Introduction

Infectious challenges using various receptor families that can identify pathogen-expressed molecules like viral RNA or bacterial DNA have been detected by the human immune system [1]. During these pathways, the involvement of Toll-like receptors (TLRs) causes a release of cytokines which lead to pro-inflammatory action along with the prevention of different diseases [2,3,4]. These abnormal actions lead to fever and in some cases, acute diseases which are becoming a serious point of discussion for various research groups. Such studies made us think that whether any ligand which is showing its efficacy toward various TLRs can be used to cure only infectious disease or it can also lead to reduce the inflammation, fever, headache etc. due to over expression of interleukin-6 (IL-6) [5,6,7,8,9,10,11,12,13,14,15].

It fascinated us to initiate our work by taking curcumin as a multifaceted agent and used against various diseases such as anti-inflammatory, antiviral, anticancer, antioxidant, antimicrobial, etc. which made it a wonderful agent [16,17,18,19,20,21,22,23,24]. The structural framework of curcumin (1) consists of two aromatic rings with a dicarbonyl linker in the center as shown in Fig. 1. This attracted us to design new molecules with bioactive precursors like Purines/pyrimidine as aromatic moieties and containing linker moiety, but with hetero atom like nitrogen in the linker part. The presence of nitrogen is proposed to increase the solubility property leading to better bioavailability and hence may better fit in the pocket of various receptor sites.

Fig. 1
figure 1

Structures of curcumin (1–2), newly designed (3–8), and reported (9–10) compounds

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Therefore, the present piece of work deals with the design, synthesis, and in silico analyses of Purines/pyrimidine derivatives, whose structural framework resembles curcumin, but with better moieties and improved properties. Detailed in silico studies involving ADMET studies, toxicology effects, docking analyses, and MD simulation were carried out to understand the responses of new compounds in comparison to curcumin as well as some reported compounds. Finally, in vitro anti-inflammatory activity will be carried out for the most efficient molecule, and a comparative analysis with curcumin and diclofenac will be taken.

Results and discussion

Computational studies

The physicochemical studies of newly designed molecules using ADMET tools

ADMET studies, using various computational tools, provide detailed data regarding the physicochemical parameters of the new set of molecules and also helps in screening and selecting the most suitable molecules for synthesis and further studies. Even though curcumin is known to be effective against numerous diseases and helps to improve the immune system, it does not cure them completely. Studies of structural features are important to design a new pharmacophore for the development of new molecules with minimum side effects. One of the major drawbacks of curcumin is its low bioavailability and poor solubility [25,26,27,28,29]. The presence of various substituents, their position, number, and pattern of linker affects the properties of the given molecule. In the case of curcumin, the presence of 1,3-dicarbonyl linker plays a vital role in the anticancer activity. To understand such features, a list of molecules was designed (3–8), and subjected to their ADMET studies. Their physicochemical properties were discussed and compared with reported compounds (9–10) along with both the tautomers of curcumin (1 and 2) (Fig. 1 and Table 1).

Table 1 Physicochemical properties of compounds (1–10) using SwissADME software
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The solubility parameter of any compound can be enhanced, either by increasing the number of hydroxyl groups or by increasing the number of hetero atoms such as N, S, or O which in turn increases the polarity and hence affects the solubility of the molecule. As shown in Table 1, the hydrogen bond acceptor value (HBA) of curcumin (1) and its enolic form (2) is more than that of hydrogen bond donor (HBD) values. On the other hand, both these values are in the range (approx 3–4) in most of the other compounds (3–10). Another major observation is the values of topological surface area (TPSA) which is lesser than or equal to 140 Å2 [30]. From (ST-1), it is observed that compound 3, 4, 5, 9, and10 which are following drug-likeness rules tends to justify the rule of five and do not enter the blood–brain barrier having the TPSA value above 70 Å2 [31, 32]. From Table 1, these compounds are also showing high GI absorption. P-glycoprotein and permeability glycoprotein substrate are involved in the efflux mechanism which is ATP dependent and is responsible for decreased drug accumulation in multidrug resistance cells and often mediates the development of resistance to anticancer drugs [33].

Toxicological studies

The toxicological behavior of these molecules was predicted using OSIRIS Data Warrior software (http://www.openmolecules.org/datawarrior/) (ST-1) [34]. Compounds (1–2, 4–5, and 9–10) were non-toxic toward mutagenic, tumorigenic, reproductive systems and irritants except compound 3, which has shown minor toxicity toward mutagenic system. So, from these results, compounds (3–5) were selected for further synthesis and docking analysis.

