Available online on 15.09.2022 at http://jddtonline.info

Journal of Drug Delivery and Therapeutics

Open Access to Pharmaceutical and Medical Research

Copyright  © 2022 The   Author(s): This is an open-access article distributed under the terms of the CC BY-NC 4.0 which permits unrestricted use, distribution, and reproduction in any medium for non-commercial use provided the original author and source are credited

Open Access   Full Text Article                                                                                                                                                                      Research Article 

ADMET informatics of Plant Derived n-Hexadecanoic Acid (Palmitic Acid) from ethyl acetate fraction of Moringa oleifera leaf extract

Krishnaveni K.¹, Murugan M.², Kalaimathi RV.², Basha AN.¹, Pallan GA.¹, Kandeepan C.¹, Senthilkumar N.³, Mathialagan B.⁴, Ramya S.⁴, Jayakumararaj R.*⁵, Loganathan T.⁶, Pandiarajan G.⁷, Kaliraj P.⁸, Ram Chand Dhakar¹⁰

¹ PG & Research Department of Zoology, Arulmigu Palaniandavar College of Arts & Culture, Palani – 624601, TN, India

² Department of Zoology, GTN Arts & Science College, Dindigul - 624005, TN, India

 ³ Institute of Forest Genetics & Tree Breeding (IFGTB), Indian Council of Forestry Research & Education (ICFRE), Coimbatore – 641002, TN, India 

⁴ PG Department of Zoology, Yadava College (Men), Thiruppalai - 625014, Madurai, TN, India

⁵ Department of Botany, Government Arts College, Melur – 625106, Madurai District, TN, India

⁶ Department of Plant Biology & Plant Biotechnology, LN Government College (A), Ponneri, TN, India

⁷ Department of Botany, Sri S Ramasamy Naidu Memorial College (A), Sattur - 626203 TN, India

⁸ Department of Zoology, Sri S Ramasamy Naidu Memorial College (A), Sattur - 626203 TN, India

⁹ Hospital Pharmacy, SRG Hospital & Medical College Jhalawar-326001, Rajasthan, India

INTRODUCTION 

Palmitic acid, (PA) is the most common saturated fatty acid found in animals, plants and microorganisms. It’s C:D (Total number of carbon atoms to the total number of carbon-carbon double-bonds) is 16:0¹. It is a major component of oil from fruits of oil palms (palm-oil), 44% of total fats. Meat, cheeses, butter, and other dairy products also contain higher amounts of palmitic acid (50–60% of total fats)². Chemically, palmitates are salts and esters of PA, on the other hand palmitate is anion form of PA at a pH of 7.4. It has been demonstrated that PA represents 20–30% of total fatty acids (FA) in membrane phospholipids (PL), and adipose triacylglycerols (TAG)³, and can be provided in the diet or synthesized endogenously via de novo lipogenesis (DNL). 

