In silico investigations of triazole -trimethoxyphenyl hybrids as anti-proliferative agents against adenocarcinomic human alveolar basal epithelial cells (A549): DFT, QSAR and Molecular Docking Approaches

doi:10.21203/rs.3.rs-3993742/v1

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In silico investigations of triazole -trimethoxyphenyl hybrids as anti-proliferative agents against adenocarcinomic human alveolar basal epithelial cells (A549): DFT, QSAR and Molecular Docking Approaches

https://doi.org/10.21203/rs.3.rs-3993742/v1

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Twenty-eight sets of synthesized triazole-trimethoxyphenyl hybrids (TPD) were considered as anti-proliferative drugs against human alveolar basal epithelial (A549) cancer cell lines using DFT, QSAR, ADMET profile and molecular docking methods. The considered compounds were used to develop a robust QSAR model, which was used to design new triazole-trimethoxyphenyl compounds that could serve as anti-proliferative drug candidate against human alveolar basal epithelial (A549) cancer. The parameters obtained from DFT calculations such as the HOMO, LUMO, Dipole moment, chemical hardness and softness favoured TPD-11 and TPD-25 over etopoxide as strong inhibitors against human alveolar basal epithelialcancer cell (A549), which agreed with the experimental data. The QSAR modeling and validation indicated the major influence of MATS4p, minHBint3, and ATSC7c descriptors on the reported anticancer activity of the drugs in the A549- MLR-GFA QSAR (R² = 0.8632, adjusted R² = 0.7951, Q²_Loo = 0.6023 and R² - Q²_Loo = 0.2609). By leveraging data from the model, four new triazole-trimethoxyphenyl hybrids were proposed (NTPD-3, NTPD-4, NTPD-6 and NTPD-9). The DFT and molecular docking analysis showed these four compounds could be good inhibitors against adenocarcinomic human alveolar basal epithelial cells (A549) than etopoxide. However, ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties revealed NTPD-6 showed excellent pharmacokinetic and toxicological profiles and might serve as a road map for new and more effective anticancer agents.

Triazole -Trimethoxyphenyl Hybrids

anti-cancer

DFT

QSAR

molecular docking

High morbidity and mortality from both communicable and non-communicable diseases still have a negative influence on humanity. Non-communicable diseases (NCDs) have a significant financial burden on population in addition to causing sickness and mortality (Budreviciute et al., 2020). NCDs include chronic lung disease, cancer, diabetes, heart disease, and stroke which account for over 70% of all fatalities globally; heart disease and cancer have been described as leading cause of death among NCDs (WHO, 2021). It had been estimated that over 10.3 million people died of cancer in 2020 with 2.26 million died of breast cancer new cases, lung cancer (2.21 million), colon and rectum cancer (1.93 million), prostate cancer (1.41 million), skin cancer (1.20 million) and stomach cancer (1.09 million); and with about 19.3 million new cases of cancer disease predicted (WHO, 2021; Ferlay et al., 2021). Muka et al., (2015) noted that NCDs place a heavy financial strain on healthcare budgets and thus on national welfare which may only get worse over time, if drastic measures are not taken. Therefore, cancer compared to the other NCDs call for special attention and consideration (Ullrich and Miller, 2014).

Recent developments in anticancer medications have considerably improved quality of life of cancer patients and have increase the rate of cancer survival. However, a favorable initial response to treatment changes afterwards, thereby leading to cancer relapse and recurrence (Nikolaou et al., 2018). Consequently, there is continuous research for new substances that can effectively act against this disease with little or no side effect. In drug development, researchers focus on the organic and biochemical reactions of drug substances with their targets as well as the synthesis and analysis of drug substances (Ozkan, 2018). Therefore, there has been an increasing interest by researchers all over the world in exploring all available options; evaluating potential anticancer agents with the hope of discovering effective drugs for cancer. Also, several ongoing efforts are being made by the medical community aimed at identifying novel anticancer targets (Palve et al., 2021).

Among a class of nitrogen-containing heterocyclic compounds with interesting structures for medicinal properties is triazole derivatives due to their important chemical and biological properties that are stable against metabolic degradations and show target selectivity as well as several pharmacological activities (Fu et al., 2018; Lang et al., 2020; Kerru et al. 2020; Al-Bayati et al. 2021; Zhao et al., 2023). Triazole functions well as a linker to connect different pharmacophores, thus, hybrid substances containing triazole have demonstrated the ability to decrease tumor growth, invasion, and metastasis by altering various signaling pathways. This is made possible by its capacity to form a variety of non-covalent interactions with various biological targets, including hydrogen bonds, van der Waals forces, hydrophobic interactions, and dipole-dipole bonds. This is what gives the compound its wide range of therapeutic properties, including its antibacterial (Grytsai et al. 2020), antimalarial (Chu et al. 2019), antifungal (Peyton et al. 2015), antiviral (El-Sebaey 2020), antitubercular (Shaikh et al. 2019), and anticancer activities (Lakkakula et al. 2019; Krishna et al. 2020)

However, it has been observed that the high prevalence of resistance makes employing single chemicals as anticancer medicines in targeting protein or enzyme ineffective in treating cancer cells (Ahmad et al., 2018). Therefore, several hybrids of triazole compounds have been synthesized and evaluated for their anticancer activities, such as triazole-benzimidazole-chalcone (Djemoui et al., 2020), triazole-quinazoline (Banerji et la., 2018), Indole-Thiazolidinedione-Triazole (Perike et al., 2022), Coumarin-triazole (Mandalapu et la., 2016), Quinolone-Triazole (Xu et al., 2018), triazolo[4,5-d]pyrimidine (Li et al., 2016, Oyebamiji and Semire, 2020), Triazole-Pyrimidine-Urea (Ma et al., 2015; Oyebamiji et al., 2020).

