Consensus-based pharmacophore mapping for new set of n-(Disubstituted-phenyl)-3-hydroxyl-naphthalene-2-carboxamides

International Journal of

Molecular Sciences

Article

Consensus-Based Pharmacophore Mapping for

New Set of N-(disubstituted-phenyl)-3-

hydroxyl-naphthalene-2-carboxamides

Andrzej Bak ^1,

*

, Jiri Kos ^2,*, Hana Michnova ², Tomas Gonec ³, Sarka Pospisilova ²,

Violetta Kozik ¹, Alois Cizek ⁴, Adam Smolinski ⁵and Josef Jampilek ²

1

Department of Chemistry, University of Silesia, Szkolna 9, 40007 Katowice, Poland; violetta.kozik@us.edu.pl

Division of Biologically Active Complexes and Molecular Magnets, Regional Centre of Advanced

2

Technologies and Materials, Faculty of Science, Palacky University, Slechtitelu 27,

78371 Olomouc, Czech Republic; michnova.hana@gmail.com (H.M.);

sharka.pospisilova@gmail.com (S.P.); josef.jampilek@gmail.com (J.J.)

3

Department of Chemical Drugs, Faculty of Pharmacy, Masaryk University, Palackeho 1,

60200 Brno, Czech Republic; t.gonec@seznam.cz

Department of Infectious Diseases and Microbiology, Faculty of Veterinary Medicine, University of

4

Veterinary and Pharmaceutical Sciences, Palackeho 1946/1, 61242 Brno, Czech Republic; cizeka@vfu.cz

Central Mining Institute, Pl. Gwarków 1, 40-166 Katowice, Poland; smolin@gig.katowice.pl

5

*

Correspondence: andrzej.bak@us.edu.pl (A.B.); jiri.kos@upol.cz (J.K.)

ꢀꢁꢂꢀꢃꢄꢅꢆꢇ

ꢀꢁꢂꢃꢄꢅꢆ

Received: 3 August 2020; Accepted: 7 September 2020; Published: 9 September 2020

Abstract: A series of twenty-two novel N-(disubstituted-phenyl)-3-hydroxynaphthalene-

2-carboxamide derivatives was synthesized and characterized as potential antimicrobial

agents. N-[3,5-bis(triﬂuoromethyl)phenyl]- and N-[2-chloro-5-(triﬂuoromethyl)phenyl]-3-hydroxy-

naphthalene-2-carboxamide showed submicromolar (MICs 0.16–0.68

µM) activity against

methicillin-resistant Staphylococcus aureus isolates. N-[3,5-bis(triﬂuoromethyl)phenyl]- and

N-[4-bromo-3-(triﬂuoromethyl)phenyl]-3-hydroxynaphthalene-2-carboxamide revealed activity

against M. tuberculosis (both MICs 10 µM) comparable with that of rifampicin. Synergistic activity

was observed for the combinations of ciproﬂoxacin with N-[4-bromo-3-(triﬂuoromethyl)phenyl]- and

N-(4-bromo-3-ﬂuorophenyl)-3-hydroxynaphthalene-2-carboxamides against MRSA SA 630 isolate.

The similarity-related property space assessment for the congeneric series of structurally related

carboxamide derivatives was performed using the principal component analysis. Interestingly,

diﬀerent distribution of mono-halogenated carboxamide derivatives with the –CF₃substituent is

accompanied by the increased activity proﬁle. A symmetric matrix of Tanimoto coeﬃcients indicated

the structural dissimilarities of dichloro- and dimetoxy-substituted isomers from the remaining ones.

Moreover, the quantitative sampling of similarity-related activity landscape provided a subtle picture

of favorable and disallowed structural modiﬁcations that are valid for determining activity cliﬀs.

Finally, the advanced method of neural network quantitative SAR was engaged to illustrate the key

3D steric/electronic/lipophilic features of the ligand-site composition by the systematic probing of the

functional group.

Keywords:

hydroxynaphthalenecarboxamides; lipophilicity; antistaphylococcal activity;

antitubercular activity; MIC; MTT assay; CoMSA; IVE-PLS; similarity-activity landscape index

1. Introduction

The comprehensive speciﬁcation of the target-ligand interaction content in the rational drug

design is a computationally intense issue that requires at least four German G’s: Geschick (skill),

Int. J. Mol. Sci. 2020, 21, 6583; doi:10.3390 /ijms21186583

ww w .mdpi.com /journal /ijms

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Geduld (patience), Geld (money), and Glück (luck) [1]. Hence, the decision-making process of hit

identiﬁcation

→

lead generation

→

lead optimization candidate nomination in the synthetic eﬀorts

→

during drug discovery ought to be supported by a variety of advanced in silico approaches. On the

whole, the computer-assisted manipulation of host-quest structures can be dichotomized into direct

target-dependent (structure-based) as well as indirect drug-related (ligand-based) methodologies [2].

Unfortunately, there is no a priori guideline based on which a procedure performs best in the

search for promising drug molecule for a given disease; therefore, the integrated (collaborative)

approach is advisable to advocate chemical intuition [

target molecule is accessible at the atomic level the pharmacophore-guided concept can be employed

for mapping of the binding/active site [ ]. The inspiration for pharmacophoric pattern study stems

3]. Whenever no structure or model of the

4

loosely from the intermolecular recognition concept assuming that interchangeable groups that are

similar in size, shape, or electronic distribution are likely to induce similar eﬀects on binding aﬃnities

(neighbor behavior) [

and complementarity inherently favors the similarity principle, especially in structure-activity (SAR)

modeling, where congeneric series of molecules should exhibit similar pharmacological proﬁle [ ].

In this context, validation is one of those words . . . that is constantly used and seldom deﬁned as was

stated by Feinstein [ ]. Obviously, a comprehensive mapping of the topology and/or topography of

5]. Conceptually, the straightforward tenet of substituent interchangeability

6

7

a compound into the property-based chemical space (CS) is not feasible in principal as was noticed

by an anonymous reviewer, since it is estimated that up to 10¹⁰⁰structures can be synthesized with

potentially 10⁶⁰small molecules of less than 30 non-hydrogen atoms and up to 10⁴⁰pharmacologically

relevant compounds [

8

]. Hence, only a limited exploration of the factual chemical space (FCS) is

10]. On the other hand, the clever

achievable (10⁸), mainly employing structurally-related structures [

9

,

management of pharmacophore-related information can provide hints that might be incorporated at

the synthesis stage.

Basically, the pseudoreceptor mapping of pharmacophoric features can be classiﬁed

into similarity-driven and hypothesis-related approaches, respectively [11]. Roughly speaking,

the hypothesis-based procedures employ frequently machine learning techniques conjugated with

variable selection/weighting algorithms to prioritize robust linear or non-linear SAR models [12].

The quantitative comparison of ligand surfaces using electrostatic potential maps (CoMSA) and

ﬁeld-based descriptors (CoMFA) can provide a more realistic picture of mutual drug-receptor

recognition phenomena; however, it is only a crude approximation of the underlying biological

reality [13,14]. On the other hand, the similarity principle is still questionable since similarity depends

strongly on both descriptor and/or similarity score used. Despite some drawbacks, distance-related

similarity searching with a numerical measure of the intermolecular resemblance between two objects,

each described by a number of attributes, contributes favorably in SAR practice [15]. Obviously, certain

molecular signatures such as lipophilicity can correlate with bioavailability, therefore in silico ﬁlters are

meaningful in restricting the values of structural or physicochemical descriptors to ADMET-friendly

property space [16]. Estimation of the sustainable equilibrium between ADMET-related properties and

desirable drug aﬃnity to the receptor can be illustrated graphically by enhancement of the planar (2D)

similarity-driven projection with activity data to form a biological response surface [17]. Intuitively,

SAR landscapes are not homogenous in nature, but rather heterogeneously distributed regions

resembling more rugged topography of Poland’s Tatra Mountains than the gently rolling Flint Hills of

Kansas [18]. Chemically, smooth and ﬂat regions of SAR landscape indicate areas where subtle structural

changes are accompanied by tiny variations in biological response according to the similarity principle.

Conversely, sharp and non-uniform areas called activity cliﬀs highlight structurally-related compounds

that are characterized by exorbitant changes in potency to target (magic methyl phenomenon) [19].

From the drug hunter’s perspective, successful exploration of jagged landscape parts provides

a guideline to a non-trivial question about the structural modiﬁcations that boost or demolish the

biological activity. In fact, a range of similarity metrics and ﬁngerprint representation is employed

Int. J. Mol. Sci. 2020, 21, 6583

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to numerically quantify the SAR smoothness, for instance, the structure-activity landscape index

(SALI) [20].

Hydroxynaphthalenecarboxanilide scaﬀolds make it possible to design a wide variety

of compounds, especially with anti-infective [21

3-Hydroxynaphthalene-2-carboxanilides (which are direct radical analogs of salicylanilides) were

investigated as well [29 30]. The activity of these structurally simple compounds is dependent

on the mutual position of the carboxamide and the phenolic moieties [21 28 31 32]. In other

–26] and antineoplastic activity [27,28].

,

–

, ,

words, various substitutions of the crucial phenolic motif determine a spectrum of diﬀerent

biological activities. Roughly speaking, the carboxamide fragment that mimics a peptide bond

seems to be pivotal for the binding aﬃnity, since a compound is bound to its targets using this

molecular anchor. One can assume that the anilide part of the molecule has a direct impact

also on the physicochemical properties and binding strength of the tested compounds to the

potential target. As a matter of fact, hydroxynaphthalenecarboxanilide analogs can be classiﬁed

as multi-target compounds, since they are able to aﬀect various target structures, especially in microbial

pathogens (similarly to salicylanilides) [33]. These types of compounds are able to inhibit glucose

uptake, oxidative phosphorylation, two-component regulatory systems of bacteria, interleukin-12p40

production, various bacterial enzymes, respectively. Moreover, they can bind to protein kinase

epidermal growth factor receptor, destruct the cellular proton gradient as proton shuttles, etc. [34].

Thus, based on the experience with various substituents on the aniline core a series of novel 22

N-(disubstituted-phenyl)-3-hydroxynaphthalene-2-carboxamides, was synthesized and in vitro tested

against several microbial strains [26,35–37]. The similarity related property space assessment for the

set of carboxamide derivatives was performed using the principal component analysis (PCA) [38 ,39].

Moreover, the quantitative sampling of similarity related activity landscape provided a subtle picture of

favorable and disallowed structural modiﬁcations that are valid for determining the activity cliﬀs [40].

Finally, the complex approach of the machine learning using neural network was employed in

quantitative SAR to specify the potentially valid 3-dimensional steric/electronic/ lipophilic features of

the ligand-receptor composition by the systematic sampling of the functional group resulting in the

production of an averaged selection-driven pharmacophore pattern [26].

