the optimized structures are true minima. The choice of B3LYP was based on previous studies [20,21,22]. The solvent effects were taken into account implicitly by using the PCM, in which, the solute is embedded into a cavity surrounded by solvent described by its dielectric constant [23].

The chemical descriptors selected to correlate with cytotoxicity were as follows: (i) electronic descriptors: frontier molecular orbital energies (EHOMO and ELUMO, which are well accepted as molecular descriptors in medicinal chemistry as they are linked to the capacity of a molecule to form a charge-transfer complex with its biological receptor), hardness (η), electrophilicity index (ω), electronic affinity (EA), softness (S), ionization potential (IP), electronegativity (χ), dipole moment (μ), and molecular polarizability (α); (ii) steric descriptors: surface area of the molecule (A), volume (V), and its molecular weight (M); and (iii) hydrophobicity descriptor: log P, where P denotes the octanol–water partition coefficient. The calculations of log P were performed using the Hyperchem Molecular package by means of the atomic parameters derived by Ghose, Pritchett, and Crippen and later extended by Ghose and co-workers. The other descriptors may be obtained at the DFT level of theory [24] by considering: (i) orbital consideration, which is based on Koopman’s theorem where IP = −EHOMO and EA = −ELUMO [25]; (ii) energy consideration, which is based on the use of the classical finite difference approximation, IP = E+1 − E0 and EA = E0 − E−1 where E0, E−1, and E+1 are the electronic energies of neutral molecule, when adding and removing an electron to the neutral molecule, respectively [24]; and (iii) internally resolved hardness tensor approach [26,27,28]. Previously, we have reported the structure cytotoxicity activity relationship of 2-thiophen-naphtho (benzo)oxazinone derivatives [29] and compounds isolated from Curcuma zedoaria [22] by considering orbital and energy methods in calculating electronic and molecular properties, and the results displayed that both methods give similar results. In another study, De Luca et al. tested the three methods to evaluate the solvent effects on the hardness values of a series of neutral and charged molecules, and they concluded that three methods give similar results in the presence of solvent [30]. Herein, to minimize computational cost of theoretical calculations, we have used the first approach in calculating other chemical descriptors. The Gaussian 16 package was used to perform all DFT calculations [31].

2.3 Statistical analyses

SLR and MLR analyses were performed to determine the regression equations, correlation coefficients R2, adjusted R2, and SDs between the calculated descriptors and cytotoxicity of the target compounds. The regression curves and statistical parameters are obtained using the DataLab package (http://www.lohninger.com/datalab/en_home.html).

  1. Ethical approval: The conducted research is not related to either human or animal use.

3 Results and discussion

3.1 Cytotoxicity evaluation

The synthetic methodology for target pyridotriazolopyrimidines (Table 1 and Scheme 1) has been previously described [10,11]. As illustrated in Table 2, the in vitro cytotoxicity of pyridotriazolopyrimidines was evaluated against A549, HepG2, MCF-7, and WRL 68 cells using the MTT assay. The percentages of cell viability at 200 µg/mL are presented in Table 2 as the cytotoxicity parameter of the tested pyridotriazolopyrimidines. A considerable cytotoxicity was demonstrated by compounds 1a, 1b, 2a, 2c, 2e, 2d, 2f, 2i, 2m, 4, 5a, 5b, and 6c against A549 cells (inhibition% of 33.44–55.87), whereas the significant cytotoxicity against MCF-7 cells (30.75–56.70%) was reported by 2c, 2f, 2m, 4, and 5b. In comparison to the aforementioned results, compounds (2b, 2d, 2h, 2j, 2k) and (2b, 2h, 3a, 5a) demonstrated moderate effects against A549 and HepG2, respectively. In contrast, 1a–b, 2a, 3a, and 6b–d exhibited moderate effects against MCF-7 cells. The other target compounds failed to demonstrate a significant activity against A549, HepG2, and MCF-7 cell lines. In regard to WRL 68 cells, only 2m appeared to demonstrate a remarkable activity (19.34%), although 1a, 1b, 2e, 2g, 2k, and 5b showed viability percentages between 10.23 and 12.94%. Based on the findings presented in Table 2, the type of substituent attached to the 2, 4, and 5 positions of the pyridotriazolopyrimidine skeleton can be considered a major determinant for the cytotoxic properties. In the case of the target 2m, the cytotoxicity increased in the order of MCF-7 > A549 > HepG2 > WRL 68, with 2m emerging as the most active compound in this series. This could be attributed to the conformation of the heteroalkyl (propyl isoindole) and thiomethyl groups, which might have played a pivotal role in the cytotoxicity profile. The aliphatic C-chain in 2b, the aromatic(hetero) group in 2c and 2f, along with the chloro group in 5a and 5b could be responsible factors for their slightly improved cytotoxicity profile against A549, Hep-G2, and MCF-7 cells compared to that of their parents 1a and 1b.