Target prediction analyses

Before undergoing docking analysis, the reference molecules 1 and 2 were subjected to Swiss Target Prediction analysis using SwissADME tools (SF-1, SF-2). As both the tautomers of curcumin (1–2) have shown highest binding probability toward TLRs, therefore, this has been taken primarily to analyze the further results. Present work deals with understanding the multi-parametric features of newly synthesized compounds against all the receptors sites, which are related to innate immune system pathways.

Molecular docking analysis

For docking analysis, Chimera 1.15 and Biovia (Discovery studio v21.1.0.20298) have been used. Three trials have been taken for the average vina score toward each receptor site (ST-2). Also, side chain A for all target sites was taken, and specifications of center of grid for all sites were optimized as shown in (ST-3). To understand the structural features of the reference compounds (1–2) and to compare the results with newly synthesized compounds (3–5) and reported compounds (9–10) [35, 36], almost all the target sites of TLR9 were taken from PDB files (PDB code:—3WPD, 3WPE, 3WPF, 3WPG, 3WPH, 3WPI, 3WPB, 3WPC, 4QDH, 5ZLN, 5Y3J, 5Y3L, 5Y3K, 5Y3M (ST-2)) and were screened using Autodock Vina.

Molecular docking studies of selected TLR9 sites with all ligands (1–5, 9–10)

Out of all the target sites mentioned above, compounds 1–2 have shown good binding energy for 5ZLN site (ST-2). All the newly synthesized compounds (3–5) and reported compounds (9–10) were subjected for docking studies toward 5ZLN site. Table 2 represents that compound 3 was the most efficient among all the other sets of molecules with average binding energy − 8.1 kcal/mol.

Table 2 Docking analysis toward various receptor sites
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Figure 2 represents various binding interactions of the 5ZLN receptor site with various compounds (1–5, and 9). Compound 1 has not shown conventional hydrogen bond interaction (CHB) with linker, whereas the enolic form i.e., compound 2 was involved in CHB interaction with that of linker portion. On the other hand, CHB interactions of compound 3 have a minimum bond distance of 2.0217 Å with ASP A: 469 (ST-4). Moreover, it has also shown interaction with hydrogen of NH (at position 1’’’ and 2a in Fig. 1), whereas no such interactions were observed in the linker part of compounds 4, 5, and 9. Overall compound 3 seems to interact better than all other compounds not only with bond distance or hydrogen bond interaction but also in terms of minimum hydrophobic interactions. With the increased HBD (hence TPSA values) for compounds 1 < 2 = 3 (from ADMET studies as shown in Table 1), an increase in the hydrogen bond interactions in docking studies was also detected (Fig. 2). Another major finding was observed while comparing the structure of compound 3 with that of compound 9 (the structure of which resembles that of compound 3) [36]. Although, there was a lot of structural similarity between the two compounds and also HBD and TPSA of these were almost similar then also compound 9 showed lesser CHB interaction and increased hydrophobic interactions which made compound 9 less potent than compound 3. This signified that not only the presence of hetero atoms mainly nitrogen, in the linker part but also the position of the hetero atom played a vital role in structural interactions.

Fig. 2
figure 2

Interactions between 5ZLN site with a Compound 1b Compound 2 c Compound 3 d Compound 4 e Compound 5 f Compound 9

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Molecular docking studies of breast cancer (PDB ID: 4RJ3) with various ligands (1–5, 9–12)

A specific piece of research was seen, in which TLR9 was somewhere indirectly related to breast cancer studies [6]. Preclinical studies for immunotherapeutic approach through TLR9 for the treatment of cancer are still under trial [6, 37]. Presently, docking analysis against breast cancer target site (4RJ3) was carried out and compared with curcumin (Table 2) [38]. Compounds 2 and 3 have shown high efficiency in their binding energies when compared with curcumin. Further, it was seen that the enolic form (2) has shown increased potency in terms of binding energy for various target sites than keto form (1). Finer details of structural features between keto and enol forms of curcumin made us extend our studies for different tautomeric forms of compound 3 as shown in Fig. 3.

