Rational Druggability Investigation Toward Selection of Lead Molecules: Impact of the Commonly Used Spices on Inflammatory Diseases

Abstract

Herbal remedies and phytochemicals have been used in traditional medicine. Most of the herbs used in human diet have some major effective elements that can affect various pathways in the human body and play a therapeutic role in healing disorders or diseases. Among the inflammatory diseases, worldwide common disorders possess well-known pathways that can be controlled by diet and behavior. There are some well-established targets that are used for anti-inflammatory drugs like cyclooxygenase type 1 and 2 (COX-1 and COX-2), lipoxygenase, prostaglandin D2 receptor, DP1, CRTH2, and so on. In this article, we investigated the role of phytochemicals, extracted from different commonly used spices in the food industry, in preventing or healing the inflammatory disorders. The ability of such bioactives to inhibit COX-2 enzyme has been investigated and compared with marketed selective and nonselective NSAIDs, aspirin and celecoxib. Thereafter, the pharmacokinetic and pharmacodynamic properties of such ingredients have been evaluated for their druggability potential. The results indicated that piperine showed the best ADME (absorption, distribution, metabolism, and excretion) and toxicity profiles among all bioactives. Also, it possessed better affinity value, −7.80518 kcal/mol and energy binding −85.08 kcal/mol, in inhibition of COX-2 with PDB Id: 1CVU rather than other compounds and significantly the higher dock score than aspirin, close to celecoxib. Therefore, piperine has been suggested to be used as the major ingredient in daily diet as a potent anti-inflammatory and anticancer agent.

Introduction

The small amount of bioactives presented in foods acts as extranutritional constituents.¹ The most representative phytochemicals can be categorized as carotenoids, flavonoids, proanthocyanidins, isothiocyanates, anthocyanins, terpenoids, and omega-3 polyunsaturated fatty acids.² Pan et al.,² detailed the mechanism of anti-inflammatory activities of natural bioactives by modulating the expression of various proinflammatory and inflammatory related genes or by directly binding to inflammatory responsible receptors like cyclooxygenase type 1 and 2 (COX-1 and COX-2) to carry out their anti-inflammatory functions,³ particularly, COX-2. COX-2 enzyme plays the key role in inflammation scenario by catalyzing the first step of prostaglandin, thromboxane, and other eicosanoid biosynthesis.⁴ The COX-2 expression can be modulated by several natural products,^5,6 but the mode of action of natural products (NPs) is not clear that either they are modulating transcription factors or directly interact with the gene product. The prostaglandin synthesis pathway is depicted in Figure 1.

Fig. 1.

The prostaglandin synthesis pathway.

A possible way to explore the mode of action of NPs is using the computational chemistry and in silico techniques to model protein–ligand interaction procedures.⁷ The receptor–ligand Docking methods are valuable tools for drug development by designing and virtually screening large numbers of druggable molecules and putative binding sites on a receptor molecule.⁸ Among all tools, Schrodinger suite 2011 was used for docking in this article.⁹ In this study, in silico drug investigation was performed to test the anti-inflammatory ability of 15 natural bioactives, isolated from different ingredient spices and food flavors, by targeting COX-2, and compare their affinity values with commonly used NSAIDs (Aspirin and Celecoxib)¹⁰ to predict their biological activity.

Materials and Methods

Selection of Proteins

The structures of COX-2 (PDB Id: 1CVU and 3LN1) were obtained from the RCSB Protein Data Bank with X-ray diffraction resolutions in 2.40 Å for both PDB Ids.

Preparation of Proteins and Ligands

The retrieved protein was prepared using Protein Preparation Wizard of Schrodinger suite 2011 (Schrödinger Suite, Epik version 2.2, Impact version 5.7, Prime version 2.3; Schrödinger, LLC, New York, NY, 2011). The geometrical optimization and energy minimization of target protein have been performed by OPLS 2005 forcefield with RMSD as 0.30. Binding site of the processed protein was predicted based on pose of presented ligands in extracted PDB file of crystalographic structure of protein.

Ligands were prepared using “LigPrep 2.5” module of Schrodinger Suite 2011 using the OPLS forcefield 2005 at biologically relevant pH. The preparation has been performed by assigning the protonation states, including disconnecting of group 1 metals in simple salts, protonating strong bases, and deprotonating strong acids, while adding topological duplicates and explicit hydrogens. The molecular properties of all compounds are presented in Table 1.