2. Results and Discussion

2.1. Synthesis and Physicochemical Properties

All studied N-(disubstituted-phenyl)-3-hydroxynaphthalene-2-carboxamides were prepared

according to Scheme 1 as described previously by Kos et al. [31]. The condensation of

3-hydroxynaphthalene-2-carboxylic acid with appropriate disubstituted anilines using phosphorus

trichloride in dry chlorobenzene under microwave conditions gave a series of target

3-hydroxy-N-arylnaphthalene-2-carboxanilides 1–22 (see Table 1).

Scheme 1. Synthesis of N-(disubstituted-phenyl)-3-hydroxynaphthalene-2-carboxamides

1–22.

Reagents and conditions: (a) PCl₃, chlorobenzene, microwave reactor, 45 min [31].

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Table 1.

Compositions of N-disubstituted 3-hydroxynaphthalene-2-carboxanilides, in vitro

antistaphylococcal activities MIC [µM] compared to ampicillin (AMP) and ciproﬂoxacin (CPX), in vitro

antitubercular activity MIC [µM] compared to isoniazid (INH) and rifampicin (RIF).

MIC [µM]

No.

R

MRSA

63718

MRSA

SA 630

MRSA

SA 3202

SA

MT

1.

2.

3.

4.

5.

6.

7.

8.

9.

10.

11.

12.

13.

14.

15.

16.

17.

18.

19.

20.

21.

22.

AMP

CPX

INH

RIF

2,5-OCH₃

3,5-OCH₃

2,5-CH₃

2,6-CH₃

3,5-CH₃

2,5-F

2,6-F

3,5-F

2,5-Cl

2,6-Cl

198

879

27.4

879

856

26.7

856

770

193

770

0.158

741

11.4

2.43

5.54

11.4

0.342

5.72

624

5.72

0.751

–

792

879

54.9

879

856

770

385

770

0.626

371

11.4

4.78

5.54

11.4

0.684

5.72

624

45.8

48.3

–

792

879

27.4

879

856

53.4

214

770

385

770

0.158

741

11.4

4.78

5.54

11.4

0.342

5.72

624

45.8

48.3

–

792

879

27.4

879

856

107

856

770

385

770

0.626

371

11.4

4.78

5.54

11.4

0.342

5.72

624

45.8

48.3

–

792

256

54.9

110

879

107

53.4

770

385

770

12.0

10.0

741

22.9

9.75

11.1

11.4

10.9

11.4

624

–

3,4-Cl

3,5-Cl

3,5-CF₃

3-CF₃-4-CH₃

3-CF₃-4-F

3-CF₃-4-Br

3-F-4-Br

5-CF₃-2-F

5-CF₃-3-F

5-CF₃-2-Cl

4-CF₃-3-F

4-CF₃-2-Br

–

36.5

9.72

–

SA = Staphylococcus aureus ATCC 29213; MRSA = clinical isolates of methicillin-resistant S. aureus 63718, SA 630,

and SA 3202 (National Institute of Public Health, Prague, Czech Republic); MT = Mycobacterium tuberculosis H37Ra

ATCC 25177.

2.2. In Vitro Antimicrobial Activity

The biological screening of all the compounds was performed against the reference and quality

control strain Staphylococcus aureus ATCC 29213, three clinical isolates of methicillin-resistant S. aureus

(MRSA) [41], and against Mycobacterium tuberculosis H37Ra ATCC 25177. The eﬃciency of the

compounds was expressed as the minimum inhibitory concentrations (MICs), that are deﬁned for

bacteria as 90% (IC₉₀) reduction of growth in comparison with the control (see Table 1 ). The MIC

(IC₉₀) value is routinely and widely used in bacterial assays as a standard detection limit according

to the Clinical and Laboratory Standards Institute (CLSI) [42,43]. Based on the range of observed

MIC values, it can be summarized that compounds expressed a wide range of potencies. Some of

them, especially substituted by a triﬂuoromethyl moiety were much more active than standards,

clinically used drugs. N-[3,5-bis(Triﬂuoromethyl)phenyl]- (13) and N-[2-chloro-5-(triﬂuoromethyl)-

phenyl]-3-hydroxynaphthalene-2-carboxamide (20) showed submicromolar (MICs 0.16–0.68

µM)

antistaphylococcal activity; and compounds 16 19 and 21 demonstrated antistaphylococcal activity

–

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ranging from 2.4 to 11.5

µM. As MICs against MRSA isolates were, in fact, comparable with the MIC

values observed against methicillin-susceptible S. aureus ATCC 29213, it could be assumed that the

presence of mecA gene did not aﬀect the activity of these compounds [44].

The same compounds, i.e., 13, N-[4-bromo-3-(triﬂuoromethyl)phenyl]-3-hydroxy-naphthalene-

2-carboxamide (16), 12, and 17–21 showed activity against M. tuberculosis ranging from 9.75 to 12.0 µM

and comparable with that of rifampicin. Therefore, a standard MTT (3-(4,5-dimethylthiazol-2-yl)-2,

5-diphenyltetrazolium bromide) assay was performed with the chosen most eﬀective compounds

against M. tuberculosis H37Ra. The MTT test can be used to assess cell growth by measuring respiration.

The MTT measured viability of mycobacterial cells less than 70% after exposure to the MIC values

for each tested compound is considered as a positive result of this assay. This low level of cell

viability indicates inhibition of cell growth by inhibition of respiration [45,46]. It can be concluded that

compounds 12 (R = 3,5-Cl, 27%), 13 (R = 3,5-CF₃, 6%), 19 (R = 5-CF₃-3-F, 41%), 20 (R = 5-CF₃-2-Cl, 5%),

and 21 (R = 4-CF₃-3-F, 46%) showed the viability of M. tuberculosis that was signiﬁcantly less than 70%

at the tested concentration equal to MICs [47].

Based on the above ﬁndings, it can be assumed that one mechanisms of actions of these so-called

multi-target compounds appears to be the inhibition of bacterial respiration by the inhibition of ATP

synthase (F₀F₁complex) or cytochrome bc1 [21

can destroy the cellular proton gradient as proton shuttles that is evidenced by their ability to inhibit

photosynthetic electron transport (PET) in chloroplasts [29 31 49 51]. In fact, a good correlation was

found between PET inhibition and the antibacterial/antimycobacterial activities [52 53]). It seems that

–26,48]. In addition, the structurally-related molecules

–

, –

,

other possible sites of action in bacterial cells are possible; therefore, further studies at the molecular

level are needed.

2.3. Molecular Similarity Assessment

The similarity-guided evaluation of the activity proﬁle for the congeneric series of

structurally-related N-(disubstituted-phenyl)-3-hydroxynaphthalene-2-carboxamide derivatives was

performed using the principal component analysis (PCA) on the pool of 2762 descriptors generated by

Dragon 6.0 software, where constant or nearly constant descriptors (standard deviation < 0.0001) were

a priori excluded (overall 2123 variables) [54]. The multi-dimensional data were arranged in X_22×2762

matrix with columns and rows representing variables and molecules, respectively. The centered and

standardized data were subsequently subjected to PCA, where compression eﬃciency is directly related

to the quantity of uncorrelated variables. The high percentage of total variance described by the ﬁrst

three principal components (72.69%) suggests that variables are highly inter-correlated. The ﬁrst two

PCs characterized 65.36% of the overall variance; therefore, the projection of the selected properties

on the plane deﬁned by the ﬁrst two PCs with respect to carboxamide chemotype was conducted as

illustrated in Figure 1.

Despite the fact, that carboxamide derivatives

hydroxynaphthalene ring, peptide-like linker, and phenyl substituent, mainly two heterogeneous

structural subfamilies can be formed along the PC1: the ﬁrst one (PC1 < 0) with compounds 12 17

1–22 have a common chemotype based on

3– ,

and the second group (PC1 > 0) containing the remaining ones (see Figure 1 a). It is noticeable that

very active compound 13 occupies a distinct location of the PC1 vs. PC2 plane, while fairly potent

compounds group together (20 < PC1 < 40), as shown in Figure 1b. Interestingly, diﬀerent distribution

of mono-halogenated carboxamide derivatives with the –CF₃substituent (molecules 15, 16, 18–22) is

accompanied by the increased activity proﬁle (see Figure 1b).

In fact, the majority of the investigated carboxamides do not strictly obey the Lipinski’s Rule

of Five (Ro5), where the set of threshold values was imposed on the speciﬁc molecular descriptors

(MW

≤

500, HBD

≤

5, HBA

≤

10, clogP

≤

5) to specify drug-like property space (see Figure 2a);

57]. On the

however, a good drug-like score does not make a molecule a drug and vice versa [55

–

other hand, the violations of any two of the Ro5 conditions reduces the probability of a compound

to be orally bioavailable as suggested anonymous Reviewer. Clearly, ADMET-friendly properties

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such as lipophilicity are meaningful in the context of speciﬁc ligand-receptor interactions; therefore,

the lipophilic proﬁle for the analyzed set of compounds is displayed in Figure 2b.

Figure 1. Projection of carboxamides

for Dragon descriptors ( ) with MRSA activity in logarithmic scale (

pMRSA activity. Molecules with pMRSA > 4 are numerically labeled.

1

–

22 on plane deﬁned by ﬁrst vs. second principal components

a

b) colors code numerical values of

Figure 2. Projection of carboxamides

for Dragon descriptors with Ro5 rule violations (

1

–

22 on the plane deﬁned by ﬁrst vs. second principal components

) and calculated lipophilicity clogP ( ) colors code

a

b

numerical values of clogP and Ro5 violations. Molecules with Ro5 violation and clogP > 5 are

numerically labeled.

Unarguably, the core of many SAR-based methods is the similarity principle in the structural

space assuming that similar compounds are expected to display similar physicochemical and biological

properties. Conceptually, medicinal chemists have adopted a simpliﬁed view that similarity between

two objects can be quantitatively expressed by the function of common features [58]. Despite obvious

oversimpliﬁcation of the similarity quantiﬁcation (e.g., some similarity scores exhibit size-dependent

behavior), it is still a powerful concept, where the number of molecular characteristics might be denoted

by a bit-string representation (sometimes augmented with the scaling coeﬃcients). The pair-wise

descriptor-based structural resemblance/relatedness can be numerically evaluated by a variety of

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relative distance metrics (Hamming or Euclidean measures) or absolute comparison using Tanimoto

coeﬃcient calculated for OpenBabel ﬁngerprints. The distribution of Tanimoto coeﬃcients for the

analyzed set of compounds was investigated revealing that the greatest frequency was recorded

in the range of 0.65 < T < 0.70, respectively. A triangular matrix of T_22×22in Figure 3 indicates

the structural dissimilarities of dichloro-substituted (compounds

(compounds ) isomers from the remaining ones that conﬁrm our previous PCA ﬁndings (see

Figure 1a). On the other hand, in the Tanimoto matrix compound 13 is high similar to compounds

14 22, but PCA showed a diﬀerent result as indicated by an anonymous reviewer. It should be noted

9–12) and dimetoxy-substituted

1, 2

–

that the PCA-based similarity analysis is performed in the reduced PC1 vs. PC2 space, that describes

65% of the overall variance.

Figure 3. Triangular matrix of Tanimoto coeﬃcients for 1–22 carboxamides.

