Biological Activities and ADMET-Related Properties of Novel Set of Cinnamanilides ?

Article abstract of DOI:10.3390/molecules25184121

A series of nineteen novel ring-substituted N-arylcinnamanilides was synthesized and characterized. All investigated compounds were tested against Staphylococcus aureus as the reference strain, two clinical isolates of methicillin-resistant S. aureus (MRSA), and Mycobacterium tuberculosis. (2E)-N-[3-Fluoro-4-(trifluoromethyl)phenyl]-3-phenylprop-2-enamide showed even better activity (minimum inhibitory concentration (MIC) 25.9 and 12.9 μM) against MRSA isolates than the commonly used ampicillin (MIC 45.8 μM). The screening of the cell viability was performed using THP1-Blue NF-κB cells and, except for (2E)-N-(4-bromo-3-chlorophenyl)-3-phenylprop-2-enamide (IC₅₀ 6.5 μM), none of the discussed compounds showed any significant cytotoxic effect up to 20 μM. Moreover, all compounds were tested for their anti-inflammatory potential; several compounds attenuated the lipopolysaccharide-induced NF-κB activation and were more potent than the parental cinnamic acid. The lipophilicity values were specified experimentally as well. In addition, in silico approximation of the lipophilicity values was performed employing a set of free/commercial clogP estimators, corrected afterwards by the corresponding pK_a calculated at physiological pH and subsequently cross-compared with the experimental parameters. The similarity-driven property space evaluation of structural analogs was carried out using the principal component analysis, Tanimoto metrics, and Kohonen mapping.

Full text of DOI:10.3390/molecules25184121

molecules
Article
Biological Activities and ADMET-Related Properties
of Novel Set of Cinnamanilides †
Jiri Kos 1, Andrzej Bak 2,
*
, Violetta Kozik 2, Timotej Jankech 3, Tomas Strharsky 1,
Aleksandra Swietlicka 2, Hana Michnova 1, Jan Hosek 1 , Adam Smolinski 4 , Michal Oravec 5,
Ferdinand Devinsky 6 , Milan Hutta 3 and Josef Jampilek 3,
*
1
Regional Centre of Advanced Technologies and Materials, Faculty of Science, Palacky University,
Slechtitelu 27, 78371 Olomouc, Czech Republic; jiri.kos@upol.cz (J.K.); tomas.strharsky01@upol.cz (T.S.);
michnova.hana@gmail.com (H.M.); jan.hosek@upol.cz (J.H.)
Department of Chemistry, University of Silesia, Szkolna 9, 40007 Katowice, Poland;
violetta.kozik@us.edu.pl (V.K.); aswietlicka@us.edu.pl (A.S.)
Department of Analytical Chemistry, Faculty of Natural Sciences, Comenius University, Ilkovicova 6,
84215 Bratislava, Slovakia; timotej.jankech@gmail.com (T.J.); milan.hutta@uniba.sk (M.H.)
Central Mining Institute, Pl. Gwarkow 1, 40166 Katowice, Poland; smolin@gig.katowice.pl
Global Change Research Institute CAS, Belidla 986/4a, 60300 Brno, Czech Republic; oravec.m@czechglobe.cz
Faculty of Pharmacy, Comenius University, Odbojarov 10, 83232 Bratislava, Slovakia;
devinsky@fpharm.uniba.sk
2
3
4
5
6
*
†
Correspondence: andrzej.bak@us.edu.pl (A.B.); josef.jampilek@gmail.com (J.J.)
Preliminary results presented at the 23rd International Electronic Conference on Synthetic Organic
Chemistry, 15 November–15 December 2019; Available online: https://ecsoc-23.sciforum.net/.
ꢀꢁꢂꢀꢃꢄꢅꢆꢇ
Academic Editor: Julio A. A. Seijas Vázquez
Received: 24 August 2020; Accepted: 8 September 2020; Published: 9 September 2020
ꢀꢁꢂꢃꢄꢅꢆ
Abstract: A series of nineteen novel ring-substituted N-arylcinnamanilides was synthesized and
characterized. All investigated compounds were tested against Staphylococcus aureus as the reference
strain, two clinical isolates of methicillin-resistant S. aureus (MRSA), and Mycobacterium tuberculosis.
(2E)-N-[3-Fluoro-4-(trifluoromethyl)phenyl]-3-phenylprop-2-enamide showed even better activity
(minimum inhibitory concentration (MIC) 25.9 and 12.9
used ampicillin (MIC 45.8 M). The screening of the cell viability was performed using THP1-Blue
NF- B cells and, except for (2E)-N-(4-bromo-3-chlorophenyl)-3-phenylprop-2-enamide (IC50 6.5 M),
none of the discussed compounds showed any significant cytotoxic effect up to 20 M. Moreover,
all compounds were tested for their anti-inflammatory potential; several compounds attenuated the
lipopolysaccharide-induced NF- B activation and were more potent than the parental cinnamic acid.
µM) against MRSA isolates than the commonly
µ
™
κ
µ
µ
κ
The lipophilicity values were specified experimentally as well. In addition, in silico approximation
of the lipophilicity values was performed employing a set of free/commercial clogP estimators,
corrected afterwards by the corresponding pKa calculated at physiological pH and subsequently
cross-compared with the experimental parameters. The similarity-driven property space evaluation
of structural analogs was carried out using the principal component analysis, Tanimoto metrics,
and Kohonen mapping.
Keywords: cinnamamides; synthesis; antistaphylococcal activity; MTT assay; cytotoxicity;
lipophilicity; PCA; IVE-PLS; quantitative structure-property relationships
1. Introduction
Inflammatory diseases of visceral organs, joints, bones, etc. can be based on an infectious
or autoimmune basis, but the body always aims to eradicate noxious agents and restore tissue
homeostasis [1]. A bacterial infection is often connected with severe inflammatory diseases such as
Molecules 2020, 25, 4121; doi:10.3390/molecules25184121
www.mdpi.com/journal/molecules

Molecules 2020, 25, 4121
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bronchial asthma [
2], gingivitis, periodontitis [3], systemic lupus erythematosus [
4], and even with
cancer [5]. Because the bacterial infection can trigger inflammation, which can cause subsequent damage
of surrounding tissue [6], it seems advantageous to follow a multi-target approach in drug design
and try to prepare molecules with dual antimicrobial and anti-inflammatory activity. Multi-target
drug discovery represents an innovative approach of medicinal chemistry to overcome a crisis in drug
design, reflected in the small number of newly approved drugs. This approach is based on the concepts
of privileged scaffolds, polypharmacology, and multifactorial diseases [7–12]. Thus, multi-target
drugs can be designed for the simultaneous treatment of, for example, autoimmune, inflammatory,
and invasive diseases. From pharmacoeconomic and patients’ comfort point of view, it seems favorable
to treat both a cause and a consequence (bacterial infection and inflammation) simultaneously with
one active substance.
Cinnamic acids are one of such privileged multi-target structures that occur naturally in all
plants [13–15]. Cinnamic acids as well as hydroxy- and phenyl-substituted derivatives of cinnamic
acids have been widely investigated due to their significant and varied biological effects including
anti-inflammatory, antioxidant, hepatoprotective, antidiabetic, antidepressant/anxiolytic, antifungal,
antibacterial, antiviral, and anticancer effects [16–24]. Derivatives of cinnamic acids are also used as
agricultural fungicides [25]. Ring-substituted N-arylcinnamanilides were recently synthesized and
tested for their antibacterial, antimycobacterial, anti-inflammatory potential and antifungal activity
as well as for their activity related to the inhibition of photosynthetic electron transport (PET) in
spinach (Spinacia oleracea L.) chloroplasts [26–28]. Since the N-phenylcinnamamide skeleton can be
considered as a privileged scaffold providing multi-target agents, new di-, tri-, and tetra-halogenated
N-arylcinnamanilides were prepared.
The biopharmaceutical profile of a compound is increasingly relevant for characterizing
both the pharmacokinetic (ADMET) and pharmacodynamic aspects of drug–receptor/enzyme
interactions [29,30]
.
The ADMET-friendly design of molecular in vivo permeability and cell
bioaccumulation belongs to the field of the quantitative structure-property relationships (QSPR),
where the physicochemical properties of a compound are the mathematical function of the chemical
composition/constitution [31
often correlates well with the bioactivity of compounds indicate the differential partitioning of a neutral
compound between two immiscible solvents (n–octanol/water) under equilibrium conditions [33 34].
The existing experimental lipophilicity dataset is negligible (3
104) when compared to the enormous
,32]. In this context, lipophilicity and its quantitative descriptor (logP) that
,
×
number of compounds under design; therefore, the reliable measure/estimation of lipophilicity is
a valid requirement at the early stages of drug design [35]. Hence, an attractive alternative for
lipophilicity estimation is in silico predictive protocols due to the additive-constitutive nature of
the logP descriptor [36]. Regrettably, the comparative evaluation of implemented computational
algorithms (linear or non-linear) revealed that the practical development of magic bullet (a global model
for a diverse set of structural types) is dubious—the quality of lipophilicity estimation varies noticeably
depending on the chemical type under consideration [37]. In order to eliminate a consequential
uncertainty (over-/under-estimation) of clogP value, a possible vast range of in silico predictors
should be engaged and compared with the available experimental data. As a matter of fact, early
lipophilicity profiling (theoretical and/or empirical) might facilitate better decision-making in the
hit
→
lead
→
seed drug route and eradicate bad actors (false positive hits) at early stages of drug
→
design/development according to the “fail fast, fail cheap” concept [38]. Due to the inaccuracy of clogP
prediction, the potential exclusion of prospective drug molecules (false negative hits) at the preliminary
step of drug discovery might happen using the Ro5 rule [39].
Depending on the target location and the track of administration, chemicals are exposed to pH
variations in the physiological environment (pH = 2–12). In the case of non-ionizable compounds,
only neutral species exist; however, the majority of commercial pharmaceuticals contain an ionizable
moiety—approximately 75% are basic, and 20% are acidic [40]. In fact, even partial ionization of
a molecule results in the depletion of calculated/measured lipophilicity; therefore, the distribution