According to reports (Lu et al., 2012; Negi et al., 2015), trimethoxyphenyl compounds have also been shown to operate as a pharmacophoric group that enhances the anticancer activities of various natural anticancer drugs, including colchicine, combretastatin, podophyllotoxin, and poly-methoxychalcone. The trimethoxyphenyl-based compounds might be a viable way to get around some of the issues with the current anticancer medications that have been described. 5-amino-1,2,4-triazoles based on trimethoxyphenyl have been identified as promising antimitotic possibilities against CEM and Hela cells (Romagnoli et al., 2014). Also, a series of 3-(benzylthio)-4H-1,2,4-triazoles bearing trimethoxyphenyl scaffold were synthesized and tested against different cancer cell lines; the results revealed that 4-[3-[(4-methoxyphenyl)methylsulfanyl]-5-(3,4,5-trimethoxyphenyl)-1,2,4-triazol-4-yl]-N,N-dimethylaniline was a promising against colon cancer (HCT116 cell line) (Romagnoli et al., 2014). Furthermore, triazolothiadiazine, 4-amino-5-aryl-4H-1,2,4-triazoles and triazolothiadiazine with trimethoxy substituent were evaluated against cancer cells and the results were promising (Banimustafa et al., 2013). Recently, triazoles-trimethoxyphenyl hybrids were synthesized and evaluated using both experimental and in silico methods against cancer cells (A549, MCF7, and SKOV3) with etoposide as standard drug. The results revealed that most of the compounds inhibitory activities were comparable to that of the standard with some compounds having outstanding inhibitory properties (Ansari et al., 2019).

Therefore, in this work, in silico methods would be used to further evaluate triazole - trimethoxyphenyl hybrids reported by Ansari et al., (2019) as displayed in Fig. 1 and Table 1. Although molecular docking of these compounds has been carried out, this study would lay emphasis on the development of robust QSAR using multiple linear regression-Genetic Algorithm (MLR-GA) to propose new set of triazole-trimethoxyphenyl hybrids with enhanced anticancer activity against adenocarcinomic human alveolar basal epithelial cells (A549). Thus, the aim of this work is to develop robust QSAR model, designed novel triazole-trimethoxyphenyl hybrids, predict their inhibitory properties, molecular docking analysis and ADMET profiling of the novel triazole - trimethoxyphenyl derivatives.

Fig. 1

Table 1

Structures, IUPAC names and observed IC₅₀ of the triazole -trimethoxyphenyl hybrids used in training and test sets against adenocarcinomic human alveolar basal epithelial cells (A549).
Molecule	R	IUPAC Name	IC₅₀ µM (A549)
TPD 1	H	2-[(4-amino-5-phenyl-1,2,4-triazolidin-3-yl)sulfanyl]-1-(3,4,5trimethoxyphenyl)ethanol	3.95
TPD 2	4-CH₃	2-[[4-amino-5-(4-methylphenyl)-1,2,4-triazol-3-yl]sulfanyl]-1-(3,4,5trimethoxyphenyl) ethanone	4.39
TPD 3	4-OH	2-[[4-amino-5-(4-hydroxyphenyl)-1,2,4-triazol-3-yl]sulfanyl]-1-(3,4,5-trimethoxyphenyl)eth2-[[4-amino-5-(4-chlorophenyl)-1,2,4-triazol-3-yl]sulfanyl]-1-(3,4,5-trimethoxyphenyl)ethanoneanone	4.20
TPD 4	3-Br	2-[[4-amino-5-(3-bromophenyl)-3,5-dihydro-1,2,4-triazol-3-yl]sulfanyl]-1-(3,4,5-trimethoxyphenyl)ethanol, 2-[[4-amino-3-(3-bromophenyl)-3,5-dihydro-1,2,4-triazol-5-yl]sulfanyl]-1-(3,4,5-trimethoxyphenyl)ethanol	3.67
TPD 5	4-OCH₃	2-[[4-amino-5-(4-methoxyphenyl)-1,2,4-triazol-3-yl]sulfanyl]-1-(3,4,5-trimethoxyphenyl)ethanone	2.58
TPD 6	4-Cl	2-[[4-amino-5-(4-chlorophenyl)-1,2,4-triazol-3-yl]sulfanyl]-1-(3,4,5-trimethoxyphenyl) ethanone	2.81
TPD 7	2,4-Cl₂	2-[[4-amino-5-(2,4-dichlorophenyl)-1,2,4-triazol-3-yl]sulfanyl]-1-(3,4,5-trimethoxyphenyl)ethanone	3.79
TPD 8	H	3-phenyl-6-(3,4,5-trimethoxyphenyl)-7H-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazine	3.71
TPD 9	4-CH₃	3-(4-methylphenyl)-6-(3,4,5-trimethoxyphenyl)-7H-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazine	4.57
TPD 10	4-OH	4-[6-(3,4,5-trimethoxyphenyl)-7H-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazin-3-yl]phenol	4.17
TPD 11	3-Br	3-(3-bromophenyl)-6-(3,4,5-trimethoxyphenyl)-7H-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazine	0.67
TPD 12	4-OCH₃	3-(4-methoxyphenyl)-6-(3,4,5-trimethoxyphenyl)-7H-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazine	3.20
TPD 13	4-Cl	3-(4-chlorophenyl)-6-(3,4,5-trimethoxyphenyl)-7H-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazine	3.94
TPD 14	2,4-Cl₂	3-(2,4-dichlorophenyl)-6-(3,4,5-trimethoxyphenyl)-7H-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazine	2.73
TPD 15	H	6-phenyl-3-(3,4,5-trimethoxyphenyl)-7H-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazine	1.04
TPD 16	4-OCH₃	6-(4-methoxyphenyl)-3-(3,4,5-trimethoxyphenyl)-7H-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazine	3.60
TPD 17	3,4-(CH₃)₂	6-phenyl-3-(3,4,5-trimethoxyphenyl)-7H-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazine	2.50
TPD 18	3,4,5-(CH₃)₃	3,6-bis(3,4,5-trimethoxyphenyl)-7H-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazine	4.00
TPD 19	4-F	6-(4-fluorophenyl)-3-(3,4,5-trimethoxyphenyl)-7H-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazine	2.80
TPD 20	2,4-F₂	6-(2,4-difluorophenyl)-3-(3,4,5-trimethoxyphenyl)-7H-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazine	2.15
TPD 21	4-Cl	6-(4-chlorophenyl)-3-(3,4,5-trimethoxyphenyl)-7H-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazine	4.97
TPD 22	4-Br	6-(4-bromophenyl)-3-(3,4,5-trimethoxyphenyl)-7H-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazine	3.15
TPD 23	4-OH	4-[3-(3,4,5-trimethoxyphenyl)-7H-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazin-6-yl]phenol	5.01
TPD 24	3,4-(OH)₂	4-[3-(3,4,5-trimethoxyphenyl)-7H-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazin-6-yl]phenol	3.23
TPD 25	4-CH₃	6-(4-methylphenyl)-3-(3,4,5-trimethoxyphenyl)-7H-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazine	0.60
TPD 26	4-Ph	6-(4-phenylphenyl)-3-(3,4,5-trimethoxyphenyl)-7H-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazine	4.01
Standard		Etoposide	2.99

