Macrocyclic peptidomimetics with antimicrobialactivity: synthesis, bioassay, and molecularmodeling studies†
Mohamed A. Ibrahim,*a,b,c Siva S. Panda,*a,d Alexander A. Oliferenko,a
Polina V. Oliferenko,a Adel S. Girgis,e Mohamed Elagawany,a,f F. Zehra Küçükbay,a,g
Chandramukhi S. Panda,a Girinath G. Pillai,a,h Ahmed Samir,i Kaido Tämm,h
C. Dennis Halla and Alan R. Katritzky‡a
Novel, cyclic peptidomimetics were synthesized by facile acylation reactions using benzotriazole chem-
istry. Microbiological testing of the synthesized compounds revealed an exceptionally high activity against
Candida albicans with a minimum inhibitory concentration (MIC) two orders of magnitude lower than the
MIC of the antifungal reference drug amphotericin B. A strikingly high activity was also observed against
three Gram-negative bacterial strains (Pseudomonas aeruginosa, Klebsiella pneumoniae and Proteus
vulgaris), two of which are known human pathogens. Thus the discovered chemotype is a potential poly-
pharmacological agent. The toxicity against mammalian tumor cells was found to be low, as demon-
strated in five different human cell lines (HeLa, cervical; PC-3, prostate; MCF-7, breast; HepG2, liver; and
HCT-116, colon). The internal consistency of the experimental data was studied using 3D-pharmacophore
and 2D-QSAR.
Introduction
Infectious diseases are the third leading cause of death in deve-loped countries and the second leading cause of death world-wide.1 Although numerous anti-infective drugs have been
developed and commercialized, bacteria and fungi develop drugresistance rather quickly by (i) producing metabolizing enzymesto degrade the drugs, (ii) modifying their targets to render thedrugs ineffective, and (iii) expressing a high level of efflux pro-teins that ‘pump’ the drug out and lower its intracellular con-centration. Drug resistance can be overcome by designinginnovative agents with different modes of action so that nocross-resistance can occur.2 Hence, there is an urgent need todiscover new chemotherapeutic agents to combat microbialresistance and ideally shorten the duration of therapy.3
Fungal infections
Humans are naturally well protected against most fungi, whichcannot grow at the higher body temperature of warm-bloodedanimals. However fungal infections can be lethal to millions ofpeople with a compromised immune system caused by HIVinfection, the immunosuppressant pharmacotherapy requiredduring organ transplantations, or treatment with corticoster-oids.4 Global warming may allow apathogenic fungal strains togrow at elevated temperatures and thus increase the infectivethreat. Candidiasis is one of the most common human fungalinfections and is especially problematic when associated withbloodstream and catheter-related infections in hospitalizedpatients.5 Fusarium, Aspergillus, and Trichosporon speciesare other pathogens that may emerge during periods ofimmunosuppression.6
†Electronic supplementary information (ESI) available: Including 1H NMR, 13CNMR, CHN/HRMS, Tables of descriptor models, 3D-pharmacophore mappedmodels and Dose–response curves of all the compounds. See DOI: 10.1039/c5ob01400j‡Professor Alan R. Katritzky passed away 10th February 2014.
aDepartment of Chemistry, University of Florida, Gainesville, FL 32611-7200, USA.
Tel: +1-352-870-9288bDepartment of Organic Chemistry, College of pharmacy, Misr University for Science
and Technology, Al-Motamayez District, 6th of the October, P.O.Box: 77, EgyptcDepartment of Pharmaceutical Chemistry, Almaarefa Colleges for Science and
Technology, Riyadh 11597, Kingdom of Saudi ArabiadDepartment of Chemistry & Physics, Georgia Reagents University, Augusta, GA
30912, USAePesticide Chemistry Department, National Research Centre, Dokki, Giza 12622,
EgyptfDepartment of Pharmaceutical Chemistry, faculty of pharmacy, Damanhour
University, Damanhour, EgyptgDepartment of Basic Pharmaceutical Sciences, Faculty of Pharmacy,
İnönü University, 44280 Malatya, TurkeyhDepartment of Chemistry, University of Tartu, 50411 Tartu, EstoniaiDepartment of Microbiology, Faculty of Veterinary Medicine, Cairo University, Cairo,
There are three major classes of antifungal drugs currentlyin clinical use: (i) Macrocyclic polyenes, the usual moleculartarget of which is ergosterol, a specific component of the cellwall of fungi and yeasts which fulfills a similar role to chole-sterol in animal cells.7,8 Selective binding of macrocyclic poly-enes to ergosterol alters membrane integrity, which results inthe leakage of cations and hence to cell death. The diastereo-meric compounds amphotericin B and nystatin are typicalexamples of macrocyclic polyene antimicotics (Fig. 1). (ii) Echino-candins, Micafungin and Caspofungin are representatives ofthis recently discovered class of antifungal compounds, inwhich the mode of action is believed to be the inhibition of1,3-β-glucan synthase required for the β-glucans buildingblocks of the cell wall membranes.9 (iii) Azole antifungalagents also interfere with the functions of ergosterol, this timethrough suppression of its biosynthesis from lanosterol viainhibition of the fungal cytochrome P450 enzyme lanosterol14α-demethylase.10
Many antifungal drugs have drawbacks such as drug resist-ance, a narrow therapeutic window, and severe side-effects.11
The development of novel, potent antifungal agents beyondthe existing classes is thus an important task for medicinalchemistry, which is currently being tackled by synthesis and bya screening of natural products.12–14
Antimicrobial peptides
These represent a family of innate host-defense polypeptidesand are found in all organisms ranging from prokaryotes to
humans. The native peptides were found to possess antimicro-bial, antifungal and antiprotozoal activity and some of themcan inhibit cell-associated production of HIV-1, influenza A,and vesicular stomatitis viruses.15 However, native peptides arerarely used as drugs due to limitations such as sensitivity toproteolytic degradation and a high risk of cross-resistance toinnate human antimicrobial peptides.16 To circumvent suchproblems, peptides have been modified into “peptidomi-metics” that mimic and/or stabilize secondary structures(e.g., α-helices and β-turns) and combine several antibioticactivities in one molecule thus allowing the study of associatedbiological processes and opportunities for drug design anddevelopment.
