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In Silico Study of Diuretic by Using the Flower of Aerva Lanata L
Haseena Parveen.N, and Dr.C.A.Annapoorani*
Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, India
DOI: https://doi.org/10.51244/IJRSI.2025.1208004123
Received: 07 Oct 2025; Accepted: 15 Oct 2025; Published: 24 October 2025
ABSTRACT
Diuretics are medications commonly used to treat conditions characterized by fluid retention, such as edema
and hypertension. Diuretics promote increased urine production, which can help flush out toxins and
potentially prevent the formation of kidney stones.This increased urine output can potentially help in flushing
out small stones or reducing the concentration of stone-forming substances in the urine. The flower of Aerva
lanataL has been studied for its potential therapeutic effects in the context of kidney stones and diuretic
properties. It may possess anti-urolithic properties, which could be beneficial in preventing or treating kidney
stones. Aervalanata flowers are known for their diuretic effects, meaning they can increase urine production.
This property has been traditionally used to treat conditions associated with fluid retention and urinary
problems. It exhibits anti-inflammatory properties, which can help reduce inflammation and associated
symptoms in various conditions. Aervalanata has been investigated for its potential to prevent the formation of
urinary stones (urolithiasis) due to its diuretic and urinary alkalizing propertiesand it also have antineoplastic
activity.
Keywords: Kidney stone, Diuretics, Aerva lanata flower, Osteopontin, Biological activity, Antineoplastic,
Anti-inflammatory.
INTRODUCTION
Nature has been a vast reservoir of remedies in the form of medicinal herbs for the treatment of numerous
ailments since ancient times. Plants have been a part of the therapeutic practice both in traditional and modern
era. Herbs contain many phytoconstituents that contribute to their vast array of pharmacological activities
leading to the production of beneficial effects. 80% of people throughout the world depend on herbal
medicines for some fraction of their primary health care according to latest reports by World Health
Organization. Herbal medicines have gained popularity over conventional medicines owing to their reduced
risk of side effects, effectiveness with chronic conditions, lower cost and widespread availability (Bitasta&
Madan2016)
Microbial diseases and metabolic disorders are gradually spreading among the human race as a result of the
dense population and malnutrition. Allopathy medicines are more effective and provide fast recovery for
variety of microbial infections, and metabolic disorders. Plant-based therapy has the potential to cure the
microbial infections while also boosting immune mechanisms and providing humans with extended resistance
to infectious microbial agents (Narayanan, et al2021).
Kidney stones, one of the most painful of the urologic disorders, are not a product of modern life.
Unfortunately, kidney stones are one of the most common disorders of the urinary tract. A large number of
people are suffering from urinary stone problem all over the globe. Kidney stones, which are solid crystals that
form from dissolved minerals in urine, can be caused by both environmental and metabolic problems. Calcium
oxalate or phosphate stones account for almost 70% of all renal stones observed in economically developed
countries. Higher consumption of fructose has been tied to kidney stone risk. A less energy dense diet may
decrease the incidence of stones. This fact has been documented during far years when diets containing
minimal fat and protein resulted in a decreased incidence of urinary stones. Those afflicted with recurrent
urinary stone disease are encouraged to maintain a diet restricted in sodium and protein intake.
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Postmenopausal women with low estrogen levels have an increased risk for kidney stones. Women who have
had their ovaries removed are also at increased risk(Sofiaet al 2016).
Globally, kidney stone disease prevalence and recurrence rates are increasing, with limited options of effective
drugs. Urolithiasis affects about 12% of the world population at some stage in their lifetime. It affects all ages,
sexes, and races but occurs more frequently in men than in women within the age of 20–49 years. If patients do
not apply meta phylaxis, the relapsing rate of secondary stone formations is estimated to be 10–23% per year,
50% in 5–10 years, and 75% in 20 years of the patient. Recent studies have reported that the prevalence of
urolithiasis has been increasing in the past decades in both developed and developing countries (Alelign&
Petros, 2018).
