In Silico ADMET and Molecular Interaction Profiles of Phytochemicals from Medicinal Plants in Dakshina Kannada

Abstract The success or failure of a potential drug depends on its absorption, distribution, metabolism, excretion and toxicity (ADMET) characteristics, and these features are usually rate-limiting in the drug development process. Hence, it is essential to know about the predicted ADMET properties of the most promising leads to avoid the risk of late-stage attrition. This project focuses on in silico screening of ADMET properties of phytochemicals found in Dakshina Kannada's medicinal plants, which include Tinospora cordifolia , Azadirachta indica , Ocimum sanctum, and Plectranthus amboinicus , mainly known for their antimicrobial properties. The physicochemical properties, bioactivity scores, ADMET, and molecular interactions of the selected phytoconstituents were determined by QikProp, Molinspiration, ADMETlab 2.0, ProTox-II, and GLIDE. In addition, molecular docking checked for their binding interactions with target proteins 1JIJ and 4 HOE of Staphylococcus aureus and Candida albicans , respectively, as they were well known for their antimicrobial properties. In this studies, rosmarinic acid was well interacted phytochemical with both target proteins and has highest docking score. The physicochemical properties showed that all compounds fell under the recommended molecular weight, volume, and polar surface area range. Xanosporic acid violated two rules of Lipinski's Rule of Five, indicating that it may have problems with oral bioavailability. The ADME properties for most of the phytocompounds were within the recommended ranges; hence, they are promising candidates for drug development. Most phytoconstituents showed good bioactivity scores, indicating they have good druglikeness properties. On the analysis of the toxicity, most of the phytoconstituents were found to be noncarcinogenic and nonmutagenic. Therefore, this data can further be utilized as primary tools for determining the biological actions of these plants. Xanosporic acid was found to violate two out of three rules of Lipinski. Similarly, ursolic acid and oleanolic acid also showed a few undesirable properties. All other compounds otherwise showed desirable properties and hence are promising candidates for drug development. This data can be further utilized as primary tool for determining the biological actions of the plants.


Introduction
Pharmaceutical drug discovery is a high-risk, tedious, and expensive process that involves choosing a specific disease, target identification, lead discovery, optimization followed by preclinical and clinical trials. 1 Animal studies usually fail to predict the clinical results because of interspecies differences in transporters, biochemical pathways, and enzymes. Millions of molecules are screened, but not many get approved due to technical, safety, and efficacy issues related to absorption, distribution, metabolism, and elimination (ADME) and various toxicities (T), leading to delayed progress in drug discovery. Unfortunately, drugs with high potency may not always have the desirable pharmacokinetic profile to be approved and marketed for human use. Usually, a successful drug is not only the one with the highest potency but the one with acceptable potency, safety, and pharmacokinetics. 2 There are various online and offline tools available for analyzing the ADMET properties of a particular compound. 3 Few among the many tools available are Molinspiration, Derek Nexus, PreADMET ADMET Prediction, ProTox-II, VolSurf, ADMEWORKS Predicto, QikProp (Schrodinger, LLC, New York), ADMETlab 2.0 and admetSAR.
QikProp predicts significant physical descriptors and pharmaceutically relevant properties of organic molecules, individually or in batches. In addition to predicting molecular properties, QikProp provides ranges for comparing a particular molecule's properties with those of 95% of known drugs. 4 Molinspiration Cheminformatics software offers fragment-based virtual screening, bioactivity prediction, and data visualization. 5 ADMETlab 2.0 is a redesigned version of the previously widely used AMDETlab web server for the predictions of pharmacokinetics and toxicity parameter of compounds, of which the supported ADMET-related endpoints are approximately twice the number of the endpoints in the previous version. 6 The ProTox-II is a webserver to predict toxicity and multiple toxicological endpoints for several chemical compounds have five different models such as oral acute toxicity prediction model as per six different toxicity classes; organ toxicity model especially liver toxicity prediction; toxicological (immunotoxicity model); and genotoxicological (cytotoxicity, mutagenicity and carcinogenicity model) endpoints. 7 In the past few years, research on traditional plants with medicinal significance has seemingly surged all over the world, because the natural sources and the plant varieties encourage scientists to complement modern pharmacological approaches. 8 This project focuses on in silico screening of ADMET properties of phytochemicals found in medicinal plants of Dakshina Kannada which include, Tinospora cordifolia (Amrita Balli), 9 Azadirachta indica (Neem), 10 Ocimum sanctum (Tulasi), 11 and Plectranthus amboinicus (Indian borage). 12,13 These selected four plants are substantially found almost everywhere in Dakshina Kannada and are traditionally significant to the native population. These plants are well known for their various actions ranging from antimicrobial, 9 anti-inflammatory 10 and anti-asthmatic, 11,14 larvicidal, 12 and so on. From review of literature, it is clear that the various extracts of Tinospora cordifolia, Azadirachta indica, Plectranthus amboinicus, and Ocimum sanctum have antifungal and antibacterial activity against Candida albicans and Staphylococcus aureus, respectively. So, these four plants were selected against S. aureus and C. albicans. 15,16 Dakshina Kannada is the abode of many such highly potent medicinal plants, containing various phytochemicals, the ADMET profiles of which, if well established, can simplify and accelerate drug discovery. Of the 500 medicinal plants listed in Arya Vaidya Sala, 320 are located in Dakshina Kannada and parts of the Udupi district. The phytoconstituents of four plants, namely Tinospora cordifolia (Amrita Balli), Azadirachta indica (Neem), Ocimum sanctum (Tulasi), and Plectranthus amboinicus (Indian borage), were selected based on their antimicrobial properties. [17][18][19] Methodology The ADMET properties of 18 phytochemicals found in Dakshina Kannada's medicinal plants were selected as Tinospora cordifolia, Azadirachta indica, Ocimum sanctum, and Plectranthus amboinicus. 19