A combination of two antimicrobial cyclic dipeptides cyclo(L-Leu–L-Pro) and cyclo(L-Phe–L-Pro) was reported to exhibit astrong synergy in inhibiting the growth of vancomycin-resist-ant enterococci and other pathogenic bacteria like Staphylo-coccus aureus and P. aeruginosa.17 Morel et al. reported that someof the cyclopeptide alkaloid extracts from Scutia buxifolia.Reiss showed significant antimicrobial activity against K. pneu-moniae, S. aureus, Escherichia coli, Salmonella setubal, Staphylo-coccus epidermidis, and Micrococcus luteus.18 Loloatin C and itsanalogues are cyclic cationic antimicrobial peptides activeagainst Gram-positive bacteria like S. aureus, S. epidermidisand Enterococcus faecalis as well as certain Gram negative bac-teria such as E. coli, P. aeruginosa, Bacillus cereus, as reportedby Tuin et al.19 The enopeptins are cyclic acyldepsipeptidesshowing antibacterial activity against multidrug-resistant
bacterial strains such as methicillin-resistant S. aureus1
(MRSA) while the syringopeptins SP508 and SP22 are cycliclipodepsipeptides produced by several plant associated pseudo-monads. Both of them displayed growth-inhibitory activitiesagainst Mycobacterium smegmatis, S. aureus, Bacillus megater-ium, E. coli, P. vulgaris, P. aeruginosa, Serratia marcescens, Sal-monella enterica (serovar typhimurium), Citrobacter freundii,Rhodotorula rubra, C. albicans and Rhodotorula pilimanae.20
Chiral macrocyclic ligands have found widespread appli-cation in asymmetric synthesis and enantiomeric reco-gnition.21,22 The incorporation of amino acids into abioticanion receptors can lead to systems that mimic the anioncoordination properties of anion binding proteins.23 Introduc-tion of cysteine subunits into a macrocycle facilitates receptorsynthesis and can control the relative spatial alignment of thetwo chains attached to a cysteine residue, as exemplified by acysteine-containing macrocycle which was designed to mimiccation binding by valinomycin.23 Most cysteine-based anionreceptors contain aromatic subunits which act as amphi-recep-tors able to interact both with cations and anions.
In the present study, the synthesis of twelve peptidomi-metics is reported along with anti-fungal activity, which wasfound to be strikingly high. In fact, the observed potency wasfifty times higher than that of amphotericin B, the well-knownpotent but rather toxic systemic antifungal agent. Activityagainst several Gram-negative bacteria was also extremelystrong, thus adding value in terms of polypharmacology. Anexcellent selectivity with respect to human cells was found,which implies low human toxicity. QSAR (quantitative struc-ture–activity relationship) analysis revealed the structural fea-tures that correlate with the antifungal activity.
Results and discussionChemistry
Preparation of N-acylbisbenzotriazoles. N-Acylbisbenzotria-zoles 2a–f were prepared in 54–85% yields by the reaction ofthe corresponding dicarboxylic acids 1a–f with 8 equiv. of 1H-benzotriazole (BtH) and 2.2 equiv. of SOCl2 in DCM at 20 °Cfor 24 h (Scheme 1, Table 1).
Selective S-acylation with N-acylbenzotriazole. The regio-selective S-acylation of cysteine esters was developed recentlyin our group and is based on the use of N-acylbisbenzotria-zoles.25 Utilizing this methodology, 2a was coupled with twoequiv. of cysteine ester hydrochloride in aqueous acetonitrileat 20 °C for 3 h to give bis(S-acylcysteine)esters 3a,b. Com-pounds 3a,b were then treated with 1 equiv. of N-acylbisbenzo-
triazoles 2a–f to synthesize cyclic enantiopure peptidomimeticproducts 4a–l via double N-acylation.
Treatment of L-cysteine ester hydrochlorides with pyridine-2,6-diylbis(1H-benzo[d][1,2,3]triazol-1-yl)methanone (2a) atroom temperature in MeCN/H2O (19 : 1) for 3 h gave S-acyl-cysteines 3a,b in yields of 88 and 92% as the only isolated pro-ducts (Scheme 2).
Preparation of cyclic cysteine peptidomimetics 4a–l. Com-pounds 2a–f were reacted with pyridine dicysteine ester hydro-chlorides 3a,b in the presence of triethylamine (4.0 equiv.) inDMF under microwave irradiations at 50 °C and 50 W powerfor 20 min to afford novel macrocyclic peptides 4a–l in mode-rate to good yields (45–83%) (Scheme 3, Table 2).
Antimicrobial bioassay
Antifungal bioassay. Antifungal bioassays were conductedby the standard serial dilution technique26,27 using CandidaScheme 1 Synthetic route towards N-acylbisbenzotriazoles 2a–f.
