Antibacterial evaluation,quantum chemical computation,and pharmacological studies of some arylidenemalononitrile and ethyl 2-cyano-3-phenylacrylate derivatives: In vitro and in silico approach

Abstract

The study emphasizes the importance of Knoevenagel condensation for forming carbon—carbon bonds to create biologically active α, β-unsaturated compounds. In this research, arylidenemalononitriles (AMN) (1–4) and ethyl 2-cyano-3-phenylacrylates (ECPA) (5–7) analogs were used to test the antibacterial efficacy against five human pathogens. Most of the compounds exhibited moderate to low inhibitory activity, whereas compounds 2 and 6 showed significant antibacterial effects, notably surpassing ampicillin in efficacy against Bacillus cereus and Escherichia coli. Molecular docking studies revealed strong binding affinities for these compounds (1–7) with bacterial proteins i.e., 1AH7 (−6.9 kcal/mol to −8.6 kcal/mol) and 6DR3 (−5.8 kcal/mol to −7.7 kcal/mol) respectively, indicating effective ligand–protein interactions. Structural analysis highlighted that specific functional groups showed enhance binding affinity, while PASS and ADMET predictions suggested favorable biological profiles for all compounds. The findings were corroborated by molecular dynamics simulations via NMA analyses and quantum chemical calculations, demonstrating strong agreement between experimental and computational results.

Keywords

Arylidenemalononitrile ethyl 2-cyano-3-phenylacrylate antibacterial activity molecular docking molecular dynamics normal mode analysis (NMA)and pharmacological prediction

Introduction

The most common complications of antibiotic resistance are neglect and maltreatment. In the world, antibiotic resistance is responsible for more than 35,000 deaths yearly.¹ It is essential to search for a new drug due to the availability of medicines and the growing incidence of drugs for inhibitors. In general, malononitriles and cyano-esters are highly reactive chemicals. In condensation reactions, the unstable methylene group is used commonly as a nucleophile. The Knoevenagel condensation frequently uses the reagents malononitrile and cyano-esters, which are methylene-active compounds. This feature makes them a vital chemical in industrial, agricultural, and medicinal applications.^2–5 For aldol condensation, base catalysts are commonly employed. Novel solid bases can be utilized to study several base-catalyzed reactions, such as the Knoevenagel condensation between benzaldehyde and malononitrile or benzaldehyde and cyano-esters.⁶ The end product of this reaction, arylidenemalononitriles (AMNs) and also ethyl cyano-phenyl-acrylates (ECPAs), have many applications in the pharmaceutical, pharmacology, biotech, and fragrance industries.⁷ The biological properties of AMNs and ECPAs derivatives, particularly employed in organic chemistry as both a transitional molecule and a target molecule, have been well explored. The relative position of the substitution on the ring structure has a significant as well as considerable impact on the physiological action of AMNs. The most active position is the 2-substituted position, followed by the 3-substituted position's decline in activity, whereas, the 4-substituent's gradually decrease in activity until it completely disappears.^4,8,9 They act as essential synthesis intermediates, mainly in cyclization reactions, for the synthesis of different organic compounds.¹⁰ Pharmaceuticals known as carbonic anhydrase inhibitors (CAIs) suppress the catalytic activity in carbonic anhydrases (CAs). Carbon dioxide is reversibly converted to carbonate and proton by CAs with Zn²⁺ at the active site.^11,12 Recently, research has demonstrated that AMNs derivatives can effectively suppress the acetylcholine esterase (AChE), CA-I and CA-II in erythrocytes in vitro and silico. They were also investigated for the prospective antibacterial effects.⁴ According to reports, AMNs and also ECPAs can be utilized as riot-control agents or as fungicides, insecticides, cytotoxic agents against tumors, and anti-fouling agents.¹³ The key reason for further in silico research against various infections is the efficacy of the produced crystals against five human pathogenic bacteria during in vitro experiments, where it could show a promising result. AMN and ECPA derivatives had distinct and optimized chemical structures, so these structures were chosen for further computational analysis. Data on computational techniques, like DFT, MD, chemical stability, protein-ligand interactions, FTIR, UV-vis, ADMET, QSAR, drug-likeness and many chemical descriptors, are computed. Then, to determine the binding score of the ligand with the active site of proteins, the study of molecular docking is the primary tool and the core idea of these studies. To determine the acceptability of these derivatives, they are compared to an FDA-approved drug: ampicillin (AMP). So far, this study clearly shows the assessment of in vitro and silico analysis, molecular docking in addition to molecular dynamics via NMA analyses, and structure-activity relationships.

Materials and methodology

Materials

Structures of arylidenemalononitriles (1–4) and ethyl 2-cyano-3-phenylacrylates (5–7)

In this research, some analogues of arylidenemalononitriles (AMN) (1–4) and ethyl 2-cyano-3-phenylacrylates (ECPA) (5–7) are selected for studying antibacterial activities through computational approaches (Figures 1 and 2). The research team of Bhuiyan et al.¹⁴ has already synthesized these analogs. The investigated, AMN and ECPA analogs (1–7) were synthesized via, a modified Knoevenagel reaction with microwave irradiation (MWI) from a variety of benzaldehydes along with active methylene compounds, such as malononitrile and cyanoacetate using a catalytic amount of NH₄OAc (Scheme 1).

Figure 1.

Chemical structures of arylidenemalononitrile (AMN) (1–4) and ethyl 2-cyano-3-phenyl acrylate (ECPA) analogs (5–7).

Figure 2.

Structural details and IUPAC names of AMN and ECPA derivatives (1–7). MF = Molecular formula; MW = molecular weight.

Scheme 1.