Pashanabheda (stone breaking) plants are a group of medicinal plants which are used in Indian traditional
medicinal system by Ayurvedic practitioners as antiurolithiatic drugs. Traditionally Aervalanata (L) also
known as Pashanabheda, used for various medicinal uses including both antiurolithiatic and diuretic. The
reported phytochemical constituents present in A.lanataL are responsible for various biological activities.
These constituents include alkaloids, flavanoids, methyl grevillate, lupeol, lupeol acetate benzoic acid, β-
sitosteryl acetate and tannic acid (Dinnimath, et al2017).
AervalanataL is also known as knot grass and it is a perennial shrub. These plants are branching shrub, roots
are like woody, and flowers are like soft spikes. The flowers bloom in the first year of cultivation. Leaves are
oval in shape, they are 0.5-1.5 in length, are alternately arranged. The leaves are present in the main stem. The
whitish flowers have two lobes and red bases, grown in leaf axils have 0.1in long, the pink, green, white
flowers are also seen. These plants are self-pollinated, bisexual and are cultivated in 90 meters above these
level, and are grown only in tropical climate. The whole plant is useful for many diseases (Athira, Nair,2017).
METHODOLOGY
Plant collection and extract preparation
The healthy, disease free Aervalanata L. was collected from natural habitats, A. Kalayam Puthur, Palani Taluk,
Dindigul district, Tamil Nadu, India. The flowers were then shade dried and sieved into fine powder, extracted
using double boiling method and evaporated stored in a sterile container for potential use.
Gas chromatography-mass spectrometer (GC-MS) analysis
The Phytocompounds present in the extract of the flower of A.lanata L was identified using GC-MS analysis.
The GC-MS system was equipped with a flame ionization detector and capillary column of HP-5 (5 per cent
phenyl methyl siloxane, film thickness 0.25mm). At a flow rate of 2.5 ml/min, nitrogen was employed as the
carrier gas, with a split injector (split ratio 50:1) and a split flow of 60 ml/min. The oven temperature was
programmed from 90°C
Kingdom: Plantae
Phylum: Tracheophyta
Class: Magnoliopsida
Order: Caryophyllales
Family: Amaranthaceae
Genus: Aerva
Figure.1 Taxonomical Classification of Mountain knotgrass or Aerva Lanata L
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for 2 min, increased to 90°C - 200°C at the rate of 8°C per min and additionally with an increase of 200°C-
250°C at the rate of 3°C per min. Temperatures for the injector and detector were set to 280°C and 250°C,
respectively. The methanolic extract (0.1mL) was injected into the GC-MS instrument for its analysis. Ion
source temperatures were maintained at 200°C and the mass spectra were taken at 70eV with a total run time
of 32.4 min (Hossain et al., 2014). The GCMS mass spectrum data were analyzed using the database of the
National Institute of Standard and Technology (NIST) to interpret the results.
Ligand generation
The data of the entire ligand is obtained from a website namely the PubChem database
(http://pubchem.ncbi.nlm.nih.gov). The 2D structure of a ligand molecule is converted into a 3D structure by
using the Biovia discovery studio application. The 2D file should be converted into .pdb format to obtain a 3D
structure. Later, the smiles of the ligand molecule are taken and given for further process.
Biological Activity Prediction
Way2Drug is a computational platform designed for predicting the biological activities of chemical
compounds. Way2Drug offers a versatile toolkit for drug discovery and development processes. By analyzing
molecular structures and their interactions with biological targets. Way2Drug can forecast various
pharmacological properties such as toxicity, potency, and mechanism of action. PASS Online predicts over
4000 kinds of biological activity, including pharmacological effects, mechanisms of action, toxic and adverse
effects, interaction with metabolic enzymes and transporters, influence on gene expression.
Receptor preparation and molecular docking
The cytotoxic activity of phytocompounds identified from A.lanataL, the crystal structures of four apoptosis
regulator protein molecule Osteopontin (PDB ID: 3CXD),which shows an essential role, especially we have
taken the effective protein of kidney stone and their three - dimensional structures were retrieved from Protein
Data Bank (PDB www.rcsb.org) (Berman et al., 2000).
The grid map and grid size were calculated using an auto grid to represent the protein binding size for docking.