PA has been for long time negatively depicted for its putative detrimental health effects, shadowing its multiple biochemical, metabolic and physiological activities³. It has been established that the level of PA content is controlled by a well-defined concentration, and changes in its intake doesn’t influence tissue concentration as exogenous source is counter balanced by the endogenous biosynthesised PA. Disruption of PA homeostatic balance, implicated in different physio-pathological conditions is often related to endogenous synthesis of PA, irrespective of its dietary intake⁴. It has been pointed out that increasing dietary PA decreases fat oxidation and daily energy expenditure in the system⁵. However, overproduction of PA by DNL, activated by physio-pathological conditions and chronic nutritional imbalance, leads to a systemic inflammatory response (SIR) and metabolic dysregulation, resulting in dyslipidemia, insulin resistance and dysregulated fat deposition and distribution⁶. Further, increase in cellular concentration of PA impairs endothelial progenitor cells and bone marrow-derived progenitor cell bioavailability⁷. PA induces central leptin resistance and impairs hepatic glucose and lipid metabolism⁸. It dysregulates the Hippo-YAP pathway and inhibits angiogenesis by inducing mitochondrial damage and activating the cytosolic DNA sensor cGAS-STING-IRF3 signalling mechanism⁹. Emerging evidence shows that PA serves as an intracellular signalling molecule regulating the progression and development of many diseases at the molecular level¹⁰. Studies depict that high-fat diet (HFD)-induced obesity is associated with increased cancer risk. PA increases invasiveness of pancreatic cancer cells AsPC-1 through TLR4/ROS/NF-kappaB/MMP-9 signalling pathway¹¹. Further, PA impairs hepatocellular carcinoma development by modulating membrane fluidity and glucose metabolism¹². PA has been reported to induce neurotoxicity and gliatoxicity in SH-SY5Y human neuroblastoma and T98G human glioblastoma cells¹³. PA reduces the autophagic flux in hypothalamic neurons by impairing autophagosome-lysosome fusion and endolysosomal dynamics¹⁴. It has been indicated that, long-term feeding with HFD increases the concentration of PA in hypothalamus; hypothalamic neuronal cells, up on exposure to PA inhibits autophagic flux from the synthesis of the autophagosomes, up to their lysosomal fusion and degradation. However, mechanism by which PA impairs autophagy in hypothalamic neurons remains unknown. Further, impaired autophagy in hypothalamic neurons promotes obesity, which suggests that a balanced autophagy is required to inhibit malignant transformation and results in tumor initiation, progression, and/ or response to therapy of obesity-related cancers¹⁵. Moringa oleifera leaf is a low cost food material that remains as excellent source of proteins, essential and non-essential amino acids, lipids, sugars, essential fatty acids, vitamin precursors, and mineral, as well as organic acid, terpenoids, alkaloids, steroids, and phenolic compounds. Moringa leaf has been commonly used as therapeutic food to prevent hypertension, hypercholesterolemia, atherosclerosis, diabetes, cancer and malnutrition among economically poor section of people in developing countries^16-30. In recent years, combinatorial chemistry and high-throughput screening have significantly increased the number of compounds for which early data on absorption, distribution, metabolism, excretion (ADME) and toxicity (T) are needed, which has in turn driven the development of a variety of medium and high-throughput in vitro ADMET screens for drug development and design with deeper dimension ad interim maintain high degree of precision taking into account time and cost economics^30-42. In the current framework of the available experimental evidences related to PA, it is clear that there exists a knowledge gap in terms of its biomedical application due to the dearth of ADMET data and preclinical trails that hampers exploitation of PA by pharma-industries on commercial basis.   

MATERIALS AND METHODS

In silico Drug-Likeliness and Bioactivity Prediction

The drug likeliness and bioactivity of selected molecule was analyzed using the Molinspiration server (http://www.molinspiration.com). Molinspiration tool is cheminformatics software that provides molecular properties as well as bioactivity prediction of compounds⁴³. In Molinspiration-based drug-likeness analysis, there are two important factors, including the lipophilicity level (log P) and polar surface area (PSA) directly associated with the pharmacokinetic properties (PK) of the compounds⁴⁴. In Molinspiration-based bioactivity analysis, the calculation of the bioactivity score of compounds toward GPCR ligands, ion channel modulators, kinase inhibitors, nuclear receptor ligands, protease inhibitors, and other enzyme targets were analyzed by Bayesian statistics⁴³. This was carried out for G protein-coupled receptors (GPCR), ion channels, kinases, nuclear hormone receptors, proteases, and other enzymes (RdRp), are the major drug targets of most of the drugs⁴⁵.

