Finding the Effect of Darjeeling Black-Tea Aromatics in CNS Function through In-silico GluR-Ligand Interaction as a Probable Means

Finding the Effect of Darjeeling Black-Tea Aromatics in CNS Function through In-silico GluR-Ligand Interaction as a Probable Means | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Finding the Effect of Darjeeling Black-Tea Aromatics in CNS Function through In-silico GluR-Ligand Interaction as a Probable Means Moumita Saha, Anup Sardar, Sirshendu Chatterjee, Anirban Ghosh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7151639/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Darjeeling tea ( Camellia sinensis var. sinensis ) is recognized for its unique aroma and taste, associated with mood and cognitive enhancement. However, the underlying neurochemical mechanisms remain elusive. Present study investigated the potential interaction of Darjeeling tea's volatile aromatic compounds with glutamate receptors (GluRs), the predominant excitatory receptors in the central nervous system. We hypothesized that these compounds target GluRs to elicit their effects. An in-silico approach was employed, involving the analysis of physicochemical properties, bioactivity scores, and toxicity profiles of the aroma compounds. Subsequently, molecular docking simulations were performed using retrieved 3D structures of relevant GluRs to predict the binding affinity of selected compounds exhibiting high bioactivity, drug-likeliness, and bioavailability with identification of key amino acid residues within the receptor binding pockets. Our findings revealed α-Ionone and Safranal as prominent ligands exhibiting strong binding interactions. Among metabotropic GluRs, mGluR1 (IEWK), GluR5 (3FUZ), and GluR6 (3G3F) showed the highest affinity. Ionotropic receptor subtypes AMPA (2WJW) and NMDA (7EOR) also displayed significant binding scores where greater structural dynamics found in metabotropic GluRs upon ligand binding compared to ionotropic subtypes. Given the nasal passage as the primary route of exposure, and the presence of GluR-expressing cells along this pathway, the high bioavailability of α-Ionone and Safranal suggests their potential to interact with neuro-glial cells and subsequently influence CNS neurons and microglia/macrophages. In conclusion, the identified binding capability between Darjeeling tea's aromatic ligands and GluRs offers a promising framework for elucidating the mechanisms underlying the tea's effects on mood, psychological states, and immune-physiological responses. Aromatic compounds Darjeeling tea Glutamate receptors Molecular docking Neuro-immune modulation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Black tea, particularly harvested and processed from the slopes of Darjeeling Himalayan region, is famous for its rich flavor and accepted worldwide as one of the valued and popular beverages. The Darjeeling tea ( Camellia sinensis var. sinensis ), famous for its rich aromatic property, supposed to have a mood enhancing effect with other bioactivities including roles in metabolic regulation, atherosclerosis, inflammation and cancer [ 1 , 2 ]. Chemical analysis of the processed black tea with specific extraction procedure shows a rich presence of polyphenol and catechin compounds with specific presence of theaflavins and thearubigens responsible for its distinctive coloration upon brewing [ 3 , 4 ]. Different recent studies demonstrated specific anti-cancer activity of theaflavins and other compounds of black tea [ 4 , 5 , 6 ]. Simultaneously, tea polysaccharides are gaining attention for intervening efficacy in metabolic diseases with potential prebiotic function, skin-care and predicted anti-tumor role [ 7 , 8 ]. However, a major class of components, the volatile aromatic compounds, responsible for the characteristic flavor of Darjeeling black tea, apparently producing the psycho-physiological mood effect is yet to be evaluated properly. Yokogoshi and team, in a comparison between Darjeeling and Assam tea, interestingly showed that black tea aroma can alleviate negative brain functions caused by stressful lifestyle by controlling autonomous nervous system (ANS) stress response upon inhelation, which lowers salivary chromogranin-A (CgA) released from adrenal chromaffin cells and adrenergic neurons, thus showed improved mood as observed in tension-anxiety (TA) score in POMS [ 9 ]. Odor stimuli derived from black tea, are the aromatic volatile compounds which reaches to olfactory tract and bulb where olfactory nerve receptors accept the molecules to trigger appropriate signals to brain [ 10 ]. This is obvious to have a ligand-receptor interaction between the aromatic compounds and receptors present on the neuronal projections. As the Darjeeling tea aroma produce a mood enhancing effect, we hypothesize that such compounds may bind with Glutamate receptors (GluR) of the cells as glutamate is the most abundant excitatory neurotransmitter found in the nervous system [ 11 ]. We observe a wide variety of receptors spread across the neurons and non-neuronal allied cells which are divided into metabotropic and ionotropic receptor families. Metabotropic receptors designated as mGluR1-8 are subdivided into 3 groups, whereas, ionotropic receptors are AMPA (GluR1-4) and NMDA [ 11 , 12 ]. Additionally, the olfactory nerve fibers around olfactory bulb are accompanied with the immunoregulatory cells of brain, i.e., microglia which are highly responsive and active protector of the CNS against any microenvironmental modifications in the tissue. Presence of glutamate receptors are also pronounced on these myeloid lineage cells [ 13 ]. Therefore, tea aroma compounds are most likely accessible to these receptors of both neurons and microglia or macrophages. If those volatile aromatic compounds can potentially bind with these receptors, they can trigger both neurophysiological and neuroimmune cascades, which may be the probable mechanism of stress relieve and mood alleviation for Darjeeling black tea. In present investigation, we have selected some specific volatile aromatic compounds designated with characteristic Darjeeling black tea aroma and validated their molecular property as ligand candidate. Both metabotropic and ionotropic glutamate receptors identified on neuronal cells and microglia and/or macrophages have been selected with stable structural forms and subjected to ligand-receptor binding and docking experiments in silico . This assessment will show the likelihood of docking and binding of tea aromatic compounds on glutamate receptors and open the potentially probable mood enhancement pathway, and also the possibilities of aroma driven modulation of immune-functions. Materials and Methods Selection and Preparation of Ligands Available characteristic aromatic compounds of Darjeeling black tea were screened and selected on the basis of literature review from online databases and articles from Web of Science, Scopus and Science Direct, PubMed and others with special emphasis on the literature analysing chemical composition and aromatic formations of black tea [3,14,15]. We selected 10 specific fragrant compounds characterizing the Darjeeling tea aroma of first and second flash harvested products ( Table 1 ). Molecules were retrieved from online chemical database: PubChem (www.pubchem.ncbi.nlm.nih.gov/) and later translated to their 3D PDB format using Open Babel software. A PDBQT format file was created after adding hydrogenating atoms and the desired torsion to a PDB format file [16] and the 3D model of the molecules are shown in Figure 1 . Validation of Ligands The lack of pharmacokinetic studies is one of the main obstacles in the commercial implementation of the plant-based products as therapeutic agents. Thus, the study was designed to use computational methods to evaluate the physicochemical, pharmacokinetic and drug-likeness properties of the citrus species based four bioactive compounds. Physiologically significant properties of a ligand designated by absorption, distribution, metabolism and excretion are the parameters all-together termed as ADME and these parameters of the selected ligands were assessed using the server of SwissADME website (https://www.swissadme.ch/) [17]. Toxicity prediction of the ligands were made essentially to evaluate the probable systemic toxicity prior administering in animal or human based on the molecular database of similar physical, structural and chemically featured molecules. The online server PreADMET (https://preadmet.bmdrc.kr/) was used. In PreADMET server, first the SDF structures retrieved from the PubChem were converted to mol2 format using Open babel software and then submitted to the online server for toxicity prediction including mutagenicity (AMES test), carcinogenicity (for rat and mouse) and hERG inhibition. Receptor Protein Selection and Validation According to the literature and database analysis, receptor proteins for glutamate which are simultaneously identified on both neuronal and non-neuronal cells in CNS, particularly, on microglia and CNS associated macrophages were selected. Both the metabotropic and ionotropic receptors for glutamate commonly present on neuronal and myelo-monocytic cells entangled with nervous systems are depicted in Table 2 , Figure 2A-2B . The 3D structure has been retrieved from Protein Databank (http://www.rcsb.org/). Then, using BIOVIA Discovery Studio 2020 software, the already-attached ligands, water molecules and other impurities were eliminated in order to stabilise the receptor structures (https://discover.3ds.com/discovery-studio-visualizer-download/). The newly generated protein PDB structure was then undergone through a series of quality analyses including ERRAT, Procheck using SAVES 6.0 (https://saves.mbi.ucla.edu/) as per standard protocol [18]. Table 2: Selected Ionotropic and Metabotropic Receptors with their Respective PDB I.D.s Type PDB I.D. Description Metabotropic 1EWK Glutamate Receptor Subtype 1 3FUZ Glutamate Receptor, GluR5 3G3F Glutamate Receptor, GluR6 3KS9 Glutamate Receptor mGluR1 6BSZ Glutamate Receptor mGlu8 Ionotropic 5L1F AMPA Subtype Glutamate Receptor GluA2 2WJW Glutamate Receptor AMPA Subtype 5L1G Glutamate Receptor AMPA Subtype 6IRH Glutamate Receptor, GluN1/GluN2A NMDA Subtype 7EOR Glutamate Receptor, GluN1/GluN2A NMDA Subtype Molecular Docking Interaction in AutoDock Vina AutoDock Vina software (http://vina.scripps.edu/) was used for molecular docking and virtual screening that significantly improved efficient binding mode predictions, thereafter, enhance accuracy in protein-ligand interaction [19]. AutoDock Vina works by calculating the grid maps and clusters. Kollman charges and other modifications were made to the purified form of the protein before moving on to the final docking phase and converting it into a properly readable PDBQT file format. The ligand is similarly converted into a PDBQT file. A grid box on the protein's active residues was created, with various grid sizes and centres, but a consistent grid spacing of 0.375. With AutoDock Vina software, binding energy affinity was predicted with an exhaustiveness value of 8. The final visualization of docked structure was performed using BIOVIA Discovery Studio 2020 (https://discover.3ds.com/discovery-studio-visualizer-download/). Assessment of Binding pockets and Structural Hotspots and Dynamicity of Receptor Proteins A systematic quantitative characterisation of the surface topography of proteins is often provided by the Computer Atlas Surface Topography of Protein (CASTp) [20]. To predict active amino acid residues, or alternatively, structural hotspots as well as different binding pockets of varied sizes on the receptor protein molecules were done using the CASTp 3.0 service. iMod Server Prediction (http://imods.chaconlab.org/) application were used to assess the transition paths in internal coordinates naturally produced in collective functional motions within the biomolecules [21] helping in advanced visualization with an improved affinity-model based representation of domain dynamics. Results SwissADME Prediction of Ligands Upon submission of ligand structure in SMILEs format, SwissADME result is generated on the basis of ADME/toxicity analysis and Lipinski filter analysis as depicted in Table 3 and Figure 3 . Upon submission of ligand structure in SMILEs format, SwissADME result is generated on the basis of ADME/toxicity analysis and Lipinski filter analysis. Here in our result, we have given different tables for each of the result