Glycyrrhetinic acid derivatives on lung cancer: molecular docking, QSAR, ADME/T and In-Vitro ...

20 Pages·2016·1.07 MB·English

Int J Clin Exp Med 2016;9(2):3062-3068
www.ijcem.com /ISSN:1940-5901/IJCEM0015766
Original Article
Glycyrrhetinic acid derivatives on lung cancer: molecular
docking, QSAR, ADME/T and In-Vitro analysis
Zhi-Qiang Sun1, Rui-Fang Li2, Sheng-Yan Jin3
1Department of Pharmacy, The 1st Affiliated Hospital of He’nan University of Science and Technology, Luoyang,
Henan 471003, P. R. China; 2Medical College, Henan University of Science and Technology, Luoyang, Henan
471003, P. R. China; 3Department of Neurology, Luoyang Center Affiliated Hospital of Zhengzhou University, Luoy-
ang, Henan 471009, P. R. China
Received September 7, 2015; Accepted January 7, 2016; Epub February 15, 2016; Published February 29, 2016
Abstract: In the present work, quantitative structure-activity relationship (QSAR) model of glycyrrhetinic acid deriva-
tive targeting lung cancer cell line (A549) were developed by multiple linear regression approaches. The r2 and rCV2
of the QSAR model were taken 0.90 and 0.80 respectively in terms of correlation. This QSAR study indicates that
the Lambda Max Visible (nm), Dipole Moment (debye), nitro and amine functional groups are within lung cancer
cytotoxicity profile. Exploration of drug likeness, absorption, distribution, metabolism and excretion (ADME) and tox-
icity analysis showed that one of the glycyrrhetinic acid derivatives (1c) may exhibit good anticancer activity and on
comparison showed two times more potency then standard drug paclitaxel. The molecular docking analysis showed
1c compound binding affectively with lung cancer target epithelial growth factor receptor (EGFR). The invitro study
of the same compound showed results that were in collaboration with our Insilico results.
Keywords: Glycyrrhetinic acid, QSAR, docking, ADME, human lung cancer (A-549)
Introduction Plants have been an important and rich source
for the novel drugs and more recent studies
Cancer, a malady defined by the uncontrolled have further confirmed their continued impor-
growth of abnormal cells is a major cause of tance in drug discovery [3]. At present more
death worldwide and accounted for 7.6 million than 60% FDA approved anticancer drugs are of
deaths in 2008 which is around 13% of total plant origin [4]. Over the past few years triterpe-
deaths developed as well as in developing noids from higher plants have shown a wide
countries [1]. In 2014, it was estimated that range of biological activities such as anti-HIV
1,665,540 new cancer cases diagnosed and activity, anti-inflammatory, antitumor and anti-
585,720 cancer deaths in the US alone and the viral activity [5]. The roots of licorice (Glycyrrhiza
same agency reported that cancer remains the glabra and Glycyrrhiza uralensis Fischer) are a
second most common cause of death in the rich source of glycyrrhizin, which on acidic
US, accounting for nearly 1 of every 4 deaths hydrolysis provides the triterpenoid, 18β-glycy-
[2]. Lung cancer is the leading cause of mortali- rrhetinic acid (GA-1) as aglycone. GA-1 has
ties than any other cancer in both men and shown many biological activities such as antitu-
women. The American Cancer Society estimat- mor, hepatoprotective, anti-inflammatory, anti-
ed that in 2013 about 174,100 cancer deaths viral, and immunomodulatory activities [6-8]. It
will be caused by tobacco use. On the other has been reported that glycyrrhizin and glycyr-
hand the World Cancer Research Fund estimat- rhetinic acid possess moderate cytotoxicity and
ed that about one quarter to one-third (approxi- apoptotic effects on the cancer cells [9] and a
mately, 1,660,290) of the new lung cancer QSAR guided structural modification of glycyr-
cases will be diagnosed in 2013. While great rhetinic acid may result in the development of
efforts have been made to tackle the disease novel anticancer compounds [10].
over past few decades, it still continues to be a
major health menace. Medicinal chemists are In the present study QSAR model was devel-
tirelessly exploring for a better and more suit- oped to study glycyrrhetinic acid derivative,
able cancer therapeutic. their cytotoxicity activity were tested and fur-
Glycyrrhetinic acid derivatives against lung cancer
Molecular docking simula-
tions
AutoDock 4 program [13] was
used to explore the ligand-
receptor interaction at the
anticancer target protein EG-
FR for human lung cancer
PDB ID: 2GS7 binding site and
to obtain an accurate binding
model for the complexes. This
program estimates the total
interaction energy between a
Figure 1. Graphical plot of multiple linear regression analysis which indicates
ligand and a selected portion
linear relationship between experimental and predicted log IC (µM).
