Pharmacophore-Based Repositioning of Approved Drugs as Novel

Subscriber access provided by HACETTEPE UNIVERSITESI KUTUPHANESI

Brief Article

Pharmacophore-based repositioning of approved drugs as novel S. aureus NorA efflux pump inhibitors Andrea Astolfi, Tommaso Felicetti, Nunzio Iraci, Giuseppe Manfroni, Serena Massari, Donatella Pietrella, Oriana Tabarrini, Glenn W Kaatz, Maria Letizia Barreca, Stefano Sabatini, and Violetta Cecchetti J. Med. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jmedchem.6b01439 • Publication Date (Web): 24 Jan 2017 Downloaded from http://pubs.acs.org on January 24, 2017

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Journal of Medicinal Chemistry is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 12

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Journal of Medicinal Chemistry

Pharmacophore-based repositioning of approved drugs as novel S. aureus NorA efflux pump inhibitors Andrea Astolfi,a‡ Tommaso Felicetti,a‡ Nunzio Iraci,a Giuseppe Manfroni,a Serena Massari,a Donatella Pietrella,a Oriana Tabarrini,a Glenn W. Kaatz,b Maria L. Barreca,a* Stefano Sabatini,a* and Violetta Cecchetti.a a

Department of Pharmaceutical Sciences, University of Perugia, via del Liceo 1, 06123 Perugia, Italy.

b

John D. Dingell Department of Veterans Affairs Medical Centre and the Department of Internal Medicine, Division of Infectious Diseases, School of Medicine, Wayne State University, Detroit, MI 48201, United States. KEYWORDS. Efflux pump inhibitor, S. aureus, NorA pump, 3D pharmacophore model, virtual screening, drug repositioning

ABSTRACT: An intriguing opportunity to address antimicrobial resistance is represented by the inhibition of efflux pumps. Focusing on NorA, the most important efflux pump of Staphylococcus aureus, an efflux pump inhibitors (EPIs) library was used for ligand-based pharmacophore modeling studies. Exploiting the obtained models, an in silico drug repositioning approach allowed for the identification of novel and potent NorA EPIs.

INTRODUCTION The use of large quantities of antibiotics to control bacterial infections has created unprecedented conditions for the development of resistance to these therapeutics. Over time, these conditions have enabled the selection of drugresistant bacteria through four main mechanisms: (1) alteration of the target site,1 (2) enzymatic drug inactivation,2 (3) decreased uptake,3 or enhanced efflux,3 and (4) biofilm formation.4 Overall, these mechanisms provide antimicrobial resistance (AMR), that results in reduced efficacy of antibacterial drugs, making the treatment of patients difficult, costly, or sometimes even impossible. Staphylococcus aureus plays an important role on AMR causing a variety of infections. The rapid development of resistance to common antibiotics has provided methicillinresistant S. aureus (MRSA) strains that caused about 11,000 deaths in 2013 in USA, exceeding even HIV/AIDSrelated deaths (7,638). Moreover vancomycin, which represents the standard of care for complicated MRSA infections, is losing its efficiency because of the wide-spreading bacterial resistance. In this scenario, it is crucial to determine effective means of addressing current antimicrobial resistance mechanisms. Thus, the co-administration of an antibiotic with a drug able to restore sufficient antibacterial activity may be a successful strategy.5 Given the importance of bacterial efflux pumps in several microbial detoxification processes6 and their involvement in intercellular pathways (e.g. quorum sensing),7 they could represent a valid target to find new antibiotic resistance breakers (ARBs). Efflux pumps are involved in the extrusion of the antibiotics, causing a

decreased intracellular drug concentration. In this way, efflux makes the bacteria resistant to the extruded antibiotic, whose minimum inhibitory concentration (MIC) grows with the increase of the efflux activity.8 Among the efflux pumps that in S. aureus extrude antimicrobial agents, the most studied is NorA, whose gene is overexpressed in 43% of strains.9 NorA is capable of extruding multiple structurally dissimilar substrates such as hydrophilic fluoroquinolones (e.g.. ciprofloxacin - CPX),10 various biocides and antibacterials, as well as dyes (i.e. ethidium bromide - EtBr).11 The use of efflux pump inhibitors (EPIs) could represent a valid approach in decreasing the MIC of antibacterial agent pump substrates by multiple modes of action, such as drug susceptibility restoration in resistant clinical strains, reduction of the capability for acquired additional resistance (i.e. target mutation) and inhibition of biofilm formation.12 To date, some NorA EPIs capable of restoring drug susceptibility in resistant strains have been identified. Among them, several non-antibiotic drugs such as reserpine,13 verapamil,14 omeprazole,14 paroxetine,15 chlorpromazine16 and other phenothiazine derivatives17 can be counted, but concentrations required for EPI activity are too high, resulting in too narrow therapeutic windows. Both natural EPI compounds18, 19 and synthetic EPI derivatives2025 have been reported. The absence of structural information about the NorA protein and its molecular interaction with EPIs and substrates has strongly hampered structure-based discovery efforts in this area. In addition, the structural diversity of NorA EPIs, coupled with the lack of sufficient structure-