parameter: physicochemical properties , lipophilicity , pharmacokinetics , and drug likeliness . According to the SwissADME result, from Table 3 , it is observed that, all the compounds have molecular weight well below the acceptable range (MW ≤ 500) (Srimai et al, 2013) and also follow the Ro5 which states that the drug-like compounds ought to have nHBA ≤ 10 and nHBD ≤ 5. This indicates that all the compounds have the potential to be easily absorbed, diffused and transported [22]. The number of rotatable bonds is a measure of molecular flexibility and is one of the widely used filters during drug discovery process (Veber et al, 2002) and in this criterion all the compounds have successfully passed as all of them fell within the acceptable range (nRB ≤ 15), indicative of their potential permeability and oral bioavailability (Muegge et al, 2001). From Table 3B , it is concluded that each of the protein shows their value of CLogP ≤ 5 that influences their solubility, selectivity, potency, permeability and promiscuity. According to Table 3C , each and all compounds have the high potential to be absorbed by gastrointestinal tract as well as blood-brain-barrier (BBB), but on the other hand 2-Acetyl-2-thiazoline and 2-Acetyl-1-pyrroline show low BBB permeation ( Figure 3 ) . The penetration across BBB is only mandatory for compounds targeting the central nervous system (CNS) where 8 out of 10 selected compounds are well capable. Metabolism prediction data of the compounds against five isoforms of cytochrome P450 is one of the main priorities during drug discovery process and the study showed that negative LogKp values indicating the skin impermeability of each compound. The drug likeliness ( Table 3D ) result reveals that all of the compounds show satisfactory result with either 0 violation of the parameters. Bioavailability scores defining the extent and rate at which compounds administered can enter systemic circulation and ultimately reach the targeted sites upon oral administration is shown in Table 3D , where all the six compounds show the similar score i.e. 0.55 except benzyl alcohol scoring 0.85. This value implies that the compounds adhere to Lipinski rule of five and have 55% and above probability of being bioavailable. Table 3A: Physicochemical Properties of the Selected Aromatic Compounds of Black Tea Compound name Formula Molecular weight Number of rotatable bonds Number of H acceptors Number of H donors (E, E)-2,4-decadienal C 10 H 16 O 153.23 gm./mol 6 1 0 (E, E)-2,4-hexadienal C 6 H 8 O 96.13 gm./mol 2 1 0 Benzyl alcohol C 7 H 8 O 108.14 gm./mol 1 1 1 2-Acetyl-3-methylpyrazine C 7 H 8 N 2 O 136.15 gm./mol 1 3 0 2-Acetyl-2-thiazoline C 5 H 7 NOS 129.18 gm./mol 1 2 0 2-Acetyl-1-pyrroline C 6 H 9 NO 111.14 gm./mol 1 2 0 α-Ionone C 13 H 20 O 192.30 gm./mol 2 1 0 Safranal C 10 H 14 O 150.22 gm./mol 1 1 0 2 acetyl 3,5-dimethylpyrazine C 8 H 10 N 2 O 150.18 gm./mol 1 3 0 5 ethyl 2,3-dimethylpyrazine C 8 H 12 N 2 136.19 gm./mol 1 2 0 Table 3B: Lipophilicity of the Selected Aromatic Compounds of Black Tea Compound name Log Pa/w (iLOGP) Log Pa/w (XLOGP3) Log Pa/w (WLOGP) Log Pa/w (MLOGP) Log Pa/w (SILICOS-IT) Consensus Log Pa/w (E, E)-2,4-decadienal 2.67 3.25 2.88 2.49 2.96 2.85 (E, E)-2,4-hexadienal 1.63 1.19 1.32 1.18 1.23 1.31 Benzyl alcohol 1.66 1.10 1.03 1.54 1.74 1.41 2-Acetyl-3-methylpyrazine 1.50 0.20 0.99 -0.75 1.61 0.71 2-Acetyl-2-thiazoline 1.51 0.22 0.34 -0.42 2.21 0.77 2-Acetyl-1-pyrroline 1.07 -0.43 0.43 -0.04 2.17 0.64 α-Ionone 2.81 3.85 3.51 2.94 3.41 3.31 Safranal 2.13 2.14 2.49 2.10 2.62 2.30 2 acetyl 3,5-dimethylpyrazine 1.29 0.60 1.30 -0.42 2.06 0.97 5 ethyl 2,3-dimethylpyrazine 2.02 1.38 1.66 0.55 2.53 1.63 Table 3C: Lipophilicity of the Selected Aromatic Compounds of Black Tea Compound name GI absorption BBB permeant P-gp substrate CYP1A2 inhibitor CYP2C19 inhibitor CYP2C9 inhibitor CYP2D6 inhibitor CYP3A4 inhibitor LOG KP (skin permeation) (E, E)-2,4-decadienal High Yes No No No No No No -4.92 cm/s (E, E)-2,4-hexadienal High Yes No No No No No No -6.02 cm/s Benzyl alcohol High Yes No Yes No No No No -6.18 cm/s 2-Acetyl-3-methylpyrazine High Yes No No No No No No -6.99 cm/s 2-Acetyl-2-thiazoline High No No No No No No No -6.93 cm/s 2-Acetyl-1-pyrroline High Yes No No No No No No -7.28 cm/s α-Ionone High Yes No No No Yes No No -4.74 cm/s Safranal High Yes No No No No No No -5.70 cm/s 2 acetyl 3,5-dimethylpyrazine High Yes No No No No No No -6.79 cm/s 5 ethyl 2,3-dimethylpyrazine High Yes No No No No No No -6.15 cm/s Table 3D: Drug Likeliness of the Selected Aromatic Compounds of Black Tea Compound name Lipinski’s rule Satisfactory Number of violations Bioavailability Score (E, E)-2,4-decadienal Yes 0 0.55 (E, E)-2,4-hexadienal Yes 0 0.55 Benzyl alcohol Yes 0 0.85 2-Acetyl-3-methylpyrazine Yes 0 0.55 2-Acetyl-2-thiazoline Yes 0 0.55 2-Acetyl-1-pyrroline Yes 0 0.55 α-Ionone Yes 0 0.55 Safranal Yes 0 0.55 2 acetyl 3,5-dimethylpyrazine Yes 0 0.55 5 ethyl 2,3-dimethylpyrazine Yes 0 0.55 Toxicity Prediction of the Ligands In the process of developing new drugs, toxicity testing of small molecules was done as an essential phase. Table 4 displays the results of the toxicological prediction using the PreADMET service, including the drugs' mutagenicity, carcinogenicity, and inhibition of hERG, where, negative prediction translates carcinogenic activity and positive means the compound possess no carcinogenic activity. Where Ames test, based on bacterial mutagenicity potential showed all the selected black tea aromatic molecules are having mutagenic properties as ligand candidates, the hERG inhibition test done to assess the effect of the molecules on the cardiac potassium channel [23] showed moderate to low risk, where, selected pyrazine, ionone and safranal compounds are of much lower risk for cardiac functioning. Probable carcinogenicity assessment in PreADMET service shows all selected candidates are non-carcinogenic for mice and rat with some exception for decadienal compound for both mice and rat ( Table 4 ). Table 4: Showing Results of Mutagenicity and Carcinogenicity along with hERG Inhibition of the Selected Aromatic Compounds of Black Tea Compound name Ames Test Carcino Mouse Carcino Rat hERG Inhibition (E, E)-2,4-decadienal Mutagen Negative Positive Medium Risk (E, E)-2,4-hexadienal Mutagen Positive Positive Medium Risk Benzyl alcohol Mutagen Negative Negative Medium Risk 2-Acetyl-3-methylpyrazine Mutagen Negative Negative Low Risk 2-Acetyl-2-thiazoline Mutagen Negative Negative Medium Risk 2-Acetyl-1-pyrroline Mutagen Negative Negative Medium Risk α-Ionone Mutagen Positive Negative Low Risk Safranal Mutagen Negative Negative Low Risk 2 acetyl 3,5-dimethylpyrazine Mutagen Negative Negative Low Risk 5 ethyl 2,3-dimethylpyrazine Mutagen Negative Negative Low Risk Validation of Protein Structures Overall quality recognition of all 3D protein PDB structures as predicted by the online tools was done ( Suppl. Fig. 1 ). By confirming the protein PDB model using a number of quality checking criteria, the ideal protein structures were approved. The "overall quality factor" that ERRAT displayed indicated that proteins with higher scores are of greater quality. All proteins, with the exception of 6IRH (43%), have quality scores that fall between 86 and 98%, indicating that they are all well-modelled. The Ramachandran plot of nearly all protein models then showed that over 80% of residues were found in the most preferred areas, followed by extra allowed, generously allowed, and banned regions, in accordance with the PROCHECK result. Together, these strong validations guarantee the precision of molecular docking investigations by proving that the interactions between the ligands and proteins are faithfully shown. Table 5: Showing Results of Maximum Binding Affinity Scores in AutoDoc Vina between Glutamate Receptors and Best Selected Aromatic Compounds of Black Tea Ranking Receptor (common name) Receptor (PDB ID) Ligand Affinity Score (kCal/mol) 1 GluR6 metabotropic receptor 3G3F α-Ionone ̶ 7.1 2 GluN1/GluN2A NMDA ionotropic receptor subtype 7EOR α-Ionone ̶ 6.4 3 GluR5 metabotropic receptor 3FUZ Safranal ̶ 6.3 4 GluR-AMPA ionotropic receptor subtype 2WJW Safranal ̶ 6.2 5 GluR1 metabotropic receptor 1EWK (E,E)-2,4-decadienal ̶ 6.1 6 Glutamate Receptor mGlu8 6BSZ 2-acetyl-3,5-dimethylpyrazine -5.7 7 Glutamate Receptor AMPA Subtype 5L1G α-Ionone ̶ 4.7 8 GluR1 metabotropic receptor 3KS9 α-Ionone ̶ 5.9 9 GluN1/GluN2A NMDA ionotropic receptor subtype 6IRH α-Ionone ̶ 5.9 10 AMPA Subtype Glutamate Receptor GluA2 5L1F α-Ionone ̶ 4.8 Molecular Docking Interaction Using AutoDock Vina On the basis of docking analysis done by AutoDock Vina, the overall results of binding affinity of all the tea bioactive compounds against the 5 metabotropic receptors are represented in pictorial manner ( Figure 4 ). When the stability of the docking interactions has been calculated, some specific ligand-receptor interactions emerges as most potent and stable having binding affinity around –6 KCal/Mol or less. It is found that α-Ionone and Safranal are the most prominent ligands and mGluR1 (IEWK), GluR5 (3FUZ) and GluR6 (3G3F) are the most common metabotropic receptors providing stable interactions. The best of the interactions and their binding affinity is presented in Table 5 . Based on the graphical view generated through BIOVIA Discovery Studio 2020, 2D and 3D mode of binding interactions of top docked compounds are presented via Figure 5A-E , where affinity score ranges between –5.7 to –7.1 kCal/mol. In case of ionotropic glutamate receptors, specific subtypes of AMPA (2WJW) and NMDA (7EOR) show stronger binding affinities predominantly to α-Ionone and Safranal ( Figure 6 ). 2D and 3D model of binding prediction using BIOVIA Discovery Studio 2020 showed binding pockets and molecular interactions with the ionotropic receptors ( Figure 7A-E ), where affinity score ranges between – 4.7 to -6.4 kCal/mol. Assessment of Binding Pockets and Active Amino Acid Residues and Dynamicity Table 6 displays the findings from the CASTp 3.0 online server for structural hotspots or active amino acid residues of the protein PDB structure. The best fitted ligand-receptor formations with maximized binding affinity are considered for identifying the active amino acid residues during binding with the ligands. Numerous amino acid types in different places are found to create the structural pockets of the receptors and are implicated in molecular interactions with the ligands. However, among the amino acid types most abundant presence of polar-uncharged serine and threonine as well as +ve charged arginine and lysine are observed for binding with α-ionone and safranal mostly as the favourable ligands within the binding pockets. Other amino acids in binding pockets are the hydrophobic representatives like tyrosine, tryptophan, valine, leucine, isoleucine, alanine and methionine which are found available to interact with aromatic ligands. iMOD server analysis showed specific eigenvalues of the receptor-ligand complex systems (eigenvectors) as per the principal component analysis (PCA) where 3 out of 5 metabotropic receptors, namely, GluR type 1 (1EWK), GluR5 (3FUZ), GluR6 (3G3F) and only 1 out of 5 ionotropic receptors, namely, AMPA receptor (5LIG) showed high eigenvalue signifying a large variance or dynamicity in the system. In general, upon ligand binding, metabotropic receptors are more structurally dynamic and ionotropic receptors showed more structural stiffness except AMPA receptor 5L1G ( Suppl. Fig. 2 ). Discussion Tea consumption is one of the commonest ethnic practices in many parts of the world which have putative values related to public health. Depending on preparation, particularly for green and black tea, it releases wide variations of bioactive compounds and flavours in infusions like catechins, flavonoids, polyphenols, l-theanine and many aromatic compounds and their derivative forms [ 3 , 14 ]. In studies it was found that l-theanine, unique to green and black tea and some mushrooms, relaxes human and improve attention [ 1 , 24 ], whereas, routine intake of tea has significant effect on mood and improve the restoration of cortisol at basal level after stress resulting in quicker stress recovery and that was also supported by different population based studies [ 9 , 25 , 26 ]. Table 6 Active Amino Acid Residues of the Receptors using CASTp 3.0 with Best Fitted Ligand Aromatic Compounds derived from Black Tea Sl. No. Receptor (PDB ID) Ligand Active Amino Acid Residues For Binding 1 3G3F α-Ionone