Topological pattern for the search of new active drugs against methicillin resistant Staphylococcus aureus

Accepted Manuscript Topological pattern for the search of new active drugs against methicillin resistant Staphylococcus aureus Pedro A. Aleman Lopez, Jose I. Bueso-Bordils, Maria T. Perez-Gracia, Beatriz SuayGarcia, Maria J. Duart, Rafael V. Martin Algarra, Luis Lahuerta Zamora, Gerardo M. Anton-Fos PII:

S0223-5234(17)30532-9

DOI:

10.1016/j.ejmech.2017.07.010

Reference:

EJMECH 9573

To appear in:

European Journal of Medicinal Chemistry

Received Date: 31 March 2017 Revised Date:

13 June 2017

Accepted Date: 6 July 2017

Please cite this article as: P.A. Aleman Lopez, J.I. Bueso-Bordils, M.T. Perez-Gracia, B. Suay-Garcia, M.J. Duart, R.V. Martin Algarra, L. Lahuerta Zamora, G.M. Anton-Fos, Topological pattern for the search of new active drugs against methicillin resistant Staphylococcus aureus, European Journal of Medicinal Chemistry (2017), doi: 10.1016/j.ejmech.2017.07.010. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Topological pattern for the search of new active drugs against methicillin resistant Staphylococcus

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aureus

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Pedro A. Aleman Lopez,* Jose I. Bueso-Bordils,* Maria T. Perez-Gracia, Beatriz Suay-Garcia, Maria J. Duart, Rafael V. Martin Algarra, Luis Lahuerta Zamora and Gerardo M. Anton-Fos. Departamento de Farmacia, Universidad CEU - Cardenal Herrera, Avenida Seminario s/n, 46113

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Moncada, Valencia, Spain. Tel: (+34) 96 136 90 00 (Ext. 61470). Fax: (+34) 96 139 52 72.

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* Corresponding author:

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E-Mail: [email protected]

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ABSTRACT

Molecular topology was used to develop a mathematical model capable of classifying

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compounds according to antimicrobial activity against methicillin resistant Staphylococcus aureus (MRSA). Topological indices were used as structural descriptors and their relation to antimicrobial activity was determined by using linear discriminant analysis.

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This topological model establishes new structure activity relationships which show that the presence of cyclopropyl, chlorine and ramification pairs at a distance of two bonds favor this

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activity, while the presence of tertiary amines decreases it.

This model was applied to a combinatorial library of a thousand and one 6-fluoroquinolones, from which 117 theoretical active molecules were obtained. The compound 10 and five new quinolones were tested against MRSA. They all showed some activity against MRSA, although

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compounds 6, 8 and 9 showed anti-MRSA activity similar to ciprofloxacin. This model was also applied to 263 theoretical antibacterial agents described by us in a previous work, from which 34 were predicted as theoretically active. Anti-MRSA activity was found

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bibliographically in 9 of them (ensuring at least 26% of success), and from the rest, 3 compounds were randomly chosen and tested, finding mitomycin C to be more active than ciprofloxacin.

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The results demonstrate the utility of the molecular topology approaches for identifying new drugs active against MRSA. KEYWORDS

QSAR, Molecular Topology, Linear Discriminant Analysis, Virtual Combinatorial Chemistry, Quinolone, SARM.

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INTRODUCTION The search for new treatments to improve the quality of life is one of the priorities of research teams around the world. The discovery of antibiotics and its improvement in the last decades

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have brought a revolution in the pharmacological treatment of infectious diseases. However, the extreme versatility and adaptability of microorganisms has avoided the prevalence of infectious diseases from decreasing, because many bacteria, in recent years, have developed mechanisms

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that protect them from many drugs [1].

Currently, the development of antibiotic resistance by microorganisms is one of the most

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important problems that have appeared in recent years in the treatment of infectious diseases. This increased resistance is associated with increased morbidity and mortality from infections, as well as an increase in healthcare costs [2]. Therefore, it is necessary to know the sensitivity of the microorganisms and be constantly alert to the emergence of resistant strains that may

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condition treatment failure.