Table 1

The synthesized pyridotriazolopyrimidines (1–6)

CPsRXR1CPsXRR1
1aOphOH2kOSCH33-Methylbenzyl
1bSCH3OH2lOSCH34-Chlorobenzyl
2aOphOBenzyl2mOSCH3Propyl isoindole
2bOphOEthyl3aSOphH
2cOphOp-NO2-benzyl3bSSCH3H
2dOphOPiperidinoethyl4—SCH3S-Ethyl
2eOphOMorpholinoethyl5a—OphCl
2fOphOPropyl isoindole5b—SCH3Cl
2gSCH3OEthyl6a—Ophp-Methyl aniline
2hSCH3OAllyl6b—Ophp-Ethoxy-aniline
2iSCH3OBenzyl6c—OphIsoniazid
2jSCH3O2-Methylbenzyl6d—OphNH-OH
Scheme 1 The synthetic route of the target pyridotriazolopyrimidines (1–6).
Scheme 1

The synthetic route of the target pyridotriazolopyrimidines (1–6).

Table 2

Maximal percentage of cell viability at 200 µg/mL of samples against A549, HepG2, MCF-7, and WRL68 cell lines

CPsMaximal inhibition (%)
A549HepG2MCF7WRL68
1a42.64 ± 2.1846.12 ± 16.4526.75 ± 0.5011.15 ± 2.75
1b49.72 ± 8.2836.22 ± 1.9224.44 ± 0.6610.48 ± 0.52
2a39.19 ± 4.3739.76 ± 8.4625.98 ± 2.304.50 ± 2.12
2b25.00 ± 3.2822.71 ± 2.253.94 ± 0.555.44 ± 1.71
2c40.61 ± 8.8538.15 ± 8.6438.01 ± 0.646.26 ± 1.83
2d24.03 ± 3.1034.61 ± 10.5616.92 ± 1.075.87 ± 1.91
2e34.27 ± 3.1335.83 ± 3.3712.61 ± 3.3612.94 ± 0.78
2f41.13 ± 3.7043.76 ± 5.5135.71 ± 3.588.11 ± 2.20
2g16.50 ± 2.2310.45 ± 11.667.59 ± 9.3710.78 ± 1.40
2h27.60 ± 1.9627.60 ± 1.962.11 ± 1.675.67 ± 1.89
2i33.60 ± 2.8312.72 ± 2.021.27 ± 0.675.07 ± 3.78
2j26.96 ± 4.4616.77 ± 5.762.92 ± 2.048.62 ± 0.25
2k26.53 ± 1.399.80 ± 3.161.43 ± 5.2810.23 ± 4.62
2l19.56 ± 4.9617.66 ± 9.275.99 ± 2.207.92 ± 0.60
2m55.87 ± 1.4140.75 ± 13.1256.70 ± 0.4319.34 ± 1.53
3a23.78 ± 1.7630.26 ± 5.4023.94 ± 0.381.07 ± 0.89
3b15.50 ± 6.6615.50 ± 6.664.83 ± 2.356.65 ± 2.20
441.05 ± 1.9813.73 ± 0.7530.75 ± 0.668.40 ± 0.36
5a33.44 ± 1.7329.31 ± 4.9410.50 ± 0.586.70 ± 1.29
5b39.71 ± 1.0714.69 ± 1.6135.34 ± 1.0211.05 ± 2.47
6a20.54 ± 2.5113.51 ± 7.5314.15 ± 0.813.39 ± 1.43
6b7.33 ± 11.745.94 ± 7.7921.92 ± 1.255.73 ± 1.29
6c34.53 ± 1.9147.37 ± 7.7620.15 ± 5.263.58 ± 3.44
6d3.79 ± 8.893.72 ± 15.1325.95 ± 2.937.12 ± 2.15

3.2 Structure–property relationships

3.2.1 SLR analysis

For the synthesized compounds, SLR analysis was performed to investigate the strength of each descriptor on the cytotoxic activity against the tested cells (Table 3). The statistical parameters R2, Radj2, and SD were determined by considering compounds 1a–2a and 2c–6d (Table 4) and the subdivision of the tilted compounds into subclasses with the same base skeleton, 2a, 2c–2m (Table 5), 2a, 2c–2f (Table 6), and 2g–2m (Table 7). By considering compounds 1a–2a and 2c–6d (Table 4), the SLR statistical parameters revealed that the influence of each separate descriptor on the observed cytotoxicity was relatively weak and varied with the tested cells. For A549, HepG2, and WRL 68 cells, best correlations were observed with IP, with correlation coefficients of 37.57, 31.05, and 16.64%, respectively. For MCF-7 cells, the best correlation was obtained with IP with a correlation coefficient of 48.9%. The weak to moderate influence of individual descriptor on the cytotoxicity is in accordance with our previous studies reported on the cytotoxicity of the synthesized 2-thiophen-naphtho (benzo)oxazinone derivatives and C. zedoaria metabolites [22,29]. Ishihara et al. tested the influence of a set of molecular descriptors of tropolone-related compounds on the cytotoxicity against HSC-2, HSC-3, and HSG cells, and they found poor to moderate correlations (0–50%) between the CC50 of cells and the chosen 11 descriptors [15]. These correlations improved for the series of 2a and 2c–2m (Table 5), demonstrating correlation coefficients of 58.92, 41.20, and 66.41% for A549, HepG2, and MCF-7 cells, respectively. However, for WRL 68 cells, correlations were relatively weak. For the series of compounds 2a and 2c–2f (Table 6), the correlations between each descriptor and tested cells strongly improved. For instance, for A549 and MCF-7 cells, maximum correlations were obtained with IP and electronegativity, with correlation coefficients of 94.22 and 85.12%, respectively. For the last series, 2g–2m (Table 7), the correlations were relatively weak. Based on this analysis, it can be concluded that, for the synthesized compounds, the strength of each descriptor in the observed cytotoxic activity against tested cells strongly depends on their basic skeletons. Thus, for these subclasses, one may conclude that the cytotoxicity relatively depends on their ability of these compounds to provide and accept electrons (best correlations are obtained with IP and electronegativity).