The 2D image of molecular similarity augmented with biological aﬃnity proﬁle provides a coherent

visualization tool for the systematic illumination of SAR trends (continuity areas and/or activity cliﬀs) in

the form of the structure-activity landscape indexes (SALI) [13–16]. The greater density of compounds

that cover the factual chemical space (FCS) the more accurate speciﬁcation of activity cliﬀs; however even

relatively sparse sampling of FCS can be suﬃcient to construct a reliable activity landscape. As a matter

of fact, the detection of sharply non-uniform regions that form steep cliﬀs in the activity landscape

depends critically on the availability of structurally-related compounds with discernible variations in

the biological response data. It is obvious that for closely related molecules (e.g., stereoisomers where

T

→

1) SALI inﬁnity; therefore, such values were substituted by the largest SALI value. Figure 4 a

→

presents the symmetrical grayscaled heatmap of SALI for the investigated set of carboxamides,

where axes correspond to a molecule number sorted according to increasing aﬃnity ATCC 29213

(

∆pATCC = 3.75) with a legend indicating the range of SALI values. In fact, two types of spots can be

noticed on the heatmap: the white representing the highest numerical SALI values and the black that

indicate minimal ones, respectively.

Located at the right lower part of the SALI plane (or symmetrically positioned in the upper left

part) are pairs of molecules that potentially form the activity cliﬀ that is formally manifested via

demolition of activity for similar structures (lighter spots in Figure 4a). Interestingly, the substitution

of hydrogen atoms in methyl groups of molecule

observed for molecule 13. Moreover, the potent molecule 13 is accompanied by inactive molecule 14

where one triﬂuoromethyl group was isomerically (meta orto) replaced with one methyl substituent.

5 with ﬂuorine resulted in the boost of potency

,

→

The mentioned structural modiﬁcations, that unfavorably aﬀect the aﬃnity proﬁle, can be tracked

down on the neighborhood plot (see Figure 4 b), where the structurally-related pairs of molecules

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(T > 0.85) are plotted versus diﬀerences in the biological activity and color-coded by higher SALI

values as well. It seems, that further dense sampling of rough regions (the upper right corner) in the

numerical SALI plane (T > 0.75 and pIC₅₀> 2) is necessary to specify the valid SAR boundaries for the

investigated series of carboxamides.

Figure 4. Grayscaled SALI plot with compounds ordered with increasing ATCC 29213 activities (

and neighboring plot (b) for 1–22 carboxamides.

a)

2.4. Probability-Driven Pharmacophore Mapping

In order to explore the key 3D steric/electronic/lipophilic features of the ligand-site composition

the systematic probing of the functional group complementarity inevitably led to the introduction

of pharmacophore concept in SAR studies [59]. In the simplest case, the relative arrangement of

pharmacophoric properties is speciﬁed for the homogenous subfamily of compounds that share

the common structural scaﬀold (chemotype). In reality, drug hunters aspire to enhance the SAR

applicability domain for non-congeneric series of molecules, but accurate de novo prediction of

modeled property for the entire universe of chemicals (CS) is still an elusive and enigmatic operation.

Moreover, the model robustness and predictive power are strongly dependent on the training/test

subset division, but no correlation between good retrospective performance and good prospective

performance was observed (Kubinyi paradox) [60,61]. In fact, there are no speciﬁc rules to place

particular molecules into the training/test groups; therefore, multiple and interchangeable training/test

assignment was proposed for the probability-oriented pharmacophore mapping stemming from

stochastic model validation (SMV) approach [

were viable for the entire pool of systematically generated training/test populations (C₂⁸₂

for CoMSA MRSA 63718 modeling. The frequency distribution of test compounds for models with

preferable q²_cv0.5 and q_t²_est

0.5 parameters (more eﬃcient than ﬂipping a coin) revealed that active

17 and 20) and inactive ( and 22) molecules were noticeably over-represented. The preferential

3,

5,13

,

57]. Fortunately, the CPU-intense PLS calculations

≈

3

×

10⁵)

≥

(

9

over-representation of some compounds in the test set suggests the possibility of optional (or random)

generation of better models; therefore, the investigation of all possible training/test set combinations

seems advisable. In other words, better models were recorded for training sets depleted of the indicated

molecules. Additionally, an averaged selection-driven pharmacophore pattern was produced based

on the regions of the pretty high model ability and predictability according to the IVE-PLS (iterative

variable elimination partial least squares) procedure described elsewhere [62]. A listing of spatial

zones with favorable and unfavorable contributions of CoMSA descriptors is provided in Figure 5.

Int. J. Mol. Sci. 2020, 21, 6583

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Figure 5. Spatial sectors with largest contribution into MRSA potency selected by CoMSA IVE-PLS for

14 training/test set samplings. Colors code their contribution ( ) and four possible combination of

22 carboxamides. Reference compound 13 was

/

8

a

mean charge q and correlation coeﬃcient b values (b) 1–

illustrated as the most potent molecule.

The impact of the surface/charge descriptors on the ligand-site aﬃnity is indicated by the

regions of positive and negative potency contribution (Figure 5 a) and four combinations of charge

values versus the mean regression coeﬃcients (Figure 5b), respectively. It is not straightforward to

translate pharmacophore-related points of the space into the corresponding pseudoreceptor model

with privileged zones, that hypothetically harbors putative drug molecule, while the bundle of

steric and electrostatic features was speciﬁed on the averaged surface of the receptor-selective ligand

molecules. The three-dimensional distribution plot shown in Figure 5a demonstrates the contribution

of hydroxynaphthalene ring, peptide-like linker, and phenyl group. Oddly enough, the negatively

charged surface areas of the hydroxyl substituent contribute unfavorably, mainly due to steric and/or

electronic factors, while the gray 3D polyhedrals surrounding carbonyl groups depict the spatial areas

that were foreseen as favorable (see Figure 5a,b). Noticeably, the dominant gray spheres in the close

proximity of position 3 in the phenyl group (Figure 5a) correspond well with the increased electron

density on ﬂuorine atoms of the –CF₃substituent, that conﬁrms the tendency recorded for carboxamide

derivative, where activity proﬁle for meta substitution can be basically ranked as triﬂuoromethyl >>

methyl. Similarly, the negatively charged substituent directly attached to the phenyl group at position

5 contributes favorably to carboxamides binding aﬃnity as suggested by dark blue zones in Figure 5a

and data recorded in Table 1, respectively. In fact, the interpretation of an average pharmacophore

pattern is common for a set of congeneric subfamilies, and provides subtle hints useful at the stage of

structure-related activity modiﬁcations. In fact, no particular pharmacophore interaction was indicated

as dominant; however, the pharmacophore visualization based on the consensus 3D QSAR modeling

provides the spatial map of chemical groups/atoms potentially relevant for increasing/decreasing the

activity proﬁle of the analyzed molecules.

2.5. Advanced Antimicrobial Evaluation

2.5.1. Combined Eﬀect

The most potent antistaphylococcal compounds 13, 16, 17, 20, and 21 were studied in combination

with ciproﬂoxacin to evaluate their potential additive eﬀect on the resistant isolate MRSA SA 630 of

a given antibacterial chemotherapeutic. The method of minimal fractional inhibitory concentration

index (FICI) in a microtitration plate was used. FICI

≤

0.5 means synergy; 0.5 < FICI < 1 means

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additivity; 1

All the results are included in Table 2.

≤

FICI < 4 means indiﬀerence; and FICI

≥

4 means antagonism, respectively [63,64].

Synergistic activity was observed for the combinations of compounds 16 and 17 with ciproﬂoxacin

(CPX) against MRSA SA 630. The combination of compound 21 with CPX showed additivity against

this isolate as well. Combinations of all tested compounds with oxacillin (OXA) were indiﬀerent.

This indiﬀerence shows no interference of the tested compounds with mecA gene and can exclude

interaction with the cell wall. The mechanism of resistance to CPX in MRSA SA 630 has not yet been

precisely described. Generally, the common mechanism of resistance to ﬂuoroquinolones is active

eﬄux; so, a possible explanation of synergy may be inhibition of eﬄux pumps.

Table 2. Eﬀect of tested compounds in combination with ciproﬂoxacin (CPX) and oxacillin (OXA)

against isolate MRSA SA 630. MICs (µg/mL) of compounds alone and observed in the synergy

experiment are shown in parentheses. In the case of additivity and synergy, concentrations of tested

compound/CPX (µg/mL) providing this eﬀect are shown.

Combination (MIC [µg/mL])

FIC Index

Comb. Eﬀect (MICs [µg/mL])

Comp. 13 + CPX (0.125/128)

Comp. 16 + CPX (8/128)

Comp. 17 + CPX (1/128)

Comp. 20 + CPX (0,5/128)

Comp. 21 + CPX (0.125/128)

Comp. 13 + OXA (0.125/64)

Comp. 16 + OXA (8/64)

Comp. 17 + OXA (1/64)

Comp. 20 + OXA (0,5/64)

Comp. 21 + OXA (0,125/64)

1.000

0.375

0.500

1.004

0.75

1.004

1.000

1.125

1.002

1.125

IND

SYN 1/32

SYN 0.25/32

IND

ADD 0.0625/32; 0.03125/64

IND

FIC = fractional inhibitory concentration; IND = indiﬀerence; ADD = additivity; SYN = synergy.

2.5.2. Time-Kill Assay

Compounds 16 17, 20, and 21 were chosen for the evaluation of the dynamics of the antibacterial

,

activity by the time-kill curves method against S. aureus ATCC 29213 and MRSA SA 3202 (Figure 6).

Compound 13 was not included in this experiment by reason of too low MIC, which is limiting for

this methodology. The activity was tested at concentrations equal to 1

timepoints 0, 4, 6, 8, and 24 h from the beginning of incubation.

×

MIC, 2

×

MIC, and 4 MIC at

×

Most of the tested compounds had a bacteriostatic eﬀect. The activity of the compound 16

depended on the concentration; the static eﬀect was observed at concentration 4 MIC at 4 and 6 h after

×

the start of incubation for S. aureus ATCC 29213 and at 4, 6, and 8 h for MRSA SA 3202. The increase in

the amount of CFU at 24 h could be caused by the selection of resistant mutants. Compound 17 showed

the highest eﬀect at 8 h for both tested strains. In the case of S. aureus ATCC 29213, there is only a small

diﬀerence between concentrations at that timepoint. On the other hand, the most active concentration

against MRSA SA 3202 was 2

eﬀect at 4 MIC at 6 h for S. aureus ATCC 29213 and 4 h for MRSA SA 3202. Bactericidal eﬀect

MIC and 4 MIC

×

MIC. Compound 20 showed concentration dependence with the highest

×

(

≥log₁₀3 decrease) was observed only in the case of compound 21 at concentrations 2

×

at 24 h of incubation against S. aureus ATCC 29213. All the tested concentrations of this compound

showed at least 90% killing at 8 and 24 h of incubation against this strain. In the case of MRSA SA 3202,

90% killing at all the three concentrations at 8 h of incubation was also observed; the concentration

4×MIC at 24 h timepoint showed actually 99% killing. Both strains showed time-dependent killing.

