QSAR-driven design, synthesis and discovery of potent chalcone derivatives with antitubercular activity

Article abstract of DOI:10.1016/j.ejmech.2017.05.026

New anti-tuberculosis (anti-TB) drugs are urgently needed to battle drug-resistant Mycobacterium tuberculosis strains and to shorten the current 6–12-month treatment regimen. In this work, we have continued the efforts to develop chalcone-based anti-TB co

Full text of DOI:10.1016/j.ejmech.2017.05.026

Accepted Manuscript
QSAR-driven design, synthesis and discovery of potent chalcone derivatives with
antitubercular activity
Marcelo N. Gomes, Rodolpho C. Braga, Edyta M. Grzelak, Bruno J. Neves, Eugene
Muratov, Rui Ma, Larry L. Klein, Sanghyun Cho, Guilherme R. Oliveira, Scott G.
Franzblau, Carolina Horta Andrade
PII:
S0223-5234(17)30387-2
10.1016/j.ejmech.2017.05.026
EJMECH 9456
DOI:
Reference:
To appear in: European Journal of Medicinal Chemistry
Received Date: 9 February 2017
Revised Date: 4 May 2017
Accepted Date: 8 May 2017
Please cite this article as: M.N. Gomes, R.C. Braga, E.M. Grzelak, B.J. Neves, E. Muratov, R. Ma,
L.L. Klein, S. Cho, G.R. Oliveira, S.G. Franzblau, C.H. Andrade, QSAR-driven design, synthesis and
discovery of potent chalcone derivatives with antitubercular activity, European Journal of Medicinal
Chemistry (2017), doi: 10.1016/j.ejmech.2017.05.026.
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to
our customers we are providing this early version of the manuscript. The manuscript will undergo
copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please
note that during the production process errors may be discovered which could affect the content, and all
legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT

ACCEPTED MANUSCRIPT
1
2
3
QSAR-driven Design, Synthesis and Discovery
of Potent Chalcone Derivatives with
Antitubercular Activity
†
†
‡
†₸
4
5
6
Marcelo N. Gomes, Rodolpho C. Braga, Edyta M. Grzelak, Bruno J. Neves, Eugene
#
,£,¥
‡
‡
‡
§
Muratov,
Rui Ma, Larry L. Klein, Sanghyun Cho, Guilherme R. Oliveira, Scott G.
‡*
†
Franzblau, Carolina Horta Andrade *
†
7
8
9
LabMol – Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia,
Universidade Federal de Goiás, Rua 240, Qd.87, Setor Leste Universitário, Goiânia, Goiás
74605-510, Brazil
‡
1
1
0
1
Institute for Tuberculosis Research, University of Illinois at Chicago, 833 South Wood
Street, Chicago, Illinois 60612, United States
₸
1
2
3
Postgraduate Program of Society, Technology and Environment, University Center of
1
Anápolis/UniEVANGELICA, Anápolis, Goiás, 75083-515, Brazil
#
1
4
5
Laboratory for Molecular Modeling, Eshelman School of Pharmacy, University of North
1
Carolina, Chapel Hill, North Carolina 27955-7568, United States
£
1
6
7
Department of Chemical Technology, Odessa National Polytechnic University, Odessa,
1
65000, Ukraine
§
1
8
9
Chemistry Institute, Universidade Federal de Goiás, Goiania, Brazil
¥
1
Currently Visiting Professor in Universidade Federal de Goiás, Goiania, Brazil
1

ACCEPTED MANUSCRIPT
1
2

ACCEPTED MANUSCRIPT
1
2
ABSTRACT
New anti-tuberculosis (anti-TB) drugs are urgently needed to battle drug-resistant
Mycobacterium tuberculosis strains and to shorten the current 6-12-month treatment regimen.
In this work, we have continued the efforts to develop chalcone-based anti-TB compounds by
using an in silico design and QSAR-driven approach. Initially, we developed SAR rules and
binary QSAR models using literature data for targeted design of new heteroaryl chalcone
compounds with anti-TB activity. Using these models, we prioritized 33 compounds for
synthesis and biological evaluation. As a result, 10 heteroaryl chalcone compounds (4, 8, 9,
11, 13, 17-20, and 23) were found to exhibit nanomolar activity against replicating
mycobacteria, low micromolar activity against nonreplicating bacteria, and nanomolar and
micromolar against rifampin (RMP) and isoniazid (INH) monoresistant strains (rRMP and
rINH) (<1 µM and <10 µM, respectively). The series also show low activity against
commensal bacteria and generally show good selectivity toward M. tuberculosis, with very
low cytotoxicity against Vero cells (SI = 11-545). Our results suggest that our designed
heteroaryl chalcone compounds, due to their high potency and selectivity, are promising anti-
TB agents.
3
4
5
6
7
8
9
0
1
2
3
4
5
6
1
1
1
1
1
1
1
1
1
7
8
KEYWORDS: Tuberculosis, in silico design, QSAR, nitroaromatic compounds, chalcone,
anti-TB agents.
1
9
3

ACCEPTED MANUSCRIPT
1
INTRODUCTION
2
3
4
5
6
7
8
Tuberculosis (TB) is a chronic infectious disease caused predominantly by
Mycobacterium tuberculosis (M. tb.). Tuberculosis is reported in every country around the
globe and the World Health Organization (WHO) estimates that about a third of the world’s
population is infected with M. tb. [1–3]. According to the WHO, in 2014 there were
registered almost 10 million of new TB cases and 1.5 million deaths; 400,000 of which were
HIV-positive. As a frequent co-infection, TB is aggravated by the spread of HIV and is a major
cause of death among HIV/AIDS patients [3–5].
9
0
1
2
3
4
5
6
7
8
9
0
Drug-sensitive TB can be cured by a combination of isoniazid (INH), rifampin
(RMP), pyrazinamide (PZA), and ethambutol (EMB) taken under supervision for 4 months,
and 2 months of treatment with only two drugs RMP and INH, consisting the basis of the
DOTS program (Directly Observed Therapy Short-course). The emergence of multidrug-
resistance (MDR-TB) and extensively drug-resistant (XDR-TB) has created substantial new
challenges for TB treatment [6,7]. The treatment of resistant strains requires a prolongation of
the therapy with drugs that are more toxic, less effective, and more costly [8]. Over the past
16 years, significant investment by academia, funding agencies, and initiatives such as WHO
Stop TB Partnership [9] and The Global Alliance for TB Drug Development [10], has led to a
renaissance of research in the field of TB and led to the discovery of bedaquiline and
delamanid, two new anti-TB drugs approved in 2012 and 2013 respectively for treatment of
adults with MDR-TB [11,12].
1
1
1
1
1
1
1
1
1
1
2
21
22
23
24
The development of computer science has found broad application in the drug
discovery area [13]. Computer-aided drug design (CADD) has become an integral part of the
drug discovery process in both academia and pharma companies [13,14]. Elucidation of
quantitative structure-activity relationships (QSAR) is one of the main approaches of CADD
4