Synthetic method of AMN (1–4) and ECPA analogs (5–7).¹⁴

Methodology

In vitro antibacterial evaluation

Five bacteria, including B. cereus, S, typhi, E. coli, S. dysenteriae, and P. aeruginosa, were utilized to investigate the antibacterial efficacy of the tested AMN and ECPA analogs (1–7). Nutrient agar (NA) served as the test bacteria's basal medium for the determination of antibacterial activities. NA was made up of an extract of beef (3 g), peptone(5 g) solution, agar (15 g), Salt (NaCl) (0.5), and 1000 mL of distilled water. The infected slants were cultured in an incubator at (37 ± 2)°C. Then, for each organism, 10 mL of distilled water was added to a screw-cap test table, which was then autoclaved to kill any remaining bacteria. One loop of the bacteria culture that was 48 h old was added to 10 mL of sterilized water that had been distilled and thoroughly mixed. During the sensitivity test, the bacteria solution was placed on the pour plate. The antibacterial efficacy of the compounds was determined by employing the disc diffusion method.¹⁵ Throughout the experiment, paper discs of 4 mm in circumference and petri plates measuring 100 mm in size were utilized. Culture plates were produced using sterilized methods at 45°C after the agar mixture had been set.¹⁶ The sterilized glass rod was employed separately to press the test microorganisms (suspensions) uniformly over the plate. The test chemicals were soaked into the paper discs at a rate of 20 g/disc, and the results were good at the core of the infected pour plate. In all cases, distilled water was used to maintain a control plate. Test chemicals have already diffused from the disc to the adjacent medium after 4 h duration at a low heat (4°C). The plates were subsequently incubated at (37 ± 2)°C for the test organisms’ growth, and they were monitored for three days at intervals of a period of 24 h. The activity is measured in terms of three-day intervals of 24 h’ diameter. The width of the area of inhibition (in mm) is used to calculate activity.

Geometry optimization of ligands

People's 6-311G + (d, p)¹⁷ basis set was used in conjunction with density functional theory (DFT) by using Gaussian 09 W (D.01).¹⁸ Lee, Yang, and Parr's (LYP) correlated functional and Backe's (B) three-parameter hybrid equations were also used to ascertain their physicochemical and spectral features.¹⁹ For the electronic absorption calculation, time-dependent (TD) density functional theory (TD-DFT)²⁰ was applied under the same hybrid functionals and basis set. The inherent reactivity of the highest occupied molecular orbital (HOMO) as well as the lowest unoccupied molecular orbital (LUMO) is a result of their unique characteristics.²¹ The HOMO and LUMO values were calculated and converted to electron volts (eV). The following formulas have been used to determine the overall chemical reactivity and molecular orbital features²²; energy gap, Δε = εLUMO−εHOMO; ionization potential, I = −εHOMO; electron affinity, A = −εLUMO; electronegativity, χ = (I + A)/2; chemical potential, μ = −(I + A)/2; hardness, and softness, S = 1/2 η.

Preparation of proteins, molecular docking and non-bonding calculations

The 3D crystal structures of the studied proteins i.e., PDB ID: 1AH7, 3FHU, 6DR3, 3FHH and 5X6F, respectively were collected from the online RCSB protein data library (https://www.rcsb.org/). PyMol (1.7.4) software was used to eradicate water molecules, heteroatoms, and co-crystalline ligands to build the protein chains.²³ The Swiss PDB program (4.1.0) was used to minimize the energy and reduce bad atom interaction. Here, four analogs of AMN (1–4) and three ECPA (5–7) were docked against five different proteins of pathogenic bacteria using PyRx docking tools.²⁴ The required non-bonding interactions and assessment of the protein-ligand binding pockets with bond type and bond distance were retrieved by Discovery Studio visualizer 2020 .²⁵

Prediction of ADMET, biological activities and drug-likeness

The ADMET (absorption, distribution, metabolism, excretion, and toxicity) parameters are essential in pharmacological studies. Here, the ADMET profile was predicted using the admetSAR (http://lmmd.ecust.edu.cn/admetsar1/predict/) server.²⁶ The “prediction of activity spectra for substances (PASS)” and Swiss ADME web applications were used to forecast the biological activities of organic compounds that resemble drugs.²⁷ All results were predicted with the aid of a “simplified molecular input line entry system (SMILES)” and structural data files in each case.

Molecular dynamics simulation

To validate the outcomes of molecular docking as well as investigate the perdurability of protein-ligand complexes, a simulation of molecular dynamics is frequently used. Here, the C-α atoms of the receptor proteins were simulated employing the NMA (normal mode analysis) dynamics utilizing the “iMODS server (https://imods.iqfr.csic.es/)”.²⁸

Quantitative structure-activity relationship (QSAR)

In computer-aided drug design, QSAR is assessed by comparing it to eight drug-related descriptors from ChemDes, (http://www.scbdd.com/chemdes/) a free online tool for calculating molecular descriptors.²⁹ “Quantitative structure-activity relationships (QSAR)”, based upon mathematical and statistical correlations, were utilized to constitute links between the physicochemical properties of chemical substances and their biological functions.^30,31 Utilizing “multi-linear regression (MLR)” equations, a model was developed to forecast the QSAR.²⁹

Here,
$\begin{aligned} {pIC}_{50} (Activity) = - 2.768483965 + 0.133928895 \\ \times (Chiv 5) + 1.59986423 \times (bcutm 1) \\ + (- 0.02309681) \times (MRVSA 9) + (- 0.002946101) \\ \times (MRVSA 6) + (0.00671218) \times (PEOEVSA 5) \\ + (- 0.1596341 5) \times (GATSv 4) + (0.207949857) \\ \times (J) + (0.082568569) \times (Diametert) . \end{aligned}$