Table 1 Preparation of N-acylbisbenzotriazoles 2a–f
Entry R Product 2 Yield (%) Mp (°C) Lit mp (°C)
1 2a 54 226–228 Novel
2 2b 68 231–233 231–23325
3 2c 59 167–169 167–16925
4 2d 72 98–100 98–10025
5 2e 85 152–154 150–16025
6 2f 83 142–144 142–14425
Scheme 2 Synthetic route towards 2,2’-[(pyridine-2,6-dicarbonyl)bis(azanediyl)]bis(3-mercaptopropanoates) 3a,b.
albicans for the synthesized peptidomimetic esters 4a–l inaddition to 2,7,13,18-tetraoxo-6,14-dithia-3,17,23-triazatricyclo[17.3.1.18,12]tetracosa-1(22),8,10,12(24),19(23),20-hexaene-4,16-dicarboxylic acid (5), 2,7,13,18-tetraoxo-3,17-dithia-6,14,23,24-tetraazatricyclo[17.3.1.18,12]tetracosa-1(22),8,10,12(24),19(23),20-hexaene-5,15-dicarboxylic acid (6) and 2,7,11,16-tetraoxo-6,12-dithia-3,14,20-triazabicyclo[14.3.1]licosa-1(19),16(20),17-triene-4,13-dicarboxylic acid (7) which were previously reported(Fig. 2)28 by our group possess extraordinary antifungalactivity. The experimental minimum inhibition concentrations(MIC) for these compounds with amphotericin B as the refer-ence standard drug are given in Table 3. The observed anti-fungal activity of compounds 4a–4l was one to two orders ofmagnitude higher than that of amphotericin B (AmpB). Thishigh level of inhibition renders macrocyclic peptidomimetics4a–4l highly potent antifungal agents, especially compounds4a and 4k with an MIC of only 7.5 ng mL−1. Although allmacrocycles 4a–4l are structurally similar, one can identify twofeatures that make 4a and 4k special: (i) the methyl versusethyl ester fragment, and (ii) presence of an electronegativeatom in the dicarboxythiolate moiety. Structure–activityrelationships deduced from these molecular features can bevisualized by comparing structures 4a (methyl ester) vs. 4b(ethyl ester), 4a (pyridine nitrogen) vs. 4c (phenyl carbon), 4k(oxygen) vs. 4i (sulfur), and also 4k (methyl ester) vs. 4l (ethylester).
Antibacterial bioassay
A surprising dichotomy was found for the antibacterial actionof the title peptidomimetics: they all except one (compound 5)exhibited low activity against Gram-positive S. aureus but highactivity against Gram-negative K. pneumoniae, P. vulgaris, andP. aeruginosa. As seen in Table 3, activity against K. pneumoniaewas the highest. The sharp difference in activity against Gram-negative and Gram-positive bacteria may be due to a thinnerpeptidoglycan layer in Gram-negative strains which may infermembrane disruption as the mechanism of action. Lipopoly-saccharide (LPS), a characteristic membrane constituent ofGram-negative bacteria, constitutes another potential targetfor compounds 4.
Antitumor bioassay
The synthesized peptidomimetics 4a–4l, and 5–7 were also bio-assayed against five human cancer cell lines: HeLa (cervical),PC-3 (prostate), MCF-7 (breast), HepG2 (liver) and HCT-116(colon) utilizing the standard in vitro Sulfo-Rhodamine-B (SRB)method,29–37 in order to evaluate the antiproliferative poten-tial. Table 4 illustrates that almost all the peptidomimeticshave low cytotoxic activity against human cell lines, with only4a (against HepG2 with IC50 = 6.03 μM, IC50 of doxorubicin =7.36 μM) and 4l (against PC3 with IC50 = 7.94 μM, IC50 of doxo-rubicin = 8.83 μM) being as active as the reference standarddoxorubicin. Additionally, compound 4a revealed promisingantitumor properties (IC50 = 6.48 μM) against MCF7 comparedwith the reference standard doxorubicin (IC50 = 5.46 μM).
Scheme 3 Synthetic route towards macrocyclic peptides 4a–l.
Table 2 Preparation of pyridine based cysteine containing macrocycles4a–l
a IC50, concentration required to produce 50% inhibition of cell growth compared to control experimental. bDoxorubicin is the referencestandard used in the present study.
3D-pharmacophore modeling. The IUPAC definition ofpharmacophore is “an ensemble of steric and electronic fea-tures that is necessary to ensure the optimal supramolecularinteractions with a specific biological target and to trigger (orblock) its biological response”.38 Pharmacophores are used todefine the essential features of one or more molecules withthe same biological activity. The 3D-pharmacophore study wasperformed using Discovery Studio 2.5 software (Accelrys Inc.,San Diego, CA, USA) which permits 3D-pharmacophore gene-ration, structural alignment, activity prediction and 3D-data-base creation.39–43 3D-pharmacophore protocol was used togenerate predictive pharmacophores via aligning different con-formations in which the molecules are likely to bind with thereceptor. A given hypothesis may be combined with knownactivity data to create a 3D-pharmacophore model that identi-fies overall aspects of molecular structure governing bio-activity. 3D-pharmacophore was constructed using collectionsof molecules with activities ranging over a number of orders ofmagnitude. Pharmacophores explain the variability of bioactiv-ity with respect to the geometric localization of the chemicalfeatures present in the molecules. The observed HYPOGENidentifies a 3D-array of three chemical features including twohydrogen bonding acceptors (HBA-1, HBA-2) and one hydrogenbonding donor (HBD) which are common to the antifungalbio-active compounds 4a–4l, and 5–7 against C. albicans thatprovides relative alignment for each input molecule consistentwith its binding mode to a proposed common receptor site(Fig. S1 of ESI† exhibits constraint distances and anglesbetween features of the generated 3D-pharmacophore). Table 5exhibits fit values and estimated/predicted antifungal activitiesof the synthesized macrocyclic peptidomimetics 4a–4l, and 5–7due to the generated 3D-pharmacophore model. Fit values areused for scoring the interaction taking place between a ligand/tested compound and a pharmacophore. The quality of themapping is indicated by the fit value. The computed fit value
depends on two parameters: the weights assigned to the pharmaco-phore features and how close the features in the molecule areto the centers of the corresponding location constraints in thepharmacophore. Fit value is computed according to eqn (1).39
Fit ¼ sum over mapped features f of weight ðf Þ � ½1� SSEðf Þ�ð1Þ
where, SSE( f ) = sum over location constraints c on f of [D(c)/T(c)],2 D is the displacement of the feature from the center ofthe location constraint, and T is the radius of the location con-straint sphere for the feature (tolerance).