Molecules 2020, 25, 4121
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coefficient logDpH (the pH dependent version of logP) should be taken into consideration [41].
In fact, logDpH potentially accumulates errors due to the logP and dissociation constant (pKa)
h
i
estimations according to the following formulas: logDpH = logP − log 1 + 10(pH−pK ) for acids and
a
h
i
logDpH = logP − log 1 + 10(pK −pH) for bases [42]. On the other hand, in many systems, ionic species
enter the non-aqueous phase, and therefore a more rigorous approach would be appropriate, but this
is beyond the scope of this paper.
a
In the presented study, a set of cinnamic acid anilide derivatives was synthesized, investigated
for biological activity, and characterized by a series of experimental lipophilicity values
generated using reversed-phase high-performance liquid (RP-HPLC) and reversed-phase thin-layer
chromatography (RP-TLC). The similarity-related property space evaluation for the congeneric series
of structurally-based analogs was carried out using the principal component analysis (PCA). Moreover,
the in silico approximation of the lipophilic values for the ensemble of anilides 1–20 was performed
employing a set of free/commercial clogP estimators, corrected afterwards by the corresponding pKa
calculated at physiological pH and subsequently cross-compared with the experimental parameter.
The mean values of the selected molecular descriptors that average over the chosen calculation
methods were subsequently correlated with the logD7.4 parameter, namely consensus clogP. Finally,
the similarity-driven investigation using Tanimoto metrics and Kohonen mapping was conducted
revealing some structural dissimilarities within the analyzed series of compounds.
2. Results and Discussion
2.1. Synthesis and Biological Screening
All compounds were synthesized from cinnamic acid in a microwave reactor: The carboxyl group
was converted with phosphorus trichloride to acyl chloride, which then reacted with the appropriately
substituted aniline to give the desired product (Scheme 1). The structures and biological activities are
reported in Table 1, and empirically evaluated lipophilicity using RP-HPLC and RP-TLC are listed in
Table 2.
Scheme 1. Synthesis of ring-substituted (2E)-N-aryl-3-phenylprop-2-enamides 1–20. Reagents and
conditions: (a) PCl3, chlorobenzene, MW (max. 500 W), 130 ◦C, 30 min [26,28].
2.1.1. In Vitro Antimicrobial Evaluation
The investigated compounds were tested on their antistaphylococcal activity against three clinical
isolates of methicillin-resistant Staphylococcus aureus (MRSA) and S. aureus ATCC 29213 as the reference
and quality control strain. Moreover, all compounds were tested against Mycobacterium tuberculosis
ATCC 25177/H37Ra (see Table 1). As a matter of fact, all compounds showed very limited antimicrobial
activity with the exception of compound 10 (R = 3-F-4-CF3) that revealed a significant effect against both
S. aureus and MRSA isolates. It confirmed our previous findings, where compounds with CF3 moiety
showed high antistaphylococcal and antitubercular activity [26
other compounds with the CF3 motif, such as 13 (R = 2-Br-5-CF3) and 19 (R = 2,6-Br-4-CF3) or compound
(R = 3,4,5-Cl), derived from 3,5-Cl (highly effective against M. tuberculosis) should have similar
,27]. Consequently, it was expected that
6
activity [26]. Unfortunately, only molecule 10 turned out to be active. Because the minimum inhibitory
concentrations (MICs) of compound 10 are the same against both S. aureus and MRSA isolates (25.9 and
12.9 µM), it can be speculated concerning the specific activity against Staphylococcus sp. In addition,

Molecules 2020, 25, 4121
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all compounds were verified against the reference strain Enterococcus faecalis ATCC 29212 and three
vanA-carrying vancomycin-resistant E. faecalis (VRE) isolates; however no activity was recorded.
It is should be emphasized that besides MIC values, minimum bactericidal concentrations (MBCs)
were also determined for compound 10. All MBC values were equal to the MIC values, indicating that
compound 10 showed not only bacteriostatic but bactericidal activity. In fact, a tested compound is
classified as bactericidal if the ratio of its MBC/MIC values is ≤4 [43].
Finally, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay of effective
antistaphylococcal compound 10 was conducted as well. The MTT test is employed to assess cell
growth by measuring respiration. The MTT measured viability of bacterial cells less than 70% [44
]
after the exposure to the MIC values of tested compound is considered as a positive result of this
assay, because this low level of cell viability indicates the inhibition of cell growth by the inhibition
of respiration [45]. It can be concluded that compound 10 showed a significant decrease in viability
to 13.4
±
0.1%, which is far below the limit of 70% viability of S. aureus ATCC 29213 at the tested
concentration equal to MIC (i.e., 25.9 µM (8 µg/mL).
Table 1. Structure of ring-substituted (2E)-N-aryl-3-phenylprop-2-enamides 1–20, in vitro anti-
Staphylococcus activities’ minimum inhibitory concentration (MIC;
ampicillin (AMP), in vitro antitubercular activity MIC ( M) in comparison with standard isoniazid
(INH), and their influence on viability of THP1-Blue™ NF-κB cell line (IC50 (µM) ± SEM, n = 6).
µM) in comparison with standard
µ
MIC (µM)
Tox IC50
(µM) ± SEM
Comp.
R
SA
MRSA 63718
MRSA SA 630
Mtb
1 *
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
AMP
INH
H
2,4,6-F
3,4,5-F
2,4-Cl
2,4,5-Cl
>1146
>924
462
>880
>790
>790
>677
931
>1146
>924
>924
>880
>790
>790
>677
>931
>805
25.9
>1146
>924
>924
>880
>790
>790
>677
>931
805
286
>20
>20
>20
>20
>20
>20
>20
>20
>20
>20
6.5 ± 1.0
>20
>20
>20
>20
>20
>20
>20
>20
>20
–
>462
>462
>440
>395
>395
>339
>465
>403
>414
>383
>383
>348
>414
>394
>348
>381
>381
>287
>298
–
3,4,5-Cl
2,4-Br
2-F-5-Cl
3-F-4-Br
3-F-4-CF3
3-Cl-4-Br
2-Br-4-Cl
2-Br-5-CF3
2-CF3-4-F
2-CF3-4-Cl
2-CF3-4-Br
2-CF3-4-NO2
3-CF3-4-NO2
2,6-Br-4-CF3
2,6-Br-3-Cl-4-F
–
>805
25.9
12.9
192
>766
>766
>696
>828
>788
>696
>762
>762
>574
>595
5.72
192
>766
>696
>828
>788
>696
>762
>762
>574
>595
45.8
>766
>696
>828
>788
>696
>762
>762
>574
>595
45.8
–
–
–
–
36.6
–
* compound was described in reference [26]; SA = Staphylococcus aureus ATCC 29213; MRSA = clinical isolates
of methicillin-resistant S. aureus 63,718 and SA 630 (National Institute of Public Health, Prague, Czech Republic);
Mtb = Mycobacterium tuberculosis H37Ra.

Molecules 2020, 25, 4121
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Table 2. Experimentally found lipophilicity determined using HPLC and TLC approaches for
N-arylcinnamanilides 1–20.
RP-HPLC
logD b
RP-TLC
Comp.
R
e
Logk a
logD c
RM
d
f
RM
RM
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
H
2,4,6-F
3,4,5-F
2,4-Cl
2,4,5-Cl
3,4,5-Cl
2,4-Br
2-F-5-Cl
0.0270
−0.0831
0.4893
0.5278
0.8373
0.9671
0.6152
0.3692
0.5025
0.5789
0.6611
0.5476
0.5561
0.1607
0.4113
0.4683
0.3794
0.5004
0.4321
0.3139
0.1251
0.0260
0.5495
0.5885
0.8879
1.0104
0.6704
0.4334
0.5597
0.6327
0.7126
0.6064
0.6157
0.2426
0.4741
0.5316
0.4431
0.5571
0.4941
0.3922
0.1090
0.0107
0.5274
0.5685
0.8647
0.9897
0.6518
0.4154
0.5408
0.6095
0.6912
0.5860
0.5936
0.2220
0.454
−0.5087
−0.6536
−0.4206
−0.3933
−0.2497
−0.1870
−0.3098
−0.4711
−0.4081
−0.5032
−0.3376
−0.4072
−0.4325
−0.6797
−0.6102
−0.5624
−0.5633
−0.4804
−0.5206
−0.5541
0.2848
0.1218
0.6074
0.6546
0.9695
1.0852
0.7331
0.5411
0.6797
0.7923
0.8388
0.6553
0.7194
0.3011
0.5305
0.5730
0.4998
0.6927
0.5301
0.4320
0.4038
0.1951
0.5495
0.5495
0.9832
1.1306
0.7295
0.5495
0.6895
0.5960
0.8181
0.6484
0.5619
0.1330
0.3219
0.4273
0.4311
0.6160
0.3733
0.4138
3-F-4-Br
3-F-4-CF3
3-Cl-4-Br
2-Br-4-Cl
2-Br-5-CF3
2-CF3-4-F
2-CF3-4-Cl
2-CF3-4-Br
2-CF3-4-NO2
3-CF3-4-NO2
2,6-Br-4-CF3
2,6-Br-3-Cl-4-F
0.5105
0.3989
0.5098
0.4731
0.3699
72:28 MeOH:H2O; b 72:28 MeOH:NaOAc buffer (pH 7.4), c 72:28 MeOH: NaOAc buffer (pH 6.5), d 100% MeOH,
a
e
72:28 MeOH:H2O, f 72:28 MeOH: NaOAc buffer (pH 7.4).
2.1.2. In Vitro Cell Viability and Anti-Inflammatory Potential
In vitro cell viability of all the compounds was estimated using the human THP1-Blue
™
NF-
M) (Table 1)
µM). It seems that the disubstitution of
κB
cell line. Almost all tested compounds showed insignificant cytotoxic effect (IC50 > 20
with exception of molecule 11 (R = 3-Cl-4-Br, IC50 = 6.5 1.0
µ
±
C(3,4)’ by highly lipophilic and electron-withdrawing moieties (combination of chlorine and bromine)
is critical for the toxic effect of the analyzed series of compounds.
With respect to the used THP1-Blue
was evaluated in order to modulate the activity of the pro-inflammatory transcription nuclear factor
(NF)- B (see Figure 1). After lipopolysaccharide (LPS) stimulation, compounds 14 (R = 2-CF3-4-F),
™ NF-κB cells, the anti-inflammatory potential of compounds
κ
18 (R = 3-CF3-4-NO2), and 20 (R = 2,6-Br-3-Cl-4-F) showed the highest attenuation of the activity of this
transcription factor within the studied set of compounds; however, the decrease was approximately
9%, which was lower compared to the previously verified cinnamides [28]. On the other hand,
the half concentration of tested compounds was used in the study. As a matter of fact, compounds
2
(R = 2,4,6-F),
4
(R = 2,4-Cl),
5 (R = 2,4,5-Cl), and 10 (R = 3-F-4-CF3) significantly increased the activity
of NF-
κB by 10–15%, respectively. It seems that the position and type of substituents on phenyl ring is
important for tuning pro-/anti-inflammatory potential of cinnamic acid anilides, which confirms similar
observations in our previous study, where the substitution of ortho and meta positions of the anilide
ring by rather lipophilic and bulky moieties was preferred for the anti-inflammatory potential [28].
Surprisingly, the insignificant anti-inflammatory potential of compound 13 (R = 2-Br-5-CF3) is
disappointing, because its chlorinated analogue showed promising activity [28]. It can be hypothesized
that the investigated N-arylcinnamanilides may have a different mode of action, which may be the
nuclear translocation of NF-κB inhibition, or affecting its binding to DNA, or acting by epigenetic
regulation, while combinations of these effects are not excluded.