Results and discussion

In-vitro antibacterial analysis

Antibacterial activity of AMN and ECPA analogs (1–7) were examined in the Department of Microbiology at University of Chittagong. In this study, the synthesized AMN and ECPA analogs (1–7) were chosen and screened out for their antibacterial efficacy against five human pathogenic bacteria (Table 1). Among the tested inhibitions by AMNs (1–4) and ECPAs (5–7), most of the chemicals showed lower inhibitions at almost less than half for B. cereus, S. typhi, S. dysenteriae except the inhibitions of compound 2 and 6 against E. coli (e.g., 15.00 ± 0.82 mm and 13.23 ± 0.82 mm respectively) were higher than standard ampicillin (12 ± 0.63 mm). Whereas, the inhibitions of compound 2 and 6 against B. cereus (e.g., 16.23 ± 0.82 mm and 14.23 ± 0.82 mm respectively) were almost nearer than the standard ampicillin (20 ± 0.33 mm). Hence, compounds 2 and 6 exhibited the most effectivity against E. coli and B. cereus. However, in case of in vitro antibacterial test, compounds 2 and 6 displayed the highest docking scores with the protein chains 1AH7 (i.e., −8.60 kcal/mol and −8.10 kcal/mol respectively) which have two 2-vinyl malononitrile groups [–CH = C(CN)₂] at 1- and 3-position of benzene ring. Whereas, both compounds 2 and 6 also showed the best binding affinities with 6DR3 (e.g., −7.30 kcal/mol and −7.10 kcal/mol respectively) which have two ethyl 2-cyano-2-ethoxycarbonyl-vinyl acrylate groups [–CH = C(CN)(COOEt)] at 1- and 3-position of benzene ring. In brief, increasing the polar functional groups with the conjugation in benzene ring, docking scores starts to increase. In overall molecular docking, almost in all cases, it is noted that the distance between functional groups is increased from the active compound, and consequently the docking score is reduced. Finally, in vitro result gives the almost identical supports to the in-silico study (Figures 3, 4 and 5).

Figure 3.
Diameter of zone of inhibition in mm of tested compounds against pathogenic bacteria (100 µg (dw)/ disc). Values are represented as Mean ± SD with all the experiments and indicate the significant differences with (P < 0.05) at the experimental conditions.

Figure 4.
Diameter of inhibitions (mm) (a) compound 6 against Escherichia coli and (b) compound 2 against Bacillus cereus respectively.

Figure 5.
Binding affinities of the tested analogs (1–8) with selected bacterial proteases B. cereus and E. coli.

Table 1.
Inhibition against the pathogenic organisms by the test chemicals 1–7.

Serial No. Diameter of zone of inhibition in mm (100 mg (dw)/ disc)

B. cereus S. typhi E. coli S. dysenteriae P. aeruginosa

1 NI 5.56 ± 0.82 NI 7.21 ± 0.33 NI

2 16.23 ± 0.82 7.55 ± 0.44 15.00 ± 0.82 NI NI

3 8.53 ± 0.82 NI NI NI NI

4 NI 6.52 ± 0.63 5.00 ± 0.44 9.21 ± 0.33 NI

5 NI NI NI NI NI

6 14.23 ± 0.82 NI 13.23 ± 0.82 NI 8.00 ± 0.82

7 NI 6.53 ± 0.82 NI NI NI

8 20 ± 0.41 24 ± 0.63 12 ± 0.63 30 ± 0.41 20 ± 0.33

*8 = ampicillin, Data are presented as Mean ± SD, Values are represented for the triplicate of all the experiments, dw = Dry weight; NI = No inhibitions.

Chemistry of the optimized structures and thermodynamic analysis

The optimized structures of AMNs (1–4) and ECPAs (5–7) which were synthesized by using a modified Knoevenagel reaction with microwave irradiation (MWI) from a variety of benzaldehydes along with active methylene compounds (Scheme 1 and Figure 2).¹⁴ The most optimal and precise molecular structure is an important factor during an in-silico study to predict molecular reactions, chemical reactivity, as well as biological activity. To understand a compound's stability, binding energy, and spontaneity of a reaction, it is important to know the thermodynamic parameters like internal energy, enthalpy, Gibbs free energy, and dipole moment.³² In the case of drug design study, the binding interaction between a compound and a receptor protein is the most important phenomenon to understand. These binding interactions are accelerated by the negative free energy and slowed down by the positive free energy.³³ In our study, all the optimized compounds showed negative Gibb's free energy values (e.g., −571.557 Hartree to −3240.105 Hartree) (Figure 6(a)). It shows that all analogs that are being studied are exothermic with spontaneous reactions and create relatively stable products. Also, the dipole moment values (Figure 6(b)) signify the enhanced polarity, which is pivotal in determining binding affinities. The presence of electronegative atoms in different functional groups increases the dipole moment of tested compounds in the range between 5.132 Debye to 7.669 Debye. Hence, almost all of the chemicals exhibited better binding affinities with the selected bacterial proteases.

Figure 6.
(a) Gibb's free energy (Hartree), (b) dipole moment (Debye) of the AMN (1–4) and ECPA analogs (5–7) and (c) DOS plot of the optimized compounds 2 and 6.

Frontier molecular orbital analysis

The electronic excitation from HOMO to LUMO explains the electronic transition in a system which is related to the change from the ground state to the first excited state³⁴ and is also associated with chemical reactivity and stability. The chemical hardness, softness and chemical potentiality are all influenced by the HOMO-LUMO energy gap (Δɛ) of molecules. A compound tends to be more stable when the difference between the energy of HOMO and LUMO increases. For this reason, more energy will be required if one electron needs to be excited so that it goes to the higher excited state LUMO from the ground state HOMO. Table S1, Figure 6(c), Figures S1 and S2 showed the energies that relate to the reactivity of chemicals, thermal stability of a portion of the electrophilic and nucleophilic attractive region, as well as biological segregation or association with protein. Energy is described in terms of chemical reactivities, hardness, softness, participation in the electrophilic or nucleophilic region, and biological dispersion or affiliation with protein.^35,36 It is believed that the energy gap (Δɛ), which covers from −3.00 eV to −5.00 eV, is the range that best fits organic molecules.³⁷ To evaluate the capabilities of atomic electrical transportation, the molecular orbital energy gaps are investigated. Here, Δɛ spans from 3.554 eV to 4.102 eV, indicating a lower Δɛ and denotes that these compounds have greater atomic systems, and thus, significantly lower chemical stability. However, depending on the functional group, results differ significantly.^38,39 Chemical potentiality, hardness, and softness can all be used to describe bioactivity. Hardness should often be higher than softness. The rate of absorption depending on the level of softness and hardness should be from 1 kcal/mol to 4 kcal/mol for adequate biological flexibility. In our study, all of the derivatives conform to the softness and hardness rules. Here, softness ranges from 0.244 to 0.272 and hardness ranges from 1.777 to 2.049, which are around the mentioned value. Chemical potentiality ranges from −4.797 eV to −5.884 eV which all are around the reference value of 5.00 eV. Hence, all analogs 1–7 are chemically promising.