ACCEPTED MANUSCRIPT
1
2
3
4
5
6
7
8
[15–18]. QSAR modeling has been widely used for identification of novel anti-TB agents. In
many studies, QSAR was used to design new anti-TB agents [2,19–32]. However, in most the
cases, QSAR has been used to modify previously discovered congeneric series of chemicals
(Table S1, supplementary material).
Chalcones or 1,3-diaryl-2-propen-1-ones represent one class of natural products and
essential intermediates in the biosynthesis of flavonoids. Chalcones are low molecular weight
compounds possessing a broad spectrum of biological activities [33–46] including
antibacterial [47,48] and anti-TB [38,49,50] activities.
9
0
1
2
3
4
5
6
7
The goal of this work was the design, synthesis and discovery of new chalcone and
heteroaryl chalcones with potent anti-TB activity. To achieve this goal, we performed the
following steps: (i) collection of available data and rigorous data curation; (ii) generation of
structure-activity relationships (SAR) using matched molecular pair analysis (MMPA) to
design new chalcones with potential anti-TB activity by bioisosteric replacement; (iii)
development of rigorously validated binary QSAR models; (iv) perform virtual screening of
designed compounds; (v), organic synthesis and structure identification (NMR, MS, and IR)
of selected VS hits; and (vi) in vitro experimental evaluation of designed hits under normoxic
(MABA) and hypoxic (LORA) conditions.
1
1
1
1
1
1
1
1
1
8
9
RESULTS AND DISCUSSION
1
Design of chalcone and heteroaryl chalcone compounds
20
21
22
23
For the initial design of new chalcone derivatives with anti-TB activity, we retrieved
604 chalcones compounds with inhibition data against M. tb. H37Rv from PubChem
Bioassay [51], ChEMBL [52], SciFinder database [53], and from literature. After collecting
and integrating all the data, chemical structures and activity values were rigorously curated
5

ACCEPTED MANUSCRIPT
1
2
3
4
5
6
7
following the protocols established by Fourches et al [54–56]. Briefly, structural
normalization of specific chemotypes, such as aromatic and nitro groups, was performed
using ChemAxon Standardizer (v. 15.10.12.0, ChemAxon, Budapest, Hungary,
http://www.chemaxon.com). Inorganic salts, organometallic compounds, and mixtures were
also removed. After structural standardization, the duplicates were identified using ISIDA
Duplicates[57] and HiT QSAR[58]. Analysis of duplicates also allowed to estimate inter- and
intra-lab variability. No suspicious data sources were found.
8
9
0
1
2
3
4
5
6
7
The curated dataset consisted of 571 chalcones, which were the subject for SAR
analysis using matched molecular pairs analysis (MMPA, Figure 1). Matched Molecular
Pairs Analysis (MMPA) is a useful approach in drug discovery to identify and compare
matched molecular pairs from a set of compounds and determining the property change
associated [59]. Matched pairs are molecules that differ only by a particular, well defined,
structural transformation and are used to study changes in biological properties [60].
Therefore, the MMPA can reveals changes in biological properties between structures with
high similarity. In our work, we used the MACCS keys descriptor [61] and Tanimoto
coefficient [62] to evaluate structural similarity related with the changes in biological activity
(Figure 1).
1
1
1
1
1
1
1
1
1
8
19
20
21
Figure 1. MMPA of selected molecular pairs of chalcones and heteroaryl chalcones with
anti-TB activity reported in the literature. The number above the arrows indicate the
Tanimoto coefficient between the molecular pair (high structural similarity, Tanimoto
6

ACCEPTED MANUSCRIPT
1
2
coefficient > 0.90). The groups in green and in red represent the structural differences
between the molecules.
3
4
5
6
7
8
9
0
The information revealed by the matched molecular pairs allowed us to derive the
following SAR rules (Figure 2): (i) hydrophobic and hydrogen bond acceptor groups, e.g.,
halogens, phenyl, and heterocyclic amines, in p-position of ring A are favorable to anti-TB
activity; (ii) substitution of benzene ring B by nitrofuran, increases the activity; (iii) any
substituent in any position of ring B decreases the activity; and (iv) halogen in ortho- or
meta-position of the ring A decreases the activity. These SAR rules were used to design new
compounds using the bioisosteric replacement in the BROOD v.2.0 software [63] and
SwissBioisosteres server [64].
1
1
1
12
13
14
Figure 2. Derived SAR rules for chalcones with anti-TB activity. Modifications in blue
shading increase the activity; with red – decrease the activity.
1
5
6
1
QSAR-DRIVEN design
17
18
19
20
QSAR modeling. MACCS [65], AtomPairs [66,67], Morgan [67,68], FeatMorgan
[69], and Avalon fingerprints [70] combined with support vector machine (SVM) [71],
gradient boosting machine (GBM) [72], and random forest (RF) [73] machine learning
methods were used for the development of 15 different binary QSAR models. These models
7

ACCEPTED MANUSCRIPT
1
2
3
4
5
6
were united in a consensus ensemble model (Table 1). The dataset was balanced prior to the
modeling to keep the ratio of active to inactive compounds as 1:1. The results of 5-fold
external cross-validation demonstrated high predictive power of the developed consensus
model (Table 1). Ten rounds of Y-randomization were performed (CCR≈0.5, see Table S2,
supplementary material) and indicated that developed models were not obtained due to
chance correlations.
7
8
Table 1. Statistical characteristics of developed QSAR models estimated by 5-fold external
CV.
Models
MACCS-GBM
AtomPairs-GBM
Morgan-GBM
FeatMorgan-GBM
Avalon-GBM
MACCS-RF
CCR
0.73
0.71
0.76
0.74
0.74
0.75
0.75
0.76
0.75
0.74
0.77
0.74
0.76
0.76
0.73
0.77
Kappa
0.46
0.41
0.51
0.47
0.47
0.51
0.50
0.52
0.50
0.49
0.53
0.48
0.53
0.51
0.46
0.53
Se
Sp
Coverage
0.71
0.71
0.68
0.66
0.77
0.71
0.71
0.68
0.66
0.77
0.71
0.71
0.68
0.66
0.77
1.00
0.76
0.71
0.77
0.76
0.79
0.79
0.73
0.79
0.70
0.76
0.77
0.74
0.80
0.75
0.72
0.79
0.70
0.70
0.74
0.71
0.68
0.72
0.77
0.73
0.80
0.73
0.76
0.74
0.73
0.76
0.74
0.74
AtomPairs-RF
Morgan-RF
FeatMorgan-RF
Avalon-RF
MACCS-SVM
AtomPairs-SVM
Morgan-SVM
FeatMorgan-SVM
Avalon-SVM
Consensus*
9
0
1
GBM: Gradient Boosting Machine; SVM: Support Vector Machine; RF: Random Forest; CCR: correct
classification rate; Kappa: Cohen’s kappa coefficient; Se: sensitivity; Sp: specificity. *Consensus model was
developed by averaging the predictions of all 15 single models.
1
1
1
2
3
1
Then, the developed consensus model was used for virtual screening of the chalcones
designed by bioisosteric replacement aiming at prioritizing the compounds for chemical
synthesis. The chalcones obtained by bioisosteric replacement (Table S3) are drug-like
1
4
5
1
8

ACCEPTED MANUSCRIPT
1
2
compounds and satisfy Veber [74] and Lipinski [75] rules. In addition, the designed
compounds contained no PAINs substructures [76,77].
3
Chemistry
4
5
6
7
8
9
0
1
2
Based on the results of the in silico design, we synthesized the selected nitrofuran- 3-
17, nitrothiophene- 18-24 and chlorothiophene 25 containing chalcones (Scheme 1). The
standard Claisen-Schmidt condensation [78] under basic condition could not be used because
the starting materials (aldehydes, nitrofurans, nitrothiophenes, and chlorothiophenes) are
alkali-sensitive. Thus, the modified Claisen-Schmidt condensation was performed using
acetic acid as solvent and sulfuric acid as catalyst [79,80]. Compounds 26-35 were
synthesized following standard Claisen-Schmidt condensation using 20% NaOH as catalyst
[78] (see Experimental Section of the Supplementary material for details of spectra and purity
data).
1
1
1
1
3
Scheme 1. Synthesis of aryl chalcones and heteroaryl chalcone derivatives.
1
4
9