Through pharmacophore mapping studies (Fig. S2 of ESI†)it is apparent that the major structural factors affecting thepotency of the synthesized compounds are related to theirbasic skeleton (macrocyclic peptidomimetic scaffold).Additionally, most of the estimated activities as well as the fitvalues derived from the generated pharmacophore are corre-lated to the experimentally observed bio-properties. Mappingof the 3D-pharmacophore with the most potent antifungalactive peptidomimetic 4a exhibits the alignment of pyridinylN-24 with HBA-1, S-3 with HBA-2 and the cyclic amidic N-6with HBD. This fit alignment in the hypothesized 3D-pharmaco-phore preserves the lead potency of this compound amongthe other synthesized macrocycles (MIC = 14.08, 15.65 nM forobserved and estimated activity, fit value = 5.52). Compound4k which is considered the second most potent synthesizedmacrocycle exhibits a relatively similar alignment to thatexhibited by compound 4a (MIC = 15.01, 19.56 nM forobserved and estimated activity, fit value = 5.43). Where, thepyridinyl N-21 is aligned with HBA-1, and the cyclic amidic N-3with HBD. However, the HBA-2 is aligned with the carbonyloxygen of ester group attached to peptidomimetic C-4. Thedifference in alignment observed by compounds 4a and 4k inthe 3D-pharmacophore may explain the error value differenceexhibited by each respective analogue (error value “differencebetween the experimentally observed and estimated bio-activity” = −1.57, −4.55 for compounds 4a and 4k, respect-ively). Peptidomimetics 4c and 4d exhibit alignment in the 3D-pharmacophore typically like compound 4k where, the pyridi-nyl N-23 is aligned with HBA-1, the carbonyl oxygen of estergroup attached to peptidomimetic C-4 with HBA-2, and thecyclic amidic N-3 with HBD (MIC = 28.22, 26.80; 25.53, 21.10nM for observed and estimated activity of compounds 4c and4d, respectively, fit value = 5.31, 5.39 for compounds 4c and4d, respectively). Although peptidomimetic 4e observes experi-mentally antifungal activity similar to 4c, its mapping in the3D-pharmacophore is slightly deviated. The carbonyl oxygen ofC-17 is aligned with HBA-1, the carbonyl oxygen of ester groupattached to C-4 with HBA-2, and the cyclic amidic N-5 withHBD. This alignment can be attributed to the chemical skele-tal difference between the two analogues and also explains theslightly high error value due to the observed and estimatedbio-properties of 4e (MIC = 28.22, 35.33 nM for observed andestimated activity, error value = −7.11, fit value = 5.17). Theethyl ester peptidomimetic 4f shows small error value due to
Table 5 Best fit values and estimated/predicted activities (MIC) of theantifungal macrocyclic peptidomimetics 4a–4l, and 5–7 mapped withthe generated 3D-pharmacophore model
its observed and estimated bio-properties contrasting thebehavior of its methyl ester analogue 4e (MIC = 53.61, 51.34 nMfor observed and estimated activity, error value = 2.27, fit value= 5.01). This can be explained in terms of its mapping in the3D-pharmacophore which is typically the same of compounds4c and 4d.
The high potency difference between the macrocyclic pepti-domimetics 4a and 4b which are methyl/ethyl ester derivativesof the same chemical structure can be explained in terms ofalignment difference in the attained 3D-pharmacophore.Where, compound 4b is mapped in the 3D-pharmacophore ina different manner than that of the highly potent antifungalagent 4a. Although the pyridinyl N-24 of compound 4b isaligned with HBA-1, and the cyclic amidic N-6 with HBD typi-cally like compound 4a, its carbonyl oxygen of ester groupattached to cyclicpeptidomimetic C-5 is aligned with HBA-2(MIC = 53.51, 44.53 nM for observed and estimated activity, fitvalue = 5.07). The same applies to compounds 4g and 4hwhich are methyl/ethyl ester analogues of the same structurepossessing relatively the same experimentally observed anti-fungal potency but with different estimated bio-properties(MIC = 57.08, 54.18; 78.65, 53.68 nM for observed and esti-mated activity of compounds 4g and 4h, respectively, fit value= 4.82, 4.99 for compounds 4g and 4h, respectively). This isexplained by mapping behavior in the attained 3D-pharmaco-phore. In compound 4g the pyridinyl N-21 is aligned withHBA-1, S-6 with HBA-2 and the cyclic amidic N-3 with HBD.However, the carbonyl oxygen of C-7 is aligned with HBA-1, thecarbonyl oxygen of ester group attached to cyclicpeptidomi-metic C-4 with HBA-2 and the cyclic amidic N-3 with HBD incompound 4h. On the other hand, compounds 4i and 4j whichare also methyl/ethyl ester analogues of the same structurereveal the same experimentally observed antifungal potencyand show the same alignment behavior in the attained phar-macophore (pyridinyl N-21 is aligned with HBA-1, carbonyloxygen of ester group attached to cyclicpeptidomimetic C-4with HBA-2 and the cyclic amidic N-3 with HBD) but exhibitdifferent estimated/predicted bio-properties ((MIC = 58.19,55.18; 28.39, 58.58 nM for observed and estimated activity,error value = 29.80, −3.40 for compounds 4g and 4h, respect-ively). This is attributed to fitness difference of each of theseanalogues in the 3D-pharmacophore (fit value = 5.26, 4.95 forcompounds 4g and 4h, respectively).
