Design, synthesis and network pharmacology-integrated evaluation of benzenesulfonamides as potential Dickkopf-1 (Dkk-1)-targeting anticancer candidates

Abstract

Sulfanilamide-based drugs are known to exhibit immense anti-cancer activities, and thus, we sought to design and formulate sulfanilamide derivatives in order to study their potential against carbonic anhydrase II, pancreatic elastase, Dickkopf-1 (Dkk1) and LRP6 proteins, which are potential anti-cancer targets for various cancers. Out of all the synthesized derivatives, compound 4d exhibited significant inhibitory potential against CA-II, with an IC₅₀ value of 0.348 ± 0.024 µM, which is about 3-fold higher than that of the reference drug acetazolamide (IC₅₀ = 0.9979 ± 0.0024 µM). The significance of Dkk1 was taken into consideration in this study. Molecular docking interaction studies revealed that compound 4d exhibited good binding potential with CA-II, pancreatic elastase, LRP6 and Dkk1, with binding energies of −8.1, −8.7, −7.9 and −10.4 kcal mol⁻¹, respectively. Furthermore, in the course of DNA binding experiments, compound 4d exhibited the highest binding constant value of 2.3 × 10⁷ mol⁻¹, validating the other biological tests, and these results are in agreement with the reported values, demonstrating the significance of compound 4d to act as a representative drug in the future. Finally, the present work has the potential to pave way for new therapeutic approaches in cancer treatment with the involvement of the less explored Dkk1 signaling target. In addition, a network pharmacology analysis of compound 4d against pancreatic cancer identified 243 shared targets that converged, through protein–protein interactions and enrichment analyses, on the PI3K-AKT and receptor tyrosine kinase signalling axes, providing a system-level rationale for its anticancer potential.

The current study is focused on designing of sulfanilamide derivatives and to study their potential against carbonic anhydrase II, pancreatic elastase, Dickkopf-1 (Dkk1) and LRP6 proteins, which are potential anti-cancer targets for various cancers.

1. Introduction

Cancer is a multifactorial disease, which is associated with progressive changes at genetic, epigenetic, metabolic and proteomic levels, leading to uncontrolled proliferation of cells, evasion of the immune system, invasion, angiogenesis and spread to distant tissues. Although significant progress has been made in chemotherapy, targeted therapy and immunotherapy, therapeutic resistance, systemic toxicity, tumour heterogeneity and disease recurrence are still significant challenges in clinical oncology. Thus, the identification of disease-associated molecular targets and the development of small molecules that can selectively modulate these targets remain important goals in anticancer drug discovery.^1–3 The abnormal expression of proteins, abnormal enzymatic activity and abnormal intracellular signaling pathways are closely linked to malignant transformation. Cancer-associated proteins play a role in the initiation, progression, metastasis and therapeutic resistance of cancer and can be used as diagnostic, prognostic and predictive biomarkers. Therefore, the identification of key proteins that play a role in tumour survival, metabolic adaptation and regulation of the tumour microenvironment is a rational approach to the development of mechanism-based anticancer therapeutics.

Dickkopf-1 (Dkk1), pancreatic elastase, and carbonic anhydrase II (CA-II) are emerging targets for cancer treatment that have gained increasing interest due to their role in oncogenic signaling, ECM remodeling, regulation of the tumour microenvironment and adaptation of cellular pH.^4–6 Carbonic anhydrase II (CA-II) is a zinc-containing metalloenzyme that catalyzes the reversible hydration of carbon dioxide to bicarbonate and protons, which helps to maintain the cellular pH and ion homeostasis. CA-II is expressed in normal tissues, but the altered carbonic anhydrase activity is involved in the metabolic adaptation of tumour cells, leading to intracellular alkalinization and extracellular acidification, which promote cancer cell invasion and migration and therapeutic resistance. Thus, CA-II inhibition has become an attractive strategy to interfere with the pH regulation of tumour cells and inhibit tumour growth.^7–9

Dickkopf-1 (Dkk1) is a secreted glycoprotein of the Dickkopf family, mainly known as a Wnt signaling regulator. It has context-dependent biological activity, both Wnt-dependent and Wnt-independent, with reports of its involvement in tumour progression. High expression of Dkk1 has been linked to various types of cancers, tumour aggressiveness, metastatic potential, immune modulation and poor clinical outcome. In addition, Dkk1 can interact with receptors like CKAP4, which can trigger downstream pathways, such as the PI3K/AKT and JNK pathways, which promote tumour cell survival and progression. These properties make Dkk1 an interesting biomarker and a potential target for anticancer drug development.^10–12

Pancreatic elastase is a serine protease that plays a role in extracellular matrix degradation, tissue remodeling and modification of the tumour microenvironment. Proteolytic activity can be dysregulated to promote tumour cell migration, stromal interactions and local invasion, especially in aggressive malignancies like pancreatic cancer. Thus, pancreatic elastase is a potential target for understanding and controlling protease-mediated tumour progression.^13,14

Sulfonamide derivatives are an important class of pharmacologically active compounds in medicinal chemistry. The sulfonamide functional group has a wide range of biological interactions such as hydrogen bonding, metal coordination, and binding to enzyme active sites. Therefore, sulfonamide derivatives have been widely studied for their antibacterial, diuretic, anti-inflammatory, antiviral, and anticancer properties. In cancer research, sulfonamide derivatives have been shown to have therapeutic potential by acting through various mechanisms, such as carbonic anhydrase inhibition, cell-cycle regulation, disruption of microtubule dynamics, modulation of signaling pathways, and induction of apoptosis. The clinical relevance of this scaffold has been further demonstrated by several sulfonamide-based anticancer drugs such as indisulam, belinostat and amsacrine (Fig. 1).^15–17

Fig. 1. FDA-approved sulfonamide-based anticancer drugs.¹⁷.

Sulfonamide-based compounds have shown great promise in the field of anticancer drug discovery, but most of the reported derivatives have been targeted toward single molecular targets, such as carbonic anhydrases, histone deacetylases (HDACs), and other cancer-associated enzymes. Structurally diverse sulfonamide frameworks that can interact with multiple cancer-related pathways are still important areas of research. Heterocyclic sulfonamide derivatives have been recently studied due to the possibility of increasing the molecular complexity, target recognition and biological activity by the introduction of other heterocyclic systems. The thiazoloisoxazole-fused benzenesulfonamide scaffold explored in the present study is a structurally different scaffold for anticancer evaluation. The fused heterocyclic system offers increased conjugation and extra heteroatom-rich interaction points that could be beneficial for better molecular interactions with biological targets.

The complexity and multifaceted nature of cancer has changed the paradigm of drug discovery from targeting a single target to targeting multiple disease-associated pathways with multifunctional molecules. Sulfonamide-containing heterocyclic derivatives are a suitable platform for the development of such multitarget anticancer agents. The inhibition of CA-II, pancreatic elastase and Dkk1-associated pathways may have complementary effects through their role in pH regulation in the tumour, extracellular matrix remodeling and oncogenic signaling pathways. Moreover, the combination of experimental biological evaluation and computational methods like molecular docking, molecular dynamics simulation, and network pharmacology can offer a holistic view of compound–target interactions and potential anticancer mechanisms.

Hence, the present study aimed to synthesize novel thiazoloisoxazole derivatives of benzenesulfonamide and assess their anticancer activity by employing synthetic chemistry, biological assays, computational modelling and network pharmacology approaches. Enzyme inhibition assays were conducted for CA-II and pancreatic elastase, and molecular docking, molecular dynamics simulations, and network pharmacology were used to explore the interactions with Dkk1/LRP6-related cancer pathways. This integrated approach provides insights into the potential of thiazoloisoxazole sulfonamide derivatives as multifunctional anticancer agents.

2. Results and discussion

2.1. Chemistry: synthesis of 4-((3-arylthiazolo[3,4-d]isoxazol-5-yl)amino)benzenesulfonamides

Sulfanilamide derivatives possess important biological and non-biological applications. Various novel sulfanilamide derivatives were synthesized (4a–j) using the synthetic approaches, which involved short reaction time and mild conditions and resulted in good yield.

Sulfanilamide was converted into a corresponding acetylated product by treatment with chloroacetyl chloride. The freshly prepared acetylated product was reacted with potassium thiocyanate in dry acetone to obtain a key thiazol-4(5H)-one based intermediate, which plays a pivotal role in the synthesis of desired product. The key intermediate was condensed with a suitably substituted aromatic aldehyde. In the last step, the condensed product was treated with hydroxylamine hydrochloride (anhydrous) to obtain the thiazole(4,5-c)isoxazole-based fused ring heterocyclic system. The crude product obtained was recrystallized using ethanol as the solvent to obtain the purified product in excellent yield.

Sulfanilamide derivatives have important applications in biological and nonbiological systems. A new series of derivatives 4a–j were synthesized using a rapid methodology, which is highlighted by short reaction times, mild reaction conditions, and good yields.

The synthesis started with the acetylation of sulfanilamide using chloroacetyl chloride to give an acetylated product. The acetylated product was then reacted with potassium thiocyanate in dry acetone to give a key thiazol-4(5H)-one derivative. The key derivative was then condensed with an aromatic aldehyde. In the final step, an anhydrous hydroxylamine hydrochloride treatment of the product afforded a derivative with a thiazole[4,5-c]isoxazole heterocyclic ring system. The crude product obtained in each step was purified by recrystallization from ethanol to give pure compounds. The whole synthesis is shown in Fig. 2.

Fig. 2. Synthesis of 4-((3-arylthiazolo[3,4-d]isoxazol-5-yl)amino)benzenesulfonamides.

