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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5345_Библиотеки_им_академика_М_И_Перельмана.pdf
QSAR Studies on Bacterial Efux Pump Inhibitors
plex one, the model is supposed to be good if the F-test is above a threshold value, LSE is least-square
2
error, r is correlation coefficient, q
is the square of the correlation coefficient of the cross-valida-
tion.
The QSAR model was proved robust by using the method proposed by Golbraikh et al., and Roy.
The achieved QSAR model had the values of
2
R
= 0.78 and r
pred
2
= 0.72, indicating the good external
m
predictability of the QSAR model.
Jurs_PNSA-1, Shadow_XZ and Hf are important descriptors responsible for the relationship between
piperine analogs’ structure and activity as S. aureus NorA efflux pump inhibitors. Jurs_PNSA-1 is the
partial negative surface area, calculated by using the sum of the solvent accessible surface area of all
negatively charged atoms in the compound. Shadow_XZ is the area of the molecular shadow in the XZ
plane. Hf is the heat of formation descriptor. These parameters were proved to be important contributors to
the potentiating activity and must be taken into consideration while designing the efflux pump inhibitors.
QSAR of Aryl Alkenyl Amides/Imines
Another QSAR study on activity of aryl alkenyl amides/imines having structural similarity to piperine
(structures shown in Figure 2) in inhibiting bacterial efflux pump was also conducted by Nargotra et al
(A Nargotra et al., 2009).
The total set of 42 compounds was divided into 34 molecules in training set and 8 molecules in test
set by arranging the molecules in ascending order of their PF and selecting every fifth molecule to be
included in the training set. About 200 descriptors were calculated using the Cerius2 4.10 software package. After the removal of the descriptors with zero values for all the compounds, remaining descriptors
were filtered using genetic function approximation (GFA) method. The study showed that the ideal
number of descriptors should be five. After analyzing the equation with five descriptors and removing
outliers, the following equation was achieved:
log (PF) = – 0.2028 – 0.000936*Hf + 0.342484*S_dssC
+ 0.441556*RadOfGyr – 0.2087*Atype-C-16
– 0.00104*Jurs-WNSA2
(N = 30; LOF = 0.051; r
2
= 0.866; r
2
= 0.833; F-test = 28.875; LSE = 0.022; r = 0.929; q2 = 0.757)
adj
Figure 2. General structures of compounds taken for present study
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QSAR Studies on Bacterial Efux Pump Inhibitors
The final QSAR model had the values of R
2
= 0.825 and r
pred
2
= 0.634, indicating the good external
m
predictability of the QSAR model. The QSAR study also gives us the revelation that E-state indices and
A log P atom type are important descriptors responsible for describing the activity of aryl alkenyl amide/
imine based efflux pump inhibitors. In addition, solvent accessible surface area, partial charged surface
area of a molecule, radius of gyration and heat of formation of the molecule are also important parameters to be taken into account while designing new inhibitors belonging to the class of compounds
mentioned above.
QSAR of 2-Aryl-5-Nitro-1H-Indole Derivatives
It has been found that the combinatorial administration of lead structure 5-nitro-2-phenylindole (INF55)
and the antibiotic significantly increases S. aureus susceptibility to ciprofloxacin (Ambrus et al., 2008).
Structure-activity relationship studies mostly aimed at disclosing the importance of the C5 position of
INF55 (Figure 3) for inhibitory activity against the NorA pump:
1. Reduction of C5 substituent will adversely affect the activity.
2. Carbonyl based electron-withdrawing groups at C5 eliminate all activity.
3. C5 substituents such as sulfonic acids/esters/amides have no effect on potentiation.
4. INF55 derivatives bearing a nitrile group show similar potentiation activity to INF55 (compound
14) (Ambrus et al., 2008).
All compounds lacking 5-NO
groups were significantly less potent than INF55. The 3′-CO2Me
2
INF55 derivative showed slightly higher potency than INF55 against all three S.aureus strains. INF55
analogue containing a 4′-CH
OH group was nearly equipotent with INF55 against the wild type and
2
NorA overexpressing strain, representing a promising candidate for incorporation into dual action mutual
prodrugs targeting the NorA pump (Ambrus et al., 2008).
