Startseite 3D-QSAR, molecular docking, ADMET, simulation dynamic, and retrosynthesis studies on new styrylquinolines derivatives against breast cancer
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3D-QSAR, molecular docking, ADMET, simulation dynamic, and retrosynthesis studies on new styrylquinolines derivatives against breast cancer

  • Reda EL-Mernissi EMAIL logo , Marwa Alaqarbeh , Ayoub Khaldan , Mohammed Kara EMAIL logo , Omkulthom Al kamaly , Anwar M. Alnakhli , Tahar Lakhlifi , Abdelouahid Sbai , Mohammed Aziz Ajana und Mohammed Bouachrine
Veröffentlicht/Copyright: 24. Juni 2024
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Open Chemistry
Aus der Zeitschrift Open Chemistry Band 22 Heft 1

Abstract

Breast cancer is the most common illness among women, accounting for 25% of all diagnoses, as stated by the American Cancer Society. Current research focuses on 43 compounds of styrylquinoline derivatives as potential inhibitors of tubulin to design a new drug that could potentially be effective against breast cancer cells in humans. The target compounds were subjected to a three-dimensional quantitative structure-activity relationship/comparative molecular similarity indices analysis (CoMSIA) approach, where CoMSIA models were used; the best results obtained are (Q 2 = 0.84, R 2 = 0.97, r ext 2 = 0.91), H-bond acceptor field was discovered to be important for increasing inhibitory activity by examining the contour maps (54%), and it plays a key role in the prediction of anticancer activity. Based on the contour maps of the CoMSIA models, we obtained information that allows us to propose four new molecules with higher cancer inhibitory than the 43 compounds found in the literature. The molecular docking was applied to determine the likely types of binding between the tubulin protein (PDB ID: 4O2B) and the proposed compounds, and the results show that M1 has a higher total score of 6.53 and two interactions with important conventional hydrogen bond type, followed by compound M2 with a total score of 5.74. Furthermore, the designed molecules showed better pharmacokinetic properties based on absorption, distribution, metabolism, excretion, and toxicity properties. Molecular dynamics simulations at 100 ns were conducted to confirm the binding stability of the selected ligands (M1 and M2) with tubulin protein. The simulation parameters used in the current study are root mean square deviation, root mean square fluctuation, H-bond, Rg, solvent accessible surface area, and binding energy. As a result, the designed compounds (ligands M1 and M2) have shown noteworthy potential as a drug candidate for experimental in vivo and in vitro testing due to their potential inhibition of breast cancer. Finally, the study of retrosynthesis in this work facilitates the synthesis of drug candidates.

Keywords: ADMET; cancer; molecular docking; molecular dynamics; 3D-QSAR; styrylquinolines; tubulin

1 Introduction

Cancer develops when cells become abnormal, expand beyond their normal limits, and then spread across the entire body or specific organs [1,2]. It is caused by abnormal cellular activities that characterize cancer, including division, morphogenesis, movement, and signaling [3,4]. Cancer affects all parts of the human body, with breast cancer being the most common type and the second leading cause of death, especially among women [5]. Chemotherapy is one of the most widely used methods for treating different types of cancer. Recently, inhibitors that stop or slow down the growth of cancer cells have been used as promising options for cancer treatment [6,7]. Microtubules consist of α and β tubulin heterodimers. They are a prime target for cancer therapy due to their crucial role in cellular processes such as division, cell shape maintenance, cellular motility, and intracellular transit [8]. The cellular functions of microtubules are based on the polymerization dynamics process, and destabilizing microtubules by decreasing microtubule polymer mass or promoting microtubule polymerization is critical for cell division, therefore controlling cancer progression [9]. Several agents, such as epothilones, paclitaxel, and docetaxel, stabilize the polymeric state of tubulin by inhibiting the polymerization process. Destabilizers and stabilizers impair the dynamics of microtubules and tubulin and halt cells during mitosis, thus resulting in cell death. Over the past decade, microtubule-targeting agents, especially vinca alkaloids and paclitaxel, have been widely used in treating different cancers.

Recently, 2-aryl-trimethoxyquinoline analogs have been reported as tubulin inhibitors, with significant action against breast cancer cells and ovarian carcinoma [10,11]. Quinoline is an alkaloid compound that undergoes secondary metabolism and is frequently used for medical, pharmacological, and biological purposes [12–14]. Natural sources include Berberidaceae, Fumariaceae, Papaveraceae, and Rutaceae plants [15–19]. Quinoline derivatives were used as antihypertensives, antiviruses [20], anticancer [21,22], antimalarial [23], antibiotics [24], antioxidants, antimicrobials, anti-inflammatory [25,26], anti-HIV [27]. For inhibition of cell-free platelet derived growth factor receptor tyrosine kinase (PDGF-RTK) activity [28], and antibacterials [25]. This work investigated previously synthesized 43 styrylquinoline compounds as tubulin polymerization inhibitors targeting human breast cancer by computational methods [11].

Computational chemistry techniques are increasingly being used in pharmaceutical research to accelerate the drug design and discovery process, which can reduce the time and cost of discovery research. Many studies have been conducted to develop anticancer drugs, and some compounds have shown great potential in fighting this disease. Our work aims to propose new drugs and design anticancer drugs using computational methods. In order to establish a correlation between the characteristic structure of styrylquinoline compounds and inhibitory activity against breast cancer, we used three-dimensional quantitative structure-activity relationship (3D-QSAR). To identify critical interactions, molecular docking was performed between four newly designed styrylquinoline molecules and tubulin receptors on virtual screen top hits. After screening the proposed compounds, we analyzed them for absorption, distribution, metabolism, excretion, and toxicity (ADMET) risk. Finally, we employed molecular dynamics (MD) simulations of 100 ns to investigate the interaction mechanism and conformational changes of the newly designed compounds (M1 and M2) at the binding site of the tubulin protein. Computational chemistry techniques are increasingly being used in pharmaceutical research to accelerate the drug design and discovery process, which can reduce the time and cost of discovery research. Many studies have been conducted to develop anticancer drugs, and some compounds have shown great potential in fighting this disease. Our work aims to propose new drugs and design anticancer drugs using computational methods.

2 Materials and methods

This study utilized 43 styrylquinoline compounds synthesized by Mirzaei et al. [11] to create 3D-QSAR models [29]. They have a median inhibitory concentration IC50(µM) ranging from 4.12 to 5.95 and are converted to pIC50 using the formula (pIC50 = −log IC50). The database of 43 styrylquinolines compounds was divided into two groups at random [30]; the first group contains 35 molecules used to create the 3D-QSAR model, and the second group (test) contains 8 compounds utilized for validation (their numbers are marked with a star [*]). The common structure of molecules is shown in Figure 1, and Table 1 displays their pIC50 values.

Figure 1 Structure of styrylquinoline derivatives used.
Figure 1

Structure of styrylquinoline derivatives used.

Table 1

PIC50 values of studied compounds

N° X 1 X 2 pIC50 N° X 1 X 2 pIC50
1*
5.06 23 —
5.59
2 —
5.12 24 —
4.12
3 —
5.30 25 —
5.02
4* —
5.05 26* —
5.02
5 —
4.62 27
5.27
6* —
4.67 28 —
5.35
7 —
4.64 29 —
5.65
8*
5.16 30 —
5.50
9 —
5.27 31 —
4.23
10 —
5.51 32 —
4.31
11* —
5.19 33 —
4.55
12 —
4.54 34
5.26
13 —
4.85 35* —
5.42
14 —
4.57 36 —
5.39
15
5.15 37 —
5.29
16 —
5.47 38 —
4.48
17 —
5.72 39
5.08
18 —
5.36 40 —
5.75
19 —
4.65 41 —
5.61
20
5.62 42 —
5.24
21 —
5.73 43 —
4.96
22 —
5.95

2.1 Minimization and alignment

The sybyl program was used to draw the structure of the compounds [31], and the geometry of these compounds was optimized via a tripos force field with a convergence threshold of 0.01 kcal/mol [32]. Using the alignment technique, the minimized structures were employed as the main conformations to create a trustworthy 3D-QSAR model [33–35]. Compound 22 was used as a template to align all 43 compounds, as shown in Figure 2.

