Introduction

Antibiotic resistance, which was made worse by overuse and misuse of the drugs, is now a major public concern1. Antibacterial medications have been losing their efficiency against diseases since new reports of bacteria that are resistant to antimicrobial treatments appear every day. Frequently, these bacteria exhibit multi-drug resistance (MDR), extensive drug resistance (XDR), or pan-drug resistance (PDR), resulting in a progressively narrowed effective spectrum of common antibiotics1,2,3. Heterocycle compounds are one of the most important classes of organic compounds because of their vital role in life and biochemical processes4. Among the different heterocycle compounds in medicinal chemistry, sulfonamides have been important motifs since the initial discovery of sulfonamide-containing antibacterial drugs5. Many sulfonamide hybrids as antibacterial drugs have been synthesized by using coupling between heterocycle primary amine and aromatic sulfonyl chloride6,7,8,9,10. In addition to antibacterial activity, sulfonamides can be used as antifungal11, antimalarial12, antioxidant13, antiviral14, anti-inflammatory15, antitumor16, and many other pharmaceutically active agents.

Moreover, Chromene derivatives are an essential class of oxygen-containing heterocyclic compounds that have attracted researcher's attention because of their applications in pigments, cosmetics, and agrochemicals, and their wide range of biological activities17,18 like anti-diabetic19, antibacterial20, antioxidant21, anti-cancer22 and antiviral activities23. Given that chromene sulfonamide hybrids have been shown variable biological activities24,25,26, and based on our previous works on the synthesis of sulfonamide hybrids27,28,29,30,31, accordingly, we were motivated to design and synthesize some novel derivatives of these hybrids (Scheme 1).

Scheme 1
scheme 1

Synthesis of novel chromene-sulfonamide hybrids.

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The Computer-aided drug design (CADD) can not only preliminarily predict the activity of inhibitors, but also save experimental costs and provide guidance for designing more effective inhibitors by exploring the structure–activity relationship of inhibitors at the molecular level32.

In this regard, in the first step of the present study, the 3D-QSAR model, and ADMET (absorption, distribution, metabolism, and excretion) were produced to predict the antibacterial activity impact of the considered compounds. In addition, the antibacterial of the synthesized compounds was screened against Staphylococcus aureus as Gram-positive and Escherichia coli as Gram-negative bacteria. Continuing our previously published study2 on the evaluation of the bioactivity impact of some novel coumarin isoxazole sulfonamide hybrid compounds, 3D-QSAR analysis was performed on the novel chromene-sulfonamide hybrid compounds. The accuracy of the 3D-QSAR model was high, as evidenced by the strong correlation between predicted and experimental activities, with high R2 and Q2 values indicating reliable predictive power. The newly synthesized compounds were also tested for cytotoxicity against fibroblast L929 cells using the MTT method. Additionally, the Annexin V binding assay was performed for detecting apoptosis. Also, the accuracy of the predicted ADMET (using the ADMET predictor) was compared with the observed mood.

Results and discussion

Chemistry

The synthetic strategies adopted to obtain the target sulfonamide hybrids (8a–l) are depicted in Scheme 2. In the present study, green methods were applied to synthesize new chromene-sulfonamide hybrids. The synthesis of target sulfonamides was accomplished in two steps. The first one, chromene compounds were synthesized using a one-pot reaction between an aromatic aldehyde, malononitrile, and aminophenol33,34. In the presence of triethylamine as a base, the reaction was carried out in ethanol under reflux conditions (Scheme 2). Several aryl aldehydes containing electron-donating substituents such as methoxy and phenoxy group and also electron-withdrawing substituents such as halogens were used to obtain high yield and purity of the corresponding amino chromenes (Table 1).

Scheme 2
scheme 2

The synthetic route of the synthesized chromene derivatives.

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Table 1 Yield/reaction time data for the preparation of chromene derivatives (1–9a).
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Then, to synthesize chromene sulfonamide hybrids, the synthesized amino chromenes were reacted with various aryl sulfonyl chlorides including benzenesulfonyl chloride, 4-toluenesulfonyl chloride, and 4-acetamidobenzenesulfonyl chloride under solvent-free conditions in the presence of sodium bicarbonate as a green base (Scheme 3). The chromene sulfonamide hybrids were obtained in 70–90% yield just by adding water and simple filtration in high purity (Table 2).

Scheme 3
scheme 3

The synthetic route of the synthesized chromene-sulfonamide hybrids.

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Table 2 Yield/reaction time data for the preparation of chromene-sulfonamide hybrids (1–11b).
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D-pharmacophore alignment of the compounds

The co-crystalized reference inhibitor was used for a 3D-pharmacophore analysis and seven features were produced based on the receptor-ligand pharmacophore generation protocol. The generated 3D-pharmacophore features include one H-bond acceptor, three H-bond donors, and three π–π stacking motifs (aromatic rings). The obtained 3D hypotheses were performed to align the compounds. The aligned compounds were applied for 3D-QSAR analysis.

3D-QSAR model development and statistical analysis

The Comparative Molecular Similarity Indices Analysis (CoMSIA) generated model had a strong predictive and informative ability, presenting good statistical analysis for the training set regression coefficient R2 (0.86, 0.82), test set regression coefficient Q2 (0.65, 0.67) and cross validation-based root mean square error (RMSE) values (0.32, 0.52) in both E. coli and S. aureus, respectively, Fig. 1.

Figure 1
figure 1

Plots of experimental activities against predicted activities using our COMSIA predictive model for both E. coli (left) and S. aureus (right). The train set is in blue and the test set is in red coded.

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The 3D-QSAR model outcomes thoroughly established its predictive capacity for that. The comparison of the predictive versus observed plot is illustrated in Fig. 1. The contributions of steric (38%, 39%), electrostatic (9%, 11%,), hydrophobic (22%, 19%), H-donor (18%, 13%), and H-acceptor (12%, 19%) fields of the compounds in both E. coli and S. aureus exhibited that the steric, H-bond donor/acceptor, and hydrophobic fields participated a crucial role in the model. Among these three fields, the steric field produced the most considerable interactions. The predicted pMIC values of the selected predictive model for both E. coli and S. aureus are placed in the Supplementary material.

CoMSIA contour maps

The activity contour map visualization method is a qualitative and beneficial procedure to summarize SAR data into a 3D map. In this analysis, compounds 8b and 11b were selected as the active compounds with the same biological activity values in both E. coli and S. aureus. Consequently, this method would probe the beneficial functional groups, impacting the potent activity. Figure 4 depicts the steric, hydrophobic, and hydrogen bond donor/acceptor fields contour maps for 8b and 11b. In the steric field, a green contour indicates the regions where bulky groups enhance biological activity (Fig. 2A,D). Figure 2B and E are the contour map of the hydrophobic field. The blue block means that the hydrophobic groups were beneficial for activity, while the orange block means the hydrophilic groups are not beneficial. As depicted, the 2-amino-3-cyano functional group of chromene moiety in both compounds had a negative impact on their biological activity, while, the phenyl acetoamide group in compound 8b and benzene group in 11b mediated positive impact.

Figure 2
figure 2

8b and 11b Structures. CoMSIA contour maps. Steric contour maps: green and yellow played the favorable and unfavorable region; Hydrophobic contour maps: blue and orange indicated favorable and unfavorable region; Hydrogen bond acceptor contour maps: red and magenta indicated favorable and unfavorable region; Hydrogen bond donor contour maps: yellow-green and cyan indicated favorable and unfavorable region. (A, B, C) are contour maps for compound 8b and (D, E, F) are represented contour maps for 11b.