THR 108, LYS 248 2 7EOR α-Ionone SER 700, TRP 795 3 3FUZ Safranal TYR 474, SER 674, THR 675 4 2WJW Safranal ARG 129, ARG 156, TYR 295 5 1EWK (E,E)-2,4-decadienal TYR 74, TRP 110, SER 165, THR 188, MET 294 6 6BSZ 2-acetyl-3,5-dimethylpyrazine LYS 57, ALA 155, SER 156, SER 157, SER 283 7 5L1G α-Ionone ILE 664, ARG 675, LYS 761, LYS 765 8 3KS9 α-Ionone SER 165, THR 188, SER 189, TYR 236 9 6IRH α-Ionone ASN 432, LYS 457, LEU 794, TRP 795 10 5L1F α-Ionone ARG 453, VAL 484, SER 654 There are a variety of aromatic compounds which are characterizing different tea types to the world population. Among which, one of the best authentic aromatic tea variety is named as Darjeeling tea, produced and processed in the cool, humid southern hill slopes of Himalaya within average altitude between 1000m–3000m, from Nepal to Bhutan centring the Darjeeling district of West Bengal, India, which are harvested in spring and summer, selected, weathered, rolled, fermented-oxidized and processed to produce signature Darjeeling black-tea and in hot, aquas infusion it releases the flavour and taste for which it is celebrated [ 14 , 27 , 28 ]. In present study, some of the prominent compounds associated with Darjeeling tea flavour has been selected which are specified in Table 1 with their fragrance characteristics. When different physical and chemical parameters of the compounds including permeability through blood-brain-barrier and drug likeliness have been tested they are found highly absorbable through gastric endothelial tissue and all are permeable through BBB except 2-Acetyl-2-thiazoline. However, all the aromatic compounds qualified Lipinski’s rule satisfying drug likeliness with persistent bioavailability score of 0.55, but maximum score with 0.85 for benzyl alcohol (Table 3 ). Most of these chemicals are low to medium risk mutagen with most of them showed no carcinogenicity, thereby, qualified as effective ligand. In contrast, prominent metabotropic and ionotropic receptors of glutamate (Table 2 ) were subjected to the structural validation of the PDB models on which the ligand docking experiments would be performed ( Suppl. Figure 1 ). Such experiments showed us a binding affinity between – 7.1 to – 4.8 kCal/mol for the top 10 docking success among the receptor-ligand pairings, which are within flexible-ligand docking range indicating biological efficiency of this interaction [ 29 ]. Hence, ligation between metabotropic receptors GluR1 (1EWK), GluR5 (3FUZ) and GluR6 (3G3F) with (E,E)-2,4-decadienal, Safranal and α-Ionone respectively; and ionotropic receptors GluN1/GluN2A NMDA subtype (7EOR) and GluR-AMPA subtype (2WJW) with α-Ionone and Safranal respectively, are found most promising and probable. Among the ligands, Safranal and α-Ionone, both are monoterpenoids and with characteristic aroma of the black tea variant famous to tea lovers [ 27 , 28 ]. Analysis through iMOD server on the structural coordinates of the receptors show that metabotropic receptors with higher eigenvalues and spread indicating higher dynamicity, whereas, ionotropic receptors involved in binding show lower eigenvalues and spread indicating structural stiffness in general ( Suppl. Figure 2 ) [ 21 , 29 ]. But most dynamic components are the metabotropic GluR5 and GluR6 with exceptionally an ionotropic AMPA receptor. The top four ligand receptor bindings show that the interactions between ligands (α-Ionone and Safranal) and receptors are occurring in between only 2–3 amino acid residues, either polar or hydrophobic in nature (Figs. 5 and 7 ). As found as the ligands are mostly capable of crossing the endothelial lining, absorbing through gastro-intestinal tract and blood-brain-barrier with sufficient bio-availability score, they are capable of performing through both oral and olfactory routes to reach their target receptors. Therefore, the black tea volatile compounds, which have been studied here, possess ample opportunity to interact, dock and produce subsequent effects to the neuro-glial cells associated to this passage. Added to it, their high absorption score through GI tract, moderate to high bioavailability and penetration capability through BBB (except 2-Acetyl-2-thiazoline) (Fig. 3 ) ensure their systemic reach to the CNS matrix and capability to interact with neuroglial population from regions of cerebral cortex to hippocampus to hypothalamus, thus opening a wide possibility of modulating neurophysiology to psycho-behavioural aspects and immunity. In a recent study, presence of glutamate ionotropic AMPA and NMDA receptors like GluA1, GluA2, GluN1 and GluN2A are found in hypothalamus and showed their critical role in glutaminergic synaptic transmission [ 30 ]. Such signalling is found significantly related to learning and memory and neuroendocrine functions and thereby associated with circadian, feeding and behavioural disorders [ 31 , 32 ]. As with the presence of glutamate receptors on the neurons throughout the CNS including olfactory circuit, a robust endogenous immunological defence is present in olfactory tissue where immune cells cross lamina propria into olfactory neurones and these cells are showing Iba-1, CX3CR1 receptors and release iNOS, IL6 and TNFα [ 33 ]. These cells, particularly microglia express many of the neurotransmitter receptors and membrane proteins like CD200, CD22, CD47 to interact with neurons, where microglia can modulate neurones by releasing glutamate, ATP, ADP, ROS, NO, PGE2, BDNF, miRNA and cytokines/chemokines [ 34 ]. It was previously found that mGluR4/6/8 (Gr.III receptors) transform microglia into neuroprotective or mGluR2/3 (Gr II receptors) into neurotoxic forms, thus release TNFs and FasR, Ca + 2 wave driven neuronal injury; NMDA expression in the cells follows NO release, NFkB signalling, IL1β and TNFα release and mediate increased excitability of hippocampal CA3 neurons [ 34 , 35 , 36 ]. Several evidences show that microglia are present throughout nasal epithelial lining to olfactory nerves extending to subventricular zone-olfactory bulb (SVZ-OB) axis and their involvement of protecting olfactory tracts and neurons against pathogenic insult or development of olfactory dysfunctions, thus they are capable of receive glutamate signals and react to modulate neuronal functions [ 37 , 38 ]. As both AMPA and NMDA receptors and metabotropic receptors for glutamate are expressed on both neurons and microglia, their reciprocal interactions and functional responses under glutamate release or storming become very crucial for a wide range of neurophysiological conditions. In all these situations, safranal, α-ionone and other aromatic compounds derived from tea may play a pivotal role from multiple perspectives. Conclusion Present study explored possibility of some prominent Darjeeling tea aroma compounds to modulate the neuro-glial function, hence, neuro-physiology and behaviour through glutamate receptors (Fig. 8 ). These compounds, prominently like α-Ionone, safranal, (E,E)-2,4-decadienal are found to sufficiently bioavailable to olfactory bulb and CNS, and capable of interacting with group I metabotropic receptors mGluR1 and mGluR5 as well as group III metabotropic receptors mGluR6 and mGluR8. As group I receptors are the moderators which are capable of increasing NMDA receptor activity, synergistically they can uplift the function of neuronal circuitry; in contrary, group III metabotropic receptors are identified with downregulation of secondary messenger dependent signalling and projects neurotoxic effects [ 11 , 12 ]. The same aromatic compounds, i.e., safranal and α-Ionone are also capable of binding with GluR-AMPA ionotropic receptor and GluN1/GluN2A NMDA ionotropic receptor subtypes which are widespread on neuroglial cells from naso-buccal cavity to cortex or hypothalamus with wider functional implications already discussed, particularly activating hypothalamo-amygdala axis. Simultaneously, they can capably bind with the GluRs present of microglia and brain macrophages, thus, instigating the neuro-immuno axis as already discussed. This research highlights a novel potential for Darjeeling tea aroma compounds and suggests a new direction for studying neuro-glial function under their influence. Declarations Conflict of Interest: The author(s) do not have any conflict of interest. Ethics Statement This research did not involve human participants, animal subjects, or any material that requires ethical approval. Informed Consent Statement: This study did not involve human participants, and therefore, informed consent was not required. Authors’ Contribution M.S performed most of the in-silico workflows and compiled in-silico data with primary drafting; A.S curated the data and parallelly performed different in silico workflow and helped A.G in combining data; S.C analyzed and validated the data and checked the manuscript; A.G conceptualized and supervised the work, analyzed data and finalized the manuscript. Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article. Acknowledgement Authors are extending their sincere thanks to the authorities of the Netaji Subhas Open University and Techno India University, West Bengal, India for providing required infrastructural and institutional supports. Data Availability Data is provided within the manuscript or supplementary information files. In this investigation no animal or human subjects or samples are involved. References Skotnicka M, Chorostowska-Wynimko J, Jankun J and Skrzypczak-Jankun E. The black tea bioactivity: an overview. Centr Eur J Immunol 2011; 36: 284-292. Fatima M and Rizvi SI. Health beneficial effects of black tea. Biomedicine 2011; 31: 3-8. Li S, Lo C-Y, Pan M-H, Laic C-S and Ho C-T. Black tea: chemical analysis and stability. Food Funct 2013; 4: 10-18. Pan M-H, Lai C-S, Wang H, Lo C-Y, Ho C-T and Li S. Black tea in chemo-prevention of cancer and other human diseases. 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Comprehensive assessment of flexible-ligand docking algorithms: current effectiveness and challenges. Brief Bioinform 2018; 19: 982-994. Royo M, Aznar Escolano B, Madrigal MP and Jurado S. AMPA Receptor Function in Hypothalamic Synapses. Front Synaptic Neurosci 2022; 14: 833449. doi: 10.3389/fnsyn.2022.833449. Rijo-Ferreira F and Takahashi JS. Genomics of circadian rhythms in health and disease. Genome Med 2019; 11: 82. doi: 10.1186/s13073-019-0704-0. Florent V, Baroncini M, Jissendi-Tchofo P, Lopes R, Vanhoutte M, Rasika S, Pruvo J-P, Vignau J, Verdun S, Johansen JE, Pigeyre M, Bouret SG, Nilsson IAK and Prevot V. Hypothalamic structural and functional imbalances in anorexia nervosa. Neuroendocrinology 2020; 110: 552–562. Herbert RP, Harris J, Chong KP, Chapman J, West AK and Chuah MI . Cytokines and olfactory bulb microglia in response to bacterial challenge in the compromised primary olfactory pathway. J Neuroinflammation 2012; 9: 109. https://doi.org/10.1186/1742-2094-9-109. Czapski GA and Strosznajder JB. Glutamate and GABA in Microglia-Neuron Cross-Talk in Alzheimer’s Disease. Int J Mol Sc 2021; 22: 11677. https://doi.org/10.3390/ijms222111677. Domercq M, Vázquez-Villoldo N and Matute C. Neurotransmitter signaling in the pathophysiology of microglia. Front Cell Neurosci 2013; 7: 49. doi: 10.3389/fncel.2013.00049. Parellada E and Gassó P. Glutamate and microglia activation as a driver of dendritic apoptosis: a core pathophysiological mechanism to understand schizophrenia. Transl Psychiatry 2021; 11: 271. https://doi.org/10.1038/s41398-021-01385-9. Kim J, Choi Y, Ahn M, Ekanayake P, Tanaka A, Matsuda H, Shin T. Microglial and astroglial reaction in the olfactory bulb of mice after Triton X-100 application. Acta Histochem 2019; 121: 546-552. Moseman EA, Blanchard AC, Nayak D and McGavern DB. T cell engagement of cross-presenting microglia protects the brain from a nasal virus infection. Sci Immunol 2020; 5: eabb1817. doi: 10.1126/sciimmunol.abb1817. Table Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files SupplFigures.pdf Table1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7151639","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":494047220,"identity":"1a96aa97-850b-4586-8567-2c2e0a533689","order_by":0,"name":"Moumita Saha","email":"","orcid":"","institution":"Techno India University","correspondingAuthor":false,"prefix":"","firstName":"Moumita","middleName":"","lastName":"Saha","suffix":""},{"id":494047221,"identity":"cc3aca5c-a9c2-4e9f-89f9-fe62dbb1b853","order_by":1,"name":"Anup Sardar","email":"","orcid":"","institution":"Netaji Subhas Open University – Kalyani