In silico ADMET Analysis

SwissADME: is a Web tool that gives free access to a pool of fast yet robust predictive models for physicochemical properties, pharmacokinetics, druglikeness and medicinal chemistry friendliness, among which in-house proficient methods such as iLOGP (a physics-based model for lipophilicity) or the BOILED-Egg (an intuitive graphical classification model for gastrointestinal absorption and brain access). It is the first online tool that enables ADME-related calculation for multiple molecules, allowing chemical library analysis and efficient lead optimization⁴⁶. PK properties, such as Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET), of fatty acids were predicted using admerSAR v2.0 server (http://lmmd.ecust.edu.cn/admetsar2/) and The admerSAR server is an open-source computational tool for prediction of ADMET properties of compounds, which makes it a practical platform for drug discovery and other pharmacological research⁴⁷. In ADMET analysis, absorption (A) of good drugs depends on factors such as membrane permeability⁴⁸ [designated by colon cancer cell line (Caco-2)], human intestinal absorption (HIA)⁴⁹, and status of either P-glycoprotein substrate or inhibitor⁵⁰. Distribution (D) of drugs mainly depends on the ability to cross blood-brain barrier (BBB)⁵¹. The metabolism (M) of drugs is calculated by the CYP, MATE1, and OATP1B1-OATP1B3 models⁵². Excretion (E) of drugs is estimated based on the renal OCT substrate. Toxicity (T) of drugs is predicted on Human Ether-A-Go-Go related gene inhibition, carcinogenic status, mutagenic status, and acute oral toxicity⁵³.

vNN model building and analysis

vNN method was used to calculate the similarity distance between molecules in terms of their structure, and uses a distance threshold to define a domain of applicability to ensures that the predictions generated are reliable. vNN models can be built keeping quantitative structure–activity relationship (QSAR) models up-to-date to maintain their performance levels. Performance characteristics of the models are comparable, and often superior to those of other more elaborate model.^54-61 One of the most widely used measures of similarity distance between two small molecules is Tanimoto distance, d, which is defined as:

where n(P∩Q) is number of features common to molecules p and q, and n(P) and n(Q) are the total numbers of features for molecules p and q, respectively. The predicted biological activity y is given by a weighted across structurally similar neighbours:

where d_i denotes Tanimoto distance between a query molecule for which a prediction is made and a molecule i of the training set; d₀ is a Tanimoto-distance threshold, beyond which two molecules are no longer considered to be sufficiently similar to be included in the average; y_i is the experimentally measured activity of molecule i; v denotes the total number of molecules in the training set that satisfies the condition d_i≤d₀; and h is a smoothing factor, which dampens the distance penalty. Values of h and d₀ are determined from cross-validation studies. To identify structurally similar compounds, Accelrys Extended-Connectivity FingerPrints with a diameter of four chemical bonds (ECFP4) were used^54-61.

Model Validation

A 10-fold cross-validation (CV) procedure was used to validate new models and to determine the values of smoothing factor h and Tanimoto distance d₀. In this procedure, data was randomly divided into 10 sets, and used 9 to develop the model and 10^th to validate it, this process was repeated 10 times, leaving each set of molecules out once.

Performance Measures

Following metrics were used to assess model performance. (1) sensitivity measures a model’s ability to correctly detect true positives, (2) specificity measures a model’s ability to detect true negatives, (3) accuracy measures a model’s ability to make correct predictions and (4) kappa compares the probability of correct predictions to the probability of correct predictions by chance (its value ranges from +1 (perfect agreement between model prediction and experiment) to –1 (complete disagreement), with 0 indicating no agreement beyond that expected by chance).

where TP, TN, FP, and FN denote the numbers of true positives, true negatives, false positives, and false negatives, respectively. Kappa is a metric for assessing the quality of binary classifiers. Pr (e) is an estimate of the probability of a correct prediction by chance. It is calculated as:

The coverage is the proportion of test molecules with at least one nearest neighbour that meets the similarity criterion. The coverage is a measure of how many test compounds are within the applicability domain of a prediction model.

