1
Research Article
Identification of Potent Hepatitis C Virus RdRp Inhibitors by Structure Based Drug Designing
Sudharsana Sundarrajan, Sweta Kumari, Sajitha Lulu and Mohanapriya Arumugam* Bioinformatics Division, School of Biosciences and Technology, Vellore Institute of Technology University, Vellore, Tamil
Nadu, India – 632014. Correspondence should be addressed to Mohanapriya Arumugam Received 2 July 2014; Accepted 9 July 2014; Published 23 July 2014 Copyright: © 2014 Sudharsana Sundarrajan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Keywords: Non-structural protein, RNA dependent RNA polymerase, docking, Andrographolide.
Year: 2014; Volume: 1; Issue: 1, Article ID: BCI14 04;
Pages: 1-14
Abstract
Hepatitis C, the silent disease caused by Hepatitis C virus (HCV) is a chronic health infection globally. HCV
causes permanent hepatic cirrhosis and carcinoma in humans. WHO estimated about 3 million incidents of
HCV infection around the world. Multiple variant genotypes along with the development of Quasi-species
limited the efficacy of drugs used for the treatment of HCV infections. This heterogeneity of the virus
hampered the drug development against them. The virus hosts many structural and non-structural (NS)
proteins. NS5B is a non-structural protein with a unique structure and function. The protein is a RNA
dependent RNA polymerase (RdRp) responsible for building the vital genetic component of the virus.
Inhibition of NS5B stops viral replication and propagation. The major role played by RdRP makes it a
preferential target for anti-HCV drug development. An association of docking and rescoring studies was
performed to 24 compounds derived from various plant sources to estimate their activity against HCV NS5B
RdRp. Based on the docking characterization and ADMET properties andrographolide, esculetin, columbin
and tinosporide were identified as they showed greater potency against HCV NS5B RdRp. However, based
on hepato bioactive spectrum and ADMET score, andrographolide from Andrographuis paniculata emerged
as a strong contender with lead like characteristics acting as a promising drug candidate.
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Introduction
Hepatitis C virus infections represent primary
public health concern. It is estimated that 3 to 4
million people are chronically infected globally.
HCV infection increases the risk of liver cirrhosis
and hepatocellular carcinoma development [1].
HCV infected individuals are treated with
interferon, Ribavirin (a nucleoside analog)
combination and other approved protease
inhibitors [2]. Hepatitis C is termed as a silent
disease since the incubation period of HCV ranges
from 6 to 8 months with no symptoms, making
early detection difficult [3]. However, the therapies
have limited efficacy against HCV genotypes. HCV
has been classified into different strains based on
their genetic differences. HCV diverges into 6
genetic variants. Genetic diversity is also reported
within the subgroups making genetic heterogeneity
responsible for differences in disease outcome and
response to treatment among HCV infected
individuals [4]. Severe drug regime, side effects and
high treatment costs limit patients’ compliance with
the treatment. Therefore, a major focus towards
development of cost effective and efficient anti-viral
therapeutics targeting viral proteins with limited
effects on the host system is of high priority.
HCV contains 9.6 kb positive – sense RNA genome
and belong to Flaviviridae family. The viral
polyprotein is processed into individual structural
proteins - Core, E1, E2 and P7 and non-
structural(NS) proteins - NS2, NS3, NS4A, NS4B,
NS5A and NS5B [5]. Host cytoplasm acts as a
factory for HCV replication. RNA dependent RNA
polymerase (RdRp) activity of HCV nonstructural
protein NS5B mediates viral RNA genome
replication. The importance of NS5B in synthesizing
HCV RNA makes it a preferential target for anti-HCV
inhibitor development. Furthermore, the host cells
lack RdRp activity leading to minimal or no side
effect on the host counterpart.
HCV NS5B has a number of enzymatic and
structural differences from cellular RNA
polymerases, which make it a promising target for
small molecule antiviral development [6].
Phytochemicals from medicinal plants shows
prominent role in prevention and therapy of many
diseases [7]. Our research focuses on the insilico
identification of promising bioactive molecules
against HCV infections by considering NS5B as a
primary target.