Electrostatic potential analysis

The variety and charge distribution in a molecule can be understood and studied with the help of molecular electrostatic potential (MEP). To understand the mechanism, it is important to know where a nucleophile or an electrophile will attack.⁴⁰ In a biological system, the specificity and the strength of the binding site are important and the most important bond in such kind of system refers to the hydrogen bond.⁴¹ For a hydrogen bond to be established, it is important to have an electronegative and electropositive site in a molecule. From an MEP map, we can predict the site, and in the maps; the green color represents the zero potential areas, the blue colored area is the more positive, and the red colored one is the electronegative site of the compound. The size and shape of the compound can also be predicted with the help of the MEP. In Figure 7 and S3, the blue-colored area shows the presence of more percentage of carbon chains and the red-colored area indicates the presence of electronegative atoms like nitrogen, oxygen, and bromine. Here, the MEP of the tested compounds ranges from −0.241 Hartree to +0.177 Hartree. These MEP values suggest the possibility of nucleophilic and electrophilic sites of the compound. The amount of positive and negative charge varied based on the number of carbons, hydrogen, and electronegative atoms. Here, the highest negative MEP was seen for analog 7 (−0.241 Hartree), whereas, chemical 6 (+0.177 Hartree) showed the maximum positive MEP (Figure 7 and S3).

Figure 7.
Electrostatic potential map of compounds 2 and 6 (remaining are presented in Figure S3).

FT-IR analysis

Vibrational spectroscopic techniques such as FT-IR, is the most popular techniques that are used in the characterization of the compound, especially to prove the presence of different functional groups in the tested compounds.⁴² In our study, vibrational frequencies were calculated from 400 and 4000 cm⁻¹ and compared with our experimental values (Table S2 and Figure 8(a)). The computed values were multiplied by the scaling factor 0.9688 for more accuracy.¹⁷ The most significant functional groups in the study were C = O, C–O, C–H, O–H, C≡N, aromatic C = C, and aliphatic C = C. The peak that is found in the region of 3000–3100 cm⁻¹ is for the presence of C-H bond in the studied compounds and the experimental analysis the region was also found from 2900 to 3350 cm⁻¹ region. Again, the region from 2240 to 2265 cm⁻¹, which matches the result of the experimental vibrational frequency for C≡N of 2200 to 2250 cm⁻¹ confirms the existence of a nitrile functional group in the compounds. The presence of C = C in both the aromatic ring and the aliphatic side chain can be proved by the significant peak in the region of 1500 to 1650 cm⁻¹ which correlates to the experimental value as well. The ester group has two carbon-oxygen bonds, in which the C-O is detected at the region near about 1100 to 1150 cm⁻¹ and C = O is detected at the region of 1650 to 1750 cm⁻¹. Again, the presence of O-H can be confirmed by the significant peak in the region of 3650–3750 cm⁻¹ (Figure 8(a)).

Figure 8.
(a) FT-IR and (b) UV-vis spectra of selected derivatives 3, 5 and 7, (remaining are presented in Figure S4).

UV-vis spectral analysis

To know a compound's electronic transition, the UV-visible technique utilizing time-dependent Density Functional Theory (TD-DFT) calculation is the standard benchmark that can ensure a balance between accuracy and computational expense. Two particular electronic transition states of every analog are shown in Table S3, Figure 8(b) and S4. The first transition of an electron from the ground state (S₀) to the first excited state (S₁) governs the kinetic stability as well as chemical reactivity. All the studied compounds show absorbance at the UV region and no absorption can be seen in the visible region. All the compounds absorb the light between 210 to 460 nm wavelength. The excitation energies are lower and all the transitions are happening comparatively in low regions. Out of all the analogs, 4.298 eV of 2 has the most excitation energy and the fewest reaction sites. Charge transfer from excited state S₀ to excited state S₁ produces configurations with wavelengths of 288.45 nm, which are as follows for compound 2: −0.273 (H-1→L), 0.429 (H-1→L + 1), 0.389 (H→L), and −0.281 (H→L + 1). Chemical reactivity increases when excitation energy is lower, while kinetic stability gets enhanced when excitation energy is higher, according to the HOMO-LUMO energy gaps.⁴³ Analog 6 showed the lowest energy of excitation e.g., 3.696 eV which made it the most reactive analog among the others. For chemical 6, the configurations with the wavelength of 335.48 nm are as follows: 0.508 (H-1→L), 0.129 (H→L), and 0.465 (H→L + 1) are produced by charge transfer from the excited state S₀ to the excited state S₁. Again, the presence of two 2-vinyl malononitrile groups [–CH = C(CN)₂] at 1- and 3-position of benzene ring in compound 2 and also the presence of two ethyl-2-cyano-2-ethoxycarbonyl-vinyl acrylate groups [–CH = C(CN)(COOEt)] at 1- and 3-position of benzene ring in compound 6 made them more reactive than the other compounds.

Molecular docking and non-bonding interaction analysis

A molecular docking simulation method is commonly used to estimate the binding affinity and the position of ligands in the binding region of a receptor protein.^40,44 Here, the docking was carried out using some analogs of AMNs (1–4) and ECPAs (5–7) against five proteins from bacteria known as B. cereus, S. typhi, E. coli, S, dysentariae and P. aeruginosa and PDB ID: 1AH7, 3FHU, 6DR3, 3FHH, and 5X6F respectively. Table 2, shows the average binding affinities of triplet docking towards the proteins and targeted ligands. Among the five bacterial proteins, both 1AH7 and 6DR3 proteins showed better binding affinities in all tested compounds 1–7. The highest docking scores observed for those following proteins were −8.60 kcal/mol and −8.10 kcal/mol for the tested compounds 2 and 6 respectively against B. cereus (1AH7), whereas, the best binding affinities for 2 (−7.30 kcal/mol) and 6 (−7.10 kcal/mol) were observed for E. coli (6DR3) (Figure 5). However, the FDA-approved medicine ampicillin (AMP) (8) likewise also had docking scores, measuring at −7.70 kcal/mol and −7.00 kcal/mol against B. cereus (1AH7) and E. coli (6DR3) respectively. Additional experiments, including in vitro as well as molecular dynamic simulations, were conducted in accordance with the docking results.