ACCEPTED MANUSCRIPT
1
2
3
4
Reagents and conditions: (i) H SO conc., AcOH, reflux, 100 °C, 4 – 24 h; (1)
2
4
acetophenones, (2) nitrofuraldehyde or nitrothiophenecarboxaldehyde, (3-25) analogs
nitrofurans or nitrotiophenes. (ii) 20% NaOH, EtOH, room temperature, 10 h; (1)
acetophenones; (2) aromatics aldehydes; (26-35) phenyl analogs, furan or pyrrole.
5
6
Among designed and synthesized compounds, 17 compounds are new and were not
published previously (6-9, 11, 14, 15, 17-23, 25, 31, and 33), and thirty compounds (6-35)
were not tested against tuberculosis before.
7
8
9
0
Antituberculosis activity
1
1
1
1
1
1
1
1
1
1
2
2
2
The compounds were submitted to biological assays against M. tb. H37Rv, under both
1
2
3
4
5
6
7
8
9
0
1
2
aerobic (replicating) and anaerobic (non-replicating) conditions using MABA and LORA
assays, respectively [81,82]. Minimum inhibitory concentrations (MICs) were defined as the
lowest compound concentration effecting ≥90% inhibition of fluorescence or luminescence,
respectively. We evaluated 33 chalcones including three known compounds (Table 2) [79].
Twenty-two compounds had low MICs in both the MABA and LORA assays. Compounds
containing substituents in the para-position of ring A, and containing nitrofurans and
nitrothiophenes as ring B (Figure 2) were the most potent. Ortho- and meta-substituted
compounds were somewhat less active. Ten designed compounds 4, 8, 9, 11, 13, 17-20, and
23 had MABA MICs of < 1 µM and LORA MICs of < 10 µM (Table 2). And four this
compounds 9, 18, and 19 were more potent than (MIC = 0.27, 0.19, and 0.22 µM
respectively) of standard drug INH (MIC = 0.41 µM) used on treatment of TB. Already the
compound 23 exhibited MIC similar (0.45 µM) to INH.
23
24
25
26
The most potent compound was the nitrothiophene analogue 18 with MABA MIC =
0.19 µM and LORA MIC = 1.73 µM. The substitution of furan ring by thiophene or nitro-
substituted thiophene (e.g., 6 and 18) led to 5.5-fold increase of the activity in the MABA
(1.05 µM to 0.19 µM) and 4-fold for LORA (6.94 µM to 1.73 µM). The compounds 19 and
10

ACCEPTED MANUSCRIPT
1
2
3
20 were the most active in MABA, however 20 lost activity in the LORA in comparison to its
nitrofuran analogue 12. Nitrothiophenes 21 and 22, unlike their nitrofuran analogues 8 and 4,
were
inactive
(MIC>10
µM)
in
both
MABA
and
LORA
assays.
11

ACCEPTED MANUSCRIPT
Table 2. In vitro antituberculosis activity reported in minimum inhibitory concentration (MIC, µM) (MABA and LORA), MABA MIC of
selected compounds against isogenic monodrug-resistant M. tb., rRMP and rINH, spectrum of activity and selectivity index of designed
chalcones
.
Code
Compounds
Minimum Inhibitory Concentration (µM)
SI
MABA
LORA
rRMP
rINH
C. alb.
E. coli
S. aureus
M. smeg.
R1
R2
R3
R4
3
4
5
6
7
8
9
H
H
Br
2.50±0.62
0.81±0.02
6.76±0.08
9.85±2.93
0.76±0.10
0.55±0.02
0.58±0.02
0.11±0.07
<0.03
4.93±2.10
>10
>10
>10
0.36±0.11
1.19±0.46
>10
7.12±0.06
>10
ND
123
ND
94
H
H
H
H
H
H
H
H
H
H
H
H
H
H
3.42±0.43 10.91±3.27 0.07±0.04
>10
>10
>10
1.05±0.29
0.35±0.11
0.81±0.04
0.27±0.06
3.91±1.95
6.94±0.05
>10
1.19±1.02
0.29±0.16
1.12±0.03
0.76±0.08
3.45±1.05
1.44±1.05
0.22±0.14
1.23±0.06
0.19±0.04
0.29±0.08
>10
3.18±1.10
>10
0.34±0.10
>10
>10
-C(CH
)
3
>10
>10
284
122
359
25
3
3.33±0.72
9.67±2.63
3.18±0.04
>10
>10
>10
>10
>10
>10
>10
>10
1
0
>10
>10
>10
>10
12

ACCEPTED MANUSCRIPT
MABA: Microplate Alamar Blue Assay; LORA: Low Oxygen Recovery Assay; rRMP: monoresistant to rifampin; rINH: monoresistant to
isoniazid. SI: Selectivity Index (Vero Cell IC /MABA MIC). C. alb: Candida albicans; M. smeg.: Mycobacterium smegmatis.
5
0
Code
Compounds
Minimum Inhibitory Concentration (µM)
SI
MABA
LORA
rRMP
rINH
C. alb.
E. coli
S. aureus
M. smeg.
R1
R2
R3
R4
1
1
1
1
1
1
1
1
1
2
3
4
5
6
7
8
-CH
3
H
H
0.57±0.01
1.31±1.01
0.60±0.34
1.27±0.04
>10
4.59±0.01
4.21±0.22
4.12±0.14
4.75±0.18
4.30±0.00
5.65±2.22
5.07±1.03
1.73±0.21
1.08±0.04
4.70±0.45
1.18±0.25
3.78±1.31
-
1.07±0.03
2.23±1.02
1.08±0.09
1.93±0.08
-
6.36±0.09
>10
>10
>10
>10
>10
>10
>10
>10
>10
1.09±0.08
2.23±0.00
>10
9.50±0.01
>10
41
57
H
H
H
H
H
H
H
H
H
(CH ) CH
2 3 3
-I
H
>10
4.62
166
4
3
-CH
H
>10
2.23±0.00
>10
>10
3
>10
>10
ND
52
-Br
H
H
1.32±0.45
0.66±0.06
0.19±0.15
1.88±0.30
0.24±0.07
0.60±0.10
1.12±1.98
0.80±0.06
0.30±0.06
>10
1.68±0.29
0.58±0.08
0.28±0.30
4.84±0.10
4.81±0.01
4.39±0.05
-SCH
>10
61
3
H
>10
225
13

ACCEPTED MANUSCRIPT
1
9
H
H
-C(CH
)
3
3
0.22±0.13
4.18±0.55
0.14±0.01
0.15±0.05
>10
>10
>10
>10
454
Code
Compounds
Minimum Inhibitory Concentration (µM)
SI
MABA
LORA
rRMP
rINH
C. alb.
E. coli
S. aureus
M. smeg.
R1
R2
R3
-(CH
R4
2
2
0
1
H
H
2
)
3
CH
3
0.54±0.31
>10
5.56±0.66
5.75±0.01
0.48±0.00
-
0.31±0.02
-
>10
>10
>10
>10
>10
>10
>10
>10
81
H
H
ND
ND
2
2
2
2
2
3
4
5
H
H
H
H
H
H
H
H
>10
>10
-
-
>10
>10
>10
>10
>10
>10
>10
>10
>10
>10
>10
>10
>10
>10
>10
>10
-SCH
0.45±0.20
1.87±0.31
9.29±0.45
5.96±2.47
2.05±0.37
9.75±0.63
0.14±0.03
1.21±0.01
8.89±2.03
0.22±0.01
0.96±0.04
9.03±1.80
222
ND
3
-CH
3
ND
ND
2
6
H
H
>10
>10
-
-
>10
>10
>10
>10
14

ACCEPTED MANUSCRIPT
2
2
7
8
H
H
H
H
>10
>10
>10
>10
-
-
-
-
>10
>10
>10
>10
>10
>10
>10
ND
ND
1.18±0.40
Code
Compounds
Minimum Inhibitory Concentration (µM)
SI
MABA
LORA
rRMP
rINH
C. alb.
E. coli
S. aureus
M. smeg.
R1
R2
R3
R4
2
3
9
0
H
H
-SCH
3
>10
>10
>10
-
-
>10
>10
>10
>10
>10
>10
>10
>10
ND
20
H
H
H
-I
H
4.90±1.83
8.31±1.01
7.75±0.08
3
3
1
2
H
>10
>10
-
-
>10
>10
>10
>10
>10
>10
>10
>10
ND
11
-Br
H
8.54±2.07
8.20±1.60
9.28±2.01
4.94±1.92
3
3
3
4
H
H
H
H
-C(CH
)
3
3
>10
>10
>10
>10
-
-
-
-
>10
>10
>10
>10
>10
>10
>10
>10
ND
ND
15