Staphylococcus aureus (SA) is a common infectious agent, at both the community and the hospital, and can cause from trivial infections to life-long commitment infections (endocarditis,

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septicemia, meningitis ...). Its leadership has grown in recent years thanks to greater isolation of methicillin-resistant Staphylococcus aureus (MRSA) and the emergence of strains resistant to

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glycopeptides. MRSA was first described in England in 1961, two years after the introduction of methicillin [3]. MRSA infections were first detected in hospitals. However, in recent years, infections have emerged in the community and also from livestock. Consequently, MRSA can no longer be considered an exclusive healthcare-associated problem and it cannot be fought by hospital infection prevention and control measures alone. MRSA is highly prevalent in hospitals

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worldwide. The highest rates (>50%) are reported in North and South America, Asia and Malta [4]. The variability of SA, its rapid adaptive response against environmental changes, and its

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continued acquisition of antibiotic resistance determinants, have made it a habitual resident of hospitals, where it causes a problem of multidrug resistance, occasionally important [5]. Besides its resistance to methicillin through various mechanisms, it has resistance to chloramphenicol,

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tetracyclines, macrolides, lincosamides, aminoglycosides, and even quinolones, describing increasingly MRSA outbreaks often sensitive only to glycopeptides. Indeed, due to the problems

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of antibiotic multiresistance and the large number of therapeutic failures, it is necessary to search for new compounds that can be used for treatment and prophylaxis [6]. Until about the fifties, the pharmacologically active substances were obtained by experimental tests for a particular activity, and once detected, structural changes were made aimed at

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improving properties. This, with a very high economic cost, caused the development of methods to develop quantitative relationships between chemical structure and activity, called Quantitative Structure-Activity Relationships (QSAR), which served as the basis for the design of new

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molecules with a particular activity. The great advantage of QSAR methods is that they are able to predict the pharmacological activity of a compound without the need to obtain or synthesize it

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previously [7,8]. This has caused a great rise of Computational Chemistry and Virtual Combinatorial Chemistry in the recent years [9]. Several QSAR methods have been used in the design and development of antimicrobials [10-12]. Within the framework of QSAR methods, molecular connectivity has widely demonstrated its ability for an easy and efficient characterization of molecular structure through the so-called topological indices (TIs) [13-15].

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Traditionally, the search for new active compounds from a QSAR model has been applied to the same family of compounds from which the model is obtained. This is practically mandatory when fragmentary parameters are used [16]. However, when topological models derived from

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overall connectivity parameters, in combination with linear discriminant analysis [17], were applied to compounds with large structural diversity, excellent results were obtained [18]. This methodology has been successfully used in the search for new oral hypoglycemic [19], anti-

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inflammatory [20], antimalarial [21], antihistaminic [22], antibacterial agents [23] and for Alzheimer’s disease [24].

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The aim of this study was to develop and assess a QSAR model based on molecular topology in order to identify new active compounds against MRSA. Quinolones is a well-known therapeutic group used in many infections, both in hospital and out-of-hospital settings. They are usually well tolerated and safe. In addition, there are numerous published activity trials with inactivity

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data, which are often the most difficult to find when conducting QSAR studies. For all these reasons, they are ideal candidates for the development of an activity prediction model.

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METHODS AND MATERIAL Compound selection

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A group of 26 compounds with known anti-MRSA activity was selected with a literature search of several medical databases. Furthermore, a group of 30 non-antimicrobial compounds was also selected. All of them had a 4-quinolone or closely related structure (the structure of all compounds as well as bibliographic references about their activity can be found in the Supporting Information file). This database with 56 compounds was used in order to obtain a discriminant function, DFMRSA.

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To consider a compound as active, it should have a minimum inhibitory concentration (MIC) ≤ 1 µg/mL, while those compounds considered non-active should have a MIC ≥ 16 µg/mL. Those with intermediate activities were not included in the study. Regarding stereoisomers, if any of

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them was active, it was included in the active group. If all individual stereoisomers or the

group. Molecular connectivity and topological descriptors

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mixture of them in any ratio were inactive, they were included as a single graph in the inactive

The molecular descriptors used are described in Supporting Information along with their

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definitions and references. Each compound was characterized by a set of 144 descriptors specific to each molecule. These descriptors do not contain 3D parameters. They were computed from the adjacency topological matrix obtained from the hydrogen-depleted graph by using MOLCONNZ [25] and DESMOL13 [26] programs.

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Linear discriminant analysis (LDA)

Stepwise LDA is a model recognition method providing a classification model based on the combination of variables that best predicts the category or group to which a given compound

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belongs. The variables used to compute the linear classification functions were chosen in a stepwise manner, based on the Fisher–Snedecor parameter F, which relates the variance

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explained by the equation with the residual variance. At each step, the variable with the greater value of F, thus, the variable that makes the larger contribution to the separation of the groups, was entered in the discriminant function. Conversely, selected variables with a small value of F, thus, variables which lowered the statistical significance of the classification function, were removed.