RESULTS AND DISCUSSION

Physiochemical properties of PA

In silico biomolecular properties of PA is provided in Table 1. Physiochemical properties (value in parenthesis) of PA viz., melting point (61.8 °C); boiling point (351.5 °C); water solubility (0.04 mg/L (at 25 °C)); logP (7.17); logS (-6.81); Molecular weight (g/mol) (256.43); Log P (5.55); Topological polar surface area (Å2) (37.3); Number of hydrogen bond acceptors (1); Number of hydrogen bond donors (1); Number of carbon atoms (16); Number of heavy atoms (18); Number of heteroatoms (2); Number of nitrogen atoms (0); Number of sulfur atoms (0); Number of chiral carbon atoms (0); Stereochemical complexity (0); Number of sp hybridized carbon atoms (0); Number of sp2 hybridized carbon atoms (1); Number of sp3 hybridized carbon atoms (15); Shape complexity (0.94); Number of rotatable bonds (14); Number of aliphatic carbocycles (0); Number of aliphatic heterocycles (0); Number of aliphatic rings (0); Number of aromatic carbocycles (0); Number of aromatic heterocycles (0); Number of aromatic rings (0); Total number of rings (0); Number of saturated carbocycles (0); Number of saturated heterocycles (0); Number of saturated rings (0); Number of Smallest Set of Smallest Rings (SSSR) (0);  Water Solubility (0.000407 mg/mL); logP (7.23); logP (6.26); logS (-5.8); pKa (Strongest Acidic) (4.95); Physiological Charge (-1); Hydrogen Acceptor Count (2); Hydrogen Donor Count (1); Polar Surface Area (37.3 Å2); Rotatable Bond Count (14); Refractivity (77.08 m3•mol-1); Polarizability (34.36 Å3); Number of Rings (0); Bioavailability (0); Rule of Five (No); Ghose Filter (No); Veber's Rule (No); MDDR-like Rule (No) respectively.  

Drug-likeness properties of PA

Drug-likeness properties of PA (value in parenthesis) Number of Lipinski’s rule of 5 violations (1); Lipinski’s rule of 5 (Passed); Number of Ghose rule violations (0); Ghose rule (Passed); Veber rule (Bad); Egan rule (Good); GSK 4/400 rule (Bad); Pfizer 3/75 rule (Bad); Weighted quantitative estimate of drug-likeness (QEDw) score (0.41 ) respectively.  

ADMET properties of PA 

One of the key issues on the physiological role of PA is the preservation of a definite tissue concentration and repartition in different lipid classes, which requires a fine regulation of its metabolism⁶². In fact, distribution and metabolism of PA in tissues is normally maintained under stringent homeostatic control, while other aspects are unknown yet⁶³. ADMET profile evaluated using admetSAR database for PA shows highest binding energy (Table 2). admetSAR predicted classification and regression values for PA and the results seems to have been calculated for different types of models such as blood brain barrier, human intestinal absorption, Caco2 permeability all of which showed positive results ensuring that the compound passes all the models and have no side effects on absorption. Similarly in case of metabolism, various CytochromeP450 (CYP) substrate and inhibitor models were calculated and the results show that they are Non substrate and Non-inhibitor except CYP450 1A2 Inhibitor. In terms of toxicity, it is found to be non-carcinogenic. Although some toxicity models show some negative results the regression profiles indicates that they have very low probability values⁶⁴.

Human Intestinal Absorption (+) - 0.9888; Blood Brain Barrier (+) - 0.9488; Caco-2 permeable (+) - 0.8326; P-glycoprotein substrate (Non-substrate) - 0.6321; P-glycoprotein inhibitor I (Non-inhibitor) - 0.9598; P-glycoprotein inhibitor II (Non-inhibitor) - 0.9277; Renal organic cation transporter (Non-inhibitor) - 0.9266; CYP450 2C9 substrate (Non-substrate) - 0.7886; CYP450 2D6 substrate (Non-substrate) - 0.8956; CYP450 3A4 substrate (Non-substrate) - 0.6982; CYP450 1A2 substrate (Inhibitor) - 0.8326; CYP450 2C9 inhibitor (Non-inhibitor) - 0.8808; CYP450 2D6 inhibitor (Non-inhibitor) - 0.9554; CYP450 2C19 inhibitor (Non-inhibitor) - 0.9578; CYP450 3A4 inhibitor (Non-inhibitor) - 0.9484; CYP450 inhibitory promiscuity (Low CYP Inhibitory Promiscuity) - 0.9647; Ames test (Non AMES toxic) - 0.9865; Carcinogenicity (Non-carcinogens) - 0.6452; Biodegradation (Ready biodegradable) - 0.8795; Rat acute toxicity (1.3275 LD₅₀, mol/kg) - Not applicable; hERG inhibition (predictor I) (Weak inhibitor) - 0.9322; hERG inhibition (predictor II) (Non-inhibitor) - 0.8868 respectively. Recently, Chowdhury et al⁶⁵. demonstrated that exogenous exposure PA significantly increased protein expression of mTOR and current density of hERG 1a/1b channels. The increase in current is mediated by a targeted and increased expression of hERG 1b, through upregulation of protein translation (Table 2). Furthermore, PA significantly affected hERG 1a/1b channel inactivation kinetics⁶⁵.

