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j.molstruc.2018.08.049

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Accepted Manuscript
Evaluation of pyrrole-2,3-dicarboxylate derivatives: Synthesis, DFT analysis,
molecular docking, virtual screening and in vitro anti-hepatic cancer study
Iqbal Azad, Asif Jafri, Tahmeena Khan, Yusuf Akhter, Md Arshad, Firoj Hassan,
Naseem Ahmad, Abdul Rahman Khan, Malik Nasibullah
PII:
S0022-2860(18)31004-4
DOI:
10.1016/j.molstruc.2018.08.049
Reference:
MOLSTR 25570
To appear in:
Journal of Molecular Structure
Received Date: 6 July 2018
Revised Date:
14 August 2018
Accepted Date: 16 August 2018
Please cite this article as: I. Azad, A. Jafri, T. Khan, Y. Akhter, M. Arshad, F. Hassan, N. Ahmad, A.R.
Khan, M. Nasibullah, Evaluation of pyrrole-2,3-dicarboxylate derivatives: Synthesis, DFT analysis,
molecular docking, virtual screening and in vitro anti-hepatic cancer study, Journal of Molecular
Structure (2018), doi: 10.1016/j.molstruc.2018.08.049.
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ACCEPTED MANUSCRIPT
ACCEPTED MANUSCRIPT
Evaluation of pyrrole-2,3-dicarboxylate derivatives: synthesis, DFT analysis, molecular
docking, virtual screening and in vitro anti-hepatic cancer study
Iqbal Azad1, Asif Jafri2, Tahmeena Khan1, Yusuf Akhter3, Md Arshad2, Firoj Hassan1,
Naseem Ahmad1, Abdul Rahman Khan1, Malik Nasibullah1
1
Department of Chemistry, Integral University, Dasauli, P.O. Bas-ha, Kursi Road, Lucknow-226026, U.P., India
Molecular Endocrinology Lab, Department of Zoology, University of Lucknow, Lucknow, 226007, UP, India
3
Department of Biotechnology, Babasaheb Bhimrao Ambedkar University, Vidya Vihar, Raebareli Road, Lucknow,
Uttar Pradesh 226025, India.
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2
malik7860@gmail.com
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Abstract
Novel anti-hepatic carcinoma drugs are of great therapeutic significance in the treatment of
different types of hepatic cancers. Since last decade, there has been a progressive improvement
in computational drug designing strategies. Human topoisomerase-II (Topo-II) and human
platelet derived growth factor receptor-α (PDGFR-α) have been identified as main enzymes
involved in hepatic carcinoma. In the current work, we assessed novel pyrrole-2,3-dicarboxylate
derivatives as potential anti-cancer agents using docking, virtual screening and experimental
analyses. Pyrrole-2,3-dicarboxylate derivatives which we have evaluated in this study were
synthesized and characterized by UV, IR, ESI-MS, 1H and 13C NMR spectroscopic techniques.
Structural validation was done using quantum chemical calculations using Density Function
Theory (DFT) employing B3LYP method and 6-311++G(d,p) basis set. Potential energy
distribution (PED) for normal vibrational modes was computed by VEDA4. The HOMO and
LUMO analysis was carried out to determine the charge transfer within the molecule. The
synthesized compounds were tested in-silico and in-vitro for anti-cancer activity; Docking
studies were performed against topo-II and PDGFR-α. By utilizing ligand based pharmacophore
generation approach and virtual screening against control drugs (Doxorubicin Hydrochloride and
Rituximab) forty two novel molecules have been proposed that displayed highest binding
affinities, least binding energies and effective druglikeness. The docking analyses revealed that
Met782, Val785, Asn786, Gly813, Lys814 and Ile43 were important interacting residues for
topo-II and Glu556, Ile557, Arg558, Arg560, Glu789 and Arg817 for PDGFR-α receptor-ligand
interaction. Absorption, distribution, metabolism, excretion and toxicological (ADMET)
calculations predicted drugs to have improved pharmacokinetic properties. The compounds may
be proven to be novel therapeutic candidates to cure cancer. The anti-hepatic carcinoma activity
of compounds 1and 29 was evaluated against human liver carcinoma HepG2 cells using MTT
assay, nuclear fragmentation and ROS generation analysis. The result of MTT assay revealed
that these synthesized compounds significantly inhibited the growth of HepG2 cells in a dosedependent manner. In addition to this, increment in condensed apoptotic nuclei and augmentation
of intracellular reactive oxygen species (ROS) at higher doses of tested compounds showed
apoptotic cell death of HepG2 cells. In brief, the cytotoxicity data revealed that compounds 1 and
29 possessed potent anti-hepatic carcinoma activities against HepG2 cells.
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Keyword: Pyrrole; Computational studies, ADMET, Drugs, Docking, Anti-cancer; Proapoptotic
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1. Introduction
The rate of liver cancer has tripled since 1980 and liver cancer death rates have increased by 3%
per year since 2000 [1]. Liver cancer is very common in men as compared to women.
Hepatocellular Carcinoma (HCC) is an antagonistic tumour arising from chronic liver disease
[2–4]. HCC is generally not treated by curative therapies and thus it is only treated by
nonsurgical methods [5]. Certain oncogenes, such as human topoisomerase-II, (Topo-II) and
human platelet derived growth factor receptor-α, (PDGFR-α) have been identified as main
targets for cancer therapy [6,7].
Computational analysis is an important strategy for drug discovery process.
Computational studies can be helpful in estimating the drug likeness of designed compounds
before experimental validation in laboratory and further clinical trials. Drug relevant properties
include several parameters like oral bioavailability and toxicity risk assessment etc. An ideal
drug compound should be efficacious, selective in its action, and should have good oral
absorbance [8]. Many of the drugs have to be withdrawn from the market due to failure in
clinical trials leading to loss of money and time [9]. Computational studies can predict an array
of potential biological activities beforehand. Heterocyclic compounds like pyrrole derivatives
are found in a broad range of natural products and drug molecules. Pyrrole-2,3-dicarboxylate
derivatives have been found to be of great importance due to their medicinal significance [10].
The present study focuses on designing new pyrrole-2,3-dicarboxylate derivatives in search of
potent anti-cancer agents [11]. This study uses several tools like OSIRIS Data Warrior,
Molinspiration, AutoDock Vina and iGEMDOCK to analyze the biological spectrum of the
compounds. Some of the drug relevant properties like LogP (cLogP), solubility, molecular
weight and topological molecular polar surface area (TPSA) were predicted by Osiris Property
Explorer which is one of the most popular and accurate web based tools and used for the analysis
of mutagenic, tumorigenic and reproductive risks associated with the compounds [12].
Molinspiration is another online software which is used to predict bioactivity of compounds
towards several receptors like GPCR ligands, ion channel modulators, kinase inhibitors, nuclear
receptor ligands, protease inhibitors and other enzymes [13]. Molecular docking interactions
were studied between the ligand and two target proteins using AutoDock Vina and iGEMDOCK.
Docking is a consequence of computational simulation methodology of an entrant ligand,
binding to the receptor which predicts the favoured alignment of binding of the molecule to form
a stable complex. Using the scoring functions, docking predicts the affinity and activity of the
ligand with the target protein [14]. The software uses the geometry of input ligand and performs
small adjustments in the confirmation of ligand with respect to the target protein and gives the
most stable configuration with least energy of binding resulted from the highest release of free
energy. In the recent years, density functional theory (DFT) has been broadly used in theoretical
modelling and drug design [15]. Numerous significant chemical and physical properties of
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biological and chemical systems can be anticipated by various quantum chemical calculations
using DFT method such as achieved on molecular structure, conformational analysis, molecular
electrostatic potential map (MEP), HOMO-LUMO analysis and spectroscopic properties [16]. In
the view of above consequence, we here reported here the synthesis of two Pyrrole-2,3dicarboxylate derivative with improved methodology. The synthesized pyrrole-2,3-dicarboxylate
(1) and (29) were characterized using different experimental and theoretical spectroscopic
methods. Molecular properties, toxicological aspects, bioactivity score and various ADMET
aspects of forty two pyrrole-2,3-dicarboxylate derivatives against suitable biological receptors
have also been analyzed. Almost all proposed pyrrole-2,3-dicarboxylate derivatives showed good
docking score as compared to the known control drugs. Additionally, the cytotoxic activity of the
two synthesized pyrrole-2,3-dicarboxylate has been tested by MTT assay [17], reactive oxygen
species (ROS) activity [18] and nuclear condensation assay by DAPI staining [19] was also
done. This synthetic approach will offer a future opportunity for designing of new pyrrole-2,3dicarboxylate derivatives with diverse biological properties.
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2. Materials and methods
2.1. Virtual Screening
All the computational studies were performed using biological databases such as PubChem,
DrugBank and PDB (Protein Data Bank). Molinspiration (Version-2016.03) was used for the
prediction of bioactivity score and for the calculation of physicokinetic properties swissADME
was used. admetSAR 1.0 was used to predict ADMET properties of the proposed compounds.
MetaPrint 2D was used for a prediction about the probable sites of metabolism. Toxicity
potential and drug likeness were predicted using OSIRIS Property Explorer 4.5.2. All chemical
structures were drawn in Discovery studio visualizer 2016 and Chemdraw Professionals 15.1.
To better predict the ADME properties of a compound, Lipinski Veber and Ghose et al.
proposed some filtering rules which can ably predict the oral availability of the drug compound.