Berberine is the active constituent of the medicinal plants echinacea and golden seal. The extrusion of
berberine by bacterial efflux pumps can make this antimicrobial agent ineffective. Nevertheless, it was found
that the use of berberine along with an effective MDR pump inhibitor could restore its activity. In another
related study, an inhibitor of Major Facilitator MDRs, namely SS14, was made on the basis of covalent link
between berberine and INF55. Compared to berberine, SS14 was 100-fold more active against S.aureus
and 200-400 fold more active against S.aureus mutants overexpressing the NorA MDR (Ball et al., 2006).
In a QSAR study on MDR inhibitory activities of indole derivatives, the 3D structures of ten 2-aryl-5-nitro1-H-indole derivatives (structures shown in Table 2) were optimized with the semi-empirical PM3 method and
density functional theory (DFT) at b3lyp/6-31 g* level successively (Dai, Zhang, Zhang, Wang, & Lu, 2008).
Figure 3. Structure of INF55
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247
QSAR Studies on Bacterial Efux Pump Inhibitors
×
Some structural and electric parameters were subsequently obtained from single point energy and
natural bond orbital analysis. These parameters included the lowest unoccupied molecular orbital energy (E
nuclear repulsion energy (E
), and the Mulliken atomic charges of the six carbons of the 2-aryl (QC1-QC6). A genetic algorithm-
(V
mol
), the highest occupied molecular orbital energy (E
LUMO
), the molecular total energy (E
rep
), the polarizability (α), the nuclear-
HOMO
), the dipole (μ), the molecular volume
tot
based multi-linear regression analysis (GA-MRA) was carried out to reduce the number of the structural and electric parameters originally from 13 parameters obtained from the quantum chemistry calculations, thus rendering the optimized set of descriptors for constructing the QSAR model. With each
data set containing m variables, a QSAR model was built. Then the optimum combinations containing
the same numbers of descriptors were screened out with GA method. For each m of 2-7, two combina-
2
tions of the descriptors with the top square correlation coefficient R
2
2
, R
of their R
, and F (a=0.05) values in accordance with changes of m values. The QSAR model was
CV
are analyzed based on the trends
built based on partial least square (PLS) analysis.
The outcomes of genetic algorithm analysis indicated that the model containing five descriptors of
, QC2, QC3 and QC4 with the highest R
μ, V
mol
2
of 0.9941 and a good R2 of 0.9982 could be considered
CV
as an ideal model for QSAR of 2-aryl-5-nitro-1-H-indole. These five descriptors were re-analyzed with
partial least square (PLS) and the LOO cross validation. The QSAR model achieved with 5 independent
variables is shown as follows:
– lg MIC = –0.397
N = 10, R
2
= 0.998, R
μ + 0.019 ×V
2
= 0.996
CV
+ 1.231 ×QC2 + 9.976 ×QC3 – 6.177 ×QC4 – 0.198 (*)
mol
Table 2. Structures and activities of ten 2-aryl-5-nitro-1H-indole derivatives used for QSAR analysis
Compound MIC of NorA++ K2361 Plus
R
1
7a COOH H 50
8a CH
9a CH
7b COOH OCH
8b CH
9b CH
8c CH
10a CH
10b CH
INF55 H H 3
OH H 12.5
2
2N3
OH OCH
2
2N3
OH OCH2Ph 0.8
2
2NH2
2NH2
H 3
3
3
OCH
3
H 12.5
OCH
3
R
2
50
6.25
1.5
12.5
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QSAR Studies on Bacterial Efux Pump Inhibitors
The equation showed that increasing QC3 and decreasing QC4 could improve the activity of 2-aryl-5nitro-1-H-indole derivatives as efflux pump inhibitor. With the model achieved, the electric and structural descriptors of a new compound, 2-(2-azidomethyl-5-phenoxy-phenyl)-5-nitro-1H-indole (8d, R
= -CH
, R2 = -OCH2Ph) were calculated with the same quantum chemistry calculation method and
2N3
they were predicted from the equation (*) to lower the MIC of berberine to 0.091 μg/ml for inhibiting
K2361 of S.aureus with NorA efflux pump protein overexpression.