2.2. Biological evaluation

2.2.1. Pancreatic elastase and carbonic anhydrase inhibition activity results of 4a–j

All the newly synthesized derivatives (4a–4j) were analyzed for their anti-oxidant potential by a DPPH free radical scavenging assay (Table 1). In addition, pancreatic elastase inhibition activity was calculated using the reported methods.^18–20 The compound 4d showed significant activity compared to the standard drug acetazolamide and 4f is more potent against pancreatic elastase with an IC₅₀ value of 0.0542 µM.

Table 1. Carbonic anhydrase and pancreatic elastase inhibitory activities of derivatives 4a–4j^a.

Compound	Carbonic anhydrase IC50 ± SEM (µM)	Pancreatic elastase IC50 ± SEM (µM)
4a	11.89 ± 2.46	2.08 ± 0.04
4b	7.73 ± 1.13	1.64 ± 0.02
4c	4.17 ± 0.43	0.13 ± 0.01
4d	0.34 ± 0.02	0.51 ± 0.02
4e	1.31 ± 0.02	0.06 ± 0.01
4f	2.82 ± 0.06	0.05 ± 0.01
4g	5.67 ± 0.09	5.76 ± 0.08
4h	3.23 ± 0.04	45.91 ± 0.09
4i	2.29 ± 0.04	3.17 ± 0.03
4j	2.61 ± 0.03	1.37 ± 0.02
Acetazolamide	0.99 ± 0.04
Oleanolic acid	—	13.4521 ± 0.551

^aSEM = standard error of the mean; the values are expressed as mean ± SEM.

2.2.2. Structure–activity relationship

The biological activity of any drug molecule is related to its chemical structure, and the measurement of this very relationship between drug activity and molecular structure is known as structure–activity relationship (SAR). The SAR is the significant part of scientific research where it enables a researcher to understand the position of different functional groups and their effect on the activities of the compound, e.g. potency, toxicity and selectivity toward particular receptors. It is helpful for the discovery and development of new more potent drugs used for various diseases and in the same way to mitigate the harmful effects of new drugs by removing the responsible groups.

All the synthesized sulfonamide derivatives were analyzed for their carbonic anhydrase and pancreatic elastase inhibitory activities using acetazolamide and oleanolic acid as reference drugs. The IC₅₀ value of all the derivatives against carbonic anhydrase enzyme is slightly higher than that of the reference drug, except 4d which is the most potent derivative. The IC₅₀ value of 4d is 0.348 µM. which is significantly lower than that of the reference compound acetazolamide (0.99 µM). In the same way, the enzyme inhibitory activity of all the derivatives against pancreatic elastase is quite impressive. Among all the derivatives, 4d, 4e and 4f are the most potent derivatives with the lowest IC₅₀ values of 0.51, 0.06, and 0.05 µM.

Although the activity profiles of all the derivatives are quite random and following no specific pattern, certain conclusions can be drawn from the structure–activity relationship of these compounds against carbonic anhydrase. The enhanced activity of compound 4d may be due to the presence of a carbazole moiety, which offers an extended π-conjugated aromatic system that can interact with the active site through enhanced hydrophobic and π–π interactions. The enhanced electron delocalization could enhance the molecular polarization and target recognition, which could be beneficial for its better CA-II inhibition and docking profile. Interestingly, the carbazole-containing scaffold present in compound 4d is structurally similar to the reported carbazole-based p53 Y220C ligands, such as PhiKan083.²¹ The direct binding of p53 to Y220C was not assessed in the current study, but the structural similarity suggests that the carbazole moiety could be a useful pharmacophoric group for further optimization of p53-related anticancer mechanisms in medicinal chemistry. The activity of 4a and 4b is lower due to the presence of electron-donating groups, i.e. methyl and dimethylamine. However, the activity of 4e, 4g and 4j is slightly higher than that of the other due to the presence of electron-withdrawing groups, i.e. Cl, Br and F. Generally, the derivatives with ortho and meta substituents are more active to some extent than the para-substituted derivatives.

In the same way, the activity profile of all the derivatives against pancreatic elastase is higher than that of the reference compound oleanolic acid, except 4g which has the lowest activity of all the derivatives. The bromine atom at the ortho position deactivates the ring in the 4g derivative.

2.2.3. Anti-oxidant activity: free radical scavenging activity

The antioxidant capacity of the synthesized compounds (4a–4j) was assessed by a DPPH free radical scavenging test, and the findings were compared to vitamin C as a standard antioxidant (Fig. 3). DPPH is also a stable free radical, and the decline in absorbance of this scavenger indicates the capacity of a compound to give out hydrogen or electrons, thereby demonstrating its free radical scavenging ability. The highest level of activity was shown by compound 4f in DPPH scavenging (approximately 65%), which is a good antioxidant, but was less than the standard of vitamin C (approximately 95%). There were also significant activities observed with compound 4g (50.4%) and 4i (28.4%), indicating moderate antioxidant activity.

Fig. 3. Percentage of free radical scavenging activity of the synthesized compounds (4a–j); the values are presented as mean ± SEM.

Conversely, compounds 4a, 4b, 4c, 4e, 4h, and 4j exhibited a relatively low scavenging activity (less than 15%), which means that they have weak antioxidant activity. Compound 4d was a little more active (improved by about 14 percent) than most of the other derivatives but with a far lower activity than the most active compounds. The structural differences between the compounds, including the presence and location of electron-donating substituents, which can facilitate hydrogen-donating capacity and radical stabilization could be the reason behind the differences in antioxidant activity between the compounds observed. In general, the findings indicate that compound 4f is the most promising antioxidant candidate in the series synthesized, and should be further investigated, whereas vitamin C refers to the validity of the assay as a positive control.

2.2.4. UV-visible absorption spectra of the synthesized compounds

The UV-visible absorption spectra of all the synthesized derivatives 4a–4j showed absorption maxima (λ_max) in the range of 208–416 nm (Fig. 4). The major electronic transitions observed were π–π* transitions, which come from the conjugated double bond systems, and n–π* transitions, and these are due to heteroatoms possessing a lone pair of electrons. In general, the absorption bands at low wavelengths are assigned to π–π* transitions, while those bands that appear at higher wavelengths are assigned to n–π* transitions.

Fig. 4. UV spectra of all the derivatives (4a–j).

The electronic spectra showed a well-noticeable trend. In the case of halogen series, the tendency of λ_max showed an increase while going down the group, as on moving from Cl to Br, it exhibited a smaller atomic size, which diminished the polarizability effect required for higher electron donation. Thus, the compound 4h with a bromine substituent at a similar position exhibited a higher λ_max value than that of its counterpart 4e with a chlorine substituent.

However, compound 4i has a para-hydroxyl (4-OH) group absorbed at a significantly higher λ_max value due to the strong electron-donating effect of the OH group. In this case, the mesomeric (conjugation) effect overcomes the inductive effect, leading to a bathochromic shift. Curiously, compound 4f with 4-OH and 3-OMe possessing two strong electron-donating groups showed a decreased λ_max value compared to 4i. This might be attributed to intramolecular hydrogen bonding between the OH and OMe groups that could reduce the availability of the oxygen lone pairs for conjugation with the aromatic ring.

In fact, 4c (2-OH and 3-OMe) absorbed at a longer λ_max value than that of 4f. In 4c, the ortho-OH group may preferentially form a stable, six-membered intramolecular hydrogen bond with the oxygen atom of the adjacent isoxazole ring. This interaction would leave the methoxy group free to donate electron density to the aromatic system. By contrast, the spatial arrangement in 4f favors a five-membered hydrogen bond between the para-OH and meta-OMe groups, therefore diminishing the electron-donating capacity of both substituents and resulting in a lower λ_max value. All the values are given in Table 2.

Table 2. UV spectroscopy results of all the derivatives (4a–j).

S. no.	Code	λ max (nm)	ε	λ max (nm)	ε	λ max (nm)	ε	Band gap
1	4a	—	—	244	0.190	350	0.406	3.17
2	4b	—	—	252	0.066	422	0.209	5.58
3	4c	—	—	250	0.009	372	0.159	5.59
4	4d	243	0.316	288	0.259	397	0.246	5.57
5	4e	215	0.284	243	0.190	331	0.419	3.21
6	4f	—	—	222	0.231	349	0.486	2.55
7	4g	—	—	235	0.429	330	0.613	3.32
8	4h	208	0.825	272	0.634	335	0.358	2.87
9	4i	—	—	246	0.146	361	0.329	2.62
10	4j	—	—	244	0.050	332	0.246	5.61

2.2.5. DNA binding analysis for the synthesized derivatives

As part of DNA binding analysis for the synthesized derivatives, the UV-visible spectrum of compound 4c was selected. The compound 4c was chosen for further studies of interaction with DNA due to the presence of a strong interaction profile with DNA, as revealed by the preliminary spectroscopic analysis, and the appropriate absorbance properties for the accurate calculation of binding parameters. DNA-binding analysis was carried out only for a structurally representative compound, 4e, as compounds 4d and 4f showed higher enzyme inhibition. Indeed, as in other intercalation experiments, a hyperchromic effect was observed upon the progressive addition of five different concentrations of DNA to a fixed concentration of the compound, signifying the formation of a drug-DNA adduct (Fig. 5). The binding constant was calculated by employing the Benesi–Hildebrand equation:where K is the binding constant, A₀ is the absorbance of the free compound, A is the absorbance of the compound-DNA complex, and ε_G and ε_G–H are the molar absorption coefficients of the free compound and the complex, respectively. A plot of (A₀/[A − A₀]) versus DNA concentration yields a straight line. The binding constant (K) was determined from the slope-to-intercept ratio of this plot. For derivative 4c, this calculation provided a K value of 2.3 × 10⁷ mol⁻¹, which aligns with the reported values for significant DNA binders. This strong and specific interaction confirms the potential of these synthesized derivatives as future drug candidates.

Fig. 5. Spectral profile: absorbance vs. wavelength for derivative 4c.