Figure 2 Alignment of molecules.
Figure 2

Alignment of molecules.

2.2 Development of 3D-QSAR models

Comparative molecular similarity indices analysis (CoMSIA) contours were used to construct 3D-QSAR models to explore the relationship between 3D structure and the biological activity of the designed molecules [33,36,37]. CoMSIA contour used steric, electrostatic, hydrogen bond donor (HBD), hydrogen bond acceptor (HBA), and hydrophobic fields to enhance activity [38]. The energy cutoff was set at 30 kcal/mol, and the column filtering value was set at 2.0 kcal/mol, but the rest parameters of the partial least square (PLS) analysis were launched by default. There are parameters to consider while choosing a better model, such as the high values of correlation coefficient R 2 and the coefficient of cross-validation correlation Q 2 (R 2 > 0.60 and Q 2 > 0.50) [39,40]. Other parameters are taken into consideration such as the optimum number of components (N), the standard error of estimate (SEE), and F-test value (F). Furthermore, based on a test set, r ext 2 parameter was utilized to determine the best predictive model [41].

2.3 Y-randomization test

The Y-randomization method was used to check the correlation between the molecule’s structures and their pIC50. The computation of the two parameters q Yrand 2 and r Yrand 2 after each random change in the pIC50 is sufficient to assess the model’s dependability [38,42].

2.4 Density functional theory (DFT)

The energies of the boundary orbits [43], the lowest unoccupied molecular orbitals (LUMO), and the highest occupied molecular orbitals (HOMO) (E LUMO, and E HOMO) for the proposed compounds were calculated using Gaussian 09 software [44] with DFT and conjugated with the B3LYP/6-31G base [45,46]. E HOMO and E LUMO are used to calculate the following properties; the energy gap ∆E gap = (E LUMO − E HOMO), chemical hardness (η = (∆E gap)/2), the chemical potential μ is the first derivative of total energy [47], it is calculated by (μ = (E LUMO + E HOMO)/2), electronegativity (χ = −μ) explains the possibility and character of bond polarization [48], the chemical softness (S = 1/η), and the global electrophilicity (ω = (μ 2/2η)) [49].

2.5 Molecular docking

In recent decades, extensive research has been conducted on using tubulins as a biological target in cancer treatment, mainly due to their crucial role in the mitosis process [50]. Various tubulin inhibitors, specifically αβ-tubulin, have been investigated, revealing four main classes of compounds that bind to specific and corresponding sites: laulimalide, taxane, Vinca alkaloid, and colchicine. For docking with potent molecules, the protein tubulin complex with colchicine (PDB ID: 4O2B) was selected as the ideal target protein [51]. Downloading the protein tubulin (4O2B) from the Protein Data Bank (PDB) is the first step in molecular docking by link (https://www.rcsb.org). After that, it is necessary to eliminate water molecules to prepare receptors in their fundamental state. This step is followed by the usage of Surflex-Dock module in Sybyl 2.0, and in the final step, it is necessary to use Discovery and Pymol software [52,53] to determine the types of interactions [54,55].

2.6 ADMET prediction

The pharmacokinetics properties of absorption, distribution, metabolism, excretion, and toxicity are abbreviated in the word ADMET [56,30,38]; the SWISSADME web server has been programmed to determine these properties of the proposed derivatives (M1–M4) and 22; SWISSADME is a free web application on a website (http://www.swissadme.ch), and this step requires in the first step the drawing of these molecules, then the transformation of these structures in the form of codes (SMILES format), pkCSM (https://biosig.lab.uq.edu.au/pkcsm) webservers used to provide a set of molecules (SMILES format) [30,38], and in the second step, we put the codes (SMILES format) in the prediction of ADMET properties [54,57,58].

2.7 MD simulations

MD simulations during 100 ns were applied to get an overview of the stability of the [Tubulin–PredM1; Tubulin–PredM2] protein–ligand interaction [59]. The dynamic simulation run was performed based on the best docking position. In order to neutralize the system, Cl−/Na+ ions are introduced in this stage. The steepest descent (5,000 steps) was applied to minimize system energy. To check if the system has reached the pressure (1 bar) and temperature (300 K), balancing is applied under an NVT ensemble (constant number of particles N, constant system volume V and constant temperature T) at 298 K. A MD simulation with an NPT ensemble (constant pressure P) was followed to relax and refine the configuration at 1 atm, to stabilize the pressure and temperature of the complexes. Each complex is simulated at a temperature of 300 K for 125 ps, with the use of 40 and 400 kJ mol−1 nm−2 positional restraints on the side chains and the backbone, respectively. In addition, the NPT assembly was used in the production of 100 ns of simulation. The production and equilibration run were obtained during the simulation time by GROMACS 5.0.7 [60], and the complexes [Tubulin–PredM1; Tubulin–PredM2] are subjected to a CHARMM36 force field [61]. The most accentuated descent approach (5,000 cycles) was used for initial minimization by the simulation annealing method. Energy minimization for the entire system is performed at 50,000 energy minimization steps. The Ewald particle mesh method deals with electrostatic interactions with a physiological condition (0.9% NaCl, pH 7.4, 300 K) [62]. After the dynamic simulation duration (100 ns) is complete, the stability of [Tubulin–PredM1; Tubulin–PredM2] complexes was analyzed by root mean square fluctuation and deviation (RMSF and RMSD), number of H bond, solvent accessible surface area (SASA), and radius of gyration (R g). The g-MMPBSA program was used to estimate the binding free energies (ΔG Bind) of the screened complexes from the molecular mechanics of the Poisson–Boltzmann surface (MM-PBSA).

Generally, the binding free energy can be expressed as

Δ G binding = Δ G complex − ( Δ G protein + Δ G ligand ) ,

where Δ G complex represents the total free energy of the complex, Δ G protein represents the total free energies of the isolated protein, and Δ G ligand represents the total free energy of ligands in a solvent. To decompose the binding energy, at first Δ E MM , Δ G polar , and Δ G non − polar were separately calculated for each residue.

2.8 Retrosynthesis

Retrosynthesis is an essential technique for the organic synthesis of drugs. It facilitates the synthesis of candidate drug molecules by reducing the cost and time of the synthesis process [63]. The current work (spaya.ai) database was used to facilitate the synthesis of compounds M1 and M2 as drug candidates against breast cancer.

2.9 Applicability domain

The Domain of Applicability determines the range in which a compound can be confidently predicted. This analysis aspect is explicitly requested [64]. It should be noted that the QSAR-built model is not intended to be used outside of this domain [65]. There are multiple ways to determine the scope of the quantitative structure-property relationship model, including the “leverage” method. The standardized residuals of the dependent variable vary depending on the leverages, which is the basis of this method. Suppose a compound has leverage that exceeds the threshold h* = 3(k + 1)/N (where N is the number of molecules in the learning set, and k is the number of descriptors). In that case, that compound is considered adequate for the developed model.

3 Results and discussion

3.1 3D-QSAR analysis

The accuracy of the 3D-QSAR model underwent both internal and external validation. CoMSIA produced the most favorable results with an R 2 value of 0.97, an optimized component of 5, a Q 2 value of 0.84, an SEE value of 0.09, and an F-test value of 224.65. Additionally, the SEE value was low at 0.09, demonstrating the created model's strong internal predictability. Hydrophobic, electrostatic, steric, HBD, and HBA contributions were 20, 9, 6, 11, and 54%, respectively. The external validation ( r ext 2 ) of CoMSIA reached 0.91. The HBA field has a remarkable role in the CoMSIA model, followed by the hydrophobic field. The statistical results of 3D-QSAR PLS analysis are summarized in Table 2, and the experimental and predicted pIC50 of molecules are listed in Table 3. The values of experiments and predictions are closer. Figure 3 shows a regular distribution of styrylquinoline derivatives that target human breast cancer activity values as tubulin polymerization inhibitors.