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Figure 2C and F are the contour map of the hydrogen bond donor field, indicating the favorable field with the yellow-green color and unfavorable region with cyan color whereas the hydrogen bond acceptor shows the beneficial and non-beneficial regions as red and magenta colors, respectively. As it is shown, the 2-amino-3-cyano functional group of chromene moiety in both compounds that are considered a favorable group in hydrogen bond acceptor fields; however, the methyl group linked to the carbonyl functional group of phenyl acetamide moiety acts as an unfavorable functional. In addition, the 2-amino functional group in chromene moiety is depicted as a beneficial group in both compounds.

In silico pharmacokinetics compliance evaluation

The ADMET Predictor software assessed pharmacokinetic parameters in ADMET terms (absorption, distribution, metabolism, elimination, and toxicity) for all compounds are shown in supplementary information S1–S7. Physicochemical and biopharmaceutical properties were performed to determine the ADMET parameters for the studied compounds. The solubility properties which were identified and quantitatively reported (Table S1), including native water solubility (Table S1), fasting state intestinal fluid, fed state intestinal fluid, and fasted state stomach fluid. The values of the MLogP (Moriguchi model of octanol–water partition coefficient), octanol–water partition coefficient, octanol–water distribution coefficient, and molecular diffusion coefficient in the water logarithm of the air–water partition coefficient were established (Table S5). Table S3 contains the ionization level (pKa), which has a significant impact on solubility and permeability. A solubility and permeability optimal range was often observed in all substances. Except for 2b, 3b, 6b, and 9b other compounds commonly exhibited high BBB‌ penetration, accordingly 5b, 1b, 7b, 8b, 10b, and 11b were fit in brain infections, especially in meningitis cases. The logarithm of the brain/blood partition coefficient (LogBB) confirmed that these compounds could penetrate in BBB.

A compound's ability to dissolve in a lipophilic (nonaqueous) environment is referred to as lipophilicity. The coordination of multiple drug pharmacological properties, including absorption, metabolism, permeability, solubility, distribution, plasma protein binding, elimination, and toxicity affects ADMET—several characteristics influencing solubility and permeability presented in Tables S2 and S3, respectively.

The membrane borders that drug compounds encounter include gastrointestinal epithelial cells, hepatocyte membrane, blood capillary wall, glomerulus, restrictive organ barriers (e.g., blood–brain barrier), and other target cells. The computed permeability estimations provide support for the interpretation of cell-based bioassays and ADMET outcomes. Human skin permeability, the MDCK COS permeability, BBB permeability (logBB), jejunal effective permeability, and (Peff) rabbit corneal permeability are among the several forms of permeability that were anticipated (Table S2). The metabolism of drugs has a significant impact on both drug-drug interactions and drug bioavailability. Drug bioavailability and drug-drug interactions are significantly influenced by metabolism. Cytochrome P450 enzymes (CYPs) are the most certainly the largest noticeable of phase I metabolism enzymes reacting to the predominance of drug Phase I metabolic transformations (Table S4). All studied compounds are substrates for CYP1A2 and CYP2C8 enzymes, and also, inhibitors for CYP1A2, CYP2C9, and CYP3A4 enzymes, therefore this property must be noticed. Since some drugs metabolize in the body by CYPs, accordingly these compounds can be respected as a co-treatment to augment the mentioned drugs' half-life and blood concentration. Inhibition of the CYP enzymes (especially about CYP3A4 enzyme which is the most prominent role in the drug Phase I metabolic transformations) exhibited the potential high rate half-life of studied compounds. These properties can reduce the effective dose in the human body, and consequently, reduce the potential cost for compound usage.

Additionally, the activity of the uridine 5′-diphosphate-glucuronosyltransferase family (UGT) enzyme, which binds glucuronic acid to small molecules while modifying their structure into one that is water-soluble form, was further explored to evaluate phase II metabolism influence on the researched compounds, consequently, this phase has made it easier for drugs to be eliminated and extruded the outside of body cells.

Table S5 indicates that none of the investigated compounds performed as UGT enzyme substrates, with the exception of the UGT1A3 enzyme. This outcome confirmed that these compounds had long half-life.

Table S6 exhibited the Possibility of being a substrate and inhibitor of compounds for P-gp efflux. All compounds not only did not substrate for the P-gp efflux pump, but also, they were inhibitors for this pump.

These outcomes were corroborated with our previous data that displayed studied compounds had long half-life and also are good options for co-treatment with some other drugs that needed to obtain high concentration levels in the blood flow since our compounds can block possible mechanisms for clearance and elimination of other drug like neurodegenerative drugs (Parkinson and Alzheimer disease).

In Table S7, potential clearance methods were presented. Except for 5b, 6b, 7b, 8b, and 9b, all of our compounds had no renal clearance, and none of them were not eliminated by hepatic metabolism.

The studied compounds were CYP enzyme inhibitors, which potentially elevate some drug plasma concentrations (drugs that CYPs were responsible for their metabolism). Accordingly, there could be a significant probability of drug-drug interaction (DDI) with simultaneous usage.

Based on the findings of the S + CL_Metab, CYP_RLM_Clint CYP_HLM_Clint, and S + CL_Mech analyses, which are included in Table S7, indicated substance clearance would most likely be concluded in the liver, however, 8b and 9b compounds had renal clearance possibility.

Predicted toxicology of identified leads

Presented study compounds were subjected to estimate the mutagenicity, allergenicity, and toxicity in this stage.

All of the anticipated adverse effects can be found in Table 3. There is in-silico evidence of hERG (human ether-a-go-go-related gene) suppression only in compound 6b, suggesting that there would not be a negative cardiac consequence, accordingly, this compound must be noted to screen subjected disease for cardiac health care. The rest of the compounds did not display cardiac adverse effects.

Table 3 The ADMET Predictor toxicity analyses of compounds.
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The adverse clinical implications, including QT prolongation, myopathy, hepatotoxicity, nephrotoxicity, and pulmonary disorder, attributed to drug-induced phospholipidosis were not shown. ADMET predictor assessment for reproductive toxicity exhibited that all compounds were high-risk toxic, while; this outcome is incompatible with MTT and flow cytometry outcomes about 1b, 2b, 3b, 5b, 7b, 9b, 10b, and 11b that demonstrated the compounds were not toxic. Drug-induced hepatotoxicity is created through AST, ALT, ALP, and LDH, Ser_AlkPhos, or Ser_GGT enzymes augmented levels, causing acute and chronic liver disease. All compounds demonstrated liver enzymes’ standard value, accordingly, there is no anti-hepatotoxic possibility.

Androgen and estrogen receptor toxicity was not observed; therefore, compounds do not impact sperm concentration. Except for compounds 5b, 8b, and 9b which exhibited skin sensitization, the rest of the compounds were safe in this case. The findings of the Ames test for mutagenicity on several strains of Salmonella typhimurium revealed that all studied compounds were safe. This finding is corroborated with the MTT and flow cytometry outcomes (special about about 1b, 2b, 3b, 5b, 7b, 9b, 10b and 11b) and also conflicted (about 1b, 4b, 6b, and 8b) with the ADMET predictor reproductive toxicity results.

Molecular docking

The subjected compounds were docked in the DHPS enzymes of S. aureus and E. coli using Autodock Vina. All compounds exhibited favorable and negative binding energy (Table S9). The binding energies of these eleven compounds (designated as 1b, 2b, 3b, 4b, 5b, 6b, 7b, 8b, 9b, 10b, and 11b) with DHPS of S. aureus and E. coli DHPS were placed in the Table S9. The produced data through Ligplot illustrated the binding configuration of the compound, the hydrogen bonds and hydrophobic interactions between the ligands and the DHPS active site residues (Fig. S1, and S2 and Table S9).