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On the other hand, the equations obtained varied according to the number and type of indices used initially. Therefore, different combinations were tested in order to obtain equations with good statistical parameters. Several equations containing 4 indices were generated using

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combinations of different types of indices (i.e. electrotopological, charge, charge and electrotopological, connectivity…). A new subgroup was then established using the indices

a final equation with a greater discriminant capability.

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present in the equations with better statistical parameters, repeating the process in order to obtain

The discriminant ability was assessed by the percentage of correct classifications attained for

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each set. The classification criterion is the minimal Mahalanobis distance (distance of each case to the mean of all the cases in a category). The quality of the discriminant function was evaluated through Wilk’s U-statistical parameter, λ, which was obtained by a multivariate analysis of variance that tests the equality of group means for the variable in the discriminant model. LDA

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was then applied to the database, except for the molecules reserved as the test group, to obtain a predictive mathematical model linking structural descriptors and activity. The independent variables in this study were the topological descriptors, and the discriminant property was

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antimicrobial activity. The software used for the LDA study was the BMDP 7M Biomedical package [27], which randomly chooses the compounds reserved for the test set.

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Pharmacological Activity Distribution Diagrams After selecting the discriminant function, the corresponding pharmacological distribution diagrams (PDD) were built up. These plots are useful to determine the intervals of the discriminant function in which the expectancy, E, to find active compounds is maximum. PDDs are histogram-like plots of connectivity functions in which the expectancies appear on the ordinate axis. For an arbitrary interval of values of a given function, we can define the

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expectancy of activity as Ea = a/(i + 1), where “a” is the number of active compounds in the interval divided by the total number of active compounds, and “i” is the number of inactive compounds. The expectancy of inactivity is defined in a symmetrical way, as Ei = i/(a + 1). This

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representation provides good visualization of the regions of minimum overlap and selects regions in which the probability of finding improved compounds is maximum. This way the number of false actives will be minimized.

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Virtual Combinatorial Chemistry.

The synthesis of 4-quinolones has been extensively studied and practically any type of

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substituent can enter at the different positions of the bicyclic ring [28]. We decided to design a virtual combinatorial library of a thousand 6-fluoroquinolones with structural variations in positions 1, 7 and 8. This library was created with the ChemBioDraw Ultra 13.0 program, and the molecules were stored in ".mol" format.

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The selection of substituents, was made based on the commercial availability of the corresponding reagent, prioritizing those fragments which frequently appear in drugs [29]. Given that there are no 3D indices in the prediction equation, substituents with chiral centers

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were also avoided, since this would force us to synthesize and test all stereoisomers of the corresponding graph with theoretical activity. In the case of the 7-position, all pyrrolidines with

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any substituent were discarded, although it is the ring that seems to provide greater activity against Gram positive bacteria [30]. Finally, a virtual combinatorial library of 1000 compounds was obtained. The nomenclature decided to refer to each of them was an alpha-numeric code. The numbers 1, 7 and 8 indicate the position of the ring, followed by a letter indicating the substituent at that position (Figure 1).

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Figure 1. Fragments used to generate the virtual combinatorial library (10x25x4 = 1000). Topological virtual screening The topological model was used to find new anti-MRSA compounds, from the Virtual

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Combinatorial Library and from the 263 theoretical antibacterial agents described by us in a previous work [31].

PDDs allowed us to carry out the assignment of thresholds useful to discriminate active from

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inactive compounds with the highest probability of success. Only the compounds predicted as active by the DFMRSA values within the predetermined thresholds were identified as potential

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anti-MRSA. Activity assays

All compounds were evaluated for their in vitro antibacterial activity against MRSA [32], using standard techniques, following the protocol recommended by the Spanish Society of

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Clinical Microbiology and Infectious Diseases (SEIMC) [33].

The commercial compounds were used directly without any further manipulation. The synthesized compounds were dried before use keeping vacuum in presence of P2O5 for at least

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one night. Stock solutions of 1 mg/mL for mitomycin C and 1.28 mg/mL for the remaining compounds were prepared, and from them, the corresponding two-fold serial dilutions [33].