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11 of 23

(a)

10

(b)

10

9

8

7

6

5

4

3

2

1

0

9

8

7

6

5

4

3

2

1

0

Bactericidal effect

0

5

10

Growth control

15

20

25

0

5

10

Growth control

15

20

25

Time (h)

1× MIC

Time (h)

1× MIC

2× MIC

4× MIC

2× MIC

4× MIC

(d)

10

(c)

10

9

8

7

6

5

4

3

2

1

0

9

8

7

6

5

4

3

2

1

0

Bactericidal effect

0

5

10

Growth control

15

20

25

Time (h)

1× MIC

0

5

10

Growth control

15

20

25

Time (h)

1× MIC

2× MIC

4× MIC

2× MIC

4× MIC

(f)

10

(e)

10

9

8

7

6

5

4

3

2

1

0

9

8

7

6

5

4

3

2

1

0

Bactericidal effect

0

5

10

15

20

25

0

5

10

Growth control

15

2× MIC

20

25

Time (h)

1× MIC

Time (h)

1× MIC

Growth control

2× MIC

4× MIC

(g)

10

9

8

7

6

5

4

3

2

1

0

(h)

10

9

8

7

6

5

4

3

2

1

0

Bactericidal effect

0

5

10

Growth control

15

2× MIC

20

25

Time (h)

1× MIC

0

5

10

15

20

25

Time (h)

1× MIC

4× MIC

Growth control

2× MIC

4× MIC

Figure 6. Comparison of dynamics of antibacterial activity of selected compounds 16

,

17,

20,

21 against

S. aureus ATCC 29213 (a–d) and MRSA SA 3202 (e–h).

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3. Materials and Methods

3.1. General Methods

All reagents were purchased from Merck (Sigma-Aldrich, St. Louis, MO, USA) and Alfa (Alfa-Aesar,

Ward Hill, MA, USA). Reactions were conducted using a CEM Discover SP microwave reactor

(CEM, Matthews, NC, USA). The melting points were speciﬁed using a Koﬂer hot-plate apparatus

HMK (Franz Kustner Nacht KG, Dresden, Germany) and are maintained uncorrected. Infrared (IR)

spectra were recorded with Smart MIRacle™ ATR ZnSe for Nicolet™ Impact 410 Fourier-transform

IR spectrometer (Thermo Scientiﬁc, West Palm Beach, FL, USA). The spectra were received by the

accumulation of 256 scans with 2 cm⁻¹resolution in the region of 4000–650 cm⁻¹. All ¹H- and ¹³C-NMR

spectra were recorded using an Agilent VNMRS 600 MHz system (600 MHz for ¹H and 150 MHz

1

for ¹³C; Agilent Technologies, Santa Clara, CA, USA) in dimethyl sulfoxide-d₆(DMSO-d₆). H 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) coupled with

an LTQ Orbitrap XL Hybrid Ion Trap-Orbitrap Fourier Transform Mass Spectrometer (Thermo

™

Scientiﬁc) equipped with a HESI II (heated electrospray ionization) source in the positive mode.

3.2. Chemistry

General Procedure for Synthesis of Carboxamide Derivatives 1–22

3-Hydroxynaphtalene-2-carboxylic acid (1.0 g, 5.3 mM) was suspended in dry chlorobenzene

(30 mL) at ambient temperature and phosphorus trichloride (0.23 mL, 2.7 mM, 0.5 eq.), and the

corresponding substituted aniline (5.3 mM, 1 eq.) was added dropwise. The reaction mixture was

transferred to the microwave reactor, where the synthesis was conducted (1st phase: 10 min, 100 ^◦C,

100 W; 2nd phase: 15 min, 120 ^◦C, 500 W; 3rd phase: 20 min, 130 ^◦C, 500 W). Subsequently, the mixture

was cooled to 60 ^◦C, and hence the solvent was removed to dryness under reduced pressure. The residue

was washed with hydrochloric acid and water. The crude product was recrystallized from EtOH.

N-(2,5-Dimethoxyphenyl)-3-hydroxynaphthalene-2-carboxamide (

3425, 2934, 2836, 1604, 1592, 1538, 1490, 1445, 1434, 1280, 1217, 1183, 1144, 1050, 1024, 872, 862, 785,

751, 737, 711; ¹H-NMR (DMSO-d₆),

: 11.79 (s, 1H), 11.13 (s, 1H), 8.70 (s, 1H), 8.24 (s, 1H), 7.98 (d, 1H,

1

). Yield 57%; Mp 186–188 ^◦C; IR (cm⁻¹):

δ

J = 8.1 Hz), 7.78 (d, 1H, J = 8.1 Hz), 7.52 (t, 1H, J = 7.1 Hz), 7.37 (t, 1H, J = 7.3 Hz), 7.36 (s, 1H), 7.03

(dd, 1H, J = 9.2 Hz, J = 1.5 Hz), 6.66 (ddd, 1H, J = 9.2 Hz, J = 7.7 Hz, J = 1.5 Hz), 3.87 (s, 3H), 3.74 (s, 3H);

¹³C-NMR (DMSO-d₆),

121.3, 111.7, 110.7, 107.5, 106.9, 56.6, 55.3; HR-MS: [M

δ

: 162.8, 153.2, 152.5, 142.7, 135.9, 132.7, 129.0, 128.8, 128.3, 127.2, 125.6, 123.9,

−

H]⁺calculated 322.1074 m/z, found 322.1080 m/z.

N-(3,5-Dimethoxyphenyl)-3-hydroxynaphthalene-2-carboxamide (

3115, 1645, 1623, 1606, 1558, 1478, 1455, 1341, 1318, 1269, 1227, 1198, 1156, 1061, 948, 838, 811, 799, 767,

678; ¹H-NMR (DMSO-d₆),

: 11.26 (s, 1H), 10.52 (s, 1H), 8.46 (s, 1H), 7.92 (d, 1H, J = 8.1 Hz), 7.76 (d, 1H,

J = 8.4 Hz), 7.51 (t, 1H, J = 7.5 Hz), 7.36 (t, 1H, J = 7.3 Hz), 7.32 (s, 1H), 7.04 (d, 2H, J = 1.8 Hz), 6.31

(t, 1H, J = 1.8 Hz), 3.76 (s, 6H); ¹³C-NMR (DMSO-d₆),

: 165.7, 160.5, 153.6, 140.2, 135.7, 130.5, 128.7,

2

). Yield 80%; Mp 179–184 ^◦C; IR (cm⁻¹):

δ

128.1, 126.9, 125.8, 123.8, 122.1, 110.5, 98.7, 96.0, 55.2; HR-MS: [M

322.1081 m/z.

−

H]⁺calculated 322.1074 m/z, found

N-(2,5-Dimethylphenyl)-3-hydroxynaphthalene-2-carboxamide (

3328, 3013, 1633, 1621, 1597, 1577, 1538, 1519, 1493, 1446, 1413, 1361, 1326, 1287, 1262, 1221, 1174, 1067,

1000, 957, 917, 849, 800, 775, 730, 682; ¹H-NMR (DMSO-d₆),

: 11.83 (s, 1H), 10.49 (s, 1H), 8.66 (s, 1H)

3

). Yield 84%; Mp 209–212 ^◦C; IR (cm⁻¹):

δ

,

7.95 (d, 1H, J = 8.1 Hz), 7.80 (s, 1H), 7.78 (d, 1H. J = 8.4 Hz), 7.52 (t, 1H, J = 7.3 Hz), 7.37 (t, 1H, J = 7.3 Hz),

7.36 (s, 1H), 7.17 (d, 1H, J = 7.7 Hz), 6.94 (d, 1H, J = 7.7 Hz), 2.31 (s, 3H), 2.28 (s, 3H); ¹³C-NMR

(DMSO-d₆), δ: 164.5, 153.6, 136.2, 135.9, 135.4, 131.5, 130.2, 128.9, 128.3, 127.0, 126.9, 125.7, 125.6, 123.9,

123.9, 120.7, 110.7, 20.8, 17.4; HR-MS: [M − H]⁺calculated 290.11756 m/z, found 290.11829 m/z.

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N-(2,6-Dimethylphenyl)-3-hydroxynaphthalene-2-carboxamide (

4

). Yield 83%; Mp 192–196 ^◦C; IR (cm⁻¹):

3054, 1652, 1635, 1622, 1590, 1509, 1470, 1446, 1393, 1358, 1346, 1269, 1229, 1168, 1070, 953, 936, 916,

869, 842, 776, 750, 712, 671; ¹H-NMR (DMSO-d₆),

: 11.73 (s, 1H), 10.24 (s, 1H), 8.63 (s, 1H), 7.92 (d, 1H,

J = 7.7 Hz), 7.78 (d, 1H, J = 8.1 Hz), 7.53 (t, 1H, J = 7.3 Hz), 7.37 (t, 1H, J = 7.3 Hz), 7.34 (s, 1H),

7.16 (s, 3H), 2.25 (s, 6H); ¹³C-NMR (DMSO-d₆),

: 166.6, 155.0, 136.0, 135.4, 134.5, 130.0, 128.7, 128.3,

δ

127.8, 126.9, 126.7, 125.8, 123.8, 119.7, 110.7, 18.1; HR-MS: [M

290.1183 m/z.

−

H]⁺calculated 290.1176 m/z, found

N-(3,5-Dimethylphenyl)-3-hydroxynaphthalene-2-carboxamide (

3303, 3042, 2011, 2916, 1635, 1621, 1598, 1557, 1519, 1454, 1396, 1358, 1335, 1224, 1210, 1169, 1145, 916,

870, 838, 772, 744, 711, 685; ¹H-NMR (DMSO-d₆),

: 11.41 (s, 1H), 10.48 (s, 1H), 8.52 (s, 1H), 7.92 (d, 1H,

J = 8.1 Hz), 7.77 (d, 1H, J = 8.4 Hz), 7.52 (t, 1H, J = 7.1 Hz), 7.40 (s, 2H), 7.36 (t, 1H, J = 7.3 Hz), 7.33

(s, 1H), 6.79 (s, 1H), 2.29 (s, 6H); ¹³C-NMR (DMSO-d₆),

: 165.6, 153.9, 138.2, 137.8, 135.8, 130.5, 128.7,

5

). Yield 83%; Mp 184–186 ^◦C; IR (cm⁻¹):

δ

128.2, 126.9, 125.8, 125.6, 123.8, 121.5, 118.3, 110.6, 21.1; HR-MS: [M

found 290.1185 m/z.