Table 2

The results obtained

Model R 2 Q 2 F S CV r ext 2 N Fraction
Ster Acc Elec Hyd Don
CoMSIA 0.97 0.84 224.65 0.09 0.91 5 0.06 0.54 0.09 0.20 0.11
Table 3

Experimental and predicted values

N° pIC50 CoMSIA predicted N° pIC50 CoMSIA predicted
1* 5.06 5.01 23 5.59 5.75
2 5.12 5.07 24 4.12 4.4
3 5.30 5.25 25 5.02 5.22
4* 5.05 5.15 26* 5.02 5.15
5 4.62 4.86 27 5.27 5.12
6* 4.67 4.79 28 5.35 5.45
7 4.64 4.74 29 5.65 5.62
8* 5.16 5.20 30 5.50 5.63
9 5.27 5.18 31 4.23 4.50
10 5.51 5.47 32 4.31 4.49
11* 5.19 5.08 33 4.55 4.85
12 4.54 4.81 34 5.26 5.27
13 4.85 4.91 35* 5.42 5.37
14 4.57 4.70 36 5.39 5.43
15 5.15 5.20 37 5.29 5.42
16 5.47 5.40 38 4.48 4.71
17* 5.72 5.60 39 5.08 5.33
18 5.36 5.48 40 5.75 5.62
19 4.65 4.76 41 5.61 5.75
20 5.62 5.72 42 5.24 5.13
21 5.73 5.65 43 4.96 5.01
22 5.95 5.85

*: Test molecule.

Figure 3 Graphical representation of the observed and predicted activity of training and test sets for CoMSIA models.
Figure 3

Graphical representation of the observed and predicted activity of training and test sets for CoMSIA models.

4 Contour map analysis

The most active compound 22 is used as a reference for building CoMSIA contours.

4.1 CoMSIA contour maps

The green area of the steric field shown in Figure 4a shows a steric contribution to potency, while the yellow zone decreases potency. A green contour region is visible near two (O–CH3) groups of R 2 substituent, suggesting a preference for small bulky groups that explains the greater biological activity of compound 17 (pIC50 = 5.72) than compounds 16 (pIC50 = 5.47) and 15 (pIC50 = 5.15). Figure 4b shows an electrostatic contour map; the blue contour represents the electronegative charge disfavoring, and the red contour represents the electropositive group disfavoring, respectively. The presence of blue near two (O–CH3) of R 2 and red near the other group (O–CH3) explains why compound 29 (pIC50 = 5.65) is more active than compound 28 (pIC50 = 5.35) and compound 27 (pIC50 = 5.27). Based on electrostatic and steric fields percents (9 and 6%, respectively), HBD, the hydrophobic fields, and HBA in CoMSIA were examined, as shown in Figure 5a, b, and c, respectively.

Figure 4 Contour maps of CoMSIA analysis: (a) steric field, and (b) electrostatic field.
Figure 4

Contour maps of CoMSIA analysis: (a) steric field, and (b) electrostatic field.

Figure 5 Contour maps of CoMSIA: (a) HBD field, (b) hydrophobic field, and (c) HBA field.
Figure 5

Contour maps of CoMSIA: (a) HBD field, (b) hydrophobic field, and (c) HBA field.

HBD (Figure 5a) has a lower stake (11%) than HBA and hydrophobic fields, making them suitable for design in other fields. The cyan and purple in the HBD field indicate favorable and unfavorable conditions for increased activity, respectively. Where H-bond donor groups cause increased activity, as shown by a cyan area around the three –O–CH3 groups of substituent R 2, which explains the higher activity of compound 11 (pIC50 = 5.19) compared to compound 8 (pIC50 = 5.16), and the higher activity of compound 18 (pIC50 = 5.36) compared to compound 13 (pIC50 = 4.85). Figure 5b demonstrates the hydrophobic field; the white and yellow colors represent the hydrophobic parties decrease and increase activity. The R 1 and R 2 substituents are covered in yellow, meaning water repellency is preferred for increasing activity at these locations. For example, the higher activity of compound 34 (pIC50 = 5.26) compared to compound 8 (pIC50 = 5.16). While the magenta and red colors are shown in Figure 5c of the HBA contour map, they represent HBA groups that can increase and decrease biological activity, respectively. Two substituents, R 1 and R 2, with (O–CH3) covered by magenta, show that this type of HBA group is preferred for increasing activity at these locations, which also explains the higher bioactivity of compound 40 (pIC50 = 5.75) compared to the bioactivity of compound 21 (pIC50 = 5.73).

4.2 Y-randomization test

It provides a further understanding of the relationship between the pIC50 and the structures of the molecules. The value of pIC50 changed randomly to determine the q Yrand 2 and r Yrand 2 values after each Y-randomization test; the results are listed in Table 4, where the lower values indicate that the correlation is not random.

Table 4

Result of the Y-randomization test

Iteration CoMSIA Iteration CoMSIA
q Yrand 2 r Yrand 2 q Yrand 2 r Yrand 2
1 0.234 0.367 4 0.345 0.356
2 0.453 0.543 5 −0.052 0.121
3 −0.234 0.053 6 0.123 0.321

4.3 Design and prediction

To design new compounds with greater activity than the most active molecule [22], the 3D-QSAR model uses the most important fields to investigate suitable substituents of styrylquinoline derivatives as tubulin polymerization inhibitors against breast cancer cells. The most contributed fields (HBA) of four newly designed styrylquinoline compounds (M1–M4) have the most tubulin polymerization inhibitory activity, and the pIC50 value of compound 22 is lower than the pIC50 of the newly designed compounds. Table 5 shows the structures and calculated pIC50 of the newly designed compounds.

Table 5

Newly designed molecules and their predicted pIC50

N° Compounds Predicted pIC50 N° Compounds Predicted pIC50
22
5.80 M3
6.42
M1
6.76 M4
6.14
M2
6.65

4.4 DFT

Molecular frontier orbitals can donate and accept electrons. HOMO and LUMO represent the capacity of a compound to donate and withdraw electrons from proposed molecules (M1–M4) electrons, respectively. The unit of energy was measured in the atomic units (au) and then converted to eV (1 au = 27.2113 eV). Table 6 presents the properties computed for proposed compounds in this article, such as hardness η, electronegativity χ, chemical potential µ, energy gap ∆E gap, and electrophilicity ω. The energy gap (∆E gap) is an important property of designed compounds, giving essential information about these compounds [33]. The smaller the gap energy, the more easily the compound can be excited. The results outlined in Table 6 demonstrate that M1 and M2 have energy gaps of 3.1382 and 3.0604 eV, respectively, indicating that these compounds are less stimulated. This information can be valuable when considering the properties of these compounds for potential applications. The electronegativity and hardness of the proposed compound M1 were found to be 3.9577 and 1.5610 eV, respectively. The LUMO and HOMO energies of the less excited molecule M1 were calculated as −2.3886 and −5.5268 eV. In contrast, the LUMO and HOMO energies of the designed molecule M2 were calculated as −2.1840 and −5.2444 eV, respectively.