The spacing of 0.375A was fixed between the grid points by Maestro
Schrodinger and included the effective protein. Schrodinger's software is used by pharmaceutical companies,
biotech firms, and academic researchers to simulate and model the behavior of molecules at the atomic level.
This accelerates the design and develops new drugs and materials more efficiently, reducing the time and cost
of bringing them to market.The docking pose with the better binding affinity score (kcal/mol) is ranked as the
top orientation for each ligand against each receptor and the binding interaction studies were analyzed. The
docking interactions were analyzed using receptor-ligand interaction options.
RESULTS AND DISCUSSION
Flowers of A.lanata L were submitted for identification in Botanical Survey of India, Tamil Nadu Agricultural
University, Coimbatore, Tamil Nadu, India. The specimen was identified and authenticated as Aerva lanata L.
The identification has been officially confirmed by the Botanical Survey of India. The authentication number
assigned to this specimen is BSI/SRC/5/23/2023-24/TECH/39
GC-MS analysis of the flower of Aerva lanata L
The outbreak of cause of kidney stones in kidney is predictable through the results of GC-MS analysis
(Mushtaq et al., 2018). Table 1 depicts the phyto compounds analyzed in the extract of the flower of A. lanata
L
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Table 1GC-MS analysis of the compounds in the flower of A.lanata L
Name of the compound Molecular weight
(g/mol)
Formula Area
(%)
Structure
1-Butanol, 4-butoxy
146.23 C8H18O2 1.72
Bicyclo [3.2.0] hepta-2,6-diene 92 .14 C7H8 6.27
9- Octadecenoic acid (Z) 282.5 C18H34O2 1.18
6H-Dibenzo [b, d] thiopyran
198.29 C13H10S 4.02
5-t-Butyl-2-(5H)-furanone
140 .18 C8H12O2 1.22
1,10-Decanediol
174.29 C10H22O2 2.07
Propanal, oxime 73.09 C3H7NO 1.38
Hepta decanoic acid, methyl
ester
284.5 C18H36O2 1.86
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1-Cyclopropyl2-fluoro ethane 88.12 C5H9F 3.01
1,3-Dioxane-2-propanol 146.18 C7H14O3 1.00
3- benzhydryl dene 6-
trithycycloh
544 C38H30 1.00
2-Oxetanone 72.06 C3H4O2 2.25
4-azido-n-benzamine 325.3 C15H11N5
O2S
1.70
Oxirane, hexyl 128.21 C8H16O 2.36
Pentadecane,2,6,10-trimethyl 254.5 C18H38 1.0
Bis-(3,5,5-trimethylhexyl) ether 270.5 C18H38O 6.69
2-(Azidomethyl)-1,3-butadiene 109.13 C5H7N3 1.84
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Buthy methacrylate 142.2 C8H14O2 2.22
Ricinoleic acid 298.5 C18H34O3 8.25
MS chromatogram analysis of the extract of flower of A. lanata L revealed 19 distinct peaks in that area
percentage was high in 11 peaks were as follows Bicyclo [3.2.0] hepta-2,6-diene (6.27), 6H-Dibenzo [b, d]
thiopyran (4.02), 1,10-Decanediol (2.07), 1-Cyclopropyl2-fluoro ethane (3.01), 2-Oxetanone (2.25), Oxirane,
hexyl (2.36), Bis-(3,5,5-trimethylhexyl) ether (6.69), Buthy methacrylate(2.22) and Ricinoleic acid (8.25)
Fig. 2. GC- MS of the flower of A.lanataL
The gas chromatogram showed the presence of distinctive compounds which were clearly depicted as peaks
(Fig. 2).
Biological activity prediction
Biological activity has been predicted using PASS server. If Pa>0.7, the substance is very likely to exhibit the
activity in experiment, but the chance of the substance being the analogue of a known pharmaceutical agent is
also high. If 0.5 < Pa < 0.7, the substance is likely to exhibit the activity in experiment, but the probability is
less.