Materials and Methods
Identification of protein structure and
preparation of small molecules
The NS5B is 65 kDa proteins with a typical ‘right
hand’ polymerase shape containing finger, palm and
thumb domain. The lambda 1 loop of HCV NS5B,
which extends from the fingers to the thumb
domain, is unique to RNA-dependent RNA
polymerases and the beta-hairpin toward the
catalytic region is specific for HCV NS5B. The
structure of NS5B encloses motifs A-F, which are
crucial for their functional activity [8]. Brookhaven
protein data bank (PDB) is a major resource
repository of three-dimensional structural
information of biological macromolecules like
nucleic acids and proteins. The X-ray structure of
NS5B protein was retrieved from PDB (PDBID-
2YOJ) [9].
Traditional plant derived medicines are proved to
be efficient in the treatment of liver disorders [10].
The bioactive compounds which can act against
HCV NS5B were collected from literature [7, 11]
and books on Indian medicine (Table 1). The
SMILES (Simplified Molecular-Input Line-Entry
System) notation of the compounds were retrieved
from PubChem [30] and converted to PDB format
using CORINA server [31]. The 2D structures of the
compounds are presented in the S. Fig. 1.
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Table 1: List of phytochemicals used in the study
Sl.No Phytochemical Plant name Family Reference
1. Andrographolide Andrographuis paniculata Acanthaceae [12]
2. Arjungenin Terminalia arjuna Combretaceae [13]
3. Arjunic Acid Terminalia arjuna Combretaceae [13]
4. Berberine Berberis aristata Berberidaceae [14]
5. Chasmanthin Tinospora cordifolia Menispermaceae [15]
6. Columbin Tinospora cordifolia Menispermaceae [16]
7. Desmethylwedelolactone Eclipta alba Asteraceae [17]
8. Esculetin Cichorium intybus Asteraceae [18]
9. Friedelin Azima tetracantha Salvadoraceae [19]
10. Gossypin Hibiscus vitifolius Malvaceae [20]
11. Hyperforin Hypericum perforatum Hypericaceae [21]
12. Hypericin Hypericum perforatum Hypericaceae [21]
13. Hypophyllanthin Phyllanthus amarus Phyllanthaceae [22]
14. Jatrorrhizine Enantia chlorantha Annonaceae [23]
15. Kutkoside Picrorhiza kurroa Plantaginaceae [24]
16. Phyllanthin Phyllanthus amarus Phyllanthaceae [22]
17. Picroside Picrorhiza scrophulariiflora Scrophulariaceae [25]
18. Pseudohypericin Hypericum perforatum Hypericaceae [21]
19. Silymarin Silybum marianum Asteraceae [26]
20. Sitosterol Melothria heterophylla Cucurbitaceae [27]
21. Swertisin Swertia chirayita Gentianaceae [28]
22. Thaliporphine Mahonia leschenaultia Berberidaceae [29]
23. Tinosporide Tinospora cordifolia Menispermaceae [16]
24. Wedelolactone Eclipta alba Asteraceae [17]
Binding site prediction – motif conservation
The full genome sequences of eighteen HCV strains
belonging to six approved HCV genotypes were
retrieved from NCBI [32] (Table 2). Considering
the NS5B sequence of 2YOJ.pdb as the reference
sequence all other sequences were multiply aligned
to identify the conservation of functionally
significant motifs A-D using CLC workbench
(www.clcbio.com). The identified conserved motifs
were considered as the ligand binding site.
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Table 2: Genbank accession numbers of complete protein sequence of HCV genome
Molecular docking and ADMET predictions
Molecular docking predicts preferred orientation of
the ligand molecule to the protein when bound
together to form a stable complex. The scoring
function gives the strength of association between
them. AutoDock 4.2 [33] was used for docking study
with the Lamarkian genetic algorithm to find
globally optimized conformation. The grid spacing
was set to 0.403 nm and grid box dimension was set
to 86 x 60 x 60 which enclosed the residues of
motifs A to D. Default settings were applied for
remaining parameters. At the end of a docking with
multiple runs, a cluster analysis was performed.
Docking solutions were clustered together and
ranked by the lowest docking energy. The lowest
binding energy cluster was selected as the
representative binding mode. Out of twenty four
bioactive compounds, ten compounds with least
binding energy were considered for the next phase
of screening.