Table 1.

Molecular Properties of All Compounds

Title	mol_MW	donorHB	accptHB	QPlogPC16	QPlogPoct‡	QPlogPw	QPlogPo/w
Ar-curcumene	202.34	0	0.00	7.21	8.06	0.11	6.33
Ar-turmerone	216.32	0	2.00	7.32	9.22	3.13	3.63
β-Caryophyllene	204.36	0	0.00	6.24	7.76	−0.23	5.13
B-turmerone	218.34	0	2.00	7.43	9.35	2.82	3.88
Caffeic acid	180.16	3	3.50	7.04	12.64	9.87	0.56
Capsaicin	275.39	0	3.75	9.17	12.17	5.46	3.83
Cinnamic acid	148.16	1	2.00	6.04	8.37	5.69	1.91
Curcumin enol	368.39	2	7.00	13.08	19.26	11.66	2.69
Curcumin keto	368.39	2	7.00	12.80	18.75	11.45	2.92
Cyclocurcumin	368.39	2	5.75	12.59	19.15	11.28	3.33
Eugenol	164.20	1	1.50	5.83	7.56	4.14	2.67
Ferulic acid	194.19	2	3.50	6.90	11.43	8.03	1.38
methyl palmitate	270.45	0	2.00	10.08	10.68	0.76	5.82
Oleic-acid	282.47	1	2.00	10.92	12.49	2.46	5.97
Piperine	285.34	0	4.50	8.64	11.98	5.89	3.26

mol_MW, molecular weight: R.V.: 130–725; donorHB, estimated number of hydrogen bonds that would be donated by the solute to water molecules in an aqueous solution: R.V.: 0.0–6.0; accptHB, estimated number of hydrogen bonds that would be accepted by the solute from water molecules in an aqueous solution: R.V. = 2.0–20.0; QPlogPC16, predicted hexadecane/gas partition coefficient: R.V. = 4.0–18.0; QPlogPoct‡, predicted octanol/gas partition coefficient: R.V. = 8.0–35.0; QPlogPw, predicted water/gas partition coefficient: R.V. = 4.0–45.0; QPlogPo/w, predicted octanol/water partition coefficient: R.V. = −2.0 to 6.5.

R.V., recommended value.

Receptor–Ligand Interactions

The receptor–ligands were docked using “Glide 5.7” module in Extra Precision (XP) mode.^11,12 The molecular mechanics/generalized born surface area (MMGBSA)¹³ has been calculated for each ligand-protein complex by Prime 3.0 application of Schrodinger Suite 2011 (Suite 2012: Prime, Version 3.1; Schrödinger, LLC, 2012). The 2D structure of all ligands, docking scores, and MMGBS are given in Table 2.

Table 2.

Docking Score and Molecular Mechanics/Generalized Born Surface Area Energy of National Toxicology Programs-Cyclooxygenase-2 Complexes

		COX-2
		3LN1		1CVU
Title	2D structures	XP GScore	DG^a	XP GScore	DG^a
Ar-Curcumene		−7.40	−71.48	−7.76	−53.47
Ar-Turmerone		−7.63	−59.75	−7.41	−64.70
β-Caryophyllene		−6.50	−28.02	−5.33	−53.26
B-Turmerone		−7.48	−86.21	−6.91	−79.64
Caffeic acid		−7.12	−28.10	−5.86	0.64
Capsaicin		−6.87	−77.46	−7.11	−75.52
Cinnamic acid		−5.51	−25.05	−5.59	−1.99
Curcumin enol		−6.07	−74.83	−10.14	−80.27
Curcumin keto		−8.39	−74.59	−9.88	−80.81
Cyclocurcumin		−4.53	−43.10	−7.46	−60.70
Eugenol		−6.85	−44.12	−7.11	−46.54
Ferulic acid		−5.70	−25.37	−6.50	−11.66
Methyl palmitate		−4.97	−73.43	−6.51	−103.02
Oleic acid		−5.83	−62.97	−7.16	−73.14
Piperine		—	—	−7.81	−85.08
Control
Aspirin		−4.27	−28.14	—	—
Celecoxib		−11.48	−88.77	—	—

^aDG (ΔGbind) = Gcomplex − (Gprotein + Gligand) where ΔGbind is ligand binding energy.