Table 3

Maximal percentage of cell viability at 200 µg/mL of samples against A549, HepG2, MCF-7, and WRL68 cell lines and molecular descriptors calculated at B3LYP/6-31+G(d,p) of the synthesized compounds

CPsIPEAχηωaµAVlog PMMaximal inhibitions
A459HepG2MCF-7WRL76
1a6.422.314.374.112.32287.432.51321.98346.562.73279.0842.6446.1226.7511.15
1b6.382.304.344.082.31230.632.32266.82281.631.03233.0449.7236.2224.4410.48
2a6.612.244.434.372.24314.882.92367.65399.762.96307.1139.1939.7625.984.50
2c6.683.134.913.553.40421.726.54465.27514.864.42414.1140.6138.1538.016.26
2d6.182.264.223.922.27403.762.99474.87524.073.32390.1824.0334.6116.925.87
2e6.362.274.314.102.27392.612.70464.35511.752.26392.1634.2735.8312.6112.94
2f6.642.674.663.972.73474.495.48534.19591.293.49466.1441.1343.7635.718.11
2g6.322.234.274.102.23263.172.95310.79333.021.62261.0716.5010.457.5910.78
2h6.342.244.294.102.25280.632.80328.23351.032.01273.0727.6027.602.115.67
2i6.342.274.314.082.28340.502.76376.76413.543.06323.0833.6012.721.275.07
2j6.362.284.324.082.28357.052.99397.87439.053.53337.1026.9616.772.928.62
2k6.342.274.304.082.27358.062.87400.71439.623.53337.1026.539.801.4310.23
2l6.352.274.314.072.28358.193.24392.34433.263.58357.0519.5617.665.997.92
2m6.342.674.513.662.77423.265.04476.67526.302.15420.1033.4429.3110.56.70
3a6.362.844.603.523.01338.381.43334.00361.863.49295.0523.7830.2623.941.07
3b6.322.834.573.492.99282.121.00280.20297.812.16249.0115.5015.504.836.65
46.292.584.443.722.65306.367.62326.56348.952.79277.0541.0513.7330.758.40
5a6.982.874.924.102.96303.606.20332.41357.863.88297.0455.8740.7556.719.34
5b6.522.854.693.673.00255.226.20276.04292.662.55251.0039.7114.6935.3411.05
6a6.242.424.333.812.46425.3611.41444.11486.865.41368.1420.5413.5114.153.39
6b5.792.364.073.432.42465.109.75479.77525.565.04398.157.335.9421.925.73
6c6.572.444.504.132.46424.9311.02465.21508.894.41397.1334.5347.3720.153.58
6d6.552.504.534.052.53309.739.71342.18368.043.36294.093.793.7225.957.12
Table 4

Correlation coefficients (R2), adjusted correlation coefficients Radj2, and SDs of SLR between chosen descriptor and cell lines considering compounds 1a–2a and 2c–6d

Descriptors/SLR on cellsA459HepG2MCF-7WRL68
%R2%Radj2SD%R2%Radj2SD%R2%Radj2SD%R2%Radj2SD
IP37.5734.5910.5031.0527.7612.1231.6434.7411.8116.6412.673.58
EA6.331.8712.862.63−2.0014.436.8333.8311.620.26−4.993.92
χ23.4819.8311.6215.8411.8313.3948.9046.4710.456.151.683.80
η7.062.6312.819.665.3613.871.57−3.1114.518.904.563.75
S7.382.9712.799.405.0913.891.63−3.0514.519.615.303.73
ω4.920.412.961.98−2.6914.4532.9029.7011.980.02−4.743.92
α3.10−1.5213.081.38−3.3114.450.00−4.7614.6312.568.403.69
DM2.26−2.3913.143.05−1.5714.3713.249.1113.622.28−2.373.88
A0.97−3.7513.234.43−0.1214.270.10−4.6614.627.142.723.78
V0.95−3.7713.234.37−0.1814.270.16−4.5914.617.102.673.78
log P4.820.2812.970.81−3.9114.534.910.3914.629.074.743.74
M0.24−4.5113.276.071.5914.150.05−4.7114.625.551.053.81
Table 5

Correlation coefficients (R2), adjusted correlation coefficients Radj2, and SDs of SLR between chosen descriptor and cell lines considering compounds 2a and 2c–2m

