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Journal of Drug Delivery and Therapeutics

Open Access to Pharmaceutical and Medical Research

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Open Access   Full Text Article                                                                                                                                                                                          Research Article 

ADMETox-informatics of Plant Derived Octadecanoic Acid (Stearic Acid) from Ethyl Acetate Fraction of Moringa oleifera Leaf Extract as a Natural Lead for Next Generation Drug Design, Development and Therapeutics

Murugan M.¹, Kalaimathi RV.¹, Krishnaveni K.², Basha AN.¹, Gilles A Pallan.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, Sutha S.⁹, Kandavel D.¹⁰, Grace Lydial Pushpalatha G¹¹, Abraham GC.¹² Ram Chand Dhakar¹³

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

 ³Institute of Forest Genetics & Tree Breeding (IFGTB), 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 Department of Medicinal Botany, Govt. Siddha Medical College, Palayamkottai, Tamil Nadu, India 

¹⁰ Government Arts College for Men (Autonomous), Nandanam, Chennai – 600 035, Tamil Nadu, India

¹¹ PG Department of Botany, Sri Meenakshi Government Arts College, Madurai – 625002, TN, India

¹² PG & Research Department of Botany, The American College, Madurai – 625002, TamilNadu, India

¹³ Hospital Pharmacy, SRG Hospital & Medical College Jhalawar-326001, Rajasthan, India

INTRODUCTION

Chemically, Octadecanoic Acid/ Stearophanic Acid (Stearic Acid) is one of the most common long-chain fatty acids, found in combined form in animal and vegetable fats. Commercial “Stearic Acid” contains equal amounts of Stearic Acid (SA) and Palmitic Acid (PA) and small amounts of Oleic Acid (OA)¹. It is one of the common saturated fatty acid naturally obtained from plant sources with the molecular formula C₁₈H₃₆O₂^2,3. Stearic Acid is widely used as the major component in the production of washing detergents, soaps, and personal care products (PCP) such as cosmetics, shampoos and shaving creams. However, it must be noted that the detergent soaps are not directly made from SA, but through saponification of triglycerides of Stearic Acid Esters (SAE)^2,3. SAE with ethylene glycol, glycol stearate, and glycol di-stearate are used in the preparation of shampoos, soaps, and other cosmetic products to impart a pearly effect. They are added to the product in the molten form and allowed to crystallize under specific conditions so as to impart desirable effect in the products. Best available detergents in the market are obtained from amides/ quaternary alkyl-ammonium derivatives of SAE^2,3.

High fatty acid content in Moringa oleifera seed oil (MOSO) has rarely been exploited by Fast-Moving Consumer Goods (FMCG) industries for the production of Food Grade Consumable Products (FGCP) due to low melting point/ lack of plasticity. Dollah et al.⁴ pointed out that enzymatic inter-esterification (EIE) of MOSO with palm stearin (PS) added to palm kernel oil (PKO) could yield fat molecules with better and harder biochemical frame-works that may contain desirable food grade nutritional and physical properties.⁴ 

So far 13 species have been reported from genus Moringa. MO is native to India, however, cultivated all over the world. MO is deciduous, with brittle stem, whitish-gray bark; leaves - pale green in color, bipinnate/ tri-pinnate with opposite, ovate leaflets. All plant parts of MO are endowed with nutraceutical/ pharmaceutical properties. MO has been traditionally used in various indigenous traditional systems of medicine (ITSM) as it is endowed with antioxidants, anti-diabetic, anti-bacterial, anti-fungal, anti-carcinogenic properties however, without side effects. Recently, MO has been considered for the development of Ready to Eat Functional Food Products, Food Grade Nutraceutical Products and therapeutic agents as like other medicinal plants^5-31.

Pharmacological studies indicate that extracts obtained from MO have significant medicinal properties in relation to health and disease, but there isn't enough information on SA. ADMETox information on effects of SA is parsimoniously available, therefore, in the present study ADMETox profile of SA from MO has been carried out. Furthermore, DMPK properties of MO have been “fine-tuned” in order to expand the chances of making SA an apt candidate for clinical trials and biomedical applications.

MATERIALS AND METHODS

In silico Drug-Likeliness and Bioactivity Prediction

Drug likeliness and bioactivity of selected molecule was analyzed using Molinspiration server (http://www.molinspiration.com). Molinspiration tool is cheminformatics software that provides molecular properties as well as bioactivity prediction of compounds. In Molinspiration-based drug-likeness analysis, there are two important factors, including lipophilicity level (log P) and polar surface area (PSA) directly associated with pharmacokinetic properties (PK) of the compounds³². In Molinspiration-based bioactivity analysis, calculation of 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 statistics³³. The analysis was carried out for G protein-coupled receptors (GPCR)³⁴, ion channels, kinases, nuclear hormone receptors, proteases, and other enzymes as major drug targets of SA

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 BOILED-Egg.³⁵ PK properties, such as Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET), of SA was predicted using admerSAR v2.0 server (http://lmmd.ecust.edu.cn/admetsar2/). 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 research. In ADMET analysis, absorption (A) of good drugs depends on factors such as membrane permeability [colon cancer cell line (Caco-2)]³⁶, human intestinal absorption (HIA)³⁷, and status of either P-glycoprotein substrate/ inhibitor³⁸. Distribution (D) of drugs mainly depends on the ability to cross blood-brain barrier (BBB) ³⁹. Metabolism (M) of drugs is calculated by the CYP, MATE1, and OATP1B1-OATP1B3 models⁴⁰. Excretion (E) of drugs is estimated based on 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 toxicity^41,42.