COX-2, cyclooxygenase-2; XP Gscore, Extra Precision Glide Score.

Pharmacodynamic, Pharmacokinetic, and Toxicity Properties

Absorption, distribution, metabolism, and excretion (ADME) study has been performed by employing Qikprop module of Schrodinger Suite 2011 (Schrödinger Press: QikProp 3.4 User Manual, LLC, 2011). The pharmacokinetic and pharmacodynamic profiles of the compounds were assessed by #start parameter, as an overall ADME-acceptance score of the drug likeness parameter for 95% of known drugs.¹⁴ These criteria include the following: FOSA (0–750), FISA (7–330), total solvent-accessible volume (volume), PISA (0–450), SASA/Smol (300–1,000), Glob (0.75–0.95 for 95% of drugs), number of likely metabolic reactions (Metab, 1–8 for 95% of drugs), molecular weight (mol_MW, 130–725), donorHB (0–6), accptHB (2–20), QPlogHERG (concern, <−5), QPlogKp (−8 to 10), QPPMDCK (nm/s, <25 poor, >500 great), QPlogKhsa (−1.5 to 1.5), partition coefficient, including QPlogPo/w (octanol/water, 2–6.5), QPlogPoct‡ (octanol/gas, 8–35), QPlogPw (water/gas, 4–45), and QPlogPC16 (hexadecane/gas, 4–18),^15
–17 central nervous system (CNS) activity (−2 to 2),^17,18 QPlogBB (−3 to 1.2),¹⁸ QPPCaco (<25 poor, >500 great),¹⁹ PM3 calculated ionization potential (IP [eV], 7.9–10.5), PM3 calculated electron affinity (EA [eV], −0.9 to 1.7), the human oral absorption level, the maximum transdermal transport rate (Jm; Kp XMWXS; μg/cm²/h), and the number of violations of Lipinski's rule of five^15,20 of the various curcumin analogs.

The toxicity profiling of compounds was predicted using online TOPKAT approaches of Accelrys Environmental Chemistry and Toxicology Workbench, Accelrys, Inc. (San Diego). TOPKAT features provide the accurate toxicity properties of compounds such as rodent carcinogenicity from the FDA dataset for both female and male (v3.1), mutagenicity (Ames test v3.1), ocular irritation (v5.1), skin sensitization (GPMT) and irritancy (v6.1), aerobic biodegradability (v6.1), weight of evidence (WOE) (v5.1), EC50, LD50, and TD50.²¹ The ADME and toxicity results of all compounds are listed in Supplementary Tables S1 and S2 (Supplementary Data are available online at www.liebertpub.com/adt), respectively.

Visualization of Interaction Between Top Score Candidate/s and Residues in Receptors

The best absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiled candidates with top dock scores have been selected and their complexes with binding site of receptor were mapped using XP visualizer approaches of Schrodinger 2011. Through this process, the receptor surface was configured based on the electrostatic potential of residues in binding packet of protein. The receptor surface was truncated in 5 Å from ligand with 20% transparency.

Results and Discussion

Herbal remedies and phytochemicals have been used in traditional medicine. Fifteen different phytochemicals have been selected for this study, which are the major components of commonly used spices and food flavors in food industry, including Ar-curcumene, Ar-turmerone, B-turmerone, curcumin enol and keto, and cyclocurcumin, all extracted from turmeric (Curcoma longa), β-caryophyllene from rosemary (Rosmarinus officinalis), caffeic acid from blue gum (Eucalyptus globulus), capsaicin from chilli pepper in Capsicum genus, eugenol and cinnamic acid from cinnamon (Cinnamomum verum), ferulic acid from seeds of apple, peanut, orange, artichoke, rice, wheat, oats, pineapple, and coffee, methyl palmitate naturally available in many plants, oleic acid in various animal and vegetable fats and oils, and piperine from black and long pepper. Figure 2 indicates the comparison between six selected compounds in inhibition of COX-2 with both PDB Ids.

Fig. 2.

