Antimicrobial activity of quasi-enantiomeric cinchona alkaloid derivatives and prediction model developed by machine learning

Article abstract of DOI:10.3390/antibiotics10060659

Bacterial infections that do not respond to current treatments are increasing, thus there is a need for the development of new antibiotics. Series of 20 N-substituted quaternary salts of cinchonidine (CD) and their quasi-enantiomer cinchonine (CN) were pr

Full text of DOI:10.3390/antibiotics10060659

antibiotics
Article
Antimicrobial Activity of Quasi-Enantiomeric Cinchona
Alkaloid Derivatives and Prediction Model Developed by
Machine Learning
1
2
3
3
ˇ
Alma Ramic´ , Mirjana Skocˇibušic´
, Renata Odžak 2 , Ana Cipak Gašparovic´
, Lidija Milkovic´
,
1
1
Ana Mikelic´ , Karlo Sovic´ , Ines Primožicˇ 1,* and Tomica Hrenar 1,
*
1
Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia; alma.ramic@chem.pmf.hr (A.R.);
ana.mikelic@chem.pmf.hr (A.M.); karlo.sovic@chem.pmf.hr (K.S.)
2
3
Faculty of Science, University of Split, 21000 Split, Croatia; mirskoc@pmfst.hr (M.S.); rodzak@pmfst.hr (R.O.)
ˇ
Rud¯er Boškovic´ Institute, 10000 Zagreb, Croatia; ana.cipak@irb.hr (A.C.G.); lidija.milkovic@irb.hr (L.M.)
*
Correspondence: ines.primozic@chem.pmf.hr (I.P.); tomica.hrenar@chem.pmf.hr (T.H.);
Tel.: +385-1-4606000 (I.P. & T.H.)
Abstract: Bacterial infections that do not respond to current treatments are increasing, thus there
is a need for the development of new antibiotics. Series of 20 N-substituted quaternary salts of
cinchonidine (CD) and their quasi-enantiomer cinchonine (CN) were prepared and their antimi-
crobial activity was assessed against a diverse panel of Gram-positive and Gram-negative bacteria.
All tested compounds showed good antimicrobial potential (minimum inhibitory concentration
ꢀꢁꢂꢀꢃꢄꢅꢆꢇ
ꢀꢁꢂꢃꢄꢅꢆ
(MIC) values 1.56 to 125.00 µg/mL), proved to be nontoxic to different human cell lines, and did not
influence the production of reactive oxygen species (ROS). Seven compounds showed very strong
bioactivity against some of the tested Gram-negative bacteria (MIC for E. coli and K. pneumoniae
Citation: Ramic´, A.; Skocˇibušic´, M.;
ˇ
Odžak, R.; Cipak Gašparovic´, A.;
Milkovic´, L.; Mikelic´, A.; Sovic´, K.;
Primožicˇ, I.; Hrenar, T. Antimicrobial
Activity of Quasi-Enantiomeric
Cinchona Alkaloid Derivatives and
Prediction Model Developed by
Machine Learning. Antibiotics 2021,
10, 659. https://doi.org/10.3390/
antibiotics10060659
6.25 µg/mL; MIC for P. aeruginosa 1.56 µg/mL). To establish a connection between antimicrobial
data and potential energy surfaces (PES) of the compounds, activity/PES models using principal
components of the disc diffusion assay and MIC and data towards PES data were built. An extensive
machine learning procedure for the generation and cross-validation of multivariate linear regression
models with a linear combination of original variables as well as their higher-order polynomial
terms was performed. The best possible models with predicted R2(CD derivatives) = 0.9979 and
R2(CN derivatives) = 0.9873 were established and presented. This activity/PES model can be used for
accurate prediction of activities for new compounds based solely on their potential energy surfaces,
which will enable wider screening and guided search for new potential leads. Based on the obtained
results, N-quaternary derivatives of Cinchona alkaloids proved to be an excellent scaffold for further
optimization of novel antibiotic species.
Academic Editor: Yuji Morita
Received: 7 May 2021
Accepted: 27 May 2021
Published: 31 May 2021
Keywords: quaternary cinchonidines; quaternary cinchonines; antimicrobial activity; cytotoxicity;
ROS; activity/PES model; machine learning
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
1. Introduction
Bacterial drug resistance is one of the major problems in public health worldwide.
Reports from different health organizations make claims that antibacterial resistance is
responsible for more than 35,000 deaths in the United States and about 33,000 deaths
in European Union, annually [1,2]. A lot of effort has been put into the research and
development of antibacterial agents against emerging new bacterial strains. In the search for
new classes of antibacterial agents, various groups of alkaloids were extensively researched
Copyright:
© 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
and employed as scaffolds, such as metronidazole, quinolones, indoles, and others [
Cinchona alkaloids are natural products isolated from the bark of the Cinchona tree and the
most known are quinine ( ), quinidine (QD), cinchonine (CN), and cinchonidine (CD).
The structure of these alkaloids consists of a bulky quinuclidine ring with a vinyl side chain,
3].
Q
Antibiotics 2021, 10, 659. https://doi.org/10.3390/antibiotics10060659
https://www.mdpi.com/journal/antibiotics