2D-QSAR study
Dataset. QSAR is capable of generating a relationshipbetween the chemical structure of an organic compound andits physico-chemical properties. All the synthesized com-pounds 4a–4l and 5–7 which reveal potent antifungal pro-perties against C. albicans were used as a training set in thepresent QSAR study (short homogeneous dataset dealing withonly macrocyclic peptidomimetics). Attempts were made toenrich the dataset with external data points previouslyreported in the literature (chemically and biologically similarto the present peptidomimetics) but were unsuccessful due tolimitations in the reports relating to the present subject. The
QSAR study was undertaken using comprehensive descriptorsfor structural and statistical analysis (CODESSA-Pro) software.
Methodology
Geometry of the training set compounds was optimized usingthe molecular mechanics force field (MM+) followed by thesemi-empirical AM1 method implemented in the HyperChem8.0 package. The structures were fully optimized without fixingany parameters, thus bringing all geometric variables to theirequilibrium values. The energy minimization protocolemployed the Polak–Ribiere conjugated gradient algorithm.Convergence to a local minimum was achieved when theenergy gradient was ≤0.05 kcal mol−1. The RHF (RestrictedHartree-Fock) method was used in spin pairing for the twosemi-empirical tools.29–33,39,40 The resulting output files wereexported to CODESSA-Pro that includes CMOPAC capability forthe final geometry optimization.
CODESSA-Pro calculated 632 molecular descriptors includ-ing constitutional, topological, geometrical, charge-related,semi-empirical, molecular-type, atomic-type and bond-typedescriptors for the exported 15 bio-active macrocyclic peptido-mimetics which were used as training set in the present study.Different mathematical transformations of the experimentallyobserved antifungal property/activity against C. albicans (MIC,nM) of the training set compounds were utilized for thepresent QSAR modeling determination including property(MIC, nM), 1/property, log(property) and 1/log(property) valuesin searching for the best QSAR model.
QSAR modeling
The best multi-linear regression (BMLR) was utilized which is astepwise search for the best n-parameter regression equations(where n stands for the number of descriptors used), based onthe highest R2 (squared correlation coefficient), R2cvOO (squaredcross-validation “leave one-out, LOO” coefficient), R2cvMO(squared cross-validation “leave many-out, LMO” coefficient),F (Fisher statistical significance criteria) values, and s2 (standarddeviation). The QSAR models up to 3 descriptor model describ-ing the bio-activity of the antifungal active agents against C.albicans were generated (obeying the rule of 5 : 1, which is theratio between the data points and the number of QSAR descrip-tor model). The observed and predicted values of the trainingset compounds 4a–4l and 5–7 according to the multi-linearQSAR models are presented in Table 6.
The statistical characteristics of the BMLR-QSAR modelattained are presented in Table 7. The established QSARmodel is statistically significant. The descriptors are sorted inthe descending order of the respective values of the Student’st-criterion, which is a widely accepted measure of statisticalsignificance of individual parameters in multiple linearregressions. Fig. 3 shows the QSAR multi-linear model plot ofcorrelations representing the observed vs. predicted log(MIC,nM) values for the antifungal active macrocyclic peptidomi-metics. The scattered plots are uniformly distributed, coveringranges, observed 1.149–1.819, predicted 1.154–1.826 logarith-mic(MIC, nM) units.
Molecular descriptors are the physico-chemical parametersused to correlate the chemical structure and the bio-propertyvalue expressed as log(MIC, nM). The descriptors wereobtained based on the BMLR method. The first descriptor con-trolling the attained BMLR-QSAR model based on its level ofsignificance (t-criterion = 6.607) is FHDCA fractional HDCA(HDCA/TMSA) (MOPAC PC) which is a charge-related descrip-tor. Fractional hydrogen bonding donor ability of the molecule(FHDCA1) is determined by eqn (2).44
FHDCA1 ¼ HDCA1TMSA
ð2Þ
where, HDCA1 stands for hydrogen bonding donor ability ofatoms, selected by threshold charge, and TMSA for total mole-cular surface area. The second descriptor of the 2D-QSARmodel (t = −4.818) is WNSA-2 weighted PNSA (PNSA2 × TMSA/1000) (MOPAC PC) which is also a charge-related descriptor.Surface weighted charged partial negative charged surface area(WNSA2) is determined by eqn (3).44
WNSA2 ¼PPSA2 � TMSA1000
ð3Þ
where, PNSA2 stands for total charge weighted partial nega-tively charged molecular surface area, and TMSA for total
molecular surface area. The third descriptor controlling theattained BMLR-QSAR model (t = −13.399) is maximum totalinteraction for bond C–C which is a semi-empirical descriptorused as a measure of the bond strength.40,45 Moleculardescriptor values controlling the attained BMLR-QSAR modelare presented in Table S1 of ESI.†
Validation of 2D-QSAR model
The reliability and statistical relevance of the QSAR model isexamined an internal validation procedure. The dataset con-tains relatively few experimental data points but is homo-geneous (macrocyclic peptidomimetics). Therefore applicationof the internal validation methodology is an appropriate tech-nique.31 Internal validation is applied by the CODESSA-Protechnique employing both leave one out (LOO), which involvesdeveloping a number of models with one example omitted at atime, and leave many out (LMO), which involves developing anumber of models with many data points omitted at a time(up to 20% of the total data points). The observed correlationsby the internal validation technique are R2cvOO = 0.879,R2cvMO = 0.895, respectively which are significantly correlatedwith the squared correlation coefficient of the attained
Table 7 Descriptor of the BMLR-QSAR model for the antifungal active macrocyclic peptidomimetics
N = 15, n = 3, R2 = 0.946, R2cvOO = 0.879, R2cvMO = 0.895, F = 63.709, s2 = 0.004
Fig. 3 BMLR-QSAR model plot of correlations representing theobserved vs. predicted log(MIC, nM) values for the antifungal activemacrocyclic peptidomimetics (compound 4e is an outlier).
