computational approach for the evaluation of ...2016/10/11 · keywords: adme, pass, computational...

www.wjpps.com Vol 5, Issue 12, 2016.

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Sateesh et al. World Journal of Pharmacy and Pharmaceutical Sciences

COMPUTATIONAL APPROACH FOR THE EVALUATION OF

BIOACTIVE COMPOUNDS FROM ETHNOBOTANICALS FOR THEIR

PHARMACOLOGICAL POTENTIAL AND BIOLOGICAL ACTIVITY

Suhail Mohammed Hussain, Arbaaz Ahmed L., Shrihith A., Sateesh M. K.*

Molecular Diagnostics and Nanobiotechnology Laboratories, Department of Microbiology &

Biotechnology, Bangalore University, Jnana Bharathi Campus, Bangalore - 560056,

Karnataka, India.

ABSTRACT

Providing safe and effective drug therapy requires knowledge on the

pharmacokinetics and pharmacodynamics of drugs. Many synthetic

drugs often fail to enter the market as a result of poor pharmacokinetic

and pharmacodynamics profile. Medicinal plants have traditionally

proven their value as a basis for molecules with therapeutic potential,

and currently they represent a significant pool for novel drug leads.

Plants contain a number of phytopharmaceuticals that are responsible

for various biological activities. Traditionally, drugs are discovered by

testing compounds synthesized in time-consuming multi-step processes

against a battery of in vivo biological screening. The use of

computational tools in prediction of pharmacological and biological

properties of phytochemical compounds is growing rapidly in drug

discovery as the benefits they provide in high throughput and early

application in drug design are realized. In the present study we have

tested 330 GC- MS derived phytocompounds from ten ethnobotanicals

viz., Eupatorium triplinerve, Abrus precatorius, Stylosanthes fruticosa, Epipremnium

aureum, Annona squamosa, Ficus carica, Punica granatum, Cassia italic, Madhuca

longifolia and Aegle marmelos for their biological activity as promising therapeutic

compounds. The drug likeliness of the selected compounds were predicted by WDI and

Lipinski‟s “rule of five” using Molsoft Online Tool. The ADMET properties, Ames test,

Carcinogenic and Mutagenic properties were checked by PreADMET server. Out of 330

compounds analyzed, sixteen compounds were reported as non-mutagenic and non-

*Corresponding Author

Dr. Sateesh M. K.

Molecular Diagnostics

and Nanobiotechnology

Laboratories, Department

of Microbiology &

Biotechnology, Bangalore

University, Jnana Bharathi

Campus, Bangalore - 560

056, Karnataka, India.

[email protected]

[email protected]

Article Received on

11 Oct. 2016,

Revised on 31 Oct. 2016,

Accepted on 20 Nov. 2016

DOI: 10.20959/wjpps201612-8228

WORLD JOURNAL OF PHARMACY AND PHARMACEUTICAL SCIENCES

SJIF Impact Factor 6.041

Volume 5, Issue 12, 1042-1056. Research Article ISSN 2278 – 4357

mailto:[email protected]:[email protected]


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carcinogenic phytocompounds. Biological activity of these sixteen compounds were

predicted individually using PASS server, activities such as antibacterial, antifungal,

antihelmenthic, antidiabetic and antieczematic etc. were reported for these compounds. In the

present article, an effort has been made to reveal various pharmacological potentiality of

phytocompounds and these compounds can be further studied in vitro and in vivo for the

discovery of novel drugs.

KEYWORDS: ADME, PASS, Computational tools, biological activity, Drug Discovery.

I. INTRODUCTION

Drugs, chemical compounds capable of influencing biological systems, have been used to

treat human disease for thousands of years, mainly in the form of plant extracts. The first

demonstrable substantiation of plants being used for medicinal purposes developed in

Sumeria 5000 years ago and was subsequently codified in meticulous way, predominantly in

India and China (Alavijeh et al., 2005; Petrovska, 2012). An application of ethnomedicine

has amplified considerably in primary healthcare, as 80% of the world‟s population relies on

ethnomedicine as testified by WHO. People of small villages and native communities in

developing countries use ethnobotanicals for the treatment of common infectious diseases

(Mussarat et al., 2014; Vedashree et al., 2014; Hosseinzadeh et al., 2015). The use of plants

in the control and treatment of diseases in recent years has gained considerable importance

and major sources of biologically active and high pharmacological active compounds are

from plants and fruits (WHO Report, 2002; Sofi et al., 2013). The multi-drug resistance had

not only increased the morbidity and mortality rate but also maximized expenditure on

patient management and enforcement of infection control measures (Woodford, 2009). The

bioactive compounds and chemicals obtained from plant source were identified before they

discovered microbes (Sofowora, 1982; Ramawat and Mérillon, 2008). Rich source of

antimicrobial agents is supplemented by ethnobotanical plants (Mahesh and Satish, 2008).