Regional Centre","correspondingAuthor":false,"prefix":"","firstName":"Anup","middleName":"","lastName":"Sardar","suffix":""},{"id":494047222,"identity":"9d6d287d-1f3f-4b12-99fa-fe7d42a36a08","order_by":2,"name":"Sirshendu Chatterjee","email":"","orcid":"","institution":"Techno India University","correspondingAuthor":false,"prefix":"","firstName":"Sirshendu","middleName":"","lastName":"Chatterjee","suffix":""},{"id":494047223,"identity":"f9d4191e-7ad1-45d8-9609-f672d4150310","order_by":3,"name":"Anirban Ghosh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIiWNgGAWjYFACxgZmEGXAwMDG8IHhAFychygtjDOI08LAANfCzIOkBSfgn93c9rmg5o7ddvb2Z49t2+7kybf3GDD8qGGQMcehReLOwebZM449S97Zc8bcOLftWbHBmTMGjD3HGHgsG3DouZHYzMzDdjjZ4EYOm3TutsOJGyRyDBh4Gxh4DHA4Uh6s5R9IS/ozaUuglvkzcgwY/+LRYgDSwtt22M7gRoKZNCNQS8ONHANmfLYYgrTM7DucAPSCmWTvv2eJG84cKzgsc0wCpxa5G+mPmQu+HbY3ON7+TOLHmTuJ89ubNz58U2Njj0sLDCQ2IPOAiiXwqwcCe4IqRsEoGAWjYOQCAI+TYtDqWUzHAAAAAElFTkSuQmCC","orcid":"","institution":"Netaji Subhas Open University – Kalyani Regional Centre","correspondingAuthor":true,"prefix":"","firstName":"Anirban","middleName":"","lastName":"Ghosh","suffix":""}],"badges":[],"createdAt":"2025-07-17 18:23:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7151639/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7151639/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88255120,"identity":"0fb0a8ca-3033-4869-8822-a4c7a160fd72","added_by":"auto","created_at":"2025-08-04 14:18:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":305533,"visible":true,"origin":"","legend":"\u003cp\u003e3D structures of selected Tea aromatic bioactive compounds\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7151639/v1/5680b09d5a274955f4e90dc7.png"},{"id":88255151,"identity":"3846468a-7b56-4da8-8b01-a84d05f480ab","added_by":"auto","created_at":"2025-08-04 14:18:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":451375,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA. \u003c/strong\u003e3D Structures of the Metabotropic Receptor Proteins; \u003cstrong\u003eB. \u003c/strong\u003e3D Structures of the Ionotropic Receptor Proteins\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7151639/v1/d91ebe481b623cc023492c8a.png"},{"id":88255129,"identity":"4a061f0b-328d-43a0-bd21-e79ee7d06459","added_by":"auto","created_at":"2025-08-04 14:18:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":257077,"visible":true,"origin":"","legend":"\u003cp\u003eBoiled Egg Structure Showing the permeability and other potential of the selected Tea aromatic compounds\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7151639/v1/43ce65009b3123054f857d24.png"},{"id":88256252,"identity":"e995b312-eb72-4c66-a1c0-147a7864212f","added_by":"auto","created_at":"2025-08-04 14:26:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":176887,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical representation of binding affinity along with the score (kCal/ mol.) towards the target metabotropic receptors of glutamate by the respective compounds/ ligands.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7151639/v1/a7286bf9810381c03324196b.png"},{"id":88255135,"identity":"efd6b7a3-9970-464a-949f-27af22f09056","added_by":"auto","created_at":"2025-08-04 14:18:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":646058,"visible":true,"origin":"","legend":"\u003cp\u003eComplete representation and close insight of 3D and 2D interactions between best scored metabotropic glutamate receptors and ligands.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7151639/v1/57159b993611766b94bf719b.png"},{"id":88256248,"identity":"66fc8e08-3efb-442a-bd13-0729e05f4502","added_by":"auto","created_at":"2025-08-04 14:26:06","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":168203,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical representation of binding affinity along with the score (kCal/ mol.) towards the target ionotropic receptors of glutamate by the respective compounds/ ligands.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7151639/v1/ada67076a0647281750bb732.png"},{"id":88255163,"identity":"d5fe78e4-76ef-4254-b896-86f1acb08e16","added_by":"auto","created_at":"2025-08-04 14:18:08","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":664654,"visible":true,"origin":"","legend":"\u003cp\u003eComplete representation and close insight of 3D and 2D interactions between best scored ionotropic glutamate receptors and ligands.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7151639/v1/32afbb82eba8155b546e737e.png"},{"id":89317959,"identity":"391ba981-37c5-4637-a5f9-87e955857b63","added_by":"auto","created_at":"2025-08-18 17:31:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4434964,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7151639/v1/4e9fb7ad-31f8-4289-937f-d98965c8fe49.pdf"},{"id":88255126,"identity":"66c3c9af-17b4-4e3f-9a03-12fd539213b2","added_by":"auto","created_at":"2025-08-04 14:18:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1133596,"visible":true,"origin":"","legend":"","description":"","filename":"SupplFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7151639/v1/8d04a5cf8f43b43a4bb71f17.pdf"},{"id":88256250,"identity":"571a2e23-a5ba-44b1-9980-a84c8a061b70","added_by":"auto","created_at":"2025-08-04 14:26:06","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":35995,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7151639/v1/3b40960a42fe18e54d52c482.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Finding the Effect of Darjeeling Black-Tea Aromatics in CNS Function through In-silico GluR-Ligand Interaction as a Probable Means","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBlack tea, particularly harvested and processed from the slopes of Darjeeling Himalayan region, is famous for its rich flavor and accepted worldwide as one of the valued and popular beverages. The Darjeeling tea (\u003cem\u003eCamellia sinensis var. sinensis\u003c/em\u003e), famous for its rich aromatic property, supposed to have a mood enhancing effect with other bioactivities including roles in metabolic regulation, atherosclerosis, inflammation and cancer [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Chemical analysis of the processed black tea with specific extraction procedure shows a rich presence of polyphenol and catechin compounds with specific presence of theaflavins and thearubigens responsible for its distinctive coloration upon brewing [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Different recent studies demonstrated specific anti-cancer activity of theaflavins and other compounds of black tea [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Simultaneously, tea polysaccharides are gaining attention for intervening efficacy in metabolic diseases with potential prebiotic function, skin-care and predicted anti-tumor role [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHowever, a major class of components, the volatile aromatic compounds, responsible for the characteristic flavor of Darjeeling black tea, apparently producing the psycho-physiological mood effect is yet to be evaluated properly. Yokogoshi and team, in a comparison between Darjeeling and Assam tea, interestingly showed that black tea aroma can alleviate negative brain functions caused by stressful lifestyle by controlling autonomous nervous system (ANS) stress response upon inhelation, which lowers salivary chromogranin-A (CgA) released from adrenal chromaffin cells and adrenergic neurons, thus showed improved mood as observed in tension-anxiety (TA) score in POMS [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Odor stimuli derived from black tea, are the aromatic volatile compounds which reaches to olfactory tract and bulb where olfactory nerve receptors accept the molecules to trigger appropriate signals to brain [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This is obvious to have a ligand-receptor interaction between the aromatic compounds and receptors present on the neuronal projections. As the Darjeeling tea aroma produce a mood enhancing effect, we hypothesize that such compounds may bind with Glutamate receptors (GluR) of the cells as glutamate is the most abundant excitatory neurotransmitter found in the nervous system [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. We observe a wide variety of receptors spread across the neurons and non-neuronal allied cells which are divided into metabotropic and ionotropic receptor families. Metabotropic receptors designated as mGluR1-8 are subdivided into 3 groups, whereas, ionotropic receptors are AMPA (GluR1-4) and NMDA [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Additionally, the olfactory nerve fibers around olfactory bulb are accompanied with the immunoregulatory cells of brain, i.e., microglia which are highly responsive and active protector of the CNS against any microenvironmental modifications in the tissue. Presence of glutamate receptors are also pronounced on these myeloid lineage cells [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Therefore, tea aroma compounds are most likely accessible to these receptors of both neurons and microglia or macrophages. If those volatile aromatic compounds can potentially bind with these receptors, they can trigger both neurophysiological and neuroimmune cascades, which may be the probable mechanism of stress relieve and mood alleviation for Darjeeling black tea.\u003c/p\u003e\u003cp\u003eIn present investigation, we have selected some specific volatile aromatic compounds designated with characteristic Darjeeling black tea aroma and validated their molecular property as ligand candidate. Both metabotropic and ionotropic glutamate receptors identified on neuronal cells and microglia and/or macrophages have been selected with stable structural forms and subjected to ligand-receptor binding and docking experiments \u003cem\u003ein silico\u003c/em\u003e. This assessment will show the likelihood of docking and binding of tea aromatic compounds on glutamate receptors and open the potentially probable mood enhancement pathway, and also the possibilities of aroma driven modulation of immune-functions.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eSelection and Preparation of Ligands\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAvailable characteristic aromatic compounds of Darjeeling black tea were screened and selected on the basis of literature review from online databases and articles from Web of Science, Scopus and Science Direct, PubMed and others with special emphasis on the literature analysing chemical composition and aromatic formations of black tea [3,14,15]. We selected 10 specific fragrant compounds characterizing the Darjeeling tea aroma of first and second flash harvested products (\u003cstrong\u003e\u003cem\u003eTable 1\u003c/em\u003e\u003c/strong\u003e). Molecules were retrieved from online chemical database: PubChem (www.pubchem.ncbi.nlm.nih.gov/) and later translated to their 3D PDB format using Open Babel software. A PDBQT format file was created after adding hydrogenating atoms and the desired torsion to a PDB format file [16] and the 3D model of the molecules are shown in \u003cstrong\u003e\u003cem\u003eFigure 1\u003c/em\u003e\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation of Ligands\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe lack of pharmacokinetic studies is one of the main obstacles in the commercial implementation of the plant-based products as therapeutic agents. Thus, the study was designed to use computational methods to evaluate the physicochemical, pharmacokinetic and drug-likeness properties of the citrus species based four bioactive compounds. Physiologically significant properties of a ligand designated by absorption, distribution, metabolism and excretion are the parameters all-together termed as ADME and these parameters of the selected ligands were assessed using the server of SwissADME website (https://www.swissadme.ch/) [17]. Toxicity prediction of the ligands were made essentially to evaluate the probable systemic toxicity prior administering in animal or human based on the molecular database of similar physical, structural and chemically featured molecules. The online server PreADMET (https://preadmet.bmdrc.kr/) was used. In PreADMET server, first the SDF structures retrieved from the PubChem were converted to mol2 format using Open babel software and then submitted to the online server for toxicity prediction including mutagenicity (AMES test), carcinogenicity (for rat and mouse) and hERG inhibition. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReceptor Protein Selection and Validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the literature and database analysis, receptor proteins for glutamate which are simultaneously identified on both neuronal and non-neuronal cells in CNS, particularly, on microglia and CNS associated macrophages were selected. Both the metabotropic and ionotropic receptors for glutamate commonly present on neuronal and myelo-monocytic cells entangled with nervous systems are depicted in\u0026nbsp;\u003cstrong\u003e\u003cem\u003eTable 2\u003c/em\u003e\u003c/strong\u003e,\u0026nbsp;\u003cstrong\u003e\u003cem\u003eFigure 2A-2B\u003c/em\u003e\u003c/strong\u003e. The 3D structure has been retrieved from Protein Databank (http://www.rcsb.org/). Then, using BIOVIA Discovery Studio 2020 software, the already-attached ligands, water molecules and other impurities were eliminated in order to stabilise the receptor structures (https://discover.3ds.com/discovery-studio-visualizer-download/). The newly generated protein PDB structure was then undergone through a series of quality analyses including ERRAT, Procheck using SAVES 6.0 (https://saves.mbi.ucla.edu/) as per standard protocol [18].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSelected Ionotropic and Metabotropic Receptors with their Respective PDB I.D.s\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"548\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePDB I.D.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 262px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetabotropic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e1EWK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 262px;\"\u003e\n \u003cp\u003eGlutamate Receptor Subtype 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e3FUZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 262px;\"\u003e\n \u003cp\u003eGlutamate Receptor, GluR5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e3G3F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 262px;\"\u003e\n \u003cp\u003eGlutamate Receptor, GluR6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e3KS9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 262px;\"\u003e\n \u003cp\u003eGlutamate Receptor mGluR1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e6BSZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 262px;\"\u003e\n \u003cp\u003eGlutamate Receptor mGlu8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIonotropic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e5L1F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 262px;\"\u003e\n \u003cp\u003eAMPA Subtype Glutamate Receptor GluA2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e2WJW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 262px;\"\u003e\n \u003cp\u003eGlutamate Receptor AMPA Subtype\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e5L1G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 262px;\"\u003e\n \u003cp\u003eGlutamate Receptor AMPA Subtype\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e6IRH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 262px;\"\u003e\n \u003cp\u003eGlutamate Receptor, GluN1/GluN2A NMDA Subtype\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e7EOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 262px;\"\u003e\n \u003cp\u003eGlutamate Receptor, GluN1/GluN2A \u0026nbsp;NMDA Subtype\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular Docking Interaction in AutoDock Vina\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAutoDock Vina software (http://vina.scripps.edu/) was used for molecular docking and virtual screening that significantly improved efficient binding mode predictions, thereafter, enhance accuracy in protein-ligand interaction [19]. AutoDock Vina works by calculating the grid maps and clusters. Kollman charges and other modifications were made to the purified form of the protein before moving on to the final docking phase and converting it into a properly readable PDBQT file format. The ligand is similarly converted into a PDBQT file. A grid box on the protein\u0026apos;s active residues was created, with various grid sizes and centres, but a consistent grid spacing of 0.375. With AutoDock Vina software, binding energy affinity was predicted with an exhaustiveness value of 8. The final visualization of docked structure was performed using BIOVIA Discovery Studio 2020 (https://discover.3ds.com/discovery-studio-visualizer-download/).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;of Binding pockets and Structural Hotspots and Dynamicity of Receptor Proteins\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA systematic quantitative characterisation of the surface topography of proteins is often provided by the Computer Atlas Surface Topography of Protein (CASTp) [20]. To predict active amino acid residues, or alternatively, structural hotspots as well as different binding pockets of varied sizes on the receptor protein molecules were done using the CASTp 3.0 service. iMod Server Prediction (http://imods.chaconlab.org/) application were used to assess the transition paths in internal coordinates naturally produced in collective functional motions within the biomolecules [21] helping in advanced visualization with an improved affinity-model based representation of domain dynamics.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSwissADME Prediction of Ligands\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUpon submission of ligand structure in SMILEs format, SwissADME result is generated on the basis of ADME/toxicity analysis and Lipinski filter analysis as depicted in \u003cstrong\u003e\u003cem\u003eTable 3\u003c/em\u003e\u003c/strong\u003e and \u003cstrong\u003e\u003cem\u003eFigure 3\u003c/em\u003e\u003c/strong\u003e. Upon submission of ligand structure in SMILEs format, SwissADME result is generated on the basis of ADME/toxicity analysis and Lipinski filter analysis. Here in our result, we have given different tables for each of the result parameter: physicochemical properties\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003elipophilicity\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003epharmacokinetics\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eand drug likeliness\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eAccording to the SwissADME result, from \u003cstrong\u003e\u003cem\u003eTable 3\u003c/em\u003e\u003c/strong\u003e,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eit is observed that, all the compounds have molecular weight well below the acceptable range (MW \u0026le; 500) (Srimai et al, 2013) and also follow the Ro5 which states that the drug-like compounds ought to have nHBA \u0026le; 10 and nHBD \u0026le; 5. This indicates that all the compounds have the potential to be easily absorbed, diffused and transported [22]. The number of rotatable bonds is a measure of molecular flexibility and is one of the widely used filters during drug discovery process (Veber et al, 2002) and in this criterion all the compounds have successfully passed as all of them fell within the acceptable range (nRB \u0026le; 15), indicative of their potential permeability and oral bioavailability (Muegge et al, 2001). From \u003cstrong\u003e\u003cem\u003eTable 3B\u003c/em\u003e\u003c/strong\u003e,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eit is concluded that each of the protein shows their value of CLogP \u0026le; 5 that influences their solubility, selectivity, potency, permeability and promiscuity. According to \u003cstrong\u003e\u003cem\u003eTable 3C\u003c/em\u003e\u003c/strong\u003e,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eeach and all compounds have the high potential to be absorbed by gastrointestinal tract as well as blood-brain-barrier (BBB), but on the other hand 2-Acetyl-2-thiazoline and 2-Acetyl-1-pyrroline show low BBB permeation (\u003cstrong\u003e\u003cem\u003eFigure 3\u003c/em\u003e\u003c/strong\u003e)\u003cstrong\u003e.\u003c/strong\u003e The penetration across BBB is only mandatory for compounds targeting the central nervous system (CNS) where 8 out of 10 selected compounds are well capable. Metabolism prediction data of the compounds against five isoforms of cytochrome P450 is one of the main priorities during drug discovery process and the study showed that negative LogKp values indicating the skin impermeability of each compound. The drug likeliness (\u003cstrong\u003e\u003cem\u003eTable 3D\u003c/em\u003e\u003c/strong\u003e)\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eresult reveals that all of the compounds show satisfactory result with either 0 violation of the parameters. Bioavailability scores defining the extent and rate at which compounds administered can enter systemic circulation and ultimately reach the targeted sites upon oral administration is shown in \u003cstrong\u003e\u003cem\u003eTable 3D\u003c/em\u003e\u003c/strong\u003e, where all the six compounds show the similar score i.e. 0.55 except benzyl alcohol scoring 0.85. This value implies that the compounds adhere to Lipinski rule of five and have 55% and above probability of being bioavailable. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3A:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ePhysicochemical Properties of the Selected Aromatic Compounds of Black Tea\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"638\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompound name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFormula\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMolecular weight\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of rotatable bonds\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of H acceptors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of H donors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(E, E)-2,4-decadienal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eC\u003csub\u003e10\u003c/sub\u003eH\u003csub\u003e16\u003c/sub\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e153.23 gm./mol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(E, E)-2,4-hexadienal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eC\u003csub\u003e6\u003c/sub\u003eH\u003csub\u003e8\u003c/sub\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e96.13 gm./mol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBenzyl alcohol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eC\u003csub\u003e7\u003c/sub\u003eH\u003csub\u003e8\u003c/sub\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e108.14 gm./mol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2-Acetyl-3-methylpyrazine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eC\u003csub\u003e7\u003c/sub\u003eH\u003csub\u003e8\u003c/sub\u003eN\u003csub\u003e2\u003c/sub\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e136.15 gm./mol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2-Acetyl-2-thiazoline\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eC\u003csub\u003e5\u003c/sub\u003eH\u003csub\u003e7\u003c/sub\u003eNOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e129.18 gm./mol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2-Acetyl-1-pyrroline\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eC\u003csub\u003e6\u003c/sub\u003eH\u003csub\u003e9\u003c/sub\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e111.14 gm./mol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026alpha;-Ionone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eC\u003csub\u003e13\u003c/sub\u003eH\u003csub\u003e20\u003c/sub\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e192.30 gm./mol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSafranal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eC\u003csub\u003e10\u003c/sub\u003eH\u003csub\u003e14\u003c/sub\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e150.22 gm./mol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2 acetyl 3,5-dimethylpyrazine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eC\u003csub\u003e8\u003c/sub\u003eH\u003csub\u003e10\u003c/sub\u003eN\u003csub\u003e2\u003c/sub\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e150.18 gm./mol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5 ethyl 2,3-dimethylpyrazine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eC\u003csub\u003e8\u003c/sub\u003eH\u003csub\u003e12\u003c/sub\u003eN\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e136.19 gm./mol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3B:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eLipophilicity of the Selected Aromatic Compounds of Black Tea\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"633\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompound name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLog Pa/w (iLOGP)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLog Pa/w (XLOGP3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLog Pa/w (WLOGP)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLog Pa/w (MLOGP)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLog Pa/w (SILICOS-IT)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConsensus Log Pa/w\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(E, E)-2,4-decadienal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e2.