Table 2 Activities of 1-Butanol, 4-butoxy C8H18O2
Pa Pi Activity
0,950 0,002 Fucasterol-epoxide lyase inhibitor
0,950 0,002 Sugar-phosphatase inhibitor
0,942 0,003 Alkenyl glycerol phosphocholine hydrolase inhibitor
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0,934 0,002 Alkanal monooxygenase (FMN-linked) inhibitor
0,922 0,002 Carboxypeptidase Taq inhibitor
0,920 0,001 Glucan 1,4-alpha-maltotriohydrolase inhibitor
0,921 0,003 Alkylacetyl glycerol phosphatase inhibitor
0,918 0,003 Dextranase inhibitor
0,919 0,004 Phobic disorders treatment
0,917 0,004 Ubiquinol-cytochrome-c reductase inhibitor
0,911 0,003 Pullulans inhibitor
0,906 0,002 Peptide-N4-(N-acetyl-beta-glucosaminyl)asparagine amidase inhibitor
0,906 0,002 Alkylglycerone-phosphate synthase inhibitor
0,908 0,005 Polypepsin inhibitor
0,903 0,002 Gluconate 5-dehydrogenase inhibitor
0,905 0,005 Sphinganine kinase inhibitor
0,900 0,003 Exoribonuclease II inhibitor
0,899 0,003 Glyceryl-ether monooxygenase inhibitor
0,898 0,003 Levanase inhibitor
0,899 0,005 Saccharopepsin inhibitor
0,899 0,005 Acrocylindropepsin inhibitor
0,899 0,005 Chymosin inhibitor
0,894 0,001 Sclerosant
0,890 0,002 Leukopoiesis stimulant
0,891 0,003 Eye irritation, inactive
0,896 0,009 Aspulvinonedimethylallyltransferase inhibitor
0,887 0,002 Coccolysin inhibitor
0,896 0,012 Membrane integrity agonist
0,884 0,003 Poly(alpha-L-guluronate) lyase inhibitor
0,886 0,005 Mannotetraose 2-alpha-N-acetylglucosaminyltransferase inhibitor
0,882 0,003 Xylan endo-1,3-beta-xylosidase inhibitor
0,880 0,002 Diuretic inhibitor
0,881 0,003 IgA-specific serine endopeptidase inhibitor
0,878 0,003 Macrophage colony stimulating factor agonist
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0,879 0,003 Sarcosine oxidase inhibitor
0,875 0,002 Poly lyase inhibitor
0,877 0,006 Acylcarnitine hydrolase inhibitor
0,874 0,002 Anthranilate-CoA ligase inhibitor
0,877 0,007 Benzoate-CoA ligase inhibitor
0,870 0,001 Oryzin inhibitor
0,872 0,004 Cardiovascular analeptic
0,870 0,003 IgA-specific metalendopeptidase inhibitor
0,868 0,002 Hydroxylamine reductase (NADH) inhibitor
0,869 0,004 Phosphatidylcholine-retinol O-acyltransferase inhibitor
0,871 0,006 G-protein-coupled receptor kinase inhibitor
0,871 0,006 Beta-adrenergic receptor kinase inhibitor
0,863 0,003 Phosphatidyl glycerol phosphatase inhibitor
0,862 0,002 Prenyl-diphosphatase inhibitor
0,861 0,004 Lysine 2,3-aminomutase inhibitor
0,858 0,002 Steroid N-acetylglucosaminyltransferase inhibitor
0,858 0,002 Protein-tyrosine sulfotransferase inhibitor
0,858 0,004 Acetylesterase inhibitor
0,872 0,018 CYP2C12 substrate
0,853 0,003 Trimethylamine-oxide aldolase inhibitor
0,853 0,003 Beta-mannosidase inhibitor
0,854 0,004 Skin irritation, inactive
0,853 0,004 Lipoprotein lipase inhibitor
0,853 0,004 N-acetylneuraminate 7-O(or 9-O)-acetyltransferase inhibitor
0,852 0,004 5-O-(4-coumaroyl)-D-quinate 3'-monooxygenase inhibitor
0,849 0,006 Glycosylphosphatidylinositol phospholipase D inhibitor
0,843 0,004 Cutinase inhibitor