ACS Paragon Plus Environment


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

activity relationship information, makes it difficult to get clues for the rational design of new EPI candidates. Following our long-standing interest in NorA EPIs research,26-30 we have previously generated a ligand-based three-dimensional (3D) pharmacophore model for NorA inhibitors, which aided the rational design and synthesis of a new series of 2-phenylquinoline derivatives.30 The biological results showed that most of the designed derivatives displayed potent EPI activity against S. aureus strain SA1199B, which overexpresses norA and also has an A116E GrlA mutation.31 Recently, new EPIs have been reported in literature, providing new structural and biological data for drug discovery programs. Thus, the aim of this work was to extend our investigation by (1) building a new data set of compounds for pharmacophore modeling studies, (2) using our previously developed ModA to screen this library to identify which known EPIs were able to fit the specified binding requirements, 3) developing new 3D model(s) and 4) performing a pharmacophore-based drug repositioning approach to identify existing drugs containing the desired 3D patterns of chemical features, and thus hopefully acting as NorA EPIs. RESULTS AND DISCUSSION In our previous work30 a data set of NorA EPIs was built by taking into account three constraints to ensure high homogeneity in the biological data within the compound library: (1) the NorA inhibitory activity had to be reported as the percentage of reduction in EtBr efflux, (2) the bacterial strain used for these experiments had to be SA-1199B, and (3) the compounds had to be defined for their chirality. The molecules possessing EtBr efflux inhibition of >70% at concentration equal or lower than 50 µM were defined as “active”. Only 14 EPIs in the literature (1-14, Table S.1, Supporting Information) satisfied our criterions, and these compounds were selected as actives and used to develop a common features 3D model (hereafter called “ModA”).30 In this work, we have extended our investigation by adding to the first set of 14 active EPIs, new compounds published by us or by other groups after the development and disclosure of ModA, and able to satisfy the above reported requirements. On this basis, the new “active set” consisted of 61 EPIs (1-14 from the previous work30 plus newly collected EPIs 15-61, Table S.1, Supporting Information). These new compounds include eighteen quinolines (1-4, 15-28), two quinolones (5 and 7), a benzothiazine derivative (6), a pyrazolobenzothiazine (8), two benzoimidazole compounds (9 and 10), twenty five 1H-benzylindole derivatives (29-53), and miscellaneous compounds such as Astemizole (11), Paroxetine (12) and its isomer (3R,4S)-3((benzo[d][1,3]dioxol-5-yloxy)methyl)-4-(4fluorophenyl)piperidine15 (NNC 20-7052 - 13), Reserpine (14), 54-57, Aripiprazole (58), Ebastine (59), Loperamide (60) and Ziprasidone (61). Due to the promiscuity of multidrug efflux pumps and the variety of structurally diverse inhibitors able to target these proteins, the selected active molecules most likely interact with NorA using different binding site and/or bind-