Antibiotics 2021, 10, 659
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an aromatic quinoline ring, and a hydroxyl group at C9. They have five chiral centers (N1,
C3, C4, C8, and C9) and two of them, C8 and C9, can have different absolute configuration
in
Q/QD and CN/CD pairs, so these diastereomers are often called quasi-enantiomers.
Structures of CN and CD are presented in Figure 1.
Compound
CD
R
‐
X−
‐
Compound
CN
CD‐Met
CD‐Bzl
CH3−
Bzl−
I
CN‐Met
CN‐Bzl
Br
Br
Br
Br
Br
Br
Br
Br
Br
CD‐(pBr)
CD‐(pCH3)
CD‐(pNO2)
CD‐(pCl)
CD‐(mBr)
CD‐(mCH3)
CD‐(mCl)
CD‐(mNO2)
para‐BrC6H4CH2−
para‐CH3C6H4CH2−
para‐NO2C6H4CH2−
para‐ClC6H4CH2−
meta‐BrC6H4CH2−
meta‐CH3C6H4CH2−
meta‐ClC6H4CH2−
meta‐NO2C6H4CH2−
CN‐(pBr)
CN‐(pCH3)
CN‐(pNO2)
CN‐(pCl)
CN‐(mBr)
CN‐(mCH3)
CN‐(mCl)
CN‐(mNO2)
Figure 1. Structures of cinchonidine (CD) and cinchonine (CN) compounds. Absolute configurations,
opposite in quasi-enantiomers at positions 8 and 9, are noted; (8S,9R) in CD and (8R,9S) in CN.
Because of their availability and interesting properties, derivatives of Cinchona alka-
loids have various applications in all fields of chemistry, for example as chiral resolving
agents or chiral stationary phases for chromatographic separation, as a chiral catalyst or
chiral ligands in asymmetric synthesis [
ical activity, which is not surprising since for decades,
malaria [ ]. Besides anti-malarial activity, they have anti-inflammatory, anti-arrhythmic,
anti-proliferative, and insecticidal activity, among others [ 10]. Qi et al. performed experi-
4
–
6]. They also possess a wide range of biolog-
Q
was used for the treatment of
7
8
–
ments in vitro which showed that CN could induce apoptosis and reduce the proliferation
of cancer cells, and experiments in animals showed that it could suppress tumor growth in
mice [11]. These findings were confirmed by Jo et al., who showed that CN inhibits osteo-
clast differentiation and promotes osteoblast differentiation [12]. Another group designed
and synthesized indocinchona alkaloids, an alkaloid with merged QN and indole rings.
They identified one of them, azaquindol, as a novel class of autophagy inhibitors, which
play a crucial role in cancer and degenerative diseases [13]. QN scaffold was used for
creating active nanostructured coatings with the ability to release antibacterial compounds
against Escherichia Coli [14]. Optochin is a Cinchona alkaloid derivative that poses highly
selective antibacterial activity towards Streptococcus pneumoniae and it is used as a labora-
tory standard for differentiation of Streptococcus pneumoniae from other streptococci [15].
Aldrich et al. recently prepared a series of new optochin derivatives, and one of them
showed increasing activity toward multidrug-resistant strains of Streptococcus pneumoniae
compared to the parent compound [16].
In this paper, we evaluate the antimicrobial activity of quaternary derivatives of
cinchonidine and cinchonine by using disc diffusion and broth microdilution assay against
a representative panel of Gram-positive and Gram-negative bacteria. For the most potent
compounds, cytotoxicity was assessed on four different human cell lines as well as their
influence on creating reactive oxygen species (ROS).
2. Materials and Methods
2.1. Synthesis of Quaternary Derivatives
The detailed synthetic protocols and spectral data of products have been previously
reported [17]. Chemical structures of compounds are presented in Figure 1.