Artificial drugs have number of drug resistant microorganisms with unpleasant side effects is

increasing (Maobe et al., 2013). Owing to lack of confined synthetic drug, WHO has

recommended for the evaluation of pharmacopeial and physiochemical parameters for their

efficacy in identification and authentication of plant material (Jadhav et.al, 2009;

Lakshmeesha et al., 2013). The pharmaceutical and pharmacopeial value of plants are mirror

image of bioactive chemical compounds which has a particular action on the human body

such as alkaloids, tannins, phenolic compounds and flavonoid are significant compared to


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other chemical components (Hill, 1952; Sateesh, 2009). Poor and pharmacodynamics as well

as pharmacokinetic profiles of drugs often fail to invade in to the market (Darvas et al. 2002;

Iram et al., 2016).

As natural product-based drug discovery is associated with some intrinsic difficulties,

pharmaceutical industry has shifted its main focus toward synthetic compound libraries and

high throughput screening (HTS) for discovery of new drug leads (Atanasov et al., 2015).

The obtained results, however, did not meet the expectations as evident in a declining number

of new drugs reaching the market (David et al., 2015; Atanasov et al., 2015). This

circumstance revitalized the interest in natural product-based drug discovery, despite its high

complexity, which in turn necessitates broad interdisciplinary research approaches. In

addition, medicinal plants are having numerous phytochemicals with different secondary

metabolites nature like Poly phenols (phenolic acids, anthocyanins, proanthocyanidins,

flavonols, tannins), isoprenoids (sesqiterpenes, diterpenes, triterpenes, steroids, saponins),

alkaloids (indole alkaloids, lysergic acid diethylamide, tropane alkaloids, ergot group) and

fatty acid are dynamic constituents are found in lot of herbal plants. Though there are many

medicinal plants in traditional use across civilizations for the treatment of various human

disorders, only a few have been studied extensively to determine the pharmacological basis

for their therapeutic effects. Several of them have raised extraordinary interest among

investigators and scholars in understanding their potential and usefulness in the treatment of

human illnesses.

Since 1960‟s, medicinal chemistry has shown an extravagant application of quantitative

structure-activity relationship (QSAR) methods to homogeneous classes of chemicals.

Articulation of efficient quantitative models is characterized by induction of same type of

biological activity and elucidation of the action mechanisms of yet untested chemicals

(Hansch, 1990; Pritchard, 2008). The early application in drug design is realized by the use of

computational tools to prognosticate of ADME and toxic properties of compounds to provide

in high throughput in drug discovery. Numerous examples are present of drugs being

withdrawn in clinical trials because of unacceptable toxicity. The application of QSAR

methods constitute as a basic building block in the design of new drugs for the study of their

biological activities including toxicity. In silico prediction of the toxicity and due to the

advent of chemo-informatics tools, has reduced the cost dramatically (Collins and Workman,

2006; Ma and Lu, 2011; Singla et al., 2013). As a typical drug costs up to 500 million US$


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and takes up 10–12 years to reach the market. Therefore, an early stage discovery of toxicity

is important to eliminate failure yet improving the efficacy and cost effectiveness of the

industry (Reddy et al., 2013). There are vast numbers of software‟s which play a crucial role

in computational drug designing to develop a novel proteins or drugs in the pharmaceutical

field. The computational drug designing software‟s are used to inspect molecular modeling of

gene, protein sequence analysis and 3D structure of proteins. Right now computational drug

designing methods have been of vast significance in target identification and in prediction of

novel drugs (Maithri et al., 2016). Hence, the current research investigation is carried out for

the evaluation of pharmaceutical and pharmacopeial value of ten folklore plants, whose

compound are selected by GC-MS analyzed reports that are available in the databases.

II. RELATED WORK

Homoeopathic floras have been a valuable and immense source of therapeutic drugs for eras

and still numerous of currently used drugs are plant-derived natural products or their

derivatives. The initial transcribed archives on medicinal uses of plants date back to 2600 BC

and report the existence of a sophisticated medicinal system in Mesopotamia, comprising

about 1000 plant-derived medicines. Egyptian medicine dates back to about 2900 BC, but its

most useful preserved record is the “Ebers Papyrus” from about 1550 BC, containing more

than 700 drugs, mainly of plant origin (Sateesh, 1998; Sofi et al., 2014; Atanasov et al., 2015;

Maithri et al., 2016). Traditional Chinese medicine (TCM) has been extensively documented

over thousands of years and the documentation of the Indian Ayurveda system dates back to

the 1st millennium BC. Plants based rational drug innovation underway at the

commencement of the 19th century, when the German apothecary assistant Friedrich

Sertürner succeeded in isolating the analgesic and sleep-inducing agent from opium called

morphium (morphine). This directed many researchers to isolate the bioactive natural

products, primarily alkaloids (e.g., quinine, caffeine, nicotine, codeine, atropine, colchicine,

cocaine and capsaicin) from the natural plant sources (Atanasov et al., 2015).