2.2.6. Density functional theory studies for the synthesized derivatives

The electronic structure and diversity of a compound is the most complex area which is related to multiple properties of a compound. Using DFT calculations, various energy levels of the molecules are estimated to predict the reactivity profile of a compound. Among the various quantum parameters that are estimated in DFT calculation is the hardness of a compound, which refers to its ability or potential to react within its vicinity.⁴⁵ According to the DFT results, among all the derivatives, 4a and 4d are most stable with distinctive hardness and softness values calculated using two different basis sets. Similarly, all other parameters including electronegativity, chemical potential and electrophilicity index are in the optimal range. The energy parameters including softness, hardness, and the energy gap of the frontier molecular orbitals, i.e. HOMO and LUMO, are the major factors that determine the reactivity profile and nature of the compound.⁴⁶ Molecules with small HOMO–LUMO gaps tend to be more reactive and easier to polarize, which has also been confirmed by the higher dipole moment values of these derivatives. To further understand the reactivity of the molecule, the surfaces of these frontier molecular orbitals were also visualized and analyzed by the red and green orbitals in the compound. All the results are given in Table 3.

Table 3. DFT parameters of sulfonamide compounds (4a–4j) calculated using the 3-21G and 6-31G basis sets.

Compound	Hardness (η)		Softness (ζ)		Electronegativity (χ)		Chemical potential (µ)		Electrophilicity index (ω)
	3-21G	6-31G	3-21G	6-31G	3-21G	6-31G	3-21G	6-31G	3-21G	6-31G
4a	0.081	0.078	6.39	6.21	0.148	0.153	−0.148	−0.153	0.135	0.149
4b	0.068	0.065	7.68	7.35	0.125	0.131	−0.125	−0.131	0.115	0.132
4c	0.077	0.077	6.49	6.49	0.137	0.146	−0.137	−0.146	0.122	0.138
4d	0.072	0.069	7.27	6.94	0.134	0.138	−0.134	−0.138	0.125	0.139
4e	0.082	0.080	6.27	6.13	0.159	0.163	−0.159	−0.163	0.154	0.166
4f	0.077	0.077	6.50	6.49	0.137	0.146	−0.137	−0.146	0.122	0.138
4g	0.081	0.079	6.31	6.21	0.150	0.156	−0.150	−0.156	0.139	0.153
4h	0.083	0.082	6.11	6.02	0.154	0.160	−0.154	−0.160	0.143	0.157
4i	0.077	0.076	6.60	6.49	0.142	0.150	−0.142	−0.150	0.131	0.148
4j	0.082	0.081	6.15	6.13	0.154	0.161	−0.154	−0.161	0.145	0.159

2.2.6.1. Electrical parameters

The electrical polarizability of the compound indicates its propensity to interact with its ligand. Any molecule with higher polarizability is more easily available for its target ligand.⁴⁷ According to the results, compound 4d has the highest polarizability and 4b is with the 2nd highest value, which is in coordination with the hardness and softness results of these derivatives. Similarly, the dipole moment of a compound is another important factor in understanding the reactivity behavior. The dipole moment value of 4b and 4c is the highest of all the derivatives, i.e. 10.35 D and 10.1 D for the 3-21G basis set and 11.28 D and 11.05 D for the 6-31G basis set. The values are given in Table 4, and detailed explanation is given in the SI file.

Table 4. DFT parameters of sulfonamide compounds (4a–4j) calculated using the 6-31G basis set.

Compound	Basis set 3-21G			Basis set 6-31G
	Optimization energy (hartree)	Polarizability	Dipole moment (debye)	Optimization energy (hartree)	Polarizability	Dipole moment (debye)
4a	−1887.07	270.06	7.17	−1896.67	287.51	8.27
4b	−1981.20	305.59	10.35	−1991.29	324.80	11.28
4c	−2036.66	279.23	10.01	−2047.03	294.79	11.05
4d	−2209.82	365.26	8.94	−2221.12	387.13	10.07
4e	−2305.36	270.37	4.77	−2316.94	287.37	6.29
4f	−2036.66	279.22	10.01	−2047.03	294.87	10.97
4g	−1922.76	259.33	7.93	−1932.55	276.32	9.01
4h	−4409.16	265.52	7.37	−4428.32	282.62	8.67
4i	−1922.76	262.19	8.13	−1932.55	279.46	9.09
4j	−1946.66	253.71	7.48	−1956.57	270.07	8.63

2.2.6.2. FMO analysis

The energy difference between HOMO and LUMO, known as the HOMO–LUMO gap, acts as an index of reactivity such that the lower this gap is, the better the reactivity will be generally.⁴⁸ DFT study indicated that compound 4b has the least HOMO–LUMO energy gap among the prepared derivatives (Table 5). The details are given in the SI file.

Table 5. HOMO and LUMO energy values: basis set comparison (3-21G and 6-31G) for 4a–4j.

Code	E HOMO (eV)		E LUMO (eV)		ΔEgap (eV)		Electron donating power (ω−)		Electron accepting power (ω+)		Electrophilicity (Δω±)
	3-21G	6-31G	3-21G	6-31G	3-21G	6-31G	3-21G	6-31G	3-21G	6-31G	3-21G	6-31G
4a	−0.228	−0.231	−0.067	−0.074	0.161	0.157	0.219	0.235	0.071	0.082	0.290	0.317
4b	−0.193	−0.1963	−0.057	−0.066	0.136	0.130	0.186	0.206	0.061	0.075	0.247	0.281
4c	−0.214	−0.2228	−0.060	−0.068	0.154	0.154	0.200	0.221	0.063	0.075	0.263	0.295
4d	−0.206	−0.2069	−0.062	−0.069	0.144	0.138	0.201	0.216	0.067	0.078	0.267	0.295
4e	−0.240	−0.2425	−0.077	−0.083	0.163	0.159	0.244	0.258	0.085	0.095	0.329	0.352
4f	−0.214	−0.2227	−0.060	−0.068	0.154	0.154	0.200	0.221	0.063	0.075	0.263	0.295
4g	−0.230	−0.2353	−0.069	−0.076	0.161	0.159	0.224	0.241	0.074	0.085	0.298	0.327
4h	−0.237	−0.2423	−0.071	−0.078	0.166	0.164	0.230	0.248	0.076	0.087	0.306	0.335
4i	−0.219	−0.2255	−0.065	−0.073	0.154	0.152	0.212	0.232	0.070	0.082	0.281	0.315
4j	−0.235	−0.242	−0.072	−0.079	0.163	0.163	0.231	0.249	0.078	0.088	0.309	0.338

The shape and position of FMOs are very important in understanding and predicting the movement and density of electrons in the molecule. The HOMO and LUMO orbitals also influence the transitions of electrons in response to the absorption of light energy. The reactive sites of a compound can also be predicted and analyzed by the electron density in the molecules after understanding its HOMO and LUMO orbitals. The HOMO and LUMO orbitals of the 4d compound along with the ESP map and optimized structures are shown in Fig. 6. The HOMO and LUMO energy values and their associated parameters for all the derivatives are given in Table 5.

Fig. 6. HOMO and LUMO diagrams of the 4d derivative along with the optimized structures and ESP bar.

2.2.6.3. Electrostatic potential (ESP) bar

The DFT calculations are quite helpful to map the electrostatic potential around a molecule. This usually appears as a colored surface map, i.e. different regions of this surface are often colored distinctly for positive, negative, or neutral electrostatic potential, thereby intuitively showing the charge distribution of the molecule.⁴⁹ The ESP map of compound 4d, for instance, indicates that the blue-colored area corresponds to an electron-deficient one, which has been identified as the methyl group attached to the carbazole ring. The red zone in the ESP points out an electron-rich zone, similarly corresponding to the sulfonamide functional group.

2.2.7. Molecular docking studies for the synthesized derivatives

The binding energies of all the derivatives were measured against 4 different proteins, i.e. carbonic anhydrase, pancreatic elastase, LRP6 and Dkk1. All the derivatives had strong interactions in the active site of these proteins. The differential molecular docking analysis reveals that the derivatives had strong binding interactions and the lowest binding energies, making them potent derivatives.⁵⁰ The docking results of all the derivatives, i.e.4a–4j, with these proteins separately are given in Table 6.

Table 6. Docking results of all the synthesized compounds (4a–4j).

Docking complexes	Binding score (kcal mol−1)
	Pancreatic elastase	Carbonic anhydrase	Dkk1	LRP6
4a	−8.0	−7.1	−7.1	−8.8
4b	−7.7	−7.0	−6.7	−9.2
4c	−7.8	−7.4	−6.7	−9.5
4d	−8.7	−8.1	−7.9	−10.4
4e	−7.8	−7.1	−6.9	−10.2
4f	−8.0	−7.4	−6.6	−9.9
4g	−7.9	−7.1	−6.8	−8.9
4h	−7.6	−7.0	−6.6	−8.5
4i	−7.7	−7.0	−6.6	−9.0
4j	−7.8	−7.5	−6.6	−9.1

2.2.7.1. Structural assessment of carbonic anhydrase II and Dkk1 complex

Carbonic anhydrase II is a zinc-containing metalloprotein that comprises 259 amino acid residues. The secondary structure analysis done with VADAR⁵¹ showed that the protein contains about 9% α-helices, 45% β-sheets, and 45% random coils. The unit cell parameters obtained upon the study of the crystal structure are as follows: a = 103.84 Å, b = 104.82 Å, c = 119.36 Å, α = 90°, β = 110.45°, and γ = 90°.

Similarly, the crystal structure with PDB ID 3S2K represents a heterodimeric complex between the LRP6 and Dkk1 proteins, stabilized by specific interactions among active site residues. The stereo-chemical quality and structural integrity of both the CA II and Dkk1 models were further validated by Ramachandran plot analysis. The plot showed that 93.8% of all residues resided in the most favored regions, affirming that the structural models were highly accurate and reliable.