Table 6

Electronic descriptors for the proposed compounds

N° Global properties (eV)
E LUMO E HOMO µ η χ ω ∆E gap
M1 −2.3886 −5.5268 −3.9577 1.5610 3.9577 4.7436 3.1382
M2 −2.1840 −5.2444 −3.7142 1.5302 3.7142 4.5076 3.0604
M3 −2.4756 −5.2085 −3.8420 1.3664 3.8420 5.4014 2.7329
M4 −2.4337 −5.2403 −3.8370 1.4033 3.8370 5.2456 2.8066
22 −2.1739 −5.5301 −3.8520 1.6781 3.8520 4.4210 3.3562

4.5 Molecular docking

αβ-Tubulin dimers have four distinct interaction sites, including the colchicine-binding site (CBS). This makes CBS a promising target for developing new tubulin modulators. The tubulin co-crystal ligand was redocked into the binding pocket before the docking investigation to ensure that the docking approach was reliable, as shown in Figure 6. The redocked conformation of 4O2B was extremely consistent with co-crystallized ligand conformation with an RMSD of 1.388Å (<2.0Å). That indicates that the docking method was reliable and might be used in future research. Molecular docking was used to examine how the newly designed compounds (M1, M2, M3, and M4) and compound 22 bind to the target tubulin–colchicine complex (PDB ID: 4O2B). The results of the 2D view are listed in Table 7, and the interaction type and total score are listed in Table 8.

Figure 6 Re-docking pose with the RMSD value of 1.388 Å (green = original, orange = docked).
Figure 6

Re-docking pose with the RMSD value of 1.388 Å (green = original, orange = docked).

Table 7

2D view of the binding conformation for the newly designed compounds (M1, M2, M3, and M4) and compound 22 with the target tubulin–colchicine complex (PDB ID: 4O2B)

N° 2D view N° 2D view
22 M1
STRM2 M3
M4
Table 8

Interaction types and total score of the newly designed compounds (M1, M2, M3, and M4) and compound 22 with the target tubulin–colchicine complex

N° Types of interactions Total score
Carbon H-bond, Pi-DHB, conventional hydrogen bond Pi-sigma, amide-Pi stacked, Pi-sulfur, Pi-cation Pi-alkyl, alkyl
22 Asn349, Asn350 Asn258 Leu248, Cys241 3.45
M1 Asn167, Asn258, Tyr202, Ala317, Lys352, Thr353, Ala354 Leu242, Val238, Leu255, Lys254 Leu252, Ala250, Leu255, Ala354 6.53
M2 Gly237, Val238 ,Lys352 , Asn258 Lys254 Leu255, Cys241, Ile318, Ala250, Lys352 5.74
M3 Gln247, Asn249, Asn350, VaL238 Leu248, Met259 Ala316, Gys241, Leu248 4.43
M4 Thr353, Gln247, Asp251 Leu255, Lys254 Ala250, Lys352 3.67

The results show that all compounds have favorable interactions, with a total score between 3.45 and 6.53. Compound M1 is the most stable because it has the highest total score of 6.53. The interaction types of M1 with the tubulin protein active site consist of two conventional hydrogen bonds: Asn167 and Ala354, six bonds mixed between carbon H-bonds and Pi-DHB: (Asn258), Tyr202, Ala317, Thr353, Lys352, five Pi sigma/amide-Pi stacked: Leu242, Val238, Leu255, Lys254, and six Pi-alkyl types: Leu252, Ala250, Leu255(4.94), Ala354. Compound M2 has a total score of 5.74 with six different interaction types with the tubulin protein active site consisting of carbon H-bond and Pi-DHB: Val238, Gly237, Asn258, Lys352, one of amide-Pi stacked Lys254, one of Pi-cation Lys254, and five of Pi-alkyl Leu255, Cys241, Ile318, Ala250, Lys352. Moreover, compound M3 has a total score of 4.43 and has four carbon–hydrogen bonds: Gln247, Asn249, Asn350, VaL238, one of amide-Pi stacked Leu248; one of Pi-cation type Met259; and three of Pi-Alkyl Ala316, Gys241, Leu248. The binding mode of compound M4 revealed that it bonds inside the binding pocket with a total score of 3.67 and has four types of carbon–hydrogen bond interaction Thr353, Gln247, Asp251, one Pi-Sigma (Leu255), one Pi-cation (Lys254), and three types of pi-alkyl interaction Ala250, Lys352, and Leu 248. Compound 22 has a total energy of 3.45 with three types of carbon–hydrogen bond interactions: Asn349, Asn350, one interaction of type Pi-sigma Leu255, one interaction of type Pi-cation Lys254, and three interactions of type Pi-Alkyl Ala250, Lys352.

These compounds are more stable than the reference compound 22, which has a total score of 3.45. It has two interactions of type carbon–hydrogen bond interaction (Asn349, Asn350), one interaction of type Pi-sigma (Asn258), and two mixed interactions between Pi-alkyl and alkyl (Leu248, Cys241). Additionally, molecular docking studies indicated that compounds M1 and M2 have a strong affinity for the CBS of tubulin and could serve as lead molecules for future pharmaceuticals.

4.6 ADMET prediction

Computational pharmacology (ADMET) is a rapidly growing field that includes developing software to integrate and capture medical and biological data worldwide. It is important to study the ADMET properties of compounds, which can be done using in silico techniques, to find and create new medicines. Table 9 shows the ADMET prediction. The results show that all the compounds have an absorbance value of almost 90%, which means the human intestine absorbs them well. The blood–brain barrier (BBB) protects the brain and maintains good health. Furthermore, it blocks the flow of hazardous chemicals, preventing them from easily entering the brain; all designed compounds have a log BB value <−1 [66,67], and they cross the BBB poorly. The second parameter is the volume of distribution (VDss), which measures the intensity of drug diffusion in the body; the values are not accepted if (VDss) <−0.15 and are considered low, while compounds with VDss >0.45 are accepted and have a high distribution. All designed compounds (M1, M2, M3, and M4) have VDss >0.45.

Table 9

ADMET prediction of the most potent (C22) and the newly designed

Model Compounds
22 M1 M2 M3 M4
(A)
Intestinal absorption (human) 83.90 89.95 98.42 86.70 87.23
(D)
VDss (human) 0.35 0. 46 0. 49 0. 56 0. 52
BBB (logBB) −0.984 −1.08 −1.41 −1.28 −1.45
(M)
Inhibition (CYP) 2C19 + + + + +
2D6 − − − — −
3A4 + + + + +
2C9 + + + + +
1A2 − − − − −
Substrate (CYP) 3A4 + + + + +
2D6 − − − − −
(E)
Clearance 0.62 0.40 0.45 0.65 0.66
(T)
AMES toxicity − − − − −

Abbreviation symbol: A: Absorption, D: Distribution, VDss: Volume of distribution, BBB: Blood–brain barrier, M: Metabolism, E: Excretion, T: Toxicity, +: Yes, −: Non.

Drug detoxification and elimination is an essential stage in metabolism; many cytochromes are involved, including 2D6, 3A4, 1A2, 2C19, 2C9, 2D6, and 3A4. All newly designed compounds (M1, M2, M3, and M4) were shown to be CYP3A4 inhibitors and substrates, but they are inhibitors only of 1A2 and 2C19 in this research. Constant clearance describes the relationship between drug concentration in the body and drug removal rate; when the clearance value is higher, the medication is considered safe. All designed compounds have a higher clearance value and are not toxic, making them the best-designed drug treatment against human breast cancer cells.

4.7 MD simulation

Under normal pressure and temperature conditions, the MD simulations were run for 100 ns for the best-newly designed compounds M1 and M2 with tubulin protein active site (PredM1 and PredM2) complexes on the “C-alpha position.” They demonstrated the best stability at molecular docking, and the results during dynamic simulation showed that the ligands (PredM1 and PredM2) remain bound to the protein (tubulin (pocket. Calculations of RMSD, RMSF, R g, hydrogen bonding, SASA, and average center of the mass distance between protein–ligand and binding free energy (MMPBSA) were carried out to assess the stability of each structure.

All the proposed complexes [Tubulin–PredM1; Tubulin–PredM2] showed good stability in 100 ns simulation.