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 reliable⁴³. vNN models can be built keeping quantitative structure–activity relationship (QSAR) models up-to-date to maintain their performance levels⁴⁴. Performance characteristics of the models are comparable, and often superior to those of other more elaborate model.^15-18 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) was used.

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 

SA is a saturated long-chain fatty acid with an 18-carbon backbone. SA is a major component of cocoa butter and shea butter. SA is a white solid with a mild odour, floats on water. SA is a saturated fatty acid present in animal and vegetable fats and oils. It is a waxy solid.

Drug-likeness properties of SA

Score from cLogP: 0.358 (cLogP = 5.581); Score from logS: 0.763 (logS = -3.826); Score from molecular weight: 0.956 (molecular weight 242.0); Score from drug-likeness: 0.0 (drug-likeness = 35.364); No Risk of Mutagenicity Score = 1.0; No Risk of Tumorigenicity Score = 1.0; No Risk of Irritating Effects Score = 1.0; No Risk of Reproductive Effects Score = 1.0 respectively were predicted and the overall predicted drug score for compound 3 was calculated as 0.293. 

Bio-molecular properties of SA

Calculated value for molecular properties of compound 1 were (values given in parenthesis) - miLogP (5.35); TPSA (26.30); Natoms (15); MW (214.35); nON (2); nOHNH (0); Nviolations (1); Nrotb (11); volume (214.74) respectively; and the calculated bioactivity scores for biological properties were - GPCR ligand³⁴ (-0.41); Ion channel modulator (-0.13); Kinase inhibitor (-0.73); Nuclear receptor ligand (-0.43); Protease inhibitor (-0.46); Enzyme inhibitor (-0.11) respectively (Table 1).

Physiochemical Properties of SA

Molecular Formula of SA = C₁₈H₃₆O₂; Molecular weight of SA = 284.48 g/mol; Number of heavy atoms in SA = 20; Number of aromatic. heavy atoms = 0; Fraction Csp3 in SA = 0.94; Number of rotatable bonds in SA = 16; Number of H-bond acceptors in SA = 2; Number of H-bond donors in SA = 1; Molar Refractivity of SA = 90.41; TPSA of SA = 37.30 Ų; Lipophilicity properties of SA = -; Log Po/w (iLOGP) = 4.30; Log Po/w (XLOGP3) = 8.23; Log Po/w (WLOGP) = 6.33; Log Po/w (MLOGP) = 4.67; Log Po/w (SILICOS-IT) = 6.13; Consensus Log Po/w = 5.93; Water Solubility properties of SA - Log S (ESOL) = -5.73; Solubility = 5.26e-04 mg/ml;   1.85e-06 mol/l; Class = Moderately soluble; Log S (Ali) = -8.87; Solubility = 3.80e-07 mg/ml;  1.33e-09 mol/l; Class = Poorly soluble; Log S (SILICOS-IT) = -6.11; Solubility = 2.19e-04 mg/ml;  7.71e-07 mol/l; Class = Poorly soluble; Pharmacokinetics properties of SA - GI absorption of SA is High BBB permeant = No; P-gp substrate = No; CYP1A2 inhibitor = Yes; CYP2C19 inhibitor = No; CYP2C9 inhibitor = No; CYP2D6 inhibitor = No; CYP3A4 inhibitor = No; Log Kp (skin permeation) = -2.19 cm/s; Druglikeness properties of SA – Lipinski’s Rule for SA is Yes; (1 violation: MLOGP>4.15); Ghose’s Rule for SA is No; (1 violation: WLOGP>5.6); Veber’s Rule for SA is No; (1 violation: Rotors>10); Egan Rule for SA is No; (1 violation: WLOGP>5.88); Muegge’s Rule for SA is No;(2 violations: XLOGP3>5, Rotors>15); Bioavailability Score for SA = 0.85 Fig. 1; Medicinal Chemistry properties of SA - PAINS for SA is 0; Brenk’s for SA is No; Leadlikeness for SA is No; (2 violations: Rotors>7, XLOGP3>3.5); Accessibility for SA = 2.54. Percentage distribution of function targets for SA using Swiss Target Prediction is given in Fig. 2; Table 2. Predicted ADMET Properties of SA is given in Table 3 and the summative physicochemical, druggable, ADMET properties of SA have been provided in Table 4. 

vNN model based ADMET analysis of SA

Implemented Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) prediction models, including their performance measures has been carried out.  Model covers diverse set of ADMET endpoints for Maximum Recommended Therapeutic Dose (MRTD), mutagenicity, human liver microsomal (HLM), Pgp inhibitor/substrates (Table 5). 