2D-QSAR model (R2 = 0.946). Standard deviation of theregression (s2 = 0.004) is also a measurable value for theattained model together with the Fisher test value (F = 63.709)that reflects the ratio of the variance explained by the modeland the variance due to their errors. A high value of the F-testrelative to the s2 value is also validation of the model.
The predicted/estimated antifungal properties due to theattained BMLR-QSAR model of most of the synthesized macro-cyclic peptidomimetics are compatible with their experi-mentally observed values preserving their relative potencies(Table 6). The estimated/predicted MIC value of compound 4awhich is considered the most potent synthesized macrocyclicpeptidomimetic correlated well with its observed bio-activity(MIC = 14.08, 14.26 nM for observed and estimated bio-properties, respectively, error = −0.18). Similar observation isalso exhibited by compound 4k which is also considered thesecond highly potent synthesized antifungal peptidomimetic(MIC = 15.01, 18.25 nM for observed and estimated bio-properties, respectively, error = −3.24). Although peptidomi-metic 4e observes experimentally antifungal activity similar to4c (MIC = 28.22 nM), slightly different estimated MIC valueswere exhibited by QSAR model (MIC = 31.20, 21.19 nM for esti-mated bio-properties of compounds 4c and 4e, respectively).The same observation was revealed by the 3D-pharmacophorestudy. The other less potent antifungal agents 4b,f–j,l showedestimated bio-properties compatible with their experimentallyobserved data (MIC = 53.51–58.19, 47.93–63.60 nM forobserved and estimated antifungal properties, respectively).The macrocyclic peptidomimetic acids 5–7 also reveal esti-mated bio-data based on the attained 2D-QSAR model corre-lated with their experimentally observed antifungal properties(MIC = 59.46–65.87, 53.47–66.96 nM for observed and esti-mated antifungal properties, respectively). All of above obser-vations are good indications of the predictive power of theattained 2D-QSAR model not only for validating the observedbio-data but also for optimizing high potent hits havingmacrocyclic peptidomimetic scaffold.
Conclusion
In conclusion, a series of novel pyridine-cysteine containingcyclic peptidomimetics was synthesized using benzotriazolederivatives of dicarboxylic acids. The substances showed strik-ingly high activity against Candida albicans and three Gram-negative bacterial strains, but were found to be of low cytotoxi-city against diverse human tumor cell lines. The potent anti-fungal properties of the synthesized peptidomimetics againstC. albicans relative to the standard reference used amphoteri-cin B (MIC = 0.39 μg mL−1 “422.31 nM”) was supported by3D-pharmacophore modeling studies possessing three features‘two hydrogen bonding acceptors and one hydrogen bondingdonor’. A robust 3-describtor 2D-QSAR model was attainedutilizing CODESSA-Pro software validating the observed anti-fungal properties and supporting the attained controllingparameters governing bio-data.
Experimental partChemistry
Melting points were determined on a capillary point apparatusequipped with a digital thermometer. NMR spectra wererecorded in CDCl3, or CD3OD on Mercury or Gemini NMRspectrometers operating at 300 MHz for 1H (with TMS as aninternal standard) and 75 MHz for 13C. Elemental analyseswere performed on a Carlo Erba-EA1108 instrument. All micro-wave assisted reactions were carried out with a single modecavity Discover Microwave Synthesizer (CEM Corporation, NC).The reaction mixtures were transferred into a 10 mL glasspressure microwave tube equipped with a magnetic stir bar.The tube was closed with a silicon septum and the reactionmixture was subjected to microwave irradiation (Discovermode; run time: 60 s; PowerMax-cooling mode).
General procedure for preparation of N-acyl benzotriazolederivatives (2a–f ). Thionyl chloride (0.95 mL, 13 mmol) wasadded to a solution of benzotriazole (5.701 g, 47.90 mmol) indichloromethane (100 mL) and the solution was stirred atroom temperature for 20 min. The dicarboxylic acids 1a–c(6 mmol) were added to each mixture which were stirred atroom temperature for 24 h. The precipitate was filtered off andthe filtrate was extracted with saturated sodium carbonatesolution (3 × 100 mL). The organic layer was dried over an-hydrous sodium sulfate, filtered and evaporated under vacuumto give compounds 2a–f.24
General procedure for preparation of pyridine dicysteinehydrochloric salt (3a,b). To a solution of 2a (1.0 g, 2.7 mmol)in tetrahydrofuran (20 mL), a solution of cysteine methyl orethyl ester hydrochloric salt (5.962 mmol) in water (5 mL) wasadded. Each heterogeneous mixture was then stirred at roomtemperature for 2 h. The precipitate was filtered off, washedwith diethyl ether (3 × 30 mL), and dried under vacuum to givecompounds 3a,b.
C17H25Cl2N3O6S2 required C, 40.46; H, 5.02; N, 8.36. foundC, 41.00; H, 5.37; N, 8.45.
General procedure for preparation of the macrocycles (4a–l).Mixture of compounds 2a–h (0.316 mmol), compounds 3a,b(0.316 mmol) and triethylamine (1.265 mmol) in DMF (3 mL)were irradiated by microwave at 50 °C and 20 watt for 20 min.Each solution was poured on crushed ice and the mixture wasextracted with ethyl acetate (3 × 50 mL). The organic layer waswashed with saturated solution of Na2CO3, dried over an-hydrous sodium sulfate, filtered and evaporated to givecompounds 4a–l.
Antifungal. Antifungal activity screening of the synthesizedcompounds was performed by determining the minimuminhibitory concentration (MIC) as recommended by the Clini-cal and Laboratory Standard Institute (CLSI).26,27 A pureculture of a single microorganism (Candida albicans) “localisolate” was grown in Mueller-Hinton broth. The culture sizewas standardized to be 1.5 × 108 cells per milliliter. The syn-thesized compounds were two fold serially diluted in DMSO.After 4a–l, and 5–7 had been diluted, a volume of the standar-dized inoculum equal to the volume of the diluted compoundswas added to each dilution in microtitre plates. These plateswere incubated at 37 °C for 18–24 hours. After incubation, theplates were observed for microbial growth, and the spot withthe lowest concentration of compound showing no growth wasdefined as the MIC (Table 3).