Descriptors/SLR on cellsA459HepG2MCF-7WRL68
%R2%Radj2SD%R2%Radj2SD%R2%Radj2SD%R2%Radj2SD
IP58.9254.825.4231.9625.1510.6664.2160.648.244.22−5.352.63
EA36.1129.736.7723.6115.9711.3051.5246.679.603.68−5.952.63
χ51.7746.955.8831.4124.5510.7066.4163.057.994.60−4.942.62
η5.67−3.778.225.38−4.0912.5713.444.7812.821.08−8.812.67
S7.40−1.868.156.62−2.7112.4915.917.5012.641.72−8.112.66
ω34.2427.676.8622.2514.4811.4050.1345.159.733.84−5.772.63
α31.2424.367.0232.5325.7810.6234.3627.8011.160.04−9.962.68
DM32.9426.236.9326.4419.0811.0855.8551.439.163.63−6.012.64
A30.6523.727.0541.2035.329.9136.4230.0610.990.36−9.602.68
V30.0823.097.0839.4833.4210.0535.5529.1111.060.36−6.602.68
log P10.741.818.002.87−6.4812.7419.0610.9712.406.23−3.152.60
M30.5023.557.0636.9530.6410.2637.5131.2710.890.20−9.782.68
Table 6

Correlation coefficients (R2), adjusted correlation coefficients Radj2, and SDs of SLR between chosen descriptor and cell lines considering compounds 2a and 2c–2f

Descriptors/SLR on cellsA459HepG2MCF-7WRL68
%R2%Radj2SD%R2%Radj2SD%R2%Radj2SD%R2%Radj2SD
IP94.2292.221.9861.6348.842.5773.3864.516.666.02−25.313.68
EA32.049.397.0012.01−17.333.8970.4460.597.022.43−30.093.74
χ60.1946.925.2030.206.933.4685.1280.154.984.20−27.733.71
η0.10−33.208.241.44−31.414.1222.33−3.5611.380.06−33.253.79
S0.50−32.668.221.26−31.654.1225.290.3911.160.40−32.813.78
ω29.546.056.928.90−21.473.9667.1056.147.412.76−29.653.74
α1.19−31.758.1911.62−17.843.9016.60−11.2011.798.98−21.363.62
DM36.1614.876.5926.191.583.5682.2976.395.433.69−28.423.72
A0.19−33.088.245.59−25.884.034.92−26.7712.5916.09−11.883.47
V0.19−33.088.245.26−26.324.045.13−26.5012.5815.93−12.093.48
log P8.60−21.867.886.34−24.754.0167.7857.047.3332.8810.513.10
M2.05−30.618.1611.75−17.673.9015.26−12.9811.8813.39−15.473.53
Table 7

Correlation coefficients (R2), adjusted correlation coefficients Radj2, and SDs of SLR between chosen descriptor and cell lines considering compounds 2g–2m

Descriptors/SLR on cellsA459HepG2MCF-7WRL68
%R2%Radj2SD%R2%Radj2SD%R2%Radj2SD%R2%Radj2SD
IP16.790.156.421.93−17.698.5619.633.563.4910.66−7.212.26
EA28.5114.225.9540.6028.376.6648.5138.212.796.76−11.892.31
X31.2817.535.8441.6129.946.6044.5133.412.907.86−10.562.30
H25.6610.796.0739.2827.146.7352.2642.712.695.69−13.172.32
S25.4810.586.0839.5427.456.7252.5243.022.685.67−13.202.32
Ω27.93135.515.9840.7528.896.6549.3539.222.776.58−12.102.31
a31.7218.075.829.27−8.878.239.18−8.983.713.70−15.562.35
DM13.32−4.016.5639.8327.806.7065.6158.732.282.60−16.882.36
A33.5020.205.7413.57−3.728.0313.55−3.733.623.26−16.092.35
V32.2617.725.7911.81−5.838.1112.59−4.903.643.05−16.342.36
log P0.98−18.827.0013.62−3.668.0324.038.833.390.35−19.582.39
M24.629.556.1114.31−2.828.0018.892.673.513.91−15.312.35

For each cancer cell line, the impact of each descriptor on the cytotoxic activity of the tested derivatives was mainly reliant on the nature of the descriptor itself. For A459 cells, the descriptors AE, χ, ω, α, μ, V, log P, and M demonstrated no significant influence (R2 ≈ 0–3%), while modest correlations were obtained with η and S (R2 ≈ 25%). In the case of HepG2 cells, the best correlation was recorded for IP, with an R2 of 27%, and an SD of 0.34; the lowest correlation was observed with surface area (A), with an R2 of 0.22% and an SD of 0.5. The η and S demonstrated similar effects to those observed with the A459 cell line. However, for MCF-7 cells, the best correlations were obtained with the hardness η and S descriptors, with an R2 of 61 and 62%, respectively. In contrast to HepG2 cells, the IP descriptor demonstrated negligible influence on the cytotoxic activity of the tested derivatives against MCF-7 cells, with an R2 and an SD of 61% and 0.54, respectively. Thus, SLR demonstrated that the cytotoxicity moderately correlated with M of the tested derivatives, with an R2 of 22%. However, for A459 and HepG2 cells, SLR displayed extremely weak effects for M, with R2 less than 2%.

3.2.2 MLR analysis

As shown in the SLR analysis, the influence of each descriptor on the cytotoxicity of the synthesized compounds against the tested cells strongly depended on the basic skeleton of these compounds. In an attempt to improve the correlations between the cytotoxicity of each of these series and their descriptors, MLR analysis was performed for each series considering all tested cell lines.