Antibiotics 2021, 10, 659
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2.2. Antimicrobial Activity
Test microorganisms were obtained from the American Type Culture Collection
(ATCC, Rockville, MD, USA) and Faculty of Science, University of Split, Croatia (FSST).
Three Gram-negative bacteria were used: Escherichia coli (FSST 982), Klebsiella pneumoniae
(FSST 011) and Pseudomonas aeruginosa (FSST 982); as well as four Gram-positive bacte-
ria: Bacillus cereus (ATTC 11778), Enterococcus faecalis (ATCC 29212), Staphylococcus aureus
(ATCC 25923), and Clostridium perfringens (FSST 4999). To reach optical density equiv◦alent
of 106 colony-forming units (cfu/mL), bacterial strains were cultured overnight at 37 C in
tryptic soy broth (TSB). Compounds were dissolved in DMSO to obtain a stock solution of
10 mg/mL.
For disc diffusion assay, sterile Mueller–Hinton agar was dispensed in sterile petri
dishes (90 mm diameter) and left at room temperature to solidify for 2 h. The paper discs
(6 mm diameter) were placed on the agar surface and 50 µL of each compound was placed
on an empty disc. Gentamicin was used as positive control and DMSO as solvent control.
Petri dishes were left to stand for 20 min at room temperature before incubation at 37 ◦C
for 24 h. The diameter of the inhibition zone was measured in mm and the experiment was
repeated trice.
Broth microdilution assay was used to assess minimum inhibitory concentration
(MIC) by standard two-fold serial microdilution assay according to Clinical and Laboratory
Standards Institute. Gentamicin and cefotaxime were used as positive controls. A detailed
description of experiments was previously reported [18,19].
2.3. MTT
To assess the influence of the compounds with antibacterial activity on human cell
lines, EZ4U MTT assay (Biomedica, Vienna, Austria) was performed according to the
manufacturer’s instructions. Briefly, after thawing, cell lines FB-35 (primary culture of
foreskin fibroblasts), NDFH (normal human dermal fibroblasts) HaCaT (spontaneously
transformed aneuploid immortal keratinocyte cell line from adult human skin), and HMEC-
1 (human microvascular endothelial cell line) cell lines were cultivated in Dulbecco’s
Modified Eagles Media (DMEM; Sigma-Aldrich, St. Louis, MO, USA) supplemented with
10% fetal calf serum (FCS). After growing to 80% of confluence, cells were trypsinized and
plated at a density of 10,000 cells/well for 24 h. The next day, cells were treated with the
compounds for additional 24 h. Compound with high antimicrobial activity, CD-(pBr)
was diluted to final concentrations of 1 M, 5 M, 10 M, 50 M, 100 M, and 200 M,
while CD-(pNO2), CD-(pCl), CN-Met, CN-Bzl, CN-(pCl), and CN-(mBr) were diluted to
M, 10 M, and 100 M, respectively. Control cells were not treated with anything, while
,
µ
µ
µ
µ
µ
µ
1
µ
µ
µ
vehicle control was DMSO in the concentration of the corresponding compound dilution.
At the end of treatment, the colorless dye was added to each well and the color development
was monitored by measuring absorbance at 450 nm, with 620 nm as a reference wavelength.
All experiments were performed in technical and biological triplicates. The obtained data
were analyzed by one-way ANOVA with Dunnett’s multiple comparisons test comparing
each compound with the control.
2.4. Measurement of ROS, GSH and Catalase Activity
To determine the effect of the selected compounds on redox level in the cells, we
measured levels of ROS as oxidative part, and GSH levels and catalase activity as antioxida-
tive parts of the cell redox system. For these assays, the two compounds with the highest
antibacterial activities were selected: CD-(pBr) and CD-(pNO2).
To assess the influence of the compounds on ROS production, FB-35, HaCaT, and
HMEC-1 cells were plated in black 96-microwell plates at a density of 10,000 cells/well
in colorless DMEM with 10% FCS and left overnight to adhere. The next day, 20,70-
dichlorofluorescin diacetate (DCF-DA) at a final concentration of 20
30 minutes in each well. After the end of incubation, media with DCF-DA was removed and
fresh media alone, or with 1 M, 10 M, 100 M of CD-(pBr), and CD-(pNO2) was added
µM was added for
µ
µ
µ

Antibiotics 2021, 10, 659
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to the cells. The ROS production was measured on fluorimeter/spectrometer plate reader
Infinite 200 PRO (Tecan Group Ltd., Männedorf, Switzerland) at an excitation wavelength
of 500 nm and emission detection at 530 nm.
GSH levels and catalase activity were assessed on FB-35, HaCaT, and HMEC-1 cell
lines. Cells were plated at a density of 0.5
×
106 cells/well overnight. The next day,
cells were treated with 100 M and 200 M of CD-(pBr), CD-(pNO2), and equivalent
µ
µ
concentrations of DMSO and were lef◦t overnight. After 24 h, cells were trypsinized
and the dry pellet was stored at
−
80 C until the GSH and catalase activity analysis.
GSH analysis was performed after diluting samples to 0.03 mg/ml, and the addition
of reaction mix (8 mM 5,5-dithio-bis-2-nitrobenzoic acid, 0.4 Units of GSH reductase,
and 0.6 mM of NADPH in phosphate buffer 100 mM NaH2PO4, 5 mM EDTA, pH 7.4).
The formation of yellow product, 2-nitro-5-thiobenzoic acid, was measured on a plate
reader at 405 nm (Easy-Reader 400 FW; SLT Lab Instruments, GmbH, Salzburg, Austria).
The catalase activity assay is based on the degradation of H2O2 by the catalase in the
cell lysate. Catalase is the enzyme with one of the highest turnover numbers, making
it the first to degrade H2O2. The reaction started with mixing 40
µL of cell lysate with
100 L of 65 mM H2O2 for 5 minutes. The addition of 100 L of 32.4 mM ammonium
µ
µ
molybdate stopped the reaction. The intensity of the resulting yellow complex between
ammonium molybdate and hydrogen peroxide was measured with a plate reader Multiskan
EX (Thermo Electron Corporation, Shanghai, China) at 405 nm. Concentrations of hydrogen
peroxide in a range from 0 to 75 mM were used as standards. One unit of catalase activity is
◦
defined as the amount of enzyme needed for degradation of 1
µ
mol of H2O2/min at 25 C.
Catalase activity was expressed as units per milligram of proteins in cell lysate (U mg−1).
2.5. Statistics
All experiments were performed in technical and biological triplicates. The obtained
data were analyzed by one-way ANOVA with Dunnett’s multiple comparisons test com-
paring each compound with the control.
2.6. Principal Component Analysis
Multivariate analyses were conducted by a second-order tensor analysis tool known
as principal component analysis (PCA) [20
,
21]. In PCA, the data matrix
X
of rank r is
decomposed in the sum of r matrices ti pτi with rank 1 (Equation (1)):
r
X =
ti pτ
(1)
∑
i
i=1
ti is a vector of scores and pτi is a vector of loadings. PCA provides the best linear
projection of multidimensional data by minimizing the least squares objective function.
Scores are used for classification, while loadings can be used for the variability identification
among the data. PCA development goes back to Beltrami [22] and Pearson [23], and the
name was introduced by Harold Hotelling [24].
Disc diffusion assay and MIC data were arranged in the data matrix
the covariance matrix was performed by our parallelized code for multi- and univariate
analysis [25 27]. Extraction of eigenvectors was based on the NIPALS algorithm [28] and
X, and PCA on
–
the obtained principal components were subsequently used as regressed variables.
2.7. Sampling of the Potential Energy Surfaces
Ab initio molecular dynamics simulations with on-the-fly calculations of forces were
used as a sampling procedure for potential energy surfaces (PES). Equations were in-
tegrated using the velocity Verlet algorithm [29]. The PM7 method [30] implemented
in MOPAC2016 [31] was used for calculation of forces in each point of the simulation.
Molecular dynamics were conducted by using our in-house developed program qcc [32,33].
Phase space coverage was ensured by setting the initial temperature for Maxwell distri-
bution of velocities to 773.15 K. During the simulation, temperature was controlled using