The compression between six selected compounds in inhibitory of COX-2 with both PDB Ids. COX-2, cyclooxygenase-2.

Protein and Ligand Preparation

The structures of COX-2, PDB Ids: 1CVU and 3LN1, have been downloaded from protein data bank website (www.rcsb.org/pdb) and analyzed by Schrödinger 2011 to predict the binding site of protein for docking of the ligands. The ligands have been prepared using LigPrep module of Schrödinger 2011 and their molecular properties have been computed by Qikprop 3.4 (Table 1).

Docking Calculations Using Schrodinger 2011

The docking of ligands and receptors has been performed by Glide v.5.7 feature of Schrodinger suite 2011. The result indicated that among all compounds in inhibitory binding site of COX-2 with PDB Id: 3LN1, curcumin (keto) with affinity value −8.385 kcal/mol was the best inhibitor, which showed significantly better dock score than aspirin and close to celecoxib. After curcumin, Ar-turmerone, B-turmerone, and Ar-curcumene with affinity values −7.625, −7.485, and −7.405 kcal/mol occupied the second, third, and fourth ranks, respectively. However, in substitute binding pocket, the first rank belonged to curcumin (enol) with affinity value −10.135, which is close to celecoxib dock score and a significantly higher score than aspirin. Then, curcumin (keto) with affinity value −9.876 kcal/mol and piperine with dock score −7.805 kcal/mol showed better affinity values than rest of compounds. The result showed curcumin in both isoform can be used as the best anti-inflammatory agent and then piperine will be the best option. Selection of the best compound depends on the pharmacokinetics, pharmacodynamics, and toxicity profiling (ADMET profiling) of such compounds. The level of absorption and bioavailability can determine the amount of usage and dose of a compound to act as proper anti-inflammatory agent.

ADME Prediction

The main parameter of pharmacological properties of each compound is to check the ability of a ligand to pass through oral and intestinal barriers, enter blood stream for distribution to the entire body, deliver to its target, be metabolized, and excrete from body during period of time. For prediction of the oral availability of the compounds, Lipinski evaluated different criteria known as Lipinski's rule of five.²² Rule of five (ro5) includes standard range for molecular weight (MW <500), number of hydrogen bond donor (HBD <5), number of hydrogen bond acceptor (HBA ≤10), and predicted octanol/water partition coefficient (log P < 5). Recently, for NPs, Ntie-Kang et al. suggested to add number of rotatable bond (NRB <10) to ro5 to assess the oral availability.²³ NRB was added due to the wide range of conformational flexibility of NPs to consider the desired pharmacokinetics and drug metabolism. Lipinski's criteria are presented in the scatter plots of the correlation between MW and other criteria for six compounds with better affinity values in Figure 3. According to the result, three out of six, piperine, Ar-turmerone, and B-turmerone, could satisfy drug-likeness criteria and have potential for considering as druggable molecules. The ADME properties of six high dock-score compounds are presented in Table 3.

Fig. 3.

The scatter plots of correlation of MW and Lipinski's criteria for six compounds with better affinity values.

Table 3.

Absorption, Distribution, Metabolism, and Excretion Properties of Six Selected Compounds

Molecule	QPlogS	CIQPlogS	QPlogHERG	QPPCaco	QPlogBB	QPPMDCK	QPlogKp	QPlogKhsa	CNS	#metab	HOA	RF	RT	Jm
Ar-Curcumene	−6.971	−6.971	−4.551	9906.038	0.984	5899.293	−0.69	1.001	2	5	1	1	15	0.004
Ar-Turmerone	−4.023	−3.099	−4.260	4390.438	−0.007	2448.076	−1.38	0.395	0	5	3	0	16	0.861
B-Turmerone	−3.994	−2.947	−4.140	4078.424	−0.095	2260.580	−1.41	0.414	0	5	3	0	16	0.863
Curcumin enol	−4.369	−4.616	−6.198	134.191	−2.278	56.431	−3.09	−0.029	−2	5	2	0	27	0.013
Curcumin keto	−4.237	−4.616	−6.143	297.557	−1.860	133.457	−2.39	−0.042	−2	5	3	0	27	0.087
Piperine	−3.493	−3.556	−4.808	3984.151	−0.122	2204.154	−1.17	−0.025	0	0	3	0	0	6.181