The objective of drug design is to find a chemical compound that can fit to a specific cavity

on a protein target both geometrically and chemically. After passing the animal tests and

human clinical trials, this compound becomes a drug available to patients. The conventional

drug design methods include random screening of chemicals found in nature or synthesized

in laboratories. The problems with this method are long design cycle and high cost. Modern

approach including structure-based drug design with the help of informatics technologies and


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computational methods has speeded up the drug discovery process in an efficient manner.

Remarkable progress has been made during the past five years in almost all the areas

concerned with drug design and discovery. An improved generation of softwares with easy

operation and superior computational tools to generate chemically stable and worthy

compounds with refinement capability has been developed. These tools can tap into

cheminformation to shorten the cycle of drug discovery, and thus make drug discovery in

more efficiency, cost effectiveness, time saving, and will provide strategies for combination

therapy in addition to overcoming toxic side effect (Mandal, 2009; Baldi, 2010; Sharma and

Sarkar, 2013).

III. METHODS AND IMPLICATIONS

Selection of plants and their phytocompounds

A total of 330 phytocompounds present in ten folklore plants viz., Eupatorium triplinerve

(Selvamangai and Anusha, 2012), Abrus precatious (Hussain and Kumaresan, 2014),

Stylosanthes fruticosa (Paul John Peter et al. 2012) Epipremnium aureum (Selvamangai and

Bhaskar, 2012), Annona squamosa (Vijayalakshmi et al.,), Ficus carica (Soni et al., 2014),

Punica granatum (Sangeetha and Vijayalakshmi, 2011), Cassia italica (Sermakkani and

Thangapandian, 2012), Madhuca longifolia (Annalakshmi et al., 2013) and Aegle marmelos

(Satyal et al., 2012) obtained from GC-MS analysis were taken from various authentic

research papers and tested for their biological activity and pharmacological activity for use as

promising therapeutic compounds. The structure of these chemical compounds were obtained

from online servers viz., PubChem (http://pubchem.ncbi.nlm.nih.gov/) and ChemSpider

(http://www.chemspider.com/) and each chemical compound was constructed using ACD/

Chemsketch bioinformatics tool and saved in the format „.mol‟.

ADME/T properties and Carcinogen Tests prediction

ADMET (Absorption Distribution Metabolism Excretion and toxicity) properties, blood brain

penetration (BBB), Plasma protein barrier (PPB), Ames mutagenic and carcinogenic

properties of individual compound were tested through computational tool such as

PreADMET server (http://preadmet.bmdrc.org/).This will predicts both physicochemical

significant descriptors and pharmacokinetically relevant properties. The server predict

Mutagenicity to Salmonella strains - TA98, TA100 and TA1535 which are often used in

Ames test (Ames et al., 1972) and the results were calculated both with consideration of

metabolite (metabolic activation by rat liver 10% homogenate,+S9) and without

http://preadmet.bmdrc.org/


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consideration of metabolite (no metabolic activation, -S9). The carcinogenicity was predicted

based on the result from its model, which was built from the data of NTP (National

Toxicology Program) and US FDA (Riju et al., 2009).

Computation of drug-likeness properties

Drug-likeness is an equilibrium amongst the molecular properties of a compound which

directly affects biological activity, pharmacodynamics and pharmacokinetics of a drug in

human body (Menezes et al., 2011).Drug likeness of the compounds was tested with WDI

rule and Lipinski‟s rule of five using (MolSoft, 2007 software). Depending on these four

molecular descriptors, the approach generates a vigilant about apparent absorption trouble, if

at least two of the following conditions are fulfilled: (1) calculated M log P (Mol. log p) >5

(2) molecular weight (Mol. wt.) >500; (3) total number of hydrogen bond acceptors (HBA)

>10; (4) total number of hydrogen-bond donors (HBD) >5. Absorption, polar surface area,

and rule of five properties.

In silico Prediction of activity spectra for substances (PASS)

The pharmacological activities of the compounds were predicted individually with the help of

computer program, PASS (Predicted Activity Spectrum for Substances) server

(http://195.178.207.233/PASS/). Software estimates predicted activity spectrum of a

compound as probable activity (Pa) and probable inactivity (Pi). Prediction of this spectrum

by PASS was based on structural activity relationship (SAR) analysis of the training set

containing more than 205,000 compounds having more than 3750 kinds of biological

activities (Goel et al., 2011). The compounds showing higher Pa value than Pi are the only

constituents considered as possible for a particular pharmacological activity (Khurana et al.,

2011; Goel et al., 2011).