2.2.7.2. Active pocket analysis of Dkk1–LRP6 complex through protein–protein docking

The crystal structure of the protein complex 3S2K from the RCSB Protein Data Bank consists of two proteins, i.e. Dickkopf-1 (Dkk1) and Low-Density Lipoprotein Receptor-Related Protein 6 (LRP6). These two proteins are associated mainly by the cysteine-rich domain (CRD) of Dkk1. An analysis of the binding interface was carried out using an online server known as PDBsum. Its generated interaction diagram provides information that chain B (LRP6) has 8 hydrogen bonds with chain C (Dkk1), along with 4 salt bridges and 101 non-bonded contacts. Dkk1 hydrogen bonding amino acid residues are Arg236, Phe234, Glu232, His204, Arg224, and Glu185.

The active pocket residues determined in the initial analysis were verified, and the proteins Dkk1 and LRP6 were computationally separated and then re-docked using the protein–protein docking server ClusPro. From all the generated conformations with more than 70 000 rotations, the model with the best energy score (−1464.7) was selected for further analysis. The best-docked model showed that its amino acid residues at the active site shared as much as 90% similarity with the native complex 3S2K from the RCSB (Fig. 7).

Fig. 7. Structural analysis of the Dkk1–LRP6 interaction site before and after docking.

2.2.7.3. Detailed binding interactions of 4d–pancreatic elastase complex

The binding interactions of compound 4d within the active pocket of pancreatic elastase involve the following bonding and non-bonding interactions: there are 5 strong hydrogen bonds, 2 pi–cation bonds and 1 pi–sulfur bond along with Pi–alkyl bonds. The amino acid residues involved in the binding interactions include Asn76, Glu80, Tyr82, Arg65, Leu73, Asn74 and Leu151. The two hydrogen atoms of amine group form 3 strong hydrogen bonds with the amino acids Asn76 and Glu80. The 4th hydrogen bond is formed by the amino acid Tyr 82 and nitrogen atom of thiazole ring. In the same way, the carbazole ring forms another hydrogen bond with the amino acid Asn74. All other weak bonding interactions including pi–alkyl and pi–sulfur bonds are formed by amino acids Arg65, Ley73, and Leu151. The detailed 2D and 3D diagrams showing the bond lengths are presented in Fig. 8.

Fig. 8. Docking interaction of 4d within the active pocket: 3D (left side) and 2D (right side) interactions of 4d with pancreatic elastase.

2.2.7.4. Detailed binding interactions of the 4d–Dkk1 complex

The results of molecular docking analysis reveal that the docking interactions of 4d with the Dkk1 protein involve 4 strong hydrogen bonds along with various weak bonds, i.e. pi–cation bond and pi–pi stacking bond. In the molecular docking studies, the hydrogen bond with a shorter bond length is considered strongest of all the bonding interactions. There are 4 hydrogen bonds formed by the following 2 amino acid residues, i.e. Arg224 and Arg259. The two sulfonamide oxygen atoms form two hydrogen bonds with Arg224 with bond lengths of 2.99 Å and 3.16 Å. Similarly, two hydrogen atoms of adjacent amine groups form another 2 hydrogen bonds with the amino acid Arg259 with bond lengths of 2.74 Å and 2.78 Å. All other weak bonds including 3 pi–cation bonds and 2 pi–pi stacked bonds are developed by the amino acids Arg236, Lys211 and Phe234. The detailed 2D and 3D diagrams showing the bond lengths are given in Fig. 9.

Fig. 9. 2D (left side) and 3D (right side) representations of the docking interactions of 4d with the Dkk1 protein.

2.2.7.5. Detailed binding interactions of the 4d–carbonic anhydrase complex

Docking analysis of the 4d–carbonic anhydrase complex revealed five stabilizing hydrogen bonds with the following active site residues: Asn66, Thr197, Thr198, and His93. The sulfonamide group of 4d forms key hydrogen bonds: one with a hydrogen atom (2.43 Å) and one with an oxygen atom (3.80 Å) from Thr197. The same sulfonamide oxygen also bonds with Arg198. A second sulfonamide oxygen atom forms a bond with His93. A fifth hydrogen bond (2.19 Å) connects Asn66 to the aromatic nitrogen of the thiazole ring. Beyond hydrogen bonding, additional stabilization is provided by pi–sulfur interactions, pi–stacking, and van der Waals forces. The 2D and 3D interaction diagrams of the complex are illustrated in Fig. 10.

Fig. 10. 2D (left side) and 3D (right side) representations of the docking interactions of 4d with the carbonic anhydrase protein.

2.2.7.6. Detailed binding interactions of the 4d–LRP6 complex

The binding interaction formed by the 4d–LRP6 complex includes 3 strong hydrogen bonds and multiple weak alkyl and pi–alkyl bonds. The hydrogen bonds are formed by the following amino acid residues: i.e. Leu696, Glu701 and Arg739, with the nitrogen atom of the sulfonamide moiety and isoxazole ring. In addition, various alkyl bonds are formed by the following amino acid residues: Pro960, Ile957, Ala918 and Pro917 with the carbazole ring of 4d. The detailed depiction of the 2D and 3D structures of the 4d–LRP6 complex along with the bond lengths are given in Fig. 11.

Fig. 11. 2D (left side) and 3D (right side) representations of the docking interactions of 4d with the LRP6 protein.

2.2.8. Molecular dynamics simulations

Molecular dynamics simulations of all the four target proteins complexed with compound 4d in its best docked pose were performed in an explicit aqueous environment for 100 nanoseconds using Desmond from Schrödinger. For the assessment of structural behavior, the following important parameters were computed for each protein and its protein–ligand complex: root mean square deviation (RMSD), root mean square fluctuation (RMSF), solvent accessible surface area (SASA), and radius of gyration (R_g). The root mean square deviation (RMSD) plot provides insights into the structural variations arising from ligand–protein interactions.⁵² To assess the outcome of the MD simulations, the RMSD plots for all proteins and their corresponding complexes were generated and compared to evaluate the complex stability. As presented in Fig. 12, the RMSD trajectory for the 4d–carbonic anhydrase complex showed initial variability before stabilizing after 20 ns around a value of 1.8 Å. In contrast, the RMSD graphs for the 4d complexes with pancreatic elastase, LRP6, and Dkk1 demonstrated greater instability, with higher and more variable RMSD values. These data clearly indicate that the carbonic anhydrase protein and its complex with 4d maintained greater stability in an aqueous environment relative to the other systems. Additionally, molecular dynamics analysis was extended through root mean square fluctuation (RMSF) plots, as shown in Fig. 12, which detect the localized flexibility of amino acid residues within the C- and N-terminal regions of each target protein.

Fig. 12. Root mean square deviation (RMSD) of the C-alpha atoms of 4d with (A) carbonic anhydrase, (B) pancreatic elastase, (C) LRP6 and (D) Dkk1.

The analysis of RMSF was used as a measure to monitor the local flexibility, mainly in terminal and loop regions. The average RMSF for the Cα atoms of the majority of residues for all proteins remained below 1.5 Å, while carbonic anhydrase showed very low fluctuations of <1.0 Å, confirming the rigidity within its structure. Small fluctuations were recorded for pancreatic elastase due to flexible loop regions. Ligand RMSF values indicated that all the binding sites had dynamic movements. The results are shown in Fig. 13.

Fig. 13. Residue-wise root mean square fluctuation (RMSF) of 4d with (A) carbonic anhydrase, (B) pancreatic elastase, (C) LRP6 and (D) Dkk1.

The radius of gyration is a measure of the compactness of the structure.⁵³R_g maintained constant values for carbonic anhydrase (∼20 Å) and LRP6 (∼5.8 Å) during the simulation trajectory, which indicated that no significant conformational changes occurred.⁵⁴ In the same vein, the SASA values, representing solvent exposure and folding stability,⁵⁵ remained similar for all proteins along time supporting the overall integrity of their native structure. The results are shown in Fig. 14.

Fig. 14. Radius of gyration (R_g), solvent-accessible surface area (SASA) and intra-molecular hydrogen bond (IntraHB) graphs of 4d with (A) carbonic anhydrase, (B) pancreatic elastase, (C) LRP6 and (D) Dkk1.

Protein–ligand interactions were monitored and classified throughout the simulation. These interactions were systematically analyzed using four categories: hydrogen bonds, hydrophobic contacts, ionic interactions, and water bridges. This analysis was performed utilizing the ‘Simulation Interactions Diagram’ panel within the Maestro software.

The interaction data, presented as stacked bar charts (Fig. 15), was normalized across the entire simulation trajectory. In this representation, a value of 0.7 indicates that a specific interaction was present for 70% of the simulation time. The values exceeding 1.0 are possible, indicating the occurrence of multiple, simultaneous interactions of the same subtype between the ligand and protein residues.⁵⁶ Hydrogen bonds, which were further classified as involving backbone or side-chain acceptors and donors, demonstrated particularly high persistence. This notable stability is significant, as such enduring hydrogen-bonding interactions are crucial determinants of a drug candidate's selectivity, its metabolic fate, and its absorption characteristics.

Fig. 15. Protein–ligand contact histograms of 4d with (A) carbonic anhydrase, (B) pancreatic elastase, (C) LRP6 and (D) Dkk1.

2.2.9. Network pharmacology analysis

2.2.9.1. Prediction of compound and disease targets

To place the most active derivative within a system-level pharmacological context, a network pharmacology workflow was undertaken for compound 4d, the derivative that displayed the strongest anti-carbonic anhydrase activity. Putative protein targets of 4d were predicted from its canonical structure using the TargetNet web server,⁵⁷ and the resulting hits were standardised to reviewed human entries through the UniProt identifier mapping service,⁵⁸ yielding 475 unique protein targets (Homo sapiens, Swiss-Prot reviewed). Genes associated with pancreatic cancer were retrieved from the GeneCards database,⁵⁹ and entries with a relevance score of at least 50 were retained as high-confidence disease genes, giving 2803 targets. The two sets were intersected to identify the targets through which compound 4d could plausibly act against pancreatic cancer, producing 243 common targets that corresponded to 8.0 percent of the combined pool (Fig. 16A). These 243 shared genes were carried forward as the candidate target set for all subsequent analyses.