Absorption, distribution, metabolism, excretion and
toxicity (ADME/T) properties receive more concern
in rational drug design, as they determine the
development of safe orally bioavailable drug. The
determination of characteristics of compounds that
are more likely to exhibit satisfying ADME/T
properties has led to the concept of “drug-
likeliness”. Drug likeliness properties obeying rule
of thumb [34] and molecular properties governing
pharmacokinetics of the drugs, including human
intestinal absorption, aqueous solubility, plasma
protein binding and Ames mutagenicity values [35]
were predicted using MolInspiration [36] and
ChemSilico server (www.chemsilico.com). These
parameters act as a filter to screen out more
potential leads.
Biological activity profiles are precise indicators not
only for molecular properties, but also for biological
response of the molecules. Biospectrum connects
chemical scaffolds with biological activity. PASS
(prediction of activity spectra for biologically active
substances) server [37] predicts the
pharmacological effects and biochemical
mechanisms of biologically active substances based
on their structural formula. The biological activity
spectrums for compounds emerging out of
screening processes were predicted. The pass
activity score for hepato-protectant activity (HPA),
hepatic disorder treatment (HPT) and anti-
inflammatory activity (AIA) were taken under
consideration for further analysis.
Molecular dynamics simulation
The molecular dynamics simulation (MDS)
calculates the time dependent behavior of the
molecular system. They are used to investigate the
thermodynamics of biological macromolecules and
their complexes. MDS was performed using
GROMACS 4.5.3 [38]. Gromos96 forcefield [39] was
used to prepare the protein topology file. Ligand
Genotype GB accession number Genotype GB accession number
1a AAB66324.1 3k BAA09890.1
1b Q9WMX2 4a CAA72338.1
1c BAA03581.1 5a CAA73640.1
2a AAY24373.1 6a CAA72801.1
2b AAP55704.1 6b BAA07103.1
2c BAA08911.1 6d AAZ85046.1
2k BAA88057.1 6g BAA09891.1
3a AAC03058.1 6h BAA32667.1
3b BAA08372.1 6k BAA32666.1
5
topology and forcefield parameter file were
prepared using PRODRG server [40]. The full
system was subjected to 5 ns MDS at 300 K
temperature and 1 bar pressure. The best protein
ligand complex was subjected for molecular
dynamics simulation study.
Results and Discussion
NS5B structural and functional analysis
The hydrophilic nature of NS5B was predicted as -
0.201 based on a Gravy index (grand average of
hydropathy) by Expasy ProtParam [41]. RdRp
(Fig.1) is a compact globular protein divided into
finger, palm and thumb domains. The finger domain
has two sub-domains, α finger with seven α helix
and β finger with four β strands. The β finger is
connected to thumb domain through two loops
Ala9-Thr41 (loop 1) and Asn142-Ala157 (loop2).
The incoming ribonucleoside tri-phosphates (rNTP)
enter through a small hole at the bottom of loop 2
to reach the binding cleft. The palm domain is a
combination of three anti-parallel β strands and
four helices [6]. It forms the most important
catalytic center of RdRP and contains highly
conserved motifs A to F. Seven α helices and an
anti-parallel β strand constitutes the thumb region.
The processed RNA duplex is released with the help
of the β sheet in the thumb domain [42].
The multiple sequence alignment of NS5B protein
sequences from six genotypes [43, 44] and their
sub-types approved by ICTV (International
Committee for the Taxonomy of Viruses) were
proved to be conserved in RNA dependent
polymerases by MSA. Motif A (Asp220-Asp225),
motif C (Gly317-Asp319) and motif D (Ala342-
Tyr346) are involved in nucleotide tri-phosphate
binding (NTP) and catalysis while motif B (Ser282-
Asn291) is involved in template-primer positioning.
The conserved patterns of motifs A-D are
highlighted in Fig.2. The residues involved in
divalent cation binding, substrate binding, template
selection and differentiation remain highly
conserved thus validating the binding site
preferences of the drugs.