In Silico Platform
The computational analysis was carried out on Maestro 12.3 version (LigPrep, QikProp) (Schrödinger 2020-4) 4,20 to determine the physicochemical properties alongside ADMET properties of the selected phytoconstituents. This software is programmed on DELL Inc.27" workstation machine running on Linux -x86_64 operating system. Bioactivity scores were predicted using the Molinspiration online tool 21 and ADMETlab 2.0 and ProTox-II online software programs were utilized to predict the toxicity profile.

Biological Data
The plants found in Dakshina Kannada were found using "Flora of Peninsular India database" developed by the research team at herbarium JCB, Centre for Ecology Sciences (CES), Indian Institute of Sciences(IISc), Bangalore. 13 The 18 phytoconstituents were obtained from four plants, that is, four from Tinospora cordifolia, six from Azadirachta indica, five from Ocimum sanctum, and three from Plectranthus amboinicus, and were used for in silico studies.

Ligand Preparation
The SMILES of the phytoconstituents were taken from Pub-Chem and the structures were derived based on SMILES using ChemSketch. The SMILES were imported to the Maestro in Schrödinger software. Maestro's sketch module function builds the three-dimensional structures of the 18 ligands, and the ionization states are produced at pH 7.0. OPLS3 force field executed the energy minimization, and the low-energy conformations of all 18 phytoconstituents were generated by LigPrep. 20

Physicochemical Properties
Physicochemical properties of ligand molecules were determined by using QikProp of Schrödinger software. 4 The scores help comprehend drug-likeness properties and bioavailability. The prepared ligands were selected and incorporated into the QikProp tool and processed. The physicochemical properties like molecular weight, logP, donor HB (hydrogen bond), and acceptor HB analyses Lipinski's Rule of Five 22 were assessed. Alongside, molecular volume, polar surface area (PSA) and Jorgensen's Rule of Three 23 were also predicted.

ADMET Properties
ADMET properties of ligand molecules were determined by QikProp by Schrödinger software (Schrödinger 2020-4: Qik-Prop). 4 The computation of ADMET parameters ahead of expensive trials can eradicate, or at the least minimize, redundant testing on leads that may not show promise in qualifying the clinical trials. The results further assist in concretizing our understanding of drug-likeness properties and bioavailability. The prepared ligands were selected and incorporated into the QikProp tool and processed. The ADMET features include Caco-2 cell permeability, blood-brain barrier (BBB) permeability, percentage human oral absorption and solvent accessible surface area (SASA), hydrophobic component of the SASA (FOSA), and hydrophilic component of the SASA (FISA), dermal penetration, plasma-protein binding, metabolism, and Half Maximal Inhibitory Concentration (IC 50 ) value for Human Ether-À-Go-Go-Related Gene Potassium Channel (HERG K þ ). This approach allows a researcher to focus on only those particular compounds that deserve further evaluation. 24