Several important molecular descriptors can be extracted from the chemical structures. The
current and most common molecular descriptors are the molecular fingerprint (FP) [20] and
quantitative structure-activity relationship (QSAR) [21]. Molecular fingerprinting predicts the
presence or absence of important chemical features in a molecule. FP has all fragments of the
molecular structure subsequent to a linear path up to an assumed number of bonds. Each
conceivable path is ground to produce the bit string [22]. A key advantage of FP is the
effectiveness by which computers handle such bit strings, agreeing for order large-scale virtual
screening. QSAR studies provide results in the form of relationship between chemical structures
and associated biological activities [23]. QSAR also plays an important role in the arrangement
of models for ADME activities assembled by support vector machine (SVM) or Bayesian
techniques [24]. Additionally, computer-aided drug design (CADD) has been a development
method for the significant role of machine learning technologies [25]. Here we have reported
some different in silico free web tools for virtual screening for the development of more accurate
drug designing such as swissADME (http://www.swissadme.ch/), Molinspiration
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(http://www.molinspiration.com/cgi-bin/properties),
an
admetSAR
server
(http://lmmd.ecust.edu.cn/admetsar1/predict/), based on the FP and QSAR and also Chem Draw
v15.1 and Osiris data warrior v4.5.2 for prediction of various drug relevant properties. These
tools are easy to handle, work by a single click only and provide the results which are more
accurate and nearly same with the experimental results. The obtained results from virtual
screening were compared with the standard anti-cancer drugs, Doxorubicin and Rituximab which
are included in the list of essential medicine by WHO (World Health Organization). Doxorubicin
and Rituximab are used in the treatment of different types of cancers including breast cancer,
bladder cancer, acute lymphocyte Leukemia etc. and also used together with another
chemotherapy agent [26–30]. But both have serious side effects as well which includes heart
damage, radiation recall, treatment-related Leukemia and reactivation of Hepatitis B especially
by Rituximab [31,32]. To overcome these problems now larger numbers of researchers are
engaged in search of new safer medicines. We designed several novel compounds with drug-like
properties and less toxicity. So before the synthesis of compounds computation studies were
performed to analyse the molecular behavior as well as druglikeness. Furthermore, the main
target of doxorubicin is topoisomerase II [33], the designed compounds also showed good
docking interactions with topoisomerase II and Rituximab also showed considerable interaction
with PDGFR-α [34,35].
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2.1.1. Pharmacokinetic study
swissADME was used to predict pharmacokinetic properties like log P, TPSA, hydrogen bond
donors and acceptors (nON & nOHNH), rotatable bonds (RB), number of atoms, MW, molar
refractivity (MR), and fragment-based drug-likeness [36]. Evaluation of drug likeness with
acceptable physicochemical properties was done based on Lipinski, Veber, Ghose and
leadlikeliness filtering rules.
Figure 1. Drug filtering tools in a nutshell
2.1.2. ADMET Prediction
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ADMET properties were predicted using admetSAR database which delivers newest and most
comprehensive manually generated data for various chemicals with recognized ADMET
properties [37]. Good ADME and toxicity properties are as critical as therapeutic properties.
Computational studies concerning these parameters at the initial stage of drug designing are
helpful in providing fast and valuable information for drug like character probability. Bloodbrain barrier (BBB) penetration, human intestinal absorption (HIA), Caco-2 cell permeability,
Ames Toxicity, Carcinogenicity and LD50 in Rat were calculated for the proposed compounds.
2.1.3. Bioactivity score prediction
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Molinspiration is a web based tool used for the calculation of bioactivity score of proposed drug
candidates against some of the human receptors like GPCR ligand, ion channel modulator,
nuclear receptor ligands, kinase inhibitor, protease inhibitor and an enzyme inhibitor [38]. The
results were compared with the standard drug. It has been found that if the probability of
bioactivity score is < -5.0 then the compound is inactive, if it is between -5.0-0.0 then the
compound is moderately active and the bioactivity score value >0, suggests the compound is
active.
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2.2. Molecular docking
AutoDock Tools (ADT) version 1.5.6, AutoDock Vina 1.1.2 and iGEMDOCK 2.1 were used for
molecular docking studies and docking poses were prepared in the PyMol 2.0.6 [39]. The
structures of target proteins for docking viz. human topoisomerase II-β (UniProtKB: Q02880,
TOP2B_HUMAN) and human platelet-derived growth factor receptor-α (UniProtKB: P16234,
PGFRA_HUMAN) were downloaded from protein database http://www.rcsb.org/pdb with PDB
ID: 5GWI & 5K5X respectively.
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2.2.1. Ligand Preparation
The two-dimensional (2D) structures were drawn by using ChemDraw professionals (Perkin
Elmer 15.1). While three-dimensional (3D) structures and PDB files were prepared using Corina
(https://www.mn-am.com/online_demos/corina_demo_interactive/) and structure of control
drugs were downloaded from DrugBank (https://www.drugbank.ca/) and PubChem
(https://pubchem.ncbi.nlm.nih.gov). Structure optimization was also performed through
Avogadro v1.2.0 and Gaussian 09W by using method DFT, B3LYP and basis set 6-311++G
(d,p).
Table 1. Selected optimized geometrical parameters of the ligand using B3LYP/6-311++G(d,p).
Bond length (Å)
C1-C2
C2-C3
C3-C4
C4-N5
N5-C1
1.3898
1.4268
1.3942
1.3744
1.3680
Bond angle ( )
Compound-1
C1-C2-C3
106.9096
C2-C3-C4
107.8461
C3-C4-N5
106.6890
C4-N5-C1
110.8695
N5-C1-C2
107.6811
5
Dihedral angle ( )
C7-C1-C2-C6
C7-C1-N5-C4
C6-C2-C3-C12
N5-C1-C2-C3
C1-C2-C3-C4
0.0365
179.1115
0.5044
0.4920
-0.6904
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N5-C1-C7
N5-C4-C8
C4-C8-O9
C8-O10-C11
O9-C8-O10
C1-C2-C6
C3-C2-C6
121.0835
120.6596
126.9820
115.5634
122.7912
126.8284
126.2471
C12-O13
C12-O14
O14-C15
1.2120
1.3439
1.4374
C2-C3-C12
C4-C3-C12
C3-C12-O13
C3-C12-O14
C12-O14-C15
O13-C12-O14
123.8131
128.3406
124.0242
112.4336
116.0688
123.5008
C7-C1-C2-C6
C7-C1-N5-C4
C6-C2-C3-C8
C13-C4-C3-C8
C1-C2-C3-C4
C2-C3-C4-N5
C3-C4-N5-C1
C4-N5-C1-C2
N5-C1-C2-C3
N5-C4-C13-O14
N5-C4-C13-C15
N5-C4-C3-C8
N5-C1-C2-C6
C1-C2-C3-C8
C2-C3-C8-O10
C2-C3-C8-O9
C3-C8-O10-C12
C3-C4-C13-O15
C3-C4-C13-O14
C4-C13-O15-C16
C4-C3-C8-O9
C4-C3-C8-O10
C13-O15-C16C17
C8-O10-C12-C11
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1.3907
1.4302
1.3947
1.3699
1.3662
1.4946
1.5010
1.4817
1.2076
1.3563
1.4476
1.5153
1.4656
1.2195
1.3373
1.4492
1.5197
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C1-C2
C2-C3
C3-C4
C4-N5
N5-C1
C1-C7
C2-C6
C3-C8
C8-O9
C8-O10
O10-C12
C12-C11
C4-C13
C13-O14
C13-O15
O15-C17
C17-C16
Compound-29
C1-C2-C3
106.9392
C2-C3-C4
107.5207
C3-C4-N5
106.9178
C4-N5-C1
111.0297
N5-C1-C2
107.5896
N5-C1-C7
121.1368
N5-C4-C13
116.5749
C7-C1-C2
131.2715
C1-C2-C6
126.0993
C3-C2-C6
126.9441
C8-C3-C2
125.9246
C3-C8-O10
111.0982
C3-C8-O9
125.9320
O9-C8-O10
122.9450
C8-O10-C12
116.1456
O10-C12-C11
107.6485
C4-C13-O14
121.9451
O14-C13-O15
124.5673
C15-C13-C4
34.9833
C13-O15-C16
112.2287
O15-C16-C17
33.3983
C2-C3-C4-N5
C3-C4-N5-C1
C4-N5-C1-C2
C4-C8-O10-C11
O9-C8-O10-C11
C3-C12-O14-C15
O13-C13-O12C15
C7-C1-C2-C3
C6-C2-C3-C4
C6-C2-C1-N5
C3-C4-C8-O9
C3-C4-C8-O10
C4-C3-C12-O13
C4-C3-C12-O14
0.6174
-0.3206
-0.1141
-177.8329
0.7596
-175.5141
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1.4944
1.5002
1.4628
1.4796
1.2072
1.3688
1.4376
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C1-C7
C2-C6
C4-C8
C3-C12
C8-O9
C8-O10
O10-C11
-178.6261
-179.3632
179.1548
5.8972
-175.5838
-140.6905
41.5769
1.4193
179.8926
3.8098
-9.0167
0.5214
-0.3160
-0.0109
0.3428
-0.5246
-10.1256
168.5407
174.9217
-179.0933
-174.7431
-43.7204
134.5059
177.7146
-7.2456
174.0879
-144.3671
-39.8835
141.8901
-106.7194
-179.9746
2.2.2. Protein Preparation
The structures of query proteins TOP2B_HUMAN and PGFRA_HUMAN were obtained from
Protein Data Bank (http://www.rcsb.org/pdb/home/home.do). Computed Atlas of Surface
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Topography of Protein (CASTp, http://sts.bioe.uic.edu/castp/) database was used for
identification of surface accessible pockets and interior inaccessible cavities in target proteins.
All the polar H-bonds were displayed. Lastly, the structure of target proteins was energy
minimized to the default Root Mean Square Deviation (RMSD) and AMBER force field 14SB
through Chimera 1.12.
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Figure 2. 3D-surface structures of proteins with pocket (green) and sequence of amino acid (1 =
human topoisomerase-II, (Topo-II) PDB ID: 5GWI and 2 = Human platelet derived growth
factor receptor-α, (PDGFR-α). PDB ID: 5K5X)
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2.3. Chemistry
All the chemicals and reagents were of analytical grade and purchased from Sigma Aldrich.
Silica Gel-G plates (Merck) were used for thin layer chromatography (TLC) with a mixture of
ethyl acetate and hexane as eluent and purification of compounds was performed by column
chromatography (Silica Gel, 230-400 mesh). Melting points were assessed using open capillary
method. UV-Visible spectra were recorded on ELICO SL-160 double beam, UV-Visible
spectrophotometer. The IR spectra were recorded as KBr discs using an FT-IR Perkin Elmer 24
instrument in the frequency range 4000-500 cm-1. The 1H &13C NMR spectra were recorded on a
Bruker Avance 400 MHz (FT-NMR) in CDCl3 using TMS as an internal standard. Mass analysis
was performed on an Agilent 6520 (Q-TOF) mass spectrometer.