IV. RESULTS AND DISCUSSION

Sixteen compounds were found to be non-mutagenic, non-carcinogenic and show drug-

likeliness of acceptable ranges from ten plants and are represented in Table1, while other 314

compounds showed toxicity either as mutagen or carcinogen. Out of 16 compounds,

Eupatorium triplinerve consists of two compounds and other plants contain as follows; Abrus

Precatorius(2), Stylosanthes fruticosa (3), Epipremnium aureum (2), Annona squamosal(1),

Ficus carica(5), and Aegle marmelos(1) rule of 5, BBB, PPB, mutagenic character,

carcinogenicity character in rat and mouse was also recorded (Table 1).

http://195.178.207.233/


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Table 1. Drug-likeness and ADMET properties prediction by Molsoft and PreADMET.

Sl.

No.

Compound

name

Lipkins rule of 5 *

BB

B

PP

B

Ames

Mutag

en

Carcino

genicity

(Rat)

Carcin

o

genicit

y

(Mous

e)

Drug

-

likeli

nes

Scor

e

HBA HBD Mol

wt

Mol

log

p

No.

of SC

1 Hexadecanoic

acid 02 01

256

.2 5.48 00

5.4

8 100

Non

mutag

en

Negative Negati

ve -1.28

2 1,14-tetra

decanediol 2 2

230

.2 4.77 0

7.5

5 100

Non

mutag

en

Negative Negati

ve -0.97

3

N-t BOC-trans-

4-Hydroxy-L-

Proline methyl

ester

5 1 245

.1 0.4 2

0.4

13

92.

88

Non

mutag

en

Negative Negati

ve -1.26

4

Methyl-3-

hydroxytetradeca

noate

3 1 258

.22 4.96 1

5.6

7 100

Non

mutag

en

Negative Negati

ve -1.47

5

(6R)-2,6-

Dimethyl-2,17-

octa decadien-8-

ol

1 0 294

.29 6.26 0 21 100

Non

mutag

en

Negative Negati

ve -2.16

6

9-(tetra

hydropyran-2-

yl)-6-[2-phenyl-

4,5,6-

tetrapropylpheny

l]-9H-purine

4 0 524

.35 9.31 1

2.9

1

98.

87

Non

mutag

en

Negative Negati

ve 0.75

7

Methyl 4-

hydroxymethyl-

3,8- dimethoxy-

1,6,9-trimethyl-

11-

oxo1Hdibenzo[b,

e][1,4]dioxepin-

11-one

8 1 402

.13 3.27 0

0.0

11

88.

06

Non

mutag

en

Negative Negati

ve -0.17

8

9-[91a,3a,4a)-4-

(diethyl

phosphono)meth

oxy-3-

hydroxycyclopen

tyl]-6-

chloropurine

8 1 404

.1 0.09 3

0.0

31

50.

9

Non

mutag

en

Negative Negati

ve 0.49

9

Spiro[2,3-

Dihydro-1-

Methylindol-2-

one-3,3‟-[2-(4-

3 1 308

.15 2.51 2

0.0

73

61.

68

Non

mutag

en

Negative Negati

ve 0.94


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* Hydrogen bond acceptors (HBA), hydrogen-bond donors (HBD), molecular weight (Mol.

wt.), M log P (Mol. log p), stereo center (S.C.).

These 16 drug-like compounds were predicted for pharmacological and biological activities

such as anti-inflammatory, antihypersensitive, antiviral, antidiabetic, antioxidant, HIV-1

integrase (3'-processing) inhibitor etc. that was shown in Table 2. Compound name,

molecular structure, molecular formula and predicted biological activity of different plants

was determined by computational methods. All the data are recovered from the reported

results of earlier research on different animal models. For centuries, ethnobotanicals are used

as remedies for human diseases in developing countries because they contain

phytopharmaceuticals of greater therapeutic value. Recent drug discovery techniques,

properly applied, reduce the danger for failure in the clinic and offer hope for healthier future

in therapeutic arena. The widespread hope for a new era in the prevention and treatment of

human disease that emerged with the sequencing of the human genome led to a marked

upturn in the funding of drug discovery research. Computational prediction methods offer the

prospect of screening libraries of actual or virtual compounds on the basis of mechanisms of

Methoxy

phenyl)]

Pyrrolidine]

10 Ergostenol 1 1 400

.37 8.74 8

19.

06 100

Non

mutag

en

Negative Negati

ve 0.49

11 8-Penta

decanone 1 0

226

.23 6.16 0

9.4

7 100

Non

mutag

en

Negative Negati

ve -1.36

12

2-hydroxy-1-

(hydroxymethyl)

ethyl ester

Hexadecanoic

acid

4 2 330

.28 5.16 0 6.4 100

Non

mutag

en

Negative Negati

ve -0.7

13 Gamma.-

Tocopherol 2 1

416

.37

10.6

8 3

19.

65 100

Non

mutag

en

Negative Negati

ve 0.5

14 alpha-tocopherol 2 1 430

.38

10.8

4 3

19.

9 100

Non

mutag

en

Negative Negati

ve 0.49

15 Campesterol 1 1 400

.37 8.9 9

19.

9 100

Non

mutag

en

Negative Negati

ve 0.71

16 (E)-Nerolidol 1 1 222

.2 5.24 1

13.