Molecular Docking
To identify novel candidate inhibitors of HCV
polymerase protein, the twenty four compounds
were docked in the catalytic center of the palm
region. Different binding poses of twenty four
compounds were searched and ranked based on
their binding affinity. Top ten compounds
(andrographolide, esculetin, tinosporide, columbin,
kutokoside, arjunic acid, chasmanthin, silymarin,
gossypin and hypercin) were identified based on
the AutoDock ranking (Fig.3). The binding energy
distribution of the first ten compounds ranged from
-5.42 kcal/mol to -10.43 Kcal/mol. The Binding
free energy and binding interaction patterns of all
the ligands were analyzed using Discovery Studio
Visualizer and PyMol. The free binding energy and
interaction patterns of the ligands represented in
Table 3 are considered as the basic criteria for
efficient activity. The binding mode analysis of the
compounds with NS5B provides a better insight
into the essential residue interactions.
Cation-п interactions [45] are strong, non-covalent
binding force conferring stability in ligand-receptor
complex. Strong cation-п interaction was reported
among the aromatic group of the ligands with the
residues Arg158, Lys141. The lower end of the β
finger of NS5B protein forms a rim of basic amino
acids (Arg48, Lys51, Lys141, Lys155 and Arg158)
to anchor negatively charged rNTPs and Arg158
plays a key role in rNTP binding. The blockage of
this region can inhibit the RdRp activity of the
protein. HCV NS5B shows a strong preference
towards RNA as a template and rNTPs as substrate.
Asp225 of motif A and Ser288 of motif B are
substrate discriminating residues. It should be
noted that, many compounds under our analysis
form hydrogen bonds and electrostatic interactions
with the above residues, silencing their pivotal
function. A hydrophobic pocket formed by Asp225
of motif A and Ser282, Thr281 and Asn291 of motif
B accommodates ribose moiety of rNTP. Interaction
with these residues further hinders the protein’s
functionality. Majority of the ligands interactions
were observed with the catalytic aspartate residues
Asp220, Asp318 and Asp319. The C-terminal region
6
constitutes the regulatory part of HCV NS5B
protein. Ser556 and Ser543 are phosphorylated via
casein kinase-2. Upon phosphorylation RdRp
becomes more active. Interaction of the ligands
with any one of these residue can inactivate RdRp
functional propagation.
Fig. 1: Three dimensional structure of HCV NS5B polymerase (2YOJ) with finger, palm and thumb domains. The domains are colored to emphasize their structural significance. Purple: α fingers, green: β fingers, cyan: palm and pink: thumb domains. The cartoon representation of motifs A – D.
Fig. 2: Multiple sequence alignment of the RdRp sequences of 6 different genotypes from region 200-400 depicting the conservation patterns. The motifs A: D220, T221, R222, C223, F224 and D225.motif B: S282, G283, V284, L285, T286, T287, S288, C289, G290 and N291. Motif C: G317, D318 and D319 and motif D: A342, M343, T344, R345 and Y346 are enclosed within a rectangular box. The amino acids are colored based on their nature.
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Fig. 3: The successors from first screening phase. Interaction of phytochemicals with the motifs A-C is highlighted. M-A*: interaction with motif A residues; M-A & B*: interaction with motifs A and B residues; M- B& C* interaction with motif B and C residues; M- C*: interaction with motif C residues. (The compounds in our study did not show preferential binding towards the residues of motif D, hence not shown in Fig).