−

H]⁺calculated 290.1176 m/z,

N-(2,5-Diﬂuorophenyl)-3-hydroxynaphthalene-2-carboxamide (

1652, 1630, 1598, 1561, 1516, 1489, 1444, 1391, 1360, 1346, 1285, 1272, 1245, 1222, 1184, 1145, 1064, 1015,

972, 949, 908, 889, 862, 833, 807, 795, 765, 736, 716; ¹H-NMR (DMSO-d₆)

: 11.93 (s, 1H); 11.11 (s, 1H),

6

). Yield 53%; Mp 264 ^◦C; IR (cm⁻¹): 3057,

δ

8.68 (s, 1H), 8.30 (ddd, 1H, J = 3.2 Hz, J = 6.2 Hz, J = 10.3 Hz), 7.99 (d, 1H, J = 8.2 Hz), 7.78 (d, 1H,

J = 8.3 Hz), 7.54 (ddd, 1H, J = 1.1 Hz, J = 6.8 Hz, J = 8.3 Hz), 7.41 (ddd, 1H, J = 5.1 Hz, J = 9.1 Hz,

J = 10.6 Hz), 7.38 (ddd, 1H, J = 1.2 Hz, J = 6.8 Hz, J = 8.2 Hz), 7.37 (s, 1H), 7.00–7.04 (m, 1H); ¹³C-NMR

(DMSO-d₆), δ: 163.6, 157.9 (dd, J = 1.7 Hz, J = 238.4 Hz), 152.5, 148.8 (dd, J = 2.4 Hz, J = 239.1 Hz), 136.1,

132.7, 129.1, 128.6, 127.6 (t, J = 12.4 Hz), 127.2, 125.7, 124.1, 120.4, 116.1 (dd, J = 10.0 Hz, J = 22.1 Hz),

110.9, 110.4 (dd, J = 8.0 Hz, J = 24.3 Hz), 108.7 (d, J = 29.9 Hz); HR-MS: [M + H]⁺calculated 300.0830

m/z, found 300.0832 m/z.

N-(2,6-Diﬂuorophenyl)-3-hydroxynaphthalene-2-carboxamide (

3357, 1662, 1628, 1596, 1511, 1466, 1304, 1285, 1213, 1147, 1134, 1009, 899, 830, 788, 778, 747; ¹H-NMR

(DMSO-d₆), : 11.48 (s, 1H), 10.41 (s, 1H), 8.61 (s, 1H), 7.94 (d, 1H, J = 8.1 Hz), 7.78 (d, 1H J = 8.1 Hz)

7.54 (t, 1H, J = 7.0 Hz), 7.33–7.44 (m, 4H), 7.25 (t, 1H, J = 7.9 Hz); ¹³C-NMR (DMSO-d₆),

: 166.0,

157.9 (dd, J = 247.3 Hz, J = 5.3 Hz), 154.1, 136.2, 131.2, 128.9, 128.7 (t, J = 19.8 Hz), 128.5, 126.8, 125.9,

7

). Yield 78%; Mp 194–198 ^◦C; IR (cm⁻¹):

δ

,

δ

125.8, 123.9, 119.4, 114.2 (t, J = 16.7 Hz), 112.0 (m); HR-MS: [M

298.0681 m/z.

−

H]⁺calculated 298.0674 m/z, found

N-(3,5-Diﬂuorophenyl)-3-hydroxynaphthalene-2-carboxamide (

3103, 1645, 1626, 1608, 1574, 1564, 1479, 1439, 1345, 1308, 1268, 1225, 1208, 1169, 1118, 999, 989, 855,

829, 767, 740; ¹H-NMR (DMSO-d₆),

: 11.05 (s, 1H), 10.80 (s, 1H), 8.38 (s, 1H), 7.93 (d, 1H, J = 8.0 Hz),

8

). Yield 79%; Mp 273–276 ^◦C; IR (cm⁻¹):

δ

7.76 (d, 1H, J = 8.4 Hz), 7.51–7.62 (m, 3H), 7.38 (t, 1H, J = 7.5 Hz), 7.34 (s, 1H), 6.99 (t, 1H, J = 8.0 Hz);

¹³C-NMR (DMSO-d₆), δ: 165.8, 162.4 (dd, J = 241.3 Hz, J = 15.2 Hz), 153.0, 141.2 (t, J = 13.7 Hz), 135.7,

130.6, 128.7, 128.2, 126.9, 125.8, 123.8, 122.8, 110.5, 103.0 (m), 98.93 (t, J = 25.8 Hz); HR-MS: [M

calculated 298.0674 m/z, found 298.0685 m/z.

−

H]⁺

N-(2,5-Dichlorophenyl)-3-hydroxynaphthalene-2-carboxamide (

1637, 1623, 1590, 1580, 1519, 1470, 1455, 1404, 1359, 1347, 1313, 1265, 1204, 1174, 1140, 1090, 1073, 1047,

1017, 979, 960, 916, 867, 846, 808, 769, 750, 707; ¹H-NMR (DMSO-d₆)

: 12.03 (s, 1H), 11.28 (s, 1H), 8.71

9

). Yield 72%; Mp 252 ^◦C; IR (cm⁻¹): 3187,

δ

(s, 1H), 8.67 (d, 1H, J = 2.5 Hz), 7.99 (d, 1H, J = 8.2 Hz), 7.78 (d, 1H, J = 8.2 Hz), 7.60 (d, 1H, J = 8.6 Hz),

7.53 (ddd, 1H, J = 1.2 Hz, J = 6.8 Hz, J = 8.3 Hz), 7.38 (s, 1H), 7.37 (ddd, 1H, J = 1.1 Hz, J = 6.8 Hz,

J = 8.2 Hz), 7.25 (dd, 1H, J = 2.6 Hz, J = 8.6 Hz); ¹³C-NMR (DMSO-d₆),

δ: 163.5, 152.4, 136.5, 136.1,

133.0, 132.1, 130.6, 129.1, 128.7, 127.2, 125.7, 124.5, 124.0, 121.39, 121.35, 120.3, 110.9; HR-MS: [M + H]⁺

calculated 332.0240 m/z, found 332.0243 m/z.

N-(2,6-Dichlorophenyl)-3-hydroxynaphthalene-2-carboxamide (10). Yield 73%; Mp 283–284 ^◦C; IR (cm⁻¹):

3209, 1623, 1614, 1570, 1515, 1463, 1450, 1433, 1405, 1328, 1236, 1205, 1155, 970, 912, 818, 793, 783, 771,

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745, 704, 679; ¹H-NMR (DMSO-d₆)

δ

: 11.57 (s, 1H), 10.66 (s, 1H), 8.66 (s, 1H), 7.95 (d, 1H, J = 8.2 Hz),

7.79 (d, 1H, J = 8.2 Hz,), 7.62 (d, 2H, J = 8.2 Hz), 7.53 (ddd, 1H, J = 7.8 Hz, J = 6.9 Hz, 1.0 Hz), 7.43 (t, 1H,

J = 8.2 Hz), 7.37 (ddd, 1H, J = 8.2 Hz, J = 6.9 Hz, J = 0.9 Hz), 7.35 (s, 1H); ¹³C-NMR (DMSO-d₆),

δ

: 165.8, 153.6, 136.0, 134.1, 133.3, 131.1, 129.3, 128.9, 128.8, 128.2, 126.8, 125.9, 125.6, 123.9, 120.4; HR-MS:

[M + H]⁺calculated 332.0240 m/z, found 332.0238 m/z.

N-(3,4-Dichlorophenyl)-3-hydroxynaphthalene-2-carboxamide (11). Yield 76%; Mp 278–279 ^◦C; IR (cm⁻¹):

3080, 1622, 1605, 1589, 1566, 1545, 1474, 1449, 1396, 1377, 1358, 1344, 1240, 1207, 1172, 1134, 1073, 1024,

955, 925, 898, 852, 837, 826, 771, 748, 680; ¹H-NMR (DMSO-d₆)

δ: 11.09 (s, 1H), 10.75 (s, 1H), 8.41 (s, 1H),

8.18 (d, 1H, J = 2.3 Hz), 7.93 (d, 1H, J = 8.2 Hz), 7.77 (d, 1H, J = 8.2 Hz), 7.72 (dd, 1H, J = 8.9 Hz,

J = 12.3 Hz), 7.64 (d, 1H, J = 8.7 Hz), 7.51 (ddd, 1H, J = 7.8 Hz, J = 6.9 Hz, J = 0.9 Hz), 7.36 (ddd, 1H,

J = 8.2 Hz, J = 6.9 Hz, J = 0.9 Hz), 7.33 (s, 1H); ¹³C-NMR (DMSO-d₆),

δ: 165.8, 153.2, 138.8, 135.7, 131.0,

130.7, 130.5,128.7, 128.2, 126.9, 125.8, 125.3, 123.8, 122.5, 121.4, 120.3, 110.5; HR-MS: [M + H]⁺calculated

332.0240 m/z, found 332.0242 m/z.

N-(3,5-Dichlorophenyl)-3-hydroxynaphthalene-2-carboxamide (12). Yield 70%; Mp 252 ^◦C; IR (cm⁻¹): 3087,

1646, 1630, 1583, 1547, 1447, 1409, 1396, 1375, 1345, 1329, 1269, 1254, 1204, 1172, 1147, 1115, 1092, 1072,

1017, 993, 938, 917, 905, 867, 861, 836, 802, 788, 764, 741, 723, 688; ¹H-NMR (DMSO-d₆)

δ: 11.04 (s, 1H),

10.75 (s, 1H), 8.39 (s, 1H), 7.92 (d, 1H, J = 8.2 Hz), 7.89 (d, 2H, J = 1.7 Hz), 7.77 (d, 1H, J = 8.3 Hz), 7.51

(ddd, 1H, J = 1.2 Hz, J = 6.8 Hz, J = 8.3 Hz), 7.36 (ddd, 1H, J = 1.1 Hz, J = 6.8 Hz, J = 8.2 Hz), 7.34

(t, 1H, J = 1.7 Hz), 7.34 (s, 1H); ¹³C-NMR (DMSO-d₆),

δ: 165.9, 153.1, 141.0, 135.7, 134.1, 130.5, 128.7,

128.2, 126.8, 125.8, 123.8, 123.0, 122.6, 118.3, 110.5; HR-MS: [M + H]⁺calculated 332.0240 m/z, found

332.0244 m/z.

N-[3,5-bis(Triﬂuoromethyl)phenyl]-3-hydroxynaphthalene-2-carboxamide (13). Yield 57%; Mp 232–235 ^◦C;

IR (cm⁻¹): 3256, 1651, 1631, 1601, 1578, 1568, 1473, 1384, 1357, 1346, 1274, 1162, 1120, 1113, 943, 883, 866,

836, 765, 744, 707, 682; ¹H-NMR (DMSO-d₆),

δ

: 11.05 (s, 1H), 10.99 (s, 1H), 8.50 (s, 2H), 8.41 (s, 1H),

7.93 (d, 1H, J = 8.1 Hz), 7.84 (s, 1H), 7.78 (d, 1H, J = 8.4 Hz), 7.52 (t, 1H, J = 6.6 Hz), 7.34–7.40 (m, 2H);

¹³C-NMR (DMSO-d₆),

: 166.4, 153.2, 140.6, 135.8, 130.8 (q, J = 32.8 Hz), 130.6, 128.6, 128.2, 126.8, 125.9,

δ

123.9, 123.3 (q, J = 272.4 Hz), 122.6, 120.0 (q, J = 3.6 Hz), 116.6 (sep, J = 3.4 Hz), 110.5; HR-MS: [M

calculated 398.0610 m/z, found 398.0620 m/z.