98 100

Non

mutag

en

Negative Negati

ve -1.03


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action and molecular structure. An enormous number of such models are existing. They have

evolved from simple regression models based on calculation of lipophilicity and polar surface

area, to grid-based approaches; the use of artificial neural networks is also becoming very

popular (Sharma and Sarkar, 2013). Drug discovery starts with identification of a „lead

molecule‟ with desired biological activity, a wide range of biological actions along with

toxicity free findings may be efficiently used to develop lead like compound for human

health care. The effects described herein with a broad spectrum of the biological effects of

these substances, strongly claims that the phytocompounds mentioned have various

therapeutic applications and implications.

Table 2. List of non-toxic compounds having predicted biological activity by

computational method.

Plant Compound Structure Predicted biological activity

Eu

pato

riu

m

trip

lin

erve

Hexadecanoic acid

C11H19O5

Anti inflammatory(intestinal),

antimutagenic, antihipoxic,

antieczamatic, antisecratoric, insulin

promoter

1,14-tetradecanediol

C15H30O3

Antidyskinetic, antiezamatic, antifungal,

antihelmenthic, antihypoxic,

antiinfective, antihypersensitive, anti-

inflammatory(intestine, ophthalmic),

antiparasitic, antiprotozoal, antitoxic,

antiviral (Adenovirus, herpis, hepatitis B,

influenza)

Abru

s pre

cati

ou

s

N-t BOC-trans-4-Hydroxy-L-Proline

methyl ester

C11H19NO5

Anti inflammation, Analgesic,

Antipyretic, Anti viral (adenovirus,

herpis virus, influenza), Antihypoxic,

Antihelmenthic, Antieczematic,

Antidiabetic.

Methyl-3-hydroxytetradecanoate

C15H30O3

Antianginal, anticataract, antidiabetic,

antieczematic, antifungal, antihypoxic,

antihelmenthic(nematode), anti-

inflammatory(intestinal, ophthalmic),

antiviral(adenovirus, herpis virus,

influenza)

Sty

losa

nth

es f

ruti

cosa

(6R)-2,6- Dimethyl-2,17-

octadecadien-8-ol

C20H38O

Analgesic, antidiabetic, antihypoxic,

antihelminthic, antiaczemetic, anti-

inflammatory(ophthalmic),

antiviral(adeno virus, picorna virus, rhino

virus)

9-(tetrahydropyran-2-yl)-6-[2-phenyl-

4,5,6-tetrapropylphenyl]-9H-purine

C34H44N4O

Antiviral (Picornavirus),

Immunosuppressant, Lysase inhibitor


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Methyl 4-hydroxymethyl-3,8-

dimethoxy-1,6,9-trimethyl-11-oxo-

11Hdibenzo[b,e][1,4]dioxepin-11-one

C22H26O8

Antiinflammatory, HIV-1 integrase (3'-

Processing) inhibitor, Antiischemic,

cerebral, Apoptosis agonist, Vasodilator,

coronary

Epip

rem

niu

m a

ure

um

E

pip

rem

niu

m a

ure

um

9-[91a,3a,4a)-4-

(diethylphosphono)methoxy-3-

hydroxycyclopentyl]-6-chloropurine

C15H22ClN4O5P

Antiviral (Hepatitis B), Antieczematic,

Antineoplastic.

Spiro[2,3-Dihydro-1-Methylindol-2-

one-3,3‟-[2-(4-Methoxy phenyl)]

Pyrrolidine]

C19H20N2O2

Antihypoxic, Antineurotic,

Antinociceptive.

An

non

a s

qu

am

osa

(3S,9S,10R,13R,14R,17R)-17-

[(E,2R,5R)-5,6-dimethylhept-3-en-2-

yl]-10,13-dimethyl-

2,3,4,9,11,12,14,15,16,17-decahydro-

1H-cyclopenta[a]phenanthren-3-ol

(Ergostenol)

C28H48O

Anesthetic general, Antieczematic,

Antihypercholesterolemic, Antiinfertility,

female, Antipruritic, Antimetastatic,

Antinociceptive, Antipsoriatic, Antiviral

(Influenza), Apoptosis agonist.

Fic

us

cari

ca

Fic

us

carc

ia

Aeg

le

marm

elos

8-Penta decanone

C15H30O

Antieczematic, Antiseborrheic,

Cardiovascular analeptic, Antimutagenic,

Antimyopathies, Antineurotic,

Antipruritic, allergic, Antiulcerative,

Antiviral (Rhinovirus).

2-hydroxy-1-(hydroxymethyl)ethyl

ester

Hexa decanoic acid

C19H38O4

Antieczematic, Antihypoxic,

Antiinfective, Antiseborrheic,

Antisecretoric, Antitoxic.