0,841 0,002 Phenylacetate-CoA ligase inhibitor
0,847 0,011 CYP2J substrate
0,837 0,003 Phenol O-methyltransferase inhibitor
0,835 0,003 Laccase inhibitor
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0,832 0,001 Undecaprenyl-diphosphatase inhibitor
0,836 0,005 Linoleate diol synthase inhibitor
0,833 0,005 Membrane integrity antagonist
0,838 0,014 Chlordecone reductase inhibitor
0,830 0,008 NADPH peroxidase inhibitor
0,825 0,003 Shikimate O-hydroxycinnamoyltransferase inhibitor
0,824 0,003 Ecdysone 20-monooxygenase inhibitor
0,837 0,017 Testosterone 17beta-dehydrogenase (NADP+) inhibitor
0,829 0,009 CYP2J2 substrate
0,825 0,007 GST A substrate
0,823 0,007 Arginine 2-monooxygenase inhibitor
0,818 0,003 Alkenyl glycerol phosphor ethanolamine hydrolase inhibitor
0,811 0,001 Deoxyribose-phosphate aldolase inhibitor
0,819 0,010 Feruloyl esterase inhibitor
0,810 0,003 Long-chain-aldehyde dehydrogenase inhibitor
0,810 0,003 Procollagen N-endopeptidase inhibitor
0,816 0,010 Glucose oxidase inhibitor
0,809 0,005 Venombin AB inhibitor
(Pa – probability of Active, Pi – probability of inactive)
Table 3 Activities of DethiobiotinC10H18N2O3
Pa Pi Activity
0,861 0,012 Testosterone 17beta-dehydrogenase (NADP+) inhibitor
0,843 0,009 Acylcarnitine hydrolase inhibitor
0,826 0,003 Leukopoiesis stimulant
0,829 0,014 CYP2J substrate
0,814 0,004 Insulin promoter
0,821 0,016 Polypepsin inhibitor
0,816 0,015 Anti-eczematic
0,811 0,011 CYP2J2 substrate
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0,810 0,016 Mucomembranous protector
0,791 0,022 Saccharopepsin inhibitor
0,791 0,022 Chymosin inhibitor
0,761 0,003 Erythropoiesis stimulant
0,761 0,004 Diuretic inhibitor
0,758 0,013 Protein-disulfide reductase (glutathione) inhibitor
0,774 0,044 Phobic disorders treatment
0,729 0,009 Methylamine-glutamate N-methyltransferase inhibitor
0,715 0,010 Pterin deaminase inhibitor
0,717 0,018 Alkylacetyl glycerol phosphatase inhibitor
(Pa – probability of Active, Pi – probability of inactive)
Table 4 Activities ofRicinoleicacidC18H34O3
Pa Pi Activity
0,955 0,002 CYP2J substrate
0,954 0,002 CYP2J2 substrate
0,946 0,002 Alkyl acetyl glycerol phosphatase inhibitor
0,940 0,003 Acylcarnitine hydrolase inhibitor
0,937 0,002 GST A substrate
0,936 0,001 CYP4A11 substrate
0,937 0,003 Diuretic inhibitor
0,937 0,003 Saccharopepsin inhibitor
0,937 0,003 Chymosin inhibitor
0,933 0,001 CYP4A substrate
0,932 0,001 Leukotriene-B4 20-monooxygenase inhibitor
0,929 0,001 Macrophage colony stimulating factor agonist
0,926 0,004 Prostaglandin-E2 9-reductase inhibitor
0,922 0,001 Prostaglandin-A1 DELTA-isomerase inhibitor
0,917 0,002 Phosphatidlglycerophosphatase inhibitor
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0,916 0,003 Lipoprotein lipase inhibitor
0,916 0,003 Linoleate diol synthase inhibitor
0,916 0,003 Lipid metabolism regulator
0,916 0,004 Sphinganine kinase inhibitor
0,915 0,004 Anti-eczematic
0,908 0,005 Polyporopepsin inhibitor
0,906 0,004 Beta-adrenergic receptor kinase inhibitor