Table 2.
Binding affinities of the arylidene malononitrile (AMN) (1–4) and ethyl 2-cyano-3-phenyl acrylate (ECPA) analogs (5–7) with selected bacterial proteases.

Serial no. B. cereus (1AH7) S. typhi (3FHU) E. coli (6DR3) S. dysenteriae (3FHH) P. aeruginosa (5X6F)

1 −7.20 −5.10 −6.10 −7.20 −6.00

2 −8.60 −5.60 −7.30 −7.80 −7.60

3 −7.00 −4.90 −5.90 −6.90 −5.90

4 −6.90 −5.30 −5.90 −7.00 −6.30

5 −6.90 −4.96 −5.80 −6.90 −5.90

6 −8.10 −5.46 −7.10 −7.50 −7.60

7 −7.00 −5.10 −6.10 −6.70 −6.40

8 −7.70 −5.83 −7.00 −8.00 −8.10

*8 = ampicillin

Against the protein 1AH7, some important non-bonding interactions were observed [e.g., two conventional hydrogens (H) with two amino acid (AA) residues (TYR56, TYR79), one pi-cation (PC) with one AA (HIS142) and one pi-pi stacked (PPS) with one AA (PHE66) for analogue 2 and two conventional hydrogens (H) with two AA's (ALA3, ASN134), four carbon-hydrogen (CH) with four AA's (SER2, THR133, SER64, GLU4), one pi-cation (PC) with one AA (HIS142), one pi-anion (Pa) with one AA (GLU146), one pi-pi stacked (PPS) with one AA (PHE66) and one pi-alkyl (PA) with one AA (PHE66) for analogue 6] (Table S4 and Figure 9(a)). In overall, both compounds 2 and 6 commonly displayed significant non-bonding interactions with two AA's (e.g., HIS142 and PHE66) against the 1AH7 protease of B. cereus. Again, against the protein 6DR3 some other important non-bonding interactions were also observed [e.g., one conventional hydrogen (H) with one AA (LYS75), one carbon-hydrogen bond (CH) with one AA (LYS75), one pi-anion (Pa) with one AA (GLU107) and two pi-alkyls (PA) with two AA's (LYS75, ARG137) for analogue 2 and three conventional hydrogens (H) (GLN44, ARG71, ASN133), two carbon-hydrogen (CH) with two AA's (ALA141, ALA45), one alkyl (A) with one AA (LYS75), and two pi-alkyls (PA) with two AA's (TYR50, ARG137) for analogue 6] (Table S5 and Figure 9(b)). In overall, against 6DR3 protease of E. coli, both compounds 2 and 6 commonly exhibited substantial non-bonding interactions with two AA's (e.g., ARG137 and LYS75). According to studies by Uzzaman et al., carbon-hydrogen interactions tend to play a pivotal role in biological systems when the contact lengths are kept at 2.3 Å.⁴⁵ All of the investigated compounds bound at the correct binding pocket and mostly hit the desired amino acid residues in both receptor proteins. Non-covalent and intermolecular interactions are essential in biological contacts to boost binding strength.⁴⁶ More crucially, Vander Waals interactions, carbon-hydrogen bonds, and regular hydrogen bonds stabilize the ligand-protein complexes.⁴⁷ In our study of AMN (1–4) and ECPA (5–7) derivatives, a number of non-covalent interactions, including carbon-hydrogen bonds, pi-sigma bonds, and hydrophobic bond interactions, have been observed.

Figure 9.
(i) Docked conformer, (ii) non-bonding interaction, and (iii) hydrogen bond surface for compounds 2 and 6 with the receptor proteins e.g., (a) for 1AH7 and (b) for 6DR3 respectively.

Molecular dynamics simulation analysis

Normal mode analysis (NMA) is commonly used to describe the overall functional mobility of macromolecules. To evaluate the deformability as well as stability of the receptor protein, different parameters including bond distances, positions, dihedral angles, torsions, frictions, and changes in conformation were observed. The ability of a protein chain's residues to deform is measured by its deformability. Here, the most fluctuation was seen for HIS14 for the 1AH7 receptor. The deformation propensity of TRP1, ALA3, GLU9, TRP16, VAL18, ASN19, and ARG20 were moderate to some extent [Figure 10(a)(i)]. Meanwhile, GLN52 displayed the highest level of deformation tendency for the 6DR3 receptor, while SER30, THR31, ALA37, GLN40, GLN44 and TYR50 are still in the mid-range deformation regions [Figure 10(b)(i)]. However, it's possible that those amino acids residues supported non-bonding interactions with the ligands.²⁸ The covariance map shows how the amino acid residues are coupled in terms of color grading, with the highly correlated (red), uncorrelated (white), as well as anti-correlated (blue) amino acid residues lying near the origin pointy straight line. Similarly, an elastic network map similarly shows the atom pairs that are linked through springs. Each dot represents a spring between the appropriate pairs of atoms on the map. Stiffer springs are signified by darker gray dots, and vice versa [Figure 10(a)(ii and iii) and 10b (ii and iii].

Figure 10.
Deformability and interaction index of the receptor proteins (a) 1AH7 and (b) 6DR3.