Fig. 16. Network pharmacology of compound 4d against pancreatic cancer. (A) Venn diagram of the overlap between the predicted targets of compound 4d (list 1, 475 targets) and pancreatic cancer-associated genes from GeneCards with a relevance score of ≥50 (list 2, 2803 targets); 243 targets (8.0 percent) are shared. (B) KEGG EGFR tyrosine kinase inhibitor resistance pathway with shared targets highlighted in red, showing convergence across the receptor tyrosine kinase, PI3K-AKT-mTOR, RAS-RAF-MEK-ERK and JAK-STAT branches. (C) Protein–protein interaction network of the 243 shared targets generated in STRING at the highest confidence score (0.900) with disconnected nodes removed (202 nodes, 728 edges, average node degree = 7.21).

2.2.9.2. Protein–protein interaction network and hub target identification

The 243 common targets were submitted to the STRING database (version 12.0)⁶⁰ to construct a protein–protein interaction (PPI) network, restricted to Homo sapiens and to the highest confidence interaction score (0.900) in order to retain only the most reliable functional associations. After the removal of disconnected nodes, the network comprised 202 nodes connected by 728 edges, with an average node degree of 7.21 (Fig. 16C). The network was substantially more connected than expected for a random protein set of the same size, indicating that the shared targets participate in coordinated biological processes rather than acting in isolation.

The node degree was used to rank the targets by their topological importance. The most highly connected hubs were SRC, PIK3CA, PIK3CB, AKT1 and PIK3CD, followed by the chaperones HSP90AA1 and HSP90AB1, the adaptor GRB2, the remaining AKT isoforms and the focal adhesion kinase PTK2 (Table 7). This hub set is dominated by components of the phosphoinositide 3-kinase/AKT axis and by non-receptor tyrosine kinase signalling, both central to pancreatic tumour cell proliferation and survival.

Table 7. Top fifteen hub targets ranked by node degree in the protein–protein interaction network.

Rank	Gene symbol	Node degree
1	SRC	40
2	PIK3CA	37
3	PIK3CB	35
4	AKT1	34
5	PIK3CD	34
6	HSP90AA1	33
7	GRB2	30
8	HSP90AB1	25
9	AKT2	25
10	AKT3	25
11	PTK2	24
12	ESR1	22
13	EGFR	22
14	MAPK1	21
15	BCL2	19

2.2.9.3. Gene ontology and KEGG pathway enrichment

Functional enrichment of the 243 shared targets was performed for Gene Ontology (GO) biological processes and KEGG pathways using ShinyGO,⁶¹ with a false discovery rate (FDR) threshold of 0.05. Enrichment was extensive, comprising 1000 GO biological process terms and 203 KEGG pathways. The most strongly enriched GO biological processes centred on cellular response to chemical stimuli (169 genes, FDR = 1.5 × 10⁻⁸⁷), response to chemical (189 genes, FDR = 4.3 × 10⁻⁸⁴), response to organic substances (165 genes, FDR = 7.9 × 10⁻⁸⁴) and response to oxygen-containing compounds (130 genes, FDR = 2.4 × 10⁻⁸⁰), together with a prominent cluster of apoptosis and programmed cell death terms and protein phosphorylation (Fig. 17). This convergence on programmed cell death and phosphorylation-dependent signalling is consistent with the pro-apoptotic and kinase-directed activity expected for this sulfonamide scaffold.

Fig. 17. Gene ontology biological process enrichment for the 243 shared targets. (A) Top twenty enriched terms; bar length represents fold enrichment, dot size represents the number of genes and colour represents the negative base-ten logarithm of the FDR. (B) Hierarchical clustering tree of the top enriched terms with their FDR values, resolving a programmed cell death branch and a chemical and organic substance response branch.

KEGG pathway enrichment was dominated by cancer-related and proliferative signalling pathways. Among them, the most significant were the pathways in cancer (82 genes, FDR = 1.8 × 10⁻⁷²) and the PI3K-AKT signalling pathway (51 genes, FDR = 7.9 × 10⁻⁴²), followed by prostate cancer, EGFR tyrosine kinase inhibitor resistance, endocrine resistance, focal adhesion, MAPK signalling and Ras signalling (Table 8). Critically, the pancreatic cancer pathway itself (hsa05212) was significantly enriched (23 genes, fold enrichment 30.0, FDR = 6.1 × 10⁻²⁷), directly linking the shared target set to the disease of interest; the mapped genes included EGFR, ERBB2, AKT1, AKT2, MTOR, PIK3CA, PIK3CB, PIK3CD, BRAF, RAF1, MAP2K1, MAPK1, TGFBR1 and the apoptotic effectors CASP9 and BCL2L1. A representative pathway populated by the shared targets is shown in Fig. 16B.

Table 8. Top ten enriched KEGG pathways for the 243 shared targets.

KEGG pathway	No. of genes	Fold enrichment	FDR
Pathways in cancer	82	15.3	1.8 × 10−72
PI3K-AKT signalling pathway	51	14.3	7.9 × 10−42
Prostate cancer	33	33.7	5.6 × 10−40
Lipid and atherosclerosis	39	18.1	7.2 × 10−36
MicroRNAs in cancer	35	21.5	5.4 × 10−35
EGFR tyrosine kinase inhibitor resistance	27	33.9	7.3 × 10−33
Proteoglycans in cancer	36	17.7	7.3 × 10−33
Endocrine resistance	28	29.2	4.1 × 10−32
Focal adhesion	35	17.3	9.7 × 10−32
Pancreatic cancer	23	30.0	6.1 × 10−27

2.2.9.4. Integrated interpretation

Taken together, the network pharmacology analysis indicates that the anti-cancer potential of compound 4d is unlikely to be mediated through a single target. The intersection of predicted and disease targets, the topology of the interaction network and the enrichment results all converge on the PI3K-AKT-mTOR and receptor tyrosine kinase signalling axes, with SRC, the PIK3C catalytic subunits, the AKT isoforms and HSP90 emerging as the principal hubs. The significant enrichment of the pancreatic cancer pathway, alongside broader cancer and apoptosis pathways, provides an integrated rationale linking the experimentally observed enzyme inhibition to a plausible multi-target mechanism in pancreatic cancer.

It is notable that Dkk1, the primary experimental target of this study, was not recovered among the shared targets of the network analysis. This absence is an expected and mechanistically informative consequence of the target-prediction step rather than a limitation of compound 4d. As a secreted Wnt antagonist that acts through the protein–protein interaction rather than a defined small-molecule binding pocket, Dkk1 is not represented in ligand-similarity or structure-based prediction; consistent with this, independent interrogation using SwissTargetPrediction and the STITCH chemical-protein database likewise did not return Dkk1 as a direct target. The role of Dkk1 is instead established directly by the molecular docking and dynamics data, in which compound 4d engaged Dkk1 and its co-receptor LRP6 with the most favourable binding energies of the four targets examined (−7.9 and −10.4 kcal mol⁻¹, respectively). Critically, the docking and network analyses are mechanistically convergent rather than independent. Dkk1 signals through the CKAP4 receptor to activate the PI3K/AKT pathway, which is precisely the axis on which the shared-target network converged, with PI3K catalytic subunits and AKT isoforms ranking among the leading hubs. In addition, orthogonal ligand-based prediction recovered the Wnt-pathway components GSK3B and WNT3A among the candidate targets of compound 4d, providing a further, independent computational thread linking the compound to Wnt/β-catenin signalling, although these were low-probability predictions and are presented as hypothesis-generating rather than as evidence of direct engagement. Taken together, direct binding of compound 4d to Dkk1 and LRP6 is positioned upstream of the same PI3K-AKT signalling module identified by the system-level analysis, offering an integrated mechanistic route by which disruption of the Dkk1-LRP6 interaction and modulation of PI3K-AKT signalling could jointly contribute to the anti-pancreatic-cancer potential of compound 4d.

It should be noted that these predictions are computational and hypothesis-generating. The target prediction is similarity-based and therefore biased towards well-characterised targets of the benzenesulfonamide chemotype, which is reflected in the recovery of the carbonic anhydrase family among the shared targets, in agreement with the biological assays. The hub targets identified here, in particular the PI3K-AKT axis and SRC, represent priorities for future experimental validation rather than established mechanisms, and dedicated in vitro and in vivo studies would be required to confirm their engagement with compound 4d.

3. Conclusion

A total of 10 sulfanilamide derivatives based on thiazole- and isoxazole-fused ring heterocyclic systems have been synthesized and tested for their biological usage. All compounds were identified and confirmed by FT-IR, ¹HNMR, ¹³CNMR and UV spectroscopy. The derivatives were screened for free radical scavenging activity, DNA binding studies, carbonic anhydrase inhibition and pancreatic elastase inhibition activity. It was found that 04-(3-(9-ethyl-9H-carbazol-3-yl)isoxazolo[3,4-d]thiazol-5-ylamino)benzenesulfonamide (4d) possesses the strongest anti-carbonic anhydrase activity (IC₅₀ = 0.348 ± 0.024 µM), which is similar to acetazolamide (IC₅₀ = 0.997 ± 0.045 µM). Similarly, derivative 4f revealed the strongest pancreatic elastase inhibition activity. All the synthesized compounds (4a–4j) revealed good drug score values, indicating their good drug-like activity and potential as drug candidate molecules. Comparative docking analysis warranted that 4d possesses good therapeutic potential in comparison to all other compounds. DNA binding studies were also conducted for 4c, which revealed a binding constant of 2.3 × 10⁷ mol⁻¹ that reflects their significance to be considered as a candidate in medicine in the future. The negative value of Gibbs free energy indicates that the interaction of the synthesized derivatives with DNA is spontaneous. Computations were carried out using the Gaussian software (5.0.8). The compounds were optimized using the DFT (basic sets 6-31G and 3-21G) method to compute various parameters like ionization energy, electron affinity, dipole moment and total energy of all the compounds. Finally, a network pharmacology analysis of compound 4d against pancreatic cancer revealed 243 shared targets, whose protein–protein interaction network and functional enrichment converged on the PI3K-AKT-mTOR and receptor tyrosine kinase signalling axes, with SRC, the PIK3C catalytic subunits, the AKT isoforms and HSP90 as principal hubs, and confirmed significant enrichment of the pancreatic cancer pathway. This multi-target profile complements the experimental enzyme inhibition and highlights these hubs as priorities for future validation.