Molecules 2020, 25, 4121
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Figure 1. Effect of tested compounds on activity of transcription factor NF-
cells were pretreated by compounds (1 M) dissolved in dimethyl sulfoxide (DMSO) for 1 h. Then,
lipopolysaccharide (LPS) 1 g/mL was added to trigger the NF- B activation. The activity of NF-
was measured 24 h after LPS stimulation using QuantiBlue assay. The graph represents mean SEM
n = 9). The red dotted line shows the value of the DMSO-only treated group. Groups were compared
with the help of the one-way ANOVA test followed by Fisher’s LSD multiple comparison test.
* indicates statistical significance (p < 0.05) to DMSO group; ** indicates statistical significance (p < 0.01
κB. THP1-Blue™ NF-κB
µ
µ
κ
κB
™
±
(
)
to DMSO group; *** indicates statistical significance (p < 0.001) to DMSO group; **** indicates statistical
significance (p < 0.0001) to DMSO group. CA—cinnamic acid.
2.2. Similarity-Driven Property Evaluation
The similarity-guided assessment of the property profile for the group of structurally alike
compounds was conducted for the congeneric set of cinnamic acid anilide analogues. The a
priori calculation of molecular descriptors, regarded as the result of mathematical transformation
of chemical information encoded within a symbolic representation of a molecule is crucial for
the compound’s bioavailability and hence critical for the prospective drug candidate properties.
The gold standard of ADMET–tailored property approximation is the generation of statistically robust
models, where a property is a function of the chemical structure, that are capable of making accurate
quantitative predictions, including those of molecular binding affinity, metabolic/pharmacokinetic/
pharmacodynamics fate, or environmental ecotoxicology. The molecular descriptors are essentially
estimated based on the molecular structure as intuitive roadmaps even before the synthesis of
the molecule has been rationalized. In fact, the majority of topological descriptors is highly
intercorrelated; therefore, it is necessary to employ the linear (e.g., PCA) or non-linear (e.g., neural
network) techniques to reduce the data dimensionality and illustrate the object similarity in the
orthogonal basis using the pair-wise descriptor-based structural resemblance/relatedness measure of
the intermolecular resemblance between two objects (e.g., Euclidean metric) [46]. Initially, the principal
component analysis (PCA) was performed on the pool of 2567 descriptors calculated using Dragon
6.0 program, where constant and nearly constant variables (standard deviation <10−4) were a priori
eradicated. Subsequently, the multidimensional matrix was generated (X20×2567) with rows and
columns representing variables (descriptors) and objects (molecules), respectively. The PCA procedure
was employed on the centered and standardized dataset. The compression efficacy of the PCA method
is strictly related to the number of uncorrelated variables; therefore, the high percentage of total
variance explained by the first three principal components (PC1 + PC2 + PC3 = 77.98%) implies,
that descriptors are highly inter-correlated. In fact, first two PCs described 71.44% of the total variance;
hence, the chosen properties were projected on the plane specified by the PC1 versus PC2-predicted
scores as illustrated in Figure 2a.

Molecules 2020, 25, 4121
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Figure 2. Projection of cinnamic acid anilides
components for Dragon descriptors ( ) with experimental logD7.4
logD at pH = 7.4.
1
–
20 on plane defined by first vs. second principal
a
(b). Colors code numerical values of
Basically, two subfamilies can be formed along the positive and negative values of PC1
for the analyzed 20 anilides that share a common chemotype based on styrene-containing
motif, peptide-like linker, and phenyl R-substituted ring. The first group (PC1 > 0) contains
di/tri/tetra-halogenated analogues (objects 11 12 20), while the second one (PC1 < 0) is composed
of mono-trifluoromethyl-based molecules (objects 10 13 19). Interestingly, compounds 19 and 20 are
located distinctly along the second principal component (PC2 < 30). Not surprisingly, unsubstituted
(only hydrogenated) molecule is separated from the remaining ones. The similarity-driven analysis
1
–
2
–9
,
,
,
,
–
−
1
of the empirical lipophilicity at the physiological pH = 7.4 is illustrated on the PC1 vs. PC2 plane.
In fact, the most lipophilic molecules 5–7 and 11 are grouped together as shown in Figure 2b.
On the whole, the vast number of the investigated anilides abide the Lipinski’s Rule of Five
(Ro5), where the ADMET-friendly properties are confined by the liminal values imposed on the
specific molecular descriptors (MW
≤
500, HBD
≤
5, HBA
≤
10, clogP
≤
5). Interestingly, the Ro5
violation observed for molecules depicted in Figure 3a is strictly related with the calculated lipophilicity
values (clogP > 5). Despite the drug-like property space is still a questionable concept, because a
good drug-like score does not make a molecule a drug and vice versa, the Ro5 rule might be handy
in differentiating a prospective drug from a non-drug molecule [47]. Moreover, almost all tested
compounds showed very low cytotoxic effect (IC50 > 20
µM) as presented in Figure 3b and Table 1.
Compound 11 (IC50 = 6.5 M) is the visible exception, which is also annotated with Ro5 violation.
µ
As mentioned above, it seems that the C(3,4)’ disubstitution by chlorine and bromine is critical for
the viability of cells unlike the disubstitution of C(2,4)’ positions. Since the lipophilicity is regarded
as a meaningful property in the context of pharmacokinetic and pharmacodynamic drug-receptor
interactions, the lipophilic profiles of the analyzed anilide analogues were thoroughly investigated.
From the drug hunter perspective, the justifiable precariousness of the validity of lipophilicity
estimation can arise, using a variety of theoretical approaches, because some methods for the theoretical
calculation of lipophilicity might be more or less suitable for specific/heterogeneous series of compounds.
In other words, the non-trivial question arises how to single out the best-suited method or combination
of methods for clogP estimation of new compounds. It is known that a jack of all trades is a master of
none; therefore, it seems advisable not to rely solely on one clogP estimator, but rather a combination
of methods should be engaged with subsequent comparison of the results with the experimental data.
Hence, a consensus procedure for clogP prediction was previously proposed based on a consensus
methodology that was adopted from the structure-based studies, where many docking programs and
scoring functions can be employed [48–50]. Thus, in silico approximation of the lipophilic values for

Molecules 2020, 25, 4121
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the ensemble of anilides
1
–20 was performed employing a set of free/commercial clogP estimators such
as clogPS, Molinspirations, OSIRIS, HyperChem 7.0, Sybyl-X, MarvinSketch 15, ACD/ChemSketch
2015, Dragon 6.0, Kowwin, XlogP3, ChemDraw, and ACD/Percepta (Table S1 in Supplementary
Materials). Moreover, the deduced clogP values were corrected afterwards by the corresponding
pKa calculated at specific pH using ACD/Percepta/pKa Classic module (Table S2 in Supplementary
Materials). The obtained clogDpH does not differ significantly from clogP specified by distinct in
silico principles, because the investigated molecules do not contain an ionizable moiety. Furthermore,
the clogD7.4 findings were inter-correlated with each other and cross-compared with the empirical
logD7.4 values as illustrated by the triangular matrix of linear correlations parameters in Figure 4.
Figure 3. Projection of cinnamic acid anilides
components for Dragon descriptors with Ro5 rule violations (
numerical values of Ro5 violations and IC50
1
–
20 on a plane defined by first vs. second principal
a
) and cytotoxic effect IC50 ). Colors code
(b
.
Figure 4. Matrix of correlation coefficients of linear relationships between experimental and calculated
logD7.4 lipophilicity specified using different programs.
In fact, relatively good correlation (r > 0.65) between experimental logD7.4 and model-predicted
clogD7.4 values was recorded for clogPS, ChemSketch, Sybyl, XlogP3, and Kowwin estimators. On the
other hand, rather poor correlation (r
predictors (see Figure 4). The noticeable variations in logD estimation at given pH probably resulted
≤
0.4) was revealed for data provided by HyperChem and Dragon
from different computational algorithms (atom/fragment- or descriptor-based) implemented in the

Molecules 2020, 25, 4121
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software and/or training data engaged at the training stage. Obviously, prediction of the individual
lipophilic contribution of each group/atom (sometimes augmented by the structural correction factors)
is as good as the modeling data used at the training stage of model generation [51]. In other words,
the poorer performance of some predictors focuses on issues arising from the lipophilicity estimation
for the in house collection of molecules with structural features uncovered by chemical classes of
compounds in the training subset—a wide range of chemical space is still not covered [52]. Luckily,
usually a few empirically measured values are sufficient to produce reliable lipophilicity estimation for
structurally related series of molecules. Hence, the integrated clogD7.4 matrix (X20×14) and empirical
logD7.4 were subjected to the backward elimination PLS-based procedure (IVE) indicating Sybyl-X,
XlogP3, Kowwin, ACD/Percepta, and HyperChem as valid contributors to the final QSPR model
(
q2cv 0.72, q2test
= = 0.9, Aopt = 7). The mean and median values of the selected estimators
that averaged over the chosen clogD7.4 values were subsequently correlated with the experimental
parameter with correlation coefficient 0.65, because not only the best inter-correlated clogD7.4 values
were specified in the consensus clogP approach.
The similarity tenet in the chemical space (CS) is the core of many SAR-driven procedures,
where structurally alike molecules are expected to display similar physicochemical and/or
pharmacological properties [53]. Conceptually, a numerical measure of molecular diversity between two
objects can be quantitatively expressed by a bit-string representation (sometimes augmented with the
scaling coefficients) in the function of (un-)common features. Unarguably, it is still a powerful concept
despite some obvious oversimplification of the similarity quantification (e.g., some similarity scores
exhibit size-dependent behavior) [54]. The pairwise relatedness between descriptor-guided structures
can be numerically evaluated by a variety of relative distance metrics (e.g., Hamming or Euclidean
measures) or absolute comparison using Tanimoto coefficient calculated for molecular fingerprints
(e.g., OpenBabel) [55]. The distribution of Tanimoto coefficients for the ensemble of investigated
anilides
range of 0.55 < T < 0.75. The detailed inspection of the symmetric Tanimoto coefficient matrix (T20×20
in Figure 5b reveals the structural dissimilarities of nitro-substituted isomers (compounds 17 and
18) as compared with the remaining ones that confirms our previous PCA (PC1 < 60) findings (see
Figure 2a). Interestingly, mono-bromo/chloro- substituted isomers (molecules 11 and 12) indicate
the structural similarity to di-/tri-bromo/chloro- substituted positional isomers (compounds ) as
1–20 is illustrated in Figure 5a, where the highest frequency was recorded in the pretty wide
)
−
4–9
was also shown in Figure 2a (PC1 > 20). Unfortunately, the similarity investigation did not provide
valuable hints that could explain the noticeable variations in the toxic effect exerted by molecule 11 (see
Figure 3b) and the remaining ones.
Figure 5. Distribution of Tanimoto coefficients (a) and triangular matrix of Tanimoto coefficients (b) for
1–20 anilides.