Table 1

A data set of twenty-six triazole-trimethoxyphenyl hybrids and their anti-cancer activities were obtained from the work of Ansari et al (2019) as shown in Fig. 1 and Table 1. The biological activity of the ligands was expressed as half-maximum inhibitory concentration (IC₅₀). The IC₅₀ values were subjected to data transformation by taking the negative logarithm to the base of 10 according to the formula:

${pIC}_{50}=-\text{l}\text{o}\text{g}({IC}_{50}\times {10}^{-6})$ 1

These compounds were optimised at DFT-B3LYP/6-31G(d,p) methods for molecular geometries using Spartan 14 software as described by (Semire and Odunola, 2019; Abdullahi et al., 2020). The frequency calculations were carried out on the optimised structure to ascertain minima equilibrium characterized by positive harmonic frequencies (Afolabi et al., 2022; Ibrahim et al., 2023). The molecular parameters estimated from the ground state optimized structures were the frontier orbital energies (the HOMO, LUMO and energy gap), chemical hardness (η = ${ (E}_{LUMO}-{E}_{HOMO })$/2), chemical potential (µ = ${ (E}_{LUMO}+{E}_{HOMO })$/2), electrophilicity index (ω =$\raisebox{1ex}{${ \mu }^{2}$}\!\left/ \!\raisebox{-1ex}{$2\eta $}\right.$) and dipole moment (Eswaramoorthy et al., 2021; Khaldan et al., 2022).

2.1 QSAR modelling

The optimized structures were converted to *.sdf format for generation of molecular descriptors using Padel-Descriptor software version 2.21 (Rivera-Delgado et al., 2019). Over 1,200 descriptors generated from 1D, 2D and 3D parameters were combined with electronic descriptors from quantum chemical calculations. These descriptors were pre-treated for carefully selected of relevant parameters to avoid multicollinearity using Data Pre-Treatment GUI 1.2" tool, which utilizes the V-WSP algorithm to eliminate these descriptors and ensure the reliability and generalization of the subsequent QSAR modelling. These data were split into training set (70%) for the QSAR model development and test set (30%) for validation of the model using Dataset Division GUI tool version 1.2 in a random manner. The QSAR model was constructed using the compounds from the training set. The independent variables (descriptors) were correlated to the dependent variable (pIC50 of adenocarcinomic human alveolar basal epithelial cells (A549) through multivariate analysis using the Genetic Function Approximation (GFA) technique in the Material Studio software (Mahmud et al., 2020). The fitness of the model was assessed during the evolution process using the Friedman lack-of-fit (LOF) measure. In Material Studio, the LOF value is calculated using Eq. 2. The developed QSAR models were validated to test the internal stability and predictive ability of the models. The procedure employed in model validation is internal and external validation (Islam et al., 2014; Oyebamiji et al., 2016; Abdullah et al., 2020).

$LOF=\frac{SSE}{{\left(1-\frac{c+dp}{M}\right)}^{2}}$ 2

where SSE is the sum of squares of errors, c is the number of terms in the model, other than the constant term, d is a user-defined smoothing parameter, p is the total number of descriptors contained in all model terms (again ignoring the constant term) and M is the number of samples in the training set.