4.8 RMSD

All complex configurations’ stability was studied using the RMSD during 100 ns MD simulation. Figure 7 shows RMSD for (PredM1 and PredM2); the RMSD of PredM1 ligand reaches its maximum value but is less than 40 ns during the first 30 s; after 40 ns, the graphs of M1 ligands and the protein need clarification during simulation. In the pred M2, generally, the whole chart remains stable during simulations (100 ns), and the RMSD values for the complex reach 2 Å. Generally, backbone RMSD stabilizes after 40 ns for the (Tubulin–PredM1) complex and after 10 ns for the (Tubulin–PredM2) complex. The average values of the backbone RMSD for M1 and M2 are 1.9 ± 0.3 and 1.3 ± 0.17 Å, respectively. Generally, all deviations of proteins, ligands, and complexes are less than 4 Å; this indicates the proteins, ligands, and complexes are stable in the majority time of simulation dynamics.

Figure 7 RMSD graphs of the complex, protein (Tubulin), and ligands (M1, M2) according to the simulation.
Figure 7

RMSD graphs of the complex, protein (Tubulin), and ligands (M1, M2) according to the simulation.

4.9 RMSF

RMSF was utilized to evaluate the average fluctuation of the overall structure for each amino acid in the protein or to identify a flexible protein region. Figure 8 shows the RMSF graphs for the two complexes (Tubulin–PredM1, Tubulin–PredM2). In the graph of Pred M2, five peaks appear at the residue numbers 50, 180, 280, 380, and 440, while in the complex Pred M1, a peak appears at the 380 residues. As a result, the (Tubulin–PredM1) complex is more adaptable and of higher quality than the (Tubulin–PredM2) complex. The fluctuation intensity remains below 3.0 Å for both complexes except for some residues representing loops or turns in the protein (Tubulin). The (Tubulin–PredM2) complex has lower RMSF values than (Tubulin–PredM1), which indicates a better effect the ligand has on stabilizing protein (Tubulin) residues than M1. Generally, the RMSF of all complexes (Tubulin–PredM1, Tubulin–PredM2) are significantly similar, and all graphs of RMSF characteristics show less fluctuation, which indicates excellent stability of the two complexes. In addition, the appearance of the same amino acids in molecular docking and dynamic simulations as Asn167, Tyr202, Leu242, Asn258, Ala317, Lys352, Thr353, and Ala354 complex (Tubulin–PredM1) and Gly237, Val238, Leu255, Asn258, and Ile318 for (Tubulin–PredM2). The molecular simulation dynamics results indicate that all docking molecular results are validated.

Figure 8 Flexibilities of the protein backbones examined by RMSF values.
Figure 8

Flexibilities of the protein backbones examined by RMSF values.

4.10 Hydrogen bond

The total number of hydrogen bonds and average center-of-mass distance formed between the ligand (M1, M2) and protein (Tubulin) are shown in Figure 9. For the (Tubulin–PredM1) complex, the maximum number of hydrogen bonds reaches three at 25 and 85 ns, respectively. After that, this number reaches two hydrogen bonds at 6, 8, 18, 22–24, 62, 71–73, 78, and 98 ns. In the rest of the simulation, this number reaches 1. Generally, ligand M1 has H-bond gaps throughout most of the simulation time, even though the ligand is stable in the binding site. For the (Tubulin–PredM2) complex, the maximum number of hydrogen bonds reaches four at 16, 18, 58, and 98 ns. After that, this number reaches three, two, and one hydrogen bonds for the rest of the simulation. Ligand M2 forms a stable hydrogen bond network with the protein (Tubulin) over the simulation time.

Figure 9 (a) and (b) Hydrogen bonds of complex (Tubulin–PredM1) and (Tubulin–PredM2), respectively.
Figure 9

(a) and (b) Hydrogen bonds of complex (Tubulin–PredM1) and (Tubulin–PredM2), respectively.

4.11 R g

R g is linked to the conformational state to show if a structure is folded and compact and has a stable conformation. The smaller the R g value, the less flexible the proteins and the more stable the complexes. According to Figure 10, low R g values for PredM1 (A) and PredM2 (B) led us to believe they were stable. R g in the simulation was used to determine the compactness of the structure. The consistency throughout the simulation and the lower degree of fluctuation during the simulation indicate that the system is more rigid and compact. Both complexes (Tubulin–PredM1) and (Tubulin–PredM2) have the lowest R g values, 21.13 and 20.97 Å, respectively. The R g values varied between 21.13 and 21.43 Å for (Tubulin–PredM1) complexes and between 20.97 and 21.35 Å for (Tubulin–PredM2) complexes. In conclusion, all complexes showed relatively similar consistent R g and compactness values, indicating they have good stability.

Figure 10 (Tubulin–PredM1) (a) and (Tubulin–PredM1) (b) R g of the complexes during 100 ns MD simulation.
Figure 10

(Tubulin–PredM1) (a) and (Tubulin–PredM1) (b) R g of the complexes during 100 ns MD simulation.

4.12 SASA

The magnitude of conformational changes that the water solvent can access was predicted using the graphs of SASA of the complexes (Tubulin–PredM1; Tubulin–PredM2). Hence, the interaction between these complexes and the solvent during the 100 ns MD simulation was analyzed using the SASA, as shown in Figure 11a. Figure 11a shows an SASA value varying between 1,825 and 1,875 Å2 for Tubulin–PredM1 in the first 50 ns of simulations. However, after 50 ns, the solvent-accessible surface area varied between 1,850 and 1,925 Å2, indicating that the surface reached in the second period and remained stable. On the other hand, Figure 11b revealed values between 1,800 and 1,850 Å2 in the first 38 ns. After the simulation interval [38–65 ns], the SASA value increases, reaching 1,825 and 1,875 Å2. In the rest of the simulation [66–100 ns], this value slowly decreases and varies between 1,800 and 1,850 Å2. The solvent-accessible zone reached by the [Tubulin–PredM1] complex is substantially similar to the zone reached by the [Tubulin–PredM1] complex; this value is higher and is very important, and SASA values for these two complexes (Tubulin–PredM1; Tubulin–PredM2) remain relatively stable, which indicates no significant changes during a 100 ns simulation.

Figure 11 SASA for 100 ns of the simulations. (a) and (b) represent Tubulin–PredM1 and Tubulin–PredM2, respectively.
Figure 11

SASA for 100 ns of the simulations. (a) and (b) represent Tubulin–PredM1 and Tubulin–PredM2, respectively.

4.13 Binding energy calculation

MM/PBSA determines how stable newly designed compounds are for binding to tubulin proteins, using the fastest force field to calculate the free energy. The different types of energies calculated by the MM-PBSA method during 100 ns of simulation are listed in Table 10. The average binding free energies of (Tubulin–PredM1) and (Tubulin–PredM2) complexes calculated were found to be −101.775 ± 26.749 and −108.355 ± 77.540 kJ/mol, respectively. The average ΔE MM for (Tubulin–PredM1) and (Tubulin–PredM2) complexes were found to be −181.36 ± 79.654 and −238.691 ± 62.879 kJ mol−1, respectively. The average values of ΔG Polar for the (Tubulin–PredM1) and (Tubulin–PredM2) complexes were −18.464 ± 3.421 kJ/mol and −22.222 ± 3.487 kJ/mol, respectively. The calculated average ΔG SASA for (Tubulin–PredM1) and (Tubulin–PredM2) complexes were −18.464 ± 3.421 and 22.222 ± 3.487 kJ/mol, respectively. From the MM-PBSA result, we notice that all complexes showed almost closer values; this indicates good and excellent stability of our complexes.

Table 10

Binding free energies of predicted compounds (kJ/mol)

Ligands
Energy (kJ/mol) M1 M2
ΔE MM Van der Waal −151.658 ±38.652 −187.563±32.693
Electrostatic −29.702 ± 41.002 −51.128 ± 30.186
ΔG Sol Polar solvation 98.050 ± 33.839 152.559 ± 68.591
ΔG SASA −18.464 ± 3.421 −22.222 ± 3.487
Binding (ΔG bind) −101.775 ± 26.749 −108.355 ± 77.540

4.14 Retrosynthesis analysis

Based on the retrosynthesis methods, the Spaya platform (https://www.spaya.ai) facilitates the synthesis of drug candidates with a high total score. Each route is a collection of similar synthetic steps documented in the literature (Scheme 1 and Table 11).