(2R)-2,7,8-trimethyl-2-[(4R,8R)-

4,8,12-trimethyltridecyl]-3,4-

dihydrochromen-6-ol

(Gama-tocopherol)

C28H48O2

Antihypercholesterolemic, Antioxidant,

Antiinflammatory, Antipruritic,

Antiulcerative, Apoptosis agonist.

(2R)-2,5,7,8-tetramethyl-2-[(4R,8R)-

4,8,12-trimethyltridecyl]-3,4-

dihydrochromen-6-ol

(Apha-Tocopherol) C29H50O2

Antianginal, Anticarcinogenic,

Anticataract, Anticonvulsant,

Antidiabetic symptomatic,

Antihypercholesterolemic,

Antiinflammatory ,Antioxidant,

Antipruritic, Antiulcerative.

Campesterol

C28H48O

Antifungal,Antihypercholesterolemic,

antiinfertility, female, Antiinflammatory,

Antitoxic, Antiviral (Influenza, rhino)

Aeg

le

marm

elos

Aeg

le m

arm

elos

(6E)-3,7,11-trimethyldodeca-1,6,10-

trien-3-ol ( (E)-Nerolidol )

C15H26O

Antibacterial, Anticarcinogenic,

Anticonvulsant, Antieczematic,

Antifungal, antihypercholesterolemic,

Antiinflammatory,Antioxidant,

Antisecretoric, Antiprotozoal

(Trypanosoma), Antiviral (CMV,

rhinovirus)


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V. CONCLUSION

Phytochemical compounds of ethnobotanical source with pharmacological importance are an

alternative approach for the discovery of novel drugs against several emerging and re-

emerging diseases. According to recent studies, usage of phytopharmaceuticals are reported

as the most effective and reliable compounds with therapeutic significance. The drug

discovery by pharmaceutical industry is a laborious, multistep processes against a series of in

vivo biological validations and further investigating the active candidates for their

pharmacokinetic properties (ADME), metabolism and potential toxicity (efficacy).

Nowadays, drug discovery process has been transformed with the advent of genomics,

proteomics, bioinformatics and efficient technologies like, combinatorial chemistry, high

throughput screening (HTS), virtual screening, de novo design, in vitro, in silico ADMET

screening and structure-based drug design.The use of computational methods in prediction of

pharmacological and biological properties of phytochemical compounds is growing rapidly in

drug discovery as the benefits they provide in high throughput and early application in drug

design are realized. Computational methods of drug designing and molecular dynamic studies

can be performed by using different methods namely homology modeling, molecular

dynamic studies, energy minimization, docking and QSAR etc. By means of computational

drug designing, it is possible to produce an active lead molecule from the preclinical

discovery stage to late stage clinical progress. The lead molecules that are developed will

help us in selection of only potent leads to cure particular diseases. Therefore, computational

methods have been of great importance in target identification and in prediction of novel

drugs. Hence, in the present research analysis of structural and pharmacological properties of

phytochemical compounds from ten ethnobotanicals was done to elucidate their

pharmacological properties. Out of 330 compounds sixteen compounds were selected as they

shows the drug likeliness properties and ADME/T properties of these compounds were

acceptable range. However, further in vitro and in vivo analysis would enhance the

understanding of various intrinsic and extrinsic properties based on metabolism, excretion,

drug induced toxicity, environmental factors as bio-concentration or biodegradability to

recommend the compounds as an ideal alternate drug molecule.


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REFERENCES

1. Alavijeh, Mohammad S., Mansoor Chishty, M. Zeeshan Qaiser, and Alan M. Palmer.,

"Drug metabolism and pharmacokinetics, the blood-brain barrier, and central nervous

system drug discovery", NeuroRx, 2005; 4: 554-571.

2. Annalakshmi, R., S. Mahalakshmi, A. Charles, and C. Savariraj Sahayam, "GC–MS and

HPTLC analysis of leaf extract of Madhuca longifolia (Koenig) Linn.", Drug Invention

Today, 2013; 5: 76-80,

3. Atanasov, Atanas G., Birgit Waltenberger, Eva-Maria Pferschy-Wenzig, Thomas Linder,

Christoph Wawrosch, Pavel Uhrin, Veronika Temml et al., "Discovery and resupply of

pharmacologically active plant-derived natural products: A review", Biotechnology

advances, 2015; 33: 1582-1614.

4. Baldi, A., "Computational approaches for drug design and discovery: an overview",

Systematic Reviews in Pharmacy, 2010; 1: 99.

5. Collins, Ian, and Paul Workman, "New approaches to molecular cancer therapeutics",

Nature Chemical Biology 2, no. 12 (2006): 689-700.

6. Darvas, F., Gy Keseru, A. Papp, Gy Dorman, L. Urge, and P. Krajcsi, "In silico and ex

silico ADME approaches for drug discovery", Current topics in medicinal chemistry,

2002; 2: 1287-1304.

7. Goel, R. K., Damanpreet S., Alexey L., and Vladimir Poroikov, "PASS-assisted

exploration of new therapeutic potential of natural products", Medicinal Chemistry

Research, 2011; 20: 1509-1514.