Case Study of Using QSAR Model for Virtual
Screening of NorA Efflux Pump Inhibitors
In a QSAR study conducted by Thai et al. (Thai et al., 2015), a total of 47 compounds belonging to dif-
ferent structural classes in company with the efflux pump inhibitory (EPI) activity against NorA mediated ethidium bromide efflux (IC
Moudgal, Seo, Hansen, et al., 2003; Kaatz, Moudgal, Seo, & Kristiansen, 2003; Michalet et al., 2007;
Pieroni et al., 2010; Sabatini et al., 2012; Sabatini, Kaatz, Rossolini, Brandini, & Fravolini, 2008; Smith,
Williamson, Wareham, Kaatz, & Gibbons, 2007; Stavri, Piddock, & Gibbons, 2007). In MOE, all
chemical structures of the data sets were built and energy minimized before computing 184 2D molecular descriptors. Subsequently, the variables were selected using RapidMiner (RapidMiner), Weka
(WEKA) and MOE (MOE) software packages respectively. This attribute selection resulted in five
descriptors which were analyzed in correlation matrix with experimental pIC
linear regression models (y=a
with the method Partial Least Square (PLS) and the activity field pIC
ferent models generated from different descriptors were internally validated by the cross-validation
technique and externally validated by the test set. The descriptors used for the best one were continuously used for modeling the whole data set to get the final model which was internally validated by the
cross-validation technique and externally validated by the external test set. As a result, the finally achieved
model with statistical values Q
following Roy et al. (Ojha, Mitra, Das, & Roy, 2011; Pratim Roy, Paul, Mitra, & Roy, 2009; Roy et al.,
2011).
The yielded model was applied on drug-likeness compounds from the mixture library of 182
flavonoids (Belsare, Pal, Kazi, Kankate, & Vanjari, 2010; Farkas, Jakus, & Héberger, 2004; Park
et al., 2006; Phosrithong, Samee, Nunthanavanit, & Ungwitayatorn, 2012; Seyoum, Asres, &
El-Fiky, 2006; Tran, Park, Kim, Ecker, & Thai, 2009; Yokozawa et al., 1998) and the traditional
Chinese medicine (C. Y. Chen, 2011) (TCM) database that met the rules of five/three to identify
novel Nor-A inhibitors with pIC
169 compounds that matched the criteria of the rule of five and none of 8 compounds that matched
the criteria of the rule of three were predicted as potential NorA inhibitors. Among 52957 TCM
compounds with the descriptor rings ≥ 2, 2866 of 15250 compounds that matched the criteria of
the rule of five and 9 of 1679 compounds that matched the criteria of the rule of three were determined as hit compounds for NorA inhibitory activity as well. Ultimately, 27 flavonoids and 2863
TCM compounds had fully satisfied at least the Lipinski’s rule of five and had good predicted
values and thus were considered as potential drugs to reverse the efflux-mediated multidrug
pIC
50
resistance of Staphylococcus aureus.
) were compiled from the literature (German et al., 2008; Kaatz,
50
values. Subsequently,
50
) were generated by using the QuaSAR-Model tool in MOE (MOE)
0+aixi
. Based on the training set, dif-
50
2
= 0.8, r
2
= 0.71 and ∆r
m
≥ 4.5. Among 182 flavonoids having the rings values ≥ 2, 27 of
50
2
= 0.18 met validation criteria for the data set
m
1
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249
QSAR Studies on Bacterial Efux Pump Inhibitors
Additional Discussion
From QSAR studies on piperine, aryl alkenyl amides/imines and 2-aryl-5-nitro-1H-indole derivatives
as ligands of NorA efflux pump, it was observed that certain portion of any kind of ligands do involve
themselves in hydrophobic interaction or electronic or in both with the receptor. The crucial binding
in most of the cases is, nonetheless, hydrogen bonds. NorA is, therefore, assumed to have certain critical features capable of interacting with all kinds of structurally different ligands. These features may
comprise some hydrogen-bond donor and acceptor sites, some lipophilic areas, and a few polar sites.