−

H]⁺

3-Hydroxy-N-[4-methyl-3-(triﬂuoromethyl)phenyl]naphthalene-2-carboxamide (14). Yield 68%; Mp 251–252

^◦C; IR (cm⁻¹): 3135, 1638, 1607, 1553, 1502, 1450, 1398, 1361, 1316, 1204, 1169, 1141, 1113, 1052,

1

1008, 955, 920, 900, 867, 837, 770, 749, 726; H-NMR (DMSO-d₆)

δ: 11.19 (s, 1H), 10.73 (s, 1H), 8.46

(s, 1H), 8.20 (d, 1H, J = 1.9 Hz), 7.92 (d, 1H, J = 8.2 Hz), 7.89 (dd, 1H, J = 1.9 Hz, J = 8.3 Hz), 7.77

(d, 1H, J = 8.3 Hz), 7.51 (ddd, 1H, J = 1.2 Hz, J = 6.8 Hz, J = 8.3 Hz), 7.44 (d, 1H, J = 8.3 Hz), 7.36

(ddd, 1H, J = 1.1 Hz, J = 6.8 Hz, J = 8.2 Hz), 7.33 (s, 1H), 2.42 (s, 3H); ¹³C-NMR (DMSO-d₆),

δ: 166.0,

153.6, 136.8, 135.8, 132.7, 131.1 (q, J = 1.7 Hz), 130.4, 128.7, 128.2, 127.5 (q, J = 29.3 Hz), 126.9, 125.8,

124.4 (q, J = 273.8 Hz), 123.8, 122.0, 117.4 (q, J = 5.9 Hz), 110.6, 18.3 (q, J = 1.9 Hz); HR-MS: [M + H]⁺

calculated 346.1049 m/z, found 346.1055 m/z.

N-[4-Fluoro-3-(triﬂuoromethyl)phenyl]-3-hydroxynaphthalene-2-carboxamide (15). Yield 72%; Mp 231–232 ^◦C;

IR (cm⁻¹): 3101, 1639, 1613, 1575, 1498, 1453, 1421, 1397, 1361, 1344, 1321, 1273, 1254, 1229, 1206, 1179,

1144, 1123, 1053, 959, 925, 903, 868, 839, 814, 771, 753, 732, 679; ¹H-NMR (DMSO-d₆)

δ: 11.10 (s, 1H),

10.81 (s, 1H), 8.43 (s, 1H), 8.29 (dd, 1H, J = 2.6 Hz, J = 6.5 Hz), 8.05 (ddd, 1H, J = 3.0 Hz, J = 4.2 Hz,

J = 8.9 Hz), 7.92 (d, 1H, J = 8.2 Hz), 7.77 (d, 1H, J = 8.3 Hz), 7.55 (dd, 1H, J = 9.0 Hz, J = 10.5 Hz), 7.51

(ddd, 1H, J = 1.2 Hz, J = 6.8 Hz, 8.3 Hz), 7.36 (ddd, 1H, J = 1.2 Hz, J = 6.8 Hz, J = 8.2 Hz), 7.34 (s, 1H);

¹³C-NMR (DMSO-d₆),

δ: 166.0, 154.8 (dq, J = 2.1 Hz, J = 250.3 Hz), 153.4, 135.8, 135.4 (d, J= 2.9 Hz),

130.4, 128.7, 128.2, 126.8, (d, J= 8.1 Hz), 126.5, 125.8, 123.8, 122.5 (dq, J = 1.1, 272.1 Hz), 122.2, 118.4

(q, J = 4.9 Hz), 117.7 (d, J = 21.4 Hz), 116.5 (dq, J = 13.4, 32.4 Hz), 110.5; HR-MS: [M + H]⁺calculated

350.0799 m/z, found 350.0801 m/z.

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N-[4-Bromo-3-(triﬂuoromethyl)phenyl]-3-hydroxynaphthalene-2-carboxamide (16). Yield 62%; Mp 234–235

^◦C; IR (cm⁻¹): 3161, 1637, 1622, 1602, 1549, 1478, 1451, 1395, 1359, 1313, 1264, 1208, 1176, 1151, 1127,

1105, 1021, 957, 924, 896, 867, 836, 770, 750, 721; ¹H-NMR (DMSO-d₆)

δ: 11.07 (s, 1H), 10.86 (s, 1H),

8.42 (s, 1H), 8.37 (d, 1H, J = 2.5 Hz), 7.97 (dd, 1H, J = 2.5 Hz, J = 8.7 Hz), 7.92 (d, 1H, J = 8.2 Hz), 7.88

(d, 1H, J = 8.7 Hz), 7.77 (d, 1H, J = 8.3 Hz), 7.51 (ddd, 1H, J = 1.2 Hz, J = 6.8 Hz, J = 8.3 Hz), 7.36

(ddd, 1H, J = 1.2 Hz, J = 6.8 Hz, J = 8.2 Hz), 7.34 (s, 1H); ¹³C-NMR (DMSO-d₆),

δ: 166.1, 153.3, 138.6,

135.8, 135.5, 130.5, 128.7, 128.6 (q, J = 30.5 Hz), 128.2, 126.9, 125.8, 125.1, 123.8, 122.9 (q, J = 273.1 Hz),

122.5, 119.3 (q, J = 5.8 Hz), 112.2 (q, J = 1.8 Hz), 110.5; HR-MS: [M + H]⁺calculated 409.9998 m/z, found

410.0005 m/z.

N-(4-Bromo-3-ﬂuorophenyl)-3-hydroxynaphthalene-2-carboxamide (17). Yield 72%; Mp 255–256 ^◦C; IR (cm⁻¹):

3053, 1634, 1619, 1599, 1557, 1486, 1447, 1400, 1356, 1344, 1270, 1244, 1221, 1207, 1168, 1145, 1128,

1078, 1043, 963, 915, 871, 844, 817, 771, 749, 704; ¹H-NMR (DMSO-d₆)

δ

: 11.10 (s, 1H), 10.78 (s, 1H),

8.41 (s, 1H), 7.95 (dd, 1H, J = 2.3 Hz, J = 11.4 Hz), 7.93 (d, 1H, J = 8.3 Hz), 7.76 (d, 1H, J = 8.3 Hz),

7.69 (dd, 1H, J = 8.2 Hz J = 8.7 Hz), 7.52 (ddd, 1H, J = 0.7 Hz, J = 2.4 Hz, J = 8.9 Hz), 7.51 (ddd,

1H, J = 1.3 Hz, J = 6.8 Hz

¹³C-NMR (DMSO-d₆),

,

J = 8.3 Hz), 7.36 (ddd, 1H, J = 1.2 Hz, J = 6.8 Hz, J = 8.2 Hz), 7.33 (s, 1H);

δ: 165.8, 158.1 (d, J = 242.4 Hz), 153.2, 139.9 (d, J = 10.2 Hz), 135.7, 133.3

(d, J = 1.2 Hz), 130.6, 128.7, 128.2, 126.9, 125.8, 123.8, 122.5, 101.5 (d, J = 21.0 Hz), 108.2 (d, J = 27.1 Hz),

117.6 (d, J = 3.0 Hz), 110.5; HR-MS: [M + H]⁺calculated 360.0030 m/z, found 360.0036 m/z.

N-[2-Fluoro-5-(triﬂuoromethyl)phenyl]-3-hydroxynaphthalene-2-carboxamide (18). Yield 77%; Mp 222–223

^◦C; IR (cm⁻¹): 3276, 1656, 1632, 1619, 1568, 1521, 1498, 1468, 1442, 1396, 1345, 1321, 1263, 1248, 1215,

1204, 1165, 1148, 1126, 1072, 926, 917, 904, 888, 865, 812, 765, 746, 716; ¹H-NMR (DMSO-d₆)

δ: 11.92

(s, 1H), 11.16 (s, 1H), 8.83 (dd, 1H, J = 1.6 Hz, J = 7.1 Hz), 8.67 (s, 1H), 7.98 (d, 1H, J = 8.2 Hz), 7.57–7.62

(m, 2H), 7.78 (d, 1H, J = 8.3 Hz), 7.53 (ddd, 1H, J = 1.2 Hz, J = 6.8 Hz, J = 8.3 Hz), 7.38 (ddd, 1H,

J = 1.1 Hz, J = 6.8 Hz, J = 8.2 Hz), 7.38 (s, 1H); ¹³C-NMR (DMSO-d₆),

δ: 164.0, 154.5 (d, J = 249.9 Hz),

152.6, 136.1, 132.7, 129.1, 128.7, 127.5 (d, J = 11.5 Hz), 125.8, 127.2, 125.5 (dq, J = 3.1 Hz, J = 32.2 Hz),

124.1, 123.8 (q, J = 272.3 Hz), 121.9 (dq, J = 3.9 Hz, 8.7 Hz), 120.4, 118.9 (m), 116.4 (d, J = 20.9 Hz), 110.9;

HR-MS: [M + H]⁺calculated 350.0799 m/z, found 350.0802 m/z.

N-[3-Fluoro-5-(triﬂuoromethyl)phenyl]-3-hydroxynaphthalene-2-carboxamide (19). Yield 66%; Mp 245–247

^◦C; IR (cm⁻¹): 3094, 1647, 1623, 1575, 1564, 1482, 1458, 1436, 1397, 1358, 1347, 1327, 1254, 1217, 1207,

1171, 1149, 1128, 1099, 1003, 995, 922, 894, 875, 867, 858, 840, 799, 766, 745; ¹H-NMR (DMSO-d₆)

δ: 11.01

(s, 1H); 10.93 (s, 1H); 8.40 (s, 1H); 8.05 (s, 1H); 8.00 (d, 1H, J = 11.0 Hz); 7.93 (d, 1H, J = 8.2 Hz); 7.77

(d, 1H, J = 8.2 Hz); 7.51 (dd, 1H, J = 8.2 Hz, 6.9 Hz); 7.43 (d, 1H, J = 8.2 Hz); 7.37 (t, 1H, J = 7.2 Hz); 7.34

(s, 1H); ¹³C-NMR (DMSO-d₆),

δ: 166.1; 162.1 (d, J = 244.2 Hz); 153.1; 141.4 (d, J = 11.6 Hz); 135.8; 131.0

(td, J = 33.2 Hz, 10.1 Hz); 130.6; 128.7; 128.2; 126.9; 125.9; 123.8; 123.3 (qd, J = 273.1 Hz, 4.3 Hz); 122.8;

112.6 (m); 110.5 (d, J = 26.0 Hz); 110.5; 107.5 (m); HR-MS: [M + H]⁺calculated 350.0799 m/z, found

350.0791 m/z.