TABLE 3: Binding energy and interacting residues of all the 24 ligands
Sl.No Compounds Binding
Energy
(Kcal/mol)
Residues involved in interactions
1. Andrographolide -10.43 Thr287, Asn291, Asp318
2. Esculetin -9.91 Ser288, Asn291, Gly317(σ-п), Asp318, Tyr555
3. Columbin -8.96 Lys141, Arg158(σ-п), Asn291
4. Tinosporide -8.6 Lys141, Asn291, Asp318
5. Kutkoside -7.7 Arg158, Tyr219, Thr221, Asp225, Asn291
6. Arjunic Acid -7.66 Arg158, Ser226
7. Chasmanthin -7.57 Lys141, Asp225, Asn291, Asp318
8. Silymarin -7.54 Tyr219, Asp319, Leu320, Ser556
9. Gossypin -7.24 Arg158(+-п), Cys223, Asp225, Asn291, Asn316, Asp318, Asp319,
Ser367
10. Hypericin -7.14 Lys141(+-п), Arg158, Ser282, Asp318
11. Picroside -6.92 Asp318, Asp319, Ser556
12. Swertinin -6.84 Arg158 (+-п), Ser226, Asp318
13. Sitosterol -6.80 Ser556
14. Hyperforin -6.67 Asp225, Asn291
15. Wedelolactone -6.65 Arg158(+-п), Asp225, Ser282, Asn291, Asp318
16. Friedelin -6.5 No interaction
17. Berberine -6.32 Arg158(+-п), Asn291
18. Arjungenin -6.08 Asp225, Asp318, Cys366, Ser556
19. Jatrorrhizine -6.00 Arg158(+-п), Asp318
20. Desmethylwedelolactone -5.94 Arg158(+-п), Asp225, Ser556
21. Pseudohypericin -5.53 Arg158(+-п), Asp225, Asp318, Asn316
22. Phyllanthin -5.42 Lys141, Arg158(+-п)
23. Thaliporphyine -5.04 Arg158 (+-п), Asn291
24. Hypophyllanthin -4.46 Lys141, Arg158, Ser556
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Physiochemical property profile and Bioactivity
predictions
The subsequent screening phase was based on
physiochemical properties of the compounds under
study. The ten phytochemical compounds were
evaluated against thirteen physiochemical
properties which form the backbone of drug
designing process. Compounds which satisfy the
Lipinski’s rule of five (RO5) and other
physiochemical parameters were selected for
further analysis.
Hypericin and kutokoside with molecular weight
504.45 Da and 512.46 Da were excluded from the
study, as high molecular weight affects drug
solubility, absorption and diffusion. Moderate
ClogP (Partition coefficient) is desired for GI track
absorption [46]. Based on RO5 compounds
possessing hydrogen bond donor <5, hydrogen
bond acceptor <10 and total hydrogen bonds <=12
were retained for further analysis [35]. Compounds
with CSLogD7.4 (Distribution coefficient) in the
range of 1-3 shows moderate solubility and
permeability along with favorable in-vivo oral
absorption and blood brain barrier (BBB)
penetration [47]. Gossypin with the distribution
coefficient value of -1.5 was excluded from further
analysis as it failed to fall within desired range.
Chasmanthin with high intrinsic solubility value
makes it an unfavorable lead compound [34]. The
intact drug binding to plasma protein affects its
displacement in the body [35] and silymarin with
98% of plasma binding was eliminated from the
study. The molar refractivity value of arjunic acid
was found to be 187.72 making it unfavorable for
entering the next phase [48]. Polar surface area
(PSA) measures drug’s ability to permeate cell [49,
50] and its cutoff were between 60 Å2 and 140Å2 for
good permeability. Compound kutokoside showed
higher PSA value along with high molecular weight.
Aromatic and heteroaromatic rings are ubiquitous
features in small-molecule drugs. The rings possess
fewer degrees of freedom compared to chains;
hence show increased drug-receptor binding
energy. Less number of aromatic rings (< 3) favors
an oral drug candidate [51]. Hypericin with 6
aromatic rings and high molecular weight was
excluded from the screening process.
Improvisation of our previous study on
phytochemical screening against NS3 protein [5]
was accomplished by performing human intestinal
absorption (HIA) and Ames test for crucial selection
of phytochemicals against our current target HCV
NS5B. Final screening tested HIA [52] and Ames
mutagenecity. The compounds which showed
positive HIA and Non-Toxic Ames test were finally
selected. After a series of crucial screening
tinosporide, columbin, andrographolide and
esculetin emerged as promising drug candidates
satisfying all the screening criteria (Table 4).