Bioactivity Prediction
Bioactivity explains the adverse or beneficial effects of a drug on human body. It depends entirely on fulfillment of the ADME criteria. Hence, to be a suitable drug candidate, a chemical compound must not just be active, but must also possess the appropriate ADME properties. The automated online tool Molinspiration was therefore used to calculate the same. 25 In the Molinspiration website, the SMILES of each of the 18 compounds were entered in the box after clicking on the "Calculation of Molecular Properties and Prediction of Bioactivity" tab. Then the command to "Predict Bioactivity" was given, and the scores were tabulated. 21

Toxicity Prediction
The toxicity of the phytoconstituents were virtually predicted using different tools, namely ADMETlab 2.0 and ProTox-II. ADMETlab 2.0 is an enhanced version of the widely used ADMETlab for systematic evaluation of ADMET properties and some physicochemical properties, and medicinal chemistry friendliness. 6 ProTox-II is a free online virtual lab for the prediction of toxicities of small molecules, which matches the similarity of the compound with already known toxic compounds. 26,27 With significant updates to functional modules, predictive models, explanations, and the user interface, ADMETlab 2.0 has a greater capacity to assist medicinal chemists in accelerating the drug research and development pro-cess. [28][29][30] The SMILES string of each compound was pasted separately and submitted. The evaluation results were then downloaded as a PDF file, and the scores were tabulated. AMES toxicity and rat oral acute toxicity scores were predicted using this tool. The mutagenicity and carcinogenicity boxes were ticked before submitting for "Search." Once the molecule was confirmed, "Start Tox Prediction" command was given. The predicted lethal dose, 50% (LD 50 ) and toxicity class along with the prediction and probability of carcinogenicity, and mutagenicity scores were calculated.

Molecular Docking
Docking studies were conducted to determine the possible interactions of 18 phyto constituents with essential enzymes, tyrosyl-tRNA synthetase (TyrRS) in S. aureus, and dihydrofolate reductase (DHFR) in C. albicans. The target proteins 1JIJ and 4HOE of TyrRS and DHFR, respectively, were downloaded from rcsb Protein Data Bank. 31 TyrRS and DHFR inhibitors are an important class of drugs, as evidenced by their use as antibacterial, antimalarial, antifungal, and anticancer agents.
Target protein 1JIJ is crystal structure of S. aureus TyrRS and classified as ligase with resolution of 3.20 Å. 4HOE target protein is C. albicans DHFR complexed with nicotinamide adenine dinucleotide phosphate (NADPH) and resolution is 1.76 Å. These all are the main selection criteria for selection of targets. Target proteins were minimized by protein preparation wizard. LigPrep application is used for preparing the phytoconstituents. The grid was generated and docking was carried between the minimized protein and phytoconstituents by GLIDE, Schrodinger, XP (extra precision) method. 32 Docking scores, hydrophobic interaction, polar interaction, and hydrogen bonding were found out.

Results and Discussions Physicochemical Properties
The physicochemical properties were determined using QikProp. The main objective was to establish the druglikeness property, that is, to check if the phytoconstituents obeyed Lipinski's Rule of Five. The physicochemical properties of the 18 phytoconstituents are listed in ►Table 1. The molecular weight of all the phytoconstituents that were analyzed was found to fall within the recommended range of 130.0 to 725.00. The lipophilicity (logP) value of 16 compounds was found to be within the acceptable range of À2 to 6.5, except for ursolic acid and oleanolic acid, whose logP values were found to be above 6.5.
Lipinski used various molecular properties in formulating his "Rule of Five." The rule states that most molecules with good druglikeness have logP less than or equal to 5, MW less than or equal to 500, number of HB donors less than or equal to 5, and the number of HB acceptors less than or equal to 10. The compounds that fulfil at least three of the four criteria are said to follow the Lipinski's "Rule of Five." 22 Eleven phytoconstituents namely, tinosponone, isocolumbin, nimbin, nimbolide, mahmoodin, margolone, margolonone, eugenol, linalool, carvacrol, and p-cymene were found to obey Lipinski's Rule of Five (RO5) with no violations. On further analysis, except xanosporic acid, the remaining compounds (nimbin, mahmoodin, ursolic acid, oleanolic acid, and βcaryophyllene) were found to violate one rule, which points out that they are considered drug-like molecules, and the violations were possibly due to the complex structures of the phytoconstituents. In the case of xanosporic acid, the molecular weight of 536.49 and acceptor HB score of 10.65 were out of the recommended range, and hence, it shows two violations. The total solvent-accessible volume of all the compounds was within the desired range of 500.0 to 2000.00. The PSA, which correlates the Van der Waals surface area for polar nitrogen and oxygen atoms, was retrieved. It was found that all the phytoconstituents were in the standard limit range of 7.0 to 200.0 Å 2 .