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Comparative Molecular Shape Analysis
The molecular shape analysis of the cinnamic acid anilides was performed, because the molecular
electrostatics and lipophilicity are two important properties used in the rational drug design. Firstly,
the 11-ordered atom trial alignment of the most active molecule 10 (active analogue approach, AAA)
was applied to cover the entire bonding topology in the maximal common structure (MCS) in the FIT
method as illustrated in Figure 6.
Figure 6. Superimposition of 1–20 anilides according to AAA hypothesis.
Secondly, the molecular electrostatic potentials (MESP) on the Connolly surface were specified
to provide information on the charge distribution of the substituted anilides. The interrelation
between electrostatic and lipophilicity potentials on molecular surfaces was reported previously [56].
The electron-rich positions seem to appear at low, possibly negative, electrostatic potential energy values
(see Figure 7). In other words, low MESP values indicate molecular areas susceptible to electrophilic
attack (nucleophilic positions), while greater electrostatic values correspond to electrophilic positions.
Noticeably, the negative MESP values (dark blue areas) in the close proximity of the substituted anilide
ring seem to contribute to the MIC activity of molecule 10. Obviously, the introduction and composition
of electronegative or electropositive substituents have an impact on the lipophilicity values as well.
Figure 7. Molecular electrostatic potential topography of 1–20 anilides. Blue color codes negative
values; red color codes positive MESP values.

Molecules 2020, 25, 4121
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The spatial MESP distribution does not provide a practical tool for molecular comparison;
therefore, simpler, comparative 2D Kohonen maps of the entire molecular surface were generated [57].
Self-organizing Kohonen neural mapping (SOM) is a nonlinear projection procedure that reduces
the input data dimensionality (e.g., converts 3D objects to 2D), while preserving the topological
relationships between the input and output data [58]. Moreover, a trained network can be engaged
to project the specified molecular property (expressed as a vector) by generating a 2D color-coded
clustering pattern called a feature map. The SOM algorithm was employed to generate an electrostatic
potential map in the form of a two-dimensional topographic pattern produced from input signals
(points) that were sampled randomly at the molecular surface as illustrated in Figure 8. The substitution
of the anilide ring by a rather bulky motif (e.g.,-CF3) was preferred for the anti-inflammatory potential
and lipophilic compound property; therefore, a mono-trifluoro- methyl-based molecule with highly
electronegative substituent (e.g.,-F) was chosen as a template (the most active molecule 10) to proceed
the remaining (counter-template) molecules. The structural inconsistences (dissimilarities) within
the analyzed set of compounds are indicated by the number of inactive (empty/white) neurons in
the map (see Figure 8). Unfortunately, there are no clear and visible variations in the surface charge
distribution within 40
×
40 maps between substituted analogues, with exception of molecules 17 and
18. Not surprisingly, the lowest number of non-active neurons of the comparative maps (less than
200 out of 1600) is prescribed to the similarly substituted (position 3 and 4 of the anilide ring) molecules
9, 11, and 18.
Figure 8. Comparative SOM 40
indicates non-active neurons.
×
40 maps for 1–20 anilides. Colors code MESP values, white color
3. Materials and Methods
3.1. Chemistry
3.1.1. General Information
All reagents were purchased from Merck (Sigma-Aldrich, St. Louis, MO, USA) or Alfa (Alfa-Aesar,
Ward Hill, MA, USA). Reactions were performed using a CEM Discover SP microwave reactor (CEM,

Molecules 2020, 25, 4121
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Matthews, NC, USA). Melting points were determined on an apparatus Stuart SMP10 (Stone, UK)
and are uncorrected. Infrared (IR) spectra were recorded on an ATR Zn/Se for a Nicolet iS 5 FT-IR
™
spectrometer (Thermo Fisher Scientific, West Palm Beach, FL, USA). The spectra were obtained by the
accumulation of 64 scans with 4 cm−1 resolution in the region of 4000–400 cm−1. All 1H- and 13C-NMR
spectra were recorded on a JEOL JNM-ECA 600II NMR spectrometer (600 MHz for 1H and 150 MHz for
13C, Jeol, Tokyo, Japan) in dimethyl sulfoxide-d6 (DMSO-d6); 1H and 13C chemical shifts (
δ) are reported
in ppm. High-resolution mass spectra were measured using a high-performance liquid chromatograph
Dionex UltiMate® 3000 (Thermo Scientific, West Palm Beach, FL, USA) coupled with an LTQ Orbitrap
XLTM Hybrid Ion Trap-Orbitrap Fourier Transform Mass Spectrometer (Thermo Scientific) equipped
with a HESI II (heated electrospray ionization) source in the negative mode.
3.1.2. Synthesis
Cinnamic acid (3.37 mM) was suspended at room temperature in dry chlorobenzene (20 mL)
inside a microwave tube, where phosphorus trichloride (1.7 mM) and the corresponding aniline
derivative (3.37 mM) were added dropwise. Following this step, a magnetic stirrer was added to the
tube and the reaction mixture was transferred to the microwave reactor (max. 500 W) at 130 ◦C for
30 min, where the synthesis at elevated pressure was performed. After the mixture was cooled to
60 ◦C, the solvent was evaporated in vacuum. A solid residue was washed with 2M HCl, and a crude
product was recrystallized, using 96% ethanol first, and then 50% ethanol [26,28].
(2E)-N-Phenyl-3-phenylprop-2-enamide (1) was described previously by Pospisilova et al. [26].
(2E)-3-Phenyl-N-(2,4,6-trifluorophenyl)prop-2-enamide (
3078, 3024, 1659, 1631, 1612, 1525, 1443, 1336, 1239, 1177, 1124, 1045, 999, 972, 861, 843, 764, 751, 705,
666, 612, 572, 533, 513, 481; 1H-NMR (DMSO-d6),
: 9.91 (s, 1H), 7.65–7.64 (m, 2H), 7.61 (d, J = 15.8 Hz
1H), 7.47–7.41 (m, 3H), 7.32 (t, J = 8.2 Hz, 2H), 6.85 (d, J = 15.8 Hz, 1H); 13C-NMR (DMSO-d6),
: 163.95,
2
), Yield 72%; Mp 134–136 ◦C; IR (cm−1): 3244,
δ
,
δ
159.93 (d, J = 245.7 Hz), 157.88 (ddd, J = 250.0 Hz, 15.9 Hz, 8.7 Hz), 141.19, 134.44, 130.02, 129.03,
127.87, 120.35, 111.48 (td, J = 17.3 Hz, J = 5.8 Hz), 100.95 (m) (Figure S1); HR-MS: C15H9ONF3 [M − H]−
calculated 276.0642 m/z, found 276.0634 m/z (Figure S2).
(2E)-3-Phenyl-N-(3,4,5-trifluorophenyl)prop-2-enamide (
1661, 1617, 1545, 1530, 1451, 1426, 1338, 1238, 1208, 1195, 1044, 1013, 976, 858, 848, 805, 770, 761, 717,
642, 628, 561, 509, 481; 1H-NMR (DMSO-d6),
: 10.58 (s, 1H), 7.64–7.67 (m, 5H), 7.46–7.41 (m, 3H), 6.74
(d, J = 15.8 Hz, 1H); 13C-NMR (DMSO-d6),
: 164.02, 150.09 (ddd, J = 244.2 Hz, J = 10.1 Hz J = 5.8 Hz),
141.33, 135.56 (td, J = 11.6 Hz, J = 4.3 Hz), 134.70 (dt, J = 244.2 Hz, J = 15.9 Hz), 134.38, 130.10, 129.05,
3
), Yield 74%; Mp 169–171 ◦C; IR (cm−1): 3303,
δ
δ
,
127.89, 121.25, 103.45 (m) (Figure S3); HR-MS: C15H9ONF3 [M
276.0633 m/z (Figure S4).
−
H]− calculated 276.0642 m/z, found
(2E)-N-(2,4-Dichlorophenyl)-3-phenylprop-2-enamide (4
), Yield: 62%; Mp 159–161 ◦C; IR (cm−1): 3264,
3071, 3027, 1654, 1620, 1578, 1524, 1471, 1448, 1380, 1335, 1283, 1238, 1202, 1182, 1144, 1099, 1072,
1053, 1029, 1005, 999, 969, 858, 826, 787, 757, 725, 710, 697, 658, 632, 560, 559, 513, 488, 445; 1H-NMR
(DMSO-d6),
δ
: 9.78 (s, 1H), 7.98 (d, J = 8.9 Hz, 1 H), 7.69 (d, J = 2.1 Hz, 1H), 7.65–7.64 (m, 2H), 7.62, (d,
J = 15.8 Hz, 1H), 7.47–7.41 (m, 4H), 7.11 (d, J = 15.8 Hz, 1H); 13C-NMR (DMSO-d6),
δ
: 164.02, 141.17,
134.62, 134.21, 129.99, 129.09, 129.05, 128.95, 127.88, 127.59, 126.57, 126.43, 121.54 (Figure S5); HR-MS:
C15H10ONCl2 [M − H]− calculated 290.01450 m/z, found 290.0140 m/z (Figure S6).
(2E)-3-Phenyl-N-(2,4,5-trichlorophenyl)prop-2-enamide (5
), Yield: 52%; Mp 170–172 ◦C; IR (cm−1): 3265,
3107, 3061, 3011, 1656, 1629, 1600, 1568, 1511, 1456, 1446, 1364, 1281, 1248, 1202, 1181, 1129, 1074, 1031,
1
962, 942, 880, 855, 798, 756, 729, 706, 688, 675, 631, 579, 564, 499, 465, 448; H-NMR (DMSO-d6),
δ:
9.85 (s, 1H), 8.33 (s, 1H), 7.94 (s, 1H), 7.66–7.65 (m, 2H), 7.64 (d, J = 15.8 Hz, 1H), 7.48–7.42 (m, 3H),
7.16 (d, J = 15.8 Hz, 1H).; 13C-NMR (DMSO-d6),
δ
: 164.23, 141.68, 135.18, 134.52, 130.55, 130.12, 129.84,