2.3.1. General procedure for the synthesis of Pyrrole-2,3-Dicarboxylate Derivatives
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Scheme 1. One-pot synthesis of pyrrole-2,3-dicarboxylate derivatives
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A mixture of triphenylphosphine (1 mmol) and ammonium acetate (1 mmol) in CCl3CN (10ml)
was stirred over the magnetic stirrer. After a few minutes, a temperature of -10oC was
maintained and drop wise dialkyl acetylene dicarboxylate (1.1 mmol, DMAD or DEAD) was
added. Then obtained mixture was subjected to room temperature and 2,3-butanedione (1.1
mmol) was added and stirred for 20 h. The solvents were distilled under reduced pressure and
obtained viscous residue was directly transferred into silica-gel column chromatographic column
and preceded by ethyl-acetate: hexane (3:7). The obtained product was allowed to recrystallize in
ethanol.
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2.3.2. Dimethyl 4,5-dimethyl-1H-pyrrole-2,3-dicarboxylate (4a or 1)
Colourless crystal, mp 116-118oC; IR (KBr) (vmax/cm-1): 3464 (-NH), 1713 (N-C-C=O), 1683
(C-C-C=O), 1563, 1459 (aromatic ring), 1132, 1068, 1027, 723, 617, 569; 1H-NMR (400 MHz,
CDCl3): δ 2.21 (s, 3H, CH3, C=CCH3), 2.532 (s, 3H, CH3, N-CCH3), 3.725 (s, 3H, -CH3, NCCOOCH3), 3.814 (s, 3H, -CH3, C=CCOOCH3), 8.939 (bs, 1H, NH); 13C-NMR (400 MHz,
CDCl3): δ 11.1 (N-CCH3) and 11.5 (C-CCH3), 52.3 and 52,3 (2-CH3), 117.2, 122.4, 128.5 and
133.4 (pyrrole ring or arom.), 165.3 (N-COO), 168.8 (C-COO); HRMS (ESI-TOF, m/z) calcd.
for C10H13NO4 (M + H+) 211.08; found: 212.0910; Anal. Calc. C, 67.02; H, 7.31; N, 7.82.
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2.3.3. Diethyl 4,5-dimethyl-1H-pyrrole-2,3-dicarboxylate (4b or 29)
Colourless crystal, mp 91-93oC; IR (KBr) (vmax/cm-1): 3445 (-NH), 1715(N-C-C=O), 1660
(C-C-C=O), 1560, 1473(aromatic ring), 1439, 1118, 1056, 722, 621, 578; 1H-NMR (400 MHz,
CDCl3): δ 1.306-1.385 (t, 6H, -COOCH2CH3), 2.059 (s, 3H, N-CCH3), 2.195 (s, 3H, -C=CCH3),
4.265-4.369 (q, 2H, -COOCH2), 9.396 (bs, 1H, NH); 13C-NMR (400 MHz, CDCl3): δ 10.5 (NCCH3), 12.3 (-C=CCH3), 14.0 and 14.7 (-CH2CH3), 63.3 and 63.7 (-COOCH2), 119.7, 120.5,
123.9, 131.9 (pyrrole ring or arom.), 162.0 (-N-CCOO), 168.1 (-C=CCOO); HRMS (ESI-TOF,
m/z) calcd. for C12H17NO4 (M + H+) 239.12 found: 240.8179; Anal. Calc. C, 60.24; H, 7.16; N,
5.86.
2.4. Quantum chemical calculation
All quantum calculations were performed by Gaussian 09 program package using Density
Functional Theory (DFT) and B3LYP functional with 6-311G++(d,p) as a basis set in the gas
phase and subsequent vibrational calculation was conducted to confirm that the stationary points
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correspond to minima on the potential energy surface [40]. The FT-IR and FT-Raman vibrational
frequencies were computed with B3LYP/6-311G++(d,p). While, the UV–Vis spectrum was
calculated by means of TD-DFT with B3LYP/6-311G++(d,p) method in the gas phase and
chloroform used as a solvent with the help of IEF-PCM model [41]. All the input files were
prepared, to utilize Gauss View 6.0. Finally, the Avogadro and Gauss View 6.0 programs were
used to observing the output files.
2.5. Biological evaluation
2.5.1. In vitro cytotoxicity analysis
2.5.1.1. Cell line and culture.
2.5.1.2. Cell viability assay by MTT.
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The human liver carcinoma cell line (HepG2) was procured from the National Centre for Cell
Sciences (NCCS), India. The HepG2 cells were cultured in Eagle’s Minimum Essential Medium
(EMEM) complemented with 10 % fetal bovine serum (FBS) at 37°C humidified atmosphere
with 5% CO2.
The cellular viability of HepG2 cells was evaluated by MTT assay after treated with different
concentrations of comp-1 and 29 as per previously described protocol [42]. In brief, the HepG2
cells were seeded and treated with 10, 25, 50, 75, and 100 µg/ml concentrations of tested
compounds for 24 h and then cell viability was calculated by MTT colorimetric assay.
TE
D
2.5.1.3. Cell morphology study.
EP
The morphological changes in HepG2 cells were studied after treatment at 10, 25, 50, 75, and
100 µg/ml concentrations under inverted phase-contrast microscope. Briefly, the HepG2 cells
were seeded and treated with various concentrations of the two compounds and after 24 h
cellular morphology were observed.
2.5.1.4. Intracellular ROS production analysis.
AC
C
The intracellular ROS intensity in HepG2 cells was observed with the help of DCFH-DA dye as
previously reported protocol [43]. Concisely, the HepG2 cells were seeded at 50 and 75 µg/ml
concentrations under an inverted phase-contrast microscope.
2.5.1.5. Nuclear apoptosis assays.
The nuclear condensation or nuclear apoptosis in HepG2 cells was examined by nuclear DAPI
staining as described previously [44]. The HepG2 cells were seeded and treated with tested dose
range, stained with DAPI dye and then subjected to fluorescence microscopy to observe the
nuclear morphological changes.
3. Results and Discussion
3.1. Virtual screening
9
ACCEPTED MANUSCRIPT
Pharmacokinetic studies.
A potential drug candidate should be easily absorbed as well as distributed throughout the body
system for effective metabolism and action. Table 2 represents the drug-like properties of
proposed compounds. All the proposed compounds passed the required measures of the realistic
rules showing drug likeness. Molar lipophilicity as indicated by cLogP (logarithm of
compound’s partition coefficient between ݊-octanol and water) was <5 for all compounds
showing good permeability and absorption across cell membranes [45]. Molecular weights of all
the compounds were <500 and therefore it can be predicted that these compounds can be easily
absorbed, transported and diffused. All compounds showed <10 rotatable bond representing
lesser molecular flexibility. TPSA is correlated with surface belonging to polar atoms in the
compound. It is also related with the moderate membrane permeability and hence, lower TPSA is
an important parameter for druglikeness [46]. TPSA of all proposed compounds was in the range
57.53-160.0Å, and the percentage of absorption calculated from TPSA ranged between 53.7989.16% for the all compounds signifying good oral bioavailability. The results were compared
with the standard anti-cancer drugs, Doxorubicin Hydrochloride and Rituximab (Table 2).
M
AN
U
SC
RI
PT
3.1.1.
Table 2. Molecular Properties of proposed compounds versus standard anti-cancer drugs
Doxorubicin Hydrochloride 52 and Rituximab 53.
15
20
21
21
26
25
23
29
25
27
31
29
23
23
21
26
27
27
32
31
29
35
31
33
37
35
29
29
17
22
23
23
28
nOHNHa
nONb
nRBc
LogPd
1
2
2
1
1
1
1
1
1
1
1
1
1
1
0
1
1
0
0
0
0
0
0
0
0
0
0
0
1
2
2
1
1
5
6
6
6
6
7
5
7
5
5
11
3
5
7
5
6
6
6
6
7
5
7
5
5
11
5
5
7
5
6
6
6
6
4
4
4
4
5
6
4
8
6
6
8
8
6
6
5
5
5
5
6
7
5
9
7
7
9
9
7
7
6
6
6
6
7
1.00
1.90
2.62
2.03
3.39
1.85
3.20
3.53
3.67
4.88
1.82
5.19
2.32
2.12
2.46
3.36
4.08
3.49
4.82
3.31
4.66
4.99
5.13
6.34
3.29
6.65
3.79
3.58
1.18
2.71
3.43
2.84
4.17
TE
D
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
nArom
.Atom
5
12
12
12
18
17
15
17
17
17
17
17
5
15
11
18
18
18
24
23
21
23
23
23
23
23
11
21
5
12
12
12
18
EP
nAtom
AC
C
S. No.
TPSAe
(Å)2
68.39
84.18
84.18
73.32
73.32
94.17
68.39
86.85
68.39
68.39
160.00
69.39
68.39
94.67
57.53
73.32
73.32
62.46
62.46
83.31
57.53
75.99
57.53
57.53
149.17
57.53
57.53
83.81
68.39
84.18
84.18
73.32
73.32
10
%ABSf
MWg
MRh
BSi
SAj
85.41
79.96
79.96
83.71
83.71
76.52
85.41
79.04
85.41
85.41
53.79
85.07
85.41
76.34
89.16
83.71
83.71
87.46
87.46
80.26
89.16
82.79
89.16
89.16
57.54
89.16
89.16
80.09
85.41
79.98
79.98
83.71
83.71
211.21
272.25
351.15
286.28
348.35
337.33
307.30
395.41
335.35
404.24
425.35
391.47
351.45
315.28
287.31
348.36
427.25
362.38
424.45
413.43
383.40
471.50
411.45
480.34
501.45
467.56
427.54
391.37
239.27
300.31
379.20
314.34
376.41
53.28
72.71
80.41
77.62
97.69
89.81
87.06
107.21
94.22
104.24
111.87
113.77
94.65
78.76
78.26
97.69
105.39
102.59
122.67
114.79
112.03
132.18
119.20
129.22
136.84
138.75
119.62
103.73
62.90
82.33
90.03
87.23
107.30
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
2.36
2.24
2.33
2.39
2.75
3.34
2.31
3.43
3.23
3.24
3.38
3.62
4.56
3.45
2.75
2.82
2.87
2.99
3.29
3.74
2.86
3.85
3.62
3.62
3.81
4.03
4.85
3.76
2.65
2.54
2.61
2.68
3.04
Fraction
Csp3
0.40
0.14
0.14
0.20
0.10
0.11
0.11
0.18
0.10
0.10
0.10
0.25
0.25
0.12
0.25
0.10
0.10
0.14
0.08
0.08
0.08
0.14
0.08
0.08
0.08
0.20
0.18
0.09
0.50
0.25
0.25
0.29
0.18
ACCEPTED MANUSCRIPT
17
15
17
17
17
17
17
8
15
5
5
5
5
5
5
5
5
5
12
6
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
7
5
7
5
7
5
5
11
5
5
7
5
5
5
5
5
5
5
5
5
12
2
8
6
10
8
8
10
10
5
8
8
10
6
10
12
8
12
8
10
5
7
a
2.66
4.01
4.34
4.48
5.69
2.64
6.00
3.14
2.93
3.04
4.61
3.65
4.08
5.20
4.69
5.17
4.69
-0.57
0.57
2.50
94.17
68.39
86.85
68.39
68.39
160.00
68.39
68.39
94.67
68.40
68.40
68.40
68.40
68.40
68.40
68.40
68.40
120.45
206.08
69.64
76.52
85.41
79.04
85.41
85.41
53.79
85.41
85.41
76.34
85.40
85.40
85.40
85.40
85.40
85.40
85.40
85.40
67.44
37.90
84.97
365.39
335.36
423.46
363.41
432.30
453.41
419.52
379.50
343.33
267.32
295.38
295.40
295.38
323.43
323.43
323.43
323.43
297.36
543.52
390.22
99.43
96.67
116.82
103.84
113.86
121.48
123.38
104.26
88.37
72.51
82.13
82.20
82.13
91.74
91.82
91.74
91.22
77.93
138.51
86.05
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.17
0.55
3.58
2.63
3.69
3.48
3.48
3.62
3.91
4.77
3.67
2.85
3.06
3.06
3.20
3.43
3.42
3.29
3.29
3.00
5.84
2.26
RI
PT
27
25
31
27
29
33
31
25
25
19
21
21
21
23
23
23
23
21
39
20
SC
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
53.