8. Hansch, Corwin, Peter George Sammes, and John Bodenhan Taylor., Comprehensive

medicinal chemistry: the rational design, mechanistic study & therapeutic applications of

chemical compounds. Vol. 5. Pergamon Press, Oxford. 1989.

9. Hill, Albert F., Economic Botany: A Textbook of Useful Plants and Plants Products. No.

SB103. H54 1937. 1952. McGarw-Hill Book Company Inc, New York.

10. Hosseinzadeh, S, Azizollah Jafarikukhdan, Ahmadreza Hosseini, and Raham Armand,

"The Application of Medicinal Plants in Traditional and Modern Medicine: A Review of

Thymus vulgaris", International Journal of Clinical Medicine, 2015; 6: 635.

11. Hussain A.Z. and Kumaresan S., “GC-MS analysis and antimicrobial activity of Abrus

precatorius L.”, J. Microbiol. Biotech. Res. 2014; 4: 24-30.

12. Iram, Farah, Sadaf Ali, Aftab Ahmad, Shah Alam Khan, and Asif Husain, "A review on

dronedarone: Pharmacological, pharmacodynamic and pharmacokinetic profile", Journal

of Acute Disease, 2016; 5: 102-108.


1054


13. Jadhav, V. M., R. M. Thorat, V. J. Kadam, and N. S. Sathe, "Eclipta alba Linn-

„„Kesharaja‟: A Review", Journal of Pharmacy Research, 2009; 2: 1236-1241.

14. Kadam, R., and N. Roy, "Recent trends in drug-likeness prediction: A comprehensive

review of in silico methods", Indian Journal of Pharmaceutical Sciences, 2007; 69: 609-

615.

15. Khurana, N, Mohan Pal Singh Ishar, Asmita Gajbhiye, and Rajesh Kumar Goel, "PASS

assisted prediction and pharmacological evaluation of novel nicotinic analogs for

nootropic activity in mice", European journal of pharmacology, 2011; 662: 22-30.

16. Lakshmeesha TR, Sateesh M.K., Vedashree S, Mohammad Shafi Sofi and Umesha S.

Sateesh, M. K., "Efficacy of botanicals on soybean seed-borne Fusarium equiseti",

Sciences Journal, 2013; 11.

17. Ma, Qiang, and Anthony YH Lu, "Pharmacogenetics, pharmacogenomics, and

individualized medicine", Pharmacological reviews, 2011; 63: 437-459.

18. Mahesh, B., and S. Satish, "Antimicrobial activity of some important medicinal plant

against plant and human pathogens", World journal of agricultural sciences, 2008; 4: 839-

843.

19. Maithri G, Manasa B, Vani SS, Narendra A and Harshita T, “Computational Drug Design

and Molecular Dynamic Studies-A Review”, Biomedical Data Mining, 2016; 5: 1-7.

20. Mandal, Soma, Mee'nal Moudgil, and Sanat K. Mandal, "Rational drug design",

European journal of pharmacology, 2009; 625: 90-100.

21. Maobe, Moses AG, Erastus Gatebe, Leonard Gitu, and Henry Rotich, "Preliminary

phytochemical screening of eight selected medicinal herbs used for the treatment of

diabetes, malaria and pneumonia in Kisii region, southwest Kenya", European journal of

applied sciences, 2013; 5: 1-6.

22. Menezes, Jose CJMDS, Shrivallabh P. Kamat, Jose AS Cavaleiro, Alexandra Gaspar,

Jorge Garrido, and Fernanda Borges, "Synthesis and antioxidant activity of long chain

alkyl hydroxycinnamates", European journal of medicinal chemistry, 2011; 46: 773-777.

23. Mussarat, S, Nasser M. AbdEl-Salam, Akash Tariq, Sultan Mehmood Wazir, Riaz Ullah,

and Muhammad Adnan, "Use of ethnomedicinal plants by the people living around indus

river", Evidence-Based Complementary and Alternative Medicine 2014 (2014).

24. Peter, M. Paul John, J. Yesu Raj, VP Prabhu Sicis, V. Joy, J. Saravanan, and S. Sakthivel,

"GC-MS analysis of bioactive components on the leaves extract of Stylosanthes fruticosa-

A potential folklore medicinal plant", Asian Journal of Plant Science and Research, 2012;

2: 243-253.


1055


25. Petrovska, Biljana Bauer, "Historical review of medicinal plants' usage", Pharmacognosy

reviews2012; 6: 1-10.

26. Prasad, G. J., S. W. Amruta, S. P. Sanjay, and G. C. Prakash, "Antioxidant, antimicrobial

activity and in silico PASS prediction of Annona reticulata Linn.", Beni-Suef University

Journal of Basic and Applied Sciences, 2014; 3: 140-148.

