In silico and in vitro studies of a number PILs as new antibacterials against MDR clinical isolate Acinetobacter baumannii

Article abstract of DOI:10.1111/cbdd.13678

QSAR analysis of a set of previously synthesized phosphonium ionic liquids (PILs) tested against Gram-negative multidrug-resistant clinical isolate Acinetobacter baumannii was done using the Online Chemical Modeling Environment (OCHEM). To overcome the pr

Full text of DOI:10.1111/cbdd.13678

DR MARIA M. TRUSH (Orcid ID : 0000-0001-5462-7026)
DR IGOR V. TETKO (Orcid ID : 0000-0002-6855-0012)
Article type : Research Article
In silico and in vitro studies of a number PILs as new antibacterials against MDR clinical
isolate Acinetobacter baumannii
Maria M. Trusha,*, Vasyl Kovalishyna, Diana Hodynaa, Olexandr V. Golovchenkoa, Svitlana
Chumachenkoa, Igor V. Tetkob,c, Volodymyr S. Brovaretsa, Larysa Metelytsiaa
aV.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry, National Academy of Science
of Ukraine, 1 Murmanska Street, 02660, Kyiv, Ukraine
bHelmholtz Zentrum München - German Research Center for Environmental Health (GmbH),
Ingolstädter Landstraße 1, DE-85764, Neuherberg
cBIGCHEM GmbH, Valerystr. 49, DE-85716 Unterschleißheim, Germany
*Corresponding author:
Maria M. Trush
aV.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry, National Academy of Science
of Ukraine, 1 Murmanska Street, 02660, Kyiv, Ukraine
Email: maria.m.trush@gmail.com
This article has been accepted for publication and undergone full peer review but has not been
through the copyediting, typesetting, pagination and proofreading process, which may lead to
differences between this version and the Version of Record. Please cite this article as doi:
10.1111/CBDD.13678
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ABSTRACT
QSAR analysis of a set of previously synthesized phosphonium ionic liquids (PILs) tested
against gram-negative multi-drug resistant clinical isolate Acinetobacter baumannii was done
using the Online Chemical Modeling Environment (OCHEM). To overcome the problem of
overfitting due to descriptor selection, five-fold cross-validation with variable selection in each
step of the model development was applied. The predictive ability of the classification models was
tested by cross-validation, giving balanced accuracies (BA) of 76-82%. The validation of the
models using an external test set proved that the models can be used to predict the activity of
newly designed compounds with a reasonable accuracy within the applicability domain (BA = 83-
89%). The models were applied to screen a virtual chemical library with expected activity of
compounds against MDR Acinetobacter baumannii. The eighteen most promising compounds
were identified, synthesized and tested. Biological testing of compounds was performed using the
disc diffusion method in Mueller-Hinton agar. All tested molecules demonstrated high anti-A.
baumannii activity and different toxicity levels. The developed classification SAR models are
freely available online at http://ochem.eu/article/113921 and could be used by scientists for design
of new more effective antibiotics.
Keywords:
 Phosphonium ionic liquids (PILs)
 Antibacterial activity
 Acinetobacter baumanii
 Machine learning
 OCHEM
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1. Introduction
It is known that the persistent change of infectious human pathogens is connected with
widespread and uncontrolled use of antibiotics, the lack of vaccination, a significant number of
individuals with immune-mediated diseases, expansion of migration processes in society,
development of new technologies, etc. In addition, a large number of resistant strains of
microorganisms occur as a result of spontaneous mutations of the bacterial genome, alteration of
the target or binding site, alteration of the metabolic pathway, drug inactivation or modification,
etc.(Davies et al., 2010) Since bacterial resistance to antibiotics is inevitable, researchers must
respond with innovative strategies to discover and develop novel drug candidates.