0.20
0.20
0.25
0.18
0.18
0.18
0.35
0.33
0.22
0.57
0.62
0.62
0.62
0.67
0.67
067
0.67
0.57
0.44
0.43
M
AN
U
number hydrogen bond doner (nOHNH)
number hydrogen bond acceptor (nON)
c
number of rotatable bonds (nRB)
d
Logarithm of compound partition coefficient between n-octanol and water (miLogP)
e
Topological polar surface area (TPSA) (defined as a sum of surfaces of polar atoms in a molecule)
f
Percentage of Absorption (% of ABS) was calculated by: % of Absorption= 109 ‒ [0.345 × Topological Polar
Surface Area]
g
molecular weight (MW)
h
molar refractivity(MR)
i
Bioavailability Score (BS)
j
Synthetic accessibility (SA)
TE
D
b
AC
C
EP
3.1.2. ADMET prediction.
ADMET properties of proposed pyrrole-2,3-dicarboxylate derivatives were investigated using
the admetSAR server. admetSAR predicts the ADMET properties via a detailed analysis of: (i)
Brain/Blood penetration coefficient (ii) Human Intestinal Absorption (iii) Caco-2 cell
permeability (iv) Prediction of IC50 value for rat model (v) Ames toxicity and (vi) Carcinogenic
effect. The ADMET results well established the fact that all the proposed pyrrole-2,3dicarboxylate derivatives could penetrate through BBB [47]. The Intestine is the primary site for
absorption of an orally administered drug. The proposed derivatives exhibited positive result,
indicating that their complexes could be absorbed or assimilated through human intestine. Caco2 is a human colon epithelial cancer cell line used as a model for human intestinal assimilation of
drugs and other compounds. Positive results for Caco-2 indicate good permeability. The
compounds showed good permeability except 2, 34 and 42, 44 and 46-51. Ames experiment is
the most widely used assay for testing the mutagenicity of the compound. The Ames test is a
short-term bacterial reverse mutation assay that can be used to detect a large number of
compounds which can induce genetic damage and frame shift mutations [48,49]. All the
proposed compounds were non-mutagenic except 5, 11, 16, 18, 19, 21, 25 and 39. Adverse
11
ACCEPTED MANUSCRIPT
carcinogenic effect of the proposed derivatives was also predicted and it was concluded that all
pyrrole-2,3-dicarboxylate derivatives were non-carcinogenic. The admetSAR server also
provides significant information about the proposed compounds’ LD50 values in a rat model as
illustrated in table 3.
Caco-2+
Caco-2Caco-2+
Caco-2+
Caco-2+
Caco-2+
Caco-2+
Caco-2+
Caco-2+
Caco-2+
Caco-2+
Caco-2+
Caco-2+
Caco-2+
Caco-2+
Caco-2+
Caco-2+
Caco-2+
Caco-2+
Caco-2+
Caco-2+
Caco-2+
Caco-2+
Caco-2+
Caco-2+
Caco-2+
Caco-2+
Caco-2+
Caco-2+
Caco-2Caco-2Caco-2Caco-2Caco-2Caco-2+
Caco-2+
Caco-2+
Caco-2+
Caco-2+
Caco-2+
Caco-2Caco-2Caco-2+
Caco-2Caco-2+
Caco-2Caco-2Caco-2Caco-2Caco-2Caco-2Caco-2-
AMES Toxicity
Results
None
None
None
None
Toxic
None
None
None
None
None
Toxic
None
None
None
None
Toxic
None
Toxic
None
None
Toxic
None
None
None
Toxic
None
None
None
None
None
None
None
None
None
None
None
None
None
Toxic
None
None
None
None
None
None
None
None
None
None
None
None
Toxic
None
Caco-2-
Probabilit
y
0.7489
0.5863
0.6705
0.5366
0.6501
0.5843
0.5736
0.5759
0.7405
0.7932
0.8951
0.8153
0.6302
0.7138
0.7299
0.6501
0.5339
0.5786
0.6039
0.5796
0.5058
0.6849
0.6846
0.7742
0.8832
0.7463
0.6413
0.6947
0.8113
0.6661
0.6914
0.6210
0.5772
0.7412
0.7643
0.7142
0.8574
0.8430
0.7158
0.8505
0.7074
0.7843
0.8550
0.8404
0.8394
0.8715
0.8630
0.8236
0.8684
0.8280
0.8670
0.8672
0.8343
Carcinogens
Results
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
12
Probabilit
y
0.8826
0.9439
0.9291
0.9133
0.8624
0.8980
0.9270
0.9117
0.8791
0.8194
0.6142
0.8051
0.8537
0.8771
0.8078
0.8624
0.8310
0.8348
0.8627
0.8590
0.8462
0.8636
0.8215
0.7469
0.6194
0.7300
0.8119
0.8444
0.8117
0.9081
0.8907
0.9038
0.8152
0.8255
0.8733
0.8817
0.7992
0.7275
0.5701
0.7686
0.7896
0.7955
0.8861
0.8937
0.8183
0.9155
0.9198
0.8376
0.8645
0.8366
0.9245
0.9293
0.7964
SC
Results
Probabilit
y
0.6018
-0.5534
0.5339
0.6030
0.6172
0.5379
0.5977
0.6112
0.6513
0.6509
0.6219
0.6560
0.5102
0.5599
0.7035
0.6172
0.5710
0.6252
0.6274
0.6849
0.6678
0.6974
0.7161
0.6529
0.5818
0.6901
0.5900
0.6853
0.5158
0.6535
0.6085
0.5313
0.5474
-0.5512
0.5000
0.5701
0.5366
0.5617
0.5000
0.5534
0.8172
-0.5372
0.5000
0.5097
0.5116
0.5735
0.5582
0.5718
0.5185
0.5151
0.6650
-0.7525
0.5091
M
AN
U
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBB+
BBBBBBBBB+
Caco-2 cell permeability
TE
D
Results
Probabilit
y
0.9242
0.7575
0.8063
0.8802
0.8680
0.7065
0.8759
0.8108
0.8568
0.9136
0.5217
0.8002
0.8217
0.9111
0.8804
0.8680
0.8640
0.8977
0.9098
0.7770
0.8761
0.8827
0.8917
0.9076
0.7012
0.8541
0.8331
0.9126
0.8737
0.7152
0.7653
0.8174
0.8776
0.5827
0.8372
0.7155
0.7918
0.8691
0.5437
0.7601
0.8172
0.8172
0.8803
0.8882
0.9253
0.7352
0.7519
0.8503
0.9179
0.9026
0.5076
-0.9902
0.5966
Human intestinal
absorption
Probabilit
Results
y
HIA+
0.9821
HIA+
0.9806
HIA+
0.9760
HIA+
0.9830
HIA+
0.9920
HIA+
0.9871
HIA+
0.9965
HIA+
1.0000
HIA+
1.0000
HIA+
1.0000
HIA+
0.9817
HIA+
1.0000
HIA+
1.0000
HIA+
0.9953
HIA+
0.9763
HIA+
0.9920
HIA+
0.9903
HIA+
0.9884
HIA+
0.9894
HIA+
0.9153
HIA+
0.9924
HIA+
0.9859
HIA+
0.9821
HIA+
0.9851
HIA+
0.9765
HIA+
0.9862
HIA+
0.9892
HIA+
0.9798
HIA+
0.9828
HIA+
0.9813
HIA+
0.9769
HIA+
0.9707
HIA+
0.9923
HIA+
0.9876
HIA+
0.9966
HIA+
0.9966
HIA+
1.0000
HIA+
1.0000
HIA+
0.9826
HIA+
0.9974
HIA+
1.0000
HIA+
1.0000
HIA+
0.9804
HIA+
0.9829
HIA+
0.9928
HIA+
0.9894
HIA+
0.9907
HIA+
0.9949
HIA+
0.9779
HIA+
0.9744
HIA+
0.8993
HIA-0.5530
HIA+
0.6208
EP
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
Blood-brain barrier
AC
C
S.
No.
RI
PT
Table 3. admetSAR prediction of different compounds versus standard anti-cancer drugs
Doxorubicin Hydrochloride 52 and Rituximab 53.