27. Pritchard, J. Fred, "Risk in CNS drug discovery: focus on treatment of Alzheimer's

disease", BMC neuroscience, 2008; 9: 1-5.

28. Rajeswari, G., M. Murugan, and V. R. Mohan, "GC-MS analysis of bioactive components

of Hugonia mystax L. (Linaceae)", Research Journal of Pharmaceutical, Biological and

Chemical Sciences, 2012; 3: 301-308,

29. Ramawat, Kishan Gopal, and Jean-Michel Mérillon, Bioactive molecules and medicinal

plants. Berlin Heidelberg: Springer, 2008.

30. Reddy, S., T. Sreenivasulu Reddy, and G. Saayi Krushna, "Advantages of Bio and

Cheminformatics tools in drug design", Current Research in Drug Targeting, 2011; 1: 6-

17,

31. Riju, A., K. Sithara, Suja S. Nair, A. Shamina, and Santhosh J. Eapen, "In silico screening

major spice phytochemicals for their novel biological activity and pharmacological

fitness", Journal of Bioequivalence & Bioavailability 2009 (2011).

32. Sangeetha, J., and K. Vijayalakshmi, "Determination of bioactive components of ethyl

acetate fraction of Punica granatum rind extract", Int J Pharm Sci Drug Res, 2011; 3:

116-22.

33. Sateesh M.K., Bioprospecting of botanicals against Phomopsis azadirachtae Sateesh et

al. on neem. 31st Annual conference and symposium on Microbial wealth-Plant health.

Indian society of mycology and Plant pathology. University of North Bengal, Siliguri,

India. October 23-25th, 2009.

34. Sateesh, M. K., "Microbiological investigations on die-back disease of neem

(Azadirachta indica A. Juss.)." Mysore, Karnataka, India, University of Mysore, PhD

Thesis (1998).

35. Satyal, Prabodh, Katherine E. Woods, Noura S. Dosoky, Sanjaya Neupane, and William

N. Setzer, "Biological activities and volatile constituents of Aegle marmelos (L.) Corrêa

from Nepal", Journal of Medicinally Active Plants, 2012; 1: 6.

36. Selvamangai, G., and Anusha Bhaskar, "GC–MS analysis of phytocomponents in the

methanolic extract of Eupatorium triplinerve", Asian Pacific Journal of Tropical

Biomedicine, 2012; 2: S1329-S1332.


1056


37. Sermakkani, M., and V. Thangapandian, "GC-MS analysis of Cassia italica leaf

methanol extract", Asian journal of pharmaceutical and clinical research, 2012; 5: 90-94.

38. Sharma, Vivekanand, and Indra Neil Sarkar, "Bioinformatics opportunities for

identification and study of medicinal plants", Briefings in bioinformatics, 2013; 14: 238-

250,

39. Singla, Deepak, Sandeep Kumar Dhanda, Jagat Singh Chauhan, Anshu Bhardwaj, Samir

K. Brahmachari, and Gajendra PS Raghava, "Open source software and web services for

designing therapeutic molecules", Current topics in medicinal chemistry, 2013; 13: 1172-

1191, Sofowora, A., Medicinal plants and traditional medicine in Africa. John Wiley and

son‟s Ltd, 1982.

40. Sofi, M.S., Sateesh, M.K., Bashir, M., Harish, G., Lakshmeesha, T.R., Vedashree, S. and

Vedamurthy, A.B., “Cytotoxic and pro-apoptotic effects of Abrus precatorius L. on

human metastatic breast cancer cell line, MDA-MB-231”, Cytotechnology, 2013; 65:

407-417.

41. Sofi, Mohammed Shafi, M. K. Sateesh, and Mohsin Bashir, "Screening of the

Ethnobotanicals against MDA-MB-231 and MCF-7 breast cancer cell lines", International

Journal of Phytopharmacy, 2014; 4: 140-147.

42. Soni, N, Sanchi Mehta, Gouri Satpathy, and Rajinder K. Gupta, "Estimation of

nutritional, phytochemical, antioxidant and antibacterial activity of dried fig (Ficus

carica)", Journal of Pharmacognosy and Phytochemistry, 2014; 3: 2.

43. Suleiman, M. N., "The in vitro phytochemical investigation on five medicinal plants in

Anyigba and its environs, Kogi State, Nigeria", Der Pharmacia Sinica, 2011; 2: 108-111.

44. Vedashree, S., Sateesh M.K., T. R. Lakshmeesha, and Mohammad Shafi Sofi,

"Antifungal activity of herbal extracts against neem die-back pathogen Phomopsis

azadirachtae", Asian Journal of Pharmaceutical Science and Technology, 2014; 4: 126-

132.

45. WHO Report, World Health Organization, Geneva, WHO/EDM/TRM/ 2002; 21: 19.

46. Woodford, Neil, and David M. Livermore, "Infections caused by Gram-positive bacteria:

a review of the global challenge", Journal of Infection, 2009; 59: S4-S16,

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