ADME Properties
QikProp was used to determine the ADME properties. It helps us establish the ADME of the compound and provides information related to the onset of action and how the drug crosses the barrier. The ADMET properties help the medicinal chemist to make necessary modification to improve the activity. QikProp determined the different variables such as bioavailability, BBB penetration, plasma-protein binding, metabolism, blockade of HERG Kþ channels, and SASA. The results are given in ►Tables 2 and 3.

Prediction of Bioavailability
The parameters that assess oral absorption are the predicted aqueous solubility, logS, the predicted percentage human oral absorption, and agreement to Jorgensen's famous "Rule of Three." According to Jorgensen's RO3, if a compound complies with all or some of the rules (logS > -5.7, Caco-2 > 22 nm/s and # Primary Metabolites< 7), 23 then it is more likely to be orally available.
When examined, the log S values of the phytoconstituents showed that all, except ursolic acid and oleanolic acid, fall within the range of À6.5 to 0.5 and hence they have good aqueous solubility.
The nonactive transport for the gut-blood barrier was assessed from Caco-2 cell permeability, and the examined 18 phytocompounds showed varied results. Phytochemicals like, tinosponone, isocolumbin, nimbin, nimbolide, eugenol, linalool, carvacrol, β-caryophyllene, p-cymene with Caco-2 value higher than 500 showed the maximum permeability to the gut-blood barrier. Xanosporic acid, quercetin and rosmarinic acid were found to have poor gut-blood barrier permeability since their values fell under 25. The remaining compounds showed acceptable permeability.
Tinosponone, isocolumbin, nimbin, nimbolide, mahmoodin, margolone, margolonone, eugenol, linalool, carvacrol, and p-cymene were found to obey Jorgensen's RO3 with no violations. On the analysis of all the compounds, few violated one or two but never violated all three rules, which infers that they are orally bioavailable. Xanosporic acid yet again violated two rules out of three.

Prediction of Blood-Brain Barrier Penetration
Too polar drugs do not cross the BBB. The blood-brain partition coefficients (logB/B) were computed and used as a predictor for access to the central nervous system. QPlogBB analyzed the entrance of a chemical to the central nervous system. Rosmarinic acid's blood/brain partition coefficient does not fall in the recommended range (À3.0-1.2), while all others can penetrate the BBB.

Prediction of Metabolism
All the 18 phytoconstituents fall inside the recommended range (1-8 reactions) of #metab that predicts the number of likely metabolic reactions they may undergo.

Prediction of Blockage of Ether-À-Go-Go-Related Gene Potassium (HERG K þ ) Channel
HERG K þ channel blockers are potentially toxic, and the predicted IC 50 value often provides reasonable prediction for the cardiac toxicity of drugs in the early stages. All phytochemicals except quercetin showed predicted IC 50 value above À5 for HERG Kþ channels, which is in compliance with the standard range.

Prediction of Dermal Penetration
The logKp value predicts the skin permeability and the same is desired to fall in the range of -8.0 and -1.0. All the phytoconstituents were found to be within the recommended range that predicts them to have good dermal penetration.

Prediction of Plasma-Protein Binding
The binding of the drugs to plasma proteins decreases the amount of drug reaching the blood circulation, affecting drug efficiency. The plasma-protein binding is determined by binding to human serum albumin (logKhsa) (recommended range is À1.5-1.5). All the compounds were found to be in the recommended range and thus are likely to reach the blood circulation freely and are hence more available to the target site.

Prediction of Solvent Accessible Surface Area (SASA, FOSA, FISA)
The measure of the contact area between the solvent and molecule represents SASA, which is usually in the range of 300.0 to 1000.0 Å 2 and the measure of FOSA, must be in the range of 0.0 to 750.0, which represents the hydrophobic component of the SASA (saturated carbon and attached hydrogen). All the phytocompounds were found to satisfy the SASA and FOSA criteria within the said ranges.

Bioactivity Prediction
Molinspiration tool was used to predict the bioactivity score of the 18 phytoconstituents of Tinospora cordifolia (Amrita Balli), Azadirachta indica (Neem), Ocimum sanctum (Tulasi), and Plectranthus amboinicus (Indian borage). A molecule having a bioactivity score of more than 0.00 is considered to exhibit good biological activity, while values À0.50 to 0.00 are expected to be moderately active, and if the score is less than À0.50, it is presumed to be inactive. 27 The bioactivity score of compounds is suggestive of moderate interaction with all drug targets. Eugenol, carvacrol, and p-cymene were found to have poor bioactivity, whereas linalool was predicted to show poor-to-moderate bioactivity. The rest of the compounds were found to have good activity since they showed scores above 0.00. The results are tabulated in ►Table 4.