Molecules 2020, 25, 4121
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129.05, 127.95, 126.89, 125.31, 124.45, 121.29 (Figure S7); HR-MS: C15H9ONCl3 [M
323.9755 m/z, found 323.9751 m/z (Figure S8).
−
H]− calculated
(2E)-3-Phenyl-N-(3,4,5-trichlorophenyl)prop-2-enamide (
3080, 1655, 1613, 1583, 1513, 1433, 1378, 1337, 1281, 1245, 1196, 1188, 1148, 1011, 998, 967, 944, 880, 860,
815, 762, 711, 685, 617, 602, 575, 536, 483; 1H-NMR (DMSO-d6),
: 10.62 (s, 1H), 7.95 (s, 2H), 7.65–7.62
(m, 3H), 7.47–7.42 (m, 3H), 6.74 (d, J = 15.8 Hz, 1H); 13C-NMR (DMSO-d6),
: 164.16, 141.63, 139.28,
6
), Yield: 75%; Mp 237–239 ◦C; IR (cm−1): 3157,
δ
δ
134.31, 132.91, 130.20, 129.08, 127.96, 123.40, 121.14, 119.19 (Figure S9); HR-MS: C15H9ONCl3 [M
calculated 323.9755 m/z, found 323.9752 m/z (Figure S10).
−
H]−
(2E)-N-(2,4-Dibromophenyl)-3-phenylprop-2-enamide (
2980, 2888, 1653, 1620, 1575, 1521, 1464, 1446, 1376, 1336, 1280, 1240, 1203, 1184, 1080, 1040, 1007, 967,
859, 825, 766, 755, 711, 688, 643, 619, 566, 546, 501, 472, 440; 1H-NMR (DMSO-d6),
: 9.68 (s, 1H), 7.94 (d,
J = 2.1 Hz, 1H), 7.78 (d, J = 8.9 Hz, 1H), 7.65–7.60 (m, 4H), 7.47–7.41 (m, 3H), 7.06 (d, J = 15.8 Hz, 1H);
13C-NMR (DMSO-d6),
: 163.93, 141.13, 135.88, 134.61, 134.55, 130.98, 129.99, 129.05, 127.88, 127.73,
7
), Yield: 49%; Mp 180–182 ◦C; IR (cm−1): 3263,
δ
δ
121.50, 117.97, 117.66 (Figure S11); HR-MS: C15H10ONBr2 [M
377.9106 m/z (Figure S12).
−
H]− calculated 377.9134 m/z, found
(2E)-N-(5-Chloro-2-fluorophenyl)-3-phenylprop-2-enamide (
3188, 3119, 3042, 1663, 1622, 1612, 1541, 1481, 1415, 1343, 1268, 1253, 1202, 1182, 1112, 996, 915, 871, 808,
761, 737, 679, 646, 574, 566, 502, 485, 454; 1H-NMR (DMSO-d6),
: 10.13 (s, 1H), 8.31 (dd, J = 6.9 Hz
J = 2.7 Hz, 1H), 7.64–7.61 (m, 3H), 7.47–7.41 (m, 3H), 7.35 (dd, J = 11.0 Hz, J = 8.9 Hz, 1H), 7.22–7.19 (m,
1H), 7.11 (d, J = 15.8 Hz, 1H); 13C-NMR (DMSO-d6),
: 164.22, 151.55 (d, J = 245.6 Hz), 141.31, 134.58,
130.04, 129.06, 128.02 (d, J = 2.9 Hz), 127.89 (d, J = 13.0 Hz), 127.87, 124.14 (d, J = 7.2 Hz), 122.23, 121.48,
8
), Yield: 59%; Mp 118–120 ◦C; IR (cm−1): 3244,
δ
,
δ
116.98 (d, J = 21.7 Hz) (Figure S13); HR-MS: C15H10ONClF [M
274.0435 m/z (Figure S14).
−
H]− calculated 274.0440 m/z, found
(2E)-N-(4-Bromo-3-fluorophenyl)-3-phenylprop-2-enamide (
1673, 1625, 1598, 1514, 1473, 1450, 1414, 1333, 1302, 1240, 1204, 1190, 1155, 1135, 1045, 988, 976, 944, 868,
857, 812, 780, 763, 738, 707, 683, 642, 572, 564, 544, 518, 518; 1H-NMR (DMSO-d6),
: 10.55 (s, 1H), 7.89
(dd, J = 8.9 Hz, J = 4.0 Hz, 1H), 7.67–7.61 (m, 4H), 7.47–7.41 (m, 3 H), 7.36 (dd, J = 8.9 Hz J = 2.1 Hz
1H), 6.79 (d, J = 15.8 Hz, 1H); 13C-NMR (DMSO-d6),
: 163.95, 158.08 (d, J = 242.8 Hz), 141.13, 140.43 (d,
J = 10.1 Hz), 134.48, 133.41, 130.06, 129.08, 127.88, 121.54, 116.59 (d, J = 2.9 Hz), 107.19 (d, J = 26.0 Hz),
9
), Yield: 69%; Mp 146–148 ◦C; IR (cm−1): 3405,
δ
,
,
δ
100.83 (d, J = 21.7 Hz) (Figure S15); HR-MS: C15H10ONBrF [M
317.9929 m/z (Figure S16).
−
H]− calculated 317.9935 m/z, found
(2E)-N-[3-Fluoro-4-(trifluoromethyl)phenyl]-3-phenylprop-2-enamide (10), Yield 69%; Mp 139–141 ◦C; IR
(cm−1): 8336, 1671, 1624, 1560, 1521, 1506, 1452, 1424, 1413, 1338, 1319, 1205, 1192, 1162, 1117, 1049,
996, 970, 666, 827, 767, 763, 738, 710, 633, 603, 565, 536, 507, 484; 1H-NMR (DMSO-d6), δ: 10.78 (s, 1H),
7.95 (dd, J = 13.7 Hz, 1H), 7.75 (t, J = 8.6 Hz, 1H), 7.67 (d, J = 15.8 Hz, 1H), 7.68–7.65 (m, 2H), 7.54 (d,
J = 8.2 Hz, 1H), 7.48–7.42 (m, 3H), 6.82 (d, J = 15.8 Hz, 1H); 13C-NMR (DMSO-d6),
δ: 164.33, 159.20 (dq,
J = 248.5 Hz, J = 2.9 Hz), 144.96 (d, J = 11.6 Hz), 141.74, 134.36, 130.20, 129.08, 127.95, 127.87 (m), 122.82
(q, J = 271.7 Hz), 121.25, 114.75, 110.57 (qd, J = 31.8 Hz, J = 11.6 Hz), 106.65 (d, J = 26.1 Hz) (Figure S17);
HR-MS: C16H10ONF4 [M − H]− calculated 308.0704 m/z, found 308.0695 m/z (Figure S18).
◦
(2E)-N-(4-Bromo-3-chlorophenyl)-3-phenylprop-2-enamide (11), Yield: 61%; Mp 170–172 C; IR (cm−1):
3282, 3097, 2980, 2888, 1663, 1627, 1579, 1521, 1470, 1449, 1376, 1338, 1288, 1253, 1229, 1180, 1113, 1071,
968, 882, 861, 808, 761, 711, 679, 676, 577, 561, 496, 482; 1H-NMR (DMSO-d6),
δ
: 10.51 (s, 1H), 8.11 (d,
J = 2.1 Hz, 1H), 7.72 (d, J = 8.2 Hz, 1H), 7.65–7.61 (m, 3H), 7.50 (dd, J = 8.6 Hz, J = 2.4 Hz, 1H), 7.47–7.41
(m, 3H), 6.78 (d, J = 15.8 Hz, 1H); 13C-NMR (DMSO-d6),
: 163.94, 141.13, 139.92, 134.47, 133.95, 133.09,
δ