Page 2 of 12

ing modes. However, we did assume that NorA EPIs sharing the same pharmacophore (i.e. ModA) should bind to the same receptor site and probably have a common mechanism of action. The 3D pharmacophore model ModA contained four features: one hydrogen-bond acceptor, one positive charge and two aromatic rings.30 The superimposition of the EPI library on this pharmacophore model highlighted that 51 out the 61 EPIs showed the specified binding requirements of ModA (fitness range 1.0-2.9) (Table S.2, Supporting Information). However, according to our experience on pharmacophore modelling with Phase, a fitness value threshold of 1.5 was chosen to define well-fitting compounds. This requirement was satisfied by 35 EPIs within the whole library. Among the matching compounds we found the eight EPIs already highlighted in our previous publication30 which belong to four different chemical families: (1) quinoline derivatives 1-4, (2) the quinolone derivative 5, (3) compound 11 and (4) the two isomers 12 and 13. In addition, 13 new quinoline derivatives (15-21 and 23-28) and, more interestingly, a new class of EPIs characterized by the presence of a 1H-benzylindole scaffold (30, 35-41, 43-45, 47, 51 and 52) were retrieved by ModA (Figure S.1, Supporting Information). Although ModA allowed the identification of new inhibitors potentially able to share a common receptor site with our quinoline derivatives, this 3D model was not capable of recognizing potent 1H-benzylindole derivatives such as 29, 31-34, 42, 46, 48-50, 53. This prompted us to develop new model(s) for highly potent EPIs where a stricter criterion for activity (i.e. an EtBr efflux inhibition of >80% at concentration equal or lower than 50 µM) was applied. Several attempts were performed to generate common features pharmacophore models using all potent EPIs belonging to the four chemical families that in principle could be able to act at the same NorA site. However, using this approach many EPIs did not properly map (i.e. fitness values were lower than the chosen 1.5 cutoff value) the obtained 3D models. Thus, given the lack of balance between scaffold diversity and scaffold representation among the collected EPIs (Table S.1, Supporting Information), to avoid overweighing of a single chemical family only the most active/representative compounds of each chemical family was selected to generate the 3D models. As a consequence, a ligand-based pharmacophore modeling study was conducted using Phase32 and a training set (TS) constituted by compounds 4 (as representative of quinolines), 13 (as representative of paroxetine isomers), 33 (as representative of indoles), and 11 (Figure 1). In parallel, two additional sets of compounds were generated. Starting from the whole “active set”, all compounds sharing the same scaffold of the compounds defining the TS were in turn used to define a “focused active set”, that in principle should be formed by EPIs acting at the same NorA site; this set was composed by 42 EPIs (Table S.1, Supporting Information). Moreover, we built a “focused inactive set” constituted by compounds reported in the literature as both showing EtBr efflux inhibition of < 30% at 50 µM concentration and belonging to the same chemical families of the TS compounds. This set included 10 compounds, among


Page 3 of 12

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 1. Fitting of TS compounds on the developed models ModB (A, B) and ModC (C,D). Red sphere and vectors: H-bond acceptor; blue sphere: positive charge; orange ring: aromatic ring; green sphere: hydrophobic moiety. In Figures B and D the compounds are differently color-coded: grey, 4; purple, 11; yellow, 13; green, 33. Comparison between ModB (green) and ModC (red) for each individual compounds (E).

which 5 were quinolines (62-66) and 5 benzylindoles (6771) (Table S.3, Supporting Information). It was later used to validate the developed hypotheses; indeed, a good 3D model should give high fitness values for actives and low fitness values for inactive molecules (i.e. the model should be able to discriminate active from inactive compounds). Given the hypothesis that all EPIs in the TS share the same receptor site, we looked for common 3D model(s) containing three, four or five pharmacophore sites. No fivesites models common to all TS compounds were found. However, Phase32 generated several four-sites hypothesis that were evaluated for their ability (1) to fit the molecules of TS (Survival score), (2) to be unique to the actives (Selectivity score) and (3) to have a low “inactive score”. The analysis of the different models culminated with the selection of the two most promising hypotheses ModB and ModC, which showed the best Selectivity and Survival scores, respectively (see Experimental Section). The alignment of the TS compounds into ModB and ModC highlighted how these EPIs were able to map simultaneously the four pharmacophoric features of the two models in at least one conformation (Figure 1). The fitness score for each EPI in both models is reported in Table S.4, Supporting Information. ModB contained the same chemical features of the previously developed ModA though the spatial arrangement was slightly different (Figure S.2, Supporting Information): one hydrogen-bond acceptor (A2), one positive charge (P6) and two aromatic rings (R7 and R8) (Figure 1.A and 1.B). The best 3D model obtained according to the Survival score (ModC) represented instead a novelty, consisting of one hydrogen-bond acceptor (A2), one positive charge

(P6), one aromatic ring (R8) and one hydrophobic region (H4) (Figure 1.C and 1.D). Interestingly, although ModB and ModC were developed using the same TS and apparently shared some pharmacophore elements, the superimposition of the two models on the individual compounds underlined that from a structural perspective the protonable group is the only 3D feature in common (Figure 1.E). Of note, the positive charge appeared to be the key feature in the discrimination between active and inactive compounds for both models (Table S.4 , Supporting Information). In fact, when analyzing the “focused inactive set”, seven compounds (i.e. 62-65, 67, 68, and 70) failed to completely map both ModB and ModC because of the absence of a protonable group in their structures. Conversely, the inactive compounds 66 and 71 did bear groups that Phase software recognized as able to assume a positive charge, and thus these two compounds fitted into both ModB and ModC despite their lack of NorA inhibitory activity. However, this discrepancy could be explained considering that Moka,33 a software able to accurately predict pKa values and tautomerism in the aqueous medium with the respective species abundance, suggested that the calculated pKa value of the nitrogen in the side chain of 66 and 71 was 6.52 and 5.51, respectively. Thus, under physiological conditions the abundance of the charged forms might be 9.5% and 1% for compound 66 and 71, respectively, suggesting the lack of a “real” protonable group in both molecules. Finally, only ModB retrieved the inactive molecule 69 even though with fitness value lower than the chosen threshold of 1.5 for active compounds. We also used ModB and ModC to perform a pharmacophore mapping of the “focused active set” (Table S.4, Supporting Information). All highly potent EPIs (i.e. EtBr efflux inhibition of > 80% at concentration equal or lower than 50 µM) fitted well both 3D models. Furthermore, the two