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RESULTS AND DISCUSSIONS Topological pattern

To obtain the discriminant function, a training group with 19 active and 24 inactive compounds and a test group with 7 active and 6 inactive compounds were formed. This test group allowed to evaluate the quality of the selected discriminant function. This discriminant function along with its corresponding statistical parameters are depicted in equation 1:

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DFSARM = – 9.51467 – 2.19S>N- + 1.37006S-Cl + 0.72921PR2 + 44.320453χch λ = 0.1637891

N = 56

F = 48.501

(Eq. 1)

The equation has a low value of λ, indicating that there is hardly any linear dependence

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between independent variables. On the order hand, the high value of F in the equation indicates that the selected independent variables contribute largely to the separation of the active and inactive groups.

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A compound was considered to be active against MRSA according the DFMRSA value. If DFMRSA was > 0, the compound was predicted to be active, and if DFMRSA was < 0 it was

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predicted to be inactive. All compounds, active and inactive, were correctly classified in both training and test sets. This means that the accuracy of this equation was 100% (Table 1). Tables with indices values are shown in Supporting Information.

Cross-validation of the training group showed that 19 out of the 19 active compounds (100%)

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and 21 out of the 24 inactive compounds (87.5%) were correctly classified. Table 1. Results obtained in the LDA and classification of compounds. Training group: actives

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and inactives. ACTIVES

Compound

FDSARM

Prob.

Clas.FD

+

Inact1

−12.886

1.000

–

1.000

+

Inact2

−5.1778

0.994

–

8.4152

1.000

+

Inact3

−6.151

0.998

–

9.1808

1.000

+

Inact4

−9.0319

1.000

–

Act7

8.3611

1.000

+

Inact5

−8.3546

1.000

–

Act8

9.2519

1.000

+

Inact6

−13.029

1.000

–

Act9

8.3526

1.000

+

Inact7

−7.9273

1.000

–

Act1 Act4 Act5 Act6

FDSARM

Prob.

Clas.FD

9.158

1.000

9.1696

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Compound

INACTIVES

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8.3517

1.000

+

Inact8

−12.422

1.000

–

Act12

9.2723

1.000

+

Inact9

−12.808

1.000

–

Act14

10.51

1.000

+

Inact10

−11.164

1.000

–

Act16

9.0432

1.000

+

Inact11

−10.534

1.000

–

Act17

9.0483

1.000

+

Inact12

−8.2228

Act18

16.037

1.000

+

Inact13

−13.726

Act20

5.6456

0.996

+

Inact14

−0.7978

Act21

7.2081

0.999

+

Inact15

−1.7116

Act23

7.1048

0.999

+

Inact16

Act24

5.899

0.997

+

Inact17

DX-619

12.485

1.000

+

Sitafloxacin

25.023

1.000

+

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Act11

–

1.000

–

0.689

–

0.847

–

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1.000

0.600

–

−13.513

1.000

–

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−0.4041

−12.965

1.000

–

Inact21

−13.799

1.000

–

Inact23

−15.154

1.000

–

Inact24

−14.607

1.000

–

Inact27

−13.596

1.000

–

Inact28

−13.656

1.000

–

PGE-6116542

−5.2238

0.995

–

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Inact18

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Regarding the indices affecting the value of DFMRSA, it is worth noting the negative sign associated with the electrotopological index for tertiary nitrogens (S>N-), indicating an

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unfavorable influence of this group. This does not mean that their presence is not necessary, as stated in the classic structure-activity relationship (SAR) of quinolones [30], since this group in position 1 is part of the pharmacophore, but an excess of this functional group would negatively affect activity. Moreover, the value of this index is also dependent on the electronegativity of groups nearby, decreasing when the electronegativities are high [34].

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Regarding the electrotopological index for chlorine (S-Cl), the equation indicates that the presence of this atom favors the activity, noting that some of the training and test active compounds possess it, while inactive compounds all have a value of zero, which implies that this

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group is not present in their structure. Since this atom is present in some of the active compounds, the possibility of developing the model ignoring this index was studied, but in all the attempts the percentage of success significantly decreased.

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The importance of this atom was evidenced experimentally by Foroumadi et al., who observed in a study of antibacterial activity that the replacement of chlorine by fluorine drastically

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decreased the activity of several analog compounds against S. aureus [35].