vNN based ADMET prediction models for PA

The implemented Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) prediction models, including their performance measures includes 15 models cover a diverse set of ADMET endpoints (Table 3).

Liver Toxicity

DILI: Drug-induced liver injury (DILI) is one of the most frequent adverse clinical reactions and a relevant cause of morbidity and mortality. DILI has been one of the most commonly cited reasons for drug withdrawals from the market. This application predicts whether a compound could cause DILI. The dataset of 1,431 compounds was obtained from four sources used⁶⁶. This dataset contains both pharmaceuticals and non-pharmaceuticals; a compound was classified as causing DILI if it was associated with a high risk of DILI and not if there was no such risk (Table 3).

Cytotoxicity (HepG2): Cytotoxicity is the degree to which a chemical causes damage to cells. A cytotoxicity prediction model was developed using in vitro data on toxicity against HepG2 cells for 6,000 structurally diverse compounds, which was collected from ChEMBL. In developing the model, the compounds with an IC₅₀ ≤ 10 μM were considered in the in vitro assay as cytotoxic. Presence of high glucose and PA, HepG2 cells undergo severe metabolic and oxidative stress, resulting in increased ROS production, lipid, protein, and DNA damage, NF-kB-, TNF-α-, IL6-, and NO-dependent inflammatory responses, increased apoptosis, and decreased mitochondrial function leading to a significant shift in the energy metabolism^67,68. 

Metabolism

HLM: The human liver microsomal (HLM) stability assay is commonly used to identify and exclude compounds that are too rapidly metabolized. For a drug to achieve effective therapeutic concentrations in the body, it cannot be metabolized too rapidly by the liver. Compounds with a half-life of 30 min or longer in an HLM assay are considered as stable; otherwise they are considered unstable. HLM data was retrieved from the ChEMBL database, manually curated the data, and classified compounds as stable or unstable based on the reported half-life (T1/2 > 30 min was considered stable, and T1/2 < 30 min unstable (Table 3).  The final dataset contained 3,654 compounds. Of these, as much as 2,313 were classified as stable and 1,341 as unstable⁶⁹.

Cytochrome P450 enzyme (CYP) inhibition: CYPs constitute a superfamily of proteins that play an important role in the metabolism and detoxification of xenobiotics⁷⁰. In vitro data derived from five main drug-metabolizing CYPs—1A2, 3A4, 2D6, 2C9, and 2C19 were used to develop CYP inhibition models. CYP inhibitors were retrieved from PubChem and classified a compound with an IC₅₀ ≤ 10 μM for an enzyme as an inhibitor of the enzyme. Predictions for the following enzymes: CYP1A2, CYP3A4, CYP2D6, CYP2C9, and CYP2C19 have been provided (Table 3).

Membrane Transporters

BBB: The blood-brain barrier (BBB) is a highly selective barrier that separates the circulating blood from the central nervous system⁴⁶. VNN-based BBB model has been developed, using 352 compounds whose BBB permeability values (log⁡BB) were obtained from the literature respectively. Compounds with log⁡BB values of less than –0.3 and greater than +0.3 were classified as BBB non-permeable and permeable (Table 3).