ACCEPTED MANUSCRIPT
ND
3
5
H
H
-C(CH
)
3
3
>10
>10
-
-
>10
>10
>10
>10
RMP
INH
Amph.B
Amp
0.05
0.41
-
-
0.19
>256
-
-
>1
-
-
-
>5
-
-
-
-
-
-
-
-
>10
>10
-
-
2000
-
<0.004
-
-
-
1.18
0.25
MABA: Microplate Alamar Blue Assay; LORA: Low Oxygen Recovery Assay; rRMP: monoresistant to rifampin; rINH: monoresistant to
isoniazid. SI: Selectivity Index (Vero Cell IC /MABA MIC). RMP: rifampin, INH: isoniazid, Amph. B: amphotericin B, Amp.: ampicillin
5
0
16

ACCEPTED MANUSCRIPT
1
Cytotoxicity assay
2
3
4
5
6
To verify the possibility that the anti-TB activity of the designed compounds arises
from general toxicity, Vero cells were used to estimate the in vitro cytotoxicity of the 18 most
potent compounds in MABA and LORA assays. These compounds demonstrated modest to
high selectivity on this assay, with selectivity indices (SI) ranging between 11 and 454 (Table
2).
7
Spectrum of activity
8
9
0
1
We also investigated selectivity of compounds with respect to activity against
Candida albicans, Escherichia coli, Staphylococus aureus, and M. smegmatis (Table 2). Most
of the tested compounds had MIC >10 µM, except 3, 4, 6, 11-14, 16-18, 20, and 28 that
exhibited MICs against S. aureus of 0.28-2.23 µM.
1
1
12
13
14
15
16
Conversely, these compounds demonstrated broad-spectrum activity against non-
tuberculosis mycobacterias (NTMs), i.e., M. abscessus, M. chelonae, M. marinum, M. avium,
M. kansasii, and M. bovis (Table S3). Compounds 3, 8-13, 15-25, 30, and 32 had MICs <10
µM against M. avium, M. kansasii, and M. bovis, and compound 10 demonstrated MICs of
0.14 µM and 0.08 µM against M. kansasii and M. bovis, respectively.
1
7
Evaluation in M. tb. resistant strains
18
19
20
21
22
23
We evaluated the subset of most potent compounds (3-14, 16-21, 24 and 25) against
rifampin- and isoniazid-resistant strains of M. tb. H37Rv (Table 2). All tested compounds
were potent against resistant strains (MIC < 10 µM), and compounds 3-5, 7, 9, and 17-21
exhibit MIC < 1 µM against resistant strains. Compound 5 was the most potent compound
with MIC of 0.07 µM against rRMP and < 0.03 µM against rINH strains, indicating that this
compound is promising for RMP and INH resistant strains.
17

ACCEPTED MANUSCRIPT
1
CONCLUSIONS
2
3
4
5
6
7
8
9
0
1
The integration of in silico design, QSAR-driven virtual screening, synthesis, and
experimental evaluation in a single pipeline led to discovery of new and promising anti-TB
compounds. After the compilation of the initial dataset and its rigorous curation, the specific
SAR rules were developed and used for designing of new chalcones by bioisosteric
replacement. For instance, hydrophobic groups and H-bond acceptors are preferred in the
para-position of ring A combined with nitrofuran or nitrothiophene serving as ring B. Then,
the developed consensus QSAR model of antimicrobial activity and applied it for virtual
screening and prioritization of designed compounds. Thirty-three chalcone derivatives were
synthesized, structures were confirmed by spectroscopic methods and tested against
normoxic, replicating (MABA), and hypoxic, non-replicating (LORA) cultures of M. tb.
1
1
12
13
14
15
16
17
18
19
20
21
We identified 20 compounds with MIC <10 µM in MABA including 10 compounds
(4, 8, 9, 11, 13, 17-20, and 23) with MIC < 1 µM in MABA and < 10 µM in LORA. All
tested compounds were also active against M. tb. resistant strains to isoniazid or rifampicin.
The compounds were mostly inactive against all other tested bacteria strains and against
mammalian (VERO) cells. Therefore, those compounds are selective for the genus
Mycobacteria with moderate activity against S. aureus. Our compounds satisfy the criteria for
developing new anti-TB hits published by Katsuno and coauthors[83] and due to their high
potency and activity against resistant strains. Therefore, the designed and synthetized
chalcones and heterochalcones could serve as perspective starting points for the development
of anti-tubercular agents.
2
2
2
3
EXPERIMENTAL SECTION
Computational Design
18

ACCEPTED MANUSCRIPT
1
2
3
4
Dataset. We retrieved 604 chalcone and heteroaryl chalcone compounds with experimental
data tested against M. tb. H37Rv from the PubChem (AID: 1626 and AID: 1949) [51],
ChEMBL [52], SciFinder [53], and from the literature. Compounds that had inconclusive
IC values were considered unreliable and were not included in the modeling.
5
0
5
6
7
8
9
0
Data curation. The compiled dataset of 604 compounds was carefully curated following the
protocols proposed by Fourches et al.[54–56] Briefly, explicit hydrogens were added,
whereas specifics chemotypes such as aromatic and nitro groups were normalized using
ChemAxon
Standardizer
(v.15.1.26.0,
ChemAxon,
Budapest,
Hungary,
http://www.chemaxon.com). Polymers, inorganic salts, organometallic compounds, mixtures,
and duplicates were removed. Modeling-ready curated dataset contained 571 compounds.
1
11
12
13
SAR analysis. SAR analysis was performed using the MMP (Matched Molecular Pairs)
approach [84], Structural similarity was calculated using Tanimoto coefficient obtained on
MACCS keys.
14
15
16
17
18
19
20
21
SAR analysis and bioisosteric replacement. SAR analysis was performed using the MMP
(Matched Molecular Pairs) approach [84]. Structural similarity was calculated using
Tanimoto coefficient [85] obtained on MACCS keys. Bioisosteric replacement was
performed in the p-substituents on the ring A (Figure 1), i.e., piperidine of the most active
chalcone (MIC = 0.19 µM), (2E)-3-(5-nitrofuran-2-yl)-1-[4-(piperidin-1-yl)phenyl]prop-2-
en-1-one, described in literature [79]. Design of these bioisosters were performed using
BROOD
v.2.0
software
[63]
and
SwissBioisosteres
webserver
(http://www.swissbioisostere.ch) [64].
2
2
3
Molecular fingerprints. Five different types of fingerprints were used: molecular access
2
system (MACCS) structural key fingerprints [65], AtomPair [66,67], Morgan, [67,68]
19