Antibiotics 2021, 10, 659
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the velocity scaling algorithm. Step size was 0.5 fs and a total of 5 million steps were
computed for each compound. PES of compounds spanned in multidimensional space
of Cartesius coordinates were evaluated by PCA, providing principal components for
further regression.
2.8. Machine Learning Multivariate Linear Regression
Reduced spaces of multi-target antimicrobial activities were used as dependent vari-
ables for estimation of Cinchona alkaloids derivatives [17] activities. A panel of various
Gram-positive and Gram-negative bacteria provided activity data whose principal compo-
nents were extracted by the second-order tensor decomposition. These principal compo-
nents were regressed on the theoretically computed energy fingerprints of all compounds
by performing extensive machine learning (ML).
The ML procedure was applied for the generation of all possible multivariate linear
regression models with a linear combination of original variables as well as their higher-
order polynomial terms. Multivariate linear regression was performed using the following
expression for matrices of coefficients B calculated by singular value decomposition:
B = (XτX)−1XτY
(2)
where
X and Y are the matrices of independent and dependent variables, respectively.
Every possible regression model of antimicrobial activity dependent on molecular dynam-
ics data was built and thoroughly validated by the leave-one-out cross-validation technique
(LOO-CV). The models were inspected up to the sixth order for 2D models and up to the
fourth order for 3D models, and the total numbers of investigated models were 134,217,728
and 17,179,869,184, respectively. The most optimal representations were selected based on
the adjusted and predicted R2 values, LOO-CV mean squared error, as well as the number
of variables in the models.
3. Results and Discussion
3.1. Synthesis
A series of differently substituted quaternary ammonium salts of CDs and their corre-
sponding quasi-enantiomeric CNs were synthesized by reaction of commercially available
cinchonidine or cinchonine and alkyl or arylalkyl halides in refluxing toluene by published
procedures [17]. Compounds CD-Met and CN-Met were prepared in reaction of the appro-
priate alkaloid with methyl iodide, CD-Bzl and CN-Bzl with benzyl bromide, and other
compounds with appropriate meta- and para-substituted benzyl bromides, different in size
and electronic properties. Compounds were characterized by standard analytical methods
(IR, 1D and 2D NMR, MS, CHN analysis).
3.2. Antimicrobial Activity
Unmodified parent alkaloids CD and CN as well as prepared quaternary derivatives
of quasi-enantiomers were screened for antimicrobial activity on different Gram-positive
and Gram-negative bacteria by disc diffusion method. Activities of the target compounds
were expressed as the mean diameter of the measured inhibition zone (mm) against selected
microorganisms along with the activity of the reference compound gentamicin, Table 1.