Another index that positively affects activity against MRSA way is the geometric topological index PR2. This index counts the number of ramification pairs separated by two bonds [36]. The equation also shows a clear dependence of the activity relative to the Kier-Hall chain type

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third order index (3χch), which implies that the presence of a cyclopropyl group greatly enhances the activity against MRSA. Most of the active compounds have this group. On the contrary, it can be seen that the inactive compounds generally lack this group. This group has proven to be a

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positive influence on the activity of a large number and variety of drugs [37]. Attending classical SAR for quinolones, the substituent of choice at position 1 for activity

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against Gram positive bacteria is cyclopropyl group [30]. In our case, its presence also favors the activity, but not necessarily in position 1. There are examples of quinolones active against MRSA with a cyclopropyl group included at position 7, with or without it in 1 [38,39], that have not been included in the development of DFMRSA. If we would only take into account DFMRSA to design a new quinolone active against MRSA, we would say that it must have a high number of branches, cyclopropyl groups, chlorine atoms

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and a minimum number of tertiary nitrogen, desirably close to electron attractor atoms or groups. If we observe the active compounds, we see that sitafloxacin fulfills all these properties, obtaining the highest value of DFMRSA, 25.023 (Table 1). However, when performing the

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distribution diagram (Figure 2), we see that there are no values in the range 18-24, so without any more data, and in order to avoid false actives, those compounds whose DFMRSA > 18 shall be considered unclassified. This also applies to values between 0 and 3, so finally, we can say that a

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compound will be active against MRSA when its DFMRSA is between 3 and 18.

Figure 2. PDD of DFMRSA. Black Bars: training inactives. White Bars: training actives. Dashed Line: test inactives. Straight Line: test actives.

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Phamacological Virtual Screening

After performing the virtual drug screening with the anti-MRSA activity predictive model, 116

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compounds of a total of 1000 were classified as active (Table 2).

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1a7j8b

1a7n8d

1a7t8a

1a7x8b

1d7b8c

1h7b8a

1a7a8b

1a7j8c

1a7o8b

1a7t8b

1a7x8c

1d7b8d

1h7b8b

1a7a8c

1a7j8d

1a7o8c

1a7t8c

1a7x8d

1d7n8b

1h7b8c

1a7a8d

1a7k8b

1a7o8d

1a7t8d

1a7y8a

1e7b8a

1h7b8d

1a7c8b

1a7k8c

1a7p8b

1a7u8a

1a7y8b

1e7b8b

1h7n8b

1a7e8b

1a7k8d

1a7p8c

1a7u8b

1a7y8c

1e7b8c

1i7b8a

1a7e8c

1a7l8a

1a7p8d

1a7u8c

1a7y8d

1e7b8d

1i7b8b

1a7e8d

1a7l8b

1a7q8b

1a7u8d

1b7b8a

1e7n8b

1i7b8c

1a7f8a

1a7l8c

1a7q8c

1a7v8a

1b7b8b

1f7b8a

1i7b8d

1a7f8b

1a7l8d

1a7q8d

1a7v8b

1b7b8c

1f7b8b

1j7b8a

1a7f8c

1a7m8a

1a7f8d

1a7m8b

1a7h8a

1a7m8c

1a7h8b

1a7m8d

1a7h8c

1a7n8a

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1a7a8a

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Table 2. Quinolones classified as active against MRSA.

1a7v8c

1b7b8d

1f7b8c

1j7b8b

1a7r8b

1a7v8d

1c7b8a

1f7b8d

1j7b8c

1a7r8c

1a7w8a

1c7b8b

1g7b8a

1j7b8d

1a7r8d

1a7w8b

1c7b8c

1g7b8b

1j7n8b

1a7s8b

1a7w8c

1c7b8d

1g7b8c

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1a7r8a

1a7h8d

1a7n8b

1a7s8c

1a7w8d

1d7b8a

1g7b8d

1a7i8b

1a7n8c

1a7s8d

1a7x8a

1d7b8b

1g7n8b

None of these 116 compounds have previously been tested against MRSA. The compounds 1 [40], 2 [41], 3 [42], 4 [43], and 5 [41] have been described as active against non-methicillin

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resistant S. aureus (Figure 3). Quinolone 6 has been previously described as antimycobacterial [44]. From the rest of the theoretical active molecules against MRSA, we find no description of

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their pharmacological activity nor their synthesis.