0,906 0,004 G-protein-coupled receptor kinase inhibitor
0,906 0,005 Alkenyl glycerol phosphocholine hydrolase inhibitor
0,903 0,002 All-trans-retinyl-palmitate hydrolase inhibitor
0,897 0,005 Pro-opiomelanocortin converting enzyme inhibitor
0,891 0,002 Xylan endo-1,3-beta-xylosidase inhibitor
0,891 0,003 Sarcosine oxidase inhibitor
0,889 0,001 BRAF expression inhibitor
0,886 0,003 Phosphatidate phosphatase inhibitor
0,877 0,003 Vaso-protector
0,872 0,003 D-lactaldehyde dehydrogenase inhibitor
0,870 0,007 Mucomembranous protector
0,866 0,004 Dextranase inhibitor
0,861 0,004 Carboxypeptidase Taq inhibitor
0,863 0,008 Sugar-phosphatase inhibitor
0,856 0,005 Fucosterol-epoxide lyase inhibitor
0,851 0,001 Platelet aggregation stimulant
0,741 0,004 Biotinidase inhibitor
0,742 0,005 Anti-infective
0,738 0,002 Uroporphyrinogen decarboxylase inhibitor
0,742 0,006 Glucan 1,4-alpha-maltotriohydrolase inhibitor
0,740 0,005 Endopeptidase So inhibitor
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0,740 0,005 Alkenyl glycerol phosphor ethanolamine hydrolase inhibitor
0,737 0,003 Protein-Npi-phosphohistidine-sugar phosphotransferase inhibitor
0,733 0,001 Leukotriene-C4 synthase inhibitor
0,740 0,008 Cutinase inhibitor
0,743 0,011 UDP-glucuronosyltransferase substrate
0,747 0,018 Protein-glutamate methylesterase inhibitor
0,731 0,002 Sclerosant
0,727 0,001 Cyclooxygenase substrate
0,729 0,003 1-Alkylglycerophosphocholine O-acetyltransferase inhibitor
0,729 0,004 CYP2E1 inhibitor
0,730 0,005 Reductant
0,727 0,004 Gastrin inhibitor
0,725 0,003 4-Hydroxybenzoate nonprenyltransferase inhibitor
0,733 0,011 Hypolipemic
0,726 0,005 Shikimate O-hydroxycinnamoyl transferase inhibitor
0,723 0,002 Platelet adhesion inhibitor
0,757 0,037 Testosterone 17beta-dehydrogenase (NADP+) inhibitor
0,722 0,004 GST P substrate
0,722 0,003 2-Oxoglutarate decarboxylase inhibitor
0,725 0,007 Anti-hypercholesterolemic
0,726 0,009 N-benzyloxycarbonylglycine hydrolase inhibitor
0,720 0,003 Phosphoenolpyruvate-protein phosphotransferase inhibitor
0,727 0,011 IgA-specific serine endopeptidase inhibitor
0,718 0,004 Vitamin-K-epoxide reductase (warfarin-insensitive) inhibitor
0,720 0,006 3-Phytase inhibitor
0,717 0,004 Galactolipase inhibitor
0,715 0,004 CYP2C8 inhibitor
0,716 0,005 MMP9 expression inhibitor
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0,714 0,004 Phosphatidylcholine-sterol O-acyltransferase inhibitor
0,713 0,004 Aspergillopepsin I inhibitor
0,722 0,014 Venombin AB inhibitor
0,710 0,002 Oxidizing agent
0,711 0,003 Catalase inhibitor
0,709 0,002 15-Hydroxyprostaglandin-D dehydrogenase (NADP+) inhibitor
0,710 0,003 Anti-inflammatory, intestinal
0,703 0,003 Acyl-CoA hydrolase inhibitor
0,703 0,003 Palmitoyl-CoA hydrolase inhibitor
0,704 0,004 GST M substrate
0,702 0,003 Carnosine synthase inhibitor
0,702 0,003 Dolichyl-phosphatase inhibitor
0,704 0,006 TNF expression inhibitor
0,701 0,004 GST P1-1 substrate
0,702 0,005 Cytoprotectant
0,742 0,049 CYP2C12 substrate
0,702 0,010 CYP2C8 substrate