Antibacterial. Antibacterial activity screening of the syn-thesized compounds 4a–l, and 5–7 was determined by the agardilution technique standard methods as recommended by theClinical and Laboratory Standard Institute (CLSI), in a methodo-logy similar that of the antifungal bioassay.26,27 The testedcompounds 4a–l, and 5–7 were dissolved in DMSO. An inocu-lum of about 1.5 × 108 colony forming units (CFU) per spotwas applied to the surfaces of Mueller-Hinton (in case of bothGram-positive and Gram-negative bacterial strains) agar platescontaining graded concentrations of the respective com-pounds. Plates were incubated at 37 °C for 18 h. The spot withthe lowest concentration of compound showing no growth wasdefined as the MIC. All organisms used in this study were stan-dard strains obtained from American Type Culture Collection(ATCC). The organisms included representatives of Gram-posi-tive bacteria (Staphylococcus aureus ATCC 25923) and Gram-negative bacteria (Klebsiella pneumoniae ATCC 33495, Proteusvulgaris ATCC 13315 and Pseudomonas aeruginosa ATCC27853). The MIC of Ciprofloxacin was determined concurrentlyas a reference standard for antibacterial activities (Table 3). Anegative control (DMSO) was carried out with each experiment.
Antitumor activity screening
Antitumor properties of the synthesized compounds (4a–l, and5–7) were screened by National Cancer Institute, Cairo Univer-sity, Egypt, using the reported in vitro Sulfo-Rhodamine-B(SRB) standard method adopting HepG2 (liver), HeLa
(cervical), HCT116 (colon), PC3 (prostate), and MCF7 (breast)carcinoma cell lines.29–37 Cells were seeded in 96-well microti-ter plates at a concentration of 5 × 104–105 cells per well in afresh medium and left for 24 h before treatment with thetested compounds to allow attachment of cells to the wall ofthe plate. The tested compounds were dissolved in DMSO anddiluted 1000-fold in the assay. Different concentrations of thecompounds under test (0, 5, 12.5, 25, and 50 μg ml−1) wereadded to the cell monolayer. Triplicate wells were prepared foreach individual dose. The monolayer cells were incubated withthe tested compounds for 48 h at 37 °C, in atmosphere of 5%CO2. After 48 h, the cells were fixed, washed and stained withSulfo-Rhodamine-B (SRB) stain. Excess stain was washed withacetic acid. The attached stain was recovered with Tris-EDTAbuffer. Cell survival and drug activity were determined bymeasuring the color intensity spectrophotometrically at564 nm using an ELISA microplate reader (Meter tech. Σ 960,USA). Data are collected as mean values for experiments thatperformed in three replicates for each individual dose whichmeasured by SRB assay. Control experiments did not exhibitsignificant change compared to the DMSO vehicle. Doxo-rubicin was used as a standard reference during the present invitro bio-activity screening assay. The percentage of cell survi-val was calculated by eqn (4).
Surviving fraction ¼ Optical density ðO:DÞ of treated cellsO:D: of control cells
ð4Þ
The IC50 (concentration required to produce 50% inhibitionof cell growth compared to control experiment) was deter-mined using Graph-Pad PRISM version-5 software. Statisticalcalculations for determination of the mean and standard errorvalues were determined by SPSS 11 software. The observedantitumor properties were presented in Table 4 (Fig. S3–S7 ofESI†).
Acknowledgements
We thank the University of Florida, United States and theKenan Foundation for financial support.
Notes and references
1 A. M. Socha, N. Y. Tan, K. L. LaPlante and J. K. Sello,Bioorg. Med. Chem., 2010, 18, 7193–7202.
2 A. B. Patel, R. V. Patel, P. Kumari, D. P. Rajani andK. H. Chikhalia, Med. Chem. Res., 2013, 22, 367–381.
3 Y. Ozkay, Y. Tunalı, H. Karaca and I. Isikdag, Eur. J. Med.Chem., 2010, 45, 3293–3298.
4 M. C. Fisher, D. A. Henk, C. J. Briggs, J. S. Brownstein,L. C. Madoff, S. L. McCraw and S. J. Gurr, Nature, 2012,484, 186–194.
5 D. M. Kuhn, T. George, J. Chandra, P. K. Mukherjee andM. A. Ghannoum, Antimicrob. Agents Chemother., 2002, 46,1773–1780.
6 J.-A. H. van Burik and P. T. Magee, Annu. Rev. Microbiol.,2001, 55, 743–772.
7 J. Sykora, S. Yilma, W. C. Neely and V. Vodyanoy, Langmuir,2003, 19, 858–864.
8 T. Brautaset, O. N. Sekurova, H. Sletta, T. E. Ellingsen,A. R. Strøm, S. Valla and S. B. Zotchev, Chem. Biol., 2000, 7,395–403.
9 D. W. Denning, J. Antimicrob. Chemother., 2002, 49, 889–891.10 C. A. Hitchcock, K. Dickinson, S. B. Brown, E. G. V. Evans
and D. J. Adams, Biochem. J., 1990, 266, 475–480.11 L. Romani, Nat. Rev., 2004, 4, 1–13.12 M. A. Pfaller, Am. J. Med., 2012, 125, 3–13.13 S. B. Singh, W. Liu, X. Li, T. Chen, A. Shafiee, D. Card,
G. Abruzzo, A. Flattery, C. Gill, J. R. Thompson,M. Rosenbach, S. Dreikorn, V. Hornak, M. Meinz, M. Kurtz,R. Kelly and J. C. Onishi, ACS Med. Chem. Lett., 2012, 3,814–817.