Gram-negative bacteria Acinetobacter spp. are one of the most dangerous multidrug-resistant
(MDR) ESKAPE pathogens for the human population.(Rice, 2010) In particular, different kinds of
hospital-acquired infections caused by opportunistic microbe A. baumannii have increased in
recent years. It is known that A. baumannii isolates are able to form biofilms on the surfaces of
surgical instruments, medical devices, implants, etc.(Asif et al., 2018) It should also be noted that
Acinetobacter strains were widely detected in acute wound infections of patients in military
conflicts in Iraq, Afghanistan, Ukraine, and other countries.(Khan et al., 2018; O'Toole et al.,
2019)
A. baumannii strains are capable of colonising skin, mucosal and epithelial surfaces, and most
often affect the respiratory, circulatory and urinogenital systems.(Doi et al., 2015; Eliopoulos et
al., 2008) Senile age, alcoholism, heavy smoking, diabetes mellitus, severe injuries, extensive
burns, maligned neoplasms, broad surgical interventions, radiation, hormonal and cytostatic
therapy, newborns pathology, immunodeficiency syndrome, chronic obstructive pulmonary
disease and renal disease are the risk factors of A. baumannii infections.(Visca et al., 2011) The
mortality rate from the diseases of the blood system caused by MDR A. baumannii is 49%, and
from resulting central nervous system damage is 70%. According to the literature, there are cases
of A. baumannii infection as a result of procedures such as catheterization, lumbar puncture,
myelography and ventriculography, intrathecal infusions or spinal anaesthesia, gastrointestinal
endoscopy, etc.(Doi et al., 2015; Eliopoulos et al., 2008; O'Toole et al., 2019)
Acinetobacter spp. are usually highly resistant to a number of commonly used antibiotics,
including ampicillin, carbenicillin, cefoxitin, gentamicin, chloramphenicol.(Seifert et al., 1993;
Vila et al., 1993) The multidrug resistance of these bacteria is mediated by all of the major
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resistance mechanisms that are known to occur in bacteria. These include the modification of
target sites (the methylation of ribosomal RNA), enzymatic inactivation (β-lactamases,
aminoglycosidases, tetraciclinases), and the slowing of drug influx and direct active
efflux.(Dijkshoorn et al., 2007) Thus, extensive research and the development of new
antibacterials for the prevention or treatment of MDR Acinetobacter infection are highly relevant
and necessary.
Phosphonium liquids/salts (PILs) are a promising class of pharmacologically active
organophosphorus compounds. They are used for imaging and diagnosing tumors, and also as
anticancer,(Madar et al., 2007) antioxidant,(Sheu et al., 2006) antiviral and antiparasitic
agents.(Spivak et al., 2014) PILs are also used extensively to fight bacterial and fungal
infections,(Trush et al., 2018) and additionally as biocides and antifouling agents.(Joseph et al.,
2016) One of the advantages of phosphonium compounds is the high thermal and chemical
stability they present due to the absence of acidic protons in their structure.(Fraser et al., 2009)
Moreover phosphonium liquids, in general, are less toxic and more environmentally friendly
compared to, for example, ammonium salts.(Kumar et al., 2009)
The main goal of this study was to investigate the anti-A. baumanii activity of a number of PILs
based on Structure Activity Relationship studies(Dearden, 2016) derived using computational
models developed by the On-line CHemical Database and Modeling Environment
(OCHEM).(Tetko et al., 2017)
2. Methods and Materials
2.1. QSAR modeling
2.1.1. Data
A series of 263 compounds and their bioactivities against A. baumannii were collected from
the literature.(Cheng et al., 2010; Singh et al., 2015; Sun et al., 2011) The compounds had MIC
values ranging from 0.137 to 900 µM. All compounds in this set were unique. The collected data
are freely accessible under the CC-BY license on the OCHEM.(Tetko et al., 2017)
2.1.2. On-line Chemical Database and Modeling Environment
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The OCHEM platform was used to develop public and freely accessible models for
predicting the antibacterial activity of compounds. The resulting OCHEM models estimate
standard mean errors for each prediction and allow users to decide whether the provided
predictions are sufficiently accurate for their studies.