Molecular docking was employed to analyze the binding affinity of these 11 compounds (1b, 2b, 3b, 4b, 5b, 6b, 7b, 8b, 9b, 10b, and 11b) with favorable docking scores. The docking results indicate that all eleven compounds exhibited favorable binding energies when docked with the DHPS enzymes of both S. aureus and E. coli using Autodock Vina (Table S1, and S2). The binding energies for these compounds ranged from − 13.4 to − 19.2 kcal/mol for S. aureus DHPS and from − 15 to − 18.6 kcal/mol for E. coli DHPS. The observed binding energies suggested that strong interactions between the compounds and the DHPS enzymes. Specifically, compounds 4b and 5b showed the highest binding energies for S. aureus DHPS, indicating potentially strong binding affinity. Similarly, compounds 4b and 6b exhibited the highest binding energies for E. coli DHPS.

These findings are significant as DHPS is a key enzyme involved in the biosynthesis of folate, which is essential for the growth and survival of bacteria. Inhibition of DHPS can therefore be a promising strategy for developing antibacterial agents. The favorable binding energies observed in this study implied that the eleven compounds identified could be potential candidates for further development as antibacterial agents targeting DHPS.

Antibacterial activity evaluation

The broth microdilution method assessed the minimum inhibitory concentrations (MIC) of all the newly synthesized compounds. MIC results suggested that 8b and 6b have a robust antibacterial effect on E. coli and S. aureus and the rest of the compounds were moderately active against these bacteria. The compound 8b exhibited a significant effect on the E. coli and S. aureus (Table 4). Sulfisoxazole, functioning as a sulfonamide antibiotic, and Gentamicin were employed in a comparative analysis of antibacterial activity, cytotoxicity, and apoptosis assessment.

Table 4 Minimum inhibitory concentrations (MIC) in µg/mL of compounds tested in microdilution method.
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Table 4 displays the IC50 of synthesized compounds. Also, a comparison of the mean IC50 values for newly synthesized compounds is placed in Fig. 1. The average cytotoxicity of the newly synthesized compounds was significant. MTT test results revealed that compounds 1b, 4b, 6b, and 8b have a higher cytotoxicity effect on the fibroblast L929 cell line compared to the rest compounds. This finding implied several points to attention. First, it is important to acknowledge that the toxicity of a compound depends on various factors, including its chemical structure, concentration, and the specific biological system it is being tested on. It would be valuable to know the specific structure and nature of the synthesized compounds and how they differ from other compounds that can produce augmented cytotoxicity levels.

Cytotoxicity evaluation

Table 5 presents the IC50 values of the synthesized compounds, while Fig. 3 compares the mean IC50 values for these newly synthesized compounds. Notably, the average cytotoxicity of the newly synthesized compounds was found to be significant. The descriptive data and IC50 values for the synthesized compounds against the fibroblast L929 cell line. Compounds 1b, 4b, 6b, and 8b showed significant cytotoxicity with IC50 values of 139 µg/mL, 152 µg/mL, 114 µg/mL, and 90 µg/mL, respectively. These findings imply that structural elements within these compounds contribute to their enhanced cytotoxicity. This observation prompts several considerations. Firstly, it is crucial to recognize that the toxicity of a compound is influenced by various factors, including its chemical structure, concentration, and the specific biological system under investigation. Thus, understanding the specific structure and characteristics of the synthesized compounds, along with how they differ from other compounds with heightened cytotoxicity, is important. While increased cytotoxicity may raise safety concerns, there are contexts where it may be desirable, such as in the development of anticancer drugs or antimicrobial agents. Understanding the SAR and conducting in-depth analyses will aid in optimizing these compounds for desired biological activities while minimizing potential adverse effects. Evaluating the effects of these compounds on other cell lines and conducting animal studies to evaluate systemic toxicity would offer a comprehensive understanding of their safety profile.

Table 5 Descriptive data and IC50 of newly synthesized compounds against fibroblast L929 cell line.
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Figure 3
figure 3

Comparison of mean IC50 of newly synthesized compounds. If the p-value is 0.05 or lower, the result is statistically significant. ns not significant. The results were subjected to analysis of one-way analysis of variance (ANOVA), followed by Tukey's test. The significance levels were indicated as follows: *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, and ****P ≤ 0.0001. Sulfisoxazole, and Gentamicin were employed in a comparative analysis of cytotoxicity assessment.

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Apoptosis Annexin V binding evaluation:

The flow cytometry method was used to check apoptosis at the IC50 concentration of each compound. Based on this assay, four newly synthesized compounds (1b, 4b, 6b, and 8b) were more cytotoxic for the fibroblast L929 cell line. The cells were labeled using Annexin V FITC and/or propidium iodide (PI) dyes. Figure 4 represents the occurred apoptosis event in treated and untreated fibroblast L929 cells after 24 h with each compound IC50 concentration. The result revealed that synthesized compounds 1b, 4b, 6b, and 8b induced cell death in with 42.29%, 22.2%, 53.85%, and 56.8%, respectively. At the IC50 concentration, neither Sulfisoxazole nor Gentamicin demonstrated apoptotic effects.

Figure 4
figure 4

Two-dimensional plot of Annexin V-FITC against PI from flow cytometric experiments, with concentration values for the IC50 concentration of each compound. The FSC/SSC plot was used to determine the analysis border in relation to the untreated cells (control). By placing the most dots in the Q4 region of the control, the borders of the four quadrants (Q1–Q4) were determined. Based on cell stages, the cells were divided into four quadrants: viable (Q4), early apoptosis (Q3), late apoptosis (Q2), and necrosis (Q1). Data are presented as percentages. (A) IC50 concentration of 8b, (B); IC50 concentration of 6b, (C); Control, (D); IC50 concentration of 1b, (E); IC50 concentration of 4b.

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SAR investigation on chromene sulfonamide derivatives

Compounds 2b, 4b, 5b, 6b, 7b, and 8b exhibit positive MIC effects on E. coli, indicating enhanced antibacterial activity. Compounds 3b, 9b, 10b, and 11b showed neutral or negative MIC effects, indicating reduced or no antibacterial activity. Steric and hydrophobic effects are generally positive for compounds with lower MIC values (e.g., 6b, 8b), suggesting these properties enhance antibacterial activity. Compounds with positive H-bond donor effects (e.g., 7b, 8b) also showed positive MIC effects, highlighting the importance of hydrogen bond interactions in antibacterial activity (Table 6).

Table 6 SAR effect diagram on the antibacterial activity of Chromene sulfonamides against E. coli.
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Table 7 presents the SAR effect diagram on the biological activity of compounds against S. aureus. Compounds 5b, 7b, and 8b exhibit positive MIC effects on S. aureus, indicating enhanced antibacterial activity. Compounds 1b, 2b, 3b, 9b, and 10b showed negative MIC effects, indicating reduced or no antibacterial activity. Similar to the E. coli results, steric and hydrophobic effects are generally positive for compounds with lower MIC values (e.g., 8b), suggesting these properties enhance antibacterial activity. Compounds with positive H-bond donor effects (e.g., 7b, 8b) also showed positive MIC effects, highlighting the importance of hydrogen bond interactions in antibacterial activity.

Table 7 SAR effect diagram on the antibacterial activity of Chromene sulfonamides against S. aureus.
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The 3D-QSAR model demonstrated a strong predictive capacity, with high R2 and Q2 values indicating a good correlation between predicted and experimental activities. The steric field was found to have the most significant impact on antibacterial activity, followed by hydrophobic and H-bond donor/acceptor fields. The contour maps generated from the CoMSIA model provided visual insights into the regions of the molecule where modifications could enhance or reduce antibacterial activity.