14 J. Janiszewska, M. Sowińska, A. Rajnisz, J. Solecka, I. Łącka,S. Milewski and Z. Urbańczyk-Lipkowska, Bioorg. Med.Chem. Lett., 2012, 22, 1388–1393.
15 A. Giuliani, G. Pirri and S. F. Nicoletto, Cent. Eur. J. Biol.,2007, 2, 1–33.
16 R. E. W. Hancock and H. Sahl, Nat. Biotechnol., 2006, 24,1551–1557.
17 K.-H. Rhee, Int. J. Antimicrob. Agents, 2004, 24, 423–427.18 A. F. Morel, G. Maldaner, V. Ilha, F. Missau, U. F. Silva and
I. I. Dalcol, Phytochemistry, 2005, 66, 2571–2576.19 A. W. Tuin, G. M. Grotenbreg, E. Spalburg, A. J. D. Neeling,
R. H. Mars-Groenendijk, G. A. V. D. Marel, D. Noort,H. S. Overkleeft and M. Overhanda, Bioorg. Med. Chem.,2009, 17, 6233–6240.
20 I. Grgurina, M. Bensaci, G. Pocsfalvi, L. Mannina,O. Cruciani, A. Fiore, V. Fogliano, K. N. Sorensen andJ. Y. Takemoto, Antimicrob. Agents Chemother., 2005, 49,5037–5045.
21 G. J. Kim, D. W. Park and Y. S. Tak, Catal. Lett., 2000, 65,127–133.
22 Z. Li and C. Jablonski, Chem. Commun., 1999, 1531–1532.23 D. Ranganathan, V. Haridas and I. L. Karle, J. Am. Chem.
Soc., 1998, 120, 2695–2702.24 A. R. Katritzky, N. K. Meher, C. Cai and S. K. Singh, Rev.
Soc. Quim. Mex., 2004, 48, 275–278.25 M. A. Ibrahim, S. S. Panda, L. Nhon, A. Hamed,
S. A. El-Feky and A. R. Katritzky, Synthesis, 2013, 767–772.26 Clinical and Laboratory Standards Institute (Formerly
NCCLS); Methods for Dilution Antimicrobial SusceptibilityTests for Bacteria Grow Aerobically. Approved Standard
M7–A4, Clinical and Laboratory Standards Institute,Wayne, Pennsylvania, USA, 2011.
27 A. S. Girgis, F. F. Barsoum and A. Samir, Eur. J. Med. Chem.,2009, 44, 2447–2451.
28 W. M. Berhanu, M. A. Ibrahim, G. G. Pillai,A. A. Oliferenko, L. Khelashvili, F. Jabeen, B. Mirza,F. L. Ansari, I. Ul-Haq, S. A. El-Feky and A. R. Katritzky,Beilstein J. Org. Chem., 2012, 8, 1146–1160.
29 A. S. Girgis, S. S. Panda, M. N. Aziz, P. J. Steel, C. D. Halland A. R. Katritzky, RSC Adv., 2015, 5, 28554–28569.
30 A. S. Girgis, S. S. Panda, E. M. Shalaby, A. F. Mabied,P. J. Steel, C. D. Hall and A. R. Katritzky, RSC Adv., 2015, 5,14780–14787.
31 A. S. Girgis, S. S. Panda, I. S. Ahmed Farag, A. M. El-Shabiny, A. M. Moustafa, N. S. M. Ismail, G. G. Pillai,C. S. Panda, C. D. Hall and A. R. Katritzky, Org. Biomol.Chem., 2015, 13, 1741–1753.
32 R. F. George, N. S. M. Ismail, J. Stawinski and A. S. Girgis,Eur. J. Med. Chem., 2013, 68, 339–351.
33 A. S. Girgis, J. Stawinski, N. S. M. Ismail and H. Farag,Eur. J. Med. Chem., 2012, 47, 312–322.
34 A. R. Katritzky, A. S. Girgis, S. Slavov, S. R. Tala andI. Stoyanova-Slavova, Eur. J. Med. Chem., 2010, 45, 5183–5199.
35 A. S. Girgis, Eur. J. Med. Chem., 2009, 44, 1257–1264.36 A. S. Girgis, Eur. J. Med. Chem., 2009, 44, 91–100.37 A. S. Girgis, H. M. Hosni and F. F. Barsoum, Bioorg. Med.
Chem., 2006, 14, 4466–4476.38 C. G. Wermuth, C. R. Ganellin, P. Lindberg and
L. A. Mitscher, Annu. Rep. Med. Chem., 1998, 33, 385–395.39 A. S. Girgis, S. S. Panda, A. M. Srour, H. Farag,
N. S. M. Ismail, M. Elgendy, A. K. Abdel-Aziz andA. R. Katritzky, Org. Biomol. Chem., 2015, 13, 6619–6633.
40 R. N. Naumov, S. S. Panda, A. S. Girgis, R. F. George,M. Farhat and A. R. Katritzky, Bioorg. Med. Chem. Lett.,2015, 25, 2314–2320.
41 A. S. Girgis, H. Farag, N. S. M. Ismail and R. F. George,Eur. J. Med. Chem., 2011, 46, 4964–4969.
42 A. S. Girgis, N. S. M. Ismail and H. Farag, Eur. J. Med.Chem., 2011, 46, 2397–2407.
43 A. S. Girgis, N. S. M. Ismail, H. Farag, W. I. El-Eraky,D. O. Saleh, S. R. Tala and A. R. Katritzky, Eur. J. Med.Chem., 2010, 45, 4229–4238.
44 CODESSA-Pro manual pp. 60, 66.45 A. S. Girgis, D. O. Saleh, R. F. George, A. M. Srour,
G. G. Pillai, C. S. Panda and A. R. Katritzky, Eur. J. Med.Chem., 2015, 89, 835–843.