(ii) R-squared (R²) is determined by Eq. 3

${R}^{2}=\frac{{\left(\sum \left\{⌊Y-{\stackrel{-}{Y}}_{tr}⌋\left[{Y}_{pred}-{\stackrel{-}{Y}}_{pred}\right]\right\}\right)}^{2}}{\sum {\left(Y-{\stackrel{-}{Y}}_{tr}\right)}^{2}\sum {\left({Y}_{pred}-{\stackrel{-}{Y}}_{pred}\right)}^{2}}$ 3

(iii) Cross validated R squared (Q²cv) is estimated using Eq. 4

${Q}_{cv}^{2}=1-\frac{{\sum \left(Y-{Y}_{pred}\right)}^{2}}{{\sum \left(Y-{\stackrel{-}{Y}}_{tr}\right)}^{2}}$ 4

${Y}_{pred}$ and Y are predicted and observed training set concentration (experimental), respectively. ${\stackrel{-}{Y}}_{tr}$ and ${\stackrel{-}{Y}}_{pred}$ are the average observed (experimental) and predicted training set response, respectively.

(iii) Coefficient of determination adjusted (R² adjusted) is determined using Eq. 5

${R}_{adj}^{2}=\frac{{R}^{2}-p(n-1)}{n-1-p}$ 5

where p = number of the descriptor in the model, n = number of compounds in the training set, R² is the correlation coefficient, and n -1- p is the degree of freedom.

2.2 Docking of the Protein-Ligand

The X-ray structure of the A549 protein target (PDB ID: 7MLR) was retrieved from protein data bank (ww.rcsb.org). It was purified and repaired using discovery Studio Visualizer version 16.1.0.15350 and Avogadro. The protein and optimized structures of the ligands were saved in PDB format and molecular docking was performed using PyRx software; the docking results were analysed with discovery Studio Visualizer version (Vázquez-Jiménez et al., 2022; Ibrahim et al., 2023).

2.3 In-silico ADMET/Pharmacokinetic Predictions

The pKCSM online tool software was utilized to predict the pharmacokinetic properties (htpp://biosig.unimelb.edu.au/pkCSM/prediction), specifically the ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity), of the compounds (Purwanto et al., 2021).

3.1 Molecular descriptors and Docking analysis

The quantum chemical descriptors of triazole-trimethoxyphenyl hybrids obtained from B3LYP/6-31G(d,p) method are provided in Table 2. The HOMO energies for the triazole-trimethoxyphenyl hybrids ranged from − 5.40 eV (TPD 25) to -8.69 eV (TPD 13), suggesting that TPD 11 and TPD 25 have higher propensity to donate electrons to the protein receptor, particularly to the electron acceptor hydrogen and carbon atoms within the receptor (Oyebamiji et al., 2021). The LUMO and chemical potential energies of TPD 11 (-1.98 and − 3.75 eV) and TPD 25 (-1.98 and − 3.69 eV), respectively, are lower than that of Etoposide (-0.10 and − 2.72 eV), indacting that TPD 11 and TPD 25 can readily receive electrons from the surrounding (protein) than Etoposide (Nehra et al., 2021; Pal and Chattaraj, 2022). Also, dipole moment (7.44 and 4.96 Debye), HOMO-LUMO energy gap (ΔEg, 3.53 and 3.42 eV), chemical hardness (1.77 and 1.71 eV) and softness (0.565 and 0.585 eV^− 1) favour interactions of TPD 11 and TPD 25, respectively, with protein than Etoposide (Miar et al., 2020; Haribabu, 2022; El-Ghamry et al., 2022; Ameji et al., 2023). This was in agreement with IC₅₀ of 0.69 and 0.60 of TPD 11 and TPD 25, respectively compared to 2.99 µM for Etoposide, as observed against adenocarcinomic human alveolar basal epithelial cells, A549 (Ansari et al., 2019) as presented in Table 1. Thus, TPD 11 and TPD 25 are expected to engage in hydrogen bonding and other hydrophobic interactions with a receptor more than Etoposide which may lead to enhance binding properties with the receptor (Meanwell and Lolli, 2021).