QPlogS, prediction aqueous solubility level, recommended range −6.5 < x < 0.5; CIQPlogS, conformation-independent predicted aqueous solubility, −6.5 < x < 0.5; QPlogHERG, predicted IC₅₀ value for blockage of human Ether-à-go-go related gene K+ channels, <−5 = concern; QPPCaco, predicted apparent gut-blood barrier permeability, <25 = poor, >500 = great; QPlogBB, predicted brain/blood partition coefficient, −3.0 to 1.2; QPPMDCK, predicted apparent Madin-Darby canine kidney cell permeability, <25 = poor, >500 = great; QPlogKp, predicted skin permeability, range = −8 < x < −1; QPlogKhsa, prediction of binding to human serum albumin, −1.5 to 1.5; CNS, central nervous system activity −2, −1, 0, 1, 2: −2 = completely inactive, −1 = very low activity, 0 = low activity, 1 = medium activity, 2 = completely active, 3 = high; #metab, number of likely metabolic reactions, 1–8; HOA, human oral absorption level, 1, 2, 3: 1 = low, 2 = medium; RF, the number of violations of Lipinski's rule of five; RT, the number of violations of Jorgensen's rule of three; Jm, maximum transdermal transport rate.

Bioavailability

Two processes, absorption and the first-pass metabolism of the liver, are to be used to determine the bioavailability of each compound. The effective factors in absorption include the solubility of compounds and the gut wall permeability to the compounds, meaning the ability of a compound to interact with shuttles in the gut wall like transporters and metabolizing enzymes depending on the functional groups in the compound structure.

Jorgensen offer rules for computing the oral absorption of a compound known as “Rule of Three” (ro3). The parameters of the likelihood of oral availability include log S >−5.7, QPPCaco >22 nm/s, and number of primary metabolites <7. Other important parameters are the prediction of the qualitative human oral absorption, the percentage of human oral absorption, and the conformation-independent aqueous solubility, CIlog S. CIlog S is computed based on the compounds' similarities with their close analogs. For similarity >0.9, adjusted formula is given in the Equation (1): \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} \begin{align*}{ \rm{Ppred}} \; = { \rm{ \;SPexp}} \; + \; ( 1 \; - \;{ \rm{S}} ) { \rm{PQP}} , \tag{1} \end{align*} \end{document}

where S is the similarity, and Pexp and PQP are the respective experimental and QikProp predictions for the most similar molecules within the training set. Log S, #metab, and Caco-2 were used to predict the aqueous solubility levels, number of likely metabolic reactions, and the gut blood barrier permeability, which is a nonactive transport in nm/s, respectively.²⁴ The results indicated that all three compounds possessed QPPCaco >500 nm/s and great gut permeability. Their metabolic behaviors were in recommended range. All three compounds had acceptable values of log S and all showed high level of oral absorption.

The Prediction of Blood/Brain Penetration (QPlogBB)

QPlogBB was used for the prediction of the blood–brain barrier (BBB) permeability of compounds and accessibility for CNS based on the polarity of compounds.²⁵ There are some other parameters to predict the BBB penetration such as the CNS activity, Madin-Darby canine kidney (MDCK), and logB/B.²⁵ The CNS activity prediction indicated that none of three compounds was active in CNS (predicted value >1). The logB/B prediction showed that all three compounds were in the acceptable range (−3.0 to 1.2) and the prediction of nonactive transportation through the MDCK signifies the great transportation (>500 nm/s) for all three compounds.

The Prediction of Plasma-Protein Binding

Pharmacodynamics of a druggable molecule depends on the ability to bind to the plasma proteins such as glycoprotein, human serum albumin, lipoprotein, and globulins (a, b, and c), which can directly affect the efficacy of a drug.²⁶ On the other hand, whatever the rate of plasma-protein binding is high, the availability of a drug for target is less, result in reducing the rate of distribution of it through general blood circulation.²⁷ Therefore, for designing a drug, the less degree of plasma-protein binding is desirable. QPlogKhsa has been computed for the estimation of plasma-protein binding tendency of the compounds. The results showed that all three compounds are in the recommended range (−1.5 to 1.5).