Acute Oral Toxicity
Result
s
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
Probabili
ty
0.5349
0.4775
0.4670
0.5010
0.5432
0.6089
0.5958
0.6856
0.5868
0.6009
0.6558
0.7114
0.6232
0.5860
0.5393
0.5432
0.5612
0.5776
0.5331
0.5576
0.5599
0.6895
0.5549
0.5857
0.6497
0.6283
0.5991
0.6131
0.6747
0.6136
0.5789
0.5934
0.6262
0.6942
0.6905
0.7437
0.6760
0.6848
0.6172
0.6878
0.6601
0.7007
0.6150
0.5932
0.5522
0.6603
0.6493
0.6154
0.6371
0.5950
0.5779
0.7415
0.6078
pLC50,
mg/L Fish
Toxicity
LD50,
mol/kg
in Rat
1.2181
1.2625
0.9638
0.9308
0.5053
0.8089
0.0618
0.5674
0.2559
-0.1119
0.5950
0.3392
1.0561
1.2689
0.6360
0.5053
0.4887
0.4618
0.4406
0.7210
-0.0642
0.1365
0.1547
-0.0167
0.5247
0.2344
0.7220
0.7777
1.3539
1.4642
1.1007
1.2358
0.7813
1.0866
0.5040
0.5589
0.6466
0.1930
0.7276
0.6180
1.2085
1.4417
0.9291
0.7818
0.6236
1.3808
1.2429
1.2274
0.8686
0.6879
1.9334
1.0930
1.8119
2.4063
2.6624
2.6367
2.5838
2.4845
2.3858
2.4791
2.2637
2.3992
2.3938
2.4445
2.3714
2.3945
2.4304
2.4704
2.4845
2.4358
2.4878
2.4771
2.3236
2.4127
2.2213
2.4499
2.4357
2.3586
2.4495
2.4462
2.3763
2.1581
2.3887
2.4656
2.4932
2.3059
2.2929
2.2376
2.2879
2.2477
2.3316
2.6150
2.2954
2.4530
2.4530
2.1666
2.1740
2.3896
2.1227
2.1328
2.4877
2.1481
2.2185
2.0371
2.7244
2.0897
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3.1.3. Bioactivity score prediction.
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Bioactivity score is an important measure to predict the molecular activity of proposed
compounds. The results indicated that all compounds possessed significant bioactivity score
against ion channel modulator, kinase inhibition and enzyme inhibition except compounds 1-3,
13-18, 25-32, 39 and 41-42 respectively (Table 4). The obtained scores were in the range -0.500.00. Compounds 4-6, 9, 12, 19-20, 33-38, 40 and 48 possessed positive bioactivity score while
compounds 1-4, 14-15, 30-32 and 43-51 showed lesser than -0.50 bioactivity score with respect
to the ICM, KI and EI. The bioactivity score was calculated by comparing structural fragments
of known drug molecules with the proposed compounds [50].
Table 4. Parameters of Bioactivity Score Prediction for Different compounds versus standard
anti-cancer drugs Doxorubicin Hydrochloride 52 and Rituximab 53.
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Bioactivity score of the proposed compounds according to Molinspiration
cheminformatics software.
Parameters of Bioactivity score
S.No.
GPCRa
ICMb
KIc
NRLd
PIe
EIf
1
-0.88
-0.52
-0.76
-1.52
-1.57
-0.74
2
-0.21
-0.07
-0.04
-0.64
-0.67
-0.03
3
-0.33
-0.20
-0.07
-0.75
-0.77
-0.14
4
-0.17
0.00
-0.02
-0.54
-0.69
-0.06
5
0.07
0.03
0.02
-0.33
-0.39
-0.04
6
0.00
0.02
0.25
-0.20
-0.32
0.O1
7
-0.04
-0.03
-0.18
-0.32
-0.49
0.00
8
-0.09
-0.19
0.10
-0.09
-0.38
-0.01
9
-0.06
-0.16
0.15
-0.11
-0.39
0.03
10
-0.06
-0.15
0.12
-0.12
-0.41
-0.01
11
-0.18
-0.18
0.00
-0.18
-0.44
-0.08
12
-0.04
-0.13
0.07
-0.06
-0.33
0.03
13
-0.22
-0.20
-0.22
-0.33
-0.47
-0.26
14
-0.14
-0.33
-0.10
-0.35
-0.60
-0.21
15
-0.46
-0.37
-0.49
-0.74
-0.86
-0.51
16
-0.14
-0.23
-0.06
-0.30
-0.39
-0.17
17
-0.25
-0.33
-0.10
-0.44
-0.50
-0.25
18
-0.15
-0.18
-0.12
-0.26
-0.44
-0.21
19
0.02
-0.12
-0.08
-0.14
-0.17
-0.12
20
-0.05
0.04
-0.01
-0.21
-0.22
0.01
21
-0.04
-0.15
-0.24
-0.17
-0.32
-0.04
22
-0.14
-0.13
-0.14
-0.18
-0.27
-0.06
23
-0.13
-0.08
-0.12
-0.20
-0.26
-0.04
24
-0.12
-0.08
-0.13
-0.21
-0.28
-0.07
25
-0.21
-0.17
-0.21
-0.25
-0.33
-0.12
26
-0.11
-0.08
-0.16
-0.16
-0.23
-0.03
27
-0.22
-0.19
-0.29
-0.33
-0.31
-0.26
28
-0.17
-0.25
-0.30
-0.35
-0.43
-0.20
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-1.16
-0.49
-0.62
-0.42
-0.28
-0.16
-0.26
-0.08
-0.08
-0.09
-0.15
-0.05
-0.27
-0.29
-0.91
-0.70
-0.58
-0.46
-0.34
-0.23
-0.58
-0.42
-0.74
0.32
-0.18
-1.31
-0.57
-0.68
-0.61
-0.40
-0.33
-0.46
-0.40
-0.40
-0.41
-0.44
-0.34
-0.44
-0.50
-1.02
-0.78
-0.67
-0.65
-0.48
-0.39
-0.63
-0.62
-0.58
0.67
-0.21
-0.64
-0.07
-0.17
-0.10
-0.07
-0.02
0.04
-0.05
-0.01
-0.04
-0.10
0.00
-0.28
-0.23
-0.45
-0.34
-0.32
-0.22
-0.16
-0.13
-0.30
-0.21
-0.24
0.66
-0.19
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-0.63
-0.05
-0.09
-0.05
-0.04
0.17
-0.22
0.04
0.08
0.05
-0.05
0.02
-0.26
-0.15
-0.45
-0.29
-0.21
-0.21
-0.13
-0.05
-0.23
-0.20
-0.04
-0.07
-0.24
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-0.39
-0.04
-0.17
0.02
0.04
0.03
0.01
-0.18
-0.14
-0.13
-0.16
-0.11
-0.17
-0.29
-0.34
-0.28
-0.10
-0.09
-0.06
0.11
-0.25
-0.12
-0.11
-0.20
-0.10
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-0.71
-0.18
-0.30
-0.16
0.04
-0.04
-0.07
-0.12
-0.09
-0.09
-0.20
-0.07
-0.23
-0.17
-0.47
-0.29
-0.24
-0.17
-0.06
-0.02
-0.21
-0.24
-0.14
0.20
-0.03
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29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
GPCR = G protein-coupled receptor ligand, bICM = Ion channel modulator, cKI = Kinase inhibitor, dNRL =
Nuclear receptor ligand, ePI = Protease inhibitor, fEI = enzyme inhibitor
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3.1.4. MetaPrint2D prediction.
MetaPrint2D predicts the results by calculating normalized occurrence ratio (NOR) of an atom
highlighted with different colours. While understanding the metabolic pathway of a new
chemical entity, it is of great importance to understand the metabolic mechanistic action in
human beings. Metaprint2D predicts the sites likely to undergo metabolism. A high NOR
indicates that the particular site is frequently reported in the metabolite database [51]. The likely
to be metabolized site atoms are indicated by different colours; Red: High, Orange: Medium,
Green: Low, White: Very low and Grey: No data as shown in figure 3. Variation in the metabolic
site is likely to be based on the presence and absence of the aromatic ring, a heteroatom, electron
withdrawing and electron releasing groups in the chemical entity. All proposed pyrrole-2,3dicarboxylate derivatives possessed excellent metabolic sites and thus, can be predicted to be
easily metabolized.
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Figure 3.
MetaPrint2D prediction of compounds and reference drugs (Doxorubicin
Hydrochloride 52 and Rituximab 53.)
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3.2. Molecular docking using Auto Dock Vina.
Through molecular docking binding interactions of the ligand with the target proteins can be
better understood. In this study, the pyrrole-2,3-dicarboxylate derivatives were proposed in
search of potential anti-cancer action. The docking interactions were studied against two most
cancer causing target proteins viz. Topo-II and PDGFR-α. The results were compared with
known anti-cancer drugs doxorubicin hydrochloride and rituximab. The docking studies were
performed using AutoDock Vina 1.1.2 [52]. The protein’s structures were downloaded from
protein data bank with PDB ID: 5GWI and 5K5X. One probable binding site for each protein
was identified. The Lamarckian genetic algorithm was employed for the molecular docking
studies. Target protein was kept in rigid mode while ligands were kept flexible to rotate. The
torsional bonds of ligands were set free to get the most feasible binding poses. All the residues
present inside the binding site of target protein were set as 40×40×40 points in x, y and z
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direction, for Topo-II grid centre values were set to be -6.70, 18.45 and -19.18 whereas for
PDGFR-α was set 25×25×25 points in x, y and z direction and grid centre values were set
70.955, 45.763 and 5.782. Rest of the parameters were set at default mode. Biovia Discovery
studio visualizer 2016 was used to visualize the results. Protein-ligand interactions were
considered in the form of minimum binding energy (kcal/mole), a number of hydrogen bonds,
atomic charge interactions, pi-pi interactions and pi-sigma interactions were found between the
active site residues of the target protein and ligand [53]. The obtained results against both target
proteins were compared with the standard drugs doxorubicin hydrochloride (-8.8 and -8.4) and
rituximab (-8.1 and -5.6). The synthesized comp-1 and 29 had the binding energy -5.8 and -5.9
kcal/mole with respect to topo-II while -5.9 and -6.1 kcal/mole with respect to the PDGFR-α
whereas comp-25 possess lowest binding energy with respect to both target proteins viz. -8.6
and -8.8 kcal/mole as given in supplementary-table 1.
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3.3. Protein-ligand docking validation by iGEMDOCK.
The ligand bonding with the protein can be explained on the basis of several interactions.
iGEMDOCK identifies various bond energies, such as hydrogen bond (H-Bond), Van Der Walls
(VDW) interaction, and electrostatic interaction [54,55]. The empirical scoring function of
iGEMDOCK was estimated asFitness = VDW+Hbond+Elec.
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Hbond= Hydrogen bonding
Elec= Electro-static energy
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The results of molecular docking have been shown in supplementary-table 1. Synthesized
compounds 1 and 29 possessed docking energies viz. -72.38 and -74.09 kcal/mol and -82.35 and
-84.53 kcal/mol with topo-II and PDGFR-α. Out of all the proposed compounds compound 25
possessed lowest docking energies viz. -120.88 and -143.68kcal/mol against both the enzymes
showing better results as compared to the standard drugs doxorubicin hydrochloride (-11.45 and
-80.35kcal/mol) and rituximab (-121.60 and -103.58kcal/mol).