Table 2

Molecular parameters calculated from DFT-B3LYP/6-31G(d,p) for the compounds
CPD	H (eV)	L (eV)	ΔEg	Dipole moment (Debye)	µ (eV)	η (eV)	S = 1/η	ω (eV)
TPD-1	-5.93	-2.02	3.91	6.61	-3.98	1.96	0.510	4.04
TPD-2	-5.65	-2.11	3.54	6.17	-3.88	1.77	0.565	4.25
TPD-3	-5.54	-1.99	3.55	5.79	-3.77	1.78	0.562	3.99
TPD-4	-5.46	-1.98	3.48	5.88	-3.72	1.74	0.575	3.98
TPD-5	-6.26	1.90	8.16	6.38	-2.18	4.08	0.245	0.58
TPD-6	-6.16	-2.08	4.08	7.02	-4.12	2.04	0.490	4.16
TPD-7	-6.46	-1.98	4.48	6.17	-4.22	2.24	0.446	3.98
TPD-8	-5.89	-2.07	3.82	8.43	-3.98	1.91	0.524	4.15
TPD-9	-5.76	-2.04	3.72	8.49	-3.90	1.86	0.538	4.09
TPD-10	-5.55	-2.02	3.53	6.93	-3.79	1.77	0.565	4.06
TPD-11	-5.51	-1.98	3.53	7.44	-3.75	1.77	0.565	3.97
TPD-12	-5.47	-1.86	3.61	7.21	-3.67	1.81	0.553	3.72
TPD-13	-8.69	1.56	10.25	10.65	-3.57	5.13	0.195	1.24
TPD-14	-5.66	-2.23	3.43	4.35	-3.95	1.72	0.581	4.54
TPD-15	-8.18	1.72	9.90	5.39	-3.23	4.95	0.202	1.05
TPD-16	-5.54	-1.91	3.63	6.29	-3.73	1.82	0.550	3.82
TPD-17	-5.64	-1.97	3.67	4.93	-3.81	1.84	0.544	3.94
TPD-18	-5.65	-2.01	3.64	5.15	-3.83	1.82	0.550	4.03
TPD-19	-5.67	-2.14	3.53	3.85	-3.91	1.77	0.565	4.32
TPD-20	-5.67	-2.17	3.50	4.13	-3.92	1.75	0.571	4.39
TPD-21	-5.70	-2.99	2.71	3.31	-4.35	1.36	0.735	6.97
TPD-22	-5.72	-2.30	3.42	3.28	-4.01	1.71	0.585	4.70
TPD-23	-5.63	-1.93	3.70	5.68	-3.78	1.85	0.540	3.86
TPD-24	-5.56	-1.92	3.64	6.92	-3.74	1.82	0.550	3.84
TPD-25	-5.40	-1.98	3.42	4.96	-3.69	1.71	0.585	3.98
TPD-26	-5.61	-2.16	3.45	5.76	-3.89	1.73	0.578	4.37
Etoposide	-5.34	-0.10	5.24	3.53	-2.72	2.62	0.382	1.41

Table 2

The analysis of docking results showed that the banding affinities ranged from − 6.8 kcal/mol (TPD-7) to -8.6 kcal/mol (TPD-25). The triazole-trimethoxyphenyl hybrids with higher binding affinities against adenocarcinomic human alveolar basal epithelial cells, A549 (ID: 7MLR) are TPD-10 and TPD-14 ( -8.0 kcal/mol), TPD-19 (-8.4 kcal/mol), TPD-20, TPD-21 and TPD-26 (-8.5 kcal/mol) and TPD-25 (-8.6 kcal/mol). TPD-10 and TPD-14 are hydrogen bonded with ILE 146; TPD-19 is hydrogen bonded with GLN 85 and PRO 86; TPP 20 is hydrogen bonded with GLN 85, TYR 97, ASN 140 and ILE 146; TPD-21 is hydrogen bonded with GLN 85; TPD-26 is hydrogen bonded with ASP 144 and ASN 140; while TPD-25 is hydrogen bonded with ASN 140, ILE 146 and CYS 137. The binding affinity of Etoposide is -8.8 kcal/mol having hydrogen binding with ASN 140 and PRO 86 (Table 3). The TPD-25 with highest inhibitory activity against adenocarcinomic human alveolar basal epithelial cells, A549 (Table 1), also presented highest binding affinity among the triazole-trimethoxyphenyl hybrids, thus it is used as QSAR reference compound for modelling and prediction of IC₅₀ for new triazole-trimethoxyphenyl hybrids in this work.

Table 3

Binding affinities (kcal/mol) of the 26 triazole-trimethoxyphenyl hybrids with **A549 (ID: 7MLR)**
	RECEPTOR A549
CPD	Binding affinity ∆G (Kcalmol^− 1)	Inhibition constant K_i (µM)	H-BOND with ligands	H bond distance(Å)
TPD-1	-7.7	2.25	GLN 85	2.41
TPD-1	-7.7	2.25	PRO 86	3.77
TPD-2	-7.4	3.74	PRO 86 GLN 85	3.37 2.08
			ASP 88	1.79
			PRO 82	2.15
			GLN 85	3.54
TPD-3	-7.6	2.67	GLN 85	2.35
			ASN 140	2.23
			MET 105	2.39
TPD-4	-7.7	2.25	GLN5 8	2.35
TPD-5	-7.3	4.43	ASN 140	2.32
			GLN 85	2.82
			MET 105	3.70
			PRO 86	3.36
TPD-6	-7.0	7.35	ASN140	2.65
TPD-7	-6.8	10.30	PRO 82	3.73
TPD-8	-7.9	1.61	ILE 146	2.67
TPD-9	-7.9	1.61	ILE 146	2.62
TPD-10	-8.0	1.36	ILE 146	2.60
TPD-11	-7.5	3.16	Nil	Nil
TPD-12	-7.8	1.90	ILE 146	2.78
TPD-13	-7.8	1.90	ILE 146	2.84
TPD-14	-8.0	1.36	ILE 146	2.60
TPD-15	-7.9	1.61	GLN 85	2.08
TPD-15	-7.9	1.61	PR0 86	3.37
TPD-16	-7.6	2.67	ILE 146	2.51
			MET 105	3.19
			MET 132	3.54
TPD-17	-7.8	1.90	GLN 85	2.23
			TYR 97	2.42
			MET 105	3.57
			PRO 86	3.23
			ASN 135	3.79
TPD-18	-6.8	10.30	GLN 85	2.36
			ASN 140	2.56
			PRO 86	3.35
TPD-19	-8.4	0.69	GLN 85	2.10
TPD-19	-8.4	0.69	PRO 86	3.52
TPD-20	-8.5	0.58	GLN 85	2.78
			TYR 97	2.15
			ASN 140	2.48
			ILE 146	3.71
TPD-21	-8.5	0.58	GLN 85	2.14
TPD-22	-7.5	3.16	GLN 85	2.45
TPD-23	-7.5	3.16	MET 132	2.50
TPD-23	-7.5	3.16	ILE 146	2.96
TPD-24	-7.5	3.16	ASN 140	2.45
			ILE 146	2.17
			CYS 136	2.72
TPD-25	-8.6	0.49	ASN 140	2.45
			ILE 146	2.72
			CYS 137	3.36
TPD-26	-8.5	0.58	ASP 144	3.51
TPD-26	-8.5	0.58	ASN 140	3.64
Etoposide	-8.8	0.35	ASN 140	2.99
Etoposide	-8.8	0.35	PRO 86	3.77