Methods. Well-known classical machine-learning methods such as Associative Neural
Networks (ASNNs),(Tetko, 2008) k-Nearest Neighbors (kNNs), and WEKA-RF (Random
Forest) (Breiman, 2001) were used to build classification QSAR models.
Descriptors. OCHEM supports multiple software packages for calculation of diverse molecular
descriptors. For this study we used electrotopological (E-state)(Hall et al., 1995) descriptors and
AlogPS,(Tetko et al., 2002) which were frequently the top-performing descriptors in our previous
studies. AlogPS estimates lipophilicity and solubility of chemical compounds while E-state
descriptors describe their electronic and topological characteristics.
Descriptor preprocessing. The unsupervised filtering of descriptors was used. Descriptors
with fewer than two unique variables or with a coefficient of variance, less than 0.01 were
excluded. Moreover, descriptors with a pairwise non-parametric Pearson’s correlation coefficient
R > 0.95 were grouped. Additionally, the Unsupervised Forward Selection (UFS) method(Whitley
et al., 2000) was used to select a representative non-redundant set of descriptors for model
development.
Model validation. Two validation protocols were used. First of all the initial set of 263
compounds was split by chance into training (210 compounds) and test (53) sets. Five-fold Cross-
Validation (CV) with variable selection in each step of the analysis was used to estimate the
accuracy of the models for the training set.(Tetko et al., 2006) The dataset of 210 compounds was
divided into 5 subsets of equal size. Out of each 5-subsets a single subset was retained for
validation while the remaining sets were used for training. For each subdivision, we first selected
descriptors using the respective training set, developed the model, and then applied it to predict the
excluded molecules from the respective validation set. Then the statistical coefficients over the
combined five validation sets were computed. The prediction performance of the final model
developed with 210 compounds was also tested using the test set of 53 compounds (see Table 1).
Estimation of prediction accuracy. The OCHEM estimates the applicability domain (AD) and
the accuracy for each prediction, which are calibrated using CV.(Sushko et al., 2010) A
probability of incorrect classification of molecules based on the standard deviation and average
prediction of a model class (PROB-STD), which provided the best accuracy for classification
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models as described elsewhere,(Sushko et al., 2010) was used as a distance-to-model. The model
AD corresponded to PROB-STD covering 90% of molecules within the training set.
A detailed description of used machine-learning methods, all selected descriptors, and
validation procedures can be found in the Supplementary materials and the OCHEM manual.
2.2. Biology
2.2.1. Anti-A. baumannii activity
The antibacterial activity of the studied PILs was estimated against gram-negative MDR
clinical isolate A. baumannii obtained from the Museum of Microbial Culture Collection of the
P.L. Shupyk National Medical Academy of Postgraduate Education.
Antibacterial properties were determined by disc diffusion method in Mueller-Hinton
agar.(Bauer et al., 1966) A final inoculum concentration of 1•105 colony-forming unit (CFU) per
mL was established using a 0.5 McFarland turbidity standard and a subsequent dilution of 0.02 ml
of the tested compounds was applied to standard paper disks (6 mm) which were placed on agar
plate. All compounds were dissolved in 0.1% dimethyl sulfoxide solution (DMSO). The
compound content on a disk was 0.3 µM. The studied bacterial culture was not sensitive to DMSO
solvent, which was used as a negative control in the experiment. The known antibiotics
Ampicillin, Oxacillin and Ceftriaxone were applied as positive control using the same
experimental protocol.
The plates were incubated for 24 h at 37 °C. The tests were repeated three times. The
antibacterial activity of the tested compounds was identified by measuring zone diameter of the
growth inhibition, which indicates the degree of susceptibility or resistance of A. baumannii
isolate against the test compounds. Inhibition zones were measured in millimeters. The
compounds with growth inhibition zones > 15 mm were selected as active.