The Prediction of Metabolism

Determination of the accessibility level of compounds for their target after entering into the blood stream is known as the number of likely metabolic reactions. #meta of QikProp is the parameter for prediction of average number of possible metabolic reactions of each compound. The results showed that all three selected compounds possessed #meta values within the recommended range of metabolic reaction 1–8.

The Prediction of Blockage of Human Ether-à-go-go Related Gene Potassium (hERG K+) Channel

Human Ether-à-go-go related gene (hERG), because of its role in the electrical activity during systolic and diastolic periods of the heart by encoding the potassium ion (K+) channel, is the target for testing the cardiac toxicity of druggable molecules.²⁸ Also, this channel can modulate the function of nervous system²⁹ and involve in disorders like long QT syndrome (torsade de pointes).³⁰ Thus, hERG K+ channel inhibitors are potentially toxic for nervous and cardiac system and for prediction of toxicity of druggable molecules, determination of IC₅₀ values is necessary in drug designing.³¹ The QPlogHERG is used to predict the IC₅₀ values of hERG channel toxicity. The results indicated that all three compounds have values in the recommended range of blockage of hERG K+ channels IC₅₀ >−5.

Toxicity

Online TOPKAT approaches of Accelrys Environmental Chemistry and Toxicology Workbench have been employed to predict the important parameters of toxicity. These parameters include carcinogenicity based on structural similarity of compounds with structures available in both the FDA (U.S. Food and Drug Administration) and NTP (National Toxicology Program) databases for male mouse (MM), male rat (MR), female mouse (FM), and female rat (FR). The data analysis indicated that based on NTP database, piperine was noncarcinogenic for both male and female rat and mouse, while Ar-turmerone and B-turmerone were carcinogenic for MM with probability 0.603 and 0.604, respectively. According to the FDA database, piperine was noncarcinogenic for MM and FM with no evidence of efficacy on FR and MR, while B-turmerone was carcinogenic for all FM, MM, FR, and MR with probabilities 0.246, 0.507, 0.393, and 0.664, and Ar-turmerone, except for FM, was carcinogenic for all MM, FR, and MR with probabilities 0.284, 0.339, and 0.529, respectively. All three compounds showed skin irritancy. Piperine was nonsensitizer for skin, while both Ar-turmerone and B-turmerone showed mild skin sensitizing. All compounds possessed ocular irritancy with probability 0.999 for piperine, and for Ar-turmerone and B-turmerone, the ocular irritancy was dose dependent, 1.97 and 5.91 g/L, respectively. The obtained rat oral LD50 values were 0.699 for piperine and the remaining two compounds were dose dependent, g/kg of body weight. The maximum rat tolerance dose by feeding for piperine was 0.19 g and the remaining two compounds were dose dependent, g/kg of body weight. According to the developmental toxicity potential, model piperine was toxic with probability 0.495 and the remaining two compounds behaved dose dependent, 0.931 and 1.841 g/kg of body weight for Ar-turmerone and B-turmerone, respectively. Based on WOE for rodent carcinogenicity, piperine was noncarcinogenic and Ar-turmerone and B-turmerone showed dose-dependent behavior, 240.78 and 96.83 mg/kg of body weight, respectively. The Daphina Magna EC50 values model was 0.930 and 0.376 mg/L for piperine and B-turmerone, respectively, and no data were generated for Ar-turmerone. Piperine was degradable through an aerobic biodegradablility pathway; however; there are no data regarding the degradability of Ar-turmerone and B-turmerone. The inhalation lethal concentration for piperine was 4.595 mg/m³/h and the two other compounds were dose dependent and in optimum ranges. Piperine did not show any mutagenicity; however, both the compounds showed mutagenicity with probabilities 0.503 and 0.522 for Ar-turmerone and B-turmerone, respectively. The toxicity profiles of all compounds are presented in Supplementary Table S1.

Visualization of Interaction Between Compounds and Residues in Receptors

In a glance, piperine has the best docking score among the three compounds and possesses better ADME, and toxicity profile has been selected for visualizing its interaction with the residues in the binding packet of COX-2 with PDB Ids: 1CVU and Ar-turmerone with better affinity value, rather than B-turmerone used for visualization in interacting with COX-2 with PDB Id: 3LN1.