4. Materials and methods

4.1. Chemistry

Sulfanilamide, triethylamine, chloroacetyl chloride, potassium thiocyanate, substituted aromatic aldehydes, sodium acetate, hydroxylamine hydrochloride, acetone, ethanol, THF, and glacial acetic acid were obtained from Sigma-Aldrich. All the solvents were distilled and dried by standard procedures before use. Melting points were determined in a Stuart SMP3 apparatus. Nuclear magnetic resonance (NMR) spectra were recorded using a Bruker Advance DMX 300 spectrometer operating at 300 MHz for ¹H and 75.5 MHz for ¹³C, and chemical shifts are expressed in parts per million (ppm) using DMSO-d₆ as the solvent. Ultraviolet-visible (UV-vis) spectra were recorded using a Shimadzu 1800 spectrophotometer scanning from 200 to 800 nm in 1 cm quartz cells. The progress of the reaction was monitored by TLC using Merck silica gel F254 aluminium sheets.

4.2. General synthesis procedure of 4-((3-arylthiazolo[3,4-d]isoxazol-5-yl)amino)benzenesulfonamides

First, 1.72 g of sulfanilamide (10 mmol) was treated in a THF solvent with 0.92 mL of chloroacetyl chloride (10 mmol) to obtain the product 2-chloro-N-(4-sulfamoylphenyl)acetamide in an ice bath. This freshly prepared dry intermediate (2.48 g, 10 mmol) was treated in the second step with potassium thiocyanate (0.97 g, 10 mmol) in dry acetone (30 mL) under reflux conditions for 8 hours, resulting in the formation of 4-(4-oxo-4,5-dihydrothiazol-2-ylamino)benzenesulfonamide. This intermediate (2.71 g, 10 mmol) was further treated with different aromatic aldehydes and sodium acetate (0.82 g, 10 mmol) (dissolved in a glacial acetic acid solvent) in an equimolar ratio (10 mmol) to obtain the condensed product (Z)-4-(5-benzylidene-4-oxo-4,5-dihydrothiazol-2-ylamino)benzenesulfonamide. In the last step, to the condensed product of chalcone derivatives 3a–j (0.01 mol) and hydroxylamine hydrochloride (0.7 g, 0.01 mol) in ethanol (10 mL), a solution of KOH (0.56 g, 0.01 mol) in ethanol (50 mL)was added. The reaction mixture was refluxed for 18 h and then kept overnight at room temperature. After cooling in ice, the reaction mixture was acidified with aqueous HCl (10%). The resulting precipitate was washed with distilled water and dried. The resulting crude was crystallized from ethanol to obtain products 4a–j.

4.2.1. 4-(3-p-Tolylisoxazolo[3,4-d]thiazol-5-ylamino)benzenesulfonamide (4a)

Yellow solid, yield: 72%, m.p.: 268, R_f = 0.66 (chloroform : methanol, 9 : 1), FTIR ν (cm⁻¹) 3422, 3239, 3222 (NH₂, NH), 3049 (ArH), 2945 (CH₃), 1665 (C N), 1478 (Ar–C), 1356 (CH3), 1224 (C S), 1156 (C–N), 1131 (C–S); ¹HNMR (300 MHz, DMSO-d₆); δ (ppm) 12.0 (s, 1H, N–H), 7.5 (s, 2H, NH₂), 7.82 (d, 2H, J = 7.6Ar–H), 7.51 (d, 2H, J = 7.9 Ar–H), 7.22 (d, 2H, J = 7.6 Ar–H) 6.88 (d, 2H, J = 7.8 Ar–H) 1.9 (s, 3H, 19); ¹³CNMR (75 MHz, DMSO-d₆), δ (ppm) 162.7 (11), 160.7 (5), 156.2 (8), 121.2 (9), 144.9, 136.2, 134.6, 133.9, 131.0, 129.2, 127.0, 121.9 (ArC), 13.97 (19). Anal. calcd. for C₁₇H₁₄N₄O₃S₂ C, 52.84; H, 3.65; N, 14.50; S, 16.59. Found: C, 52.81; H, 3.63; N, 14.52; S, 16.57. HRMS (ESI): m/z calcd for [C₁₇H₁₄N₄O₃S₂+H]⁺ 386.0507, found 386.0509.

4.2.2. 4-(3-(4-(Dimethylamino)phenyl)isoxazolo[3,4-d]thiazol-5-ylamino)benzenesulfonamide (4b)

Yellow solid, yield: 67%, m.p.: 302 °C, R_f = 0.53 (chloroform : methanol, 9 : 1), FTIR ν (cm⁻¹) 3482, 3279, 3276 (NH₂, NH), 3081 (Ar–H), 2975 (CH₃), 1697 (C N), 1496 (Ar–C), 1379 (CH₃), 1237 (C S), 1174 (C–N), 1161 (C–S); ¹HNMR (300 MHz, DMSO-d₆); δ (ppm) 11.97 (s, 1H, N–H), 7.50 (s, 2H, NH₂), 7.84 (d, 2H, J = 8.2 Ar–H), 7.53 (d, 2H, J = 7.8 Ar–H), 7.22 (d, 2H, J = 8.2 Ar–H) 6.84 (d, 2H, J = 7.8 Ar–H) 4.01 (s, 6H, 19); ¹³CNMR (75 MHz, DMSO-d₆); δ (ppm) 160.66 (11), 159.58 (5), 154.25 (8), 118.24 (9), 143.98, 135.34, 133.71, 132.78, 130.12, 128.32, 128.15, 122.85 (ArC), 32.16 (19). Anal. calcd. for C₁₈H₁₇N₅O₃S₂ C, 52.03; H, 4.12; N, 16.86; S, 15.43. Found: C, 52.01; H, 4.10; N, 16.84; S, 15.40. HRMS (ESI): m/z calcd for [C₁₈H₁₇N₅O₃S₂+H]⁺ 415.0773, found 415.0776.

4.2.3. 4-(3-(2-Hydroxy-3-methoxyphenyl)thiazolo[3,4-d]isoxazol-5-ylamino)benzenesulfonamide (4c)

Yellow solid, yield: 70%, m.p.: 315 °C, R_f = 0.52 (chloroform : methanol, 9 : 1), FTIR ν (cm⁻¹) 3445, 3394, 3293, 3127 (OH, NH₂, NH), 3027 (ArH), 2922, 2849 (CH₃), 1642 (C N), 1471 (Ar–C), 1471 (CH3), 1221 (C S), 1182 (C–N), 1152 (C–S); ¹HNMR (300 MHz, DMSO-d₆); δ (ppm) 12.38 (s, 1H, N–H), 9.47 (s, 1H, OH), 7.37 (s, 2H, NH₂), 7.86 (d, 2H, J = 8.1 Ar–H), 7.22 (d, 2H, J = 8.1 Hz Ar–H) 7.53 (d, 1H, J = 7.8 Ar–H), 7.09 (t, 1H, J = 8.1 Ar–H), 6.93 (d, 1H, J = 8.0 Ar–H), 3.79 (s, 3H, OCH₃); ¹³CNMR (75 MHz, DMSO-d₆); δ (ppm) 162.64 (13), 151.39 (7), 150.14 (10), 112.87 (11), 147.32, 140.46, 130.93, 127.74, 126.32, 123.84, 122.02, 120.73, 119.61, 115.67 (ArC), 56.08 (20).). Anal. calcd. for C₁₇H₁₄N₄O₅S₂ C, 48.80; H, 3.37; N, 13.39; S, 15.33. Found: C, 48.82; H, 3.35; N, 13.32; S, 15.36 HRMS (ESI): m/z calcd for [C₁₇H₁₄N₄O₅S₂+H]⁺ 418.0406, found 418.0408.