Pgp Substrates and Inhibitors: P-glycoprotein (Pgp) is an essential cell membrane protein that extracts many foreign substances from the cell. Cancer cells often overexpress Pgp, which increases the efflux of chemotherapeutic agents from the cell and prevents treatment by reducing the effective intracellular concentrations of such agents - a phenomenon known as multidrug resistance⁵⁸. For this reason, identifying compounds that can either be transported out of the cell by Pgp (substrates) or impair Pgp function (inhibitors) is of great interest. Models to predict both Pgp substrates and Pgp inhibitors were developed. The Pgp substrate dataset contained of measurements of 422 substrates and 400 non-substrates. To generate a large Pgp inhibitor dataset and both the datasets were combined and removed duplicates to form a combined dataset consisting of training set of 1,319 inhibitors and 937 non-inhibitors (Table 3).

hERG (Cardiotoxicity): The human ether-à-go-go-related gene (hERG) codes for a potassium ion channel involved in the normal cardiac repolarization activity of the heart. Drug-induced blockade of hERG function can cause long QT syndrome, which may result in arrhythmia and death⁶⁵. As much as 282 known hERG blockers from the literature were retrieved known hERG blockers from the literature and classified compounds with an IC₅₀ cut-off value of 10 μM or less as blockers. A set of 404 compounds with IC50 values greater than 10 μM were collected from ChEMBL and classified them as non-blockers (Table 3).

MMP (Mitochondrial Toxicity): Given the fundamental role of mitochondria in cellular energetics and oxidative stress, mitochondrial dysfunction has been implicated in cancer, diabetes, neurodegenerative disorders, and cardiovascular diseases. A largest dataset of chemical-induced changes in mitochondrial membrane potential (MMP), was used based on the assumption that a compound that causes mitochondrial dysfunction is also likely to reduce the MMP¹¹. A vNN-based MMP prediction model was developed using 6,261 compounds collected from a previous study that screened a library of 10,000 compounds (~8,300 unique chemicals) at 15 concentrations, each in triplicate, to measure changes in the MMP in HepG2 cells. The study found that 913 compounds decreased the MMP, whereas 5,395 compounds had no effect (Table 3).

Mutagenicity (Ames test): Mutagens are chemicals that cause abnormal genetic mutations leading to cancer. A common way to assess a chemical’s mutagenicity is the Ames test⁷¹. A prediction model was developed using a literature dataset of 6,512 compounds, of which 3,503 were Ames-positive (Table 3).

MRTD: The Maximum Recommended Therapeutic Dose (MRTD) is an estimated upper daily dose that is safe⁷². A prediction model was developed based on a dataset of MRTD values publically disclosed by the FDA, mostly of single-day oral doses for an average adult with a body weight of 60 kg, for 1,220 compounds (most of which are small organic drugs). Organometallics, high-molecular weight polymers were excluded (>5,000 Da), nonorganic chemicals, mixtures of chemicals, and very small molecules (<100 Da). An external test set of 160 compounds collected by the FDA was used for validation. The total dataset for the model contained 1,185 compounds. The predicted MRTD value is reported in mg/day unit based upon an average adult weighing 60 kg (Table 3).