All compounds have been docked in the same binding pocket of COX-2 enzyme with both PDB Ids: 1CVU and 3LN1. Ar-turmerone interacts with COX-2 with PDB Id: 3LN1 in inhibitory binding pocket with residues, including Gly512, Ala513, Met508, Val509, Tyr341, Phe504, Leu338, Val335, Trp373, ser516, arg106, try334, leu517, and Try371, and without any hydrogen bond to the residues inside the receptor binding pocket. Piperine interacts with the residues inside the substrate binding pocket (PDB Id: 1CVU) of COX-2, including LEU534, SER530, MET113, LEU384, ALA527, TYR385, GLY526, PHE381, LEU352, VAL349, PHE518, PHE209, PHE205, TYR384, VAL344, VAL523, TRP387, and TRY385 with no hydrogen bond between ligand and receptor binding pocket.

The 3D structures of receptor–ligand complexes are depicted in Figure 4.

Fig. 4.

The 3D structures of receptor–ligand complexes.

Conclusion

Nowadays, the diet pattern has been changed as a result of modernity in both developing and developed countries, and few people still have enough time to prepare foods at home. Most people due to the pressure of daily duties are completely depending on manufactured food. From this angle, at this moment, food industry can play the key role in public health. Manufactures can improve the level of health in societies by selecting high quality material and selecting the ingredients that contain the essential elements for human health. Among, spices are the cornerstone of medicinal ingredients which can directly affect on human health and healing some common disorders. In this article, we have focused on some important bioactives that are mainly available in daily used spices to suggest the best ingredients for most inflammatory disorders. We have selected 15 different constituents of commonly used spices, predicted the biological activities of such compounds in terms of the anti-inflammatory efficacy, and investigated the pharmacological properties. Among all ingredients, piperine extracted from black and long peppers showed the best ADME and toxicity profile, and higher affinity value than other potent candidates. Piperine could inhibit COX-2 enzyme in substrate binding site as a competitor for arachidonic acid with affinity value −7.80518 kcal/mol and energy binding −85.08 kcal/mol, much better than aspirin and close to celecoxib. All ADMET properties were in recommended ranges and can be considered a drug candidate in daily diet. According to several reports, it can also increase the bioavailability of curcumin in simultaneously up-taking. Data suggested that black pepper can be used as regular spice in diet to prevent inflammatory disorders and inflammatory caused cancers.

Footnotes

Acknowledgment

The authors are grateful to Dr. Shikha Singh of Centre of Biotechnology, SOA University, Bhubaneswar, Orissa, India, for providing technical support and encouraging throughout.

Disclosure Statement

No competing financial interests exist.

Abbreviations Used

References

Kitts

: Bioactive substances in food: identification and potential uses. Can J Physiol Pharmacol, 1994; 72:423–434.

Pan

M-H

, Lai

C-S

, Dushenkov

, Ho

C-T

: Modulation of inflammatory genes by natural dietary bioactives. J Agric Food Chem, 2009; 57:4467–4477.

Chakraborti

, Garg

, Kumar

, Motiwala

, Jadhavar

: Progress in COX-2 inhibitors: A journey so far. Curr Med Chem, 2010; 17:1563–1593.

Kiefer

, Pawlitz

, Moreland

, et al.: Structural insights into the stereochemistry of the cyclooxygenase reaction. Nature, 2000; 405:97–101.

Wilken

, Veena

, Wang

, Srivatsan

: Curcumin reviewed as a nontoxic potential adjuvant in resistant head and neck cancers. Mol Cancer, 2011; 10:12.

Jin

, Suh

S-J

, Yang

, et al.: Anti-inflammatory activity of bark of Dioscorea batatas DECNE through the inhibition of iNOS and COX-2 expressions in RAW264. 7 cells via NF-κB and ERK1/2 inactivation. Food Chem Toxicol, 2010; 48:3073–3079.

Shahbazi

, Sahrawat

, Ray

, Dash

, Kar

, Singh

: Drug targets for cardiovascular-safe anti-inflammatory: In silico rational drug studies. PLoS One, 2016; 11:e0156156.

May

, Zacharias

: Accounting for global protein deformability during protein–protein and protein–ligand docking. Biochim Biophys Acta, 2005; 1754:225–231.