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Figure 3. Compounds from the library previously described as active against S. aureus Molecules 5-9 were selected to be synthesized and tested against MRSA (Figure 4). The

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quinolone 7 is a common reactive used to obtain quinolones with different substituents on position 7. We found no activity data against MRSA. Another reagent commonly used in the

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synthesis of quinolones that has not been previously tested against MRSA was 10. This compound was not included in our initial virtual combinatorial library. According to our equation, by containing chlorine, it should present higher activity than its corresponding fluorinated derivative 7. Therefore, it was included in our virtual combinatorial library of quinolones. The anti-MRSA activity predictive model was applied on it, and classified it as theoretically active. This compound was included among the compounds to be tested (Figure 4).

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O

O

F

N

O F

OH

N

O

N

O

O OH

N

N

O

F F

OH N

5

6

7

O O OH

N

F

O

OH

N N

N

N 9

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8

O

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O F

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O

F

Cl

O OH

N

10

Figure 4. Compounds selected to be tested against MRSA Chemistry

The selected quinolones were obtained by coupling the corresponding amine with the reactive

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7, according to well-established literature procedures (Table 3) [45]. All compounds were obtained with yields above 70% (Table 3, entries 1-3) except 9, which

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reacted very slowly, and after 7 days of reaction, although there was still many unreacted starting quinolone, we decided to stop it, obtaining the desired product with only 12% yield (Table 3,

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entry 4). The compound 6 was obtained after 16 h of reaction, but 5 and 8 needed 54 h and 41 h respectively to be completed (Table 3, entries 1 and 3). Although there are methods to improve these syntheses, such as the formation of boric chelates with the starting quinolone [46], or using microwave techniques [47], or through Buchwald–Hartwig coupling [48], they were not conducted since the amounts obtained were sufficient to continue with the activity assays.

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Table 3. Synthesis of quinolones from 7. mmol.

Et3N (mmol)

2.25

-

1

O

2

NH HCl

O

1.5

12

1.5

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3

1.5

Quinolone

54 h

Yield

5

71 %

3.4

16 h

6

80 %

3.4

41 h

8

75 %

9

12 %

144 3 h

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4

Time

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Amine

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Entry

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MRSA activity assays

The minimum inhibitory concentration values (MICs) and minimum bactericidal concentration values (MBCs) were compared with those of ciprofloxacin (Table 4). Of the 5 synthesized quinolones, 9 has been particularly active since it shows the same bacteriostatic activity as ciprofloxacin (MIC = 0.5 mg/L) but a much higher bactericidal activity, since its CMB is 8-fold lower (Table 4, entries 1 and 6).

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Table 4. MIC and MBC values obtained for the tested products. Entr Compound

MIC (mg/L) MBC (mg/L)

y ciprofloxacin

0.5

256

2

5

32

>512

3

6

1

128

4

7

64

>512

5

8

1

6

9

0.5

7

10

8

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1

128

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32

512

λ-cyhalothrin

>512

>512

9

lamotrigine

>512

>512

10

mitomycin C

0.5

4

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32

Compounds 6 and 8 have shown activity similar to ciprofloxacin, since although their MICs

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are higher, both compounds have a MBC of 128 mg/L (Table 4, entries 1, 3 and 5). Finally, compound 5 has shown moderate activity (Table 4, entry 2).

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In turn, compounds 7 and 10 have also shown some activity, being more active 7 (Table 4, entries 4 and 7), which would be consistent with the model, which establishes that the presence of chlorine favours the activity. On the other hand, among the non-quinolone structure compounds, λ-cyhalothrin and lamotrigine have been found to be inactive (Table 4, entries 8 and 9), while mitomycin C has

19

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been found to be very active against MRSA, with a MIC of 0.5 mg/L and a very low MBC of only 4 mg/L compared to 256 mg/L obtained for ciprofloxacin (Table 4, entries 1 and 10).

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Generally, MIC and MBC, in antibacterials considered bactericidals, are very close together. Usually, they are one or two dilutions apart. When the difference is higher we are normally facing one of three phenomena: paradoxical, of tolerance or of persistence. The phenomenon of

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tolerance is the decrease in the killing ability of a bactericidal agent in some species (a fact that has been proven in resistant staphylococcal infections), which is evidenced when MIC and MBC

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are separated by at least 5 double dilutions. In clinical settings, it can determine the need for treatment with more than one antibacterial [33]. This phenomenon occurs in the case of 6 of the 7 quinolones tested, including ciprofloxacin (Table 4, entries 1-6). The difference was 4 double dilutions for quinolone 10 (Table 4, entry 7). In the case of mitomycin C, it was 3 double

CONCLUSIONS

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dilutions (Table 4, entry 10).