3.2.2.1 Considering all compounds

Equations (1)–(4) show the reliable descriptors dependent on the observed cytotoxic activities against the tested cell lines for all compounds (Table 8). The correlations are relatively moderate for A459, HepG2, and MCF-7 cells, with correlation coefficients of 41, 64, and 58%, respectively. However, for WRL 68 cells, the correlation was relatively weak, with a correlation coefficient of 26%. For A459, HepG2, and WRL 68 cells, IP demonstrated the strongest contribution in equations (1), (2) and (4) with regression coefficients of 35.42, 44.69, and 6.37, respectively. This may indicate that these compounds provide an electron to the targeted enzyme of the tested cells. In addition to the strongest contribution of IP in these models (1–2 and 4), some other descriptors show moderate contribution hydrophobicity. For instance, hydrophobicity (log P) has a negative contribution in models 1 and 4 with regression coefficients of 2.37 and 0.009, while it has a positive contribution for model 2 (a regression coefficient of 1.69). However, for MCF-7 cells, the electronegativity reported the strongest contribution with a regression coefficient of 44.27, which may indicate that the compounds accept electrons from the targeted enzyme in MCF-7 cells (equation (3)). The moderate and weak correlations may be attributed to the basic skeleton of the synthesized compounds. Table 8 displays the predicted cytotoxic activities and residuals of the observed values. The best reproduction of the observed cytotoxicity values for A459, HepG2, MCF-7, and WRL 68 cells was obtained for compounds 2k, 4, 5b, and 2f, with residual values of 0.76, 0.02, 0.97, and 0.04, respectively (Table 8).

(1)lnPPred.=−189+35.42IP–2.37logPR2=41.34%;RAdj2=35.47%andSD=10.43
(2)lnPPred.=−371+44.69IP−3.37µ+3.77A−3.17V+1.69logPR2=63.71%;RAdj2=53.03%andSD=9.77
(3)lnPPred.=−176.42+44.27χ–0.02α+1.52µR2=57.62%;RAdj2=50.93%andSD=10.01
(4)lnPPred.=−27.88+6.37IP–0.64α–0.009logPR2=26.00%;RAdj2=14.31%andSD=3.54
Table 8

Predicted percentage inhibition and residuals obtained by using MLR equations (1)–(4) and considering all compounds

CPsA459 (equation (1))HepG2 (equation (2))MCF-7 (equation (3))WRL68 (equation (4))
(ln P)Pred.Resid.(ln P)Pred.Resid.(ln P)Pred.Resid.(ln P)Pred.Resid.
1a32.0910.5527.3118.6913.9012.858.802.35
1b34.5115.2120.9315.0713.6710.7710.090.39
2a38.270.9238.301.716.589.409.625.12
2c37.203.4134.693.3140.732.728.181.92
2d21.912.1229.215.795.1811.745.820.05
2e31.003.2736.100.19.273.347.775.17
2f38.053.0852.508.526.828.898.070.04
2g31.1614.6620.1910.1911.023.439.071.71
2h30.853.2530.782.7811.179.068.783.11
2i28.485.1217.614.6110.309.037.612.54
2j27.820.8616.920.0810.707.787.251.37
2k27.290.7625.5415.549.968.537.143.09
2l27.327.7613.174.8310.754.767.140.78
2m30.472.9727.301.720.6610.167.430.73
3a28.114.3326.303.721.412.537.476.40
3b29.7614.2623.847.8420.8115.988.541.89
427.3413.7114.020.0224.206.557.760.64
5a48.926.9544.983.9843.7512.9511.447.90
5b35.903.8116.541.5434.370.979.801.25
6a19.071.479.044.9622.398.244.681.29
6b4.003.335.634.637.5314.391.704.03
6c33.151.3833.1313.8729.499.347.433.85
6d35.1931.417.9613.9631.335.399.031.91
3.2.2.2 Considering compounds 2a and 2c–2m

Equations (5)–(8) and Table 9 represent the best correlations between the observed cytotoxic activities of the subclass of compounds (2a and 2c–2m) against the tested cell lines. The improved correlations were observed compared to the ones obtained considering all compounds (equations (5)–(8)). Indeed, for A459, HepG2, MCF-7, and WRL 68 cells, the obtained correlation coefficients were 77, 83, 98, and 79%. In accordance with correlations obtained considering all compounds, the observed activities against A459 and HepG2 cells were strongly dependent on IP with regression coefficients of 41.51 and 46.74, respectively (equations (5) and (6)). For MCF-7 cells (equation (7)), the observed activities were strongly related to the electronegativity and EA of the tested compounds with a positive contribution of the former (a regression coefficient of 78.60) and a negative contribution of the latter (a regression coefficient of 74.58). In this model (equation (7)), dipole moment and hydrophobicity show a moderate contribution with regression coefficients of 14.15 and 6.83, respectively. For WRL 68 cells (equation (8)), the observed activity depended on several descriptors with different contributions. The softness, electronegativity, electrophilicity, and hardness show strongest effects with regression coefficients of 13.544, 1.615, 1.214, and 473, respectively; while dipole moment and hydrophobicity display moderate contributions (equation (8)). Similarly, the SD and residuals were improved (equations (5)–(8) and Table 9). The improved correlations between the observed cytotoxic activities and the selected descriptors may be attributed to the fact that this subclass of compounds has the same basic skeleton and that these compounds only differ in the substituted groups.

