Simone Simone BrogiBrogi

Dipartimento Farmaco Chimico TecnologicoDipartimento Farmaco Chimico Tecnologico

UniversitUniversitàà degli Studi di Sienadegli Studi di SienaVia Aldo Moro, IVia Aldo Moro, I--53100 Siena, Italy53100 Siena, Italy

We have applied a novel approach to generate a liga ndWe have applied a novel approach to generate a liga nd --based based pharmacophorepharmacophore model. The model. The pharmacophorepharmacophore was built from a set of 42 compounds showing activ ity against was built from a set of 42 compounds showing activ ity against MCFMCF--7 cell 7 cell line derived from human mammary adenocarcinoma,line derived from human mammary adenocarcinoma, 11 using the program PHASE,using the program PHASE, 2 2 implemented in the Schrimplemented in the Schr öödinger suite software package. PHASE is a highly fl exible systemdinger suite software package. PHASE is a highly fl exible system for for common common pharmacophorepharmacophore identification and assessment and 3Didentification and assessment and 3D --database creation and searching. The best database creation and searching. The best pharmacophorepharmacophore hypothesis showed five features: two hydrogenhypothesis showed five features: two hydrogen --bond bond acceptors, one hydrogenacceptors, one hydrogen --bond donor, and two aromatic rings. The structurebond donor, and two aromatic rings. The structure ––activity relationship (SAR) so acquired was applied within PHASEactivity relationship (SAR) so acquired was applied within PHASE for molecular alignment in a for molecular alignment in a comparative molecular field analysis 3Dcomparative molecular field analysis 3D --QSAR study.QSAR study. 33 The 3DThe 3D--QSAR model yielded a test set rQSAR model yielded a test set r 2 2 equal to 0.97 and demonstrated to be highly predict ive with respequal to 0.97 and demonstrated to be highly predict ive with resp ect to an external test ect to an external test set of 18 compounds (rset of 18 compounds (r 22 =0.93). In summary, in this study we improved a pre viously deve=0.93). In summary, in this study we improved a pre viously deve loped Catalyst MCFloped Catalyst MCF --7 inhibitory pharmacophore,7 inhibitory pharmacophore, 44--55 and established a predictive 3Dand established a predictive 3D --QSAR QSAR model. We have further used this model to detect no vel MCFmodel. We have further used this model to detect no vel MCF --7 cell line inhibitors through 3D database searchin g and docking7 cell line inhibitors through 3D database searchin g and docking simulation simulation

AcknowledgmentAcknowledgment : : WeWe are are gratefulgrateful toto Prof. Prof. VassiliosVassilios RoussisRoussis (University of (University of AthensAthens ) and ) and coco--workersworkers forfor the the chemicalchemical entitiesentities and the and the biologicalbiologicalassaysassays

ReferencesReferences : : (1)(1) Soule H. D. Soule H. D. et al.et al. J. J. NatlNatl Cancer Inst.Cancer Inst. 1973, 1973, 51,51, (5)(5), 1409; , 1409; (2)(2) PHASE 2.5 (PHASE 2.5 (SchrSchr öödinger, LLC, New York, NYdinger, LLC, New York, NY ); ); (3)(3) Dixon S. L. Dixon S. L. et al. J. et al. J. ComputComput . Aided Mol. Des. . Aided Mol. Des. 2006, 2006, 20,20, (10(10--11),11), 647; 647; (4)(4) KladiKladi , M. , M. et al. J. Nat. Prod. et al. J. Nat. Prod. 2009, 2009, 72, (2), 72, (2), 190; 190; (5)(5) BrogiBrogi , S. , S. et al.et al. J. Chem. Inf. Model. J. Chem. Inf. Model. 2009, 2009, Accepted in pressAccepted in press ; ; (6)(6) PHASE 2.5 user manual; PHASE 2.5 user manual; (7)(7) Walters, W. P. Walters, W. P. et al.et al. Adv. Drug Deliv. Rev.Adv. Drug Deliv. Rev. 2002, 2002, (54),(54), 255; 255; (8)(8) Jones, G. Jones, G. et al.et al. J. Mol. Biol. J. Mol. Biol. 1995,1995, (1), (1), 4343

3D3D--Database Database searchingsearching and and MolecularMolecular DockingDocking