50 of the protein containing the
binding site using a genetic
ther screening was done on basis of drug like- algorithm. The first step of the study was the
ness and ADME properties. The top compound validation of the docking method performance
generated from this analysis was tested using on the system to be modeled. The starting point
molecular docking and simulation analysis, fur- was the 3D structure of a co-crystallized com-
ther to validate our results the compound was plex EGFR (PDB code 2GS7). Docking scoring
subjected to invitro analysis. function considers several factors involved in
the ligand-receptor interaction, hydrophobicity,
Materials and method polarity, repulsiveness, entropy and solvation.
Docking parameters include ligand flexibility
Structural modeling and energy minimization
and rigid protein structure; all other parameters
were assigned to their default values.
The glycyrrhetinic acid derivatives and their “in
-vitro” biological activity (IC values) were Development of QSAR model for lung cancer
50
taken for QSAR studies. The molecular struc- (A-549)
tures of all of these compounds and their cor-
responding IC values are displayed in Table To predict the cytotoxicity activity against lung
50
S1. ChemBioDraw Ultra v12.0 software provid- cancer cell line (A-549) a QSAR model was
ed by Cambridge Soft Corporation, Washington developed, where multiple linear regression
was used for designing structure of the mole- QSAR model was used for validation. It was
cules. SPDBV viewer [11] was used for energy observed that training set cytotoxicity drugs/
minimization of the EGFR structure [12]. compounds fit well in this model and four
molecular descriptors; Lambda Max Visible
Chemical descriptors and QSAR modeling
(nm), Dipole Moment (debye), nitro and amine,
parameters
functional groups counts strongly correlated
well with cytotoxicity activity.
28 known anticancer drugs with activities were
selected in the training data set for the devel-
QSAR model equation -
opment of QSAR model for activity prediction
against human lung cancer cell line A-549
Predicted log IC (µM) = -0.012363 × Lambda
using standard 50 chemical descriptors calcu- 50
Max Visible (nm) +0.782079 × Group Count
lated for each compound (Table S2). A total of 4
(amine) +0.14071 × Dipole Moment (debye)
known anticancer drugs with experimental
-0.769541 × Group Count (nitro) -3.83299.
activities were selected in the test data set.
The anti-cancer activity was in IC (µM). Default The r2 = 0.9048, refers regression coefficient
50
parameters were set as selection criteria and which indicates 80% of correlation between
highly correlated descriptors were excluded by activity and chemical descriptors of training
using correlation matrix or co-variance analysis data set while rCV2 = 0.8040, refers cross vali-
(Table S3). Finally, robust QSAR model was dation regression coefficient which indicates
developed by using forward stepwise multiple 80% prediction accuracy of QSAR model (Figure
linear regression (MLR) statistical method. 1). It is evident from the above equation that
3063 Int J Clin Exp Med 2016;9(2):3062-3068
Glycyrrhetinic acid derivatives against lung cancer
Table 1. Predicted lung cancer (A-459) activ- logBB penetration and permeability studies.
ity (IC µM) of glycyrrhetinic acid dervatives Process of excreting the compound from
50
human body depends on MW and logP. Higher
S. No. Compound Name Pred. log IC (µM)
50 compound lipophilicity leads to increased
1. 1a 1.462 metabolism and poor absorption. The descrip-
2. 1b 1.112 tors values of 90% orally active compounds fol-
3. 1c 1.044 lows Lipinski’s rule. The bioavailability of com-
pounds was evaluated by topological polar
4. 1d 1.354
surface area (TPSA) value. The number of rotat-
5. 1e 1.252
able bonds should be less than 10 for oral bio-
6. 1f 1.331
availability. Some researchers use the sum of
7. 1g 1.172 H-bond donors and acceptors may be ≤12 as a
8. 1h 1.110 secondary determinant of fractional absorp-
tion.