2.2.2. Acute Daphnia magna toxicity testing
In order to exclude the observed activity of compounds due to nonspecific toxicity, we
evaluated the acute toxicity test of the investigated compounds to Daphnia magna. LD50 was
estimated according to the procedures set out in the Organization for Economic Co-operation and
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Development Guideline 202.(OECD, 2014) D. magna neonates (6-24 h-old) were used for the
controls and for the geometric series of concentrations of each test compound. The mortality of the
neonates was observed after 48 h. The LD50 values in mg/L with their 95% confidence intervals
(CI) were determined using Statistica 7 program. All experiments were run in triplicate. The
sensitivity of D. magna to the reference toxicant potassium dichromate (K2Cr2O7) was determined
as well.
3. Results and discussion
3.1. Models
The activity data were collected from different sources and thus could have some variations
due to differences across laboratory protocols. In order to decrease this variation we decided to
develop classification models. All compounds were assigned to active (147 compounds with MIC
≤ 50 µM) and inactive (116, MIC > 50 µM) classes. The classification models were developed
using the protocols described in section 2.2.
Table 1. Statistical coefficients calculated for classification models.
N
Method
Specificity (%)
Sensitivity (%) Balanced Accuracy (%)
Training
Test
85
Training Test
Training
76 ± 3
81 ± 3
81 ± 3
82 ± 3
Test
87 ± 4
83 ± 5
89 ± 4
83 ± 5
1
2
3
4
ASNN
WEKA-RF
kNN
73
84
80
83
78
82
82
83
88
85
86
85
81
92
Consensusb
81
aThe training and test sets included 210 and 53 molecules, respectively. The cross-validation
results are reported for the training set bThe consensus model was built by averaging outputs of all
individual models.
All models had similar performance in terms of sensitivity, specificity and balanced
accuracy (BA) as summarized in Table 1 (see also Fig. 1S of the Supplementary materials). The
BAs for the training sets were in the range of 76-82 % (Table 1). The compounds in the test sets
were predicted with similar accuracies: BA = 83-89%. The statistical parameters for both training
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and test sets have large standard mean errors due to small dataset sizes. Despite some variations,
the model performances for both these sets were not significantly different.
A consensus model was built as a simple average of the three models. Despite its statistical
parameters being similar to those of individual models (see Table 1), the consensus model was
expected to be have a smaller variance. Moreover, the predictions of the consensus model were
used to estimate its applicability domain.(Sushko et al., 2010)
3.2. Evaluation activity of new compounds
A virtual set of drug-like compounds was generated based on available synthetic
blocks and reactions. It included 41 compounds with different substitution patterns (Table
2S in Supplementary materials,). 14 compounds predicted as inactive as well as three
compounds predicted outside of the AD of the model were excluded. The remaining 24
compounds were evaluated for synthetic feasibility by organic chemists, as well as for
absence of potential toxicity (mutagenicity, tumorigenicity, irritation and reproductive
effectiveness) using the DataWarrior software (Datawarrior, 2019). As a result of this
analysis, 18 compounds were selected for synthesis and testing (Tables 1 and 1S in
Supplementary materials). The biological testing (Table 2) confirmed their biological
activity.
3.3. Chemistry
Oxazoles 1-17 were synthesized according to the known methods(Abdurakhmanova et al.,
2017; Brovarets et al., 2001; Van Meervelt et al., 1995) from the starting amides and
trichloroacetaldehyde (Schema 1). Obtained N-(2,2,2-trichloro-1-hydroxyethyl)amides were
treated with thionyl chloride to form N-(1,2,2,2-tetrachloroethyl)amides. These compounds were
converted into phosphonium chlorides by reaction with triphenylphosphine running in benzene by
heating to 70-80 °C. Sequential interaction of phosphonium salts with an excess of sodium
hydrosulfide and alkyl iodide leads to the formation of 5-alkylthio-1,3-oxazol-4-ylphosphonium
salts 1-17 (Scheme 1). The structures of all obtained phosphonium salts 1-17 were in accordance
with data of elemental analysis, 1H, 13C, 31P NMR, mass-spectrometry.