This view was justified by two docking studies using NorA homology models: Thai et al. developed
homology model of NorA based on the transporter (PDB ID: 2gfp) while Kalia et al. used GlpT (PDB
ID: 1PW4) as the protein template to create homology model. Thai et al. docked 6 traditional Chinese
medicine (TCM) and 27 flavonoids into the central channel and the Walker B. Most molecules had better
affinities to the Walker B than the central channel due to the hydrophilic property of the latter. Compared
with TCM, flavonoids have higher affinities towards NorA binding site because of tight interactions
(hydrogen bonding and π-π stacking) with NorA homology model made by substituents in flavonoid
structures (Thai et al., 2014). Kalia et al. conducted docking studies of two ligands namely reserpine and
capsaicin, which are two known NorA inhibitors, in the binding site of NorA. The most important finding
was that a hydrophobic cleft of NorA provided stability as well as affected the orientation of the ligand
within the binding pocket. Hydrogen bondings between substituents of ligands and amino acid residues
of the binding site attributed to the stability of the ligand-protein complex (Kalia et al., 2012). These
results consolidated the outcomes of above-presented QSAR studies that most of parameters selected
for final models are associated with surface charge, polarity and molecular weight.
Compared with QSAR studies of specific derivatives, the QSAR analysis of natural products for
NorA EPIs was performed with a larger and more diversified database including the compounds from
different structural classes. However, this strong point became an obstacle that made the validation
results of obtained models be at a disadvantage compared with those of the model generated by Koul et
al. (A. Nargotra et al., 2009). Among three descriptors used for the model of these authors (A. Nargotra
et al., 2009), this study revealed that the descriptor “partial negative surface area” might be relevant
to the descriptor “total negative vdw surface area” (PEOE_VSA_NEG) that was determined as a less
important variable and removed from final models.
QSAR Approaches in Search for EPIs in Gram Negative Bacteria
Gram-negative bacteria seem to be more dangerous than Gram-positive bacteria in some cases. Some
can be named such as P. aeruginosa, K. pneumoniae, C. jejuni, E. coli, S. typhimurium and E. aerogenes,
etc. Moreover, their levels of MDR have increased alarmingly. Nonetheless, no efflux pump inhibitor
has been licensed for use in the treatment of bacterial infections in human or veterinary medicine. It
must stimulate research that leads to the development of new EPI molecules.
With the recent advent of high-resolution 3D-structure of drug transporters, the co-crystallization of
AcrB and MexB with some substrates, and the analyses of the biological effects of several mutations in
the pump, it is now possible to rationally design functional targets located inside the bacterial pumps.
Consequently, the synthesis of efficient molecules with strong EPI capacity will be more readily achieved.
In this context, there should be more QSAR models that help to save time and money on searching “new
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QSAR Studies on Bacterial Efux Pump Inhibitors
drugs” for mediation of efflux pumps in Gram-negative bacteria. However, it is noticed with deep concern that scientists have not paid enough attention on this kind of QSAR studies. In comparison with
a large number of other efflux pumps in Gram-negative bacteria, studies on the two kinds of efflux
pumps (MATE efflux pump VmrA from V.parahaemolyticus, and AcrAB–TolC from Escherichia coli)
are likely insufficient to deal with Gram-negative bacterial resistance. Therefore, it is hoped that new
and solid QSAR models for MDR efflux proteins would be yielded in order to provide a more complete
picture of the molecular requirements.
QSAR Study of β-Lactam Antibiotic Efflux by the Bacterial MDR Pump AcrB
A QSAR study related to Gram-negative bacterial efflux pumps was conducted by researchers Marcia
Ferreira and R.Kiralj in 2004 (Ferreira & Kiralj, 2004). The target of the study was AcrAB-TolC
– an efflux pump identified in several Gram-negative bacteria including Salmonella typhymurium.
AcrAB-TolC is a tripartite efflux pump, which consists of the inner-membrane transporter AcrB
(Edward, McDermott, Zgurskaya, Nikaido, & Koshland, 2003; Elkins & Nikaido, 2003; Murakami,
Nakashima, Yamashita, & Yamaguchi, 2002; Pos & Diederichs, 2002), the outer-membrane channeltunnel protein (Koronakis et al., 2000) of the TolC family (Elkins & Nikaido, 2003), and the
periplasmic linker lipoprotein AcrA from the membrane fusion family (Thanabalu, Koronakis,
Hughes, & Koronakis, 1998).
The study was aimed at establishing multivariate quantitative lipophilicity–MIC and β-lactam
structure–MIC relationships for S. typhimurium strains (Nikaido, Basina, Nguyen, & Rosenberg, 1998).