Currently, the development of resistance of microorganisms such as Staphylococcus aureus is one of the most important problems that have appeared in recent years in the treatment of

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infectious diseases. Molecular topology has demonstrated to be a useful methodology for

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identifying new compounds with antimicrobial activity against MRSA. By using multilinear regression and LDA, a pattern of topological similarity of antimicrobial activity against MRSA was developed that was successfully applied to the search for new compounds exhibiting significant activity against MRSA. One additional advantage is that it affords screening of large databases in a short time. EXPERIMENTAL SECTION

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Chemistry. General. All reagents and solvents were purchased from Aldrich and used without purification unless stated otherwise. All reactions were made under anhydrous atmosphere. The solid reagents were dried before use keeping vacuum in presence of P2O5 for at least one night.

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Melting points were determined by using open capillary method and are uncorrected. Thinlayer chromatography (TLC) was run on Merck silica gel 60 F-254 plates and preparative TLC (PTLC) was run on Glass Plates from EMD/Merck KGaA. IR spectra were obtained on a

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Spectrum Two FT-IR PerkinElmer instrument directly from the corresponding pure solid.

1H-NMR spectra were recorded on a Bruker AC-300 instrument in DMSO-d6, unless

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otherwise indicated. Chemical shifts (δ values) are given in ppm relative to internal tetramethylsilane, and coupling constants (J values) are expressed in Hz. Mass spectra were obtained on a hybrid mass spectrometer with quadrupole time analyzer flight TRIPLETOFTTM 5600 LC/MS/MS System, (ABSCIEX). The conditions for all of them

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were direct infusion, electrospray technique (ESI) (Ion source gas 1 (GC1): 30 psi; Ion source gas 2 (GC2): 30 psi; Curtain gas 1: 25 psi; Temperature: 450 ºC; Ion Spray Voltage (ISVF): 5500; Mass range: 80-950 m/z). Data was evaluated using the PeakView™.

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Compound purity was confirmed by a combination of LC-MS (HPLC), and high resolution

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mass spectrometry and NMR analysis. All compounds are ≥ 95% purity. 1-cyclopropyl-6-fluoro-7-(2-methyl-1H-imidazol-1-yl)-4-oxo-1,4-dihydroquinoline-3carboxylic acid (5).

A mixture of 7 (200mg, 0.754 mmol) and 11 (185 mg, 2.25 mmol) in 11 mL of anhydrous CH3CN was refluxed with drying tube. After 54 h, the resulting suspension was filtered and the solid was washed first with CH3CN (3 x 3 mL) and later with water (3 x 3 mL). The solid was dried under high vacuum to afford pure fluoroquinolone 5 (174 mg, 71%) as an off white solid,

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mp 290.5-292ºC. IR (cm-1) 3148.64; 3114.24; 3033.28; 2973.71; 1727.08; 1615.01; 1553.18; 1491.91; 1461.72 cm-1. NMR 1H (300 MHz, DMSO) δ 14.67 (s, 1H); 8.82 (s, 1H); 8.47 (d, J = 6.5 Hz; 1H); 8.30 (d; J = 10.0 Hz; 1H); 7.47 (br s, 1H); 7.05 (d; J = 1.3 Hz; 1H); 3.94-3.78 (m,

C17H14FN3O3 328.1092. Found 328.1106.

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1H); 2.33 (s, 3H); 1.38-1.14 (m, 4H). HRMS (ESI-TOF) m/z: [M+H]+ calculated for

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1-cyclopropyl-7-(6,7-dimethoxy-3,4-dihydroisoquinolin-2(1H)-yl)-6-fluoro-4-oxo-1,4dihydroquinoline-3-carboxylic acid (6).

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A mixture of 7 (200mg, 0.754 mmol), 12 (345 mg, 1.5 mmol), 11 mL of anhydrous CH3CN and Et3N (0.47 mL 3,.4 mmol) was refluxed with drying tube. After 16 h, the resulting suspension was filtered and the solid was washed first with CH3CN (3 x 3 mL) and later with water (3 x 3 mL). The solid was dried under high vacuum to afford pure fluoroquinolone 6 (263

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mg, 80 %) as an off white solid, mp 224-227 ºC. IR (cm-1) 3049.92; 3024.13; 2914.22; 2835.26; 1710.79; 1612.44; 1554.48; 1498.38. RMN 1H (300 MHz, CDCl3) δ 15.10 (s, 1H); 8.74 (s, 1H); 7.92 (d; J = 12.6 Hz; 1H); 7.39 (d; J = 8.4 Hz; 1H); 7.28 (s, 1H), 6.68 (s, 1H); 4.52 (s, 2H); 3.89

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(s, 6H); 3.76 (br t; J = 5.7 Hz; 2H); 3.58-3.47 (m, 1H); 2.97 (br t; J = 5.4 Hz; 2H); 1.42-1.12 (m,

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4H). HRMS (ESI-TOF) m/z: [M+H]+ calculated for C24H23FN2O5 438.1591. Found: 438.1664.