Fig. 3
figure 3

Tautomeric forms of compound 3 enolic form (11) iminium form (12)

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Out of these 3 different forms of compound 3, the enolic form (11) has shown most efficient results in terms of binding energy toward 4RJ3 − 8.5 kcal/mol (Fig. 3 and Table 3). It may be due to the CHB interaction of hydroxyl group in the linker, similar to the enolic form of curcumin (2) (Fig. 4).

Table 3 Binding energies of compounds 11 and 12 toward 4RJ3, 4YOJ, 4YO9, and 1I1R (kcal/mol)
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Fig. 4
figure 4

Interactions between 4RJ3 site with g Compound 2 h Compound 3 i Compound 5 j Compound 11 k Compound 12

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While comparing the structural interactions of tautomeric forms i.e., compound 3, 11, and 12, it was found that compound 12 interacted via CHB interaction with LYS 122 has a minimum bond distance of ~ 1.921 Å (ST-5). Further, minimum hydrophobic interactions were observed in the case of compound 12 as compared to the large number of these interactions of enolic (11) and keto form (3). Although, compound 12 has increased binding energy, but due to the minimum hydrophobic interactions, 12 may have better results. Moreover, such studies have explored the new point for discussion that outer environmental parameters i.e., solvent potency which affects the flipping of H from one form to another might be involved in the interaction studies.

Molecular docking studies of MERS-CoV (PDB ID: 4YOJ and 4YO9) with various ligands (1–5, 9–12)

Both SARS-CoV and MERS-CoV are emerging zoonotic pathogens that crossed the barriers to contaminate the human body [39]. Viral adaptations to human cells may have contributed to the increased replication, transmission, and pathogens of MERS-CoV in humans On the other hand, TLRs play an important role in reorganization of viral particles and the activation of the innate immune system [8]. Unfortunately, the exact pathway for understanding the pathogenesis of SARS-CoV and MERS-CoV is still under consideration.

Bat corona virus HKU4-3CLpro bound to non-covalent inhibitor viral sites (PDB Code:—4YO9, 4YOG, 4YOI, 4YOJ) were targeted by keto and enol form of curcumin as shown in Table (ST-6). Curcumin has shown the most efficient binding results against two receptor sites i.e., 4YO9 and 4YOJ. Therefore, all other compounds were docked against both these target sites as shown in Table 2. Interestingly, compound 3 has shown minimum binding energy among all other compounds. Broadly, it has been observed that hydrogen bond interactions and hydrophobic interaction greatly influenced the binding energies. Introduction of hetero atom in the linker part as well as in the aromatic system was found to be important for the binding efficacy of a particular ligand.

Molecular docking studies of cytokine site (PDB ID: 1I1R) with various ligands (1–5, 9–12)

Cytokines are responsible for regulating the immune system and generate T cells and B cells which resulted in pro-inflammation. Out of various interleukin cells, IL-6 has been widely expanded due to its upregulation during acute viral infection. Further, IL-6 invention has been associated with both pro and anti-inflammatory actions [40]. After critically investigating the binding energies of the compounds (1–5 and 9–12), using 1I1R as receptor site for IL-6, it has been examined that here also, compound 3 has resulted with greater efficiency in terms of binding energies as well as structural interactions (Tables 2 and 3).

Structural features of compound 3 toward various target sites as a multi-targeted action

As discussed previously, the efficiency of compound 3 toward 5ZLN, 4RJ3, 4YOJ, 4YO9, and 1I1R was due to its lowest binding energies (Table 2). Further, structural interactions of compound 3 toward these sites have clearly shown that the presence of nitrogen in the linker is responsible for good binding interaction with minimum bond distance (Table 4).

Table 4 Linker interactions toward various target sites of compound 3
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Moreover, Compound 3 exhibited only two conventional hydrogen bond interactions toward 4RJ3, whereas it showed three hydrogen bond interactions toward 1I1R, five and four for 4YO9 and 4YOJ, respectively. Although a greater number of interactions may not be the only factor in binding toward the receptors, but the orientation of a ligand in the binding pocket of these sites played crucial role for better interactions. Both binding energies and structural interactions of compound 3 proved it to be a multifaceted compound with increased potency toward interleukin-6 (IL-6), 4YOJ, and 4YO9 sites. The molecular dynamics simulation of compound 3 is further selected to study and analyze its stability toward 5ZLN, 4RJ3, 4YO9, and 1I1R target sites (Fig. 5).