Compounds 8b and 11b were identified as the most active against both E. coli and S. aureus, with specific structural features contributing to their high activity. The steric and hydrophobic interactions, particularly around the chromene moiety and sulfonamide group, were crucial for enhancing antibacterial properties. The model's predictions were consistent with experimental findings, validating the utility of the 3D-QSAR approach in guiding the design of potent antibacterial agents.

The 3D-QSAR study provided valuable insights into the structure–activity relationships of chromene-sulfonamide hybrids. By accurately predicting the antibacterial activity based on molecular structure, the model aids in the rational design of new compounds with improved efficacy. This computational approach complements experimental assays, reducing the need for extensive trial-and-error synthesis and testing.

Methods

In-silico-based assay

3D‑QSAR data set. The data set was gathered against Dihydropteroate Synthase (CHEMBL4032, CHEMBL1075045) for sulfonamides from our previously published study, this study synthesized compounds and reviewed literature37,38,39,40,41,42,43,44. The database includes compounds that had the antibacterial effect and sulfonamides functional group. The initial dataset derived from MIC was selected for further investigation on the dataset. Compounds that did not the MIC were omitted. Then 73, and 77 Compounds were gained to assess their antibacterial impact against S. aureus and E. coli, respectively. All collected data sets are placed in the supplementary file (predicted vs experimental file).

Conformation minimization, pharmacophore alignment, predictive model calculations

The accumulated CSV format was transformed to SDF format. All extracted compounds were created using the ChemSketch program. Preparation of compounds was accomplished using the LigPrep45 module of the Schrodinger Suite, which adds hydrogen atoms, realistically adjusts bond lengths and angles, and corrects any chirality issues, ionization states, tautomers, stereo chemistries, and ring conformations. Partial charges were assigned using the OPLS-2005 force field, and the resulting structures were subjected to minimized energy until their average RMSD reached 0.001 Å. The ionization state was set for pH 7.0 using the ionization tool within Epik45. Finally, the compounds were converted to the SDF format.

The crystallized target protein (PDB code: 3TYE) was extracted from the Protein Data Bank (PDB) and separated into protein receptor and ligand references. The structure of the enzyme was pre-processed, minimized, and refined using the Protein Preparation Wizard46 implemented in the Schrodinger Suite47. This step involved eliminating crystallographic waters, adding missing hydrogens/side chain atoms, and assigning the appropriate charge and protonation state of the receptor structure (for pH 7.0) while considering the appropriate ionization states for the acidic and basic amino acid residues. The prepared macromolecular structure was subjected to energy minimization using the OPLS-2005 force field48,49,50,51 with a root mean square deviation (RMSD) cut-off value of 0.30 Å to relieve steric clashes among closely spaced residues arising from the addition of hydrogen atoms. Preparation of the reference inhibitor was accomplished using LigPrep.

The quality of molecular alignment is considered as a key factor for the robustness and predictive power of 3D-QSAR models. 3D-pharmacophore alignment was performed to build 3D-QSAR models. In this regard, the bound drug was utilized as a reference ligand to produce 3D-pharmacophore features and subsequently as a bioactive reference conformation, while the target receptor was applied as a protein receptor excluding volume. The PHASE module45,52,53 implemented in Schrödinger suite software 2022-0254 was used For the structure-based pharmacophore modeling with the default set of seven chemical features: hydrogen bond acceptor (A), hydrogen bond donor (D), hydrophobic contacts (H), negative ionizable group (N), positive ionizable group (P), and aromatic ring (R) to construct the most representative features of the enzyme active sites. The 3D features were generated using Hypothesis Generation for Energy-Optimized Structure Based Pharmacophores considering the excluded volumes within 5 Å of the refined reference inhibitor for the enzyme55,56. According to the essential interactions with the key residues of the enzyme involving the inhibitor, pharmacophore features were selected. The resultant pharmacophore features contained the functional groups that were involved in their bioactivity against the mentioned enzyme. The excluded volumes engaged all atoms within 5 Å of the refined inhibitor.

Consequently, the reference conformer was utilized to align the compounds based on the generated 3D-pharmacophore features. The compounds observed biological activities were converted to a logarithmic scale for E. coli and S. aureus via the formula of pMIC =  − log (MIC).

3D-QSAR calculation

CoMSIA52 was used to evaluate the steric field and electrostatic field parameters by Lennard Jones and coulombic potential functions, hydrophobic field, hydrogen bond acceptor field, and hydrogen bond donor field parameters57. During the calculation process, the compound was placed in the spatial grids, which consisted of many grids with a side length of 2 Å. In this space sp3 hybridized carbon atom was performed as a probe particle to calculate the structural characteristics of the compound58. The van der Waals radius of the probe particle and the net charge were set on 1.52 Å + 1.0, respectively. The energy cut-off value was adjusted to 30 kcal mol−1 and the default value was adopted for other parameters. Therefore, for the probe particle, in addition to the van der Waals radius of 1.52 Å and the net charge of + 1.0, while hydrophobic parameters, hydrogen bond acceptor parameters, and hydrogen bond donor parameters also should be set to + 1 for calculating the compound characteristics. The attenuation coefficient was configured to 0.3 by default. In the CoMSIA model, Gaussian functions were used to determine the distance between molecule atoms and probe atoms, and the partial least square (PLS) method was applied to analyze CoMSIA models59,60.

Validation of the 3D‑QSAR

The correlation coefficient (R2), the cross-validation regression coefficient Q2, and the root mean square error (RMSE) were considered to the 3D-QSAR predictive model.

In silico pharmacokinetics, pharmacodynamics, and toxicity studies using a ADMET™ predictor

The compound's pharmacokinetic and some potential pharmacodynamics parameters were predicted using ADMET Predictor version 9.0 software (Simulations Plus Inc., USA). In this step, drug-like properties quantitative evaluation was assessed, such as permeability, solubility, pKa (a negative logarithmic measure of the acid dissociation constant), lipophilicity, absorption, plasma protein binding, blood–brain barrier penetration, transporters, dermal and ocular penetration, metabolism and drug-drug interaction. A quantitative evaluation of drug-like properties, including pKa, lipophilicity, permeability, solubility, plasma protein binding, absorption, blood–brain barrier permeation, transporters, dermal and ocular penetration, metabolism, and drug-drug interaction was computed. A crucial consideration in the development of effective medications is the safety of the components. A crucial consideration in the development of effective medications is the safety of the components. The compounds pharmacokinetics impact on the hepatocellular enzymes, including gamma-glutamyltransferase (GGT), lactate dehydrogenase (LDH), aspartate transaminase (AST), alanine transaminase (ALT), and alkaline phosphatase (ALP) enzymes were predicted. Furthermore, other parameters such as neurotoxicity, androgen receptor toxicity, allergenicity, mutagenicity, and developmental toxicity were also estimated. The ADMET evaluation was assessed to discover the dose range and define the potential compound's pharmacokinetics and pharmacodynamics in the human body.

Molecular docking studies

The X-ray diffraction structures for the Dihydropteroate synthase (DHPS) enzyme of both E. coli and S. aureus, associated with PDB IDs 5AZS, and 3D5K, respectively, were obtained from the Protein Data Bank (https://www.rcsb.org/pdb). To prepare the structures for docking, all water molecules were first removed. Hydrogen atoms were then added at pH 7 using Discovery Studio version 2.5 (Accelrys Inc., San Diego)61. The proteins and their ligands were subsequently subjected to energy minimization using the simulation module of Discovery Studio. In the minimization, the conjugate gradient method and the CHARMM force field were employed, until the energy gradient was reduced to below 0.1 cal/Å62.