ACCEPTED MANUSCRIPT
1
2
FeatMorgan, [69] and Avalon.[70] All fingerprints were calculated using the open-source
cheminformatics toolkit RDKit v.2.4.0 [86].
3
4
5
6
7
8
9
0
Dataset analysis and under-sampling. The curated dataset was unbalanced (148 active and
423 inactive compounds), which is not recommended to build binary QSAR models.
Therefore, we decided to balance the dataset using linear under-sampling strategy developed
by Braga, R.C. (Neves et al, 2016) [87]. Unlike the traditional under-sampling methods
which randomly balance the dataset, this strategy retains the most representative inactive
compounds in the balanced dataset, thus assuring as high as possible coverage of original
chemical space. As a result, balanced dataset containing 148 active and 148 inactive
compounds was used for the modeling.
1
11
12
13
14
15
Machine learning techniques. SVM[71], GBM [72], and Random Forest (RF) [73],
approaches implemented in R v.3.0.3 [88] were used for the building and optimization of
statistically acceptable QSAR models. All machine learning classifiers were implemented
using the R v.3.0.3 [88]. More details about these machine modeling techniques are given in
Supplementary material.
16
17
18
19
20
21
22
23
24
External validation of developed QSAR models. 5-fold external cross-validation is the
standard approach for the estimation of predictive power of QSAR models [89]. In this
procedure, the dataset is randomly divided in five subsets of equal size (20% of compounds
each). One of these subsets serve as an external validation fold and the other four subsets are
used building of the model. The same procedure is repeated five times to place each
compound once in the corresponding external fold. Then, the predictivity of the models is
estimated based on these external folds. Description of statistical characteristics used for
estimation of robustness and external predictivity of developed models is provided in
supplementary material.
20

ACCEPTED MANUSCRIPT
1
2
3
4
5
6
7
8
Consensus modeling. The underlying idea of consensus predictions is that an implicit SAR
for a given dataset can be formally manifested by a variety of QSAR models built with
different types of molecular descriptors and diverse machine learning approaches. Rigorously
built individual models form an ensemble that allows for consensus bioactivity prediction
using all models at once. The development of consensus models is generally recommended
because usually they result in better predictivity and better coverage of chemical space during
virtual screening [90]. To obtain consensus prediction, we have averaged the predictions of
all individual models.
9
0
1
2
3
4
5
6
7
8
9
0
1
2
3
4
5
Chemical synthesis
®
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
All the chemicals and solvents were purchased from Sigma Aldrich . The progress of
all reactions was monitored on Merck KGaA precoated silica gel plates 0.25 mm (with
fluorescence indicator UV ) using ethyl acetate/n-hexane as solvent system. Spots were
2
54
visualized by irradiation with ultraviolet light (254 nm). Melting points (mp) were
1
determined using open capillary method on Melting Point III Marte® apparatus. Proton ( H)
1
3
and ( C) NMR spectra were recorded on Bruker Avance 400 spectrometer at 400 MHz for
1
13
H and 100 MHz for C using DMSO-d and CDCl as solvents referenced. Chemical shifts
6
3
1
13
are given in parts per million (ppm) (δ relative to residual solvent peak for H and C).
Spectra Mass was performed on a LCMS-2020 Liquid Chromatograph Mass Spectrometer
Shimadzu, the column was Agilent XDB-C18, 35µM, 21x20 nm. IR spectra were recorded
-1
on a PerkinElmer model Spectrum 400 (medium, sweep of 4000 to 400 cm ). Synthesized
compounds were ≥96% pure as determined by high performance liquid chromatography
(HPLC) Shimadzu with PDA detector, Nucleodur 100-5 CN-RP column 205x4.6mm, mobile
phase water/0.1% TFA and acetonitrile with flow of 1 mL/min.
For the synthesis of 3-25, substituted acetophenones (0.5 equiv, 0.5 mmol) and
nitroaromatics (0.5 equiv., 0.5 mmol) were dissolved in acetic acid (1 mL) and concentrated
21

ACCEPTED MANUSCRIPT
1
2
3
4
5
6
7
8
sulfuric acid (0.05 mL) and were stirred at 100° C until completion of the reaction (4-24 h).
The cooled mixture and the solid was washed with iced methanol (200 mL) for purification.
For the synthesis of 26-35, 0.4 mL of aqueous NaOH (20% w/v) was added to the solution of
the acetophenones substituted in 4’ position (1 mmol) in EtOH. The resulting mixture was
stirred at the room temperature for 10 hours. The formed precipitate was filtered and washed
with cold water. If no precipitation occurred, the resulting mixture was neutralized with 5%
HCl filtered and dried. The crude was then subjected to chromatography column with
EtOAc/Hexane (7:3, v/v) as eluent.
9
0
1
2
3
4
5
6
7
8
9
0
1
2
3
4
5
(2E)-1-(4-bromophenyl)-3-(5-nitrofuran-2-yl)prop-2-en-1-one (LabMol-63) 3. Yellow
1
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
solid; yield 33% (107 mg, 0.33 mmol); mp 182°C; HPLC purity 98.13%. H NMR (CDCl ):
3
δ = 7.95 (d, 2H, J = 8.0 Hz), 7.70 (d, 2H J = 8.0 Hz), 7.72 (d, 1H, J = 15.0 Hz), 7.58 (d, 1H, J
1
3
= 15.0 Hz), 7.39 (d, 1H, J = 4.0 Hz), 6.87 (d, 1H, J = 4.0 Hz). C NMR (CDCl ): δ = 187.1,
3
152.4, 135.4, 131.8 (2 C), 130.4, 129.7 (2 C), 128.5, 128.1, 123.9, 116.4, 112.7. IR (KBr): ν =
1663 (s; ν(C=O)), 1607 (s; ν(C=C )), 1475, 1301 (s; ν(Ar-NO ).
αβ
2
(2E)-1-[4-(morpholin-4-yl)phenyl]-3-(5-nitrofuran-2-yl)prop-2-en-1-one (LabMol-64) 4.
1
Red solid, yield 13% (42 mg, 0.12 mmol); mp 86°C; HPLC purity 98.19%. H NMR ([D ]
6
DMSO): δ = 8.02 (d, 2H, J = 8.0 Hz), 7.88 (d, 1H, J = 15.0 Hz), 7.80 (d, 1H, J = 3.6 Hz),
7.51 (d, 1H, J = 15.0 Hz), 7.42 (d, 1H, J = 3.6 Hz), 7.04 (d, 2H, J = 9.2 Hz), 3.73 (m, 4H),
1
3
3.41 (m, 4H). C NMR ([D ] DMSO): δ = 185.2, 154.1, 153.8, 130.8 (2 C), 127.1, 126.6,
6
125.6, 116.9, 114.9, 113.1 (2 C), 65.8 (2 C), 46.6 (2 C). IR (KBr): ꢀ = 1660 (s; ν(C=O)),
1601 (s; ν(C=C )), 1515, 1355 (s; ν(Ar-NO )), 1238 (s; ν(C-N)), 1119 (s; ν(C-O)). ESI (+)-
αβ
2
+
MS (MeOH): m/z = 329 [M+H]
(2E)-3-(5-nitrofuran-2-yl)-1-[4-(piperidin-1-yl)phenyl]prop-2-en-1-one (LabMol-65) 5.
1
Red solid, yield 17% (56 mg, 0.17 mmol); mp 220°C; HPLC purity 98.41%. H NMR ([D ]
6
22