Halgren

, Murphy

, Friesner

, et al.: Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem, 2004; 47:1750–1759.

10.

Mamdani

, Juurlink

, Lee

, et al.: Cyclo-oxygenase-2 inhibitors versus non-selective non-steroidal anti-inflammatory drugs and congestive heart failure outcomes in elderly patients: a population-based cohort study. Lancet, 2004; 363:1751–1756.

11.

Friesner

, Murphy

, Repasky

, et al.: Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J Med Chem, 2006; 49:6177–6196.

12.

Eldridge

, Murray

, Auton

, Paolini

, Mee

: Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. J Comput Aided Mol Des, 1997; 11:425–445.

13.

Lyne

, Lamb

, Saeh

: Accurate prediction of the relative potencies of members of a series of kinase inhibitors using molecular docking and MM-GBSA scoring. J Med Chem, 2006; 49:4805–4808.

14.

Duffy

, Jorgensen

: Prediction of properties from simulations: free energies of solvation in hexadecane, octanol, and water. J Am Chem Soc, 2000; 122:2878–2888.

15.

Lipinski

, Lombardo

, Dominy

, Feeney

: Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev, 2012; 64:4–17.

16.

Meraj

, Mahto

, Christina

, Desai

, Shahbazi

, Bhaskar

: Molecular modeling, docking and ADMET studies towards development of novel Disopyramide analogs for potential inhibition of human voltage gated sodium channel proteins. Bioinformation, 2012; 8:1139.

17.

Ajay, Bemis

, Murcko

: Designing libraries with CNS activity. J Med Chem, 1999; 42:4942–4951.

18.

Kelder

, Grootenhuis

, Bayada

, Delbressine

, Ploemen

J-P

: Polar molecular surface as a dominating determinant for oral absorption and brain penetration of drugs. Pharm Res, 1999; 16:1514–1519.

19.

Egan

, Lauri

: Prediction of intestinal permeability. Adv Drug Deliv Rev, 2002; 54:273–289.

20.

Cheng

, Merz

: Prediction of aqueous solubility of a diverse set of compounds using quantitative structure-property relationships. J Med Chem, 2003; 46:3572–3580.

21.

Venkataramana

, Sravani

, Singh

, Madhavan

: In-silico ADME and toxcity studies of some novel indole derivatives. J Appl Pharm Sci, 2011; 1:159.

22.

Anand

, Thomas

, Kunnumakkara

, et al.: Biological activities of curcumin and its analogues (Congeners) made by man and Mother Nature. Biochem Pharmacol, 2008; 76:1590–1611.

23.

Ntie-Kang

, Zofou

, Babiaka

, Meudom

, Scharfe

: AfroDb: A select highly potent and diverse natural product library from African. PLoS One, 2013; 8:e78085.

24.

Chen

I-J

, Foloppe

: Tackling the conformational sampling of larger flexible compounds and macrocycles in pharmacology and drug discovery. Bioorg Med Chem, 2013; 21:7898–7920.

25.

, Kamon

, Personett

, et al.: Pharmacokinetics of Agelastatin A in the central nervous system. MedChemComm, 2012; 3:233–237.

26.

Singh

, Bast

: In silico molecular docking study of natural compounds on wild and mutated epidermal growth factor receptor. Med Chem Res, 2014; 23:5074–5085.

27.

Nerkar

, Ghone

, Thaker

: In silico screening and ADME predictions of some quinazolinones as potential dihydrofolate reductase inhibitors for anticancer activity. Asian J Chem, 2009; 21:5363.

28.

Vandenberg

, Walker

, Campbell

: HERG K+ channels: Friend and foe. Trends Pharmacol Sci, 2001; 22:240–246.

29.

Chiesa

, Rosati

, Arcangeli

, Olivotto

, Wanke

: A novel role for HERG K+ channels: Spike‐frequency adaptation. J Physiol, 1997; 501:313–318.

30.

Hedley

, Jørgensen

, Schlamowitz

, et al.: The genetic basis of long QT and short QT syndromes: A mutation update. Hum Mutat, 2009; 30:1486–1511.

31.

Aronov

: Predictive in silico modeling for hERG channel blockers. Drug Discov Today, 2005; 10:149–155.