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 4 of 12

Figure 2. 2D structures of drugs selected for biological evaluation. The fitness values for both ModB and ModC and the corresponding EtBr efflux inhibition activity (both % of inhibition and IC50 values) are reported for each compound.

models efficiently identified all the remaining active EPIs but 22. These results clearly demonstrated the mining power improvement of the new pharmacophore models with respect to the previously developed ModA. Encouraged by the predictive ability of ModB and ModC, we employed a drug repositioning strategy to identify exiting drugs with previously unknown inhibitory activity against NorA efflux pump. Indeed, drug repositioning is a popular reduced-risk approach for developing new drugs, and offers key advantages over traditional drug discovery.34, 35 The two discussed pharmacophore models were thus used as queries to screen an approved drug library (1620 compounds) in the search for drugs containing all of the specified 3D patterns of chemical features. The results of each screening (57 and 52 drugs for ModB and ModC, respectively) were analyzed through a consensus approach by selecting only the compounds with a fitness score for both models ≥ 1.5. This approach led to the identification of potentially potent NorA EPIs, among which compound 12 was found. Five drugs were selected for further analyses on the basis of their availability and cost. These included moricizine (72), dasatinib (73), gefitinib (74), sildenafil (75), and mebeverine (76); furthermore, nicardipine (77) and 1-(3-(tert-butyl)-1-(p-tolyl)-1Hpyrazol-5-yl)-3-(4-(2-morpholinoethoxy)naphthalen-1yl)urea36 (BIRB 796 - 78) were purchased given their promising fitness scores on ModB or ModC, respectively. (Figure 2 and Figure S.3, Supporting Information). Biological data. The seven virtual hits (72-78) were initially evaluated for their ability to inhibit EtBr efflux from SA-1199B at 50 µM concentration. Compounds 77, 74, and 73 showed an activity of 77.1, 94.2, and 95.8 %, respectively. Furthermore, 76 and 78 showed a lower activity with an EtBr efflux inhibitory effect of 45.2 and 47.0%, respectively (Figure 2). Compounds 72 and 75 inhibited EtBr efflux poorly. To deepen the knowledge about the activity of the three best compounds (73, 74, and 77), dose-response curves of EtBr efflux inhibition were built and related IC50 values

were obtained (Figures 2 and 3); our best in house EPI (1)28 and reserpine (14)13 were included as reference controls.

Figure 3. Effect of compounds 73, 74, 77, 1, and 14 on EtBr efflux of SA-1199B

Then, to establish whether the EtBr efflux inhibitory activity of 73, 74, and 77 resulted in a synergistic interaction with CPX, it was necessary to evaluate the MICs of the three compounds against the S. aureus strains included in the biological assay (i.e. SA-1902, SA-K2378, SA-1199, and SA-1199B). Notably, none of the tested drugs showed any antibacterial activity at least up to the highest concentration used in the test (i.e. 100 µg/mL). Checkerboard assays for 73, 74, and 77 and CPX were performed, and again 1 and 14 were included as positive controls. Data collected in Figure 4 reveal that the three selected drugs showed a poor synergistic activity when tested against both SAK1902 and SA-1199. These data are consistent with the lack (SA-K1902) or the poor expression (SA-1199) of the NorA pump in these strains. On the contrary, compounds 73, 74, and 77 exhibited a strong synergistic activity against strains overexpressing norA (SA-K2378 and SA1199B). In particular, the three compounds showed an effect against SA-K2378 comparable to that of the reference controls 1 and 14 (Figure 4). When tested against SA1199B, all drugs exhibited a synergistic activity with CPX greater than 14, with compound 77 showing an effect even better than 1 (Figure 4). Thus, it is evident that the three drugs, at concentrations well below their MIC values,


Page 5 of 12

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 4. Effect of compounds 73, 74, 77, 1, and 14 on the MIC of CPX against SA-K1902 (norA−), SA-K2378 (norA++), SA-1199 (norA wt), and SA-1199B (norA+/A116E GrlA).