4.2.4. 4-(3-(9-Ethyl-9H-carbazol-3-yl)isoxazolo[3,4-d]thiazol-5-ylamino)benzenesulfonamide (4d)

Yellow solid, yield: 75%, m.p.: 322 °C, R_f = 0.60 (chloroform : methanol, 9 : 1), FTIR ν (cm⁻¹) 3349, 3256, 3171 (NH₂, NH), 3049 (ArH), 2956, 2924 (alkyl region), 1627 (C N), 1485 (Ar–C), 1386 (CH3), 1226 (C S), 1150 (C–N), 1095 (C–S); ¹HNMR (300 MHz, DMSO-d₆); δ (ppm) 12.48 (s, 1H, N–H), 7.38 (s, 2H, NH₂), 8.43 (t, 1H, J = 6.9, 7.5 Hz, Ar–H), 8.35 (d, 1H, J = 7.5 Hz, Ar–H), 8.17 (d, 1H, J = 7.2, Hz, Ar–H), 7.97 (dd, 1H, J = 7.8, 2.1 Hz, Ar–H), 7.74 (d, 2H, J = 7.2 Hz, Ar–H), 7.66 (t, 1H, J = 8.1 Hz, Ar–H), 7.53 (d, 1H, J = 8.7 Hz, Ar–H) 7.51 (d, 2H, J = 7.2 Hz, Ar–H), 7.26 (d, 1H, J = 6.9 Hz, Ar–H), 4.32 (q, J = 7.1, 2H, 14), 1.44 (t, J = 7.1, 3H, 15); ¹³CNMR (75 MHz, DMSO-d₆), δ(ppm) 158.97 (22), 154.84 (16), 153.47 (19), 114.37 (20), 148.40, 146.88, 146.70, 141.63, 140.42, 137.63, 127.69, 125.36, 123.93, 122.02, 120.96, 120.26, 120.07, 119.97, 119.83, 119.60 (ArC), 37.71 (14), 15.69 (15). Anal. calcd. for C₂₄H₁₉N₅O₃S₂ C, 58.88; H, 3.91; N, 14.31; S, 13.10. Found: C, 58.84; H, 3.93; N, 14.28; S, 13.13 HRMS (ESI): m/z calcd for [C₂₄H₁₉N₅O₃S₂ +H]⁺ 489.0929, found 489.0932.

4.2.5. 4-(3-(4-Chlorophenyl)isoxazolo[3,4-d]thiazol-5-ylamino)benzenesulfonamide (4e)

Yellow solid, yield: 71%, m.p.: 308 °C, R_f = 0.57 (chloroform : methanol, 9 : 1), FTIR ν (cm⁻¹) 3373, 3189, 3102 (NH₂, NH), 1696 (C N), 1451 (Ar–C), 1231 (C S), 1123 (C–N), 1076 (C–S); ¹HNMR (300 MHz, DMSO-d₆); δ (ppm) 12.6 (s, 1H), 7.94 (d, 2H, J = 8.1 Ar–H), 7.86 (d, 2H, J = 8.4 Ar–H), 7.66 (d, 2H, J = 8.6 Ar–H), 7.53 (s, 2H NH₂) 7.21 (d, 2H, J = 8.1 Ar–H), ¹³C NMR (75 MHz, DMSO-d₆); δ (ppm) 171.7 (C–S), 167.7(C N), 151.1 (C–N), 141.4, 135.0, 132.54, 130.4, 129.8, 129.1, 127.7, 123.8 (ArC), 120.9(C C).). Anal. calcd. for C₁₆H₁₁ClN₄O₃S₂ C, 47.23; H, 2.73; N, 13.77; S, 15.76. Found: C, 48.82; H, 3.35; N, 13.32; S, 15.36 HRMS (ESI): m/z calcd for [C₁₆H₁₁ClN₄O₃S₂+H]⁺ 405.9961, found 405.9964.

4.2.6. 4-(3-(4-Hydroxy-3-methoxyphenyl)isoxazolo[3,4-d]thiazol-5-ylamino)benzenesulfonamide (4f)

Yellow solid, yield: 69%, m.p.: 306 °C, R_f = 0.72 (chloroform : methanol, 9 : 1), FTIR ν (cm⁻¹) 3482, 3299, 3156 (NH₂, NH), 3076 (ArH), 2932 (CH₃), 1667 (C N), 1474 (Ar–C), 1378 (CH₃), 1256 (C S), 1123 (C–N), 1108 (C–S); ¹HNMR (300 MHz, DMSO-d₆); δ (ppm) 12.37 (s, 1H, 15), 7.34 (s, 2H, 19), 7.81 (s, 1H, 21), 7.90 (d, 2H, J = 8.1 Ar–H), 7.80 (d, 2H, J = 8.1 Ar–H), 7.55 (d, 1H, J = 7.3 Hz, Ar–H), 7.45 (d, 1H, J = 7.3, Hz, Ar–H), 7.20 (s, 1H, J = 7.6, Hz, Ar–H), ¹³CNMR (75 MHz, DMSO-d₆); δ (ppm) 150.49 (13), 149.24 (7), 139.56 (10), 110.47(11), 146.32, 130.83, 126.84, 125.42, 122.74, 122.01, 120.63, 119.51, 115.57, 112.77 (ArC), 54.18 (20). Anal. calcd. for C₁₇H₁₄ClN₄O₅S₂ C, 48.80; H, 3.37; N, 13.39; S, 15.33. Found: C, 48.83; H, 3.32; N, 13.36; S, 15.35 HRMS (ESI): m/z calcd for [C₁₇H₁₄ClN₄O₅S₂+H]⁺ 418.0406, found 418.0409.

4.2.7. 4-(3-(3-Hydroxyphenyl)isoxazolo[3,4-d]thiazol-5-ylamino)benzenesulfonamide (4g)

Yellow solid, yield: 72%, m.p.: 312 °C, R_f = 0.64 (chloroform : methanol, 9 : 1), FTIR ν (cm⁻¹) 3576, 3326, 3248, 3128 (OH, NH₂, NH), 3046 (ArH), 1635 (C N), 1479 (Ar–C), 1244 (C S), 1183 (C–N), 1154 (C–S); ¹HNMR (300 MHz, DMSO-d₆); δ (ppm) 9.91 (s, 1H, N–H), 7.92 (s, 1H, OH), 7.52 (s, 2H, NH₂), 7.69 (d, 2H, J = 8.7 Hz, Ar–H), 7.50 (dd, 1H, J = 7.8, 2.1 Hz, Ar–H), 7.40 (t, 1H, J = 8.1 Hz, Ar–H), 7.13 (d, 1H, J = 8.1 Hz, Ar–H) 7.06 (d, 1H, J = 8.1 Hz, Ar–H) 6.94 (d, 2H, J = 7.8 Hz, Ar–H), ¹³CNMR (75 MHz, DMSO-d₆); δ (ppm) 170.17 (13), 165.32 (7), 158.43 (10), 116.47 (11), 167.32, 144.98, 136.24, 134.61, 133.98, 131.02, 129.22, 127.05, 121.95, 121.61 (ArC). Anal. calcd. for C₁₆H₁₂N₄O₄S₂ C, 49.47; H, 3.11; N, 14.42; S, 16.51. Found: C, 49.44; H, 3.15; N, 14.40; S, 16.55 HRMS (ESI): m/z calcd for [C₁₆H₁₂N₄O₄S₂+H]⁺ 388.0300, found 388.0303.

4.2.8. 4-(3-(2-Bromophenyl)isoxazolo[3,4-d]thiazol-5-ylamino)benzenesulfonamide (4h)

Yellow solid, yield: 67%, m.p.: 3014 °C, R_f = 0.59 (chloroform : methanol, 9 : 1), FTIR ν (cm⁻¹) 3463, 3253, 3171 (NH₂, NH), 1590 (C N), 1488 (Ar–C), 1230 (C S), 1122 (C–N), 1032 (C–S); ¹HNMR (300 MHz, DMSO-d₆); δ (ppm) 12.43 (s, 1H, 15), 7.60 (s, 2H, 19), 7.92 (d, 1H, J = 8.2 Hz, Ar–H), 7.54 (d, 2H, J = 7.2 Hz, Ar–H), 7.49 (d, 1H, J = 7.6 Hz Ar–H), 7.40 (t, J = 8.1 Hz, 1H) 7.23 (d, 2H, J = 7.5 Hz, Ar–H) ¹³CNMR (75 MHz, DMSO-d₆); δ (ppm) 171.9 (13), 167.7 (10), 152.9 (7), 123.1 (11), 158.33, 151.81, 143.72, 138.60, 134.39, 132.31, 128.82, 127.43, 126.01, 125.31 (ArC). Anal. calcd. for C₁₆H₁₁BrN₄O₃S₂ C, 42.58; H, 2.46; N, 12.41; S, 14.21. Found: C, 42.56; H, 2.49; N, 12.44; S, 14.25 HRMS (ESI): m/z calcd for [C₁₆H₁₁BrN₄O₃S₂+H]⁺ 449.9456, found 449.9459.

4.2.9. 4-(3-(4-Hydroxyphenyl)isoxazolo[3,4-d]thiazol-5-ylamino)benzenesulfonamide (4i)

Yellow solid, yield: 68%, m.p.: 305 °C, R_f = 0.69 (chloroform : methanol, 9 : 1), FTIR ν (cm⁻¹) 3373, 3187, 3123 (NH₂, NH), 1559 (C N), 1462 (Ar–C), 1370 (CH₃), 1227 (C S), 1117 (C–N), 1018 (C–S); ¹HNMR (300 MHz, DMSO-d₆); δ (ppm) 9.91 (s, 1H, N–H), 7.65 (d, 2H, J = 8.1 Hz, Ar–H), 7.82 (s, 1H, OH), 7.43 (s, 2H, NH₂), 7.38 (d, 2H, J = 8.1 Hz, Ar–H), 7.32 (d, 2H, J = 7.8 Ar–H), 7.25 (d, 1H, J = 7.8 Ar–H), ¹³CNMR (75 MHz, DMSO-d₆); δ (ppm) 165.17 (11), 163.32 (5), 156.43 (8), 114.47 (9), 166.42, 143.98, 135.34, 132.71, 130.12, 128.32, 126.15, 120.71 (ArC). Anal. calcd. for C₁₆H₁₂N₄O₄S₂ C, 49.47; H, 3.11; N, 14.42; S, 16.51. Found: C, 49.44; H, 3.15; N, 14.40; S, 16.55 HRMS (ESI): m/z calcd for [C₁₆H₁₂N₄O₄S₂+H]⁺ 388.0300, found 388.0303.