Molecules 2020, 25, 4121
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130.07, 129.08, 127.88, 121.53, 120.31, 119.45, 114.48 (Figure S19); HR-MS: C15H10ONBrCl [M
calculated 333.9640 m/z, found 333.9635 m/z (Figure S20).
−
H]−
◦
(2E)-N-(2-Bromo-4-chlorophenyl)-3-phenylprop-2-enamide (12), Yield: 68%; Mp 173–175 C; IR (cm−1):
3258, 2980, 2888, 1653, 1621, 1572, 1524, 1465, 1447, 1380, 1336, 1279, 1263, 1238, 1201, 1180, 1093, 1039,
1006, 999, 969, 858, 824, 775, 757, 714, 700, 653, 632, 568, 551, 512; 1H-NMR (DMSO-d6),
δ
: 9.69 (s, 1H),
7.84–7.82 (m, 2H), 7.65–7.64 (m, 2H), 7.62 (d, J = 15.8 Hz, 1H), 7.49 (dd, J = 8.6 Hz, J = 2.4 Hz, 1H),
7.47–7.41 (m, 3H), 7.07 (d, J = 15.8 Hz, 1H); 13C-NMR (DMSO-d6),
: 163.94, 141.08, 135.50, 134.60,
δ
131.87, 129.96, 129.72, 129.02, 128.05, 127.85, 127.38, 121.49, 117.69 (Figure S21); HR-MS: C15H10ONBrCl
[M − H]− calculated 333.9640 m/z, found 333.9635 m/z (Figure S22).
(2E)-N-[2-Bromo-5-(trifluoromethyl)phenyl]-3-phenylprop-2-enamide (13), Yield 59%; Mp 134–135 ◦C; IR
(cm−1): 3270, 1658, 1631, 1606, 1529, 1467, 1423, 1329, 1262, 1170, 1112, 1078, 1035, 963, 928, 894, 856, 816,
759, 709, 685, 643, 615, 560, 491, 454; 1H-NMR (DMSO-d6),
δ
: 9.84 (s, 1H), 8.26 (d, J = 1.4 Hz, 1H), 7.94 (d,
J = 8.2 Hz, 1H), 7.67–7.65 (m, 2H), 7.65 (d, J = 15.8 Hz, 1H), 7.49–7.42 (m, 4H), 7.14 (d, J = 15.8 Hz, 1H);
13C-NMR (DMSO-d6),
: 164.25, 141.54, 137.26, 134.54, 134.06, 130.08, 129.06, 128.59 (q, J = 31.8 Hz),
δ
127.94, 123.71 (q, J = 273.1 Hz), 122.77 (q, J = 4.3 Hz), 122.07 (q, J = 2.9 Hz), 121.35, 120.81 (Figure S23);
HR-MS: C16H10ONBrF3 [M − H]− calculated 367.9903 m/z, found 367.9893 m/z (Figure S24).
(2E)-N-[4-Fluoro-2-(trifluoromethyl)phenyl]-3-phenylprop-2-enamide (14), Yield 76%; Mp 145–147 ◦C; IR
(cm−1): 3257, 3086, 1653, 1622, 1521, 1492, 1429, 1332, 1316, 1271, 146, 1174, 1127, 1119, 1049, 974, 914,
883, 865, 845, 823, 761, 745, 727, 709, 691, 664, 652, 569, 539, 499, 481; 1H-NMR (DMSO-d6),
δ
: 9.79 (s,
1H), 7.67 (dd, J = 8.9 Hz, J = 2.7 Hz, 1H), 7.65–7.63 (m, 2H), 7.61–7.58 (m, 2H), 7.59 (d, J = 15.8 Hz, 1H),
7.46–7.40 (m, 3 H), 6.97 (d, J = 15.8 Hz, 1H); 13C-NMR (DMSO-d6),
: 164.79, 159.51 (d, J = 245.7 Hz),
δ
140.86, 134.55, 132.54 (d, J = 8.7 Hz), 131.76 (d, J = 2.9 Hz), 129.92, 129.02, 127.81, 126.40 (qd, J = 31.8 Hz,
J = 10.1 Hz), 122.67 (qd, J = 274.6 Hz, J = 2.9 Hz), 121.18, 120.00 (d, J = 21.7 Hz), 113.72 (dq, J = 26.0 Hz,
J = 5.8 Hz) (Figure S25); HR-MS: C16H10ONF4 [M
(Figure S26).
−
H]− calculated 308.0704 m/z, found 308.0696 m/z
(2E)-N-[4-Chloro-2-(trifluoromethyl)phenyl]-3-phenylprop-2-enamide (15), Yield 71%; Mp 165–167 ◦C; IR
(cm−1): 3268, 3083, 3030, 1655, 1624, 1584, 1522, 1483, 1449, 1410, 1337, 1306, 1277, 1268, 1243, 1171, 1122,
1109, 1052, 967, 890, 872, 857, 833, 810, 761, 711, 696, 684, 562, 539, 509; 1H-NMR (DMSO-d6),
δ: 9.80 (s,
1H), 7.84 (d, J = 2.1 Hz, 1H), 7.79 (dd, J = 8.6 Hz, J = 2.4 Hz, 1H), 7.69 (d, J = 8.9 Hz, 1H), 7.65–7.64
(m, 2H), 7.60(d, J = 15.8 Hz, 1H), 7.47–7.41 (m, 3H), 7.00 (d, J = 15.8 Hz, 1H); 13C-NMR (DMSO-d6).
δ
: 164.65, 141.13, 134.52, 134.38, 132.96, 131.51, 130.67, 129.98, 129.03, 127.86, 126.26 (q, J = 5.8 Hz),
125.72 (q, J = 30.3 Hz), 122.7 (q, J = 273.1 Hz), 121.12 (Figure S27); HR-MS: C16H10ONClF3 [M
calculated 324.0409 m/z, found 324.0399 m/z (Figure S28).
−
H]−
(2E)-N-[4-Bromo-2-(trifluoromethyl)phenyl]-3-phenylprop-2-enamide (16), Yield 64%; Mp 176–178 ◦C; IR
(cm−1): 3289, 3083, 3030, 1657, 1624, 1525, 1481, 1404, 1338, 1305, 1278, 1265, 1247, 1172, 1160, 1124,
1
1052, 967, 889, 866, 831, 760, 747, 710, 682, 631, 560, 529, 501, 463; H-NMR (DMSO-d6),
δ: 9.78 (s,
1H), 7.94 (m, 1H), 7.92 (dd, J = 8.2 Hz, J = 2.1 Hz, 1H), 7.65–7.62 (m, 3H), 7.60 (d, J = 15.8 Hz, 1H),
7.46–7.41 (m, 3H), 7.00 (d, J = 15.8 Hz, 1H); 13C-NMR (DMSO-d6),
: 164.59, 141.16, 135.94, 134.81 (q,
J = 2.9 Hz), 134.52, 131.64, 129.99, 129.03, 129.02 (q, J = 5.8 Hz), 127.86, 125.87 (q, J = 30.3 Hz), 122.60 (q,
δ
J = 273.1 Hz), 121.12, 118.62 (Figure S29); HR-MS: C16H10ONBrF3 [M
found 367.9894 m/z (Figure S30).
−
H]− calculated 367.9903 m/z,
(2E)-N-[4-Nitro-2-(trifluoromethyl)phenyl]-3-phenylprop-2-enamide (17), Yield 61%; Mp 164–166 ◦C; IR
(cm−1): 3314, 3086, 1662, 1620, 1594, 1544, 1505, 1449, 1424, 1338, 1319, 1277, 1172, 1112, 1052, 1000,
973, 921, 901, 854, 840, 790, 760, 744, 709, 686, 604, 592, 560; 1H-NMR (DMSO-d6),
δ
: 9.95 (s, 1H), 8.54
(dd, J = 8.9 Hz, J = 2.7 Hz, 1H), 8.47 (d, J = 2.7 Hz, 1H), 8.17 (d, J = 8.9 Hz, 1H), 7.68 (d, J = 15.8 Hz,

Molecules 2020, 25, 4121
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1H), 7.68–7.67 (m, 2H), 7.48–7.43 (m, 3H), 7.17 (d, J = 15.8 Hz, 1H); 13C-NMR (DMSO-d6),
δ: 164.64,
143.84, 142.29, 141.30, 134.41, 130.25, 129.06, 128.84, 128.04, 127.96, 122.50 (q, J = 30.3 Hz), 122.48 (q,
J = 273.10 Hz), 122.27 (q, J = 4.3 Hz), 120.91 (Figure S31); HR-MS: C16H10O3N2F3 [M
335.0649 m/z, found 335.0637 m/z (Figure S32).
−
H]− calculated
(2E)-N-[4-Nitro-3-(trifluoromethyl)phenyl]-3-phenylprop-2-enamide (18), Yield 60%; Mp 181–183 ◦C; IR
(cm−1): 3314, 3086, 1662, 1620, 1594, 1544, 1505, 1424, 1338, 1319, 1277, 1247, 1172, 1112, 1052, 983,
1
921, 901, 854, 840, 790, 760, 750, 709, 686, 645, 604, 593, 560, 505; H-NMR (DMSO-d6),
δ: 11.03 (s,
1H), 8.38 (d, J = 2.1 Hz, 1H), 8.23 (d, J = 8.9 Hz, 1H), 8.13 (dd, J = 8.9 Hz, J = 2.1 Hz, 1H), 7.70 (d,
J = 15.8 Hz, 1H),7.67–7.66 (m, 2H), 7.48–7.43 (m, 3H), 6.81 (d, J = 15.8 Hz, 1H); 13C-NMR (DMSO-d6),
164.54, 143.95, 142.27, 141.30, 134.23, 130.35, 129.10, 128.04, 127.84, 123.02 (q, J = 33.2 Hz), 122.07 (q,
δ
:
J = 273.1 Hz), 120.94, 117.32 (q, J = 5.8 Hz) (Figure S33); HR-MS: C16H10O3N2F3 [M
335.0649 m/z, found 335.0635 m/z (Figure S34).
−
H]− calculated
(2E)-N-[2,6-Dibromo-4-(trifluoromethyl)phenyl]-3-phenylprop-2-enamide (19), Yield 78%; Mp 227–229 ◦C; IR
(cm−1): 3086, 2970, 2855, 1655, 1613, 1520, 1450, 1395, 1340, 1303, 1197, 1167, 1132, 1096, 1072, 1010, 984,
881, 862, 768, 743, 715, 692, 666, 566, 497; 1H-NMR (DMSO-d6),
δ
: 10.36 (s, 1H), 8.19 (s, 2H), 7.67–7.66
(m, 2H), 7.64 (d, J = 15.8 Hz, 1H), 7.47–7.42 (3H), 6.90(d, J = 15.8 Hz, 1H); 13C-NMR (DMSO-d6),
163.42, 141.14, 140.13, 134.38, 130.08, 129.99 (q, J = 33.2 Hz), 129.16 (q, J = 4.3 Hz), 129.07, 127.88, 124.96,
δ
:
122.29 (q, J = 273.1 Hz), 120.45 (Figure S35); HR-MS: C16H9ONBr2F3 [M
found 445.9002 m/z (Figure S36).
−
H]− calculated 445.9008 m/z,
(2E)-N-(2,6-Dibromo-3-chloro-4-fluorophenyl)-3-phenylprop-2-enamide (20), Yield: 74%; Mp 234–235 ◦C;
IR (cm−1):3197, 3001, 1656, 1664, 1611, 1571, 1523, 1442, 1357, 1339, 1296, 1285, 1206, 1179, 1113, 988,
1
979, 854, 784, 763, 753, 723, 696, 684, 640, 581, 562, 530, 486, 442; H-NMR (DMSO-d6),
δ: 10.26 (s,
1H), 8.07 (d, J = 8.9 Hz, 1H), 7.67–7.65 (m, 2H), 7.62 (d, J = 15.8 Hz, 1H), 7.47–7.42 (m, 3H), 6.87
(d, J = 15.8 Hz, 1H); 13C-NMR (DMSO-d6),
: 163.64, 156.15 (d, J = 252.9 Hz), 141.25, 134.40, 134.28
(d, J = 2.9 Hz), 130.04, 129.07, 127.85, 126.30, 122.82 (d, J = 10.1 Hz), 121.30 (d, J = 20.2 Hz), 120.52,
δ
119.79 (d, J = 24.6 Hz) (Figure S37); HR-MS: C15H8ONBr2FCl [M
429.8648 m/z (Figure S38).
−
H]− calculated 429.8651 m/z, found
3.2. Biological Testing
3.2.1. In Vitro Antibacterial Evaluation
The synthesized compounds were evaluated for their in vitro antibacterial activity
against representatives of multidrug-resistant bacteria, clinical isolates of methicillin-resistant
Staphylococcus aureus (MRSA) 63718, and SA 630 that were obtained from the National Institute
of Public Health (Prague, Czech Republic). S. aureus ATCC 29213 was used as the reference and quality
control strain. Ampicillin (Sigma, St. Louis, MO, USA) was employed as the standard. All compounds
and controls were prepared in triplicate. The screening was performed as described previously [26].
The results are summarized in Table 1. Antibacterial activity was assessed by determining MIC and
MBC values. The broth microdilution method was applied. MIC values were determined in a microtiter
plate. Aliquots from this plate were subcultivated on the Petri dish with Mueller-Hinton broth for the
determination of MBC [43].
3.2.2. In Vitro Antimycobacterial Evaluation
The assessment of the in vitro antimycobacterial activity of the compounds was performed
against Mycobacterium tuberculosis H37Ra/ATCC 25177 by means of the methodology described recently
(e.g., [26]). Isoniazid (Sigma) was used as the standard. All compounds and controls were prepared in
triplicate. The results are summarized in Table 1.