Table 3

3.2 QSAR model using Multilinear Regression Genetic Algorithm (MLR-GFA).

The QSAR model developed via MLR-GFA, as shown in Eq. 6, of inhibitory activity (pIC₅₀ = -log (IC₅₀)) of triazole-trimethoxyphenyl hybrids against adenocarcinomic human alveolar basal epithelial cells (A549) contains four independent 2D-molecular descriptors. These descriptors symbols and definitions are displayed in Table 4.

QSAR model displayed a negative contribution form MATS4p and naasC descriptors, while minHBint3 and ATSC7c descriptors contributed positively. Thus, increase in MATS4p and naasC values will negatively influence the predicted value of pIC₅₀, meanwhile, minHBint3 and ATSC7c descriptors will have positive impact on the model. Figure 2 shows the percentage contributions of each descriptor to the predicted pIC₅₀. The Pearson correlation analysis of the descriptor and pIC₅₀ values of the model shows nonexistence of inter-correlation among the descriptors as displayed in Table 5.

Table 4

Symbols definitions and descriptors used in the MLR-GFA equation.
S/N	Descriptor	Definition	Class	Contribution
1	MATS4p	Moran autocorrelation - lag 4 / weighted by polarizabilities.	2D	Negative
2	ATSC7c	Centered Broto-Moreau autocorrelation - lag 7 / weighted by charges.	2D	Positive
3	minHBint3	Minimum E-State descriptors of strength for potential Hydrogen Bonds of path length 3	2D	Positive
4	naasC	Count of atom-type E-State: C:	2D	Negative

pIC₅₀ = 0.3773–6.4175*MATS4p + 0.3043* minHBint3 + 0.5099* ATSC7c

− 0.1974* naasC 6

Table 5

Table of correlation matrix for the descriptors.
	pic50	MATS4p	ATSC7c	minHBint3	naasC
pic50	1
MATS4p	0.074446	1
ATSC7c	-0.00223	0.414277	1
minHBint3	0.663251	0.595809	0.184444	1
naasC	0.075153	-0.29125	0.07373	0.282719	1

Table 4

Figure 2

Table 5

The model is statistically validated with R-squared (R²), adjusted R², Cross validated R squared (Q²) are 0.8632, 0.7951 and 0.6023, respectively. The model general robustness is also examined using the differences in R² and Q²cv which is < 0.3 and Friedman LOF is 0.001 (Table 6). The inter-correlated descriptor characteristics of model are evaluated based on their multicollinearity estimated using the variation inflation factor (VIF) as shown in Eq. 7.

$VIF={\left(1-{R}^{2}\right)}^{-1}$ 7

In this case, R² is the correlation coefficient, and VIF value approaching a unity, is an indication of no inter-correlation among each descriptor in the model. The VIF scores between 1 and 5 means the model is within acceptability and stability arena; nevertheless, if the VIF score > 10, then the model is unstable and unacceptable (Driouche and Messadi 2019). The VIF scores in Fig. 3 shows that the model is stable and acceptable, thus the model can be used to predict bioactivity (pIC₅₀) of similar triazole-trimethoxyphenyl hybrids against adenocarcinomic human alveolar basal epithelial cells (A549).

Table 6

QSAR validation parameters.
S/N	Parameter	Threshold value	Computed value	Statement
1	R-squared (R²)	> 0.6	0.8632	Good
2	Adjusted R² (R²adj)	> 0.6	0.7951	Good
4	Cross validated R squared (Q²)	> 0.5	0.6023	Good
5	Friedman LOF	< 0.1	0.0010	Good
6	R² - Q² _LOO	< 0.3	0.2609	Good

Table 6

Figure 3

The experimental IC₅₀ (pIC₅₀) triazole-trimethoxyphenyl hybrids against adenocarcinomic human alveolar basal epithelial cells (A549) reported by Ansari et al., (2019) and the predicted pIC₅₀ and the residual values (difference between the experimental pIC₅₀ and predicted pIC₅₀) are displayed in Table 7. The lower residual score implies that the model has good predictive potential. Figures 4 and 5 display the observed versus predicted activity and residual plots, demonstrating the accuracy of the model with R²_Pred of 0.8632, which is greater than 0.6 threshold value.