Scheme 1 The different stages of the prepared compound M1.
Scheme 1

The different stages of the prepared compound M1.

Table 11

Reactions for the synthesis of compound M1

4.15 Applicability domain

Figure 12 shows the standardized prediction errors as a function of the lever values (hi) of the molecules in training, test sets, and the proposed M1 and M2 molecules.

Figure 12 Williams plot to evaluate the applicability domain of the model (h* = 0.53 and residual limits = ±2).
Figure 12

Williams plot to evaluate the applicability domain of the model (h* = 0.53 and residual limits = ±2).

All compounds have levers hi < h ∗ = 0.53, and all the tailings including M1 and M2 are found to be in the range of (2SD) (horizontal lines); this explains is a reliable model.

5 Conclusion

This work uses various in silico techniques, such as 3D-QSAR/CoMSIA, molecular Docking, ADMET, MD, MM-PBSA binding energy calculations, and retrosynthesis. In order to examine and identify the essential designed compounds for tubulin inhibition against human breast cancer. The statistical results (Q 2 = 0.84, R 2= 0.97, r ext 2 = 0.91) obtained from the best CoMSIA-HA model are reasonable, predictive, and reliable, which gives credibility to the molecules designed (M1, M2, M3, and M4). Molecular docking was used to predict the interaction types and total score of all designed compounds at the active site of tubulin protein. The designed styrylquinolines compounds (M1 and M2) showed great stability and demonstrated excellent pharmacokinetic characteristics (ADMET). Moreover, MD simulations combined with free energy evaluated the stability of compounds M1 and M2. Therefore, the two proposed compounds (M1 and M2) were found to be more stable in the tubulin protein binding site.

It is important to synthesize these compounds, which may be useful in developing tubulin polymerization inhibitors against human breast cancer candidate drugs.

Acknowledgements

The authors extend their appreciation for Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2024R489) Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

  1. Funding information: The research was finically supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2024R489) Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

  2. Author contributions: R.E.M., M.A., A.K., M.A.A., and M.B.: methodology; formal analysis; R.E.M, A.K., and M.K.: writing-review and editing; R.E.M, O.A, A.M.A and M.K.: software, project administration, resources, R.E.M., T.L., A.S and M.A.A.: writing-original; M.A.A., and M.B.: supervision, review and editing, R.E.M., M.A.A., A.M.A., and M.B.: conceptualization. All authors have read and agreed to the published version of the manuscript.

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

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

  5. Data availability statement: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Received: 2024-03-13
Revised: 2024-05-02
Accepted: 2024-05-06
Published Online: 2024-06-24

© 2024 the author(s), published by De Gruyter

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

Artikel in diesem Heft

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https://doi.org/10.1515/chem-2024-0041
Schlagwörter für diesen Artikel
ADMET; cancer; molecular docking; molecular dynamics; 3D-QSAR; styrylquinolines; tubulin
Creative Commons
BY 4.0