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O
OH
O
Cl
O
PPh3
CCl3CHO
SOCl2
R
N
CCl3
R
N
CCl3
R
NH2
H
H
O
P+Ph3Cl-
Cl
P+Ph3I-
N
1. NaSH
2. AlkI
R
N
H
R
SAlk
O
1-17
Cl
Scheme 1. General synthesis of compounds 1-17
R = CH3 (5, 8, 11), CH2F (9), C(CH3)3 (14), C6H5 (4, 7, 13, 17), 4-CH3C6H4 (15), 2-ClC6H4 (6), 4-ClC6H4 (1, 3, 16),
2,4-ClC6H3 (12), furan-2-yl (2, 10); AlkS = CH3S (3, 7, 14), C2H5S (2, 6, 8, 9, 13, 15, 16), C3H7S (1, 5), C4H9S (4),
CH2=CHCH2S (10-12,17).
13
Particular attention was paid to C NMR data. These spectra were remarkable as signals of a
1,3-oxazole ring appear as doublets due to the spin-spin coupling with the phosphorus atom. Thus,
C5 and C2atoms resonate at 156.4-174.2 ppm as doublets with coupling constants of 18.9-29.4 Hz.
The signal of the C4 is a doublet at 115.3-128.5 ppm with spin-spin coupling constants of 122.5-
146.1 Hz.
31
The signal of phosphorus nuclei in P NMR spectra of compounds is observed at 9.80-13.21
ppm.
The detailed description of the chemical synthesis of selected compounds (Table 1S)
was provided in the Supplementary materials.
3.4. Biology
The results of biological activity are summarized in Table 2.
Table 2. Antibacterial activity and acute toxicity of the studied PILs
Compound
The inhibition zones
diameters (mm) of MDR
clinical isolate A.
Lethal doses of compounds to D. magna in 48-h
acute toxicity bioassays
LD50 (mg/L)
95%
Toxicity
classificationa
baumannii culture
confidence
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intervals
(by Passino and
Smith)
1
29
30
24
28
30
27
29
28
25
29
28
22
31
28
32
30
32
28
0.12 ± 0.04
2.0 ± 0.6
0.11 ± 0.03
0.15 ± 0.05
2.8 ± 0.7
0.18 ± 0.05
0.36 ± 0.09
3.1 ± 0.6
9.4 ± 2
0.03-0.2
0.88-3.18
0.05-0.18
0.05-0.25
1.28-4.25
0.08-0.29
0.18-0.55
1.9-4.36
4.72-14.01
0.72-2.36
1.69-3.92
0.028-0.097
0.032-0.19
0.4-1.06
0.33-0.93
0.046-0.22
0.11-0.34
0.12-0.34
ND
++++
2
+++
++++
++++
+++
3
4
5
6
++++
++++
+++
7
8
9
+++
10
1.54 ± 0.4
2.8 ± 0.5
0.06 ± 0.02
0.12 ± 0.04
0.7 ± 0.2
0.6 ± 0.2
0.13 ± 0.04
0.23 ± 0.05
0.23 ± 0.05
NDc
+++
11
+++
12
+++++
++++
++++
++++
++++
++++
++++
ND
13
14
15
16
17
18
NAb
NA
15
Ampicillinum
Oxacillinum
Ceftriaxone
ND
ND
ND
ND
ND
ND
aToxicity classification and LD50 range: “+” - practically harmless (100–1000 mg/L); “++” -
slightly toxic (10–100 mg/L); “+++” - moderately toxic (1–10 mg/L); “++++” - highly toxic (0.1–
1mg/L); “+++++” - extremely toxic (0.01–0.1mg/L). bNA – no activity; cND – not determined.
All synthesized PILs exhibited high activity against MDR clinical isolate A. baumannii with
diameters of inhibition zones ranging from 22 to 32 mm (Table 2) while Ampicillin, Oxacillin and
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Ceftriaxone antibiotics were inactive. The acute toxicity of PILs ranged from 0.063 to 9.36 mg/L.
According to the D.R. Passino classification six (33%) compounds were classified as moderately
toxic (1–10 mg/L), 11 (61%) were highly toxic (0.1–1 mg/L) and only one compound was
extremely toxic (0.01–0.1 mg/L). However, despite the analysed compounds having different
toxicity classes, all of them exhibited strong in vitro activity against A. baumannii. Thus their
inhibitory activity was specific and was not connected to their toxicity.