Mass concentration MICs for 16 penicillines and cephalosporins (structures shown in Table 3) with
respect to bacterial strains S. typhimurium SH5014 (parent strain) and its mutants low resistant SH7616
and AcrAB overproducer HN891, were collected from the literature (Nikaido et al., 1998). Around
50 molecular properties of 16 β-lactam antibiotics were calculated using different methods based on
their modeled molecular structures. These computed properties included lipophilicity parameters
and other molecular properties which were prepared based on two-dimensional chemical formulae
of studied penicillins and cephalosporins. HCA (hierarchical cluster analysis) and PCA (principal
component analysis) studies indicated that the presence of charged groups and hydrophobic moieties
determined the antibiotic behavior with respect to AcrAB-TolC pumps. Highly charged β-lactams with
small hydrophobic fraction w
pump–drug interaction in molecular recognition. Nonetheless, the HCA-PCA approaches could not
give answers to the question of which lipophilicity parameters were most reliable for these antibiotics,
so the QSAR studies were carried out.
QSAR studies, which were performed by means of the partical least squares (PLS) and based on
lipophilicity and electronic and hydrogen bonding molecular descriptors, demonstrated that
or Sf might be poor AcrB substrates due to energetically unfavorable
C
1. Biological activities (pMICs) strongly depend both on properties of bacterial strains and drug
molecules. β-lactams were classified as good, moderately good and poor AcrAB-TolC substrates;
2. Among the most important β-lactam molecular properties quantitatively related to pMICs are
lipophilicity and electronic and hydrogen bonding properties; and
3. Lipophilicity parameters calculated in different ways do not necessarily present the same infor-
mation about drugs, and cannot produce parsimonious and accurate regression models for MICs
originated by active AcrAB-TolC pumps.
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251
QSAR Studies on Bacterial Efux Pump Inhibitors
Table 3. Chemical structures of β-lactams used in QSAR study
No. R No. R R
1 4
2 5
3 6
7 9
8 11
1
15 12
13
14
10
16
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QSAR Studies on Bacterial Efux Pump Inhibitors
Two strains HN981 and SH5014 were found to act similarly, and the third one – SH7616 is significantly different from each of them (Table 4). This was manifested by the similarity, even with the same
molecular descriptors, of the PLS models related to strains HN891 and SH5014.
Additionally, penicillins and cephalosporins which can be characterized as hydrophilic, with good
hydrogen bonding properties and able to establish polar-polar interactions with bacterial pump receptors, are bad pump substrates, and so potentially good drugs. This finding has been supported by recent
studies of AcrB in which most pharmacophore models generated by 49 AcrB substrates contain hydrophobic centers and hydrogen bond acceptors or donors. Molecular interactions of phytochemicals with
the active site residues in the binding site of AcrB also consist of hydrogen bondings and hydrophobic
contacts (Aparna, Dineshkumar, et al., 2014). Vargiu and Nikaido confirmed the similar features of
AcrB binding sites. Distal pocket and proximal pocket were shown to be two main active regions of
AcrB. 15 hydrophobic residues and 11 polar or charged amino acid residues in the distal binding pocket
were found to contribute to the binding of all ligands examined (Vargiu & Nikaido, 2012). In another
study publicized on Nature magazine in 2013, scientists found that AcrAB-MexAB specific inhibitor of
pyridopyrimidine derivative (ABI-PP) is deeply inserted into a hydrophobic trap rich in phenylalanine
residues. Based on the results of site-directed mutagenesis and molecular dynamics simulations, the
trap has been considered to indirectly affect drug-binding site. Phe178 located at the edge of this trap
in AcrB contributes to the tight binding of the inhibitor molecule through a π-π interaction with the
pyridopyrimidine ring (Nakashima et al., 2013).
QSAR/SAR Study of Structurally Unrelated Substrates of a
MATE Efflux Pump VmrA from V.Parahaemolyticus
Multidrug transporters of MATE family have been known to expel dissimilar lipophilic and cationic
drugs across cell membranes by dissipating a preexisting Na+ or H+ gradient (Lu et al., 2013). Brown et
al. classified the MATE family into three subfamilies. Subfamily 1 includes NorM, subfamily 2 includes
ERC1, and subfamily 3 includes DinF. VmrA has been considered to be a member of subfamily 3 (DinF
branch). The vmrA gene consisted of 1,341 nucleotides, with a deduced popypeptide (VmrA) consisting
of 447 amino acids residues with a calculated molecular mass of 49 kDa. VmrA was very rich in hy-
Table 4. PLS regression models for pMICs
, NCH, Hf, N
WIN
a
WIN
WIN
WIN
NS
, SlogK
NS
, SlogK
NS
, SlogK
WIN
WIN
WIN
,
,
,
pMIC Parameters
HN891 w
SH5014 w
SH7616 w
a
Transformation of lipophilicity descriptors;b Standard error of validation; c Correlation coefficient from validation; d Correlation
coefficient from prediction.