ADMET analysis

The body's reaction to a drug is explored in pharmacokinetics and referred to as “absorption, distribution, metabolism, excretion, and toxicity (ADMET)”.⁴⁸ Now, it is important for the early development and research of medications. Thus, ADMET forecasting is used to analyze all of the potential drug's features (Table 3).^49,50 The ADMET features of the tested AMN and ECPA derivatives (1–7) were obtained from the admetSAR online database. According to AdmetSAR results (Table 3), showed that all chemicals react to HIA effectively, therefore nothing can be removed from the body more rapidly through the urine and rectal systems.²⁶ Here, both the analog 5 and 7 exhibited a more favorable HIA (+1.000) performance than the others (+0.978 to +0.995). Again, all analogs demonstrated positive human oral bioavailability (HOB) levels in the range of +0.571 to +0.743. Positive HOB may create health problems in humans.²⁶ The permeability co-efficient across the monolayers of the colon carcinoma cell line Caco-2, which is grown on permeable scaffolds, are frequently used to forecast how xenobiotics and other oral medications will be distributed.^51,52 In our study, all tested chemicals showed positive C2P responses indicating that the body absorbs these drugs quickly. Here, C2P permeability of tested compounds (1–7) lies in the range of +0.649 to +0.903. The “blood-brain barrier (BBB)” is a highly restrictive barrier that regulates the flow of cells, ions, and molecules between the brain and other body regions. This barrier protects the central nervous system (CNS) of the brain from pathogens, illness, inflammation, injury, and disease. Unexpectedly, in this research, all derivatives of AMNs and ECPAs showed positive responses to BBB in the range of +0.650 to +1.000 except compound 4 (−0.525). Positive BBB response is worrying and shows that they do not quickly pass BBB.⁵³ Drug-binding sites can be blocked, ATP hydrolysis can be reduced, or the stability of cell membranes can be changed to inhibit p-glycoprotein (p-GpI).⁵⁴ All of the chemicals for evaluation exhibited negative p-Gp inhibition values, with the exception of compound 6 (+0.627), indicating that the p-Gp is not suppressed as well as cannot affect permeability, absorption, or retention.⁵⁴ In this study, the important cytochrome P450 enzyme CYP2C9 was the target of chemicals 3, 4, and 7, which displayed positive inhibitions. Many harmful interactions with drugs involving the enzyme are affected by inhibition.^54,55 Because all of the medications under study only mildly suppress the human “ether-α-go-go-related gene (hERG)”, the threat of long QT syndromes and other significant cardiac consequences, like sudden death, is raised.⁵⁶ Carcinogens are agents or chemical combinations that can lead to cancer in people under specific circumstances and after intensive exposure.⁵⁷ In our investigation, none of the chemicals exhibit carcinogenic efficacy. The term “acute toxicity” refers to the hazardous effects of the material on humans as well as other living things by a variety of biochemical systems. Research on the probabilities of a substance's adverse effects demands to take into account factors such as acute oral, cutaneous, and inhalation rodent toxicity.⁵⁸ With the exception of 4 which showed acute oral toxicity (AOT) level II and rest of the chemicals exhibited a category III AOT indicating relatively less harmful. Again, a significant measure used to classify drugs in terms of the probable risk they pose to public health after initial consumption (LD₅₀) is the median fatal dose of rat oral acute toxicity.^59,60 For the tested compounds (1–7), the predicted LD₅₀ varied from 2.014–3.507 mol/Kg, meaning it's quite significant.⁶⁰ Acute liver failure (ALF) is often a result of drug poisoning. The main cause of ALF is drug hepatotoxicity caused by NSAIDs overdose and atypical drug reactions.⁶¹ In our study, all compounds exhibited substantial hepatotoxicity in the range of +0.585 to +0.775.

Table 3.
Absorption, distribution, metabolism, and toxicological properties of some AMNs (1–4) and ECPAs (5–7).

Serial no. Absorption Distribution Metabolism Toxicity

HIA HOB C2P BBB p-GpI CYP4502C9 hERG Carcinogen AOT RAT Hepatotoxicity Nephrotoxicity

1 +0.987 +0.571 +0.890 +0.900 −0.962 −0.802 −0.590 NC (0.538) III 2.839 +0.712 +0.533

2 +0.987 +0.729 +0.764 +0.900 −0.879 −0.802 −0.500 NC (0.555) III 2.839 +0.623 −0.684

3 +0.978 +0.743 +0.903 +0.975 −0.969 +0.562 −0.671 NC (0.576) II 2.927 +0.752 −0.657

4 +0.995 +0.700 +0.862 −0.525 −0.970 +0.627 −0.723 NC (0.808) II 3.507 +0.775 +0.606

5 +1.000 +0.614 +0.837 +0.925 −0.829 −0.665 −0.399 NC (0.560) III 2.014 +0.585 −0.557

6 +0.986 +0.600 +0.649 +0.650 +0.627 −0.544 +0.785 NC (0.530) III 2.154 +0.650 −0.692

7 +1.000 +0.700 +0.782 +1.000 −0.941 +0.613 −0.459 NC (0.582) III 2.709 +0.738 −0.664

HIA = Human intestinal absorption, HOB = Human oral bioavailability, C2P = CACO-2 permeability, p-GpI = p-Glyco protein inhibitor, BBB = Blood-brain barrier, hERG = Human ether-α-go-go related gene, NC = Non-carcinogen, AOT = Acute oral toxicity, RAT = Rat acute toxicity (LD₅₀, mol/kg).

Biological activities

A computer-aided novel method named “prediction of activity spectra for substances (PASS)” has been used to forecast the results of 1000 biological and toxicological investigations.^40,62 Based on the study of “structure-activity relationships (SARs)”, the biological functions of the PASS program showed 90% accuracy. Additionally, biological activity is impacted by the features of the drug, biological entity, and dosage manner.⁶³ The following rules can be applied to calculate the PASS prediction: (a) “If P_a > 0.7”, the molecule is very likely to exhibit experimental activity, but there has also high pharmacological drug effect; (b) “If 0.5 < P_a < 0.7”, the molecule is likely to exhibit the experimental action, but there has less pharmacological efficacy of the drug; and (c) “If P_a < 0.5”, the molecule is unlikely to exhibit the desired experimental activity.⁵⁸ Here, Table 4 illustrates the interpretation of the prediction outcomes. The outcomes of the PASS prediction indicate that AMN analogue 3 showed significant antileukemic (0.804), anti-ulcerative (0.607) and antineoplastic (0.806) activities, whereas ethyl 2-cyano-3-phenylacrylate analogue 6 also revealed anti-inflammation (0.626), anti-ulcerative (0.564), gastrin inhibitor and antiviral (0.486) activities. In addition, the AMN analogue 1 also showed gastrin inhibitor (0.583) and antineoplastic activities (0.669). It concludes that many of these tested compounds have substantial antileukemic, anti-inflammation, gastrin inhibitor, anti-ulcerative and antineoplastic activities.