Table 7

The experimental and predicted ${pIC}_{50}$ of the *ligands.*
Ligand	Experimental	Predicted
Ligand	pIC₅₀ (µM)	pIC₅₀ (µM)	Residual
TPD 1*	-0.60	-0.35	-0.25
TPD 2	-0.64	-0.57	-0.07
TPD 3	-0.62	-0.57	-0.06
TPD 4	-0.57	-0.45	-0.11
TPD 5*	-0.41	-0.32	-0.09
TPD 6	-0.45	0.09	0.25
TPD 7*	-0.58	-0.27	-0.31
TPD 8	-0.57	-0.52	-0.05
TPD 9*	-0.66	-0.72	0.06
TPD 10	-0.62	-0.75	0.13
TPD 11*	0.17	-0.88	1.05
TPD 12	-0.51	-0.43	-0.08
TPD 13*	-0.60	-0.75	0.31
TPD 14	-0.44	-0.53	-0.17
TPD 15*	-0.02	-0.28	-0.28
TPD 16	-0.56	-0.44	0.04
TPD 17	-0.40	-0.62	0.02
TPD 18	-0.60	-0.54	0.09
TPD 19*	-0.45	-0.25	-0.08
TPD 20*	-0.33	-0.25	-0.45
TPD 21	-0.70	-0.23	-0.27
TPD 22	-0.50	-0.56	-0.14
TPD 23	-0.70	0.67	-0.16
TPD 24	-0.51	0.06	0.16
TPD 25	0.22	-0.58	0.10
Note test set for the cancer cell line.*

Table 7

Figure 4

Figure 5

3.3 Design of new triazole-trimethoxyphenyl hybrids and their computed pIC₅₀ values

New twenty-eight molecules with their computed pIC50/IC₅₀ values were predicted, from which the best ten were selected in all out of which eight were from TPD-25 (NTPD-1 to NTPD-8) and other two from TPD-11 (NTPD-9 and NTPD-10) as shown in Fig. 6. The compounds TPD-11 and TPD-25 were selected as lead molecules based on experimental bioactivity against adenocarcinomic human alveolar basal epithelial cells (Table 1). The new molecules with outstanding IC₅₀ than Etoposide (2.99 µM) are NTPD-3 (0.594 µM), NTPD-4 (0.813 µM), NTPD-6 (1,514 µM), NTPD-8 (2,743 µM) and NTPD-9 (1.090 µM).

Figure 6

The frontier energies and reactivity descriptors of the four compounds, NTPD-3, NTPD-4, NTPD-6 and NTPD-9 (out of ten new compounds) with outstanding predicted bioactivity against adenocarcinomic human alveolar basal epithelial cells (A549) are listed in Table 8 with orbital overlay in Fig. 7. The LUMO and chemical potential (µ) energies of these compounds showed that they can readily receive electrons from the surrounding (protein) than Etoposide (Pal and Chattaraj, 2022). Also, ΔEg, chemical hardness (η), and softness favour interactions of valence electrons of these compounds with adenocarcinomic human alveolar basal epithelial cells (A549). However, the HOMO supports the ease of electron donating ability of Etoposide with the protein than these new compounds (Haribabu, 2022); thus, Etoposide is expected to donate electrons to the protein readily than these NTPD compounds.

The molecular docking analysis of predicted NTPD compounds showed that binding affinity for NTPD-3, NTPD-4, NTPD-6 and NTPD-9 are − 7.9, -7.5, -8.0 and − 7.8 kcalmol^− 1, respectively, as compared to -8.8 kcalmol^− 1 for Etoposide (Fig. 8).

Table 8

Figure 7

Figure 8

Table 8

Chemical descriptors calculated for new predicted triazole -trimethoxyphenyl hybrids
CPD	H (eV)	L (eV)	ΔEg	Dipole moment	µ (eV)	η (eV)	Softness	ω (eV)
NTPD-3	-5.59	-2.00	3.59	5.95	-3.795	1.795	0.557	4.012
NTPD-4	-5.56	-1.89	3.67	6.22	-3.725	1.835	0.545	3.781
NTPD-6	-5.86	-3.11	2.75	1.50	-4.485	1.375	0.727	7.315
NTPD-9	-5.81	-2.02	3.79	6.41	-3.915	1.895	0.528	4.044
Etoposide	-5.34	-0.10	5.24	3.53	-2.720	2.620	0.382	1.412

Table 9

ADMET properties of the predicted ligands.
Property	Model Name	NTPD-3	NTPD-4	NTPD-6	NTPD-9	Etoposide
ABSORBTION	H₂O solubility	-5.648	-4.195	-4.399	-4.288	-3.69
	CaCO₂ Permeability	1.458	1.215	0.732	1.361	0.56
	Intestinal absorption (human)	97.24	99.147	93.742	99.836	91.44
	Skin permeability	-2.741	-2.739	-2.746	-2.744	-2.74
	P- glycoprotein substrate	No	No	No	No	Yes
	P-glycoprotein I inhibitor	Yes	Yes	Yes	Yes	No
	P-glycoprotein II inhibitor	Yes	Yes	No	Yes	No
DISTRIBUTION	VDSS (human)	0.015	-0.126	-0.193	-0.152	0.42
	Fraction on bound (human)	0.009	0.072	0.098	0.064	0.11
	BBB permeability	-1.009	-1.263	-1.393	-1.281	-1.61
	CNS permeability	-1.962	-3.489	-3.402	-3.254	-3.71
METABOLISM	CYP2D6 Substrate	No	No	No	No	NO
	CYP3A4 Substrate	Yes	Yes	Yes	Yes	NO
	CYP1A2 inhibitor	Yes	Yes	No	Yes	NO
	CYP2C19 inhibitor	Yes	Yes	No	Yes	NO
	CYP2C9 inhibitor	Yes	Yes	No	Yes	NO
	CYP2D6 inhibitor	No	No	No	No	NO
	CYP3A4 inhibitor	Yes	Yes	Yes	Yes	NO
EXCREATION	Total clearance	0.585	0.596	0.61	0.783	0.00
EXCREATION	Renal OCT2 substrate	No	No	No	No	NO
TOXICITY	AMES toxicity	No	No	No	No	NO
	Max. tolerated dose (human)	0.541	0.627	0.668	0.532	0.10
	hERG I inhibitor	No	No	No	No	NO
	hERG II inhibitor	Yes	Yes	No	Yes	NO
	Oral- Rat Acute toxicity (LD₅₀)	2.752	2.845	2.427	2.648	3.00
	Oral- Rat Chronic toxicity (LOAEL)	1.266	1.365	0.97	1.34	2.94
	Hepatotoxicity	Yes	Yes	No	Yes	NO
	Skin sensitization	No	No	No	No	NO
	T. Pyriformis toxicity	0.31	0.294	0.287	0.3	0.29
	Minnow toxicity	-0.215	-1.432	-1.443	-0.715	5.08