The Autodock Vina module within LigandScout 4.3 (Inte: Ligand GmbH, Vienna, Austria)62 was utilized to dock a collection of studied compounds to E. coli and S. aureus DHPS proteins. For the E. coli’s DHPS, a grid map was established with dimensions of 22 × 22 × 22 points and a grid spacing of 1.0 Å. The center of the grid box was positioned at coordinates 38.987 Å (x-axis), 4.272 Å (y-axis), and 8.749 Å (z-axis).

For the DHPS protein of S. aureus, a grid map was configured, maintaining the 22 × 22 × 22 point dimensions and a grid spacing of 1.0 Å. The center grid box coordinates were set at 82.803 Å along the x-axis, 124.447 Å along the y-axis, and 11.493 Å along the z-axis.

The default settings of the Lamarckian genetic algorithm were employed, and the number of runs was set at 40 for profile docking for E. coli’s DHPS and S. aureus’s DHPS. Interaction profiles between selected ligands and protein were analyzed using LigPlot (European Bioinformatics Institute, Hinxton, UK) and Discovery Studio Visualizer (Biovia, Dassault Systèmes, San Diego, CA, USA) software62,63,64.

Experimental

Materials and measurements

All chemicals were purchased from Merck and Fluka chemical companies. melting points obtained with an Amstead Electro Thermal 9200 apparatus and IR spectra were recorded on a PerkinElmer VIR spectrophotometer. NMR spectra were obtained on a Bruker 400 MHz FT spectrometer (1HNMR at 250 Hz, 13CNMR at 63 Hz for 4H-chromenes, and, 1HNMR at 400 Hz, 13CNMR at 100 Hz for chromene-sulfonamides) in DMSO‑d6 using TMS as an internal standard.

General procedure for synthesis of 4H-chromene-3-carbonitrile derivatives

Synthesis of 4H-chromene-3-carbonitrile derivatives carried out as reported in the literature with minor modification65,66. 0.25 mmol of triethylamine were added to a mixture of substituted benzaldehyde (1 mmol), malononitrile (1 mmol), and 3-aminophenol (1 mmol) in absolute ethanol (5 ml) at room temperature. Then the reaction mixture was refluxed for 2–3 h. The progress of the reaction was monitored by TLC. After completion of the reaction, the reaction mixture was filtrated and the obtained precipitate was washed with cold ethanol. All the obtained products were pure, but if purification is needed, the recrystallization method from ethanol can be used.

General procedure for synthesis of chromene-sulfonamides

The synthesized chromene derivatives (1mmol) and anhydrous NaHCO3 (18 mmol) were ground together into fine powder in a mortar, and aryl benzene sulfonyl chlorides (3 mmol) namely benzene sulfonyl chloride, p-methylbenzene sulfonyl chloride, and p-acetamido benzene sulfonyl chloride was added and ground at room temperature. The discoloration of the reaction mixture was a sign of the beginning of product formation. Progress and completion of the reaction were monitored by TLC. After the reaction was completed, distilled water (25 ml) was added to the mixture. The precipitate was collected by filtration, washed with additional water, and finally dried at room temperature. All of the products were obtained very pure in good to high yield. The chemical structures of the products were elucidated by IR, 1HNMR, and 13CNMR spectroscopy and also the melting point.

Spectral data of the novel synthesized chromene derivatives

2,7-Diamino-4-(2-methoxyphenyl)-4H-chromene-3-carbonitrile (2a)

M.p = 217–218 °C; Rf = 0.63 (70% ethyl acetate, 30% n-hexane); IR (KBr, cm−1) = 3445, 3365, 3312, 3189 (NH2), 2188 (CN), 1633, 1574 (C=C); 1H NMR (250 MHz, DMSO) δ 3.68 (d, J = 7.1 Hz, 3H), 4.47 (s, 1H), 5.20 (s, 2H), 6.07–6.37 (m, 2H), 6.45–6.94 (m, 5H), 7.06 (d, J = 8.0 Hz, 2H); 13C NMR (63 MHz, DMSO) δ 36.8, 52.5, 54.3, 97.5, 108.0, 108.6, 111.4, 115.0, 118.5, 125.9, 126.9, 129.4, 136.4, 146.2, 146.4, 155.4, 157.8.

2,7-Diamino-4-(3,4-dimethoxyphenyl)-4H-chromene-3-carbonitrile (3a)

M.p = 194–196 °C; Rf = 0.50 (70% ethyl acetate, 30% n-hexane); IR (KBr, cm−1) = 3443, 3416, 3361, 3189 (NH2), 2201(CN), 1652, 1577 (C=C); 1H NMR (250 MHz, DMSO) δ 3.69 (s, 6H), 4.48 (s, 1H), 5.20 (s, 2H), 6.16–6.34 (m, 2H), 6.60–6.92 (m, 6H); 13C NMR (63 MHz, DMSO) δ 37.0, 53.0, 53.1, 54.2, 97.5, 107.9, 108.6, 108.8, 109.5, 111.2, 116.9, 118.5, 126.9, 136.8, 145.0, 146.1, 146.4, 157.9.

2,7-Diamino-4-(3-phenoxyphenyl)-4H-chromene-3-carbonitrile (6a)

M.p = 185–186 °C C; Rf = 0.78 (70% ethyl acetate, 30% n-hexane); IR (KBr, cm−1) = 3450, 3372, 3317, 3190 (NH2), 2190 (CN), 1634, 1574 (C=C); 1H NMR (400 MHz, DMSO) δ 4.56 (s, 1H), 5.28 (s, 2H), 6.22 (d, J = 2.2 Hz, 1H), 6.31 (dd, J = 8.3, 2.2 Hz, 1H), 6.65–6.70 (m, 1H), 6.78–6.87 (m, 4H), 6.94 (dt, J = 7.8, 1.2 Hz, 1H), 6.98– 7.04 (m, 2H), 7.15 (tt, J = 7.3, 1.1 Hz, 1H), 7.31 (t, J = 7.9 Hz, 1H), 7.35–7.43 (m, 2H); 13C NMR (101 MHz, DMSO) δ 39.5, 56.0, 99.8, 109.6, 111.1, 116.2, 117.6, 118.5, 120.8, 122.4, 123.4, 129.3, 130.0, 130.1, 148.9, 148.9, 149.0, 156.4, 156.6, 160.4.

2,7-Diamino-4-(4-(methylthio) phenyl)-4H-chromene-3-carbonitrile (8a)

M.p = 247–248 °C; Rf = 0.52 (70% ethyl acetate, 30% n-hexane); IR (KBr, cm−1) = 3446, 3364, 3313, 3188 (NH2), 2188 (CN), 1632, 1573 (C=C); 1H NMR (250 MHz, DMSO) δ 2.42 (s, 3H), 4.48 (s, 1H), 5.21 (s, 2H), 6.12–6.33 (m, 2H), 6.59 (d, J = 8.1 Hz, 1H), 6.75 (s, 2H), 7.03–7.23 (m, 4H); 13C NMR (63 MHz, DMSO) δ 12.3, 37.0, 53.8, 97.4, 107.4, 108.6, 118.4, 123.8, 125.5, 126.9, 133.5, 141.1, 146.3, 146.4, 157.9.