Antibiotics 2021, 10, 659
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Table 1. Antimicrobial activity of CD and CN derivatives against a panel of Gram-positive and Gram-negative bacterial strains determined by disc diffusion assay. According to the sizes
of the inhibitory zone (including the diameter of a disc), the antimicrobial activity is graded as inactive (0–9 mm); mildly active (10–15 mm); moderately active (16–20 mm); and highly
active (≥21 mm). All values are expressed as mean ± SD of three parallel measurements (n = 3).
Diameters of the Inhibition Zone (mm) a
Compounds
Gram-Positive Bacteria
E. faecalis
Gram-Negative Bacteria
K. pneumoniae
10.2 ± 1.1
12.4 ± 0.9
16.7 ± 1.6
14.7 ± 0.6
15.8 ± 0.9
26.1 ± 2.1
19.5 ± 1.3
17.6 ± 1.3
12.7 ± 0.6
18.2 ± 0.7
12.4 ± 3.1
12.6 ± 1.4
13.4 ± 1.0
13.9 ± 0.6
12.1 ± 2.3
14.2 ± 1.5
14.7 ± 1.2
13.5 ± 1.6
14.6 ± 1.1
10.2 ± 0.9
9.2 ± 1.4
B. cereus
10.9 ± 1.7
12.7 ± 1.1
9.4 ± 0.5
16.6 ± 1.4
15.8 ± 2.2
22.8 ± 0.8
15.3 ± 1.0
14.8 ± 1.3
9.8 ± 1.0
15.0 ± 1.6
15.3 ± 0.6
13.2 ± 0.9
12.4 ± 1.0
22.7 ± 1.9
15.7 ± 1.8
8.8 ± 1.3
13.3 ± 1.6
21.5 ± 1.5
16.6 ± 1.3
7.7 ± 1.6
S. aureus
13.3 ± 0.6
15.7 ± 1.7
18.5 ± 1.2
17.4 ± 1.8
17.8 ± 2.3
17.4 ± 0.3
15.5 ± 1.7
21.4 ± 0.7
20.3 ± 1.8
19.0 ± 1.4
19.2 ± 1.5
13.2 ± 1.8
16.4 ± 1.2
13.4 ± 1.6
16.2 ± 1.7
10.8 ± 1.4
15.7 ± 2.5
17.5 ± 1.3
20.6 ± 2.2
11.5 ± 1.2
16.2 ± 1.6
15.4 ± 2.4
23.9 ± 0.9
C. perfringens
14.6 ± 0.7
14.2 ± 1.3
8.4 ± 2.2
E. coli
P. aeruginosa
6.4 ± 0.9
CD
14.2 ± 0.9
11.2 ± 1.4
15.7 ± 1.9
15.6 ± 2.3
16.8 ± 0.9
27.4 ± 1.7
16.3 ± 1.3
19.6 ± 1.2
17.3 ± 2.3
16.0 ± 1.8
16.2 ± 2.5
14.2 ± 1.9
13.4 ± 1.5
9.6 ± 1.6
17.2 ± 1.4
14.8 ± 1.2
15.6 ± 1.6
25.7 ± 2.7
14.8 ± 0.9
23.1 ± 1.4
24.5 ± 1.8
14.6 ± 1.2
17.7 ± 0.7
15.2 ± 2.4
15.4 ± 1.8
16.3 ± 1.2
14.4 ± 2.1
15.6 ± 2.5
11.2 ± 0.7
13.4 ± 1.5
10.5 ± 1.2
14.5 ± 1.3
11.6 ± 2.3
11.9 ± 1.5
12.2 ± 0.9
14.4 ± 2.3
11.5 ± 0.9
CD-Met
15.8 ± 2.1
16.3 ± 1.7
25.7 ± 2.4
11.8 ± 0.9
25.1 ± 1.6
17.2 ± 1.2
16.3 ± 1.9
12.1 ± 2.7
17.2 ± 1.3
17.4 ± 1.4
19.2 ± 2.7
25.4 ± 3.2
27.6 ± 3.2
13.2 ± 0.5
10.1 ± 1.3
10.2 ± 2.1
28.5 ± 2.8
24.6 ± 3.0
14.4 ± 1.7
13.9 ± 2.1
7.4 ± 1.9
CD-Bzl
CD-(pBr)
CD-(pCH3)
CD-(pNO2)
CD-(pCl)
CD-(mBr)
CD-(mCH3)
CD-(mCl)
CD-(mNO2)
CN
15.6 ± 0.9
18.1 ± 0.9
21.8 ± 0.9
23.3 ± 1.7
13.8 ± 0.6
10.3 ± 1.1
16.0 ± 0.8
17.2 ± 1.3
10.2 ± 1.5
11.4 ± 1.1
12.1 ± 1.5
10.7 ± 1.3
11.8 ± 2.6
19.4 ± 2.1
10.5 ± 1.1
12.6 ± 1.4
12.1 ± 1.3
15.2 ± 3.2
15.7 ± 2.2
CN-Met
CN-Bzl
CN-(pBr)
CN-(pCH3)
CN-(pNO2)
CN-(pCl)
CN-(mBr)
CN-(mCH3)
CN-(mCl)
CN-(mNO2)
GENb
17.7 ± 0.9
9.2 ± 1.7
12.9 ± 1.7
14.5 ± 1.7
17.2 ± 1.3
16.7 ± 0.6
13.2 ± 2.3
12.1 ± 1.3
14.6 ± 1.4
15.2 ± 0.6
9.4 ± 1.5
11.2 ± 2.0
18.8 ± 0.6
18.2 ± 0.7
21.7 ± 0.4
9.7 ± 1.4
a
b
Diameter of inhibition zone (values in mm) around the disc: 200 µg/disc. Gentamicin standard antibiotic disc (15 µg/disc).