Table 4.
Predicted biological activities of analogs 1–7.

Serial no. Anti-inflammation Gastrin inhibitor Anti-ulcerative Antineoplastic Antifungal Antiviral

Activity Virus

1 0.375 0.583 0.471 0.669 0.337 0.329 Picornavirus

2 0.251 0.396 0.440 0.336 0.112 0.326 Picornavirus

3 0.478 0.471 0.607 0.806 0.396 0.468 Picornavirus

4 0.220 0.319 0.452 0.442 0.464 0.397 Rhinovirus

5 0.298 0.347 0.383 0.351 0.341 0.386 Picornavirus

6 0.626 0.486 0.564 0.381 0.479 0.486 Picornavirus

7 0.365 0.361 0.452 0.389 0.476 0.408 Picornavirus

Drug likeness

The qualitative analysis of a molecule during the early stages of drug design is termed as “drug-likeness”.⁶⁴ It could be stated as the similarity between drugs and compounds.⁶⁵ This strategy, which is heuristic in nature and depends upon the physicochemical characteristics of the molecules, speeds up the process of developing novel drugs.⁶⁵ To evaluate a compound's drug-likeness and establish whether it is consumed as a therapeutic molecule, Lipinski's criterion of five is followed (Table S6). Poor oral bioavailability indicates that a drug violates more than a single rule.⁶⁶ According to Lipinski et al.,⁶⁷ Ghose et al.,⁶⁸ Veber et al.,⁵³ Egan et al.,⁶⁹ and Muegge et al.,⁷⁰ all of the compounds in this study correspond to all drug-likeness standards and have good oral bioavailability scores of 0.571 to 0.729.

pIC₅₀ studies

Utilizing the multi-linear regression (MLR) formula, pIC₅₀ values were examined. A MLR equation is used to analyze the relationship between a dependent variable (pIC₅₀) and a collection of independent variables (descriptors Chiv5, bcutm1, MRVSA9, MRVSA6, PEOEVSA5, GATSv4, J, and Diametert). It has been observed that the pIC₅₀ limit for many drugs is 4.0–10.⁷¹ Here, pIC₅₀ varies from 4.24 to 4.59 (Table S7 and Figure 11). In our study, all drugs have shown the typical range of pIC₅₀ values and analogue 2–3 and 5–6 exhibited remarkable pIC₅₀ values 4.49 and 4.59 respectively.

Figure 11.
pIC₅₀ of the tested compounds.

Conclusion

This work investigated in vitro and in silico antibacterial analyses using some analogs of arylidene malononitrile (AMN) (1–4) and ethyl 2-cyano-3-phenylacrylate (ECPA) (5–7). Among the AMN and ECPA derivatives, compounds 2 and 6 showed significant activity against Bacillus cereus and Escherichia coli, comparable to ampicillin. Molecular docking and dynamics simulations via NMA analysis indicated strong binding to bacterial proteins, while PASS and ADMET analyses suggested moderate-to-low acute oral toxic effects implying that oral administration is safe for those. All of the drugs also met various criteria for drug-likeness rules. DFT calculations confirmed their stability and reactivity, correlating with biological effectiveness. These compounds are potential candidates for new antibacterial agents, but further in vivo evaluations and toxicity assessments are needed.

Supplemental Material

sj-docx-1-mgc-10.1177_10241221261446763 - Supplemental material for Antibacterial evaluation, quantum chemical computation, and pharmacological studies of some arylidenemalononitrile and ethyl 2-cyano-3-phenylacrylate derivatives: In vitro and in silico approach

Supplemental material, sj-docx-1-mgc-10.1177_10241221261446763 for Antibacterial evaluation, quantum chemical computation, and pharmacological studies of some arylidenemalononitrile and ethyl 2-cyano-3-phenylacrylate derivatives: In vitro and in silico approach by Emranul Kabir, Md. Mosharef Hossain Bhuiyan, M. R. O. Khan Noyon, Monir Uzzaman, Mahbub Alam and S. M. Rahatul Alam in Main Group Chemistry

Serial No.	Diameter of zone of inhibition in mm (100 mg (dw)/ disc)
1	NI	5.56 ± 0.82	NI	7.21 ± 0.33	NI
2	16.23 ± 0.82	7.55 ± 0.44	15.00 ± 0.82	NI	NI
3	8.53 ± 0.82	NI	NI	NI	NI
4	NI	6.52 ± 0.63	5.00 ± 0.44	9.21 ± 0.33	NI
5	NI	NI	NI	NI	NI
6	14.23 ± 0.82	NI	13.23 ± 0.82	NI	8.00 ± 0.82
7	NI	6.53 ± 0.82	NI	NI	NI
8	20 ± 0.41	24 ± 0.63	12 ± 0.63	30 ± 0.41	20 ± 0.33

Serial no.	B. cereus (1AH7)	S. typhi (3FHU)	E. coli (6DR3)	S. dysenteriae (3FHH)	P. aeruginosa (5X6F)
1	−7.20	−5.10	−6.10	−7.20	−6.00
2	−8.60	−5.60	−7.30	−7.80	−7.60
3	−7.00	−4.90	−5.90	−6.90	−5.90
4	−6.90	−5.30	−5.90	−7.00	−6.30
5	−6.90	−4.96	−5.80	−6.90	−5.90
6	−8.10	−5.46	−7.10	−7.50	−7.60
7	−7.00	−5.10	−6.10	−6.70	−6.40
8	−7.70	−5.83	−7.00	−8.00	−8.10