2,7-Diamino-4-(2-fluorophenyl)-4H-chromene-3-carbonitrile (9a)

M.p = 232–233 °C; Rf = 0.70 (70% ethyl acetate, 30% n-hexane); IR (KBr, cm−1) = 3427, 3350, 3310, 3156 (NH2), 2195 (CN), 1647, 1589 (C=C); 1H NMR (250 MHz, DMSO) δ 4.82 (s, 1H), 5.25 (s, 2H), 6.10–6.41 (m, 2H), 6.62 (d, J = 8.1 Hz, 1H), 6.82 (s, 2H), 7.15 (dq, J = 14.2, 8.1 Hz, 4H); 13C NMR (63 MHz, DMSO) δ 31.7, 52.3, 97.5, 106.4, 108.7, 112.9, 113.3, 118.2, 122.2, 126.0, 126.2, 126.5, 127.3, 130.4, 130.6, 146.5, 146.7, 155.4, 158.4, 159.3.

Spectral data of the synthesized chromene sulfonamide hybrids

N-[2-amino-3-cyano-4-(4-methoxy-phenyl)-4H-chromene-7-yl]-4-methyl-benzene sulfonamide (1b)

Yellow solid; m.p = 206–209 °C; Rf = 0.68 (70% EtOAc, 30% n-hexane); FTIR (KBr)/νmax (cm−1) = 3478, 3402 (NH2), 3149 (N–H sulfonamide), 2196 (CN), 1649, 1579 (C = C), 1336, 1159 (SO2); 1HNMR (400 MHz, DMSO) δ (ppm) = 2.18 (s, 3HCH3); 3.82 (s, 3HOCH3), 4.68 (s, 1HCH(4)), 6.64–7.13 (m, 7HAr, 2HNH2), 7.79 (s, 4HAr), 10.42 (s, 1HNH-SO2); 13CNMR (100 MHZ, DMSO) δ (ppm) = 21.0, 39.4, 55.0, 55.8, 106.4, 114.0, 115.8, 119.1, 120.4, 126.6, 128.4, 129.8, 130.0, 136.4, 137.5, 137.8, 143.5, 148.3, 158.0, 159.9. Anal. Calcd. for C24H21N3O4S, %: C, 64.42; H, 4.73; N, 9.39. Found, %: C, 64.56; H, 4.62; N, 9.51.

N-[2-amino-3-cyano-4-(2-methoxy-phenyl)-4H-chromene-7-yl]-4-methyl-benzene sulfonamide (2b)

Yellow solid; m.p = 180–183 °C; Rf = 0.68 (70% EtOAc, 30% n-hexane); FTIR (KBr)/νmax (cm−1) = 3470, 3403 (NH2), 3155 (N–H sulfonamide), 2195 (CN), 1649, 1581 (C=C), 1335, 1159 (SO2); 1HNMR (400 MHz, DMSO) δ (ppm) = 2.45 (s, 3HCH3), 3.82 (s, 3HOCH3), 4.69 (s, 1HCH(4)), 6.88 (d, J = 7.3 Hz, 2HAr), 6.96 (d, J = 7.5 Hz, 3HAr), 7.03 (s, 2HNH2), 7.15 (d, J = 8.0 Hz, 2HAr), 7.47 (d, J = 8.1 Hz, 2HAr), 7.75 (d, J = 7.9 Hz, 2HAr), 10.54 (s, 1HNH-SO2); 13CNMR (100 MHZ, DMSO) δ (ppm) = 21.4, 40.6, 55.5, 56.6, 106.9, 114.5, 116.3, 119.6, 120.9, 126.0, 127.1, 128.6, 128.9, 130.3, 130.5, 136.9, 138.0, 138.3, 144.0, 148.8, 158.5, 160.4. Anal. Calcd. for C24H21N3O4S, %: C, 64.42; H, 4.73; N, 9.39. Found, %: C, 64.52; H,4.67; N, 9.54.

N-[2-amino-3-cyano-4-(3,4-dimethoxy-phenyl)-4H-chromene-7-yl]-4-methyl-benzene sulfonamide (3b)

Yellow solid; m.p = 127–130 °C; Rf = 0.51 (70% EtOAc, 30% n-hexane); FTIR (KBr)/νmax = 3449, 3346 (NH2), 3246 (N–H sulfonamide), 2190 (CN), 1650, 1605 (C=C), 1332, 1157 (SO2); 1HNMR (400 MHz, DMSO) δ (ppm) = 2.47 (s, 3HCH3), 3.80 (s, 3HOCH3), 3.83 (s, 3HOCH3), 4.80 (s, 1HCH(4)), 6.74–7.05 (m, 6HAr, 2HNH2), 7.48 (d, J = 7.9 Hz, 2HAr), 7.76 (d, J = 7.8 Hz, 2HAr), 10.56 (s, 1HNH-SO2); 13CNMR (100 MHZ, DMSO) δ (ppm) = 20.9, 39.2, 55.3, 55.4, 56.0, 106.4, 111.1, 111.9, 115.8, 118.9, 119.3, 120.4, 126.6, 129.8, 130.0, 136.3, 137.5, 138.2, 143.5, 147.6, 148.2, 148.6, 159.9. Anal. Calcd. for C25H23N3O5S, %: C, 62.88; H, 4.85; N, 8.80. Found, %: C, 63.01; H,4.93; N, 8.68.

N-[2-amino-3-cyano-4-(4-chloro-phenyl)-4H-chromene-7-yl]-4-methyl-benzene sulfonamide (4b)

Orange solid; m.p = 179–182 °C; Rf = 0.75 (70% EtOAc, 30% n-hexane; FTIR (KBr)/νmax (cm–1) = 3418, 3331 (NH2), 3210 (N–H sulfonamide), 2195 (CN), 1652, 1585 (C=C), 1329, 1161 (SO2); 1HNMR (400 MHz, DMSO) δ (ppm) = 2.34 (s, 3HCH3), 4.69 (s, 1HCH(4)), 6.75–6.81 (m, 2HAr), 6.87 (d, J = 8.3 Hz, 1HAr), 7.03 (s, 2HNH2), 7.16 (d, J = 8.1 Hz, 2HAr), 7.36 (d, J = 7.9 Hz, 4HAr), 7.64 (d, J = 8.0 Hz, 2HAr), 10.48 (s, 1HNH-SO2); 13CNMR (100 MHZ, DMSO) δ (ppm) = 21.4, 39.6, 56.0, 106.9, 116.3, 118.7, 120.7, 127.1, 129.1, 129.8, 130.3, 130.5, 131.9, 136.9, 138.3, 144.0, 145.2, 148.8, 160.5. Anal. Calcd. for C23H18ClN3O3S, %: C, 61.13; H, 4.01; N, 9.30. Found, %: C, 61.35; H, 3.97; N, 9.43.

N-[4-(2-amino-3-cyano-4-phenyl-4H-chromen-7-yl sulfamoyl)-phenyl]-acetamide (5b)

Yellow solid; m.p = 240–243 °C; Rf = 0.42 (90% EtOAc, 10% n-hexane); FTIR (KBr)/νmax = 3426, 3343(NH2), 3130 (N–H sulfonamide), 3065 (N–H amide), 2189 (CN), 1674 (C=O), 1655, 1590 (C=C), 1336, 1159 (SO2); 1HNMR (400 MHz, DMSO) δ (ppm) = 2.18 (s, 3HCH3), 4.75 (s, 1HCH (4)), 6.88 (d, J = 8.3 Hz, 2HAr), 6.99 (d, J = 8.2 Hz, 1HAr), 7.07 (s, 2HNH2), 7.23 (d, J = 7.6 Hz, 2HAr), 7.31 (t, J = 7.2 Hz, 1HAr), 7.41 (t, J = 7.5 Hz, 2HAr), 7.80 (q, J = 8.7 Hz, 4HAr), 10.45 (s, 1HNH-SO2), 10.49 (s, 1HNH amide); 13CNMR (100 MHZ, DMSO) δ (ppm) = 24.6, 40.1, 56.8, 107.1, 116.4, 116.5, 119.1, 120.8, 120.9, 127.3, 127.9, 128.4, 129.1, 130.5, 133.3, 143.7, 146.3, 148.9, 160.5, 169.5. Anal. Calcd. for C24H20N4O4S, %: C, 62.60; H, 4.38; N, 12.17. Found, %: C, 62.72; H, 4.26; N, 12.27.