(5)lnPPred.=−254.55+41.51IP+0.20A–1.2logP–0.16MR2=77.01%;RAdj2=63.87%andSD=4.85
(6)(lnP)Pred.=−315.47+46.74IP–0.63α+0.66AR2=83.28%;RAdj2=77.01%andSD=5.91
(7)(lnP)Pred.=−203.58–74.58EA+78.60χ–0.97α+14.15µ+0.73V+6.83logPR2=97.69%;RAdj2=94.92%andSD=2.96
(8)(lnP)Pred.=3775−1615.39χ+473.64η−13544S+1214ω+1.71α+29.66µ−0.59V−20logP–0.62MR2=79.21%;RAdj2=−14.35%andSD=2.74
Table 9

Predicted percentage inhibition and residuals obtained by using MLR equations (5)–(8) and considering compounds 2a and 2c–2m

CPsA459 (equation (5))HepG2 (equation (6))MCF-7 (equation (7))WRL68 (equation (8))
(ln P)Pred.Resid.(ln P)Pred.Resid.(ln P)Pred.Resid.(ln P)Pred.Resid.
2a37.671.5236.533.2325.110.874.790.30
2c40.260.35436.381.7737.770.246.310.05
2d26.302.27330.454.1615.501.426.820.95
2e32.931.34439.253.4213.060.4512.360.58
2f44.653.51646.592.8335.800.098.410.30
2g23.697.19118.227.776.960.6311.260.48
2h25.372.22919.488.123.841.734.780.89
2i25.538.0713.710.99−2.864.137.902.83
2j27.300.34217.740.976.393.487.431.19
2k27.230.69818.278.473.972.548.501.73
2l22.512.9512.884.784.661.337.770.15
2m29.993.45426.912.4010.830.336.340.36
3.2.2.3 Considering subclasses 2a and 2c–2f

By considering the subclasses 2a and 2c–2f of compounds, the correlations between the cytotoxic activities and the reliable descriptor were highly improved (equations (9)–(12) and Table 10). For instance, the correlation coefficient and SD obtained with A459 cells were 99.86% and 0.53, respectively. In agreement with the above results, IP demonstrated the strongest positive effect on the observed activity of the tested compounds against A459 cells with a correlation coefficient of 3.79; while hardness and hydrophobicity display strongest negative effects with correlation coefficients of 5.99 and 4.15, respectively. Similar behaviors were observed with HepG2, MCF-7, and WRL 68 cells. For HepG2, IP has the strongest effect with a regression coefficient of 13.34. For MCF-7, the electronegativity displays the strongest contribution with a moderate effect on electrophilicity contribution (equation (11)). Hardness (with a negative contribution of 8.70) shows the strongest effect on the activity of the tested compounds against WRL68 (equation (12)).

(9)(lnP)Pred.=−173+3.79IP–5.99η–4.15logPR2=99.86%;RAdj2=99.45%andSD=0.53
(10)(lnP)Pred.=−56.61+13.34IP+0.02AR2=70.78%;RAdj2=41.55%andSD=2.75
(11)(lnP)Pred.=−199.47+56.71χ–11.68ωR2=87.93%;RAdj2=75.87%andSD=5.49
(12)(lnP)Pred.=68.97–8.70η–10.02µ+1.71logPR2=99.93%;RAdj2=99.71%andSD=0.18
Table 10

Predicted percentage inhibition and residuals obtained by using MLR equations (9)–(12) and considering compounds 2a and 2c–2f

CPsA459 (equation (9))HepG2 (equation (10))MCF-7 (equation (11))WRL68 (equation (12))
(ln P)Pred.Resid.(ln P)Pred.Resid.(ln P)Pred.Resid.(ln P)Pred.Resid.
2a39.380.1938.301.4625.480.504.440.06
2c40.820.2140.982.8339.201.196.180.08
2d23.990.0434.410.2013.163.765.950.08
2e34.350.0836.700.8718.616.0012.890.05
2f40.690.4441.732.0432.782.938.220.11
3.2.2.4 Considering compounds 2g–2m

For the latest subclasses 2g–2m, the correlations were strongly reliant on the tested cells (equations (13)–(16) and Table 11). Indeed, the correlations were relatively moderate for A459 and HepG2 cells, with a correlation coefficient of 54% and SD values of 6.14 and 6.56, respectively. However, for MCF-7 and WRL 68 cells, the correlations were relatively good, with coefficient correlations of 96 and 99%, respectively. In equation (13) (A459 cells), IP has the strongest effect with a regression coefficient of 98.27. For HepG2 cells (equation (14)), the electronegativity showed the strongest effect. However, for WRL68, the softness displayed the strongest contribution with a regression coefficient of 58,834, while hydrophobicity displayed a moderate contribution with a correlation coefficient of 234.