4. Conclusion
We investigated antibacterial activity of new PILs. Firstly, predictive in silico models based
on different machine learning techniques were built using the OCHEM platform. The created
classification models demonstrated good stability, robustness and predictive power. Eighteen new
compounds were synthesized and their activity against A. baumanii MDR isolate was evaluated.
All PILs showed high activity against MDR clinical isolate A. baumannii. According to the acute
toxicity results all tested PILs belonged to moderately and highly toxic classes, and only one
substance was classified as extremely toxic. Therefore, the activity of the analyzed structures
against A. baumannii was not related to their toxicity and these structures need to be further
investigated
as
promising
antibacterial
agents.
The
developed
models
(http://ochem.eu/article/113921) can be used by scientific community to search for new antibiotics
against MDR A. baumannii strains.
Declaration of interest
IVT is CEO of BIGCHEM GmbH, which licenses the OCHEM software. Other authors
declare no conflict of interest.
Supplementary materials
Supplementary data associated with this article can be found, in the online version, at
http://dx.doi.org/XXXX.
Data Availability Statement
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All data used in this article are publicly available under CC-BY license at
http://ochem.eu/article/113921.
References
Abdurakhmanova, E. R., Pil’o, S. G., Kondratyuk, K. M., Golovchenko, A. V., & Brovarets, V. S. (2017).
1,3-oxazole derived cytisines. Russian Journal of General Chemistry, 87(2), 244-251.
doi:10.1134/S1070363217020153
Asif, M., Alvi, I. A., & Rehman, S. U. (2018). Insight into Acinetobacter baumannii: pathogenesis, global
resistance, mechanisms of resistance, treatment options, and alternative modalities. Infect Drug
Resist, 11, 1249-1260. doi:10.2147/IDR.S166750
Bauer, A. W., Kirby, W. M., Sherris, J. C., & Turck, M. (1966). Antibiotic susceptibility testing by a
standardized single disk method. Am J Clin Pathol, 45(4), 493-496.
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32.
Brovarets, V. S., Vydzhak, R. N., Pil'o, S. G., Zyuz, K. V., & Drach, B. S. (2001). Synthesis and
transformations of 4-phosphorylated 2-alkyl(aryl)-5-hydrazinooxazoles. Russian Journal of
General Chemistry, 71(11), 1726-1728.
Cheng, T. J., Wu, Y. T., Yang, S. T., Lo, K. H., Chen, S. K., Chen, Y. H., . . . Wong, C. H. (2010). High-
throughput identification of antibacterials against methicillin-resistant Staphylococcus aureus
(MRSA)
doi:10.1016/j.bmc.2010.10.036
Datawarrior. (2019). Data Warrior. Retrieved from http://www.openmolecules.org/datawarrior
and
the
transglycosylase.
Bioorg
Med
Chem,
18(24),
8512-8529.
Davies, J., & Davies, D. (2010). Origins and evolution of antibiotic resistance. Microbiol Mol Biol Rev,
74(3), 417-433. doi:10.1128/MMBR.00016-10
Dearden, J. C. (2016). The History and Development of Quantitative Structure-Activity Relationships
(QSARs). International Journal of Quantitative Structure-Property Relationships (IJQSPR), 1(1),
1-44. doi:10.4018/IJQSPR.2016010101
Dijkshoorn, L., Nemec, A., & Seifert, H. (2007). An increasing threat in hospitals: multidrug-resistant
Acinetobacter baumannii. Nat Rev Microbiol, 5(12), 939-951. doi:10.1038/nrmicro1789
Doi, Y., Murray, G. L., & Peleg, A. Y. (2015). Acinetobacter baumannii: evolution of antimicrobial
resistance-treatment options. Semin Respir Crit Care Med, 36(1), 85-98. doi:10.1055/s-0034-
1398388
This article is protected by copyright. All rights reserved

Eliopoulos, G. M., Maragakis, L. L., & Perl, T. M. (2008). Acinetobacter baumannii: Epidemiology,
Antimicrobial Resistance, and Treatment Options. Clinical Infectious Diseases, 46(8), 1254-1263.