N-[4-[2-amino-3-cyano-4-(3-phenoxy-phenyl)-4H-chromene-7-yl sulfamoyl]-phenyl}-acetamide (6b)

Yellow solid; m.p = 141–144 °C; Rf = 0.48 (90% EtOAc, 10% n-hexane); FTIR (KBr)/νmax = 3447, 3357 (NH2), 3213 (N–H sulfonamide), 3063 (N–H amide), 2190 (CN), 1691 (C = O), 1649, 1590 (C = C), 1322, 1156 (SO2); 1HNMR (400 MHz, DMSO) δ (ppm) = 2. 27 (s, 3HCH3), 4. 87 (s, 1HCH (4)), 6. 95 -7.21 (m, 7HAr, 2HNH2), 7. 29–7. 40 (m, 2HAr), 7. 47–7.64 (m, 3HAr), 7. 91 (q, J = 8. 7 Hz, 4HAr), 10. 55 (s, 1HNH-SO2), 10. 61 (s, 1HNH-amide); 13CNMR (100 MHZ, DMSO) δ (ppm) = 24.1, 39.6, 64.4, 106.5, 115.9, 116.4, 117.5, 118.3, 118.6, 119.3, 120.1, 122.3, 123.5, 124.6, 127.8, 130.0, 130.3, 132.7, 137.7, 143.3, 148.0, 148.2, 156.0, 156.7, 160.1, 169.0. Anal. Calcd. for C30H24N4O5S, %: C, 65.21; H, 4.38; N, 10.14. Found, %: C, 65.43; H, 4.3; N, 10.02.

N-[4-[2-amino-3-cyano-4-(4-fluoro-phenyl)-4H-chromene-7-yl sulfamoyl]-phenyl}-acetamide (7b)

Yellow solid; m.p = 230–233 °C; Rf = 0.45 (90% EtOAc, 10% n-hexane); FTIR (KBr)/νmax = 3433, 3326 (NH2), 3271 (N–H sulfonamide), 3210 (N–H amide), 2192 (CN), 1674 (C=O), 1651, 1594 (C=C), 1326, 1157 (SO2); 1HNMR (400 MHz, DMSO) δ (ppm) = 2.27 (s, 3HCH3), 4.88 (s, 1HCH(4)), 6.97 (d, J = 7.8 Hz, 2HAr), 7.06 (d, J = 8.2 Hz, 1HAr), 7.18 (s, 2HNH2), 7.30–7.38 (m, 4HAr), 7.89 (q, J = 8.9 Hz, 4HAr), 10.54 (s, 1HNH-SO2), 10.59 (s, 1HNH-amide); 13CNMR (100 MHZ, DMSO) δ (ppm) = 24.1, 39.0, 55.8, 106.6, 115.3, 115.5, 116.0, 118.5, 118.6, 120.3, 127.9, 129.2, 129.3, 130.0, 132.6, 137.7, 141.9, 142.0, 143.3, 148.3, 159.8, 159.9, 162.2, 169.1. Anal. Calcd. for C24H19FN4O4S, %: C, 60.24; H, 4.00; N, 11.71. Found, %: C, 60.29; H, 4.11; N, 11.59.

N-[4-[2-amino-3-cyano-4-(4-chloro-phenyl)-4H-chromene-7-yl sulfamoyl]-phenyl}-acetamide (8b)

Yellow solid; m.p = 227–230 °C; Rf = 0.45 (90% EtOAc, 10% n-hexane); FTIR (KBr)/νmax = 3440, 3367 (NH2), 3320 (N–H sulfonamide), 3275 (N–H amide), 2170 (CN), 1674 (C=O), 1648, 1593 (C=C), 1326, 1157 (SO2); 1HNMR (400 MHz, DMSO) δ (ppm) = 2.19 (s, 3HCH3), 4.81 (s, 1HCH(4)), 6.89 (d, J = 7.3 Hz, 2HAr), 6.98 (d, J = 7.4 Hz, 1HAr), 7.13 (s, 2HNH2), 7.26 (d, J = 8.2 Hz, 2HAr), 7.48 (d, J = 7.7 Hz, 2HAr), 7.81 (q, J = 8.7 Hz, 4HAr), 10.46 (s, 1HNH-SO2), 10.53 (s, 1HNH-amide); 13CNMR (100 MHZ, DMSO) δ (ppm) = 24.1, 39.1, 55.9, 106.6, 116.0, 118.1, 118.6, 120.2, 127.9, 128.6, 129.3, 130.0, 131.4, 132.6, 137.8, 143.3, 144.7, 148.3, 160.0, 169.1. Anal. Calcd. for C24H19ClN4O4S %: C, 58.24; H, 3.87; N, 11.32. Found, %: C, 58.34; H, 3.98; N, 11.43.

N-[4-[2-amino-3-cyano-4-(3,4-dimethoxy-phenyl)-4H-chromene-7-yl sulfamoyl]-phenyl}-acetamide (9b)

Yellow solid; m.p = 235–238 °C; Rf = 0.27 (90% EtOAc, 10% n-hexane); FTIR (KBr)/νmax = 3412, 3322 (NH2), 3265 (N–H sulfonamide), 3210 (N–H amide), 2193 (CN), 1695 (C=O), 1651, 1592(C=C), 1314, 1161 (SO2); 1HNMR (400 MHz, DMSO) δ (ppm) = 2.24 (s, 3HCH3), 3.72–3.91 (m, 6H(2OCH3)), 4.73 (s, 1HCH(4)), 6.78–7.06 (m, 6HAr, 2HNH2), 7.85 (s, 4HAr), 10.46 (s, 2HNHamide, NHSO2); 13CNMR (100 MHZ, DMSO) δ (ppm) = 24.1, 39.4, 55.3, 55.4, 56.1, 106.4, 111.1, 111.9, 116.2, 118.0, 118.5, 119.3, 120.5, 127.7, 129.7, 133.8, 138.3, 138.9, 142.8, 147.5, 148.2, 148.6, 159.9, 169.0. Anal. Calcd. for C26H24N4O6S%: C, 59.99; H, 4.65; N, 10.76. Found, %: C, 60.08; H, 4.82; N, 10.87.

N-(2-amino-3-cyano-4-(4-(methylthio)phenyl)-4H-chromen-7-yl)benzenesulfonamide (10b)

Orange solid; m.p = 135–138 °C; Rf = 0.45 (50% EtOAc, 50% n-hexane); FTIR (KBr)/νmax = 3472, 3360 (NH2), 3185 (N–H sulfonamide), 2182 (CN), 1643, 1582 (C=C), 1330, 1155 (SO2); 1HNMR (400 MHz, DMSO) δ (ppm) = 2.46 (s, 3HCH3), 4.38 (s, 1HCH(4)), 6.84–6.92 (m, 3HAr), 7.08 (s, 2HNH2), 7.20 (s, 2HAr), 7.56–7.66 (m, 4HAr), 7.78 (s, 3HAr), 10.55 (s, 1HNH-SO2); 13CNMR (100 MHZ, DMSO) δ (ppm) = 14.7, 39.1, 55.9, 106.7, 116.0, 118.8, 120.3, 126.2, 126.6, 128.0, 129.4, 130.0, 133.2, 136.5, 137.5, 139.3, 142.4, 148.3, 160.0. Anal. Calcd. for C23H19N3O3S2%: C, 61.45; H, 4.26; N, 9.35. Found, %: C, 61.57; H, 4.13; N, 9.44.