(13)(lnP)Pred.=−632.4+98.27IP+0.35η–0.29MR2=54.31%;RAdj2=8.36%andSD=6.14
(14)(lnP)Pred.=−457+117.14χ−0.08VR2=53.92%;RAdj2=30.89% and SD=6.56
(15)lnPPred.=401.16+12.98IP–100.96A–0.51MR2=96.41%;RAdj2=92.83%andSD=0.95
(16)(lnP)Pred.=5,814−58,834S+3.69A–242.81logP+2.22MR2=98.8%;RAdj2=96.39%andSD=0.41
Table 11

Predicted percentage inhibition and residuals obtained by using MLR equations (13)–(16) and considering compounds 2g–2m

CPsA459 (equation (13))HepG2 (equation (14))MCF-7 (equation (15))WRL68 (equation (16))
(ln P)Pred.Resid.(ln P)Pred.Resid.(ln P)Pred.Resid.(ln P)Pred.Resid.
2g19.663.1618.037.586.960.6310.580.20
2h23.893.7118.698.913.121.016.030.36
2i26.257.3515.442.720.610.664.700.37
2j30.703.7414.642.132.290.638.770.15
2k30.233.7013.153.352.060.6310.180.05
2l21.752.1914.273.396.280.298.020.10
2m31.711.7430.080.7710.500.006.710.01

4 Conclusions

Quantum chemical calculations and statistical analyses allow a better understanding of the structure–cytotoxicity relation between cytotoxicity with electronic, steric, and hydrophobic descriptors of the synthesized pyridotriazolopyrimidines. The obtained results demonstrated that the cytotoxicity depends on the cell line type and the combined molecular descriptors. SLR analysis revealed that the correlation of each descriptor on the observed cytotoxicity is relatively weak to moderate by considering a whole series of compounds (37–49%), and its improved by considering subclasses of compounds with similar basic skeleton (85–95%). For these subclasses of compounds, the best correlations were obtained with IP and electronegativity, with correlation coefficients of 94.22 and 85.12%, respectively. In accordance with SLR analysis, MLR analysis reveals that correlations related to different models are relatively weak to moderate by considering a whole series of compounds (26–58%), and these correlations are strongly improved by considering subclasses of compounds with similar basic skeletons (63–99%). MLR models reveal that the influence and impact of different descriptors vary with the tested cell lines and subclasses of compounds. For instance, for 2a and 2c–2f series, IP has the strongest effect against HepG2, while for MCF-7, the electronegativity displays the strongest effect.

tel: +966 114 677 194

Acknowledgments

The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through the research group project no. RG-1435-068.

  1. Author contributions: Conceptualization: RA and HAA; software and data curation: EHA; writing—original draft preparation: RA, EHA, and HAA; revise-review and editing: NSA, HA, and MM; and funding acquisition: RA. All the authors have read and agreed to the published version of the manuscript.

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

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Received: 2020-03-06
Revised: 2020-05-22
Accepted: 2020-06-03
Published Online: 2020-06-30