Artikel in diesem Heft

  1. Regular Articles
  2. Porous silicon nanostructures: Synthesis, characterization, and their antifungal activity
  3. Biochar from de-oiled Chlorella vulgaris and its adsorption on antibiotics
  4. Phytochemicals profiling, in vitro and in vivo antidiabetic activity, and in silico studies on Ajuga iva (L.) Schreb.: A comprehensive approach
  5. Synthesis, characterization, in silico and in vitro studies of novel glycoconjugates as potential antibacterial, antifungal, and antileishmanial agents
  6. Sonochemical synthesis of gold nanoparticles mediated by potato starch: Its performance in the treatment of esophageal cancer
  7. Computational study of ADME-Tox prediction of selected phytochemicals from Punica granatum peels
  8. Phytochemical analysis, in vitro antioxidant and antifungal activities of extracts and essential oil derived from Artemisia herba-alba Asso
  9. Two triazole-based coordination polymers: Synthesis and crystal structure characterization
  10. Phytochemical and physicochemical studies of different apple varieties grown in Morocco
  11. Synthesis of multi-template molecularly imprinted polymers (MT-MIPs) for isolating ethyl para-methoxycinnamate and ethyl cinnamate from Kaempferia galanga L., extract with methacrylic acid as functional monomer
  12. Nutraceutical potential of Mesembryanthemum forsskaolii Hochst. ex Bioss.: Insights into its nutritional composition, phytochemical contents, and antioxidant activity
  13. Evaluation of influence of Butea monosperma floral extract on inflammatory biomarkers
  14. Cannabis sativa L. essential oil: Chemical composition, anti-oxidant, anti-microbial properties, and acute toxicity: In vitro, in vivo, and in silico study
  15. The effect of gamma radiation on 5-hydroxymethylfurfural conversion in water and dimethyl sulfoxide
  16. Hollow mushroom nanomaterials for potentiometric sensing of Pb2+ ions in water via the intercalation of iodide ions into the polypyrrole matrix
  17. Determination of essential oil and chemical composition of St. John’s Wort
  18. Computational design and in vitro assay of lantadene-based novel inhibitors of NS3 protease of dengue virus
  19. Anti-parasitic activity and computational studies on a novel labdane diterpene from the roots of Vachellia nilotica
  20. Microbial dynamics and dehydrogenase activity in tomato (Lycopersicon esculentum Mill.) rhizospheres: Impacts on growth and soil health across different soil types
  21. Correlation between in vitro anti-urease activity and in silico molecular modeling approach of novel imidazopyridine–oxadiazole hybrids derivatives
  22. Spatial mapping of indoor air quality in a light metro system using the geographic information system method
  23. Iron indices and hemogram in renal anemia and the improvement with Tribulus terrestris green-formulated silver nanoparticles applied on rat model
  24. Integrated track of nano-informatics coupling with the enrichment concept in developing a novel nanoparticle targeting ERK protein in Naegleria fowleri
  25. Cytotoxic and phytochemical screening of Solanum lycopersicum–Daucus carota hydro-ethanolic extract and in silico evaluation of its lycopene content as anticancer agent
  26. Protective activities of silver nanoparticles containing Panax japonicus on apoptotic, inflammatory, and oxidative alterations in isoproterenol-induced cardiotoxicity
  27. pH-based colorimetric detection of monofunctional aldehydes in liquid and gas phases
  28. Investigating the effect of resveratrol on apoptosis and regulation of gene expression of Caco-2 cells: Unravelling potential implications for colorectal cancer treatment
  29. Metformin inhibits knee osteoarthritis induced by type 2 diabetes mellitus in rats: S100A8/9 and S100A12 as players and therapeutic targets
  30. Effect of silver nanoparticles formulated by Silybum marianum on menopausal urinary incontinence in ovariectomized rats
  31. Synthesis of new analogs of N-substituted(benzoylamino)-1,2,3,6-tetrahydropyridines
  32. Response of yield and quality of Japonica rice to different gradients of moisture deficit at grain-filling stage in cold regions
  33. Preparation of an inclusion complex of nickel-based β-cyclodextrin: Characterization and accelerating the osteoarthritis articular cartilage repair
  34. Empagliflozin-loaded nanomicelles responsive to reactive oxygen species for renal ischemia/reperfusion injury protection
  35. Preparation and pharmacodynamic evaluation of sodium aescinate solid lipid nanoparticles
  36. Assessment of potentially toxic elements and health risks of agricultural soil in Southwest Riyadh, Saudi Arabia
  37. Theoretical investigation of hydrogen-rich fuel production through ammonia decomposition
  38. Biosynthesis and screening of cobalt nanoparticles using citrus species for antimicrobial activity
  39. Investigating the interplay of genetic variations, MCP-1 polymorphism, and docking with phytochemical inhibitors for combatting dengue virus pathogenicity through in silico analysis
  40. Ultrasound induced biosynthesis of silver nanoparticles embedded into chitosan polymers: Investigation of its anti-cutaneous squamous cell carcinoma effects
  41. Copper oxide nanoparticles-mediated Heliotropium bacciferum leaf extract: Antifungal activity and molecular docking assays against strawberry pathogens
  42. Sprouted wheat flour for improving physical, chemical, rheological, microbial load, and quality properties of fino bread
  43. Comparative toxicity assessment of fisetin-aided artificial intelligence-assisted drug design targeting epibulbar dermoid through phytochemicals
  44. Acute toxicity and anti-inflammatory activity of bis-thiourea derivatives
  45. Anti-diabetic activity-guided isolation of α-amylase and α-glucosidase inhibitory terpenes from Capsella bursa-pastoris Linn.
  46. GC–MS analysis of Lactobacillus plantarum YW11 metabolites and its computational analysis on familial pulmonary fibrosis hub genes
  47. Green formulation of copper nanoparticles by Pistacia khinjuk leaf aqueous extract: Introducing a novel chemotherapeutic drug for the treatment of prostate cancer
  48. Improved photocatalytic properties of WO3 nanoparticles for Malachite green dye degradation under visible light irradiation: An effect of La doping
  49. One-pot synthesis of a network of Mn2O3–MnO2–poly(m-methylaniline) composite nanorods on a polypyrrole film presents a promising and efficient optoelectronic and solar cell device
  50. Groundwater quality and health risk assessment of nitrate and fluoride in Al Qaseem area, Saudi Arabia
  51. A comparative study of the antifungal efficacy and phytochemical composition of date palm leaflet extracts
  52. Processing of alcohol pomelo beverage (Citrus grandis (L.) Osbeck) using saccharomyces yeast: Optimization, physicochemical quality, and sensory characteristics
  53. Specialized compounds of four Cameroonian spices: Isolation, characterization, and in silico evaluation as prospective SARS-CoV-2 inhibitors
  54. Identification of a novel drug target in Porphyromonas gingivalis by a computational genome analysis approach
  55. Physico-chemical properties and durability of a fly-ash-based geopolymer
  56. FMS-like tyrosine kinase 3 inhibitory potentials of some phytochemicals from anti-leukemic plants using computational chemical methodologies
  57. Wild Thymus zygis L. ssp. gracilis and Eucalyptus camaldulensis Dehnh.: Chemical composition, antioxidant and antibacterial activities of essential oils
  58. 3D-QSAR, molecular docking, ADMET, simulation dynamic, and retrosynthesis studies on new styrylquinolines derivatives against breast cancer
  59. Deciphering the influenza neuraminidase inhibitory potential of naturally occurring biflavonoids: An in silico approach
  60. Determination of heavy elements in agricultural regions, Saudi Arabia
  61. Synthesis and characterization of antioxidant-enriched Moringa oil-based edible oleogel
  62. Ameliorative effects of thistle and thyme honeys on cyclophosphamide-induced toxicity in mice
  63. Study of phytochemical compound and antipyretic activity of Chenopodium ambrosioides L. fractions
  64. Investigating the adsorption mechanism of zinc chloride-modified porous carbon for sulfadiazine removal from water
  65. Performance repair of building materials using alumina and silica composite nanomaterials with electrodynamic properties
  66. Effects of nanoparticles on the activity and resistance genes of anaerobic digestion enzymes in livestock and poultry manure containing the antibiotic tetracycline
  67. Effect of copper nanoparticles green-synthesized using Ocimum basilicum against Pseudomonas aeruginosa in mice lung infection model
  68. Cardioprotective effects of nanoparticles green formulated by Spinacia oleracea extract on isoproterenol-induced myocardial infarction in mice by the determination of PPAR-γ/NF-κB pathway
  69. Anti-OTC antibody-conjugated fluorescent magnetic/silica and fluorescent hybrid silica nanoparticles for oxytetracycline detection
  70. Curcumin conjugated zinc nanoparticles for the treatment of myocardial infarction
  71. Identification and in silico screening of natural phloroglucinols as potential PI3Kα inhibitors: A computational approach for drug discovery
  72. Exploring the phytochemical profile and antioxidant evaluation: Molecular docking and ADMET analysis of main compounds from three Solanum species in Saudi Arabia
  73. Unveiling the molecular composition and biological properties of essential oil derived from the leaves of wild Mentha aquatica L.: A comprehensive in vitro and in silico exploration
  74. Analysis of bioactive compounds present in Boerhavia elegans seeds by GC-MS
  75. Homology modeling and molecular docking study of corticotrophin-releasing hormone: An approach to treat stress-related diseases
  76. LncRNA MIR17HG alleviates heart failure via targeting MIR17HG/miR-153-3p/SIRT1 axis in in vitro model
  77. Development and validation of a stability indicating UPLC-DAD method coupled with MS-TQD for ramipril and thymoquinone in bioactive SNEDDS with in silico toxicity analysis of ramipril degradation products
  78. Biosynthesis of Ag/Cu nanocomposite mediated by Curcuma longa: Evaluation of its antibacterial properties against oral pathogens
  79. Development of AMBER-compliant transferable force field parameters for polytetrafluoroethylene
  80. Treatment of gestational diabetes by Acroptilon repens leaf aqueous extract green-formulated iron nanoparticles in rats
  81. Development and characterization of new ecological adsorbents based on cardoon wastes: Application to brilliant green adsorption