Antibiotics 2021, 10, 659
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Most of the quaternary Cinchona alkaloid derivatives showed potent and broad-
spectrum activity against selected clinically important pathogens with mean diameters of
inhibition zone of compounds in the range from 6.4
±
0.9 to 28.5
±
2.8 mm. Tested com-
pounds demonstrated antibacterial effects against both Gram-positive and Gram-negative
bacteria. Generally, quaternary derivatives of cinchonidine showed more antibacterial
activity than corresponding quaternary derivatives of quasi-enantiomer cinchonine. In-
terestingly, most of the tested compounds were very effective on E. coli and P. aeruginosa,
showing potential use for P. aeruginosa which is on the critical list for resistance [
In detail, considerable zones of growth inhibition were observed for two tested strains
of Gram-negative bacteria, E. coli (from 10.5 1.1 to 25.7 2.7 mm) and P. aeruginosa
(from 7.4 1.9 to 28.5 2.8 mm). Compounds CD-(pBr) and CD-(pNO2), which have
bromine atom or nitro group in para position on the benzene ring, showed the most potent
1].
±
±
±
±
activity toward E. coli with the mean inhibition diameters of 25.7
and 23.1 ± 1.4 mm for CD-(pNO2).
±
2.7 mm for CD-(pBr)
Quaternary derivatives of CD and CN were then tested against the same panel of
Gram-positive and Gram-negative bacteria to determine MIC values by a broth microdilu-
tion method. The results of antimicrobial assays are summarized in Table 2.
All prepared quaternary derivatives demonstrated potent and broad-spectrum activi-
ties against selected microorganisms with MIC values in the range of 1.56 to 125.00 µg/mL.
Compounds CD-(pBr) and CD-(pNO2) were found to possesses not only strong and very
strong activity against all tested Gram-positive bacteria (MIC values in the range of 6.25–
12.00
µg/mL) but also very strong activity against all tested Gram-negative bacteria (MIC
values 6.25
µg/mL), which are fivefold more potent than gentamicin toward E. coli and
tenfold more potent than gentamicin toward P. aeruginosa, thereby supporting the disc dif-
fusion assay. Compound CD-(pCl) showed very strong activity against E. coli (MIC value
6.25
of CD were also quite active toward E. coli, with MIC values in the range of 25.00 to 50.00
g/mL, while quaternary derivatives of quasi-enantiomers in CN series did not show
similar activity against E. coli. Compounds CD-(pBr) and CN-Bzl showed very strong
activity against P. aeruginosa with a MIC value of 1.56 g/mL which is fortyfold more active
µg/mL) which is fivefold more active than gentamicin. Other quaternary derivatives
µ
µ
than gentamicin and tenfold more active than cefotaxime.
All prepared quaternary derivatives of CD exhibited strong activity toward P. aerugi-
nosa (MIC values in the range of 1.56 to 50.00
CD (MIC value 125.00 g/mL). Most of the prepared quaternary derivatives of CN dis-
played moderate activity toward P. aeruginosa with MIC values up to 125.00 g/mL,
but some of them have very strong activity with MIC values from 3.12 g/mL to 12.50
µg/mL) except unmodified parent alkaloid
µ
µ
µ
µg
/mL, which is twentyfold more active than gentamicin and fivefold more active than cefo-
taxime. Based on the acquired results, the stereochemistry of the antibacterial compound is
important to some extent for the bioactivity toward P. aeruginosa. Compounds CD-(pBr)
and CN-Bzl are 32 times more active than their quasi-enantiomers CN-(pBr) and CD-Bzl.
Taking these two assays together, CD-(pBr) and CD-(pNO2) were effective for both
Gram-positive and Gram-negative bacteria, indicating that the tested compounds do not
inhibit cell wall synthesis. A possible target of these cinchonine derivatives might be
the bacterial ATP synthase, which also appears to be the target for another cinchone
derivate [16]. Certainly, the mechanism should be further investigated, especially due to
reactivity toward P. aeruginosa.
3.3. Cytotoxicity
Compounds with strong activity toward tested bacterial strains were further evaluated
for cytotoxicity on four different human cell lines (Figure 2). There was no change in cell
viability in any of the tested compounds compared to both control and vehicle control.
For compound CD-(pBr), testing a wider concentration range showed no changes in cell
viability (Figure 2).

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Table 2. Determined MIC values for CD and CN quaternary derivatives, gentamicin (GEN) and cefotaxime (CFT) against a panel of Gram-positive and Gram-negative bacterial strains.
No bioactivity was defined as a MIC > 1000 g/mL, mild bioactivity as a MIC in the range 512–1000 g/ mL, moderate bioactivity as a MIC in the range 128–512 g/mL, good bioactivity
µ
µ
µ
as a MIC in the range 32–128 µg/mL, strong bioactivity as a MIC in the range 10–32 µg/mL, and very strong bioactivity as a MIC < 10 µg/mL.
MIC (µg/mL)
Gram-Positive Bacteria
Gram-Negative Bacteria
Compounds
B. cereus
E. faecalis
S. aureus
C. perfringens
E. coli
K. pneumoniae
P. aeruginosa
CD
CD-Met
CD-Bzl
100.00
50.00
100.00
25.00
25.00
12.50
25.00
25.00
100.00
25.00
50.00
50.00
50.00
12.50
25.00
100.00
50.00
12.50
25.00
100.00
50.00
100.00
4.00
50.00
50.00
25.00
25.00
12.50
6.25
50.00
25.00
25.00
25.00
25.00
12.50
25.00
12.50
12.50
25.00
25.00
50.00
25.00
50.00
25.00
100.00
25.00
25.00
12.50
50.00
25.00
25.00
1.00
50.00
50.00
125.00
25.00
25.00
12.50
6.25
50.00
100.00
25.00
25.00
100.00
50.00
50.00
50.00
100.00
12.50
100.00
50.00
50.00
25.00
25.00
0.50
25.00
25.00
50.00
6.25
25.00
6.25
100.00
50.00
50.00
25.00
25.00
6.25
125.00
50.00
50.00
1.56
50.00
6.25
25.00
25.00
50.00
25.00
25.00
12.50
3.12
CD-(pBr)
CD-(pCH3)
CD-(pNO2)
CD-(pCl)
CD-(mBr)
CD-(mCH3)
CD-(mCl)
CD-(mNO2)
CN
25.00
12.50
25.00
25.00
50.00
50.00
50.00
100.00
25.00
100.00
50.00
50.00
25.00
25.00
50.00
50.00
4.00
6.25
12.50
25.00
50.00
25.00
50.00
50.00
50.00
50.00
50.00
50.00
50.00
50.00
25.00
50.00
100.00
50.00
8.00
50.00
25.00
50.00
25.00
25.00
50.00
25.00
50.00
50.00
100.00
50.00
50.00
50.00
50.00
25.00
32.00
0.50
CN-Met
CN-Bzl
1.56
CN-(pBr)
CN-(pCH3)
CN-(pNO2)
CN-(pCl)
CN-(mBr)
CN-(mCH3)
CN-(mCl)
CN-(mNO2)
GEN
50.00
100.00
100.00
3.12
3.12
25.00
50.00
125.00
64.00
16.00
CFT
0.25
0.50
0.50
0.10
0.50