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Table 5. Docking results of the proposed compounds.
Docking results (binding energies, statistical study)
PDB
ID
Vina
iGEM
Vina
iGEM
5GWI
5K5X
Average
Median
Mode
Range
Standard
deviation
Variance
-6.99
-97.14
-7.22
-100.83
-7.15
-97.58
-7.2
-100.63
-6.9
-7.5
-
14.4
48.48
3.2
61.33
2.13
10.38
0.65
10.66
4.54
107.81
0.43
113.74
Most instructing amino acids
VDW
Other
H Bonds
forces
Bonds
Met782,
Gly813,
Ile843,
Asn786
Lys814
Val785
Arg560,
Arg817,
Arg558,
Gln789
Ile557
Glu556
Docking results through both AutoDock Vina and iGEMDOCK revealed that the proposed
pyrrole-2,3-dicarboxylate derivatives especially compound 25 exhibited extremely good docking
scores than the reference drugs as shown in supplementary-table 1. All the proposed compounds
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offered average docking scores between −6.99 to−97.14 with topo-II and -7.22 to -100.83 with
PDGFR-α shown in Table 5.
Figure 4. (A) Docking pose 1, 2 and 3 of comp-1, 29 and 25 with Topo-II, (B) - Docking pose 1,
2 and 3 of comp-1, 29 and 25 with PDGFR-α
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From the virtual screening and docking analysis of pyrrole-2,3-dicarboxylate derivatives, we got
many results out of which some were most considerable and effective which were obtained after
the substitutions on the pyrrole-2,3-dicarboxylate moiety. Our initial investigation showed that in
pyrrole-2,3-dicarboxylate three sites are substitutable with an alkyl group and the amine group.
During the preliminary investigation, we substituted the isatin moiety at the alkyl site in pyrrole2,3-dicarboxylate and these changes significantly enhanced the biological interactions, molecular
properties, and drug-likeness. Therefore, we designed some variable isatin substituted
compounds and got the results in the form of enhanced activity, especially those in those isatin
derivative having halogen substitution. Similarly, we also investigated the substitution of the
simple heterocyclic moiety such as furan, thiophene, pyridine etc. During primary search we
found out that substitution of simple heterocycles moderately affects the properties of pyrrole2,3-dicarboxylate. Furthermore, phenyl substitution showed very impressive observations.
Computationally we predicted the substitutions of phenyl ring at alkyl side greatly enhanced the
quality and interactive properties of the compound, thus we designed different phenyl substituted
derivatives and we received surprising observations. Electron withdrawing substituted phenyl
derivatives showed higher interactions, molecular behavior, drug-likeness and least toxicity. The
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phenyl substitution at the hydrogen atom of nitrogen, pyrrole-2,3-dicarboxylate also attracted
considerable attention, these substitutions enhanced all molecular properties of the compounds.
In our docking analysis, it was also verified that phenyl substitutions at alkyl site with the NO2
functional atom as well as also phenyl substitution at hydrogen of nitrogen atom possessed best
interactions. We also substituted simple acenaphthene moiety at the alkyl side and observed an
enhancement of interaction ability. At the end of our investigation, we observed the effect of the
alkoxy group. On changing the length of alkoxy, no doubt it increased the molecular properties
of the compound. Slight change in the chain length was more beneficial. Furthermore, the
substitution of variable substituted alkoxy chain also produced pronounced effect in the
interaction quality but decreased other ADMET properties of the compounds. In the process of
our virtual screening, we found out these all changes were beneficial for the drug design and
development of more accurate molecular system.
3.4. Chemistry
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One-pot multi-component relations in organic chemical synthesis has great value, as the target
molecules are frequently achieved in a single step rather than multiple steps, In continuation of
our efforts towards the improvement in the synthetic procedure of pyrrole-2,3-dicarboxylate
derivatives methodology. Previously Javad et al. proposed the synthesis of highly substituted
pyrrole with good yield 70% and he reported their reaction in acetonitrile at room temperature
within 23 h [56]. Herein, we have reported a mild, efficient and modified one-pot protocol for
the synthesis of pyrrole-2,3-dicarboxylate derivatives by using ammonium acetate, triphenyl
phosphine, DMAD/DEAD and 2,3 butanedione in catalyst free condition (Scheme 1) [57,58].
During our initial studies towards the modification of this methodology, triphenyl phosphine (1
mmol) was reacted with DMAD (1.1 mmol), ammonium acetate (1 mmol) and 2,3 butanedione
(1.1 mmol) in ethanol and found that no reaction occurred within 12 h. It was observed that the
same reaction proceeded efficiently when continued for 24 h at room temperature yielding the
corresponding substituted pyrrole derivative in 54% yield. When the same reaction was
attempted in trichloroacetonitrile at 87 °C the reaction proceeded to completion within 20 h and
yielded the corresponding pyrrole derivative in 87% yield. While evaluating the influence of
different solvent systems for synthesis of substituted pyrroles, a few solvents were screened such
as ethanol, dichloromethane, triethylamine and trichloroacetonitrile, and among them
trichloroacetonitrile appeared to be a better choice (Table 6).
COOCH 3
H3 C
H3 C
N
H
H3 C
COOCH 3
H3 C
4a or 1
COOC2H 5
N
H
COOC2 H5
4b or 29
Figure 5. Structure of compounds 1 and 29.
Table 6. Reaction optimization Conditions
S. No.
Solvent System
Time (Hors.)
Temperature(°C)
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Diketone(mmol)
Yield%
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Ethanol
Ethanol
DCM
DCM
Et3N
Trichloroacetonitrile
Trichloroacetonitrile
24
24
48
24
20
24
20
r.t.
Reflux
r.t.
r.t.
r.t.
r.t.
r.t.
a
1
1
1
1.1
1
1
1
54
42
73
66
61
80
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2
3
4
5
6
7
Reaction conditions: ammonium acetate (1 mmol), Triphenyl phosphine (1 mmol), DMAD/DEAD (1.1 mmol), and
2,3 butanedione (1.1 mmol).
b
Isolated yield.
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The plausible mechanism for the synthesis of highly substituted pyrroles, initial
triphenylphosphine (I) and DMAD (II) react together and generate a zwitterionic intermediate;
in continuation zwitterionic intermediate receives a proton from ammonium action and produces
an intermediate vinyl triphenyl phosphonium cation (III). Ammonium ion attacks on the vinyl
triphenyl phosphonium cation (III) and converted into phosphorane intermediate (IV). After the
addition of nitrogen atom of the intermediate (IV), through simple hydrolysis diketone attack on
the secondary amine and formed a double bond and intermediate (V) is obtained, now due to
conjugation intermediate (V) is converted into intermediate (VI) and undergoes intramolecular
Wittig reaction which can proceed through two ways viz. betaine formation or 2+2 cycloaddition. Both ways finally lead to the formation of intermediate (VII). In the last step, in
intermediate (VII) 1, 5-proton shift occurs and it finally leads to the formation of target
compound (Scheme 2).
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Scheme 2. Proposed mechanism for the formation of pyrrole-2,3-dicarboxylate.
3.5. Quantum chemical calculations
3.5.1. Chemical reactivity
The chemical reactivity of the synthesized compounds can be described by molecular
electrostatic potential map (MEP), Global reactivity descriptors and molar refractivity (MR).
3.5.1.1. Molecular electrostatic potential map (MEP)
The electrostatic potential V(r) illustrates the shape, size, charge density and reactive sites of the
title compounds and is significant for studying drug-receptor and enzyme-substrate interactions
[59,60].
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3.5.1.2.
Global reactivity descriptors
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Figure 6. Structurally optimized 3D (boll stick) and potential energy map presentation of the
synthesised compounds
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Global reactivity descriptors, such as electronegativity (χ), chemical potential (µ), global
hardness (η), global electrophilicity index (ω) and global softness (S) are calculated according to
Koopman’s, by the energies of FMOs, εHOMO and εLUMO are given in the table 7 [61–63];
According to Parr et al. theorem, ω is a global reactivity index similar to the chemical potential
and chemical hardness, which is positive and fixed measure. ω processes the balance in energy
when the system obtains an extra electronic charge (∆N) from the surroundings.
When chemical species like an electrophile accepts electrons from the surroundings, then the
electronic chemical potential of the molecule completely defines the path of the charge transfer.
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Therefore, its energy is lower as compared to the accepting electronic charge and also its
electronic chemical potential is negative [64]. The energies of εHOMO, εLUMO, energy band gap
(εLUMO−εHOMO), χ, µ, η, S, ω and ∆Nmax for compound 1 and 29 are shown in table 7. The
calculated values of the two compounds are near about same, but the S, ω and ∆Nmax values of
compound 29 are higher as compared to compound 1. It indicates that the compound 29 acts
have much global softness with strong electrophilic nature than compound 1.
1
29
EHOMO
-6.6073
-6.4584
ELUMO
-2.2922
-2.3704
EH-EL
4.3151
4.0880
3.5.1.3. Molar refractivity (MR)
4.4497
4.4144
-4.4497
-4.4144
2.1575
2.0440
S
0.2317
0.2446
∆Nmax
4.5884
4.7668
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Table 7. Calculated εHOMO, εLUMO, energy band gap (εL−εH), chemical potential (µ),
electronegativity (χ), global hardness (η), global softness (S), global electrophilicity index (ω)
and extra electronic charge (∆Nmax) (in eV) at 298.15 K for compound 1 & 29 at B3LYP/6311++G (d, p) level.
The Molar refractivity is the most common measure of total polarizability of a material in a
mode. It is a constitutive-additive property and can be calculated by the Lorenz-Lorentz equation
[65–67].
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Where n is the refractivity index, MW is the molecular weight, MW/ is the molecular volume,
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N is the Avogadro number, α is the polarizability of the molecular system and its value depends
on the wavelength of the light used to measure ‘n’. This equation holds for both liquid and solid
state of the system. MR is directly determined by the refractive index, molecular weight and
density of steric bulk and important for the binding property and lipophilicity of the investigated
system and also for the study of quantitative structure activity relationship (QSAR) and
molecular modelling [68,69]. MR is found to be related to the London dispersive forces that act
in the drug-receptor interaction. The MR values for compounds 1 and 29 have been found to be
53.28 and 62.90 esu. respectively.