ACCEPTED MANUSCRIPT
1
2
3
4
5
6
DMSO): δ = 7.97 (d, 2H, J = 9.2 Hz), 7.86 (d, 1H, J = 15.0 Hz), 7.80 (d, 1H, J = 4.0 Hz),
7.44 (d, 1H, J = 15.0 Hz), 7.40 (d, 1H, J = 4,0 Hz), 6.99 (d, 2H, J = 9.2 Hz), 4.43 (s, 4H),
1
3
1.60 (s, 6H). C NMR ([D ] DMSO): δ = 184.9, 154.1, 153.9, 131.0 (2 C), 126.7, 125.8,
6
125.3, 116.8, 115.0, 112.9 (2 C), 47.6 (2 C), 24.9 (2 C), 24.0. IR (KBr): ν = 1642 (s, ν(C=O)),
1607 (s; ν(C=C )), 1578, 1354 (s, ν(Ar-NO )), 1235 (s; ν(C-N)). ESI (+)-MS (MeOH): m/z
αβ
2
+
= 327 [M+H]
7
8
9
0
1
2
3
4
(2E)-1-[4-(1H-imidazol-1-yl)phenyl]-3-(5-nitrofuran-2-yl)prop-2-en-1-one (LabMol-66)
1
6. Brown solid; yield 12% (36 mg, 0.12 mmol); mp 232°C; HPLC purity 99.08%. H NMR
([D ] DMSO): δ = 9.07 (s, 1H); 8.32 (d, 2H, J = 8.0 Hz), 8.15 (s, 1H), 7.97 (d, 2H, J = 8.0
6
1
1
1
1
1
Hz), 7.94 (d, 1H, J = 16.0 Hz), 7.83 (d, 1H, J = 4.0 Hz), 7.63 (d, 1H, J = 16.0 Hz), 7.52 (s,
13
1H), 7.48 (d, 1H, J = 4.0 Hz). C NMR ([D ] DMSO): δ = 187.2, 153.2, 152.1, 139.6, 135.9,
6
135.8, 130.7 (2 C), 129.0, 126.3, 121.0 (2 C), 119.2, 118.0, 114.9. IR (KBr): ν = 1662 (s;
ν(C=O)), 1609 (s; ν(C=C )), 1566, 1352 (s, ν(Ar-NO )). ESI (+)-MS (MeOH): m/z = 310
αβ
2
+
[M+H]
15
16
17
18
19
20
21
(2E)-1-(4-tert-butylphenyl)-3-(5-nitrofuran-2-yl)prop-2-en-1-one (LabMol-68) 7. Yellow
1
solid; yield 22% (67 mg, 0.22 mmol); mp 180°C; HPLC purity 99.89%. H NMR (CDCl ): δ
3
= 1.37 (s, 9H), 6.84 (d, 1H, J = 4.0 Hz), 7.39 (d, 1H, J = 4.0 Hz), 7.55 (d, 1H, J = 15.0 Hz),
1
3
7.55 (d, 2H, J = 8.4Hz), 7.77 (d, 1H J = 15.0Hz), 8.01 (d, 2H, J = 8.4Hz). C NMR (CDCl ):
3
δ = 188.3, 157.2, 152.8, 151.8, 134.2, 128.2 (2 C), 127.3, 125.4 (2 C), 124.8, 115.8, 112.8,
34.8, 30.6 (3 C). IR (KBr): ν = 1651 (s; ν(C=O)), 1596 (s; ν(C=C )), 1527, 1354 (s, ν(Ar-
αβ
+
NO )), 1391 (m, ν(CH )). ESI (+)-MS (MeOH): m/z = 300 [M+H]
2
3
22
23
24
(2E)-1-(4-cyclohexylphenyl)-3-(5-nitrofuran-2-yl)prop-en-2-one (LabMol-72) 8. Yellow
1
solid; yield 44% (72 mg, 0.22 mmol); mp 162° C; HPLC purity 98.59%. H NMR (CDCl ): δ
3
= 8.00 (d, 2H, J = 8.0 Hz), 7.77 (d, 1H, J = 15.6 Hz), 7.54 (d, 1H, J = 15.6 Hz), 7.38 (d, 1H, J
23

ACCEPTED MANUSCRIPT
1
2
3
4
5
= 3.6 Hz), 7.37 (d, 2H, J = 8.0 Hz), 6.84 (d, 1H, J = 3.6 Hz), 2.61 (s, 1H); 2.18 (s, 2H); 1.89
13
(s, 2H), 1.78 (s, 1H), 1.39 (m, 6H). C NMR (CDCl ): 187.7, 154.1 (2 C), 152.9, 134.6,
3
128.5 (2 C), 127.3, 127.0 (2 C), 124.8, 115.8, 112.8, 44.3, 33.6 (2 C), 26.3 (2 C), 25.6. IR
(KBr): ν = 2925 (s; ν(C-H)) 1651 (s; ν(C=O)), 1593 (s; ν(C=C )), 1526, 1353 (s, ν(Ar-NO )),
αβ
2
+
1481 (m, ν(CH )). ESI (+)-MS (MeOH): m/z = 326 [M+H] .
2
6
7
8
9
0
1
2
3
(2E)-3-(5-nitrofuran-2-yl)-1-[4-(piperazin-1-yl)phenyl]prop-2-en-1-one (LabMol-73) 9.
1
Red solid; yield 42% (59 mg, 0.12 mmol); mp 221° C; HPLC purity 98.07%. H NMR
(CDCl ): δ = 8.00 (d, 2H, J = 8.8 Hz), 7.79 (d, 1H, J = 15.2 Hz), 7.51 (d, 1H, J = 15.2 Hz),
3
7.38 (d, 1H, J = 4.0 Hz), 6.90 (d, 2H, J = 8.8 Hz), 6.78 (d, 1H J = 4.0 Hz), 3.44 (s, 4H), 1.69
1
3
1
1
1
1
(s, 5H). C NMR (CDCl ): δ 185.2, 154.2 (2 C), 153.5, 130.8 (2 C), 125.9, 125.7, 125.3,
3
115.1, 112.9, 112.7 (2 C), 47.9 (2 C), 24.9 (2 C), 23.9. IR (KBr) ν = 1618 (s; ν(C=O)), 1609
(s; ν(C=C )), 1580, 1354 (s, ν(Ar-NO )), 1513 (m, ν(N-H)), HR-MS (m/z) (ESI): calcd for
αβ
2
+
C17H18N3O4 [M + H ]: 328.1291; found: 328.1289.
14
15
16
17
18
19
20
21
(2E)-3-(5-nitrofuran-2-yl)-1-(4-phenylphenyl)prop-2-en-1-one (LabMol-74) 10. Yellow
1
solid; yield 62% (100 mg, 0.31 mmol); mp 200°C; HPLC purity 99.29%. H NMR (CDCl ):
3
δ = 8.16 (d, 2H, J = 8.4 Hz), 7.83 (d, 1H, J = 15.5 Hz), 7.77 (d, 2H, J = 8.4 Hz), 7.67 (d, 2H,
J = 7.2 Hz), 7.59 (d, J = 15.5 Hz, CH α, 1H), 7.50 (s, 2H); 7.44 (d, 1H J = 7.2 Hz), 7.40 (s,
1
3
1H), 6.86 (s, 1H). C NMR (CDCl ): δ = 187.6, 152.7 (2 C), 145.9, 139.2, 135.4, 128.0 (2
3
C), 128.6 (2 C), 128.0, 127.6, 127.1 (2 C), 126.9 (2 C), 124.6, 116.1, 112.8. IR (KBr): ν =
1660 (s; ν(C=O)), 1598 (s; ν(C=C )), 1597, 1352 (s, ν(Ar-NO )), 1513, 1474 (s, ν(ArC=C)).
αβ
2
+
ESI (+)-MS (MeOH): m/z = 320 [M+H] .
22
23
24
(2E)-1-(2-methylphenyl)-3-(5-nitrofuran-2-yl)prop-2-en-1-ona (LabMol-75) 11. Yellow
1
solid; yield 9% (12 mg, 0.04 mmol); mp 114° C; HPLC purity 99.20%. H NMR (CDCl ): δ
3
= 7.61 (s, 1H), 7.44 (s, 1H), 7.41 (s, 1H), 7.35 (s, 1H), 7.37 (d, 1H, J = 3.6 Hz), 7.31 (s, 2H),
24