, Sf, GlogKOW, logPs, SlogPs, logK
C
logPIA, logP
logP
, Sf, GlogKOW, logPs, SlogPs, logK
C
logP
logP
, Sf, GlogKOW, logPs, SlogPs, logK
C
logPIA, logP
logP
X
, GlogKOW, SlogPs, Hf, AHB, Dy, N
IA
, logP
IA
X
, GlogKOW, SlogPs, Hf, AHB, Dy, N
IA
X
, GlogKOW, SlogK
IA
b
SEP
0.467 0.912 0.967 3 (82)
0.209 0.982 0.993 3 (85)
0.391 0.942 0.975 2 (82)
0.316 0.962 0.982 3 (85)
0.792 0.645 0.886 2 (76)
0.461 0.851 0.930 3 (83)
c
Q
d
R
PCs (%)
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253
QSAR Studies on Bacterial Efux Pump Inhibitors
drophobic residues, indicating that the protein was an integral membrane protein (J. Chen et al., 2002).
This characterization of MATE pump favoring hydrophobic substrates were illustrated in a QSAR study
of VmrA substrates done by Rudolf Kiralj and Marcia M.C. Ferreira in 2008 (Kiralj & Ferreira, 2008).
Kiralj and Ferreira aimed at correlating the efflux activity of V. parahaemolyticus VmrA with molecular properties of 12 diverse agents at QSAR and SAR levels. The training set in this work consists
of 12 agents (organic molecules/ions including 4’,6-diamino-2-2phenylindole, tetraphenylphosphonium
chloride, acriflavine chloride, ethidium bromide, chloramphenicol, norfloxacin, rhodamine 6G chloride,
tetracycline, erythromycin, streptomycin, sodium deoxycholate and sodium dodecyl sulfate). The cor-
3
responding activities pMIC = -log(MIC/mol/dm
) are pMIC(KAM) and pMIC(pVCJ6), which are efflux
activity pMIC of the E.coli strains KAM32 and KAM32/pVCJ6, respectively. The prediction of the MDR
character of VmrA with respect to 19 diverse substrates was performed at both QSAR and SAR levels.
Analysis of descriptors in correlation with the biological activities pMIC indicates that MDR of VmrA
was directed against agents with shorter bonds, a few polar atoms, elevated contents of rings, planar
structures, and aromatic carbon atoms. This is generally a rigid aromatic structure with exo- and/or en-
2
docyclic heteroatoms. Satisfactory prediction power statistics Q
2
be observed for all the models. The low R
and Q2 values indicated that the good results in the original
>0.5 and R2>0.6 for QSAR models can
models were not due to a chance correlation or structural dependency of the training set.
There were also common structural features related to efflux from KAM32 and KAM32/pVCJ6
strains. In general, agent efflux was not determined by specific functional groups due to the strictly
nonbonding nature of pump-mediated MDR. Exploratory analysis related to the two strains points out
that good substrates of efflux systems in E.coli strains KAM32 and KAM32/pVCJ6 are neither linear
nor extended, preferably were ring structures with some branching, rather hydrophobic, weak dipoles,
difficult to polarize, possess some polar groups, and could establish hydrogen bonds with receptors. The
strains differed in their preferences for good substrates: KAM32 binds rather hydrophobic species, while
KAM32/pVCJ6 attracts heteroaromatics. Therefore, aromatic-aromatic and hydrophobic-hydrophobic
VmrA-substrate interactions seemed to be crucial. Agent-receptor interaction statistics show that agentreceptor interaction descriptors depend on agent molecular properties, receptor molecular properties,
agent position and binding mode relative to the receptor, and the presence of other molecular/ionic species. Supposing that agent properties might substantially affect agent-receptor interactions, variations in
receptors and agent binding modes/positions were expected to have a secondary importance. In addition,
good substrates of VmrA efflux pump from V. parahaemolyticus strain AQ3334 must also be rather rigid
and condensed heteroaromatic systems, with very few or no flexible side-chains, with exo- or endocyclic
heteroatoms, and establish aromatic-aromatic contacts with VmrA.