Table 4: Physiochemical and pharmacokinetic properties of final hits
C*
P1a P2
b P3
c P4
d P5
e P6
f P7
g P8
h P9
i P10
j P11
k P12
l P13
m P14
n
C1a 1.7 1.7 -2.8 68.1 127.9 55 86.9 350 5 3 6 0 + NT
C2b 1.3 1.1 -2.4 76.4 53.5 19 70.6 178 4 2 0 1 + NT
C3c 1.7 1.7 -1.7 81.2 113.1 49 98.5 374.2 7 1 1 1 + NT
C4d 1.5 1.5 -3.2 88.7 113.7 48 85.9 358.4 6 1 1 1 + NT
C*: Compounds; C1a: Andrographolide; C2b: Esculetin; C3c: Tinosporide; C4d: Columbin;
P1a: CSLogP-Partition co-efficient; P2b: CSLogD7.4-Distribution co-efficient at pH 7.4; P3c: CSLogSo-aqueous solubility; P4d:Plasma binding (%);P5e: Molar refractivity; P6f: Number of atoms; P7g: Polar surface Area,Å2; P8h: Molecular Weight (Daltons); P9i: Number of hydrogen bond acceptor; P10j: Number of hydrogen bond donor; P11k: Number of rotatable bonds; P12l: Number of rings ; P13m: Human intestinal absorption; P14n: AMES test; NT: non toxic
9
Bioactivity prediction forms the final phase of
screening. The Hepato-protectant, hepatic disorder
treatment and anti-inflammatory activity were
predicted for the final four hits using the Pass
server (Fig.4). The compound with Pa (probability
to be active) > 0.7 was considered to exhibit the
activity in an experiment by the PASS server.
Andrographolide was finally narrowed down as it
showed high Hepato-protectant activity (Pa=0.98)
and a higher binding energy of -10.43 Kcal/mol
(Fig.5).
Fig. 4: Bioactive spectrum plot of top 4 hits (tinosporide, columbin, andrographolide and esculetin).
Fig 5. (a) Binding of Andrographolide with NS5B polymerase. Binding site residues are colored yellow and ligand is represented in ball and stick model. (b) 2D plot showing ligand binding site and interacting residues. Arrows indicate hydrogen bond formation between the ligand and the protein. The surface representation of the protein was generated using PyMol and 2D plot was generated using Discovery Studio Visualizer.
Molecular dynamics simulation
10
For a stronger and better insight into the validity of
screening strategy, andrographolide-NS5B complex
was subjected to molecular dynamic simulation
study. The stability of the complex was monitored
over the entire simulation time. The lowest binding
energy conformation was taken as initial
conformation and solvated using SPC (single point
charge) water molecule and neutralized by adding
chlorine ions. In the first equilibration phase a 30 ps
molecular dynamics was performed at 300K and
the system was relaxed by 1000 steps of conjugate
gradient energy minimization. In the second
equilibration phase the system was subjected to
2000 ps dynamics at 300 K and 1 bar pressure.
Finally a 5 ns MD simulation was performed to
relax the protein-ligand complex. The potential
energy fluctuations and RMSD of Cα atoms were
monitored. The potential energy of the whole
system remained constant throughout the
simulation process. The protein structural flexibility
during the simulation process was evident from the
small drifts observed in the RMSD plot. But the
RMSD fluctuations converged after 3 ns simulation
proving that the stability was conferred in the
protein structure (Fig.6).
Fig 6. (a) The RMSD plot obtained by fitting C-alpha of the protein after MD simulation showing convergence after 3 ns. (b) Potential energy plot depicting the stability of the protein.
Conclusion
A high throughput in silico method curtails the time
and cost spent in the synthesis and testing of
compounds before entering the clinical trials.
Modern drugs have their lineage in traditional
medicines. The Indian system of medicines based
on herbs is gaining importance globally in recent
years because of their efficacy, safety, easy
accessibility and cost effectiveness. ADMET has
become an integrated part of the drug discovery
setup, providing guidance in lead selection and
optimization. In our study andrographolide,
columbin, esculetin and tinosporide have exhibited
the characteristics of a lead compound. In addition
to the lead like characteristics, andrographolide has
shown good bioactive spectrum as hepato-
protectant. On further enhancement these leads can
evolve as promising therapeutic agents against HCV
induced hepatic disorders.
Conflict of Interest
We have no conflicts of interest to disclose.
Acknowledgements
We acknowledge VIT University, India for providing
computational facility and support throughout the
work.
11
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S. Fig. 1: 2D structures of Phytochemicals used in the study
Andrographolide Arjungenin Arjunic acid Berberine
Chasmanthin Columbin Desmethylwedelolactone Esculetin
Friedelin Gossypin Hyperforin Hypericin
14
Hypophyllanthin Jatrorrhizine Kutkoside Phyllanthin
Picroside Pseudohypericin Silymarin Sitosterol
Swertisin Thaliporphine Tinosporide Wedelolactone