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Figure 2. Effects of compounds on FB-35 (normal foreskin fibroblasts), NDFH (normal human dermal
fibroblasts), HaCat (keratinocytes), and HMEC-1 (microvascular endothelial cell line) viability. p >
0.05.
3.4. Effects of the Compounds on Cellular Reactive Oxygen Species and Antioxidative Defense
After MTT showed no difference in cell viability, we assessed the influence of the
compounds with strong antimicrobial activity on ROS production. Although there are
differences in ROS levels after treatments (Figure 3), these are not statistically significant.
Effects of the CD-(pBr) and CD-(pNO2) compounds on cellular ROS levels are cell
line-specific. While the two compounds did not significantly affect ROS levels in FB-35
fibroblast, CD-(pBr) decreased levels of ROS in HaCaT (control vs. 100 M p = 0.044; and
µ
1
µ
M vs. 100 M p = 0.011) and in HMEC-1 cells (control vs. 10 M or 100 µM, p = 0.017 and
µ
µ
p = 0.029, respectively). The CD-(pNO2) compound decreased ROS levels only in the
HaCaT cell line (control vs. 100 µM, p = 0.013).
Despite obvious differences between cell lines, both tested compounds did not show
statistical differences at the tested concentrations.
The influence of the compound CD-(pBr), which had the most potent antibacterial
activity, on GSH levels was assessed on FB-35, HaCaT, and HMEC-1 cell lines. GSH levels
in all tested cell lines were not affected (Figure 4). Likewise, catalase activity was not
affected by the compound CD-(pBr).

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✱
✱
✱
✱
✱
Figure 3. Effects of compounds CD-(pBr) and CD-(pNO2) on ROS levels in FB-35, HaCaT, and
HMEC-1 cell lines after 2 h treatment.* p > 0.05.
Figure 4. Effects of CD-(pBr) and CD-(pNO2) on GSH levels in FB-35, HaCaT, and HMEC-1.

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3.5. PCA Analysis and Activity/PES Model
To classify investigated compounds according to their activities set side-by-side with the
tested standard antibiotics (Tables 1 and 2), we performed PCA on the disc diffusion assay data
and MIC values [23]. In both cases, the first three principal components explained more than
82% of the total variance among the data, ensuring the proper description of the activities in
this reduced three-dimensional space. Effectively, the seven-dimensional space of multi-target
antimicrobial activities was reduced to only three dimensions and retained the majority of
the information present in the original data. An additional advantage of using data space
reduced to three dimensions is the possibility of visualization and presenting graphical layouts.
Therefore, we used 3D models, presented in Figures 5 and 6 (together with all 2D projections),
to perform classification and identification of principal component directions that are the most
important for evaluating activities. From the classification model for disc diffusion assay
data presented in Figure 5, it is evident that the second principal component describes the
antimicrobial activity. Using the position of compounds in this new reduced space, several
promising candidates were found, e.g., CD-(pNO2) was having a score of 11.28 which is even
higher than for GEN (10.36, Figure 5). In the group of promising candidates, there was also
CD-(pCl) with the score of 9.09.
Figure 5. Classification of compounds based on values calculated by PCA performed on the mean-
centered covariance matrix of their disc diffusion assay values.
According to the MIC values, the first principal component in the negative direction was
the most important in describing the antimicrobial activity of the compounds (Figure 6). Inter-
pretation of principal components is invariant to the sign of the component (the component with
the negative sing is still an eigenvector of covariance matrix), so the fact that some compounds
are shifted along this axis in the negative direction (or in the positive) is not important for further
analysis. Likely candidates were identified as CD-(pNO2), CD-(pBr), and CD-(pCl).
To establish a connection between antimicrobial data and calculated potential energy
surfaces of the compounds, an activity/PES model was created by using the first two
and first three principal components of the reduced PES data and the selected principal
components from the disc diffusion assay and MIC data. As identified in the classification
models obtained by PCA, for disc diffusion data the factor scores along the second principal
component were regressed on the first two and the first three principal components of the
reduced PES data. For MIC data, the factor scores along the first principal component were
regressed on the first two and the first three principal components of the reduced PES data.