This QSAR study proved that chemometric methods are very useful in practice and the agent-receptor
interaction descriptors, based on PDB data, correlate reasonably with biological activities, further rationalizing the VmrA-agent interactions.
DESCRIPTORS AND METHODS
It has been recognized that most descriptors employed to build QSAR models for prediction of interactions between chemical agents and Gram-negative bacterial efflux pumps were mostly parameters of
molecular field (steric, electrostatic and hydrophobic). In the study of AcrAB-TolC, critical β-lactam
molecular descriptors related to pMICs were associated with lipophilicity. For the examination of VmrA
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QSAR Studies on Bacterial Efux Pump Inhibitors
substrates, VmrA in strain KAM32 extrudes agents characterized by three lipophilicity descriptors
(Nh, sighyd and wh2) and two electronic descriptors (E4 and HOMO) while the regression vectors for
KAM32/pVCJ6 have contributions of three electronic descriptors (FF, Np2 and Mrefn2), a steric (wr)
and a hydrophobic (sighyd) descriptors accounting for agent efflux by VmrA. In studies of MDR efflux pumps of Gram-positive bacteria, in addition to molecular field descriptors as main contributors to
QSAR models, other relevant descriptors relate to quantum chemical (heat of formation), constitutional
(number of rings), topological and geometrical (molecular volume and radius of gyration) properties.
These observations were consistent with the fact that even though the mechanisms of drug transport in
bacterial efflux pumps have remained unknown and controversial, empiric observations on properties
of known pump substrates and inhibitors have indicated several regular features, of which amphiphilic
nature of compounds play a key role. Further discussion regarding this character of MDR pump inhibitors can be found in the review and the commentary done by Van Bambeke et al. in which the presence
of well-defined lipophilic and hydrophilic regions of antibiotics was mentioned (Van Bambeke, Balzi,
& Tulkens, 2000; Van Bambeke & Lee, 2006).
All QSAR studies concerning NorA inhibitors included in this chapter principally relied on twodimensional (2D) descriptors to generate the models. Among the used methods, the most common was
PLS, others included PCR and HCA. However, 2D descriptors could not give relevant information regarding the ligand – efflux pump protein interaction. Besides, there was only a limited number of studied
transport proteins, as the researchers focused on several efflux pumps, namely NorA, AcrAB-TolC, and
VmrA, out of a large number of identified antibiotic-resistant efflux pumps mentioned in previous sections of this chapter. Therefore, the need of broadening the scale of QSAR research and studies based
on bacterial efflux pumps is evident.
FUTURE RESEARCH AND OUTLOOK
As far as we are concerned, since the parameters used for presented 2D-QSAR models are mostly
molecular field descriptors, the COMFA/COMSIA-based 3D-QSAR might be an appropriate alternative for the exploration of relationships between certain structural properties and the binding mode
in which the ligand interacts with the efflux pump at the active domains. However, this method
also has several limitations. The alignment protocols used in 3D-QSAR assumes that the ligands
observed in the model bind to the efflux pump at the same binding site in the same manner. As a
result, researchers may have difficulties achieving 3D-QSAR models in the circumstances where the
efflux pumps have either one large binding site or multiple binding sites which can accommodate
simultaneous ligands in various ways. In such cases, VolSurf/GRIND-based descriptors should be
employed to obtain 3D-QSAR models with acceptable predictive capability. These are alignment
free and thus allow the analysis of structurally diverse compound sets. Although QSAR approaches
do not enable one to decipher the underlying mechanism of ligand-protein interaction, they might
represent versatile tools for in silico filtering large combinatorial libraries for compounds with
bacterial efflux pump inhibitory activity.
Moreover, the studies mentioned in the chapter mainly relied on the PLS analysis to yield predictive
models. This approach was limited to sets of compounds whose biological activity and molecular descriptors are in linear correlations. Thus, there should be QSAR studies using machine learning and neuron
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