9. 1i 1.254
10. 1j 1.132 Invitro assessment
11. 1k 1.254
All the experiments were carried out on A-549
12. 1l 1.154
NSCLC cells, which were obtained from the hos-
13. 1o 1.265
pitals cell Collection center. The obtained cells
14. 1m 1.235 were grown DMEM subjected with 10% fetal
15. 1n 1.026 bovine serum and were kept in humidified 95%
16. 1p 1.251 air, 5% CO at 37°C temperature. The cells used
2
for the experiment were tested for mycoplasma
17. 1q 1.251
and only mycoplasm free cells were used. The
18. 1r 1.087
culture of the cells was adjusted to exponential
19. 1s 1.254 growth before the experiments were per-
20. 1t 1.294 formed. Varying concentration of the top lead
compounds was subjected to the cells and
were incubated for 72 hr. MTT (3-(4,5-Dime-
the molecular descriptors, dipole moment and
thylthiazol-2-yl)-2,5-diphenyltetrazolium bromi-
amine group counts are showing positive cor-
de) assay was performed . The experimental
relation. On the other hand, Lambda Max culture was given 100 μl of MTT reagent and
Visible and nitro group counts are showing neg- incubated for 45 min at 37°C, MTT solution
ative correlation. was removed and the pallets were then incu-
bated for 20 min at 37°C after addition of 180
Evaluation of pharmacokinetic parameters μl of DMSO in each well. The absorbance was
measured at 560 nm with reference of 690 nm
Most of drugs failed during clinical trials due to using a Tecan Infinite F 200 micro-plate
poor pharmacokinetics parameters due to mis- reader.
match of standard pharmacokinetic properties,
described as absorption, distribution, metabo- Results and discussion
lism, excretion and toxicity [14]. These proper-
ties are important in order to determine the A QSAR model was developed in this study to
success of the compound for human therapeu- calculate the physico-chemical properties of
known anticancer compounds having experi-
tic use. Some important chemical descriptors
mental anticancer activity against lung cancer
correlate well with ADMET properties such as
cell line (A-549) for the training set. QSAR
polar surface area (PSA) as a primary determi-
model validation used leave-one-out (LOO)
nant of fraction absorption, low molecular
approach and evaluated the model through
weight (MW) for oral absorption. The distribu-
test data set, which also showed significant
tion of compound in human depends on factors
accuracy of QSAR model.
e.g., permeability (apparent Caco-2, blood-
brain barrier (logBB), logKp for skin and MDCK
High throughput virtual screening of glycyrrhe-
permeability), the volume of distribution and
tinic acid derivatives for cytotoxic activity
plasma protein binding refer by logKhsa for
serum protein binding. The octanol-water parti- We screened a virtually designed glycyrrhetinic
tion coefficient (logP) has been implicated in acid derivatives after developing a QSAR model
3064 Int J Clin Exp Med 2016;9(2):3062-3068
Glycyrrhetinic acid derivatives against lung cancer
Table 2. Comparison of docking score and binding site residues of studied compounds against the
anticancer target EGFR
S. Total
Compound Biding pocket residue in (4Å)
No energy
1. 1c 4.1953 VAL-726, ALA-743, CYS-775, MET-790, GLN-791, ASP-800, GLU-804, LEU-844, THR-854,
2. 1b 5.3674 VAL-726, GLU-762, MET-766, LEU-777, CYS-797, ASP-800, GLU-804, LEU-844, THR-854, PHE-856, ASP-855,
3. 1n 3.5291 VAL-726, ALA-743, LEU-745, MET-766, MET-790, GLY-796, CYS-797, ASP-800, GLU-804, LEU-844, THR-854, PHE-856,
4. Paclitaxel 2.5354 ALA-743, MET-790, ASP-800, GLU-804, PHE-856, ASP-855, PHE-856,
Figure 2. A: Compound 1b was docked onto the anticancer binding site of epidermal growth factor receptor (EGFR)
tyrosine kinase inhibitors (TKIs) (PDB: 2GS7) conformation and total docking score of 5.3674. B: Compound 1c was
docked onto the anticancer binding site of epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs)
(PDB: 2GS7) conformation and total docking score of 4.1367. C: Compound 1n was docked onto the anticancer
binding site of epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) (PDB: 2GS7) conformation
and total docking score of 4.3564.
(Figure S1) and results summarized in the Table acid 1c, 1b and 1n were most active and pos-
1. QSAR results showed that out of 20 deriva- sessed almost equivalent cytotoxic activity as
tives, glycyrrhetinic acid derivatives (1b, 1c, 1h, control compound.