exhibited a strong synergistic activity with CPX against strains overexpressing norA gene. In detail, 73, 74, and 77 were able to reduce the CPX MIC 16-fold at concentrations ranging from 12.5 to 25 µg/mL against SA-K2378, and 8fold at concentrations between 6.25 and 12.5 µg/mL against SA-1199B (Figure 4). Anyway, considering that the plasma concentration of CPX is around 1.5-2.5 µg/mL for standard regimens, concentration between 0.78 and 3.13 µg/mL of 74 and 77 are already enough to recovery the antibacterial activity of CPX on both SA-K2378 and SA1199B resistant strains (Figure 4). A preliminary comparison between EPI activity and cytotoxicity on human cells (Table S.5, Supporting Information) suggested a promising selectivity profile of the three studied drugs. CONCLUSION In summary, we report herein the development of two new 3D pharmacophore models (ModB and ModC) for S. aureus NorA EPIs. Pharmacophore-based virtual screening of a drug library revealed three non-antibiotic approved drugs (73, 74, and 77) that were able to restore the CPX antibacterial activity against S. aureus strains overexpressing the MDR efflux pump NorA. Employing these promising results, further in-depth analysis will be carried out against different S. aureus resistant clinical isolates. Furthermore, EPI activity of the 73, 74 and 77 will be also assayed against other microbial species, such as Mycobacterium avium, driven by our interest in this field.37 Overall, the biological data provide evidence for the validity of our in silico strategy, encouraging its extension by virtually screening further drug libraries. EXPERIMENTAL SECTION Preparation of the Data Set Compounds. The “active set” (1-61) and “focused inactive set” (62-71) were built using the fragment library tools of Maestro GUI38 and then submitted to a conformational search using MacroModel.39 To enhance the conformational sampling, the maximum number of steps was set to 10000 per molecule. Conform-

ers in an energy window of 10 kcal/mol were saved, discarding the redundant ones on the basis of their atomic RMSD (0.5 Å cutoff). This energy threshold was used because for some ligands a high-energy difference (>9 Kcal/mol) between the strain energy of the bioactive conformation and the global minimum has been calculated.40, 41 Minimization of conformers was performed using the Polak−Ribiere conjugate gradient method, using a maximum of 500 minimization steps and 0.0005 kJ/(Å mol) as gradient convergence threshold. Pharmacophore modeling. The pharmacophore modeling study was performed using Phase 4.6.32 The “active set” conformers were used to create a Phase database with pharmacophoric sites generated using the default feature definitions. This database was screened using ModA as query, and the Fitness score aided the evaluation of the results. Analysis of structural and biological data then guided the selection of 4, 11, 13 and 33 as the TS for the development of new models. The best-identified hypotheses were ModB (survival score: 2.748, selectivity score: 1.743; inactive score: 1.648) and ModC (survival score: 2.858, selectivity score: 1.660; inactive score: 1.871). The quality of both models was evaluated by using the “focused active set” and “focused inactive set” Phase databases. Drug Repositioning. The approved drug libraries from Selleck (1018 compounds) and Prestwick (1280 compounds) were downloaded and merged. Duplicates were deleted in Canvas42 to obtain a unique library of nonredundant entries (1620 compounds), which were submitted to LigPrep.43 The neutral form of the ligands was prepared and the library was then submitted to the same conformational search used for the “active set”. Finally, the obtained conformers were used to create a Phase database. Bacterial Strains. The strains of S. aureus employed included SA-K2378, which overexpresses norA from a multicopy plasmid and was produced by cloning norA and its promoter into plasmid pCU1 and then introducing the con-

5 ACS Paragon Plus Environment


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

struct into SA-K1902 (norA−).44 In addition, SA-1199B (overexpressing norA and also possessing an A116E GrlA substitution) and its isogenic parent SA-1199 (norA wt) were also used.31 EtBr Efflux. The loss of EtBr from S. aureus SA-1199B was determined fluorometrically as previously described.45 The effect of various concentrations of tested compounds on the EtBr efflux of SA-1199B was compared to that in their absence, allowing the calculation of the percentage reduction in efflux. Dose-response experiments were performed by testing increasing concentrations of inhibitors, and the 50% inhibitory concentration (IC50) was determined by inspection of the resultant plots. Microbiologic Procedures. MICs were determined by microdilution techniques according to CLSI guidelines. The effect of compounds on the MICs of CPX was determined employing a checkerboard as described previously.46 Purity. Purity of the active compounds (73, 74 and 77) was determined by LC-MS and 1H-NMR and resulted ≥95%.

ASSOCIATED CONTENT Supporting Information. Compound data sets composition with associated chemical structures and biological activity; Fitness values for the different sets on the 3D models; Alignment of selected compounds/drugs on ModA and ModB; Data analysis for active compounds; Molecular formula strings and some data (CSV); Cytotoxicity of compounds 73, 74 and 77. This material is available free of charge via the Internet at http://pubs.acs.org.