4.2.10. 4-(3-(2-Fluorophenyl)isoxazolo[3,4-d]thiazol-5-ylamino)benzenesulfonamide (4j)

Yellow solid, yield: 63%, m.p.: 305 °C, R_f = 0.63 (chloroform : methanol, 9 : 1), FTIR ν (cm⁻¹) 3473, 3223, 3154 (NH₂, NH), 1559 (C N), 1479 (Ar–C), 1238 (C S), 1121 (C–N), 1013 (C–S); ¹HNMR (300 MHz, DMSO-d₆); δ (ppm) 12.51 (s, 1H, N–H), 7.64 (s, 2H, NH₂), 7.96 (d, 1H, J = 8.4 Hz, Ar–H), 7.55 (d, 2H, J = 7.5 Hz, Ar–H), 7.53 (d, 1H, Ar–H), 7.44 (t, J = 8.1 Hz, 1H) 7.27 (d, 2H, J = 7.5 Hz, Ar–H). ¹³CNMR (75 MHz, DMSO-d₆); δ (ppm) 172.9 (13), 168.7 (10), 153.9 (7), 124.1 (11), 159.33, 152.8, 143.7, 138.60, 135.29, 131.21, 129.9, 128.33, 127.21, 126.31 (ArC). Anal. calcd. for C₁₆H₁₁FN₄O₃S₂: C, 49.22; H, 2.84; N, 14.35; S, 16.43. Found: C, 49.26; H, 2.82; N, 14.34; S, 16.42 HRMS (ESI): m/z calcd for [C₁₆H₁₁FN₄O₃S₂ +H]⁺ 388.0300, found 388.0303.

4.3. Biological evaluation

4.3.1. Anti-foxidant activity: free radical scavenging activity

The radical scavenging activity of all the sample derivatives was estimated by a modified DPPH assay (2,2-diphenyl-1-picrylhydrazyl) according to standard protocols with minor modifications. In the assay, to each 200 µL reaction mixture, 100 µL of DPPH (150 µM) was combined with 20 µL of the test compound at 100 µM concentration and the volume was completed with methanol. After 30 min of incubation at room temperature, the absorption was recorded at 517 nm using a tunable microplate reader (OPTI Max). Ascorbic acid served as the reference standard. The inhibition percentage for each test compound was computed based on the decrease in the reaction rate. All samples were prepared in triplicate, and the experiment was repeated in three independent runs.

4.3.2. Pancreatic elastase inhibition assay

Pancreatic elastase inhibition was assayed using the modified method of the previously published protocols,^20–23 wherein hydrolysis of N-succinyl-Ala-Ala-Ala-p-nitroanilide was followed. The release of p-nitroaniline was followed at 410 nm. In detail, in a 96-well plate, 130 µL of a 0.8 mM substrate solution (in 0.2 M Tris–HCl, pH 8.0) was added to 10 µL of the test sample. After 10 minutes of pre-incubation at 25 °C, the reaction was initiated with the addition of 10 µL of elastase solution (0.0375 U mL⁻¹). After an additional 30 minutes at 25 °C, the absorbance was read. All assays were performed in triplicate, and the percent inhibition was calculated using the following standard formula:Elastase inhibition activity (%) = (OD_control − OD_sample × 100)/OD_controlwhere OD_control and OD_sample represent the optical densities in the absence and presence of samples, respectively. Oleanolic acid was used as the standard inhibitor for pancreatic elastase.

4.3.3. DNA binding studies

DNA binding was investigated using a stock solution of commercial salmon sperm DNA (2.6 × 10⁻⁴ M) prepared in double-distilled water and then diluted according to the procedure reported in the literature.²⁴ The stock concentration was checked by determining the absorbance at 260 nm using ε of 6600 M⁻¹ cm⁻¹. An A₂₆₀/A₂₈₀ ratio of approximately 1.0 indicated that the DNA was protein-free. UV-visible spectra were recorded using a Shimadzu 1800 with 1 cm quartz cuvettes. We initially scanned the complex alone from 200 to 800 nm, and then, added aliquots of DNA in equal volumes to both the sample and reference cuvettes to correct for DNA's own absorbance.

4.3.4. Computational studies

The characterization of the synthesized sulfonamide derivatives was performed via an integrative multi-technique approach, i.e. DFT calculations combined with molecular docking and MD simulations. DFT analyses were performed using the Gaussian software, and the results were visualized using the GaussView software.^25,26 In the frame of molecular docking, AutoDock Vina²⁷ was used against three targets, i.e. pancreatic elastase, Dkk1, and LRP6. In addition, protein–protein docking analysis was performed between Dkk1 and LRP6.²⁸

4.3.5. Density functional theory

DFT is a quantum-mechanical method that helps in the prediction of the basic characteristics of sulfonamide derivatives. The fact that the electronic structure and interactions, which are barely recorded by experiments, can be calculated by DFT inspires drug design and materials science. Optimized geometries, energy transitions, and frontier orbital distributions of electronic properties were calculated for the understanding of activity and reactivity. The calculations used the functional B3LYP with 6-31G and 3-21G basis sets in Gaussian 09, and the results were visualized using GaussView 6.^25,26

4.3.6. Computational methodology for molecular docking

4.3.6.1. Retrieval of porcine pancreatic elastase and Dkk1 protein from RCSB data bank

The three-dimensional crystal structures of the target proteins, i.e. pancreatic elastase (PDB ID: 7EST), carbonic anhydrase (PDB ID: 1V9E), and the Dkk1–LRP6 complex (PDB ID: 3S2K), were obtained from the RCSB Protein Data Bank.^29–31 Energy minimization of all the derivatives was completed using UCSF Chimera 1.10.1.³² The stereochemical quality of the pancreatic elastase model was evaluated via Ramachandran plot analysis on the MolProbity server,³³ while its surface hydrophobicity profile was generated using Discovery Studio 4.1 Client.³⁴

4.3.6.2. Molecular docking

In molecular docking, all the synthesized derivatives 4a–4j were docked using AutoDock Vina. All the required structures were prepared by sketching in ChemDraw, and then, energy minimization was done using the OpenBabel software.^35,36 The prepared protein structures were assigned Gasteiger and Kollman charges using AutoDock tools. A docking grid was centered on active-site residues, ensuring sufficient space for exploration. All the docking poses were visualized and analysed in the PyMOL and LigPlot+ software.^37,38

4.3.6.3. Protein–protein docking

To investigate the protein–protein complex of Dkk1–LRP6, where Dkk1 binds via its CDR1 domain, we employed the crystal structure of 3S2K, separated both proteins and then redocked them using the protein–protein docking protocol. Initial contact residues were checked with an online platform called PDBsum. To confirm native interface, we separated Dkk1 and LRP6 and re-docked them using default settings (70 000 rotations) with ClusPro. The resulting poses were re-analyzed on PDBsum in order to validate the site of interaction.^39,40

4.3.7. Molecular dynamics simulations

Molecular dynamics (MD) simulations of the most active compound, 4d, bound to each target protein were completed using the Desmond software from the Schrödinger suite. The simulation was initiated from the highest-scoring docking pose, and the protein–ligand complex was simulated for 100 nanoseconds. While molecular docking estimates the binding affinity under static conditions, MD simulations track the motion of atoms within the protein's binding pocket by solving Newton's classical equations of motion.⁴¹ This approach allows for the prediction of ligand binding behavior under dynamic, simulated conditions.^42,43

The protein–ligand complex was initially prepared and optimized using standard preprocessing tools, which included structural refinement and energy minimization. Each simulation system was constructed using a simulation builder tool. The system was solvated within a defined three-dimensional boundary using a standard water model. All molecular dynamics simulations employed the OPLS-AA 2005 force field.⁴⁴ To replicate the physiological conditions, the system was neutralized and an ionic concentration of 0.15 M sodium chloride (NaCl) was introduced. Prior to the main simulation, the system was relaxed to ensure stability. During the simulation, structural data were recorded every 100 picoseconds for subsequent evaluation. The stability of each simulation was assessed by analyzing the root mean square deviation (RMSD) and root mean square fluctuation (RMSF) for both the protein and the ligand throughout the simulated timeframe, providing metrics for structural integrity and flexibility.

4.3.8. Network pharmacology analysis

The potential protein targets of the most active derivative, compound 4d, were predicted from its canonical structure using the TargetNet web server,⁵⁷ and the predicted hits were mapped to reviewed human protein entries through the UniProt identifier mapping service⁵⁸ to obtain a standardised set of gene symbols. Genes associated with pancreatic cancer were retrieved from the GeneCards database,⁵⁹ and entries with a relevance score of at least 50 were retained as high-confidence disease genes. The compound and disease target sets were intersected using the Venny tool,⁶² and the shared targets were taken forward for network analysis. A protein–protein interaction network of the shared targets was constructed in the STRING database version 12.0,⁶⁰ restricted to Homo sapiens and to the highest confidence interaction score of 0.900, with disconnected nodes removed. The node degree was used to rank the hub targets. Gene Ontology biological process and KEGG pathway enrichment of the shared targets were performed with ShinyGO,⁶¹ applying a false discovery rate threshold of 0.05, and the resulting terms were ranked by fold enrichment and statistical significance.

Author contributions

The study plan was designed and supervised by Syeda Abida Ejaz, Pervaiz Ali Channar and Aamer Saeed. The whole experimental work was carried out by Muhammad Sajjad Bilal and Sadia Naseem following the directions of Pervaiz Ali Channar and Aamer Saeed. Data curation and analysis were performed by Muhammad Sajjad Bilal, Rabil Ujan and Rehsma Sahito. The manuscript was written by Muhammad Sajjad Bilal, Sadia Naseem, and Qamar Abbas under the guidance of S. A. Ejaz. The complete manuscript was finally edited and approved by S. A. Ejaz.

Conflicts of interest

The authors declare no competing interests.

Data availability

All the associated data will be available on request to the corresponding author.

Supplementary information (SI): characterization spectra of synthesized compounds and also the binding interactions of all the compounds within the active pocts of targeted enzymes. See DOI: https://doi.org/10.1039/d6ra06998c.