1-cyclopropyl-6-fluoro-7-(methyl(naphthalen-1-ylmethyl)amino)-4-oxo-1,4-dihydroquinoline3-carboxylic acid (8).

The same procedure to that described in the synthesis of 6 was used. The quinolone 8 (234 mg, 75%) was obtained as an off white solid, mp 239.5 – 240.6 ºC. IR (cm-1) 3094.14; 3045.10; 3013.54; 2875.16; 1724.65; 1630.77; 1504.44; 1457.50. RMN 1H (300 MHz, DMSO) δ 15.36

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(s, 1H); 8.57 (s, 1H); 8.57 (s, 1H); 8.19-7.94 (m, 2H); 8.14-7.95 (m, 2H); 7.89 (d; J = 9.0 Hz; 1H); 7.85 (d; J = 15.0 Hz; 1H); 7.68-7.31 (m, 5H); 5.21 (s, 2H); 3.62 (m, 1H); 3.27 (br d; J = 2,1 Hz; 3H); 0.99 (br s, 4H). HRMS (ESI-TOF) m/z: [M+H]+ calculated for C25H21FN2O3

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417.1609. Found: 417.1616.

7-(1H-benzo[d]imidazol-1-yl)-1-cyclopropyl-6-fluoro-4-oxo-1,4-dihydroquinoline-3-

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carboxylic acid (9).

A mixture of 7 (200mg, 0.754 mmol), 14 (545 mg, 1.5 mmol), anhydrous CH3CN (11 mL) and

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Et3N (0.41 mL 3mmol) was refluxed with drying tube. After 144 h, the resulting suspension was filtered and washed first with CH3CN (3 x 3 mL) and later with water (3 x 3 mL). The solid was purificated by PTLC with a mixture CH2Cl2:MeOH 20:1 as eluent. The compound 9 (33 mg, 12%) was obtained as an off white solid, mp 291.5-293 ºC). IR (cm-1) 3066.64; 1712.91;

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1616.99; 1504.51; 1479.92; 1447.08. NMR 1H (300 MHz, DMSO) δ 14.70 (s, 1H); 8.85 (s, 1H); 8.75-8.60 (m, 2H); 8.39 (d; J = 10.4 Hz; 1H); 7.92-7.79 (m, 1H); 7.67-7.57 (m, 1H); 7.497.30 (m, 2H); 3.99-3.84 (br s, 1H); 1.35-1.20 (m, 4H). HRMS (ESI-TOF) m/z: [M+H]+

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calculated for C20H14FN3O3 364.1092. Found 364.1108.

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ACKNOWLEDGEMENTS This work was supported by the ICB (Biomedical Science Institute of the CEU - Cardenal

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Herrera University) [grant BC/ICB-Santander 05/12]. J. I. Bueso Bordils acknowledges his grant [BC/ICB-Santander 06/12] from the Biomedical Science Institute of the CEU - Cardenal Herrera University. We would also like to express our gratitude to Syngenta, Inibsa Hospital SLU, Galenicum Health SL and Menadiona SL for supplying us free samples of the compounds

Conflict of interest: none. SUPPLEMENTARY DATA

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CONFLICT OF INTEREST DISCLOSURE

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mitomycin C, lamotrigine and λ-cyhalothrin with no strings attached.

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Table of indices. Structure and references on MRSA activity of the compounds used to develop the model. DFMRSA values of all compounds. IR, NMR-1H and Mass spectrums of all

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Piperazine Surrogates: Synthesis and Biological Activity of a Ciprofloxacin Analogue,

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Active and inactive quinolones against MRSA were selected.

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An anti-MRSA prediction model has been developed using Molecular Topology-LDA.

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New structure activity relationships have been established.

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A virtual screening was performed on 1264 quinolone and non-quinolone compounds.

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Four of the nine assayed compounds showed similar activity to Ciprofloxacin.

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•