0,704 0,016 Peptidyl-dipeptidase inhibitor
0,707 0,053 Gluconate 2-dehydrogenase (acceptor) inhibitor
(Pa – probability of Active, Pi – probability of inactive)
Table 5 Activities of (1R,2R)-2-(2-Benzenesulfonylethyl)-1-(tert-butyldimethylsilyloxy) cyclopropane
C17H28O3SSi
Pa Pi Activity
0,700 0,015 Apoptosis agonist
0,618 0,005 Antiprotozoal
0,608 0,002 HIV-1 reverse transcriptase inhibitor
0,614 0,010 Anti-ulcerative
0,575 0,004 Transcription factor NF kappa B inhibitor
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0,540 0,009 Transcription factor inhibitor
0,529 0,006 Antiviral
0,520 0,010 Thiol protease inhibitor
0,512 0,004 Antiviral (HIV)
0,504 0,022 Anti-infective
0,404 0,017 RNA directed DNA polymerase inhibitor
0,455 0,085 Antineoplastic
0,371 0,003 Falcipain inhibitor
0,371 0,003 Falcipain 2 inhibitor
0,371 0,004 Catalase stimulant
0,439 0,073 Phosphatidylcholine-retinol O-acyltransferase inhibitor
0,363 0,001 Diuretic inhibitor
0,454 0,101 Sugar-phosphatase inhibitor
0,359 0,011 Prostaglandin E1 antagonist
0,439 0,103 Gastrin inhibitor
0,479 0,147 Aspulvinonedimethylalltransferase inhibitor
0,387 0,077 Alcohol O-acetyltransferase inhibitor
0,363 0,053 Antiviral (Herpes)
0,312 0,016 DNA directed RNA polymerase inhibitor
0,353 0,065 All-trans-retinyl-palmitate hydrolase inhibitor
0,350 0,091 Lactase inhibitor
0,372 0,142 Macrophage colony stimulating factor agonist
0,309 0,079 Glucan 1,4-alpha-maltotriohydrolase inhibitor
0,403 0,176 Polypepsin inhibitor
0,318 0,092 Gluconate 5-dehydrogenase inhibitor
0,328 0,137 Anti-inflammatory
0,312 0,125 GST A substrate
0,348 0,162 Antiviral (Rhinovirus)
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0,340 0,196 Platelet aggregation stimulant
0,314 0,202 Complement factor D inhibitor
(Pa – probability of Active, Pi – probability of inactive)
Table 6 Activities of 1,3-Dioxane-2-propanolC13H26O3
Pa Pi Activity
0,816 0,015 CYP2H substrate
0,757 0,040 CDP-glycerol glycerol phosphotransferase inhibitor
0,708 0,014 CYP3A5 substrate
0,713 0,031 Sugar-phosphatase inhibitor
0,725 0,059 Ubiquinol-cytochrome-c reductase inhibitor
0,708 0,042 Saccharopepsin inhibitor
0,708 0,042 Diuretic inhibitor
0,708 0,042 Acrocylindropepsin inhibitor
(Pa – probability of Active, Pi – probability of inactive)
The results indicated that all the compounds exhibit Diuretic inhibitor, phosphotransferase inhibitor, Antiviral,
Anti-infective, Insulin promoter, Anti-inflammatory and anti-urolithiatic activities given in table 2 to table 6.
The findings suggest that the plant has the potential to exhibit multiple biological activities and could be a
promising candidate for further in vivo studies.
Molecular docking
Totally 19 compounds have been selected for docking studies, out of 19 compounds 17 compounds have
binding affinity with the selected protein which causes kidney stones. Out of 17 of the compounds only 5
ligands have much higher docking scores. The glide score, number of H- bonds, Distance of H- Bonds,
interacted residues and ligand atom.