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e3.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e2.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(E, E)-2,4-hexadienal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBenzyl alcohol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2-Acetyl-3-methylpyrazine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2-Acetyl-2-thiazoline\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2-Acetyl-1-pyrroline\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026alpha;-Ionone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e2.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e3.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e3.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSafranal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e2.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e2.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2 acetyl 3,5-dimethylpyrazine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5 ethyl 2,3-dimethylpyrazine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e2.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e2.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3C:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eLipophilicity of the Selected Aromatic Compounds of Black Tea\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"119%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompound name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGI absorption\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBBB permeant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-gp substrate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCYP1A2 inhibitor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCYP2C19 inhibitor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCYP2C9 inhibitor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCYP2D6 inhibitor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCYP3A4 inhibitor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLOG KP (skin permeation)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(E, E)-2,4-decadienal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eHigh\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;No\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-4.92 cm/s\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(E, E)-2,4-hexadienal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eHigh\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;No\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-6.02 cm/s\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBenzyl alcohol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eHigh\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eYes \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;No\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-6.18 cm/s\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2-Acetyl-3-methylpyrazine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eHigh\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;No\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-6.99 cm/s\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2-Acetyl-2-thiazoline\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eHigh\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;No\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-6.93 cm/s\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2-Acetyl-1-pyrroline\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eHigh\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;No\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-7.28 cm/s\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026alpha;-Ionone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eHigh\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;Yes \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-4.74 cm/s\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSafranal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eHigh\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;No\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-5.70 cm/s\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2 acetyl 3,5-dimethylpyrazine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eHigh\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;No \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-6.79 cm/s\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5 ethyl 2,3-dimethylpyrazine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eHigh\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;No \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e-6.15 cm/s\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3D:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eDrug Likeliness of the Selected Aromatic Compounds of Black Tea\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompound name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLipinski\u0026rsquo;s rule Satisfactory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of violations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBioavailability Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(E, E)-2,4-decadienal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(E, E)-2,4-hexadienal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBenzyl alcohol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2-Acetyl-3-methylpyrazine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2-Acetyl-2-thiazoline\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2-Acetyl-1-pyrroline\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026alpha;-Ionone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSafranal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2 acetyl 3,5-dimethylpyrazine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5 ethyl 2,3-dimethylpyrazine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eToxicity Prediction of the Ligands\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the process of developing new drugs, toxicity testing of small molecules was done as an essential phase. \u003cstrong\u003e\u003cem\u003eTable 4\u003c/em\u003e\u003c/strong\u003e displays the results of the toxicological prediction using the PreADMET service, including the drugs\u0026apos; mutagenicity, carcinogenicity, and inhibition of hERG, where, negative prediction translates carcinogenic activity and positive means the compound possess no carcinogenic activity. Where Ames test, based on bacterial mutagenicity potential showed all the selected black tea aromatic molecules are having mutagenic properties as ligand candidates, the hERG inhibition test done to assess the effect of the molecules on the cardiac potassium channel [23] showed moderate to low risk, where, selected pyrazine, ionone and safranal\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ecompounds are of much lower risk for cardiac functioning. Probable carcinogenicity assessment in PreADMET service shows all selected candidates are non-carcinogenic for mice and rat with some exception for decadienal compound for\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eboth mice and rat (\u003cstrong\u003e\u003cem\u003eTable 4\u003c/em\u003e\u003c/strong\u003e). \u0026nbsp;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: \u0026nbsp;Showing Results of Mutagenicity and Carcinogenicity along with hERG Inhibition of the Selected Aromatic Compounds of Black Tea\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompound name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAmes Test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCarcino Mouse\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCarcino Rat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ehERG Inhibition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(E, E)-2,4-decadienal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eMutagen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eNegative \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003ePositive\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eMedium Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(E, E)-2,4-hexadienal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eMutagen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003ePositive\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003ePositive \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eMedium Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBenzyl alcohol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eMutagen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eNegative\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eMedium Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2-Acetyl-3-methylpyrazine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eMutagen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eNegative\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eLow Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2-Acetyl-2-thiazoline\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eMutagen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eNegative\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eMedium Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2-Acetyl-1-pyrroline\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eMutagen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eNegative \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eMedium Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026alpha;-Ionone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eMutagen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003ePositive\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eNegative \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eLow Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSafranal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eMutagen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eNegative \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eLow Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2 acetyl 3,5-dimethylpyrazine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eMutagen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eNegative \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eLow Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5 ethyl 2,3-dimethylpyrazine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eMutagen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eNegative \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eLow Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eValidation of Protein Structures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOverall quality recognition of all 3D protein PDB structures as predicted by the online tools was done (\u003cstrong\u003e\u003cem\u003eSuppl. Fig. 1\u003c/em\u003e\u003c/strong\u003e). By confirming the protein PDB model using a number of quality checking criteria, the ideal protein structures were approved. The \u0026quot;overall quality factor\u0026quot; that ERRAT displayed indicated that proteins with higher scores are of greater quality. All proteins, with the exception of 6IRH (43%), have quality scores that fall between 86 and 98%, indicating that they are all well-modelled. The Ramachandran plot of nearly all protein models then showed that over 80% of residues were found in the most preferred areas, followed by extra allowed, generously allowed, and banned regions, in accordance with the PROCHECK result. Together, these strong validations guarantee the precision of molecular docking investigations by proving that the interactions between the ligands and proteins are faithfully shown.