Molecules 2020, 25, 4121
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3.2.3. MTT Assay
A compound was diluted in Mueller-Hinton broth to achieve the desired final concentrations of
0.5 and 1 g/mL, respectively. S. aureus ATCC 29213 bacterial suspension in sterile distilled water at
µ
0.5 McFarland was diluted 1:3. Inocula were added to each well by a multi-inoculator. Diluted bacteria
in broth free from inhibiting compounds were used as the growth control. The procedure was
performed three times. Plates were incubated at 37 ◦C for 24 h. After the incubation period, 10% well
volume of MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) reagent (Sigma) was
mixed into each well and incubated at 37 ◦C for 1 h. Then 100
µL of 17% sodium dodecyl sulphate in
40% dimethylformamide was added to each well. The plates were read at 570 nm. The absorbance
readings from the cells grown in the presence of the tested compounds were compared with uninhibited
cell growth to determine relative percent inhibition. The percent inhibition was determined through
the MTT assay. The percent viability is calculated through the comparison of a measured value and
that of the uninhibited control: % viability = OD570E/OD570P
the compound-exposed cells, while OD570P is the reading from the uninhibited cells (positive control).
×
100, where OD570E is the reading from
Cytotoxic potential is determined by a percent viability of <70% [43,59].
3.2.4. In Vitro Cell Viability Assay
The cytotoxic effect of the tested compounds was specified on THP1-Blue
™ NF-κB cell line
(Invivogen; San Diego, CA, USA), as described previously [28]. Briefly, cells resuspended in serum-free
RPMI 1640 medium (Merck, Darmstadt, Germany) supplemented with antibiotics (100 U/mL penicillin
and 100 mg/mL streptomycin (Merck)) and 10% FBS (Merck) were seeded into 96-well plates (100
µL/well,
i.e., 50,000 cells per each well). After 2 h, tested compounds dissolved in DMSO (1.25–20 M) were added
µ
to the cells. The final concentration of DMSO was 0.1% (v/v) in each well. The viability analysis was
performed after 24 h incubation with the tested substances using the WST-1 Cell Proliferation Reagent
kit (Roche Diagnostics, Basel, Switzerland) according to the manufacturer’s manual. The amount
of formazan formed, which corresponded to the number of metabolically active cells in the culture,
was calculated as a percentage of the control cells, which were treated only with serum-free RPMI
1640 medium and were assigned as 100%. The IC50 values were calculated by four-parameter logistic
(4PL) analysis from obtained viability curves by GraphPad Prism 8.0.1 (San Diego, CA, USA) software.
The results are summarized in Table 1 as mean ± SEM (n = 6).
3.2.5. Determination of NF-κB Activity
To evaluate the anti-inflammatory potential of novel compounds, their ability to attenuate
the lipopolysaccharide (LPS)-activated pro-inflammatory transcription nuclear factor (NF)-
measured as was described previously [28]. Briefly, THP1-Blue NF- B cells were pretreated by tested
compounds dissolved in DMSO in the non-toxic concentration 1 M for 1 h. After LPS (1 g/mL)
stimulation, the NF- B activity was evaluated by Quanti-Blue medium (Invivogen) according to the
manufacturer’s instructions. The results from 3 independent experiments performed in triplicate
n = 9) were analyzed by GraphPad Prism 8.0.1 software. The outlaying values were excluded by
κB was
™
κ
µ
µ
κ
(
ROUT algorithms (Q = 5%).
3.3. Lipophilicity Determination by RP-HPLC (Capacity Factor k/Calculated log k)
The HPLC separation system Merck Hitachi LaChrom Elite® equipped with a Merck Hitachi
LaChrom Elite® L-2455 Diode-Array Detector (Hitachi High Technologies America, San Jose, CA, USA)
was used. A chromatographic column Symmetry® C18 5
µm, 4.6
×
250 mm, Part No. W21751W016
(Waters Corp., Milford, MA, USA) was used. The HPLC separation process was monitored by the
EZ CHROM Elite® software (Hitachi High Technologies America). The total flow of the column was
1.0 mL/min, injection 10
wavelength 214 nm was chosen. A KI methanolic solution was used for the dead time (td) determination.
µ
L, column temperature 40 ◦C, and sample temperature 10 ◦C. The detection

Molecules 2020, 25, 4121
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Retention times (tr) were measured in minutes. Isocratic elution by a mixture of Metanol ChromasolvTM
(Honeywell, St. Louis, MO, USA) (72%) and H2O-HPLC Mili-Q grade (Labconco, Kansas City, MO,
USA) (28%) as a mobile phase was used for the determination of capacity factor k, while isocratic
elution by a mixture of Metanol ChromasolvTM (Honeywell) (72%) and NaOAc buffer, pH 6.5 and
7.4 (28%) as a mobile phase were used for the determination of distribution coefficients D6.5 and
D
7.4. The capacity factors and distribution coefficients were calculated according to the formula
k(D) = (tr − td)/td, where tR is the retention time of the solute and td is the dead time obtained using an
unretained analyte. Each experiment was repeated three times. The calculated log k, log D6.5, and log
D
7.4 values of individual compounds are shown in Table 1.
3.4. Lipophilicity Determination by RP-TLC
Rm values were determined from the RP-18 TLC measurements. The solutions of compounds in
Metanol ChromasolvTM (Honeywell) were spotted on a RP-TLC plate (Silica gel Nano-SIL C18-100 UV
254, 10
×
10 cm, Macherey-Nagel, Duren, Germany), 1.5 cm from the edge. The volatiles were carefully
evaporated, and the plate was developed by MeOH (100%) or MeOH: H2O-HPLC (72:28 v/v) or MeOH:
NaOAc buffer, pH 7.4 (72:28 v/v). After drying, the spots were visualized under UV (λ = 365 nm).
Rm data were obtained from equation: Rm = log(1/Rf-1). Each experiment was repeated three times.
The Rm values of individual compounds are shown in Table 1.
3.5. Model Building and Experimental vs. Theoretical Lipophilicity Prediction
CACTVS/csed and CORINA editors were engaged to produce each structural model and its
initial spatial geometry. OpenBabel (inter)change file format converter was employed for data
conversion. Sybyl-X 2.0/Certara software package running on a HP Z200 workstation with a Debian
10.0 operating system was used to conduct the molecular modeling simulations. The initial compound
geometry optimization with MAXMIN2 module was performed using the standard Tripos force field
(POWELL conjugate gradient algorithm) with a 0.01 kcal/mol energy gradient convergence criterion.
The specification of the electrostatic potential values based on the partial atomic charges was carried
out with the Gasteiger–Hückel method implemented in Sybyl-X.
An ensemble of freely/commercially available in silico predictors can be engaged to specify
theoretically the numerical value of partition coefficients (clogP) as follows:
AlogPS—algorithm implemented by Tetko et al. [37] based on atom-type electrotopological-state
(E-state) indices and neural networks (NN);
milogP—procedure proposed by Molinspiration for practical logP calculations of almost all
organic molecules as a sum of fragment-based contributions and correction factors;
ClogP—fragment-based approach for estimation of lipophilicity based on structure-dependent
correction values retrieved from Hansch and Leo’s database that is implemented in Sybyl/
Centara software;
HyperChem logP—an atom-additive method that estimates lipophilicity using the individual
atomic contribution based on procedure proposed by Ghose, Prichett and Crippen;
MarvinSketch logP—the overall lipophilicity of a molecule is composed of the contributing values
of its atom types that were redefined to accommodate electron delocalization and contributions of
ionic forms;
ChemSketch logP—a comprehensive fragment-based algorithm with high quality models built
using empirical data. Well-characterized logP contributions have been compiled for atoms; structural
fragments and intramolecular interactions provided for more than 12
×
103 experimental logP values;
Dragon AlogP—statistical estimators of the Ghose-Crippen-Viswanadhan model were specified
on the basis of known experimental logP for the training set of 8364 compounds. The overall approach
of the lipophilic atomic-based constant is estimated with the contribution of 115 atom types;

Molecules 2020, 25, 4121
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Dragon MlogP—the calculated partition coefficient includes VdW volume and Moriguchi polar
parameters as correction factors. A regression MlogP model is based on 13 structural parameters that
were rated on the training group of 1,230 organic molecules;
Kowwin—estimates the log octanol-water partition coefficient of chemicals using the
atom/fragment contribution protocol;
XlogP3—an atom-additive procedure with well-defined correction factors that engages an
optimized atom typing approach calibrated on a big training set;
OSIRIS clogP—in house approach based on the cumulative sum of atom contributions calculated
for more than 5000 compounds with experimentally measured logP values as a training set.
Predicting engine distinguishes 369 atom types;
ChemDraw clogP—the combination of Classic and GALAS algorithms for the prediction of
partition coefficient based on a training set of >11
×
103 compounds provides coverage for a broad
chemical space;
Percepta clogP—based on >12
×
103 of empirical logP values with the algorithm that uses the
principal of isolating carbons.
In order to detect the redundant descriptors in QSAR/QSPR studies, several methods of variable
selection/elimination have been proposed, including the uninformative variable elimination (UVE–PLS),
as well as its modifications, namely iterative variable elimination (IVE–PLS) [60]. Overall, the entire
algorithm comprises of the following stages:
Stage 1. Standard PLS analysis with LOO–CV to evaluate the performance of the PLS model
Stage 2. Elimination of the matrix column with the lowest abs(mean(b)/std(b)) value
Stage 3. Standard PLS analysis of the new matrix without the column eliminated in Stage 2.
Stage 4. Iterative repetition of the Stages 1–3 to maximize the LOO q2cv.
3.6. Similarity Assessment Using PCA and Tanimoto Coefficient
The proper mapping of the molecular diversity in the theoretically infinite chemical space
(
CS ≈ 1060–10200) into the corresponding property space basically requires the multi-dimensional
description of a molecule by a set of structural (S) and physicochemical (P) properties organized
in a vector [61]. The molecular distribution of the experimental-based (FCS) and virtual-derived
(VCS) compounds/models might be graphically displayed using the human friendly 2D/3D plots,
e.g., in the procedure called Principal Component Analysis (PCA) [62]. PCA is a linear projection
method designed to model multivariate data with a relatively small number of so-called principal
components (scores and loadings) constructed in order to maximize the variance description of input
data [63]. The PCA model with f principal components for a data matrix X can be presented as follows:
X = TPT + E
(1)
where X is a data matrix with m objects and n variables, T is the score matrix with dimensions
(
m × f), PT is a transposed matrix of loadings with dimensions (f
×
n) and E is a matrix of the residual
variance (m × n) that is not explained by the first f principal components. Basically, the first few
principal components often capture interesting information about the data structure and uncover
groups of objects.
The structural relatedness between molecule library is usually estimated using a function mapping
(dis)similarities between the pairs of bit-string descriptors given by the Tanimoto equation as follows:
nxy
T(x, y) =
(2)
(nx + ny − nxy)
where nxy is the number of bits set into 1 shared in the fingerprint of molecules x and y, nx is the
number of bits set into 1 in molecule x, y is the number of bits set into 1 in molecule y. Top-ranked
n
objects are supposed to have similar properties, although the validity of this assumption is fairly