doi:10.1086/529198
Fraser, K. J., & MacFarlane, D. R. (2009). Phosphonium-Based Ionic Liquids: An Overview. Australian
Journal of Chemistry, 62(4), 309-321. doi:10.1071/CH08558
Hall, L. H., & Kier, L. B. (1995). Electrotopological State Indexes for Atom Types - a Novel Combination
of Electronic, Topological, and Valence State Information. Journal Of Chemical Information And
Computer Sciences, 35(6), 1039-1045. doi:10.1021/Ci00028a014
Joseph, R., Kaizerman, D., Herzog, I. M., Hadar, M., Feldman, M., Fridman, M., & Cohen, Y. (2016).
Phosphonium pillar[5]arenes as a new class of efficient biofilm inhibitors: importance of charge
cooperativity and the pillar platform. Chem Commun (Camb), 52(70), 10656-10659.
doi:10.1039/c6cc05170g
Khan, I. D., Gupta, R. M., Sen, S., Sahni, A., Khan, S., Jindal, A. K., . . . Rajmohan, K. S. (2018). Military
superbug’ acinetobacter baumanii induced sepsis in craniofacial gunshot injuries. Journal of
Military and Veterans' Health, 26, 13-19.
Kumar, V., & Malhotra, S. V. (2009). Study on the potential anti-cancer activity of phosphonium and
ammonium-based ionic liquids. Bioorg Med Chem Lett, 19(16), 4643-4646.
doi:10.1016/j.bmcl.2009.06.086
Madar, I., Ravert, H., Nelkin, B., Abro, M., Pomper, M., Dannals, R., & Frost, J. J. (2007).
Characterization of membrane potential-dependent uptake of the novel PET tracer 18F-
fluorobenzyl triphenylphosphonium cation. Eur J Nucl Med Mol Imaging, 34(12), 2057-2065.
doi:10.1007/s00259-007-0500-8
O'Toole, B., Pierse, K., Friedrich, B., Outram, S., Dadds, M., & Catts, S. (2019). Combat, posttraumatic
stress disorder and health of australian vietnam veteran conscripts and volunteers in the three
decades after return. Journal of Military and Veterans' Health, 27, 42-45.
OECD. (2014). Guidelines for the OECD Testing of Chemicals. Test No. 202: Daphnia sp. acute toxicity
test.
Retrieved
from
https://www.oecd.org/env/ehs/testing/oecdguidelinesforthetestingofchemicals.htm
Rice, L. B. (2010). Progress and challenges in implementing the research on ESKAPE pathogens. Infect
Control Hosp Epidemiol, 31 Suppl 1, S7-10. doi:10.1086/655995
Seifert, H., Baginski, R., Schulze, A., & Pulverer, G. (1993). Antimicrobial susceptibility of Acinetobacter
species. Antimicrob Agents Chemother, 37(4), 750-753. doi:10.1128/aac.37.4.750
Sheu, S. S., Nauduri, D., & Anders, M. W. (2006). Targeting antioxidants to mitochondria: a new
therapeutic direction. Biochim Biophys Acta, 1762(2), 256-265. doi:10.1016/j.bbadis.2005.10.007
This article is protected by copyright. All rights reserved

Singh, S. B., Kaelin, D. E., Wu, J., Miesel, L., Tan, C. M., Gill, C., . . . Fukuda, Y. (2015). Hydroxy
tricyclic 1,5-naphthyridinone oxabicyclooctane-linked novel bacterial topoisomerase inhibitors as
broad-spectrum antibacterial agents-SAR of RHS moiety (Part-3). Bioorg Med Chem Lett, 25(12),
2473-2478. doi:10.1016/j.bmcl.2015.04.063
Spivak, A. Y., Keiser, J., Vargas, M., Gubaidullin, R. R., Nedopekina, D. A., Shakurova, E. R., . . .