Fig. 5
figure 5

2D Interactions of compound 3 toward different target sites

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Molecular dynamics simulation

Molecular dynamics simulations were performed on the top hits containing high binding energies. Over the simulation period, the projected conformational changes from the initial structure were presented in terms of root mean square deviation (RMSD). Moreover, structural stability, atomic mobility, and residue flexibility at times of interaction of protein-hit were expressed with root mean square fluctuation (RMSF) values [41, 42]. The peaks of the RMSF graph represent the fluctuation portion of the protein through the simulation. The N- and C-terminal have shown more changes than any other portion of the protein. Alpha helices and beta strands showed less fluctuation, as they were stiffer than the unstructured part of protein, as well as the loop portion. The RMSD showed a small deviation initially till 40 ns and then there was a small flip and the system gets equilibrated throughout the simulation. The complex was stable throughout the simulation, ligand remained inside the binding pocket and made important interactions and the backbone was consistent. The deviation might be due to conformational changes as shown in Fig. 6. Similarly, estimated RMSF values less than 3 Å indicated high stability of the complex and there was very small fluctuation where ligand made interaction with the receptor as shown in Fig. 7. The RMSD of 4RJ3 complex showed variation initially till 50 ns and then simulation was equilibrated and there was not much abnormal deviation observed throughout 100 ns. The deviation may be due to flexibility of the ligand. For RMSF, there was fluctuation for LUE 37, PRO 38, and SER 39, and the remaining structure was more stable comparatively, and there was not much fluctuation where ligand made contacts with protein. The complex of 4YO9 showed a very small deviation till 50 ns and then it was consistent and conserved over the MD simulation. The variation of RMSF was observed with small fluctuation for only ILE 72, GLY 73, and ALA 74. There was no abnormal fluctuation where the ligand made contact. The complex of 5ZLN showed deviation till almost 60 ns after that simulation was equilibrated. The fluctuation in the RMSF graph was a little high for SER 404, THR 405, and LYS 406. There was a very small fluctuation where ligands made contacts.

Fig. 6
figure 6

Root mean square deviation plot of 1I1R Complex

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Fig. 7
figure 7

Root mean square fluctuation plot of 1I1R Complex

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The complex of 1I1R has shown significantly different types of intermolecular interactions during the entire simulation including hydrogen bonds, ionic, water bridges, and hydrophobic. The residues participating in these interactions include GLUY 105, ASP 125, GLY 126, GLY 127, ARG 128, GLU 129, THR 130, HIS 131, GLU 133, ASN 135, THR 134, PHE 136, LYS 151, ALA 152, LYS 153, ARG 154, ASP 155, PRO 157, and THR 158 as shown in Fig. 8. Complex of 4RJ3 showed interactions with LYS 9, GLY 11, GLY 13, VAL 18, ALY 33, VAL 57, PHE 75, LEU 76, HIS 77, LYS 81, ASP 120, LYS 122, ASN 125, and ALA 137. Similarly, the interacting residues of complex of 4YO9 include MET 25, LEU 27, HIS 41 ALA 46, CYS 145, GLY 146, SER 147, CYS 148, GLN 167, MET 168, GLU 169, GLU 189, ASP 190, LYS 191, THR 193, HIS 194, and GLN 195.

Fig. 8
figure 8

Protein–ligand contacts of 1I1R Complex

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The complex of 5ZLN also showed interactions with LYS 406, ASN 407, PHE 408, ARG 411, ARG 423, SER 446, HIS 447, VAL 467, ASP 469, SER 471, ALA 491, LEU 492, ASP 493, SER 495, TYR 496, SER 521, LEU 522, SER 523, ALA 525, PHE 544, ASP 546, LYS 574, and SER 578.

The 1I1R complex was studied to find out the influence of the ligand on over all proteins. Six properties were examined to illustrate the stabilities of the selected ligands in the binding pocket during the simulation of 100 ns as shown in (a), (b), and (c): (1) Ligand RMSD: Root mean square deviation of a ligand with respect to the reference conformation (typically the first frame was used as the reference and it was regarded as time t = 0); (2) Radius of gyration (rGyr): It was used to measure the ‘extendedness’ of a ligand, and was equivalent to its principal moment of inertia; (3) intramolecular hydrogen bond (intraHB): Number of internal hydrogen bonds (HB) within a ligand molecule. (4) Molecular surface area (MolSA): Molecular surface was calculated with a value of 1.4 Å probe radius; (5) Solvent accessible surface area (SASA): Molecular surface area of accessible by a water molecule; (6) Polar surface area (PSA): Solvent accessible surface area in a molecule contributed only by oxygen and nitrogen atoms.