AUTHOR INFORMATION Corresponding Author *M.L.B.: phone, +39-075-5855157; e-mail, [email protected]. *S.S.: phone, +39-075-5855130; e- mail, [email protected]. Author Contributions All authors have given approval to the final version of the manuscript. ‡These authors contributed equally.

ACKNOWLEDGMENT This work was partially supported by Università degli Studi di Perugia (Fondo per il sostegno della Ricerca di Base 2015), and in part by Veterans Affairs Merit grant Abbreviations: AMR, antimicrobial resistance; ARBs, antibiotic resistance breakers; CPX, ciprofloxacin; EtBr, ethidium bromide; EPI, efflux pump inhibitor.

REFERENCES 1. Amyes, S. G.; Smith, J. T. R-factor mediated dihydrofolate reductases which confer trimethoprim resistance. J. Gen. Microbiol. 1978, 107, 263-271. 2. Jacoby, G. A.; Medeiros, A. A. More extended-spectrum beta-lactamases. Antimicrob. Agents Chemother. 1991, 35, 16971704. 3. Van Bambeke, F.; Balzi, E.; Tulkens, P. M. Antibiotic efflux pumps. Biochem. Pharmacol. 2000, 60, 457-470. 4. Hengzhuang, W.; Ciofu, O.; Yang, L.; Wu, H.; Song, Z.; Oliver, A.; Hoiby, N. High beta-lactamase levels change the

Page 6 of 12

pharmacodynamics of beta-lactam antibiotics in Pseudomonas aeruginosa biofilms. Antimicrob. Agents Chemother. 2013, 57, 196-204. 5. Brown, D. Antibiotic resistance breakers: can repurposed drugs fill the antibiotic discovery void? Nat. Rev. Drug Discovery. 2015, 14, 821-832. 6. Sun, J.; Deng, Z.; Yan, A. Bacterial multidrug efflux pumps: mechanisms, physiology and pharmacological exploitations. Biochem. Biophys. Res. Commun. 2014, 453, 254267. 7. Waters, C. M.; Bassler, B. L. Quorum sensing: cell-to-cell communication in bacteria. Annu. Rev. Cell Dev. Biol. 2005, 21, 319-346. 8. Marquez, B. Bacterial efflux systems and efflux pumps inhibitors. Biochimie. 2005, 87, 1137-1147. 9. Patel, D.; Kosmidis, C.; Seo, S. M.; Kaatz, G. W. Ethidium bromide MIC screening for enhanced efflux pump gene expression or efflux activity in Staphylococcus aureus. Antimicrob. Agents Chemother. 2010, 54, 5070-5073. 10. Neyfakh, A. A.; Borsch, C. M.; Kaatz, G. W. Fluoroquinolone resistance protein NorA of Staphylococcus aureus is a multidrug efflux transporter. Antimicrob. Agents Chemother. 1993, 37, 128-129. 11. Li, X. Z.; Nikaido, H. Efflux-mediated drug resistance in bacteria. Drugs. 2004, 64, 159-204. 12. Kvist, M.; Hancock, V.; Klemm, P. Inactivation of efflux pumps abolishes bacterial biofilm formation. Appl. Environ. Microbiol. 2008, 74, 7376-7382. 13. Schmitz, F. J.; Fluit, A. C.; Luckefahr, M.; Engler, B.; Hofmann, B.; Verhoef, J.; Heinz, H. P.; Hadding, U.; Jones, M. E. The effect of reserpine, an inhibitor of multidrug efflux pumps, on the in-vitro activities of ciprofloxacin, sparfloxacin and moxifloxacin against clinical isolates of Staphylococcus aureus. J. Antimicrob. Chemother. 1998, 42, 807-810. 14. Aeschlimann, J. R.; Dresser, L. D.; Kaatz, G. W.; Rybak, M. J. Effects of NorA inhibitors on in vitro antibacterial activities and postantibiotic effects of levofloxacin, ciprofloxacin, and norfloxacin in genetically related strains of Staphylococcus aureus. Antimicrob. Agents Chemother. 1999, 43, 335-340. 15. Kaatz, G. W.; Moudgal, V. V.; Seo, S. M.; Hansen, J. B.; Kristiansen, J. E. Phenylpiperidine selective serotonin reuptake inhibitors interfere with multidrug efflux pump activity in Staphylococcus aureus. Int. J. Antimicrob. Agents. 2003, 22, 254261. 16. Kaatz, G. W.; Moudgal, V. V.; Seo, S. M.; Kristiansen, J. E. Phenothiazines and thioxanthenes inhibit multidrug efflux pump activity in Staphylococcus aureus. Antimicrob. Agents Chemother. 2003, 47, 719-726. 17. Tsakovska, I.; Pajeva, I. Phenothiazines and structurally related compounds as modulators of cancer multidrug resistance. Curr. Drug Targets. 2006, 7, 1123–1134. 18. Stavri, M.; Piddock, L. J.; Gibbons, S. Bacterial efflux pump inhibitors from natural sources. J. Antimicrob. Chemother. 2007, 59, 1247-1260. 19. Gibbons, S. Anti-staphylococcal plant natural products. Nat. Prod. Rep. 2004, 21, 263-277. 20. Ambrus, J. I.; Kelso, M. J.; Bremner, J. B.; Ball, A. R.; Casadei, G.; Lewis, K. Structure-activity relationships of 2-aryl1H-indole inhibitors of the NorA efflux pump in Staphylococcus aureus. Bioorg. Med. Chem. Lett. 2008, 18, 4294-4297. 21. Lepri, S.; Buonerba, F.; Goracci, L.; Velilla, I.; Ruzziconi, R.; Schindler, B. D.; Seo, S. M.; Kaatz, G. W.; Cruciani, G. Indole based weapons to fight antibiotic resistance: a structure-activity relationship study. J. Med. Chem. 2016, 59, 867–891. 22. Fontaine, F.; Hequet, A.; Voisin-Chiret, A. S.; Bouillon, A.; Lesnard, A.; Cresteil, T.; Jolivalt, C.; Rault, S. First identification of boronic species as novel potential inhibitors of