3.5.2. Electronic absorption spectra
To obtain the nature of electronic transitions, electronic absorption spectra, electronic excitation
energies and oscillatory strength through time-dependent density functional theory (TD-DFT)
have been calculated [70]. The TD-DFT excitations were calculated in the gas phase and in the
solvent (chloroform) phase using the PCM model. Experimental UV-Vis spectrum of the
compounds in the solvent is shown in Figure 7.
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Figure 7. Experimental UV-Visible spectra of compound-1 (A) and 29 (B) with TD-DFT
calculated spectra.
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The UV-Vis bands are detected at 227 and 326 nm for compounds 1, 252 and 331 nm for
compound 29. The band obtained at 227 and 252 nm is due to a π→π* transition from H-5→L
and H-3→L. The furthermost strong band detected at 326/331 nm is due to dipole allowed π→π*
from HOMO to LUMO. After comparing the experimental and calculated spectra it is displayed
that solvatochromic effects do not affect significantly calculated electronic transitions. The
calculated and observed electronic transitions of high oscillatory strength, besides experimental
wavelength in solvent chloroform, are shown in Table 8. [55]. The TD-DFT calculation showed
that the considered, initially observed highest energy band of strong oscillator strength (f =
0.1801, H→L) was at 335 nm and the lowest oscillator strength (f = 0.0486, H-5→L,) at 236 nm
for comp-1 in solvent state and in the gaseous state the highest energy band of oscillator strength
(f = 0.1528, H→L) was obtained at 320 nm for comp-1 and the lowest oscillator strength (f =
0.0020, H→L) at 422 nm for comp-29 was observed. On the basis of calculated molecular
orbital coefficients investigation and molecular orbital plots for the compound 1 and 29, the
Frontier molecular orbitals (FMOs) are generally composed of π-atomic orbital’s and
consequently, the behaviour of main electronic excitations are assigned to be π→π*. The TDDFT calculated electronic excitations are in good agreements with the experimental spectrum.
Table 8. TD-DFT calculated excitations and approximate assignments for compound 1 and 29 in
chloroform.
Comp.
1
Excitation
energy
(eV)
Oscillator
strength (f)
Experimental
wavelength (nm)
3.7024
5.2536
0.1801
0.0486
326.40
226.63
3.8732
0.1528
-
Calculated
Wavelength (nm)
Major trasition and
expansion coefficient
In solvent phase
334.87
H(56) → L(57) (0.64191)
236.00
H(51) → L(57) (0.62190)
In gaseous phase
320.11
H(56) → L(57) (0.61815)
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Assignment
s
Contribution
%
π → π*
π → π*
41.20
38.67
π → π*
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-
3.6489
4.6279
0.1672
0.1127
331.05
252.29
2.9410
4.8676
0.0020
0.1276
-
235.47
In solvent phase
339.78
267.90
In gaseous phase
421.57
254.71
H(51) → L(57) (0.62190)
π → π*
38.67
H(64) → L(65) (0.50505)
H(61) → L(65) (0.58872)
π → π*
π → π*
25.51
34.66
H(64) → L(65) (0.67764)
H(61) → L(65) (0.56890)
π → π*
π → π*
45.91
32.36
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0.0361
3.5.3. HOMO–LUMO energy gap
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To illustrate the highest reactive position in the conjugated system, molecular orbital and their
properties such as energy are used. The highest occupied molecular orbital (HOMO) and lowest
unoccupied molecular orbital (LUMO) are very common quantum chemical parameters. HOMO
and LUMO are the key orbitals that take part in the chemical stability [71].
The study of the energy gap between the HOMO and LUMO is a complex parameter in defining
molecular electrical transport properties because it is a measure of electron conductivity. FMOs,
HOMO and LUMO plots beside other molecular orbitals plot, dynamic in the electronic
transition in solvent chloroform and their transition energy (eV) are shown in figure 8. The
HOMO and LUMO energies and the energies of neighbouring orbitals are negative, which show
the compounds 1 and 29 are stable. The difference between the HOMO and LUMO energies, the
band gap assists as a measure of the excitability of the molecule, Excitation will be easy if the
energy difference is less. The 3D plots of the HOMOs and the LUMOs are shown in figure 8.
The HOMO–LUMO energy gap is a significant stability index which reveals the chemical
stability of the molecule.
HOMO energy = -6.6073eV.
LUMO energy = -2.2922eV.
HOMO–LUMO energy gap = -4.3151eV.
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According to B3LYP/6-311++G(d,p) calculation, The calculated self-consistent field (SCF)
energies are given in table 8. The comparatively lower HOMO and LUMO energy gap explains
the subsequent charge transfer (CT) interaction taking place inside the molecule, which is
responsible for the bioactivity of the molecule.
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5.2655
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Figure 8. Selected orbital transitions along their energy (eV) for title compound obtained from
TD-DFT calculations using PCM model for solvent chloroform (contour value 0.02 au.).
3.5.4. Analysis of vibrational spectra
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In order to obtain the spectroscopic predictions of the synthesized compounds, vibrational
frequency calculations for normal modes have been done at DFT- B3LYP/6-311++G (d, p) level
of the theory [72]. The total number of atoms in compound-1 and 29 are 28 and 34, respectively,
which gives 78 and 96 vibrational modes (3n-6). The detailed assignments of theoretical (scaled
and selected) and experimental vibrational wave numbers for normal modes along with their
assignments using %PED are given in Supplementary Table 1. The calculated vibrational wave
numbers for the majority of vibrational bands of the normal modes is higher than their
experimental values. The calculated and experimental IR spectrum in the region 4000–500 cm-1
is shown in Figure 10 and 11. The calculated values were scaled down by using a single scaling
factor 0.9613 to discard any harmonicity present in the real system. The value of correlation
coefficient obtained between DFT computed, theoretical and experimental wavenumber for
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compound-1 (r2 = 0.999) and compound-29 (r2 = 0.999) shows good agreement with
experimental results [73]. The correlation plots are shown in figure 9. The potential energy
distribution (PED) data was obtained from VEDA4 and modes analyzed from Gauss-View aid in
the assignment of the calculated and experimental harmonic vibrational wave numbers, peaks of
FT-IR spectrum [74].
Figure 9. the correlation graph between experimental and calculated wavenumbers of
compound-1 (A) and 29 (B)
N-H vibrations
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The N-H stretching vibrations are reported in the literature in the region 3500-3300 cm-1 [75]. In
the present study, the N-H stretch of pyrrole ring (>N-H) was observed at 3464 and 3446 cm-1,
whereas the calculated bands were obtained at 3520 and 3484 cm-1. In the computed IR spectrum
frequency of N-H with a strong intensity shows the weakening of the N-H bond due to the
elongation of conventional hydrogen bond donor (N-H bond) as compare to the hydrogen bond
free N-H group. The experiential value is in agreement with previously reported value for similar
compounds.
CH3 group vibrations
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The C-H stretching vibrations were reported in the region 3000-3100 cm-1. Methyl (CH3) group
is associated with five types of vibrational frequencies specifically viz. symmetric stretching,
asymmetric stretching, symmetric deformation, asymmetric deformation and rocking. The
observed stretching vibration (OCH3 and CH3) at as 3074, as3049, s3017, s2945, and as2966,
s2930, s2916 for compound-1 and as3021, as2979, as2977, s2954, s2928, and as2990, as2982,
as2968 s2917 cm-1 for compound 29 were in agreement with the calculated wave numbers in
region 3051-2913 and 3010–2901 cm-1. An asymmetric deformation of methyl (CH3) was
observed at 1461 cm-1 whereas the calculated value was found as a merged peak at 1460 cm-1.
Symmetric deformations of OCH3 and CH3 were observed at 1438, 1428, and 1425 cm-1,
whereas the calculated peaks were obtained between1498-1360 cm-1. The observed methyl
(OCH3) rocking at 1124 cm-1, agrees well with the calculated wave number at 1126 cm-1. The
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observed methyl (CH3) rocking at 1026 cm-1, agrees well with the calculated wave number at
1027 cm-1.
>C=O group vibrations
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Esters (C=O) with conjugated aromatic ring associated with stretching vibrations were reported
as very strong band in the range 1740-1715 cm-1 and C-O bands were also obtained in the range
of 1300-1000 [76]. In the virtual FTIR spectra of the proposed compounds, calculated
wavenumbers observed for C=O carbonyl group stretching between 1717-1654 cm-1 showed
similarity with the experimentally observed wavenumbers at 1715-1661 cm-1 for both
compounds. For C-O calculate bands were found between at 1240-811 cm-1 and are greatly
agreed with the experimental vibrational wavenumbers at 1305, 1065, 883, 794 for compound 1
and 1197, 1118,1055, 988, 831 for compound 29.
C-N vibrations
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The observed band for N-C stretching at 1457 cm-1 was in good agreement with the calculated
value at 1459 cm-1. A scissoring vibration band H16N5C1 was observed at 1569 cm-1, whereas it
was calculated as 1560 cm-1. A combination band of the N-H twisting H16N5C1C7, C-N
wagging C3C4N5C1 and in-plane bending C7N5C2C1 was observed at 616 cm-1 and calculated
value was 619 cm-1. A N-H in-plane bending band of H16N5C1C7 was observed at 555 cm-1,
whereas the calculated value was 554 cm-1.
Ring vibrations
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The C-C stretches of pyrrole ring is observed at as1569 and s1482 cm-1. This was calculated to
be found in region 1560 and 1482 cm-1. C-C ring stretching is also observed at 1132 and
calculated value is 1131. Pyrrole ring deformation band (medium to weak) was also observed at
989, 567 and 555 cm-1. These deformation bands are calculated to be found at 962, 569 and 554
cm-1.
Figure 10. Comparison of Calculated FT-IR spectra of Compound 1 (I) with the
experimental spectra (II).
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Figure 11. Comparison of Calculated FT-IR spectra of Compound 29 (I) with the experimental
spectra (II).