ACCEPTED MANUSCRIPT
1
3
1
2
3
4
5
6.83 (d, 1H, J = 3.6 Hz), 2.50 (s, 3H). C NMR (CDCl ): δ = 193.0, 152.5, 137.6, 137.3,
3
131.4, 131.1, 128.7, 128.2, 127.9, 125.3, 115.7, 112.7 (2 C), 20.2. IR (KBr): ν = 1663 (s;
ν(C=O)), 1608 (s; ν(C=C )), 1483, 1348 (s, ν(Ar-NO )), 1476 (s, ν(CH )). ESI (+)-MS
αβ
2
3
+
+
(MeOH): m/z = 258 [M+H] , HR-MS (m/z) (ESI): calcd for C H NO [M + H ]: 258.0760;
1
4
12
4
found: 258.0770.
6
7
8
9
0
1
2
3
4
5
(2E)-1-(4-butylphenyl)-3-(5-nitrofuran-2-yl)prop-2-en-1-one (LabMol-77) 12. Yellow
1
solid, yield 10% (16 mg, 0.05 mmol); mp 100°C; HPLC purity 97.93%. H NMR (MHz
CDCl ): δ = 8.00 (d, 2H, J = 8.4 Hz), 7.78 (d, 1H, J = 15.6 Hz), 7.55 (d, 1H, J = 15.6 Hz),
3
7.39 (d, 1H, J = 4.0 Hz), 7.35 (d, 2H, J = 8.4 Hz), 6.84 (d, 1H, J = 4.0 Hz), 2.71 (t, 2H, J =
13
1
1
1
1
1
1
8.0 Hz), 1.65 (q, 2H, J = 8.0 Hz), 1.39 (s, 2H J = 8.0 Hz), 0.95 (t, 3H, J = 8.0 Hz). C NMR
(CDCl ): δ = 188.5, 161.5, 145.1, 140.8, 139.9, 136.9, 130.0 (2 C), 129.8, 127.1, 126.9,
3
118.6, 114.1 (2 C), 94.0, 55.0. IR (KBr): ν = 2934 (s; ν(C-H), 1652 (s; ν(C=O)), 1607 (s;
ν(C=C )), 1594, 1351 (s; ν(Ar-NO )), 1481 (s; ν(CH )), 810 (s; ν(CH )). ESI (+)-MS
αβ
2
3
2
+
+
(MeOH): m/z = 300 [M+H] , HR-MS (m/z) (ESI): calcd for C H NO [M + H ]: 300.1230;
1
7
18
4
found: 300.1235.
16
17
18
19
20
21
(2E)-1-(4-iodophenyl)-3-(5-nitrofuran-2-yl)prop-2-en-1-one (LabMol-78) 13. Brown
1
solid; yield 33% (61 mg, 0.16 mmol); mp 194°C; HPLC purity 99.49%. H NMR (CDCl ): δ
3
= 7.91 (d, 2H, J = 8.4 Hz), 7.78 (d, 2H, J = 8.4 Hz), 7.70 (d, 1H, J = 15.6 Hz), 7.57 (d, 1H, J
1
3
= 15.6 Hz), 7.39 (d, 1H, J = 4.0 Hz), 6.87 (d, 1H, J = 4.0 Hz). C NMR (CDCl ): δ = 187.4,
3
152.4 (2 C), 137.8 (2 C), 136.0, 129.5 (2 C), 128.1, 123.9, 116.4, 112.7, 101.4. IR (KBr): ν =
1658 (s; ν(C=O)), 1606 (s; ν(C=C )), 1579, 1351 (s; ν(Ar-NO )).
αβ
2
22
23
24
(2E)-1-(3-methylphenyl)-3-(5-nitrofuran-2-yl)prop-2-en-1-one (LabMol-79) 14. Yellow
1
solid; yield 17% (22 mg, 0.08 mmol); mp 141° C; HPLC purity 97.92%. H NMR (CDCl ):
3
δ = 7.86 (s, 2H); 7.76 (d, 1H, J = 15.6 Hz), 7.55 (d, 1H, J = 15.6 Hz), 7.45 (s, 2H), 7.39 (d,
25

ACCEPTED MANUSCRIPT
1
3
1
2
3
4
5
1H, J = 4.0 Hz), 6.85 (d, 1H, J = 4,0 Hz), 2,48 (s, 3H). C NMR (CDCl ): δ = 188.3, 152.8,
3
138.4, 138.8, 134.0, 128.7, 128.3, 127.5, 125.5, 124.8, 116.0, 112.8 (2 C), 20.2. IR (KBr): ν =
3131 (s; ν(C-H)) 1661 (s; ν(C=O)), 1606 (s; ν(C=C )), 1579, 1349 (s; ν(Ar-NO )). ESI (+)-
αβ
2
+
+
MS (MeOH): m/z = 258 [M+H] , HR-MS (m/z) (ESI): calcd for C H NO [M + H ]:
1
4
12
4
258.0760; found: 258.0764.
6
7
8
9
0
1
2
3
(2E)-3-(5-nitrofuran-2-yl)-1-[4-(pyrrolidin-1-yl)phenyl]prop-2-en-1-one (LabMol-81) 15.
1
Red solid; yield 36% (57 mg, 0.18 mmol); mp 242° C; HPLC purity 96.87%. H NMR
(CDCl ): δ = 8.03 (d, 2H, J = 8.8 Hz), 7.82 (d, 1H, J = 15.6 Hz), 7.52 (d, 1H, J = 15.6 Hz),
3
7.38 (d, 1H, J = 4.0 Hz), 6.77 (d, 1H, J = 3.6 Hz), 6.59 (d, 2H, J = 8.8 Hz), 3.42 (s, 4H); 2.07
1
3
1
1
1
1
(s, 4H). C NMR (CDCl ): δ = 185.0, 153.6, 151.1, 130.9 (2 C), 125.6, 125.5, 124.3, 114.9,
3
113.0, 110.7 (2 C), 42.2 (2 C), 24.0 (2 C). IR (KBr): ν = 2854 (s; ν(C-H)), 1643 (s; ν(C=O)),
1610 (s; ν(C=C )), 1578, 1354 (s; ν(Ar-NO )) 1199 (s; ν(C-N)). ESI (+)-MS (MeOH): m/z =
αβ
2
+
313 [M+H]
14
15
16
17
18
19
20
21
(2E)-1-(3-bromophenyl)-3-(5-nitrofuran-2-yl)prop-2-en-1-one (LabMol-82) 16. Brown
1
solid, yield 32% (62 mg, 0.19 mmol); mp 158°C; HPLC purity 99.98%. H NMR (CDCl ): δ
3
= 8.18 (s, 1H); 7.98 (d, 1H, J = 7.6 Hz), 7.76 (d, 1H, J = 8.0 Hz), 7.69 (d, 1H, J = 15.6 Hz),
7.57 (d, 1H, J = 15.6 Hz), 7.44 (t, 1H, J = 7.6Hz, J = 8.0 Hz), 7.39 (d, 1H, J = 4.0 Hz), 6.88
1
2
1
3
(d, 1H, J = 4.0 Hz). C NMR (CDCl ): δ = 186.9, 152.3, 151.9, 138.5, 136.0, 131.2, 130.0,
3
128.4, 126.7, 123.9, 122.8. 116.5, 112.7. IR (KBr): ν = 1664 (s; ν(C=O)), 1607 (s; ν(C=C )),
αβ
+
1566, 1354 (s; ν(Ar-NO )). ESI (+)-MS (MeOH): m/z = 321 [M+H] ; HR-MS (m/z) (ESI):
2
+
calcd for C13H9BrNO4 [M + H ]: 321.9709; found: 321.9701
22
23
24
(2E)-1-[4-(methylsulfanyl)phenyl]-3-(5-nitrofuran-2-yl)prop-2-en-1-one (LabMol-92) 17.
1
Brown solid; yield 24% (35 mg, 0.12 mmol); mp 160° C; HPLC purity 99.15%. H NMR
(CDCl ): δ = 8.00 (d, 2H, J = 8.0 Hz), 7.76 (d, 1H, J = 16.0 Hz), 7.56 (d, 1H, J = 16.0 Hz),
3
26