N-[2-Chloro-5-(triﬂuoromethyl)phenyl]-3-hydroxynaphthalene-2-carboxamide (20). Yield 60%; Mp 185–188

^◦C; IR (cm⁻¹): 3193, 1642, 1622, 1601, 1586, 1544, 1519, 1425, 1329, 1253, 1204, 1167, 1150, 1119, 1080,

1046, 916, 895, 873, 848, 826, 770, 752; ¹H-NMR (DMSO-d₆)

δ

: 12.09 (s, 1H), 11.41 (s, 1H), 8.98 (d, 1H,

J = 2.2 Hz), 8.72 (s, 1H), 7.99 (d, 1H, J = 8.1 Hz), 7.82 (d, 1H, J = 7.7 Hz), 7.78 (d, 1H, J = 8.1 Hz), 7.50–7.57

(m, 2H), 7.33–7.41 (m, 2H); ¹³C-NMR (DMSO-d₆),

: 163.7, 152.5, 136.3, 136.2, 133.1, 130.5, 129.2, 128.7,

δ

128.4 (q, J = 32.0 Hz), 127.1, 126.8 (q, J = 1.5 Hz), 125.7, 124.1, 123.7 (q, J = 272.4 Hz), 121.3 (q, J = 3.9 Hz),

120.3, 118.2 (q, J = 3.8 Hz), 110.9; HR-MS: [M − H]⁺calculated 364.0347 m/z, found 364.0355 m/z.

N-[3-Fluoro-4-(triﬂuoromethyl)phenyl]-3-hydroxynaphthalene-2-carboxamide (21). Yield 61%; Mp 273–274

^◦C; IR (cm⁻¹): 3061, 1622, 1605, 1557, 1451, 1432, 1417, 1398, 1360, 1342, 1317, 1271, 1244, 1210, 1174,

1120, 1069, 1047, 977, 962, 945, 915, 873, 860, 842, 815, 772, 754, 732, 702; ¹H NMR (CDCl₃)

δ: 11.05

(s, 1H), 10.96 (s, 1H), 8.39 (s, 1H), 8.02 (dd, 1H, J = 1.4 Hz, J = 13.5 Hz), 7.94 (d, 1H, J = 8.2 Hz),

7.78 (t, 1H, J = 8.6 Hz), 7.77 (d, 1H, J = 8.3 Hz), 7.71 (dd, 1H, J = 1.4 Hz, J = 8.7 Hz), 7.51 (ddd, 1H,

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J = 1.2 Hz

(DMSO-d₆),

107.5 (d, J = 25.4 Hz), 128.7, 128.2, 127.8 (m), 126.9, 125.8, 123.8, 122.9, 122.8 (q J = 271.1 Hz), 115.6

d, J = 1.4 Hz

J = 8.7 Hz), 111.0 (dq, J = 12.5, 32.6 Hz), 110.5; HR-MS: [M + H]⁺calculated 350.0799 m/z,

,

J = 6.8 Hz, J = 8.3 Hz), 7.36 (ddd, 1H, J = 1.2, J = 6.8 Hz, J = 8.2 Hz), 7.34 (s, 1H); ¹³C-NMR

: 166.0, 159.2 (dq, J = 2.0 Hz, J = 250.6 Hz), 152.9, 144.5 (d, J = 11.4 Hz), 135.8, 130.7,

δ

(

,

found 350.0801 m/z.

N-[2-Bromo-4-(triﬂuoromethyl)phenyl]-3-hydroxynaphthalene-2-carboxamide (22). Yield 67%; Mp 231–233

^◦C; IR (cm⁻¹): 3225, 3093, 1643, 1626, 1601, 1588, 1541, 1492, 1451, 1404, 1359, 1319, 1274, 1212,

1169, 1120, 1078, 1042, 955, 919, 894, 871, 838, 803;¹H NMR (CDCl₃)

δ: 12.09 (s, 1H); 11.32 (s, 1H);

8.76 (d, 1H, J = 8.9 Hz); 8.73 (s, 1H); 8,12 (s, 1H); 8.01 (d, 1H, J = 8.2 Hz); 7.85 (dd, 1H, J = 8.2 Hz,

J = 1.4 Hz); 7.79 (d, 1H, J = 8.2 Hz); 7.56–7.53 (m, 1H); 7.40 (s, 1H); 7.39–7.73 (m, 1H); ¹³C-NMR

(DMSO-d₆), δ: 163.6; 152.5; 140.4; 136.2; 133.2; 129.6 (q, J = 4.3 Hz); 129.2; 128.8; 127.2; 125.7; 125.7

(

q, J = 4.3 Hz); 125.2 (q, J = 33.2 Hz); 124.1; 123.4 (q, J = 271.7 Hz); 122.4; 120.3; 113.4; 110.9; HR-MS:

[M + H]⁺calculated 409.9998 m/z, found 409.9988 m/z.

3.3. Biological Testing

3.3.1. In Vitro Antibacterial Evaluation

The synthesized compounds were evaluated for in vitro antibacterial activity against

representatives of multidrug-resistant bacteria and clinical isolates of methicillin-resistant

Staphylococcus aureus (MRSA) 63718, SA 630, and SA 3202 that were obtained from the National

Institute of Public Health (Prague, Czech Republic) [41]. S. aureus ATCC 29213 was used as a reference

and quality control strain. Ampicillin and ciproﬂoxacin (Sigma, St. Louis, MO, USA) were employed as

the standard. Prior to testing, each strain was passaged onto nutrient agar (Oxoid, Basingstoke, UK) with

5% of bovine blood, and bacterial inocula were prepared by suspending a small portion of a bacterial

colony in sterile phosphate-buﬀered saline (pH 7.2–7.3). The cell density was adjusted to 0.5 McFarland

units using a densitometer (Densi-La-Meter, LIAP, Riga, Latvia). This inoculum was diluted to reach

the ﬁnal concentration of bacterial cells 5

in DMSO (Sigma), and the ﬁnal concentration of DMSO in the cation-adjusted Mueller–Hinton (CaMH)

broth (Oxoid) did not exceed 2.5% of the total solution composition. The ﬁnal concentrations of the

×

10⁵CFU/mL in the wells. The compounds were dissolved

evaluated compounds ranged from 256 to 0.008 µg/mL. The broth dilution micro-method, modiﬁed

according to the NCCLS (National Committee for Clinical Laboratory Standards) guidelines in

Mueller–Hinton (MH) broth, was used to determine the minimum inhibitory concentration (MIC) [42].

Drug-free controls, sterility controls, and controls consisting of MH broth and DMSO alone were

included. The determination of results was performed visually after 24 h of static incubation in the

darkness at 37 ^◦C in an aerobic atmosphere. The results are reported in Table 1.

3.3.2. In Vitro Antimycobacterial Assessment

Mycobacterium tuberculosis ATCC 25177/H37Ra was grown in Middlebrook broth (MB),

supplemented with Oleic-Albumin-Dextrose-Catalase (OADC) supplement (Difco, Lawrence, KS,

USA). At log phase growth, a culture sample (10 mL) was centrifuged at 15,000 rpm/20 min using

a bench top centrifuge (MPW-65R, MPW Med Instruments, Warszawa, Poland). Following the removal

of the supernatant, the pellet was washed in fresh Middlebrook 7H9GC broth and resuspended in

fresh, ODAC-supplemented MB (10 mL). The turbidity was adjusted to match McFarland standard

No. 1 (3

broth. The antimicrobial susceptibility of M. tuberculosis was investigated in a 96-well plate format.

In these experiments, sterile deionized water (300 L) was added to all outer-perimeter wells of the

plates to minimize evaporation of the medium in the test wells during incubation. Each evaluated

compound (100 L) was incubated with M. tuberculosis (100 L). Dilutions of each compound were

prepared in duplicate. For all synthesized compounds, ﬁnal concentrations ranged from 128 to 4 g/mL.

All compounds were dissolved in DMSO, and subsequent dilutions were made in supplemented MB.

×

10⁸CFU) with MB broth. A further 1:10 dilution of the culture was then performed in MB

µ

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The plates were sealed with Paraﬁlm and incubated at 37 ^◦C for 14 days. Following incubation, a 10%

addition of alamarBlue (Difco) was mixed into each well, and readings at 570 nm and 600 nm were

taken, initially for background subtraction and subsequently after 24 h reincubation. The background

subtraction is necessary for strongly colored compounds, where the color may interfere with the

interpretation of any color change. For noninterfering compounds, a blue color in the well was

interpreted as the absence of growth, and a pink color was scored as growth. Isoniazid and rifampicin

(Sigma) used as positive control, as it is a clinically used antitubercular drug. The results are shown in

Table 1.

3.3.3. MTT Assay

The percent inhibition was determined through the MTT assay. Compounds were prepared

according to the previously described procedure and diluted in MB broth for M. tuberculosis ATCC

25177/H37Ra. M. tuberculosis ATCC 25177/H37Ra was suspended in ODAC supplemented MB at

1.0 McFarland and then diluted 1:10, using MB as a diluent. The diluted mycobacteria (50 µL) were

added to each well containing the compound to be investigated. Diluted mycobacteria in broth free

from inhibiting compounds were employed as the growth control. All compounds were prepared

◦

in duplicate. Plates were incubated at 37 C for 7 days for M. tuberculosis. After the incubation

period, 10% well volume of MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide)

reagent (Sigma) was added into each well and the plates were incubated at 37 ^◦C for 4 h in dark for

mycobacteria. Subsequently, 100 µL of 17% sodium dodecyl sulfate in 40% dimethylformamide was

added to each well. The plates were read at 570 nm. The absorbance readings for cells grown in the

presence of the tested compounds were compared with those representing uninhibited cell growth to

specify the relative percent inhibition. The percent viability is calculated through the comparison of

a measured value and that of the uninhibited control: % viability = OD_570E/OD_570P

is the reading from the compound-exposed cells, while OD_570Pis the reading from the uninhibited

×

100, where OD_570E

cells (positive control). Cytotoxic potential is evaluated by percent viability <70% [44–46,64].

3.3.4. Combined Eﬀect with Clinically Used Drugs

For the synergy eﬀect study, a method of fractional inhibitory concentration was engaged. A tested

compound (A) and antibiotic (B) (ciproﬂoxacin and oxacillin, purchased from Sigma) were diluted in

the microtitration plate in cation adjusted Mueller–Hinton (CaMH) broth to get an original combination

of concentrations in every well. Raw H was employed for the evaluation of MIC(A); column 12 was

applied for assessment of MIC(B). The plate was inoculated by the bacterial suspension to reach a ﬁnal

concentration of 5

×

10⁵CFU/mL in the wells. The fractional inhibitory concentration index (FICI) was

measured using the concentrations in the ﬁrst nonturbid (clear) well found in each row and column

along with the turbidity/nonturbidity interface. To interpret the combined eﬀect, the lowest FICI was

used [60]. A

indiﬀerence; and

and the results were averaged. The results are reported in Table 2.