Serial no.	Absorption	Distribution	Metabolism	Toxicity
1	+0.987	+0.571	+0.890	+0.900	−0.962	−0.802	−0.590	NC (0.538)	III	2.839	+0.712	+0.533
2	+0.987	+0.729	+0.764	+0.900	−0.879	−0.802	−0.500	NC (0.555)	III	2.839	+0.623	−0.684
3	+0.978	+0.743	+0.903	+0.975	−0.969	+0.562	−0.671	NC (0.576)	II	2.927	+0.752	−0.657
4	+0.995	+0.700	+0.862	−0.525	−0.970	+0.627	−0.723	NC (0.808)	II	3.507	+0.775	+0.606
5	+1.000	+0.614	+0.837	+0.925	−0.829	−0.665	−0.399	NC (0.560)	III	2.014	+0.585	−0.557
6	+0.986	+0.600	+0.649	+0.650	+0.627	−0.544	+0.785	NC (0.530)	III	2.154	+0.650	−0.692
7	+1.000	+0.700	+0.782	+1.000	−0.941	+0.613	−0.459	NC (0.582)	III	2.709	+0.738	−0.664

Serial no.	Anti-inflammation	Gastrin inhibitor	Anti-ulcerative	Antineoplastic	Antifungal	Antiviral
1	0.375	0.583	0.471	0.669	0.337	0.329	Picornavirus
2	0.251	0.396	0.440	0.336	0.112	0.326	Picornavirus
3	0.478	0.471	0.607	0.806	0.396	0.468	Picornavirus
4	0.220	0.319	0.452	0.442	0.464	0.397	Rhinovirus
5	0.298	0.347	0.383	0.351	0.341	0.386	Picornavirus
6	0.626	0.486	0.564	0.381	0.479	0.486	Picornavirus
7	0.365	0.361	0.452	0.389	0.476	0.408	Picornavirus

Footnotes

Acknowledgements

We thank the Computer in Chemistry & Medicine Laboratory, Bangladesh, for their valuable support.

ORCID iDs

Emranul Kabir

Md. Mosharef Hossain Bhuiyan

M. R. O. Khan Noyon

Monir Uzzaman

Mahbub Alam

S. M. Rahatul Alam

Authors contributions

Emranul Kabir: Conceptualization, Investigation, Software, Data curation, Writing-original draft, Formal analysis. Md. Mosharef H. Bhuiyan: Conceptualization, Investigation, Supervision. M. R. O. Khan Noyon: Software, Data curation, Formal analysis, Writing-original draft. Monir Uzzaman: Conceptualization, Investigation, Methodology, Writing-review and editing, Supervision. Mahbub Alam: Formal analysis, Writing-review and draft. S. M. Rahatul Alam: Formal analysis, Writing-review and draft.

Funding

This research did not receive any external or internal funding.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability

Data will be available upon the request to the authors.

Statement of usage of artificial intelligence

This research did not use any support from artificial intelligence.

Supplemental material

Supplemental material for this article is available online.

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3.67 MB

0.00 MB

Serial No.	Diameter of zone of inhibition in mm (100 mg (dw)/ disc)
Serial No.	B. cereus	S. typhi	E. coli	S. dysenteriae	P. aeruginosa
1	NI	5.56 ± 0.82	NI	7.21 ± 0.33	NI
2	16.23 ± 0.82	7.55 ± 0.44	15.00 ± 0.82	NI	NI
3	8.53 ± 0.82	NI	NI	NI	NI
4	NI	6.52 ± 0.63	5.00 ± 0.44	9.21 ± 0.33	NI
5	NI	NI	NI	NI	NI
6	14.23 ± 0.82	NI	13.23 ± 0.82	NI	8.00 ± 0.82
7	NI	6.53 ± 0.82	NI	NI	NI
8	20 ± 0.41	24 ± 0.63	12 ± 0.63	30 ± 0.41	20 ± 0.33

Serial no.	Absorption			Distribution		Metabolism	Toxicity
	HIA	HOB	C2P	BBB	p-GpI	CYP4502C9	hERG	Carcinogen	AOT	RAT	Hepatotoxicity	Nephrotoxicity
1	+0.987	+0.571	+0.890	+0.900	−0.962	−0.802	−0.590	NC (0.538)	III	2.839	+0.712	+0.533
2	+0.987	+0.729	+0.764	+0.900	−0.879	−0.802	−0.500	NC (0.555)	III	2.839	+0.623	−0.684
3	+0.978	+0.743	+0.903	+0.975	−0.969	+0.562	−0.671	NC (0.576)	II	2.927	+0.752	−0.657
4	+0.995	+0.700	+0.862	−0.525	−0.970	+0.627	−0.723	NC (0.808)	II	3.507	+0.775	+0.606
5	+1.000	+0.614	+0.837	+0.925	−0.829	−0.665	−0.399	NC (0.560)	III	2.014	+0.585	−0.557
6	+0.986	+0.600	+0.649	+0.650	+0.627	−0.544	+0.785	NC (0.530)	III	2.154	+0.650	−0.692
7	+1.000	+0.700	+0.782	+1.000	−0.941	+0.613	−0.459	NC (0.582)	III	2.709	+0.738	−0.664

Antibacterial evaluation,quantum chemical computation,and pharmacological studies of some arylidenemalononitrile and ethyl 2-cyano-3-phenylacrylate derivatives: In vitro and in silico approach

Abstract

Keywords

Introduction

Materials and methodology

Materials

Structures of arylidenemalononitriles (1–4) and ethyl 2-cyano-3-phenylacrylates (5–7)

Methodology

In vitro antibacterial evaluation

Geometry optimization of ligands

Preparation of proteins, molecular docking and non-bonding calculations

Prediction of ADMET, biological activities and drug-likeness

Molecular dynamics simulation

Quantitative structure-activity relationship (QSAR)

Results and discussion

In-vitro antibacterial analysis

Chemistry of the optimized structures and thermodynamic analysis

Frontier molecular orbital analysis

Electrostatic potential analysis

FT-IR analysis

UV-vis spectral analysis

Molecular docking and non-bonding interaction analysis

Molecular dynamics simulation analysis

ADMET analysis

Biological activities

Drug likeness

pIC50 studies

Conclusion

Supplemental Material

sj-docx-1-mgc-10.1177_10241221261446763 - Supplemental material for Antibacterial evaluation, quantum chemical computation, and pharmacological studies of some arylidenemalononitrile and ethyl 2-cyano-3-phenylacrylate derivatives: In vitro and in silico approach