© 2020 Hatem A. Abuelizz et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  41. Physicochemical evaluation of the fruit pulp of Opuntia spp growing in the Mediterranean area under hard climate conditions
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  43. New Schiff bases of 2-(quinolin-8-yloxy)acetohydrazide and their Cu(ii), and Zn(ii) metal complexes: their in vitro antimicrobial potentials and in silico physicochemical and pharmacokinetics properties
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  46. Chirality of β2-agonists. An overview of pharmacological activity, stereoselective analysis, and synthesis
  47. Fe3O4@urea/HITh-SO3H as an efficient and reusable catalyst for the solvent-free synthesis of 7-aryl-8H-benzo[h]indeno[1,2-b]quinoline-8-one and indeno[2′,1′:5,6]pyrido[2,3-d]pyrimidine derivatives
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  49. Enhancement of thermal properties of bio-based microcapsules intended for textile applications
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  65. Saliva proteomic analysis reveals possible biomarkers of renal cell carcinoma
  66. Withania frutescens: Chemical characterization, analgesic, anti-inflammatory, and healing activities
  67. Design, synthesis and pharmacological profile of (−)-verbenone hydrazones
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  69. Stability-indicating HPLC-DAD assay for simultaneous quantification of hydrocortisone 21 acetate, dexamethasone, and fluocinolone acetonide in cosmetics
  70. A novel lactose biosensor based on electrochemically synthesized 3,4-ethylenedioxythiophene/thiophene (EDOT/Th) copolymer
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  79. The combined use of GC, PDSC and FT-IR techniques to characterize fat extracted from commercial complete dry pet food for adult cats
  80. MALDI-TOF MS profiling in the discovery and identification of salivary proteomic patterns of temporomandibular joint disorders
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  87. Synthesis and efficacy of herbicidal ionic liquids with chlorsulfuron as the anion
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  89. Synthesis, spectral and thermo-kinetics explorations of Schiff-base derived metal complexes
  90. An improved reduction method for phase stability testing in the single-phase region
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  93. A turbidity sensor development based on NL-PI observers: Experimental application to the control of a Sinaloa’s River Spirulina maxima cultivation
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  99. Antimicrobial and antifungal activities of bifunctional cooper(ii) complexes with non-steroidal anti-inflammatory drugs, flufenamic, mefenamic and tolfenamic acids and 1,10-phenanthroline
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  102. Synthesis and physicochemical studies of a series of mixed-ligand transition metal complexes and their molecular docking investigations against Coronavirus main protease
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  104. A new phenyl alkyl ester and a new combretin triterpene derivative from Combretum fragrans F. Hoffm (Combretaceae) and antiproliferative activity
  105. Erratum
  106. Erratum to: A one-step incubation ELISA kit for rapid determination of dibutyl phthalate in water, beverage and liquor
  107. Review Articles
  108. Sinoporphyrin sodium, a novel sensitizer for photodynamic and sonodynamic therapy
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  110. Plant description, phytochemical constituents and bioactivities of Syzygium genus: A review
  111. Evaluation of elastomeric heat shielding materials as insulators for solid propellant rocket motors: A short review
  112. Special Issue on Applied Biochemistry and Biotechnology 2019
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  114. Study on online soft sensor method of total sugar content in chlorotetracycline fermentation tank
  115. Studies on the Anti-Gouty Arthritis and Anti-hyperuricemia Properties of Astilbin in Animal Models
  116. Effects of organic fertilizer on water use, photosynthetic characteristics, and fruit quality of pear jujube in northern Shaanxi
  117. Characteristics of the root exudate release system of typical plants in plateau lakeside wetland under phosphorus stress conditions
  118. Characterization of soil water by the means of hydrogen and oxygen isotope ratio at dry-wet season under different soil layers in the dry-hot valley of Jinsha River
  119. Composition and diurnal variation of floral scent emission in Rosa rugosa Thunb. and Tulipa gesneriana L.
  120. Preparation of a novel ginkgolide B niosomal composite drug
  121. The degradation, biodegradability and toxicity evaluation of sulfamethazine antibiotics by gamma radiation
  122. Special issue on Monitoring, Risk Assessment and Sustainable Management for the Exposure to Environmental Toxins
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  124. Source apportionment of soil contamination based on multivariate receptor and robust geostatistics in a typical rural–urban area, Wuhan city, middle China
  125. Special Issue on 13th JCC 2018
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  127. Preparation of magnetite-silica–cetyltrimethylammonium for phenol removal based on adsolubilization
  128. Topical Issue on Agriculture
  129. Size-dependent growth kinetics of struvite crystals in wastewater with calcium ions
  130. The effect of silica-calcite sedimentary rock contained in the chicken broiler diet on the overall quality of chicken muscles
  131. Physicochemical properties of selected herbicidal products containing nicosulfuron as an active ingredient
  132. Lycopene in tomatoes and tomato products
  133. Fluorescence in the assessment of the share of a key component in the mixing of feed
  134. Sulfur application alleviates chromium stress in maize and wheat
  135. Effectiveness of removal of sulphur compounds from the air after 3 years of biofiltration with a mixture of compost soil, peat, coconut fibre and oak bark
  136. Special Issue on the 4th Green Chemistry 2018
  137. Study and fire test of banana fibre reinforced composites with flame retardance properties
  138. Special Issue on the International conference CosCI 2018
  139. Disintegration, In vitro Dissolution, and Drug Release Kinetics Profiles of k-Carrageenan-based Nutraceutical Hard-shell Capsules Containing Salicylamide
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  141. Special Issue on the International Conf on Science, Applied Science, Teaching and Education 2019
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  145. Special Issue on the Joint Science Congress of Materials and Polymers (ISCMP 2019)
  146. Effect of natural boron mineral use on the essential oil ratio and components of Musk Sage (Salvia sclarea L.)
  147. A theoretical and experimental study of the adsorptive removal of hexavalent chromium ions using graphene oxide as an adsorbent
  148. A study on the bacterial adhesion of Streptococcus mutans in various dental ceramics: In vitro study
  149. Corrosion study of copper in aqueous sulfuric acid solution in the presence of (2E,5E)-2,5-dibenzylidenecyclopentanone and (2E,5E)-bis[(4-dimethylamino)benzylidene]cyclopentanone: Experimental and theoretical study
  150. Special Issue on Chemistry Today for Tomorrow 2019
  151. Diabetes mellitus type 2: Exploratory data analysis based on clinical reading
  152. Multivariate analysis for the classification of copper–lead and copper–zinc glasses
  153. Special Issue on Advances in Chemistry and Polymers
  154. The spatial and temporal distribution of cationic and anionic radicals in early embryo implantation
  155. Special Issue on 3rd IC3PE 2020
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  157. Special Issue on IC3PE 2018/2019 Conference
  158. Exergy analysis of conventional and hydrothermal liquefaction–esterification processes of microalgae for biodiesel production
  159. Advancing biodiesel production from microalgae Spirulina sp. by a simultaneous extraction–transesterification process using palm oil as a co-solvent of methanol
  160. Topical Issue on Applications of Mathematics in Chemistry
  161. Omega and the related counting polynomials of some chemical structures
  162. M-polynomial and topological indices of zigzag edge coronoid fused by starphene
https://doi.org/10.1515/chem-2020-0138
Keywords for this article
pyridotriazolopyrimidines; DFT; cytotoxicity; MLR; SLR
Creative Commons
BY 4.0

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  129. Size-dependent growth kinetics of struvite crystals in wastewater with calcium ions
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  133. Fluorescence in the assessment of the share of a key component in the mixing of feed
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  162. M-polynomial and topological indices of zigzag edge coronoid fused by starphene
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