Odinokov, V. N. (2014). Synthesis and activity of new triphenylphosphonium derivatives of
betulin and betulinic acid against Schistosoma mansoni in vitro and in vivo. Bioorg Med Chem,
22(21), 6297-6304. doi:10.1016/j.bmc.2014.07.014
Sun, C., Hunt, D. K., Clark, R. B., Lofland, D., O'Brien, W. J., Plamondon, L., & Xiao, X. Y. (2011).
Synthesis and antibacterial activity of pentacyclines: a novel class of tetracycline analogs. J Med
Chem, 54(11), 3704-3731. doi:10.1021/jm1015395
Sushko, I., Novotarskyi, S., Körner, R., Pandey, A. K., Kovalishyn, V. V., Prokopenko, V. V., & Tetko, I.
V. (2010). Applicability domain for in silico models to achieve accuracy of experimental
measurements. Journal Of Chemometrics, 24(3-4), 202-208. doi:10.1002/cem.1296
Tetko, I. V. (2008). Associative neural network. Methods Mol Biol, 458, 185-202. doi:10.1007/978-1-
60327-101-1_10
Tetko, I. V., Maran, U., & Tropsha, A. (2017). Public (Q)SAR Services, Integrated Modeling
Environments, and Model Repositories on the Web: State of the Art and Perspectives for Future
Development. Mol Inform, 36(3). doi:10.1002/minf.201600082
Tetko, I. V., Solov'ev, V. P., Antonov, A. V., Yao, X., Doucet, J. P., Fan, B., . . . Varnek, A. (2006).
Benchmarking of linear and nonlinear approaches for quantitative structure-property relationship
studies of metal complexation with ionophores. J Chem Inf Model, 46(2), 808-819.
doi:10.1021/ci0504216
Tetko, I. V., & Tanchuk, V. Y. (2002). Application of associative neural networks for prediction of
lipophilicity in ALOGPS 2.1 program. Journal of Chemical Information & Computer Sciences,
42(5), 1136-1145. doi:10.1021/ci025515j
Trush, M. M., Kovalishyn, V., Ocheretniuk, A. D., Kalashnikova, L. E., Prokopenko, V. M., Holovchenko,
O. V., . . . Metelytsia, L. O. (2018). New 1,3-oxazolylphosphonium Salts as Potential Biocides:
QSAR Study, Synthesis, Antibacterial Activity and Toxicity Evaluation. Letters in Drug Design &
Discovery, 15(12), 1259-1267. doi:10.2174/1570180815666180219164334
Van Meervelt, L., Schuerman, G. S., Brovarets, V. S., Mishchenko, N. I., Romanenko, E. A., & Drach, B.
S. (1995). Structure and properties of phosphonium ylides-betaines, derivatives of 2-phenyl-2-
oxazolin-5-one and its thio- and seleno-analogues. Tetrahedron, 51(5), 1471-1482.
doi:10.1016/0040-4020(94)01041-W
This article is protected by copyright. All rights reserved

Vila, J., Marcos, A., Marco, F., Abdalla, S., Vergara, Y., Reig, R., . . . Jimenez de Anta, T. (1993). In vitro
antimicrobial production of beta-lactamases, aminoglycoside-modifying enzymes, and
chloramphenicol acetyltransferase by and susceptibility of clinical isolates of Acinetobacter
baumannii. Antimicrob Agents Chemother, 37(1), 138-141. doi:10.1128/aac.37.1.138
Visca, P., Seifert, H., & Towner, K. J. (2011). Acinetobacter infection--an emerging threat to human health.
IUBMB Life, 63(12), 1048-1054. doi:10.1002/iub.534
Whitley, D. C., Ford, M. G., & Livingstone, D. J. (2000). Unsupervised forward selection: a method for
eliminating redundant variables. J Chem Inf Comput Sci, 40(5), 1160-1168. doi:10.1021/ci000384c
This article is protected by copyright. All rights reserved