Table 7Docking score of inhibitory molecules against complex epitope binding protein (PDB id: 3CXD) with
the flower ofAervalanata
S No Compound G score No of H bonds Distance Protein Residues
1. 7211 1-Butanol, 4-butoxy -5.40 5 1.74 GLY H: 130
1.91 GLY H: 132
1.94 SER L: 116
2.62 THR H: 140
2.74 LYS L: 207
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
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2. 445027 Dethiobiotin -4.45 4 1.95 ILE L: 134
1.97 LYS L: 207
1.97 ILE H: 117
2.32 SER L: 116
3. 68764 Ricinoleic acid -4.43 3 1.76 GLY H: 131
1.85 SER H: 135
2.28 LYS L: 207
4. 162405619 (1R,2R)-2-(2-
Benzenesulfonylethyl)-1-(tert-
butyldimethylsilyloxy)cyclopro
pane
-4.20 1 3.21 LYS L: 207
5. 7833 1,3-Dioxane-2-propanol -3.09 2 2.14 THR H: 140
2.53 ILE L: 117
a) 7211 (1-Butanol, 4-butoxy) b) 445027 (Dethiobiotin)
c) 68764 (Ricinoleic acid)
Figure3 Compounds docked against complex epitope binding protein
(PDB id 3CXD) with flower of A.lanataL
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
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The compound 1-Butanol, 4-butoxy(7211) has a good glide score of -5.40A0and 5H- bonds with high
interaction against target protein followed by compound Dethiobiotin (445027) with glide score about -4.45 A0
and 4H bond and compound 12-Hydroxy-9-octadecenoic acid (68764) with glide score -4.43 A0 and 3H bond
binding against diuretic inhibitor. But the glide score is lesser than1-Butanol, 4-butoxy. Hence, the compound
1-Butanol, 4-butoxy (7211) has been chosen for further analysis and the complex structure –binding protein is
taken for maestro docking approach.
Kidney stones remain a significant health concern in modern times due to several contributing factors.
Changes in diet and lifestyle, including increased consumption of processed foods high in sodium and animal
proteins, as well as decreased water intake, have contributed to a rise in kidney stone prevalence. Additionally,
obesity rates have increased, which is linked to a higher risk of developing kidney stones due to altered
metabolic factors and increased urinary excretion of calcium oxalate. Environmental factors such as climate
change and rising temperatures can lead to dehydration, promoting the formation of kidney stones.
Furthermore, certain medical conditions and medications that affect urinary tract function or calcium
metabolism can predispose individuals to kidney stone formation. The impact of kidney stones is not only
limited to the acute pain they cause but can also lead to complications such as urinary tract infections, kidney
damage, and recurrent stone formation. As a result, kidney stones represent a considerable burden on
healthcare systems globally. The flower of A.lanataL has been studied for its potential therapeutic effects in
the context of kidney stones and diuretic properties. Diuretics promote increased urine production, which can
help flush out toxins and potentially prevent the formation of kidney stones. Research suggests that A. lanataL
may contain bioactive compounds that contribute to its diuretic activity.A. lanataL may possess anti-urolithic
properties, which could be beneficial in preventing or treating kidney stones. Molecular docking is a
computational technique used to predict the binding interactions between small molecules (ligands) and target
proteins (receptors). In the context of kidney diuretic activity, researchers can use molecular docking to
simulate how specific compounds from A.lanataL interact with proteins involved in regulating fluid balance
and urine production in the kidneys. Overall, the integration of GC-MS analysis with molecular docking
techniques provides valuable insights into the pharmacological properties of natural products like A.lanataL
and contributes to the discovery of novel drug candidates for kidney diuretic and related therapeutic
applications.
CONCLUSION
In this research project, an insilico study was conducted to explore the diuretic and anti-urolithiatic potential of
compounds derived from the flower of Aerva lanata L. Gas chromatography-mass spectrometry (GC-MS)
analysis was employed to identify the chemical constituents of the flower extract. Subsequently, molecular
docking studies using the Maestro Schrödinger platform were carried out to predict the binding affinity of
these compounds with relevant target proteins associated with diuretic and anti-urolithiatic activities. The
molecular docking results revealed promising interactions, with docking scores reaching -5.4, suggesting
strong potential for these compounds as therapeutic agents in the treatment of conditions related to kidney
stone formation, fluid retention and antineoplastic. This study underscores the utility of computational methods
in drug discovery and natural product research.
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