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Figure 6. Classification of compounds based on values calculated by PCA performed on the mean-centered covariance
matrix of their MIC values.
An extensive machine learning procedure for multivariate linear regression was
performed. The objective of the machine learning was the determination of the best
possible regression model that can explain compounds’ multi-target antimicrobial activities
regressed on the theoretically computed potential energy surfaces. All possible regression
models were generated, and the B-matrices of coefficients (Equation 2) were determined.
Each model was validated using the leave-one-out cross-validation. The best regression
models were selected based on the adjusted R2 and predicted R2 values, LOO-CV mean
squared error, as well as the number of variables in the models [34].
Disc diffusion assay data for CD derivatives were regressed and the best calculated 2D
and 3D regression models are presented in Figure 8. Despite the very high valued of R2 and
adjusted R2 in 2D model (Figure 7a), the value of predicted R2 had a lower value of only 0.7318,
indicating the overfitting. Due to this reason, we also calculated the 3D regression model. The
3D model had an excellent value of predicted R2 = 0.9979 (Figure 7b), confirming the validity
of this model.
Figure 7. Machine learning determined best multivariate regression models of CD derivatives
disc diffusion assay data dependent on the (a) first two and (b) first three principal component of
compounds potential energy surfaces. (In ( ), spheres represent points in 3D-reduced space, and the
b
planes are cuts of polynomial regression model; for easier interpretation, the fourth dimension is
represented redundantly with the color and size of the spheres.)

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For derivatives of CN, the best 2D and 3D regression models are presented in Figure 8.
Although the R2 in the 2D model had value of 0.9979 (Figure 8a), the value of predicted R2
was 0.5985, again indicating the overfitting. As in the previous case, we extended the model
to three dimensions, producing the 3D model, which had very good value of predicted R2
= 0.9873 (Figure 8b).
Figure 8. Machine learning determined best multivariate regression model of CN derivatives disc
diffusion assay data dependent on the (
a) first two and (b) first three principal component of
compounds potential energy surfaces. (In (
b
), spheres represent points in 3D-reduced space, and the
planes are cuts of polynomial regression model; for easier interpretation, the fourth dimension is
represented redundantly with the color and size of the spheres.)
Regression of MIC data provided models for derivatives of CD (Figure 9a) and
derivatives of CN (Figure 9b). In both cases, the best established models have very high
values of predicted R2, confirming the quality of the models.
Figure 9. Machine learning determined best multivariate regression models of (
CN derivatives MIC data dependent on the principal component of compounds potential energy
surfaces. (In ( ), spheres represent points in 3D-reduced space, and the planes are cuts of polynomial
a) CD derivatives and
(b)
a
regression model; for easier interpretation, the fourth dimension is represented redundantly with the
color and size of the spheres.)
An established activity/PES model can be used for the prediction of antimicrobial
activities for new compounds based solely on the reduced space of compounds potential
energy surfaces. The models will work good for similar compounds, i.e., CD or CN
derivatives, while for the different type of compounds with significantly different chemical
structure, one cannot expect the models to work.
4. Conclusions
The biological activity of unmodified alkaloids CD and CN and their N-alkyl and
N-aryl quaternary derivatives was determined. Compounds CD-(pBr)
(pCl) CN-CH3 CN-Bzl CN-(pNO2), and CN-(pCl) showed strong antimicrobial activity
toward the tested representative panel of bacteria, especially the emerging pathogen
Pseudomonas aeruginosa (the lowest MIC value was 1.56 g/mL). Compounds do not show
, CD-(pNO2), CD-
,
,
,
µ
a toxic effect or an effect on the production of reactive oxygen species in different human
cell lines. An extensive machine learning procedure for the generation of multivariate linear
regression models with a linear combination of original variables as well as their higher-
order polynomial terms was performed. Among all statistically possible regression models,

Antibiotics 2021, 10, 659
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the best possible models with predicted R2 > 0.98 were determined. This activity/PES
model can be used for accurate prediction of activities for new CD and CN derivatives
based solely on their potential energy surfaces, which will enable wider screening and
faster search for new potential leads. Based on obtained results, N-quaternary derivatives
of Cinchona alkaloids proved to be an excellent scaffold for further optimization of novel
antibiotic species.
Author Contributions: Preparing of compounds, I.P. and A.R.; Antimicrobial assays, A.R., M.S. and
ˇ
ˇ
R.O.; Writing—original draft preparation, A.R., A.C.G., I.P. and T.H.; Methodology, A.R., A.C.G. and
ˇ
L.M.; Writing and analyses regarding cell line assays, A.C.G. and L.M.; Writing—review & editing,
I.P., R.O., A.C.G. and T.H.; Principal component analysis and machine learning multivariate linear
ˇ
regression, A.M. and K.S.; Machine learning software and model establishment, T.H.; Conceptu-
ˇ
alization, I.P. and T.H; Supervision, A.C.G., I.P. and T.H. All authors have read and agreed to the
published version of the manuscript.
Funding: This research was funded by the Croatian Science Foundation, grant numbers IP-2016-06-
3775”. ADESIRE“ and ESF-DOK-2018-09-3416.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The data for this manuscript is available from correspondence author.
ˇ
Acknowledgments: I.P. and A.C.G. would like to acknowledge networking contributions by the
COST Action CM1407 “Challenging organic syntheses inspired by nature—from natural products
chemistry to drug discovery”.
Conflicts of Interest: The authors declare no conflict 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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