3.5.5. NMR spectroscopy
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Quantum chemical calculations through density functional (DFT) method are precise enough in
the prediction of NMR spectra and exploring the association between molecular structure and
their chemical shifts [77,78]. For the chemical shifts analysis completely optimized structure of
the titled compounds have been subjected to the calculation by using the DFT-B3LYP method
and GIAO approach with 6-311++G(d, p) as basis set [79,80]. The IEFPCM model with CDCl3
solvent was used to study the solvent induced effects [81]. The chemical shifts of title
compounds; experimental and calculated are given in Table 9, along with their assignments. In
the experimental 1H NMR spectra of comp-1 and comp-29 a singlet for N-H proton is found at δ
8.94 and s 9.40 ppm respectively. Three proton triplets for CH3 (C6 and C7) proton were found
at δ 1.30-1.38 and singlets for CH3 at δ 2.05 and 2.19 ppm and for OCH3 (C11 and C15) proton
were found at δ 3.81 and δ 3.73 ppm for compound 1 and three proton singlets for CH3 (C6 and
C7) proton were found at δ 2.53 and δ 2.22 ppm and for OCH2 (C12 and C17) proton a quartered
peak is found at δ 4.26-4.36 ppm for compound 29 respectively. Further important support, in
favour of the structure of synthesized compounds was provided by its 13C NMR spectra. The
chemical shift value of the carbon attached to oxygen C12 = O13 and C8 = O9 for title
compounds, found at d 168.84 and d 165.38 ppm (Comp-1) and d 162.06 and d 168.16 ppm
(Comp-29), corroborated well with reported similar with calculated values. The values of the
correlation coefficient for 1H and 13C NMR (r2 = 0.9821, 0.9963) for compound 1 and (r2 =
0.9867, 0.9988) for compound 29 showed that there is a good agreement between experimental
and calculated assignment. In the support of 1H, 13C NMR chemical shifts, mass spectrum, also
approved the incidence of molecular ion peak at 212.09 and 240.81 for M+1, confirming the
molecular formula C10H13NO4 and C12H17NO4 of compound 1 and compound 29
respectively.
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Exp. 1H NMR
H-16
H-26,27,28
H-23,24,25
H-20,21,22
H-17,18,19
8.94
3.81
3.73
2.53
2.22
H-18
H-33,34
H-28,29
H-19,20,21
H-22,23,24
H-30,31,32
H-25,26,27
9.40
4.37
4.27
2.19
2.06
1.39
1.31
C NMR chemical shifts (δ/ppm)) in CDCl3 for
Calcd 1H NMR
Atom no.
Compound-1
7.78
C-12
3.77
C-8
3.70
C-1
1.95
C-3
1.86
C-2
C-4
C-15
C-11
C-6
C-7
Compound-29
7.75
C-8
4.39
C-13
3.74
C-1
1.96
C-4
1.95
C-3
1.16
C-2
1.12
C-12
C-17
C-16
C-11
C-6
C-7
Exp. 13C NMR
Calcd 13C NMR
168.840
165.381
133.405
128.569
122.449
117.222
52.316
52.316
11.513
11.196
166.16
156.83
129.86
122.90
122.26
120.55
49.37
49.20
7.76
7.41
168.16
162.06
131.97
123.94
120.57
119.75
63.74
63.33
14.73
14.07
12.33
10.59
162.89
159.42
126.65
122.81
122.16
120.10
60.06
59.38
12.87
12.06
9.45
7.75
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Atom no.
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Table 9. Calculated and experimental 1H and
Comp-1 and Comp-29.
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3.6. Biological activity analysis
3.6.1. Reduction of cell viability and alteration in the morphology of HepG2 cells
The cell viability analysis by MTT dye revealed that after 24 h exposure of these synthesized
compounds significantly reduced the viability of HepG2 cells in a concentration-dependent
manner (Figure 12). Compound 29 was found to be more cytotoxic to HepG2 cells as compared
to compound 1 and decreased the cell viability about 85.73, 71.52, 54.69, 41.23 and 33.43% at
10, 25, 50, 75 and 100 µg/ml concentrations respectively. Whereas, at the same concentrations
compound 1 reduced about 90.19, 78.38, 65.77, 59.31 and 47.97% cell viability as compared to
untreated HepG2 cells. The result of MTT assay evidently revealed that these synthesized
compounds, significantly reduced the HepG2 cells viability in a concentration-dependent
manner. The cellular morphological study of treated HepG2 cells also supports the cell viability
data Figure 12 (A and B). Compound 1 and 29 at 10, 25, 50, 75 and 100 µg/ml concentrations
encouraged typical altered morphology of apoptosis like cell shrinkage, rounding off, decreased
cell number and detachment from the surface as compared to the control HepG2 cells suggesting
cell death by apoptosis (Singh et al., 2017).
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Figure 12. Anti-proliferative and cytotoxic effect of various concentrations of compounds on
human hepatic carcinoma HepG2 cells. Cellular alterations showing the anti-proliferative effect
of compound 1 (A) and compound 29 (B) at 10, 25, 50, 75 and 100 µg/ml concentrations against
HepG2 cells of human hepatic carcinoma. (C) The percent cell viability of HepG2 cells was
measured after the exposure of compounds 1 and 29 by MTT assay. The three independent
experiments were performed and the values are represented as means ± SEM as compared with
the control.
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3.6.2. Production of intracellular ROS
As depicted in Figure 13 A and B, these synthesized compounds, significantly enhanced
apoptosis in HepG2 cells via a ROS-mediated pathway. Compound 29 at 50 and 75 µg/ml
concentrations induced more ROS intensity as compared to compound 1. The quantitative data
representing % DCF fluorescence intensities exposed that compound 29 at 50 and 75 µg/ml
concentrations induced about 120.99 and 141.99% (***p < 0.05), respectively as compared to
control. While, compound 1 induced about 110.84 and 121.33% ROS increment at 50 and 75 µM
Concentrations (Figure 13 C). The results of intracellular ROS revealed that both compounds
significantly augment the ROS mediated apoptosis in human liver carcinoma HepG-2 cells.
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Figure 13. Intracellular ROS production in HepG2 cells of human hepatic carcinoma after
treated with various concentrations of compounds. Photomicrograph representing the
intracellular ROS production of compound 1 (A) and compound 29 (B) at 50 and 75 µg/ml
concentrations on HepG2 cells of human hepatic carcinoma. (C) Graph representing %DCF
fluorescence at 50 and 75 µg/ml concentrations of compound 1 and compound 29 against
HepG2 cells as compared to the control cells. Data were represented as Mean ± SEM of three
independent experiments.
3.6.3. Nuclear condensation enhancement
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The nuclear condensation assay revealed that both these synthesized, comp-1 and 29 at 50 and
75 µM Concentrations considerably induce nuclear condensation and fragmentation in liver
carcinoma HepG2 cells stained by nuclear fluorescence dye DAPI. The typical morphological
feature of apoptosis viz. condensed and fragmented apoptotic nuclei, advocates nuclear
apoptotic death in HepG2 cells (Figure 14 A and B). The quantitative data expressing %
apoptotic cells showed that compound 29 induces about 14.33 and 28.67 % apoptotic cells at
50 and 75 µg/ml concentrations with respect to untreated HepG2 cells. Whereas, at the same
concentrations, compound 1 encouraged about 7.33 and 19.67 % apoptotic cells (Figure 14 C).
The result of both nuclear apoptotic photomicrographs of the condensed and fragmented
nucleus as well as quantitative apoptotic cells in HepG2 cells suggested that both compounds
induced nuclear cell death in hepatic carcinoma HepG2 cells.
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Figure 14. Nuclear condensation and fragmentation in HepG2 cells of human hepatic
carcinoma after treated with various concentrations of compounds. Photomicrograph showing
morphological changes, nuclear condensation and cellular apoptosis in HepG2 cells, treated
with 50 and 75 µg/ml concentrations of (A) compound 1 and (B) compound 29. (C) Graph
representing % apoptotic cells in HepG2 cells at 50 and 75 µg/ml concentrations of compound
1 and compound 29. Data were represented as Mean ± SEM of three independent experiments.
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4. Conclusions
Computation tools may help to predict the probable activity spectrum of a feasible drug
compound. In this study, we have proposed fifty one pyrrole-2,3-dicarboxylate based derivatives,
out of which we synthesized two derivatives and predicted their molecular properties,
toxicological aspects and bioactivity score against several receptors and simultaneously
providing an insight into various ADMET aspects. The structures of synthesized compounds
were characterized by the UV, IR, ESI-MS, 1H and 13C NMR. HOMO-LUMO band energy gap
calculation showed chemical reactivity of the synthesized compounds. Almost all proposed and
synthesized pyrrole-2,3-dicarboxylate derivatives had good docking score as compared to the
control drug. Topo-II and PDGFR-α can be considered as the most suitable dual target protein
through which the proposed compounds, as well as synnthesized compounds show their anticancer activity. Virtual screening computed good pharmacokinetics and ADME properties
having positive bioactivity score against human receptors. The metabolic site likely to undergo
metabolism were also predicted. Docking results clearly referred compound-25 (maximum Hbonds, and favorable Van der Waals between the ligand and target) to be an encouraging anticancer agent and it could be considered as potential “lead molecule” for the development of the
novel anti-cancer drug. Additionally, The TPSA data range predicted that all the compounds
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have good oral bioavailability. The results obtained from this study may be valuable, which may
be helpful to strategize out the further synthesis, in vivo testings which could lead to the
emergence of novel inhibitors against Topo-II and PDGFR-α. The in vitro cytotoxicity analysis
by MTT assay and cellular morphological study proposed that this synthesized comp-1 and 29
significantly declined the growth of HepG2 cells in a concentration-dependent manner. The
increased number of condensed apoptotic nuclei and accumulation of intracellular ROS
production evidently recommended that both synthesized compounds possess apoptotic and antihepatic carcinoma efficacy against HepG2 cells of human hepatic carcinoma.
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Acknowledgments- The authors gratefully acknowledge to the R&D wing of Integral
University, Lucknow, for editing of the manuscript and providing communication number
(IU/R& D/2017-MCN000226) for the manuscript. YA research group is funded by grants from
Department of Biotechnology (Govt. of India), and India Council of Medical Research and
Science and Engineering Research Board (DST, Govt. of India).
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Highlights:
• Design forty two pyrrole-2,3-dicarboxylate derivatives and subjected to virtual screening
against ADMET, Bioactivity, Molecular, metabolic transformed and toxicological
properties
• Two compounds synthesized and characterized with different spectral tools and
spectroscopic data were validate by using DFT analysis
• Molecular docking performed against two targets human topoisomerase-II, (Topo-II) and
human platelet derived growth factor receptor-α, (PDGFR-α) to understand drug-receptor
interactions
• Anticancer evaluation performed against HepG2 cells, intracellular ROS and Nuclear
condensation enhancement also evaluate
• This reports valuable for research and development of novel strategies for the synthesis
and designing of anti-hepatic carcinoma
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