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5: \u0026nbsp;Showing Results of Maximum Binding Affinity Scores in AutoDoc Vina between Glutamate Receptors and Best Selected Aromatic Compounds of Black Tea\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"103%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRanking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReceptor (common name)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReceptor (PDB ID)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLigand\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAffinity Score\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(kCal/mol)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eGluR6 metabotropic receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3G3F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026alpha;-Ionone\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e̶ \u0026nbsp;7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eGluN1/GluN2A NMDA ionotropic receptor subtype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e7EOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026alpha;-Ionone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e̶ \u0026nbsp;6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eGluR5 metabotropic receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3FUZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eSafranal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e̶ \u0026nbsp;6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eGluR-AMPA ionotropic receptor subtype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e2WJW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eSafranal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e̶ \u0026nbsp;6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eGluR1 metabotropic receptor\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1EWK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e(E,E)-2,4-decadienal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e̶ \u0026nbsp;6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eGlutamate Receptor \u0026nbsp;mGlu8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e6BSZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e2-acetyl-3,5-dimethylpyrazine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eGlutamate Receptor AMPA Subtype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e5L1G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026alpha;-Ionone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e̶ \u0026nbsp;4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eGluR1 metabotropic receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3KS9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026alpha;-Ionone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e̶ \u0026nbsp;5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eGluN1/GluN2A NMDA ionotropic receptor subtype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e6IRH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026alpha;-Ionone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e̶ \u0026nbsp;5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eAMPA Subtype Glutamate Receptor GluA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e5L1F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026alpha;-Ionone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e̶ \u0026nbsp;4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular Docking Interaction Using AutoDock Vina\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOn the basis of docking analysis done by AutoDock Vina, the overall results of binding affinity of all the tea bioactive compounds against the 5 metabotropic receptors are represented in pictorial manner (\u003cstrong\u003e\u003cem\u003eFigure 4\u003c/em\u003e\u003c/strong\u003e). When the stability of the docking interactions has been calculated, some specific ligand-receptor interactions emerges as most potent and stable having binding affinity around \u0026ndash;6 KCal/Mol or less. It is found that \u0026alpha;-Ionone and Safranal are the most prominent ligands and mGluR1 (IEWK), GluR5 (3FUZ) and GluR6 (3G3F) are the most common metabotropic receptors providing stable interactions. The best of the interactions and their binding affinity is presented in \u003cstrong\u003e\u003cem\u003eTable 5\u003c/em\u003e\u003c/strong\u003e. Based on the graphical view generated through BIOVIA Discovery Studio 2020, 2D and 3D mode of binding interactions of top docked compounds are presented via \u003cstrong\u003e\u003cem\u003eFigure 5A-E\u003c/em\u003e\u003c/strong\u003e, where affinity score ranges between \u0026ndash;5.7 to \u0026ndash;7.1 kCal/mol.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn case of ionotropic glutamate receptors, specific subtypes of AMPA (2WJW) and NMDA (7EOR) show stronger binding affinities predominantly to \u0026alpha;-Ionone and Safranal (\u003cstrong\u003e\u003cem\u003eFigure 6\u003c/em\u003e\u003c/strong\u003e). 2D and 3D model of binding prediction using BIOVIA Discovery Studio 2020 showed binding pockets and molecular interactions with the ionotropic receptors (\u003cstrong\u003e\u003cem\u003eFigure 7A-E\u003c/em\u003e\u003c/strong\u003e), where affinity score ranges between \u0026ndash; 4.7 to -6.4 kCal/mol.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment of Binding Pockets and Active Amino Acid Residues and Dynamicity \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 6\u003c/em\u003e\u003c/strong\u003e displays the findings from the CASTp 3.0 online server for structural hotspots or active amino acid residues of the protein PDB structure. The best fitted ligand-receptor formations with maximized binding affinity are considered for identifying the active amino acid residues during binding with the ligands. Numerous amino acid types in different places are found to create the structural pockets of the receptors and are implicated in molecular interactions with the ligands. However, among the amino acid types most abundant presence of polar-uncharged serine and threonine as well as +ve charged arginine and lysine are observed for binding with \u0026alpha;-ionone and safranal mostly as the favourable ligands within the binding pockets. Other amino acids in binding pockets are the hydrophobic representatives like tyrosine, tryptophan, valine, leucine, isoleucine, alanine and methionine which are found available to interact with aromatic ligands. iMOD server analysis showed specific eigenvalues of the receptor-ligand complex systems (eigenvectors) as per the principal component analysis (PCA) where 3 out of 5 metabotropic receptors, namely, GluR type 1 (1EWK), GluR5 (3FUZ), GluR6 (3G3F) and only 1 out of 5 ionotropic receptors, namely, AMPA receptor (5LIG) showed high eigenvalue signifying a large variance or dynamicity in the system. In general, upon ligand binding, metabotropic receptors are more structurally dynamic and ionotropic receptors showed more structural stiffness except AMPA receptor 5L1G (\u003cstrong\u003e\u003cem\u003eSuppl. Fig. 2\u003c/em\u003e\u003c/strong\u003e). \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTea consumption is one of the commonest ethnic practices in many parts of the world which have putative values related to public health. Depending on preparation, particularly for green and black tea, it releases wide variations of bioactive compounds and flavours in infusions like catechins, flavonoids, polyphenols, l-theanine and many aromatic compounds and their derivative forms [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In studies it was found that l-theanine, unique to green and black tea and some mushrooms, relaxes human and improve attention [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], whereas, routine intake of tea has significant effect on mood and improve the restoration of cortisol at basal level after stress resulting in quicker stress recovery and that was also supported by different population based studies [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eActive Amino Acid Residues of the Receptors using CASTp 3.0 with Best Fitted Ligand Aromatic Compounds derived from Black Tea\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSl. No.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReceptor\u003c/p\u003e\u003cp\u003e(PDB ID)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLigand\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eActive Amino Acid Residues\u003c/p\u003e\u003cp\u003eFor Binding\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3G3F\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eα-Ionone\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTHR 108, LYS 248\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7EOR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eα-Ionone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSER 700, TRP 795\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3FUZ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSafranal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTYR 474, SER 674, THR 675\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2WJW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSafranal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eARG 129, ARG 156, TYR 295\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1EWK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(E,E)-2,4-decadienal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTYR 74, TRP 110, SER 165, THR 188, MET 294\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6BSZ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2-acetyl-3,5-dimethylpyrazine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLYS 57, ALA 155, SER 156, SER 157, SER 283\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5L1G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eα-Ionone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eILE 664, ARG 675, LYS 761, LYS 765\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3KS9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eα-Ionone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSER 165, THR 188, SER 189, TYR 236\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6IRH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eα-Ionone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eASN 432, LYS 457, LEU 794, TRP 795\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5L1F\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eα-Ionone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eARG 453, VAL 484, SER 654\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThere are a variety of aromatic compounds which are characterizing different tea types to the world population. Among which, one of the best authentic aromatic tea variety is named as Darjeeling tea, produced and processed in the cool, humid southern hill slopes of Himalaya within average altitude between 1000m\u0026ndash;3000m, from Nepal to Bhutan centring the Darjeeling district of West Bengal, India, which are harvested in spring and summer, selected, weathered, rolled, fermented-oxidized and processed to produce signature Darjeeling black-tea and in hot, aquas infusion it releases the flavour and taste for which it is celebrated [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In present study, some of the prominent compounds associated with Darjeeling tea flavour has been selected which are specified in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e with their fragrance characteristics. When different physical and chemical parameters of the compounds including permeability through blood-brain-barrier and drug likeliness have been tested they are found highly absorbable through gastric endothelial tissue and all are permeable through BBB except 2-Acetyl-2-thiazoline. However, all the aromatic compounds qualified Lipinski\u0026rsquo;s rule satisfying drug likeliness with persistent bioavailability score of 0.55, but maximum score with 0.85 for benzyl alcohol (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Most of these chemicals are low to medium risk mutagen with most of them showed no carcinogenicity, thereby, qualified as effective ligand. In contrast, prominent metabotropic and ionotropic receptors of glutamate (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) were subjected to the structural validation of the PDB models on which the ligand docking experiments would be performed (\u003cb\u003eSuppl. Figure\u0026nbsp;1\u003c/b\u003e). Such experiments showed us a binding affinity between \u0026ndash; 7.1 to \u0026ndash; 4.8 kCal/mol for the