Toxicity Prediction
The in silico toxicity of the 18 compounds were found using two online tools. The AMES toxicity and rat oral acute toxicity were found with the help of ADMETlab 2.0 and; LD 50 , toxicity class, carcinogenicity, and mutagenicity were found using ProTox-II online software. The properties are tabulated in ►Table 5.

AMES Toxicity
The reference for the evaluation for toxicity was taken from ADMETlab 2.0 that mentions empirical decision of 0 to 0.3 as excellent toxicity, 0.3 to 0.7 as moderate toxicity, and 0.7 to 1 as poor toxicity. Except for Tinocordiside, which was found to be moderately toxic, all the other compounds were found to have high toxicity.

Rat Oral Acute Toxicity
Except for tinosponone, isocolumbin, Tinocordiside, nimbin, nimbolide, and mahmoodin, which were found to have poor toxicity results, all the other compounds were found to be highly toxic. The reference for evaluation for toxicity was taken from ADMETlab 2.0, which mentions empirical decision of 0 to 0.3 as excellent toxicity, 0.3 to 0.7 as moderate toxicity, and 0.7 to 1 as poor toxicity.

LD 50
The compounds were evaluated based on the upper threshold for high toxicity according to the globally harmonized system of classification, which is 50mg/kg and any values higher than this were considered toxic. All compounds except p-cymene had a value more than 50 mg/kg and were predicted to be nontoxic. The LD 50 of p-cymene was found to be 3mg/kg and hence was considered to be highly toxic.

Toxicity Class
Lower the class of the compound, higher is its toxicity. p-Cymene was predicted to be in class I. Tinosponone, xanosporic acid, and quercetin were predicted to be in class III. Linalool, rosmarinic acid, and β-caryophyllene were expected to be class V and the remaining compounds were found to fall under class IV.

Carcinogenicity
The carcinogenicity of nimbin, nimbolide, quercetin, ursolic acid, oleanolic acid, and p-cymene was found to be active with probability ranging from 0.51 to 0.68. As for the remaining phytoconstituents, carcinogenicity was found to be inactive, with a probability ranging from 0.52 to 0.74.

Mutagenicity
The mutagenicity of quercetin was found to be active with probability of 0.51. All the other compounds were found to be inactive with a possibility ranging from 0.99 to 0.56.

Molecular Docking
All the 18 phytoconstituents obtained, four from Tinospora cordifolia, six from Azadirachta indica, five from Ocimum sanctum, and three from Plectranthus amboinicus, were docked with two proteins 1JIJ and 4HOE. Most of the phytoconstituents had excellent docking scores. Among the 18 phytoconstituents rosmarinic acid from Ocimum sanctum (Tulasi) showed excellent molecular interaction with TyrRS in S. aureus and DHFR in C. albicans. The docking scores of both target proteins with rosmarinic acid, quercetin, Tino-

Conclusion
In silico ADMET profiles of 18 phytoconstituents from four plants, namely Amrita Balli, Neem, Tulasi and Indian borage, abundantly found in Dakshina Kannada, were screened. The physicochemical properties when screened via QikProp showed that all compounds fell under the recommended range of molecular weight, volume, and PSA. Xanosporic acid violated two rules of RO5, indicating that it may have problems with oral bioavailability. The ADME properties include bioavailability, logS, PCaco, percentage human oral absorption, BBB permeation, log-HERG, dermal penetration, plasma protein binding, SASA for most of the phytocompounds were within the recommended ranges; hence they are promising candidates for drug development. Most of the phytoconstituents showed good bioactivity scores, which infers that they have good druglikeness properties. On the analysis of the toxicity, most of the phytoconstituents were found to be noncarcinogenic and non-mutagenic. In molecular docking studies, the 18 phytoconstituents of different categories were     computationally analyzed for the possible interactions with the TyrRS enzyme target protein 1JIJ in S. aureus and DHFR enzyme target protein 4HOE in C. albicans, most of them interacted excellently. Their docking scores are the evidence for that. Therefore, this data can be utilized for forthcoming studies.

Conflict of Interest
None declared.