Predicted human target protein associations

TARGET (PROBABILITY); Fatty acid binding protein adipocyte (0.9359); Peroxisome proliferator-activated receptor alpha (0.9359); Fatty acid binding protein muscle (0.9359); Fatty acid binding protein epidermal (0.9359); Peroxisome proliferator-activated receptor delta (0.9359); Fatty acid binding protein intestinal (0.9359); Free fatty acid receptor 1 (0.5982); Solute carrier family 22 member 6 (0.2079); Dual specificity phosphatase Cdc25A (0.1988); 11-beta-hydroxysteroid dehydrogenase 1 (0.1534); Aldo-keto reductase family 1 member B10 (0.1262); DNA polymerase beta (0.1080); Vitamin D receptor (0.0899); Bile acid receptor FXR (0.0899); Histone lysine demethylase PHF8 (0.0899); UDP-glucuronosyltransferase 2B7 (0.0899); Cytochrome P450 19A1 (0.0899); Corticosteroid binding globulin (0.0899); Testis-specific androgen-binding protein (0.0899); Estradiol 17-beta-dehydrogenase 3 (0.0899); Glucose-6-phosphate 1-dehydrogenase (0.0899); GABA-B receptor (0.0899); Prostanoid EP2 receptor (0.0899); Lysine-specific demethylase 2A (0.0899); Lysine-specific demethylase 5C (0.0899); G-protein coupled bile acid receptor 1 (0.0899); Niemann-Pick C1-like protein 1 (0.0626); GABA A receptor alpha-2/beta-2/gamma-2 (0.0626); Protein farnesyltransferase (0.0626); Androgen Receptor (0.0626); Hydroxyacid oxidase 1 (0.0536); Prostanoid FP receptor (0.0536); Protein-tyrosine phosphatase 1B (0.0536); 11-beta-hydroxysteroid dehydrogenase 2 (0.0536); Glutathione S-transferase kappa 1 (0.0536); Leukotriene A4 hydrolase (0.0536); Carbonic anhydrase II (0.0536); Carbonic anhydrase I (0.0536); Nuclear receptor subfamily 0 group B (0.0536); CDC45-related protein (0.0536); Leukocyte common antigen (0.0536); Peroxisome proliferator-activated receptor (0.0536); Cytochrome P450 26A1 (0.0536); Retinoid X receptor alpha (0.0536); Retinoic acid receptor gamma (0.0536); Retinoic acid receptor beta (0.0536); Retinoic acid receptor alpha (0.0536); Anandamide amidohydrolase (0.0536); Telomerase reverse transcriptase (0.0536); Fatty acid-binding protein, liver (0.0536); Cytochrome P450 26B1 (0.0536); Retinoid X receptor gamma (0.0536); G-protein coupled receptor 120 (0.0536); Voltage-gated calcium channel alpha2 (0.0536); Retinoid X receptor beta (0.0536); Monocarboxylate transporter 1 (0.0536); Arachidonate 15-lipoxygenase (0.0536); Arachidonate 12-lipoxygenase (0.0536); Prostanoid EP4 receptor (0.0536); Glycine receptor subunit alpha-1 (0.0536); Epoxide hydratase (0.0536); Multidrug resistance-associated protein 1 (0.0536); P-glycoprotein 1 (0.0536); Neuronal acetylcholine receptor protein alpha-7 (0.0536); Cytosolic phospholipase A2 (0.0536); Prostaglandin E synthase (0.0536); Acyl-CoA desaturase (0.0536); SUMO-activating enzyme (0.0536); Metabotropic glutamate receptor 5 (0.0536); Plasminogen (0.0536); Serotonin 2b (5-HT2b) receptor (0.0536); Nuclear receptor ROR-beta (0.0536); MAP kinase ERK2 (0.0536); Nuclear receptor ROR-alpha (0.0536); GPCR 44 (0.0536); Intercellular adhesion molecule (ICAM-1), Integrin (0.0536); Thromboxane A2 receptor (0.0000); Hepatocyte nuclear factor 4-alpha (0.0000) respectively (Table 4; Fig. 1). 

CONCLUSION

PBNPs from wild plants still remain an ideal storehouse of phyto pharmaceutical compounds⁷³. Data obtain in the present study depict that PA could be a potential NS5B polymerase inhibitor besides other ADMET properties predicted for PA indicate that it is non-carcinogenic and biodegradable. Finally, the results may have some implications in elucidating the molecular mechanisms that critically regulates pathophysiological metabolic events in the regulations of hyperglycemia/ hyperlipidemia-induced complications associated with diabesity and CVD and provide better understanding to identify key molecular targets for the therapeutic management of PA induced metabolic disorders.

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Table 1 In silico biomolecular properties of PA

Table 2 Summary of Physicochemical, Druggability & ADMET Properties of PA

Table 3 Performance measures of vNN models in 10-fold cross validation using a restricted or unrestricted applicability domain

Table 4 In silico target predication and the probability for PA