Page 7 of 12

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


the Staphylococcus aureus NorA efflux pump. J. Med. Chem. 2014, 57, 2536-2548. 23. Doleans-Jordheim, A.; Veron, J. B.; Fendrich, O.; Bergeron, E.; Montagut-Romans, A.; Wong, Y. S.; Furdui, B.; Freney, J.; Dumontet, C.; Boumendjel, A. 3-aryl-4-methyl-2quinolones targeting multiresistant Staphylococcus aureus bacteria. Chemmedchem. 2013, 8, 652-657. 24. Hequet, A.; Burchak, O. N.; Jeanty, M.; Guinchard, X.; Le Pihive, E.; Maigre, L.; Bouhours, P.; Schneider, D.; Maurin, M.; Paris, J. M.; Denis, J. N.; Jolivalt, C. 1-(1H-Indol-3-yl)ethanamine derivatives as potent Staphylococcus aureus NorA efflux pump inhibitors. Chemmedchem. 2014, 9, 1534-1545. 25. Liger, F.; Bouhours, P.; Ganem-Elbaz, C.; Jolivalt, C.; Pellet-Rostaing, S.; Popowycz, F.; Paris, J. M.; Lemaire, M. C2 arylated benzo[b]thiophene derivatives as Staphylococcus aureus NorA efflux pump inhibitors. Chemmedchem. 2016, 11, 320-330. 26. Sabatini, S.; Kaatz, G. W.; Rossolini, G. M.; Brandini, D.; Fravolini, A. From phenothiazine to 3-phenyl-1,4-benzothiazine derivatives as inhibitors of the Staphylococcus aureus NorA multidrug efflux pump. J. Med. Chem. 2008, 51, 4321-4330. 27. Pieroni, M.; Dimovska, M.; Brincat, J. P.; Sabatini, S.; Carosati, E.; Massari, S.; Kaatz, G. W.; Fravolini, A. From 6aminoquinolone antibacterials to 6-amino-7thiopyranopyridinylquinolone ethyl esters as inhibitors of Staphylococcus aureus multidrug efflux pumps. J. Med. Chem. 2010, 53, 4466-4480. 28. Sabatini, S.; Gosetto, F.; Manfroni, G.; Tabarrini, O.; Kaatz, G. W.; Patel, D.; Cecchetti, V. Evolution from a natural flavones nucleus to obtain 2-(4-propoxyphenyl)quinoline derivatives as potent inhibitors of the S. aureus NorA efflux pump. J. Med. Chem. 2011, 54, 5722-5736. 29. Sabatini, S.; Gosetto, F.; Serritella, S.; Manfroni, G.; Tabarrini, O.; Iraci, N.; Brincat, J. P.; Carosati, E.; Villarini, M.; Kaatz, G. W.; Cecchetti, V. Pyrazolo[4,3-c][1,2]benzothiazines 5,5-dioxide: a promising new class of Staphylococcus aureus NorA efflux pump inhibitors. J. Med. Chem. 2012, 55, 3568-3572. 30. Sabatini, S.; Gosetto, F.; Iraci, N.; Barreca, M. L.; Massari, S.; Sancineto, L.; Manfroni, G.; Tabarrini, O.; Dimovska, M.; Kaatz, G. W.; Cecchetti, V. Re-evolution of the 2phenylquinolines: ligand-based design, synthesis, and biological evaluation of a potent new class of Staphylococcus aureus NorA efflux pump inhibitors to combat antimicrobial resistance. J. Med. Chem. 2013, 56, 4975-4989. 31. Kaatz, G. W.; Seo, S. M. Mechanisms of fluoroquinolone resistance in genetically related strains of Staphylococcus aureus. Antimicrob. Agents Chemother. 1997, 41, 2733-2737. 32. Small-Molecule Drug Discovery Suite 2016-2: Phase, version 4.6, Schrödinger, LLC, New York, NY, 2016.