As shown in Fig. 9, the RMSD of the ligand was nearly 1.5 Å from almost 0 ns to 45 ns and it was almost 2.8 Å till 100 ns. The rGyr value of ligand in the binding pocket can be divided into two stages, the values were 3–4 Å from 0 ns to almost 45 ns and then it was stable at 4–4.5 Å after 45 ns till the end of simulation and the constant values indicated the steady behavior. The intraHB, MolSA, SASA, and PSA plots also indicated the consistency of the ligand during the whole simulation process. In the MolSA, SASA, and PSA plots for the ligand, there was small fluctuation till almost 40 ns consistently and curves were smooth after 40 ns. These curves discovered the consistency of the ligands in the binding pocket over the simulation trajectory.

Fig. 9
figure 9

Variation in the ligand’s properties w.r.t time during the course of 100 ns simulation

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Further, compound 3 was subjected for in vitro anti-inflammatory activity based on docking analysis and molecular dynamics simulation because it has shown good binding results.

Biological activity

Membrane stability method

A variety of disorders was found as a result of inflammation caused by lysosomal enzymes. They are well known for their extracellular activity such as acute inflammation. (NSAIDS) are best known for their anti-inflammatory activity. They act either by the inhibition of lysosomal enzymes or by stabilizing the lysosomal membrane. It is well known that HRBC membrane is similar to the lysosomal membrane. Therefore, this method was used to check the stability of HRBC membrane to predict the anti-inflammatory activity of the new compound in comparison to the reference molecule [43]. Thus, compound 3 along with reference molecule was incubated at 100 µg/ml for 30 min at 37 °C with HRBC solution. The percentage of hemolysis and percentage of protection was calculated.

Anti-inflammatory activity in vitro

Compound 3 was screened for anti-inflammatory activity using Membrane Stability Method [43]. The result of compound 3 has been compared with curcumin and diclofenac. It has shown significant results as compared to curcumin in hemolysis as shown in Table 5.

Table 5 Percentage (%) of hemolysis and protection
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Conclusion

Multifaceted actions of compound 3 have shown remarkable results against TLR9 sites, breast cancer (4RJ3), viral proteins (4YO9 and 4YOJ) sites, and most important interleukin-6 (1I1R) site. The protein–ligand interactions of compound 3 toward all these sites in comparison to curcumin and some similar compounds have depicted its efficient results not only in terms of binding energy but in their structural interactions also. Moreover, out of three tautomeric forms of compound 3 i.e., 3, 11, and 12, it was seen that the presence of enolic form was important for the better interactions as compared to its keto form. More refined results were obtained after analyzing the stabilization of compound 3 toward all their sites at 100 ns in molecular dynamics simulation. Compound 3 has shown excellent stabilization results for 1I1R site. In vitro studies for anti-inflammatory activity further extended the results and exhibited the efficacy of compound 3 in all fields.

Methods

Material

Α-chloroacetyl chloride, Guanine, adenine, and cytosine were purchased from Avra synthesis. The melting points were recorded by the open capillary method. Sodium chloride, methanol, acetonitrile, and dichloromethane were of Lobachem. The IR spectra were obtained by Perkin Elmer RXIFT Infrared spectrophotometer. NMR spectra were recorded on Bruker Avance 500 MHz spectrophotometer with TMS as an internal standard. The mass spectra were recorded on the Waters Micromass Q-T of Micro (HRMS) spectrometer.

Experiment

Synthesis of N-benzyl-2-chloroacetamide (3’)

9 mmol (0.963 g) of benzylamine and 9 mmol of trimethylamine were stirred with 40 ml dichloromethane (DCM) for 36–37 h with temperature variation of 0 °C-5 °C initially for 1 h followed by 1.1 ml (14 mmol) of ɑ-chloroacetyl chloride. The formation of the product was monitored by thin-layer chromatography using methanol and chloroform (1:4) as mobile phase. The reaction mixture was filtered, and the resulting solid was dissolved in dichloromethane. The organic layer was dried over anhydrous sodium sulfate, and after the removal of solvent, pure sample was obtained (Scheme 1) [44, 45].