N-(2-amino-3-cyano-4-(2-fluorophenyl)-4H-chromen-7-yl) benzenesulfonamide (11b)

Yellow solid; m.p = 122–125 °C; Rf = 0.54 (50% EtOAc, 50% n-hexane); FTIR (KBr)/νmax = 3468, 3354 (NH2), 3154 (N–H sulfonamide), 2194 (CN), 1643, 1585 (C=C), 1329, 1156 (SO2); 1HNMR (400 MHz, DMSO) δ (ppm) = 4.99 (s, 1HCH(4)), 6.84–6.99 (m, 3HAr), 7.08 (s, 2HNH2), 7.23 (s, 3HAr), 7.37 (s, 1HAr), 7.62–7.73 (m, 3HAr), 7.85 (d, J = 7.3 Hz, 2HAr), 10.62 (s, 1HNH-SO2); 13CNMR (100 MHZ, DMSO) δ (ppm) = 13CNMR (100 MHZ, DMSO) δ (ppm) = 34.4, 54.3, 106.6, 115.7, 115.9, 116.0, 117.6, 120.1, 124.7, 124.8, 126.5, 129.0, 129.1, 129.4, 129.6, 129.8, 129.9, 131.9, 132.0, 133.1, 137.7, 139.2, 148.6, 158.6, 160.3, 161.1. Anal. Calcd. for C22H16FN3O3S%: C, 62.70; H, 3.83; N, 9.97. Found, %: C, 62.84; H, 3.76; N, 10.08.

All of the spectral data were placed in the supplementary file (Figs. S3–S54).

Antibacterial activity evaluation

A broth microdilution method was utilized to explore the minimum inhibitory concentrations (MIC) of all the newly synthesized compounds against E. coli ATCC 25922 and S. aureus ATCC 25923. Test compounds were prepared using dimethyl sulfoxide (DMSO) as a stock solution, followed by serial dilutions in Mueller Hinton broth containing up to one percent DMSO. At the end, a diluted bacterial suspension from a 0.5 McFarland tube was added to all the wells (the final volume of each well was 1 × 105 CFU/ml). In the growth control, a culture medium with bacteria was used as a positive control, and a culture medium without bacteria was used as a negative control (sterility control). The MIC was the lowest concentration of the synthesized compounds that produced no growth on the plate2. Sulfisoxazole, functioning as a sulfonamide antibiotic, and gentamicin were employed in a comparative analysis of antibacterial activity, cytotoxicity, and apoptosis assessment.

Cytotoxicity assay by MTT

MTT was applied to evaluate the cytotoxicity of the newly synthesized compounds against fibroblast L929 cells (Pasteur Institute, Tehran, Iran). Briefly, 100 μl of cell suspension (1 × 104 cells/well) in RPMI 1640 medium, as seeded in 96-well microplates and incubated for 24 h (37 °C, 5% CO2 air humidified). After removing the old media, 100 µl of each of the newly synthesized compounds was added to each well at different concentrations. The plates were incubated for another 24 h in the same condition. To determine the positive and negative controls, doxorubicin and DMSO were used. Cell survival was determined by adding 10 µL of an MTT solution (5 mg/ml in PBS) to each well and incubating at 37 °C for 3 h. The previous MTT-containing media was then removed, and 100 µL DMSO was pipetted into each well to dissolve the produced formazan crystals. Using an ELISA plate reader (Startfix-2100, Awareness, USA), absorbance was measured at 570 nm. By plotting the percentage of cytotoxicity versus concentration of the produced compounds, the concentrations that inhibit half of the cell population (IC50) were determined. A mean and standard deviation (SD) are used to represent the results67. The percentage cell viability was calculated using the formula:

$$\text{Viability}=\frac{\text{Absorbance \, of \, treated \, cells}-\text{background \, absorbance }(\text{b})}{\text{Absorbance \, of \, untreated \, cells }(\text{c})-\text{background \, absorbance }(\text{b})}\times 100$$

where (b) = blank, and (c) = control.

Apoptosis evaluation by an Annexin V binding assay

To detect apoptosis, the Annexin V binding assay was used. The assay was performed according to the manufacturer's instructions using a FITC Annexin V Apoptosis Detection Kit with PI from BioLegend® (San Diego, California). To conduct this test, the fibroblast L929 cell line (Pasteur Institute, Tehran, Iran) was treated with various concentrations of the newly synthesized compounds and then incubated for 24 h at 37 °C with 5% CO2. Subsequently, each well's supernatant was removed and the cells were washed with PBS. Each well was then filled with 500 μl of Annexin-binding buffer (10 mM HEPES, 140 mM NaCl, and 2.5 mM CaCl2, pH 7.4) and 10 μl of Annexin V and PI. For 15 min, the plates were incubated at room temperature and kept away from light. After incubation, using a BD FACSCalibur flow cytometer (Biosciences, San Jose, CA, USA), the cells were examined after incubation. FlowJo software (version 10.5.3, TreeStar Incorporated, Ashland, OR) was used to analyze flow cytometry data.

Conclusion

The 3D-QSAR analysis showed that the predictive model had good prediction ability and statistically is reliable. The contour maps also indicated the relationship between the structure and activity of small molecules. Our study would provide informative experimental and theoretical guidance for the future design of chromene-sulfonamide hybrid inhibitors.

Interestingly our experimental results supported and conflicted with ADMET prediction about compounds carcinogenicity of our compounds. ADMET predictor assessments are set up on the QSAR of its databases, hence it sounded like this program needed to improve the quality of these types of evaluations for its software.

In conclusion, several chromene-sulfonamide hybrids were designed and synthesized as antibacterial agents under solvent-free conditions. The used method for the synthesis of these compounds had advantages such as high yield and purity, no need to use toxic solvents and base, simple work-up just by adding water to the reaction medium, and filtration to remove the base and room temperature. Also, according to investigations and antibacterial results obtained on chromene-sulfonamide compounds, among all the compounds, two compounds N-{4-[2-amino-3-cyano-4-(3-phenoxy-phenyl)-4H-chromene-7-yl sulfamoyl]-phenyl}z-acetamide (compound 6b) and N-[4-[2-amino-3-cyano-4-(4-chloro-phenyl) -4H-chromene-7-yl sulfamoyl]-phenyl}-acetamide (compound 8b) showed more antibacterial activity than other chromene-sulfonamide derivatives against both Gram-positive and negative bacteria, so it can be concluded that chromene compounds that had phenoxy and chlorine groups showed better antibacterial activities against Gram-positive and Gram-negative bacteria. In addition, MTT results demonstrated that compounds 1b, 4b, 6b, and 8b have a greater cytotoxicity effect on the fibroblast L929 cell line than other compounds. Several variables must be considered when evaluating a substance's toxicity, including its chemical structure, concentration, and the experimental system. Despite safety concerns, greater cytotoxicity may be desirable in some circumstances, such as when developing anticancer drugs or antimicrobial products. Further investigation into the compounds' safety profile would involve assessing their effects on additional cell lines and doing animal tests to evaluate their systemic toxicity.