ACCEPTED MANUSCRIPT
1
3
1
2
3
4
5
7.39 (d, 1H, J = 4.0 Hz), 7.34 (d, 2H, J = 8.0 Hz), 6.84 (d, 1H, J = 4.0 Hz), 2.56 (s, 3H). C
NMR (CDCl ): δ = 186.6, 152.8 (2 C), 146.6, 132.9, 128.7 (2 C), 127.4, 124.7 (2 C), 124.5,
3
115.9, 112.8, 14.3. IR (KBr): ν = 3117 (m, ν(C-H)), 1658 (s; ν(C=O)), 1607 (s; ν(C=C )),
αβ
+
1589, 1354 (s; ν(Ar-NO )), 1394 (s; ν(CH )). ESI (+)-MS (MeOH): m/z = 290 [M+H] , HR-
2
3
+
MS (m/z) (ESI): calcd for C14H12NO4 [M + H ]: 258.0760; found: 258.0770
6
7
8
9
0
1
2
3
4
(2E)-1-[4-(1H-imidazol-1-yl)phenyl]-3-(5-nitrothiofen-2-yl)prop-2-en-1-one (LabMol-84)
1
18. Green solid; yield 55% (180 mg, 0.55 mmol); mp 223° C; HPLC purity 98.99%. H NMR
([D ] DMSO): δ = 9.80 (s, 1H), 8.42 (d, 3H, J = 8.0 Hz), 8.17 (d, 1H, J = 4.0 Hz), 8.05 (d,
6
1H, J = 16.0 Hz), 8.05 (d, 2H, J = 8.0 Hz), 7.95 (d, 1H, J = 8.0 Hz), 7.94 (d, 1H, J = 16.0
1
3
1
1
1
1
1
Hz), 7.84 (d, 1H, J = 4.0 Hz). C NMR ([D ] DMSO): δ = 187.2, 151.9, 146.4, 138.5, 137.1,
6
135.5, 135.2, 131.5, 130.6 (2 C), 130.6, 125.1, 122.0 (2 C), 121.7, 120.6. IR (KBr): ν = 1663
(s; ν(C=O)), 1605 (s; ν(C=C )), 1595, 1339 (s; ν(Ar-NO )), 1284 (m; ν(C-NAr)). ESI (+)-
αβ
2
+
+
MS (MeOH): m/z = 326 [M+H] . HR-MS (m/z) (ESI): calcd for C H N O S [M+H ]:
1
6
12
3
3
326.0593; found: 326.0607.
15
16
17
18
19
20
21
22
23
(2E)-1-(4-tert-butylphenyl)-3-(5-nitrothiophen-2-yl)prop-2-en-1-one (LabMol-86) 19.
1
Green solid; yield 63% (99 mg, 0.31 mmol); mp 192° C; HPLC purity 99.55%. H NMR
(CDCl ): δ = 7.96 (d, 2H, J = 8.4 Hz), 7.88 (d, 1H, J = 4.4 Hz), 7.80 (d, 1H, J = 15.6 Hz),
3
1
3
7.55 (d, 2H, J = 8.4 Hz), 7.52 (d, 1H, J = 15.6 Hz), 7.27 (d, 1H, J = 4.4 Hz), 1.37 (s, 9H). C
NMR (CDCl ): δ = 187.8, 157.1, 151.6, 146.1, 134.2, 134.09, 129.1, 128.7, 128.1 (2 C),
3
125.4 (2 C), 124.6, 34.8, 30.6 (3 C). IR (KBr): ν = 2962 (s; ν(C-H)), 1657 (s; ν(C=O)), 1691
(s; ν(C=C )), 1586, 1334 (s; ν(Ar-NO )), 1366 (m; ν(CH )). ESI (+)-MS (MeOH): m/z =
αβ
2
3
+
+
316 [M+H] , HR-MS (m/z) (ESI): calcd for C H NO S [M + H ]: 316.1001; found:
17
18
3
316.1006.
27

ACCEPTED MANUSCRIPT
1
2
3
4
5
6
7
8
9
0
(2E)-1-(4-butylphenyl)-3-(5-nitrothiophen-2-yl)prop-2-en-1-one (LabMol-87) 20. Green
1
solid; yield 38% (61 mg, 0.19 mmol); mp 145° C; HPLC purity 99.76%. H NMR (CDCl ): δ
3
= 7.95 (d, 2H, J = 8.0 Hz), 7.89 (d, 1H, J = 4.4 Hz), 7.80 (d, 1H, J = 15.6 Hz), 7.51 (d, 1H, J
= 15.6 Hz), 7.34 (d, 2H, J = 8.0 Hz), 7.27 (d, 1H, J = 4.0 Hz), 2.71 (t, 2H, J = 8.0 Hz), 1.65
1
3
(q, 2H, J = 8.0 Hz), 1.38 (s, 2H, J = 8.0 Hz), 0.95 (t, 3H, J = 8.0 Hz) 3H). C NMR (CDCl ):
3
δ = 187.7, 151.5, 149.1, 146.1, 134.5, 134.0, 129.1, 128.7, 128.5 (2 C), 128.3 (2 C), 124.6,
35.3, 32.8, 21.9, 13.4. IR (KBr): ν = 2928 (s; ν(C-H)), 1657 (s; ν(C=O)), 1598 (s; ν(C=C )),
αβ
1593, 1330 (s; ν(Ar-NO )), 1366 (m; ν(CH )), 816 (m; ν(CH )). ESI (+)-MS (MeOH): m/z =
2
3
2
+
+
316 [M+H] , HR-MS (m/z) (ESI): calcd for C H NO S [M + H ]: 316.1001; found:
17
18
3
1
316.1005
11
12
13
14
15
16
17
18
(2E)-1-(4-cyclohexylphenyl)-3-(5-nitrothiofen-2-yl)prop-2-en-1-one (LabMol-88) 21.
1
Green solid; yield 64% (110 mg, 0.32 mmol); mp 182° C; HPLC purity 99.79%. H NMR
(CDCl ): δ = 7.95 (d, 2H, J = 8.0 Hz), 7.88 (d, 1H, J = 4.0 Hz), 7.79 (d, 1H, J = 15.2 Hz),
3
7.52 (d, 1H, J = 15.2 Hz), 7.37 (d, 2H, J = 8.0 Hz), 7.27 (d, 1H, J = 4.0 Hz), 2.61 (s, 1H),
1
3
1.89 (s, 4H), 1.79 (s, 1H), 1.47 (s, 4H), 1.30 (s, 1H). C NMR (CDCl ): δ = 187.7, 154.0,
3
151.5, 146.1, 134.6, 134.0, 129.1, 128.7, 128.4 (2 C), 126.9 (2 C), 124.7, 44.3, 33.6 (2 C),
26.3 (2 C), 25.6. IR (KBr): ν = 2926 (s; ν(C-H)), 1656 (s; ν(C=O)), 1606 (s; ν(C=C )), 1589,
αβ
1334 (s; ν(Ar-NO )), 1427 (m; ν(CH )).
2
2
19
20
21
22
23
24
(2E)-1-[4-morpholin-4-yl)phenyl]-3-(nitrothiofen-2-yl)prop-2-en-1-one (LabMol-89) 22.
1
Yellow solid; yield 9% (17 mg, 0.04 mmol); mp 240° C; HPLC purity 98.62%. H NMR
(CDCl ): δ = 7.99 (d, 2H, J = 9.2 Hz), 7.89 (d, 1H, J = 4.0 Hz), 7.79 (d, 1H, J = 15.2 Hz),
3
7.53 (d, 1H, J = 15.6 Hz), 7.25 (d, 1H, J = 4.4 Hz), 6.93 (d, 2H, J = 9.2 Hz), 3.88 (t, 4H, J =
13
4.0 Hz), 3.38 (t, 4H, J = 4.0 Hz). C NMR (CDCl ): δ = 185.6, 154.1, 146.6 (2 C), 133.1,
3
130.4 (2 C), 128.7, 127.3, 124.8, 112.9 (2 C), 66.1 (2 C), 46.8 (2 C). IR (KBr): ν = 1648 (s;
28