Scheme 1
scheme 1

Synthesis of new compounds

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Coupling reaction of N-benzyl-2-chloroacetamide (3’) with various DNA bases to synthesize 3, 4, and 5

35 ml of acetonitrile (ACN) was added to 1.2 mmol of N-benzyl-2-chloroacetamide. The reaction mixture was stirred for 49–50 h after the addition of 2.4 mmol triethylamine, 1.2 mmol of sodium iodide, and 2.4 mmol of DNA bases. The formation of the product was examined by thin-layer chromatography by methanol and chloroform (1:4). The reaction was filtered and resulting solid was dissolved in acetonitrile. The compound was extracted using petroleum ether, further pure compound was collected after recrystallization using acetonitrile [45,46,47,48,49].

Physical properties and spectroscopic data

N-benzyl-2-chloroacetamide (3’)

Brown, yield = 60.9%, mp = 148 °C–150 °C. IR cm−1: νmax 3279.27 (N–H str.); 3063.55, 3030.51 (Ar C–H str.); 2881.88 (Aliphatic C-H str.); 1H-NMR (400 MHz, CDCl3) δ (ppm): 7.32 (m, 3H), 7.22 (m, 2H), 6.84 (s, 1H), 4.42 (d, 2H), 4.02 (s, 2H).

N-benzyl-2-((6-oxo-6,7-dihydro-1H-purine-2-yl)amino)acetamide (3)

Cream, yield = 64.5%, mp = 300 °C–302 °C. IR cm−1: νmax 3276.55 (N–H str.); 3061.95 (Ar C–H str.); 2917.76, 2849.51 (Aliphatic C–H str.), 1640.29 (C = O; amide), 1604.40 (C = O; amide); 1H-NMR (400 MHz, CDCl3) δ (ppm): 7.37 (dd, J = 15.2 Hz, 3H), 7.31 (dd, J = 14.4 Hz, 4H), 6.88 (brs, NH, D2O exchangeable), 6.31 (brs, NH, D2O exchangeable), 4.56 (dd, 2H), 4.13 (s, 1H), 3.74 (s, 1H), 1.66 (s, 1H, D2O exchangeable); 13C-NMR (125 MHz, CDCl3) δ (ppm): 167.68, 166.71, 166.03, 139.76, 137.40, 130.11, 129.48, 128.49, 127.82, 45.05, 30.26; HR-MS: Molecular ion peak (M + 3) = 301, base peak = 275, daughter ion peaks = 274, 212, 198, 150.

N-benzyl-2-((2-oxo-1,2-dihydropyrimidine-5-yl)amino)acetamide (4)

Yellow, yield = 67.2%, mp = 278 °C–280 °C. IR cm−1 3288.12 (N–H str.), 3062.53 (Ar C–H str.), 2924.90 (Aliphatic C–H str.), 1651.41 (C = O str), 1241.82 (C–N str.); 1H-NMR (500 MHz, CDCl3) δ (ppm): 7.73 (brs, 1H), 7.36 (dd, J = 9.4 Hz, 4H), 7.30 (dd, J = 7.5, 3H), 6.38 (brs, 1H), 4.51 (dd, 2H), 4.18 (s, 1H), 3.73 (s, 1H), 1.68 (s, 1H); 13C-NMR (125 MHz, CDCl3) δ (ppm): 167.50, 166.66, 162.78, 138.18, 137.34, 136.97, 130.47, 129.99, 128.65, 127.86, 127.42, 126.56, 55.57, 44.81; HR-MS: Molecular ion peak (M + H2O) = 276, base peak = 249, daughter ion peaks = 197, 91.

2-((9H-purin-6-yl)amino)-N-benzylacetamide (5)

Light Yellow, yield = 69.3%, mp = 270 °C–272 °C. IR cm−1 3277.34 (N–H str.), 3060.69 (Ar C–H str.), 2923.36 (Aliphatic C–H str.), 1639.41 (C = O str), 1237.70 (C–N str.); 1H-NMR (500 MHz, CDCl3) δ (ppm): 7.35 (m, 3H), 7.29 (m, J = 9.4 Hz, 4H), 6.87 (brs, 1H), 6.35 (s, 1H), 4.50 (d, 1H), 4.46 (s, 1H), 4.10 (s, 1H), 2.05 (s, 1H); 13C-NMR (125 MHz, CDCl3) δ (ppm): 167.68, 166.85, 140.10, 137.96, 130.62, 129.80, 128.88, 128.48, 128.04, 125.07, 115.14, 45.36, 30.34; HRMS: Molecular ion peak = (M + 1) = 283, base peak = 298.