33. Milletti, F.; Storchi, L.; Sforna, G.; Cruciani, G. New and original pKa prediction method using grid molecular interaction fields. J. Chem. Inf. Model. 2007, 47, 2172-2181. 34. Ashburn, T. T.; Thor, K. B. Drug repositioning: identifying and developing new uses for existing drugs. Nat. Rev. Drug Discovery. 2004, 3, 673-683. 35. Hodos, R. A.; Kidd, B. A.; Shameer, K.; Readhead, B. P.; Dudley, J. T. In silico methods for drug repurposing and pharmacology. Wiley Interdiscip. Rev.: Syst. Biol. Med. 2016, 8, 186-210. 36. Regan, J.; Breitfelder, S.; Cirillo, P.; Gilmore, T.; Graham, A. G.; Hickey, E.; Klaus, B.; Madwed, J.; Moriak, M.; Moss, N.; Pargellis, C.; Pav, S.; Proto, A.; Swinamer, A.; Tong, L.; Torcellini, C. Pyrazole urea-based inhibitors of p38 MAP kinase: from lead compound to clinical candidate. J. Med. Chem. 2002, 45, 29943008. 37. Machado, D.; Cannalire, R.; Costa, S. S.; Manfroni, G.; Tabarrini, O.; Cecchetti, V.; Couto, I.; Viveiros, M.; Sabatini, S. Boosting effect of 2-phenylquinoline efflux inhibitors in combination with macrolides against Mycobacterium smegmatis and Mycobacterium avium. ACS Infect. Dis. 2015, 1, 593-603. 38. Schrödinger Release 2016-2: Maestro, version 10.6, Schrödinger, LLC, New York, NY, 2016. 39. Schrödinger Release 2016-2: MacroModel, version 11.2, Schrödinger, LLC, New York, NY, 2016. 40. Perola, E.; Charifson, P. S. Conformational analysis of drug-like molecules bound to proteins: an extensive study of ligand reorganization upon binding. J Med Chem. 2004, 47, 2499-2510. 41. Avgy-David, H. H.; Senderowitz, H. Toward Focusing Conformational Ensembles on Bioactive Conformations: A Molecular Mechanics/Quantum Mechanics Study. J Chem Inf Model. 2015, 55, 2154-2167. 42. Schrödinger Release 2016-2: Canvas, version 2.8, Schrödinger, LLC, New York, NY, 2016. 43. Schrödinger Release 2016-2: LigPrep, version 3.8, Schrödinger, LLC, New York, NY, 2016. 44. Augustin, J.; Rosenstein, R.; Wieland, B.; Schneider, U.; Schnell, N.; Engelke, G.; Entian, K. D.; Gotz, F. Genetic analysis of epidermin biosynthetic genes and epidermin-negative mutants of Staphylococcus epidermidis. Eur. J. Biochem. 1992, 204, 1149-1154. 45. Kaatz, G. W.; Seo, S. M.; O'Brien, L.; Wahiduzzaman, M.; Foster, T. J. Evidence for the existence of a multidrug efflux transporter distinct from NorA in Staphylococcus aureus. Antimicrob. Agents Chemother. 2000, 44, 1404-1406. 46. Eliopoulos, G. M.; Moellering, R. C. J. Antimicrobial combinations. In Antibiotics in Laboratory Medicine; Lorian, V., 3rd Ed.; Williams and Wilkins: Baltimore, MD. 1991; pp 432−492.

Table of Contents Graphic



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

76x33mm (300 x 300 DPI)


Page 8 of 12

Page 9 of 12

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


65x24mm (600 x 600 DPI)



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

144x76mm (150 x 150 DPI)


Page 10 of 12

Page 11 of 12

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


149x76mm (300 x 300 DPI)



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

49x21mm (300 x 300 DPI)


Page 12 of 12

Pharmacophore-Based Repositioning of Approved Drugs as Novel

Recommend Documents