The assessment of drug likeness and oral bioavailability is crucial, especially for orally administered drugs. Lipinski's Rule of Five is a widely accepted guideline in this context. The rule states that a molecule should not violate more than one of the following parameters: molecular weight (Mw) < 500, number of hydrogen bond donors (HBD) < 5, octanol/water partition coefficient (Log P) < 5, and number of hydrogen bond acceptors (HBA) < 10. The physicochemical properties of the new designed triazole-trimethoxyphenyl hybrids against adenocarcinomic human alveolar basal epithelial cells indicated that these compounds adhere to Lipinski's rule except NTPD-6 with one violation. The ADMET properties predicted for these new compounds showed that NTPD-6 displayed good oral absorption, water solubility, skin permeability, non P-glycoprotein substrate which may enhance bioavailability. Blood-brain barrier (BBB) permeability predictions indicated that the ligands can cross the BBB, but they showed low permeability, indicating moderate distribution to the central nervous system (CNS). The ligand acted as non-inhibitor of human cytochrome P450 enzymes, especially CYP1A2, CYP2C19, CYP2C9 and CYP2D6 (Table 10). This suggests that it may undergo metabolic transformations. The ligands exhibits low Oral- Rat Chronic toxicity (LOAEL), Hepatotoxicity and Skin sensitization total clearance values, suggesting prolonged stability in the body and enhanced drug efficacy.

Table 10

In silico methods were used to evaluate twenty-six triazole-trimethoxyphenyl hybrids anticancer activity against adenocarcinomic human alveolar basal epithelial cells (A549) through DFT, QSAR and ADMET profile. The DFT results showed that TPD 11 and TPD 25 have better molecular properties to receive electrons from and donate electrons to adenocarcinomic human alveolar basal epithelial cells readily than Etoposide. Thus, they are expected to engage in hydrogen bonding and other hydrophobic interactions with a receptor more than Etoposide which led to enhance binding properties. The QSAR developed using multiple linear regression-Genetic Approximation (MLR-GA) statistically has very good predictive ability, which was used to predict twenty-eight molecules of which NTPD-3, NTPD-4 NTPD-6 and NTPD-9 have computed pIC50/IC₅₀ values better than etoposide. The physicochemical and ADMET properties showed that NTPD-6 displayed good oral absorption, water solubility, skin permeability, non P-glycoprotein substrate, exhibited low Oral- Rat Chronic toxicity (LOAEL), Hepatotoxicity and Skin sensitization total clearance values, suggesting it could be a good candidate inhibitor against adenocarcinomic human alveolar basal epithelial cells.

Author Contribution

Conceptualization: SB and AS; Materials and Resources: SB and AS; Investigation: SOA and MEO: Analysis: GKO and SB; Writing: SOA, GKO and SB; Proof Reading: SB, AS and GKO

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In silico investigations of triazole -trimethoxyphenyl hybrids as anti-proliferative agents against adenocarcinomic human alveolar basal epithelial cells (A549): DFT, QSAR and Molecular Docking Approaches

Status:

Version 1

Abstract

Figures

1.0 Introduction

2.0 In silico Methods

2.1 QSAR modelling

2.2 Docking of the Protein-Ligand

2.3 In-silico ADMET/Pharmacokinetic Predictions

3.0 Results and discussion

3.1 Molecular descriptors and Docking analysis

3.2 QSAR model using Multilinear Regression Genetic Algorithm (MLR-GFA).

3.3 Design of new triazole-trimethoxyphenyl hybrids and their computed pIC₅₀ values

4.0 Conclusion

Declarations

Author Contribution

References

Additional Declarations

Status:

Version 1

In silico investigations of triazole -trimethoxyphenyl hybrids as anti-proliferative agents against adenocarcinomic human alveolar basal epithelial cells (A549): DFT, QSAR and Molecular Docking Approaches

Status:

Version 1

Abstract

Figures

1.0 Introduction

2.0 In silico Methods

2.1 QSAR modelling

2.2 Docking of the Protein-Ligand

2.3 In-silico ADMET/Pharmacokinetic Predictions

3.0 Results and discussion

3.1 Molecular descriptors and Docking analysis

3.2 QSAR model using Multilinear Regression Genetic Algorithm (MLR-GFA).

3.3 Design of new triazole-trimethoxyphenyl hybrids and their computed pIC50 values

4.0 Conclusion

Declarations

Author Contribution

References

Additional Declarations

Status:

Version 1

3.3 Design of new triazole-trimethoxyphenyl hybrids and their computed pIC₅₀ values