  82. A fast, sensitive, greener, and stability-indicating HPLC method for the standardization and quantitative determination of chlorhexidine acetate in commercial products
  83. Assessment of Se, As, Cd, Cr, Hg, and Pb content status in Ankang tea plantations of China
  84. Effect of transition metal chloride (ZnCl2) on low-temperature pyrolysis of high ash bituminous coal
  85. Evaluating polyphenol and ascorbic acid contents, tannin removal ability, and physical properties during hydrolysis and convective hot-air drying of cashew apple powder
  86. Development and characterization of functional low-fat frozen dairy dessert enhanced with dried lemongrass powder
  87. Scrutinizing the effect of additive and synergistic antibiotics against carbapenem-resistant Pseudomonas aeruginosa
  88. Preparation, characterization, and determination of the therapeutic effects of copper nanoparticles green-formulated by Pistacia atlantica in diabetes-induced cardiac dysfunction in rat
  89. Antioxidant and antidiabetic potentials of methoxy-substituted Schiff bases using in vitro, in vivo, and molecular simulation approaches
  90. Anti-melanoma cancer activity and chemical profile of the essential oil of Seseli yunnanense Franch
  91. Molecular docking analysis of subtilisin-like alkaline serine protease (SLASP) and laccase with natural biopolymers
  92. Overcoming methicillin resistance by methicillin-resistant Staphylococcus aureus: Computational evaluation of napthyridine and oxadiazoles compounds for potential dual inhibition of PBP-2a and FemA proteins
  93. Exploring novel antitubercular agents: Innovative design of 2,3-diaryl-quinoxalines targeting DprE1 for effective tuberculosis treatment
  94. Drimia maritima flowers as a source of biologically potent components: Optimization of bioactive compound extractions, isolation, UPLC–ESI–MS/MS, and pharmacological properties
  95. Estimating molecular properties, drug-likeness, cardiotoxic risk, liability profile, and molecular docking study to characterize binding process of key phyto-compounds against serotonin 5-HT2A receptor
  96. Fabrication of β-cyclodextrin-based microgels for enhancing solubility of Terbinafine: An in-vitro and in-vivo toxicological evaluation
  97. Phyto-mediated synthesis of ZnO nanoparticles and their sunlight-driven photocatalytic degradation of cationic and anionic dyes
  98. Monosodium glutamate induces hypothalamic–pituitary–adrenal axis hyperactivation, glucocorticoid receptors down-regulation, and systemic inflammatory response in young male rats: Impact on miR-155 and miR-218
  99. Quality control analyses of selected honey samples from Serbia based on their mineral and flavonoid profiles, and the invertase activity
  100. Eco-friendly synthesis of silver nanoparticles using Phyllanthus niruri leaf extract: Assessment of antimicrobial activity, effectiveness on tropical neglected mosquito vector control, and biocompatibility using a fibroblast cell line model
  101. Green synthesis of silver nanoparticles containing Cichorium intybus to treat the sepsis-induced DNA damage in the liver of Wistar albino rats
  102. Quality changes of durian pulp (Durio ziberhinus Murr.) in cold storage
  103. Study on recrystallization process of nitroguanidine by directly adding cold water to control temperature
  104. Determination of heavy metals and health risk assessment in drinking water in Bukayriyah City, Saudi Arabia
  105. Larvicidal properties of essential oils of three Artemisia species against the chemically insecticide-resistant Nile fever vector Culex pipiens (L.) (Diptera: Culicidae): In vitro and in silico studies
  106. Design, synthesis, characterization, and theoretical calculations, along with in silico and in vitro antimicrobial proprieties of new isoxazole-amide conjugates
  107. The impact of drying and extraction methods on total lipid, fatty acid profile, and cytotoxicity of Tenebrio molitor larvae
  108. A zinc oxide–tin oxide–nerolidol hybrid nanomaterial: Efficacy against esophageal squamous cell carcinoma
  109. Research on technological process for production of muskmelon juice (Cucumis melo L.)
  110. Physicochemical components, antioxidant activity, and predictive models for quality of soursop tea (Annona muricata L.) during heat pump drying
  111. Characterization and application of Fe1−xCoxFe2O4 nanoparticles in Direct Red 79 adsorption
  112. Torilis arvensis ethanolic extract: Phytochemical analysis, antifungal efficacy, and cytotoxicity properties
  113. Magnetite–poly-1H pyrrole dendritic nanocomposite seeded on poly-1H pyrrole: A promising photocathode for green hydrogen generation from sanitation water without using external sacrificing agent
  114. HPLC and GC–MS analyses of phytochemical compounds in Haloxylon salicornicum extract: Antibacterial and antifungal activity assessment of phytopathogens
  115. Efficient and stable to coking catalysts of ethanol steam reforming comprised of Ni + Ru loaded on MgAl2O4 + LnFe0.7Ni0.3O3 (Ln = La, Pr) nanocomposites prepared via cost-effective procedure with Pluronic P123 copolymer
  116. Nitrogen and boron co-doped carbon dots probe for selectively detecting Hg2+ in water samples and the detection mechanism
  117. Heavy metals in road dust from typical old industrial areas of Wuhan: Seasonal distribution and bioaccessibility-based health risk assessment
  118. Phytochemical profiling and bioactivity evaluation of CBD- and THC-enriched Cannabis sativa extracts: In vitro and in silico investigation of antioxidant and anti-inflammatory effects
  119. Investigating dye adsorption: The role of surface-modified montmorillonite nanoclay in kinetics, isotherms, and thermodynamics
  120. Antimicrobial activity, induction of ROS generation in HepG2 liver cancer cells, and chemical composition of Pterospermum heterophyllum
  121. Study on the performance of nanoparticle-modified PVDF membrane in delaying membrane aging
  122. Impact of cholesterol in encapsulated vitamin E acetate within cocoliposomes
  123. Review Articles
  124. Structural aspects of Pt(η3-X1N1X2)(PL) (X1,2 = O, C, or Se) and Pt(η3-N1N2X1)(PL) (X1 = C, S, or Se) derivatives
  125. Biosurfactants in biocorrosion and corrosion mitigation of metals: An overview
  126. Stimulus-responsive MOF–hydrogel composites: Classification, preparation, characterization, and their advancement in medical treatments
  127. Electrochemical dissolution of titanium under alternating current polarization to obtain its dioxide
  128. Special Issue on Recent Trends in Green Chemistry
  129. Phytochemical screening and antioxidant activity of Vitex agnus-castus L.
  130. Phytochemical study, antioxidant activity, and dermoprotective activity of Chenopodium ambrosioides (L.)
  131. Exploitation of mangliculous marine fungi, Amarenographium solium, for the green synthesis of silver nanoparticles and their activity against multiple drug-resistant bacteria
  132. Study of the phytotoxicity of margines on Pistia stratiotes L.
  133. Special Issue on Advanced Nanomaterials for Energy, Environmental and Biological Applications - Part III
  134. Impact of biogenic zinc oxide nanoparticles on growth, development, and antioxidant system of high protein content crop (Lablab purpureus L.) sweet
  135. Green synthesis, characterization, and application of iron and molybdenum nanoparticles and their composites for enhancing the growth of Solanum lycopersicum
  136. Green synthesis of silver nanoparticles from Olea europaea L. extracted polysaccharides, characterization, and its assessment as an antimicrobial agent against multiple pathogenic microbes
  137. Photocatalytic treatment of organic dyes using metal oxides and nanocomposites: A quantitative study
  138. Antifungal, antioxidant, and photocatalytic activities of greenly synthesized iron oxide nanoparticles
  139. Special Issue on Phytochemical and Pharmacological Scrutinization of Medicinal Plants
  140. Hepatoprotective effects of safranal on acetaminophen-induced hepatotoxicity in rats
  141. Chemical composition and biological properties of Thymus capitatus plants from Algerian high plains: A comparative and analytical study
  142. Chemical composition and bioactivities of the methanol root extracts of Saussurea costus
  143. In vivo protective effects of vitamin C against cyto-genotoxicity induced by Dysphania ambrosioides aqueous extract
  144. Insights about the deleterious impact of a carbamate pesticide on some metabolic immune and antioxidant functions and a focus on the protective ability of a Saharan shrub and its anti-edematous property
  145. A comprehensive review uncovering the anticancerous potential of genkwanin (plant-derived compound) in several human carcinomas
  146. A study to investigate the anticancer potential of carvacrol via targeting Notch signaling in breast cancer
  147. Assessment of anti-diabetic properties of Ziziphus oenopolia (L.) wild edible fruit extract: In vitro and in silico investigations through molecular docking analysis
  148. Optimization of polyphenol extraction, phenolic profile by LC-ESI-MS/MS, antioxidant, anti-enzymatic, and cytotoxic activities of Physalis acutifolia
  149. Phytochemical screening, antioxidant properties, and photo-protective activities of Salvia balansae de Noé ex Coss
  150. Antihyperglycemic, antiglycation, anti-hypercholesteremic, and toxicity evaluation with gas chromatography mass spectrometry profiling for Aloe armatissima leaves
  151. Phyto-fabrication and characterization of gold nanoparticles by using Timur (Zanthoxylum armatum DC) and their effect on wound healing
  152. Does Erodium trifolium (Cav.) Guitt exhibit medicinal properties? Response elements from phytochemical profiling, enzyme-inhibiting, and antioxidant and antimicrobial activities
  153. Integrative in silico evaluation of the antiviral potential of terpenoids and its metal complexes derived from Homalomena aromatica based on main protease of SARS-CoV-2
  154. 6-Methoxyflavone improves anxiety, depression, and memory by increasing monoamines in mice brain: HPLC analysis and in silico studies
  155. Simultaneous extraction and quantification of hydrophilic and lipophilic antioxidants in Solanum lycopersicum L. varieties marketed in Saudi Arabia
  156. Biological evaluation of CH3OH and C2H5OH of Berberis vulgaris for in vivo antileishmanial potential against Leishmania tropica in murine models
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Heruntergeladen am 2.10.2025 von https://www.degruyterbrill.com/document/doi/10.1515/chem-2024-0041/html?lang=de
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