1j, 1n, and 1r) were predicted more active than
Molecular docking simulation against EGFR
control compound paclitaxel. Further robust
analysis of the most active derivatives showed As part of this study molecular docking studies
that ethyl oxalyl derivatives of glycyrrhetinic were performed to simulate the coupling of a
3065 Int J Clin Exp Med 2016;9(2):3062-3068
Glycyrrhetinic acid derivatives against lung cancer
Table 3. Compliance of pharmacokinetics (ADME) computational parameters of glycyrrhetinic acid
derivatives
Principal Descriptors: 1b 1c 1n Paclitaxel Stand. Range
Apparent Caco-2 Permeability (nm/sec) 118 31 15 55 (<25 poor, >500 great)
logBB for brain/blood 0.771 -1.716 2.06 -2.489 (-3.0/1.2)
Log IC for HERG K+ Channel Blockage 1.774 -2.842 3.57 -6.021 (concern below -5)
50
Log Kp for skin permeability 3.865 -4.798 4.89 -3.302 (-8.0 to -1.0, Kp in cm/hr)
Log S for aqueous solubility 6.333 -8.027 9.02 -5.797 (-6.5/0.5)
Log Khsa Serum Protein Binding 1.040 1.311 1.74 0.799 (-1.5/1.5)
Apparent MDCK Permeability (nm/sec) 62 14 7 21 (<25 poor, >500 great)
Lipinski Rule of 5 Violations 1 2 2 3 maximum is 4
%Human Oral Absorption in GI (±20%) 81 61 61 49 (<25% is poor)
Qual. Model for Human Oral Absorption low low low low (>80% is high)
Foot Note: logS = Predicted aqueous solubility. logHERG = Predicted IC value for blockage of HERG K+ channels. PCaco =
50
Predicted apparent Caco-2 cell permeability in nm/sec. logBB = Predicted brain/blood partition coefficient. PMDCK = Predict-
ed apparent MDCK cell permeability in nm/sec. logKp = Predicted skin permeability. logKhsa = Prediction of binding to human
serum albumin. Human Oral Absorption = Predicted qualitative human oral absorption. %Human-Oral Absorption = Predicted
human oral absorption on 0 to 100% scale.
series of top three glycyrrhetinic acid deriva- dues within a radius of 4Å were basic residue
tives (1b, 1c, and 1n) modulate the human lung ARG-110, THR-854, CYS-797, Phe-856, VAL-
cancer target EGFR and to study their interac- 726, ASP-800, Glu-804, Ala-743, Gly-796, Leu-
tion. Results of our molecular docking studies 745, Leu-844, Met-776 and Met-790; thus
also showed the similar mechanism of action. bound compound showed strong hydrophobic
These docking results suggest that studied interaction and lead to more stability and activ-
compounds inhibit the activity of EGFR (Table ity (Figure 2C).
2). On the other hand, the molecular docking
Compliance with pharmacokinetic parameters
analysis results for 1b against target protein
EGFR showed high binding affinity docking
In this studies, considered many physiochemi-
score indicated by total score of 5.3674. In
cal properties related to pharmacokinetic,
docking pose, the chemical nature of binding
when screening for active glycyrrhetinic acid
site residues within a radius of 4Å residue VAL-
derivatives. All of the derivatives explore fol-
726; Thr-854, CYS-229, Asp-855, Glu-800,
lowed by Lipinski’s rule of five. Similarly, the
Glu-804, Leu-844, Met-766, Phe-856, there-
ADME parameters were calculated for the
fore bound compound showed strong hydro-
active glycyrrhetinic acid derivatives viz., com-
phobic interaction and lead to more stability
pounds 1b, 1c and 1n. The values of these
and activity (Figure 2A). Similarly, docking
parameters also showed close resemblance
results for 1c against anti-cancer target EGFR
with these derivatives and lie within the stan-
showed high binding affinity docking score indi-
dard range of values exhibited by 95% of all
cated by total score of 4.1367. In docking pose
known available drugs, these values for the
of the 1c, EGFR complex, the chemical nature
predicted active glycyrrhetinic acid derivatives
of binding site residues within a radius of 4Å
lie within the standard range, generally
were acidic residue Asp-800, Glu-804, Ala-743,
observed for the drugs (Table 3). Most of the
Gly-796, Leu-844, Met-790, Val-726, Thr-854,
glycyrrhetinic acid derivatives have polarities
CYS-229 and Gln-791 therefore bound com-
that enabled better permeation and absorp-
pound showed strong hydrophobic interaction
tion, as revealed by the number of H-bond
with EGFR, thus lead to more stability and
donors and H-bond acceptors.
activity in this compound (Figure 2B). Lastly,
the molecular docking analysis results for 1n Compliance with toxicity risk assessment
against target protein target protein EGFR parameters
showed high binding affinity docking score indi-
cated by total score of 4.3564. In docking It is now possible to predict activity and toxicity
pose, the chemical nature of binding site resi- risks of compounds through Osiris calculator.