3.3.5. Dynamic of Antibacterial Activity

Σ

FICI

≤

0.5 means synergy; 0.5 <

Σ

FICI < 1 means additivity; 1 ≤ ΣFICI < 4 means

Σ

FICI

≥

4 means antagonism, respectively [61]. The tests were made in duplicate,

The method of time-kill curves was employed to investigate the bactericidal eﬀect of the speciﬁed

compounds [65]. The experiment was conducted with S. aureus ATCC 29213 and MRSA SA 3202

isolate. The compounds were diluted in CaMH broth to reach concentrations equal to 1

×

MIC, 2 MIC,

×

and 4 MIC, respectively. The tubes were inoculated by bacterial inoculum in the exponential phase of

×

growth to get a ﬁnal concentration of 7.5 × 10⁶CFU/mL. The tubes were incubated statically at 37 ^◦C.

Immediately after inoculation and after 4, 6, 8, and 24 h, 100 µL of the sample was serially diluted

(1:10) in phosphate-buﬀered saline. Subsequently, 2

×

20 µL from each dilution were put onto an

MH agar plate and cultivated at 37 ^◦C for 24 h. After incubation, the CFUs of dilutions containing

5–50 colonies were counted. Results were expressed as a decrease of log₁₀(CFU) in each time compared

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to the starting inoculum. Bactericidal eﬀect is speciﬁed as a

the growth control at timepoint 0. The test was performed in duplicate on two separate occasions, and

the results were averaged. The growth curves with error bars are illustrated in Figure 6.

−

3log decrease of CFU/mL compared to

3.4. Theoretical Calculations

3.4.1. Model Building and Molecular Modeling

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 as

well. Sybyl-X 2.0/Certara software package running on an 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. One 13-ordered atom trial alignment

on the most active molecule 13 according to the active analog approach (AAA) was used in the FIT

method to cover the entire bonding topology in the maximal common structure (MCS).

SONNIA software was applied for simulation of 10

×

10 to 30

×

30 SOMs with a winning distance

ranging from 0.2 to 2.0 in CoMSA analysis. Cartesian coordinates of the molecular surfaces were used

as input to Kohonen SOM network to generate a 2D map of the electrostatic potential (MEP) for the set

of superimposed molecules. The output maps were reshaped into a 100- to 900-element vectors that

were subsequently transformed by the PLS method implemented in the MATLAB environment [66].

3.4.2. Similarity-Driven Activity Landscape

The quantitative sampling of similarity-related activity landscape delivers a subtle picture of

favorable and disallowed structural modiﬁcations that are valid for the speciﬁcation of the activity

cliﬀs [11]. The smoothness of this surface can be numerically quantiﬁed using structure-activity

landscape index (SALI) calculated according to the following formula:

ꢀ

A_x− A_y

SALI_x,y

=

(1)

1 − sim(x, y)

where A_xand A_yare activity proﬁles for the x-th and y-th molecule and sim(x,y) is the similarity

estimation between the corresponding molecules [13 16]. The combination of the pairwise dissimilarity

–

and absolute discrepancies in the molecular activities assigns the score to the speciﬁc spot of the

response surface. The negligible impact of the speciﬁc similarity metric on the overall appearance

of the symmetric SALI matrix has been observed; therefore, Tanimoto coeﬃcient was applied in the

ﬁngerprint-based similarity analysis [14]. In this case, the structural relatedness between molecules

library is usually estimated using a function mapping (dis)similarities between the pairs of bitstring

descriptors given by the following equation:

n_xy

T(x, y) =

(2)

(n_x+ n_y− n_xy)

where n_xyis the number of bits set into 1 shared in the ﬁngerprint of the molecule x and y, n_xis

the number of bits set into 1 in the molecule x, n_yis the number of bits set into 1 in the molecule

y, respectively. Top-ranked objects are supposed to have similar properties, although the validity

of this assumption is fairly questionable because 70% of compounds with T(x,y) > 0.85 to an active

analog have a comparatively low opportunity of being active in the same way [67]. A relatively sparse

sampling of chemistry space may be suﬃcient to identify the potential activity cliﬀs.

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3.4.3. Selection-Driven Surface Analysis

The hypothesis-based similarity in a structural space can be expressed by the number of descriptors

that capture information about the pharmacophore pattern. Some machine learning techniques

combined with weighting and selecting algorithms were applied to specify the minimal set of

pharmacophoric features potentially valid for ligand-site interactions [59]. A meaningful comparison

of the shape/charge intensities produced on the molecular surface and values of target aﬃnity

can be performed using comparative molecular surface analysis (CoMSA) [68]. A self-organizing

network (SOM) is composed of a single layer of neurons arranged in a 2D plane with well-deﬁned

topology [69]. The closely related objects are located in the proximal neurons of the square map

because a neural network automatically adapts itself to the input data. In other words, a planar image

of the multidimensional property space is generated, where objects located in neighboring neurons

possess theoretically similar properties—most groups have a distinct location in speciﬁc regions of the

map. The presence of electrostatic and/or steric features, that are potentially valid for ligand-receptor

complementarity and recognition, can be detected using the recurrent variable selection method

conjugated with the PLS procedure (IVE-PLS) [70]. Brieﬂy, the whole computer implementation

includes four stages: (

model, ( ) elimination of a matrix column with the lowest abs(mean(b)/std(b)) value, (

analysis of the new matrix without the column eliminated in stage ( ), ( ) recurrent repetition of stages

)–( ) to maximize the LOO parameter. In order to estimate the model robustness and SAR predictive

1

) standard PLS analysis with LOO-CV to assess the performance of the PLS

2

3) standard PLS

2

4

(1

3

power, the multiple interchangeable division of the dataset into training/test subsets was applied.

The descriptor-driven consensus pharmacophore pattern is derived on the basis of the validated

models; however, our models are not expected to be predictive externally—extrapolation might be

elusive [71]. The chosen training/test samplings within highly populated areas of q²_cv> 0.5 versus

q²_test

> 0.5 performance were selected to produce ‘an average’ pharmacophore. On the contrary to

the standard procedure for a single training/test subset, an attempt was taken to specify a common

ensemble of variables that contribute (un)-favorably to the observed activity simultaneously in all

chosen models. Thus, the columns annotated with the highest stability abs(mean(b)/std(b)) for each of

the chosen models were identiﬁed using the IVE-PLS methodology. The cumulative sum of common

columns for the selected models was speciﬁed and normalized to the range of [0–1]. The relative

contribution of each variable is weighted by the magnitude and sign of the corresponding regression

coeﬃcient; therefore, color code signs of the descriptor aﬀect/inﬂuence compound potency. The sign

of inﬂuence is color-coded depicting not only the regions with a positive and/or negative activity

contribution, but also four possible combinations of a mean charge/correlation coeﬃcient, respectively.

A simpliﬁed visual inspection of the generated pharmacophore pattern gives a clear 3D picture of the

regions that should be modiﬁed to modulate the biological activity [59].

4. Conclusions

A series of twenty-two novel multi-target N-(disubstituted-phenyl)-3-hydroxynaphthalene-2-

carboxamide derivatives were synthesized and characterized by the set of in vitro potency proﬁles.

N-[3,5-bis(triﬂuoromethyl)phenyl]-3-hydroxynaphthalene-2-carboxamide (13) and N-[2-chloro-5-

(triﬂuoromethyl)phenyl]-3-hydroxynaphthalene-2-carboxamide (20) demonstrated high activity against

methicillin-resistant S. aureus isolates in the range of MICs from 0.16 to 0.68

N-[4-bromo-3-(triﬂuoromethyl)phenyl]-3-hydroxynaphthalene-2-carboxamide (16) showed activity

against M. tuberculosis (both MICs approx. 10 M) comparable with rifampicin. Synergistic

µM. Compound 13 and

µ

in vitro studies showed an increase in activity of ciproﬂoxacin in combinations with compound

16 or N-(4-bromo-3-ﬂuorophenyl)-3-hydroxynaphthalene-2-carboxamides (17) against MRSA SA

630 isolate, which could be related to the inhibition of eﬄux pumps. The similarity-related

property space assessment for the congeneric series of structurally related carboxamide derivatives

was performed using the principal component analysis. It is noticeable that very active

N-[3,5-bis(triﬂuoromethyl)phenyl]-3-hydroxynaphthalene-2-carboxamide (13) occupies a distinct

Int. J. Mol. Sci. 2020, 21, 6583

20 of 23

location of the PC1 vs. PC2 plane, while fairly potent compounds are grouped together. Interestingly,

diﬀerent distribution of mono-halogenated carboxamide derivatives with the –CF₃substituent

(compounds 15, 16, 18–22) is accompanied by the increased activity proﬁle. Moreover, the quantitative

sampling of similarity-related activity landscape provided a subtle picture of favorable and disallowed

structural modiﬁcations that are valid for determining the activity cliﬀs. A symmetric matrix of

Tanimoto coeﬃcients indicated the structural dissimilarities of dichloro-substituted (compounds

9

–

12) and dimetoxy-substituted (compounds

compound 13 (R = 3,5-CF₃) is accompanied by inactive 3-hydroxy-N-[4-methyl-3-(triﬂuoromethyl)

phenyl]naphthalene-2-carboxamide (14) where one triﬂuoromethyl group was isomerically (meta orto)

1, 2) isomers from the remaining ones. The potent

→

replaced with one methyl substituent. It seems that further dense sampling of rough regions (the

upper right corner) in the numerical SALI plane (T > 0.75 and pIC₅₀> 2) is necessary to specify

valid SAR boundaries for the investigated series of carboxamides. An advanced method of neural

network quantitative SAR was engaged to illustrate the key 3D steric/electronic/lipophilic features

of the ligand-site composition by the systematic probing of the functional group. On the other hand,

it cannot be expected that even robust and validated models can reliably predict the modeled property

for the entire universe of chemicals; therefore, the application boundary should be investigated using

similarity concept and biological surface analysis. From a philosophical point of view, it is impossible

to specify an absolute measure of predictivity, as it considerably depends on the selection of datasets

and the statistical approach that is engaged, but the great advantage of the QSAR paradigm lies not

in the extrapolation, as was stated by Hansch [14]. Combining molecular similarity concept with

hypothesis-based pharmacophore mapping may enhance the probability of ﬁnding potent compounds

in drug discovery projects.

Author Contributions: J.K. and T.G. synthesized and characterized the compounds. H.M., S.P. and A.C. performed

biological screening. J.J. designed the compounds. A.B. and V.K. and A.S. performed theoretical calculations,

CoMSA, 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 Ministry of Education of the Czech Republic (project LO1305) and

Palacky University in Olomouc (IGA_PrF_2020_023).

Acknowledgments: The authors thank Johann Gasteiger for facilitating access to the SONNIA programs. We

would like to acknowledge the OpenEye and OpenBabel Scientiﬁc Software for providing free academic licenses.

Conﬂicts of Interest: The funders had no role in the design of the study; in the collection, analyses, or interpretation

of data; in the writing of the manuscript, or in the decision to publish the results.

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Sample Availability: Samples of the compounds are available from the authors.

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Consensus-based pharmacophore mapping for new set of n-(Disubstituted-phenyl)-3-hydroxyl-naphthalene-2-carboxamides

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