Molecules 2020, 25, 4121
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questionable, because 70% of compounds with T(x, y) > 0.85 to an active analog have comparatively
low probability of being active in the same way [64].
4. Conclusions
A set of 20 cinnamic acid anilides (1 published recently and 19 newly synthesized) was investigated
in relation to their biological activities and characterized by a series of experimental lipophilicity
values generated using RP-HPLC and RP-TLC methods. All compounds were tested against the
reference strain Staphylococcus aureus, two MRSA clinical isolates, and Mycobacterium tuberculosis.
(2E)-N-[3-Fluoro-4-(trifluoromethyl)phenyl]-3-phenylprop-2-enamide (10) showed comparable or
even better activity than the ampicillin. It was found that the activity of compound 10 is
bactericidal. The screening of the cell viability performed using THP1-Blue
™ NF-κB cells
demonstrated that only (2E)-N-(4-bromo-3-chlorophenyl)-3-phenylprop-2-enamide (11) showed
significant cytotoxic effect. Moreover, all compounds were tested for their anti-inflammatory
potential; (2E)-N-(2,6-dibromo-3-chloro-4-fluorophenyl)-3-phenylprop-2-enamide (20) attenuated
the lipopolysaccharide-induced NF-κB activation and was more potent than the parental cinnamic
acid. Unfortunately, the design of compound that combines both high antimicrobial activity and
anti-inflammatory effect is a great challenge; compounds with preferential anilide ring substitution at
the meta positions and optionally in combination with para show the highest antimicrobial activity,
while the ortho position optionally in combination with meta are anti-inflammatory preferential.
The similarity-related property space evaluation for the congeneric series of structural analogues was
carried out using the principal component analysis (PCA). Moreover, the in silico approximation of the
lipophilic values for the ensemble of anilides 1–20 was performed employing a set of free/commercial
clogP estimators, subsequently corrected using the corresponding pKa calculated at physiological
pH and subsequently cross-compared with the experimental parameter. The mean and median
value of the selected estimators that averaged over the chosen logD7.4 values were subsequently
correlated with the experimental parameter with correlation coefficient of 0.65, because not only
the best inter-correlated clogD7.4 values were specified in the consensus clogP approach. Finally,
the similarity-driven investigation using Tanimoto metrics was conducted revealing the structural
dissimilarities of nitro-substituted isomers (compounds 17 (R = 2-CF3-4-NO2) and 18 (R = 3-CF3-4-NO2))
as compared with the remaining ones, which confirms our previous PCA findings. Interestingly,
mono-bromo/chloro-substituted isomers (compounds 11 (R = 3-Cl-4-Br) and 12 (R = 2-Br-4-Cl))
indicate the structural similarity to di-/tri-bromo/chloro-substituted positional isomers (molecules
4
–9). Unfortunately, the similarity investigation did not provide valuable hints that could explain the
noticeable variations in the toxic effect exerted by molecule 11 (IC50 = 6.5 M on THP1-Blue NF-
µ
™
κB
cells) and the remaining ones. In general, the distribution coefficient provides a more realistic estimation
of lipophilicity in the relevant pH environments; therefore, logDpH ought to be preferentially engaged in
the QSPR study, especially for compounds that are likely to ionize in physiological media. Based on this
study, the antiproliferative/antineoplastic activity of compound 11 will be investigated, while the rest of
the tested compounds will be studied in relation to their antimicrobial and anti-inflammatory activities.
Supplementary Materials: The following are available online, Table S1: Theoretically estimated partition
coefficient calculated by set of alternative methods for anilides 1– . Table S2: Theoretically estimated
20
Figure S1: 13C-NMR (DMSO-d6) spectrum of
). Figure S2: HR-MS record of (2E)-3-phenyl-
). Figure S3: 13C-NMR (DMSO-d6) spectrum of (2E)-3-phenyl-
). Figure S4: HR-MS record of (2E)-3-phenyl-N-(3,4,5-trifluorophenyl)-
). Figure S5: 13C-NMR (DMSO-d6) spectrum of (2E)-N-(2,4-dichlorophenyl)-3-phenylprop-
). Figure S6: HR-MS record of (2E)-N-(2,4-dichlorophenyl)-3-phenylprop-2-enamide ( ). Figure S7:
13C-NMR (DMSO-d6) spectrum of (2E)-3-phenyl-N-(2,4,5-trichlorophenyl)prop-2-enamide (
). Figure S8: HR-MS
record of (2E)-3-phenyl-N-(2,4,5-trichlorophenyl)prop-2-enamide (
). Figure S9: 13C-NMR (DMSO-d6) spectrum
of (2E)-3-phenyl-N-(3,4,5-trichlorophenyl)prop-2-enamide ( ). Figure S10: HR-MS record of (2E)-3-phenyl-
N-(3,4,5-trichlorophenyl)prop-2-enamide (
). Figure S11: 13C-NMR (DMSO-d6) spectrum of (2E)-N-(2,4-dibromo-
phenyl)-3-phenylprop-2-enamide ( ). Figure S12: HR-MS record of (2E)-N-(2,4-dibromophenyl)-3-phenylprop-
pKa calculated by ACD/Percepta/pKa for anilides
(2E)-3-phenyl-N-(2,4,6-trifluorophenyl)prop-2-enamide (
N-(2,4,6-trifluorophenyl)prop-2-enamide (
N-(3,4,5-trifluorophenyl)prop-2-enamide (
prop-2-enamide (
2-enamide (
1–20.
2
2
3
3
4
4
5
5
6
6
7

Molecules 2020, 25, 4121
20 of 23
2-enamide (
2-enamide (
Figure S15: 13C-NMR (DMSO-d6) spectrum of (2E)-N-(4-bromo-3-fluorophenyl)-3-phenylprop-2-enamide (
7
). Figure S13: 13C-NMR (DMSO-d6) spectrum of (2E)-N-(5-chloro-2-fluorophenyl)-3-phenylprop-
8
). Figure S14: HR-MS record of (2E)-N-(5-chloro-2-fluorophenyl)-3-phenylprop-2-enamide (
8
).
).
9
Figure S16: HR-MS record of (2E)-N-(4-bromo-3-fluorophenyl)-3-phenylprop-2-enamide (
9
). Figure S17: 13C-NMR
(DMSO-d6) spectrum of (2E)-N-[3-fluoro-4-(trifluoromethyl)phenyl]-3-phenylprop-2-enamide (10). Figure S18:
HR-MS record of (2E)-N-[3-fluoro-4-(trifluoromethyl)phenyl]-3-phenylprop-2-enamide (10). Figure S19: 13C-NMR
(DMSO-d6) spectrum of (2E)-N-(4-bromo-3-chlorophenyl)-3-phenylprop-2-enamide (11). Figure S20: HR-MS
record of (2E)-N-(4-bromo-3-chlorophenyl)-3-phenylprop-2-enamide (11). Figure S21: 13C-NMR (DMSO-d6)
spectrum of (2E)-N-(2-bromo-4-chlorophenyl)-3-phenylprop-2-enamide (12). Figure S22: HR-MS record of
(2E)-N-(2-bromo-4-chlorophenyl)-3-phenylprop-2-enamide (12). Figure S23: 13C-NMR (DMSO-d6) spectrum
of (2E)-N-[2-bromo-5-(trifluoromethyl)phenyl]-3-phenylprop-2-enamide (13). Figure S24: HR-MS record of
(2E)-N-[2-bromo-5-(trifluoromethyl)phenyl]-3-phenylprop-2-enamide (13). Figure S25: 13C-NMR (DMSO-d6)
spectrum of (2E)-N-[4-fluoro-2-(trifluoromethyl)phenyl]-3-phenylprop-2-enamide (14). Figure S26: HR-MS record
of (2E)-N-[4-fluoro-2-(trifluoromethyl)phenyl]-3-phenylprop-2-enamide (14). Figure S27: 13C-NMR (DMSO-d6)
spectrum of (2E)-N-[4-chloro-2-(trifluoromethyl)phenyl]-3-phenylprop-2-enamide (15). Figure S28: HR-MS record
of (2E)-N-[4-chloro-2-(trifluoromethyl)phenyl]-3-phenylprop-2-enamide (15). Figure S29: 13C-NMR (DMSO-d6)
spectrum of (2E)-N-[4-bromo-2-(trifluoromethyl)phenyl]-3-phenylprop-2-enamide (16). Figure S30: HR-MS record
of (2E)-N-[4-bromo-2-(trifluoromethyl)phenyl]-3-phenylprop-2-enamide (16). Figure S31: 13C-NMR (DMSO-d6)
spectrum of (2E)-N-[4-nitro-2-(trifluoromethyl)phenyl]-3-phenylprop-2-enamide (17). Figure S32: HR-MS record
of (2E)-N-[4-nitro-2-(trifluoromethyl)phenyl]-3-phenylprop-2-enamide (17). Figure S33: 13C-NMR (DMSO-d6)
spectrum of (2E)-N-[4-nitro-3-(trifluoromethyl)phenyl]-3-phenylprop-2-enamide (18). Figure S34: HR-MS record
of (2E)-N-[4-nitro-3-(trifluoromethyl)phenyl]-3-phenylprop-2-enamide (18). Figure S35: 13C-NMR (DMSO-d6)
spectrum of (2E)-N-[2,6-dibromo-4-(trifluoromethyl)phenyl]-3-phenylprop-2-enamide (19). Figure S36: HR-MS
record of (2E)-N-[2,6-dibromo-4-(trifluoromethyl)phenyl]-3-phenylprop-2-enamide (19). Figure S37: 13C-NMR
(DMSO-d6) spectrum of (2E)-N-(2,6-dibromo-3-chloro-4-fluorophenyl)-3-phenylprop-2-enamide (20). Figure S38:
HR-MS record of (2E)-N-(2,6-dibromo-3-chloro-4-fluorophenyl)-3-phenylprop-2-enamide (20).
Author Contributions: J.K., T.S. and F.D. synthesized the compounds. M.O., T.J., M.H. analyzed the compounds.
H.M. and J.H. performed biological screening. J.J. designed the compounds. A.B., V.K., A.S. (Adam Smolinski)
and A.S. (Aleksandra Swietlicka) performed theoretical lipophilicity calculations, PCA, IVE-PLS. A.B. and J.J.
wrote the paper. All authors have read and agreed to the published version of the manuscript.
Funding: This study was supported by the Slovak Research and Development Agency (APVV-17-0373) and by
Palacky University Olomouc (IGA_PrF_2020_023). Knowledge gained within project APVV-0516-12 has been
used in the results. M.O. was supported by SustES (CZ.02.1.01/0.0/0.0/16_019/0000797).
Acknowledgments: We would like to acknowledge the OpenEye and OpenBabel Scientific Software for providing
free academic licenses.
Conflicts of Interest: The authors declare no conflict of interest.
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Sample Availability: Samples of the compounds are available from the authors.
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