3066 Int J Clin Exp Med 2016;9(2):3062-3068
Glycyrrhetinic acid derivatives against lung cancer
Table 4. Compliance of the active glycyrrhetinic acid derivatives with the and the readings were
standard intervals for computational toxicity risk parameters calculated after 72 hrs,
the 1n compounds (Fig-
Compound- Toxicity risk parameters Drug likeness parameters (Osiris)
ure 3) is showing the
name MUT TUMO IRRI REP CLP S MW DL DS
best results at all given
1b No risk No risk No risk No risk 5.1 -5.96 484 -2.25 0.2
concentration and its
1c No risk No risk No risk No risk 5.12 -6.27 584 -16.08 0.14
result at all concentra-
1n No risk No risk No risk No risk 8.33 -8.01 623 -13.23 0.08 tions is at par with
Paclitaxel No risk No risk No risk No risk 3.15 -5.88 811 0.4 0.25 Paclitaxel.
Foot note: mut = mutagenicity, tumo = tumorogenicity, irri = irritation, rep = reproduction,
mw = molecular weight, clp = clogp, s= solubility, dl = drug-likeness, ds = drug-score, # = Conclusion
indicates a predicted active derivative.
The molecular modeling
based prediction of gly-
cyrrhetinic acid derivatives showed that 1b, 1c
and 1n possess significant cytotoxic activity for
the human lung cancer. The binding site resi-
dues of EGF receptor showed hydrophobic
interaction with active compounds, while in
docking studies all the derivatives showed high
binding affinity against EGF receptor. The calcu-
lated parameters for the drug likeness and
ADMET for the predicted active compounds
were within the acceptable limit. In QSAR study,
chemical properties viz., Lambda Max Visible
Figure 3. The invitro analysis of the top three com-
(nm), Dipole Moment (debye), nitro and amine
pounds in comparison with Paclitaxel.
functional groups correlated well with the activ-
ity. Since, the oxalyl derivative 1b, 1c and 1n
The aqueous solubility of a compound signifi- showed equal cytotoxic activity to that of stan-
cantly affects its absorption and distribution dard anticancer drug paclitaxel, this lead may
characteristics. There are more than 80% of be further optimized so that these derivatives
the drugs which showed estimated logS value as candidates for further investigation towards
greater than -4. This aqueous solubility can be the management of anticancer related
calculated and represented by logS value. On diseases.
this basis, all the studied compounds are in
acceptable limit of aqueous solubility. In this Disclosure of conflict of interest
study we calculated toxicity risks parameters
such as mutagenicity, tumorogenicity, irritation, None.
and reproductive/developmental toxicity of the
predicted active compounds 1b to 1t. Toxicity Address correspondence to: Dr. Zhi-Qiang Sun, De-
screening results showed that all compounds partment of Pharmacy, The 1st Affiliated Hospital
possess no risk of tumorogenicity, skin irrita- of He’nan University of Science and Technology, No.
tion, and reproductive toxicity (Table 4). 24 Jinghua Road, Luoyang, Henan 471003, P. R.
China. Tel: [phone]; Fax: [phone]-
Invitro assessment 64830604; E-mail: [email protected]
The top three compounds 1b, 1c and 1n were References
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3068 Int J Clin Exp Med 2016;9(2):3062-3068
Glycyrrhetinic acid derivatives against lung cancer
Table S1. List of training data set anti lung cancer (A-549) drugs/compounds with their structures and experi-
mental 50% inhibitory concentration (IC ) values used for the development of QSAR model
50
Exp. log IC Pred. log IC
S. No. Structure 50 50 Residual Reference
(µM) (µM)
1 0.433 0.286 0.147 10
2 1.371 1.529 -0.158 18
3 1.394 1.206 0.188 19
4 1.233 1.401 -0.168
5 0.712 0.785 -0.073
1
Glycyrrhetinic acid derivatives against lung cancer
6 -0.097 0.21 -0.307
7 1.013 0.665 0.348
8 -0.301 -0.113 -0.188
9 0.063 0.166 -0.103
2
Glycyrrhetinic acid derivatives against lung cancer
10 0.155 0.124 0.031
11 0.52 0.536 -0.016
12 0.401 0.539 -0.138
3