Liver Toxicity

Drug-induced liver injury (DILI) has been one of the most common reasons for drug withdrawal from market. This application predicts whether a compound could cause DILI. A dataset of 1,431 compounds was obtained from online sources. Dataset contained 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 risk⁴⁵ that includes SA (Table 5).

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, including SA were collected from ChEMBL. In developing the model, the compounds with an IC₅₀ ≤ 10 μM were considered in the in vitro assay as cytotoxic (Table 5).

Metabolism - HLM

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 were considered as stable; otherwise unstable. HLM data was retrieved from ChEMBL database, manually curated and classified compounds as stable or unstable based on reported half-life (T1/2 > 30 min was considered stable, and T1/2 < 30 min unstable. The final dataset contained 3,654 compounds. Of these, as much as 2,313 including SA were classified as stable and 1,341 as unstable (Table 5).

Cytochrome P450 enzyme (CYP) inhibition

CYPs play an important role in metabolism and detoxification of xenobiotics. In vitro data derived from five main drug-metabolizing CYPs-1A2, 3A4, 2D6, 2C9 and 2C19 was 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 for SA in Table 5. 

Membrane Transporters - Blood-Brain Barrier (BBB) 

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 Compounds with log BBB values of less than –0.3 and greater than +0.3 were classified as BBB non-permeable and permeable. Calculated BBB value of SA is -0.195 based on WLOGP vs TPSA using BOILED-Egg Fig. 3; Table 5.

Pgp Substrates/ 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 known as multidrug resistance. For this reason, identifying compounds that can either be transported out of the cell by Pgp (substrates) or impair Pgp function (inhibitors) is required. Models to predict both Pgp substrates and Pgp inhibitors were developed. Pgp substrate dataset consists of measurements of 422 substrates and 400 non-substrates. To generate a large Pgp inhibitor dataset, both the datasets were combined and duplicates were removed to form a combined dataset consisting of a training set of 1,319 inhibitors and 937 non-inhibitors. Analysis indicates that SA is neither a P-glycoprotein substrate nor P-glycoprotein I/II inhibitor as indicated⁴⁷ (Table 5). 

hERG (Cardiotoxicity): 

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 death⁴⁸. As much as 282 known hERG blockers from the literature were retrieved 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 classified as non-blockers. Prediction indicated SA as hERG I non - inhibitor and hERG II as non - inhibitor (Table 5).

MMP (Mitochondrial Toxicity)

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 MMP. vNN-based MMP prediction model was developed using 6,261 compounds collected from a previous study that screened a library of more than 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 5). SA was predicted to be Non-carcinogens with a calculated value of 0.575.

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 test. A prediction model was developed using a literature dataset of 6,512 compounds, of which 3,503 were Ames-positive. Prediction indicated SA as Non AMES toxic with a calculated value of 0.963 (Table 5).

Maximum Recommended Therapeutic Dose (MRTD)

MRTD is an estimated upper daily dose that is considered to be safe. A prediction model was developed based on a dataset of MRTD values 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 (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 FDA was used for validation. The total dataset for the model contained 1,185 compounds⁴⁹. The predicted MRTD value is reported in mg/day unit based upon an average adult weighing 60 kg. MRTD for SA was calculated as -0.791 (Table 5).

CONCLUSION

ADMET-informatics of Octadecanoic Acid (Stearic Acid) from ethyl acetate fraction of Moringa oleifera leaves has been envisaged to predict drug metabolism and pharmacokinetics (DMPK) outcomes. ADMET informatics contributes to the deeper understanding of SA as a major source of drug lead from Moringa oleifera with immense therapeutic potential. Results indicate that SA is of GRAS standard drug with predicted values within the range suitable for human consumption. Data generated herein could be useful for the development of SA as PBNPL for next generation drug design, development and therapies.

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Figure 1: Schematic diagram of Bioavailability Radar for Drug likeness of SA (lipophilicity: XLOGP3 between-0.7 and+5.0, size: MW between 150 and 500 g/mol, polarity: TPSA between 20 and 130 A2, solubility: log S not higher than 6, saturation: fraction of carbons in the sp3 hybridization not less than 0.25, and flexibility: no more than 9 rotatable bonds

Figure 2: Percentage distribution of function targets for SA using SwissTargetPrediction 

Figure 3: Schematic representation of perceptive evaluation of passive gastrointestinal absorption (HIA) and Brain penetration (BBB) of SA with WLOGP-versus-TPSA using BOILED-Egg

Table 1: In silico Drug-Likeliness and Bioactivity Prediction

Table 2: Predicted Target/ Target Class and Functional Probabilities of SA

Table 3: Predicted ADMET Properties of SA

 
 Table 4: Summative Physicochemical, Druggable, ADMET Properties of SA

Table 5 Performance measures of vNN models in 10-fold cross validation using a restricted or unrestricted applicability domain