top 10 docking success among the receptor-ligand pairings, which are within flexible-ligand docking range indicating biological efficiency of this interaction [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Hence, ligation between metabotropic receptors GluR1 (1EWK), GluR5 (3FUZ) and GluR6 (3G3F) with (E,E)-2,4-decadienal, Safranal and α-Ionone respectively; and ionotropic receptors GluN1/GluN2A NMDA subtype (7EOR) and GluR-AMPA subtype (2WJW) with α-Ionone and Safranal respectively, are found most promising and probable. Among the ligands, Safranal and α-Ionone, both are monoterpenoids and with characteristic aroma of the black tea variant famous to tea lovers [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Analysis through iMOD server on the structural coordinates of the receptors show that metabotropic receptors with higher eigenvalues and spread indicating higher dynamicity, whereas, ionotropic receptors involved in binding show lower eigenvalues and spread indicating structural stiffness in general (\u003cb\u003eSuppl. Figure\u0026nbsp;2\u003c/b\u003e) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. But most dynamic components are the metabotropic GluR5 and GluR6 with exceptionally an ionotropic AMPA receptor. The top four ligand receptor bindings show that the interactions between ligands (α-Ionone and Safranal) and receptors are occurring in between only 2\u0026ndash;3 amino acid residues, either polar or hydrophobic in nature (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAs found as the ligands are mostly capable of crossing the endothelial lining, absorbing through gastro-intestinal tract and blood-brain-barrier with sufficient bio-availability score, they are capable of performing through both oral and olfactory routes to reach their target receptors. Therefore, the black tea volatile compounds, which have been studied here, possess ample opportunity to interact, dock and produce subsequent effects to the neuro-glial cells associated to this passage. Added to it, their high absorption score through GI tract, moderate to high bioavailability and penetration capability through BBB (except 2-Acetyl-2-thiazoline) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) ensure their systemic reach to the CNS matrix and capability to interact with neuroglial population from regions of cerebral cortex to hippocampus to hypothalamus, thus opening a wide possibility of modulating neurophysiology to psycho-behavioural aspects and immunity.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn a recent study, presence of glutamate ionotropic AMPA and NMDA receptors like GluA1, GluA2, GluN1 and GluN2A are found in hypothalamus and showed their critical role in glutaminergic synaptic transmission [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Such signalling is found significantly related to learning and memory and neuroendocrine functions and thereby associated with circadian, feeding and behavioural disorders [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. As with the presence of glutamate receptors on the neurons throughout the CNS including olfactory circuit, a robust endogenous immunological defence is present in olfactory tissue where immune cells cross lamina propria into olfactory neurones and these cells are showing Iba-1, CX3CR1 receptors and release iNOS, IL6 and TNFα [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. These cells, particularly microglia express many of the neurotransmitter receptors and membrane proteins like CD200, CD22, CD47 to interact with neurons, where microglia can modulate neurones by releasing glutamate, ATP, ADP, ROS, NO, PGE2, BDNF, miRNA and cytokines/chemokines [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. It was previously found that mGluR4/6/8 (Gr.III receptors) transform microglia into neuroprotective or mGluR2/3 (Gr II receptors) into neurotoxic forms, thus release TNFs and FasR, Ca\u003csup\u003e+\u003c/sup\u003e2 wave driven neuronal injury; NMDA expression in the cells follows NO release, NFkB signalling, IL1β and TNFα release and mediate increased excitability of hippocampal CA3 neurons [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Several evidences show that microglia are present throughout nasal epithelial lining to olfactory nerves extending to subventricular zone-olfactory bulb (SVZ-OB) axis and their involvement of protecting olfactory tracts and neurons against pathogenic insult or development of olfactory dysfunctions, thus they are capable of receive glutamate signals and react to modulate neuronal functions [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. As both AMPA and NMDA receptors and metabotropic receptors for glutamate are expressed on both neurons and microglia, their reciprocal interactions and functional responses under glutamate release or storming become very crucial for a wide range of neurophysiological conditions. In all these situations, safranal, α-ionone and other aromatic compounds derived from tea may play a pivotal role from multiple perspectives.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003ePresent study explored possibility of some prominent Darjeeling tea aroma compounds to modulate the neuro-glial function, hence, neuro-physiology and behaviour through glutamate receptors (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). These compounds, prominently like α-Ionone, safranal, (E,E)-2,4-decadienal are found to sufficiently bioavailable to olfactory bulb and CNS, and capable of interacting with group I metabotropic receptors mGluR1 and mGluR5 as well as group III metabotropic receptors mGluR6 and mGluR8. As group I receptors are the moderators which are capable of increasing NMDA receptor activity, synergistically they can uplift the function of neuronal circuitry; in contrary, group III metabotropic receptors are identified with downregulation of secondary messenger dependent signalling and projects neurotoxic effects [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The same aromatic compounds, i.e., safranal and α-Ionone are also capable of binding with GluR-AMPA ionotropic receptor and GluN1/GluN2A NMDA ionotropic receptor subtypes which are widespread on neuroglial cells from naso-buccal cavity to cortex or hypothalamus with wider functional implications already discussed, particularly activating hypothalamo-amygdala axis. Simultaneously, they can capably bind with the GluRs present of microglia and brain macrophages, thus, instigating the neuro-immuno axis as already discussed. This research highlights a novel potential for Darjeeling tea aroma compounds and suggests a new direction for studying neuro-glial function under their influence.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of Interest:\u003c/h2\u003e\n\u003cp\u003eThe author(s) do not have any conflict of interest.\u003c/p\u003e\n\u003ch2\u003eEthics Statement\u003c/h2\u003e\n\u003cp\u003eThis research did not involve human participants, animal subjects, or any material that requires ethical approval.\u003c/p\u003e\n\u003ch2\u003eInformed Consent\u0026nbsp;Statement:\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThis study did not involve human participants, and therefore, informed consent was not required.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026rsquo; Contribution\u003c/h2\u003e\n\u003cp\u003eM.S performed most of the \u003cem\u003ein-silico\u003c/em\u003e workflows and compiled \u003cem\u003ein-silico\u003c/em\u003e data with primary drafting; A.S curated the data and parallelly performed different in silico workflow and helped A.G in combining data; S.C analyzed and validated the data and checked the manuscript; A.G conceptualized and supervised the work, analyzed data and finalized the manuscript.\u003c/p\u003e\n\u003ch2\u003eFunding:\u003c/h2\u003e\n\u003cp\u003eThe author(s) received no financial support for the research, authorship, and/or publication of this article.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eAuthors are extending their sincere thanks to the authorities of the Netaji Subhas Open University and Techno India University, West Bengal, India for providing required infrastructural and institutional supports.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eData is provided within the manuscript or supplementary information files. In this investigation no animal or human subjects or samples are involved.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSkotnicka M, Chorostowska-Wynimko J, Jankun J and Skrzypczak-Jankun E. The black tea bioactivity: an overview. Centr Eur J Immunol 2011; 36: 284-292.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eFatima M and Rizvi SI. Health beneficial effects of black tea. Biomedicine 2011; 31: 3-8.\u003c/li\u003e\n \u003cli\u003eLi S, Lo C-Y, Pan M-H, Laic C-S and Ho C-T. Black tea: chemical analysis and stability. Food Funct 2013; 4: 10-18.\u003c/li\u003e\n \u003cli\u003ePan M-H, Lai C-S, Wang H, Lo C-Y, Ho C-T and Li S. Black tea in chemo-prevention of cancer and other human diseases. Food Sci Hum Well 2013; 2: 12-21.\u003c/li\u003e\n \u003cli\u003eCao J, Han J, Xiao H, Qiao J and Han M. 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Acta Histochem 2019; 121: 546-552.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMoseman EA, Blanchard AC, Nayak D and McGavern DB. T cell engagement of cross-presenting microglia protects the brain from a nasal virus infection. Sci Immunol 2020; 5: eabb1817. doi: 10.1126/sciimmunol.abb1817.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Aromatic compounds, Darjeeling tea, Glutamate receptors, Molecular docking, Neuro-immune modulation","lastPublishedDoi":"10.21203/rs.3.rs-7151639/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7151639/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDarjeeling tea (\u003cem\u003eCamellia sinensis\u003c/em\u003e var. \u003cem\u003esinensis\u003c/em\u003e) is recognized for its unique aroma and taste, associated with mood and cognitive enhancement. However, the underlying neurochemical mechanisms remain elusive. Present study investigated the potential interaction of Darjeeling tea's volatile aromatic compounds with glutamate receptors (GluRs), the predominant excitatory receptors in the central nervous system. We hypothesized that these compounds target GluRs to elicit their effects. An \u003cem\u003ein-silico\u003c/em\u003e approach was employed, involving the analysis of physicochemical properties, bioactivity scores, and toxicity profiles of the aroma compounds. Subsequently, molecular docking simulations were performed using retrieved 3D structures of relevant GluRs to predict the binding affinity of selected compounds exhibiting high bioactivity, drug-likeliness, and bioavailability with identification of key amino acid residues within the receptor binding pockets. Our findings revealed α-Ionone and Safranal as prominent ligands exhibiting strong binding interactions. Among metabotropic GluRs, mGluR1 (IEWK), GluR5 (3FUZ), and GluR6 (3G3F) showed the highest affinity. Ionotropic receptor subtypes AMPA (2WJW) and NMDA (7EOR) also displayed significant binding scores where greater structural dynamics found in metabotropic GluRs upon ligand binding compared to ionotropic subtypes. Given the nasal passage as the primary route of exposure, and the presence of GluR-expressing cells along this pathway, the high bioavailability of α-Ionone and Safranal suggests their potential to interact with neuro-glial cells and subsequently influence CNS neurons and microglia/macrophages. In conclusion, the identified binding capability between Darjeeling tea's aromatic ligands and GluRs offers a promising framework for elucidating the mechanisms underlying the tea's effects on mood, psychological states, and immune-physiological responses.\u003c/p\u003e","manuscriptTitle":"Finding the Effect of Darjeeling Black-Tea Aromatics in CNS Function through In-silico GluR-Ligand Interaction as a Probable Means","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-04 14:18:01","doi":"10.21203/rs.3.rs-7151639/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d5879951-46c4-40eb-9711-bb4c4a0c2f7a","owner":[],"postedDate":"August 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-18T17:23:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-04 14:18:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7151639","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7151639","identity":"rs-7151639","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}