��3D3D--Database searching is a powerful tool to discover Database searching is a powerful tool to discover new structures and design new ligands of a biologic al new structures and design new ligands of a biologic al target target �� In our study, the computational 3DIn our study, the computational 3D --QSAR model QSAR model developed by PHASE was used to search developed by PHASE was used to search AsinexAsinexchemical databases (about 250,000 structurally chemical databases (about 250,000 structurally diversified small molecules) for new chemical struc tures diversified small molecules) for new chemical struc tures active against MCFactive against MCF --7 cell line7 cell line�� Compounds with a predicted activity cutoff value of 0.5 Compounds with a predicted activity cutoff value of 0.5 (pIC(pIC5050) were selected. Other filters were applied to iden tify ) were selected. Other filters were applied to iden tify entries against MCFentries against MCF --7 cell line: the compounds must 7 cell line: the compounds must satisfy the satisfy the Lipiniski'sLipiniski's rule of fiverule of five��The query identified 34 topThe query identified 34 top --ranking compounds with ranking compounds with high predicted activity against MCFhigh predicted activity against MCF --7 cell line. These 7 cell line. These molecules were considered likely to be wellmolecules were considered likely to be well --absorbed absorbed because because satisfied satisfied Lipiniski'sLipiniski's rule of fiverule of five 77

��These compounds were selected for docking analysisThese compounds were selected for docking analysisbyby GOLD softwareGOLD software 8 8 in the binding site (LBD) of ERin the binding site (LBD) of ER --αα. For . For each compound each compound goldscoregoldscore function were used to evaluate function were used to evaluate and rank the ERand rank the ER --αα ligandligand binding affinities binding affinities (Fig. 6)(Fig. 6)��The 14 topThe 14 top --ranking compounds with score value of 50 or ranking compounds with score value of 50 or higher and appropriate binding (in terms of interac tions higher and appropriate binding (in terms of interac tions with the protein) were subjected to biological eval uation with the protein) were subjected to biological eval uation against MCFagainst MCF --7 cell line giving interesting results 7 cell line giving interesting results (Table 2)(Table 2)

ValidationValidation of PHASE 3Dof PHASE 3D--QSAR modelQSAR model

��18 new 18 new potentialpotential SERMsSERMs werewere testedtested againstagainst MCFMCF--7 7 cellscells and and thenthen usedused asas anan externalexternal test set test set forfor PHASE 3DPHASE 3D--QSAR model QSAR model validationsvalidations forfor predictivepredictive abilityability . The . The predictionprediction resultsresults of of thisthisexternalexternal test set are show in test set are show in Figure 3Figure 3��The large value of F indicates a statistically sign ificant The large value of F indicates a statistically sign ificant regression model, which is supported by the small v alue of the regression model, which is supported by the small v alue of the variance ratio (P), an indication of a high degree of confidencevariance ratio (P), an indication of a high degree of confidence , , the Qthe Q 22 value suggesting the model is robust value suggesting the model is robust (Table(Table 1)1)6 6

��Therefore, the correlation between the actual and p redicted Therefore, the correlation between the actual and p redicted values of activities suggested that the PHASE 3Dvalues of activities suggested that the PHASE 3D --QSAR model QSAR model was reliable based result and the was reliable based result and the stericsteric , electrostatic, and , electrostatic, and hydrogen bond acceptor and donor field effects were nicely hydrogen bond acceptor and donor field effects were nicely related with variation of activityrelated with variation of activity

PharmacophorePharmacophore generationgeneration

��PHASE 2.5 implemented in the Maestro 8.0 PHASE 2.5 implemented in the Maestro 8.0 modelingmodelingpackage (Schrpackage (Schr öödinger, LLC, New York, NY) was used to dinger, LLC, New York, NY) was used to generate generate pharmacophorepharmacophore models for MCFmodels for MCF --7 cell line 7 cell line inhibitorsinhibitors��Some highly active SERM, were selected for generating Some highly active SERM, were selected for generating the the pharmacophorepharmacophore hypotheses hypotheses (Fig. 1)(Fig. 1)��PharmacophorePharmacophore feature sites for the best PHASE model feature sites for the best PHASE model was: two hydrogenwas: two hydrogen --bond acceptors (A3, A5), one bond acceptors (A3, A5), one hydrogenhydrogen --bond donor (D6) and two aromatic sites (R9, bond donor (D6) and two aromatic sites (R9, R10) R10) (Fig.2)(Fig.2)��Common Common pharmacophorepharmacophore hypotheses were identified, hypotheses were identified, scored and ranked. scored and ranked. The regression is performed by a The regression is performed by a partial least squares (PLS) methodpartial least squares (PLS) method��All the molecules used for QSAR studies were aligned All the molecules used for QSAR studies were aligned to the to the pharmacophorepharmacophore hypothesis obtained in PHASEhypothesis obtained in PHASE

DevelopmentDevelopment of a PHASE 3Dof a PHASE 3D--QSAR modelQSAR model

��The The PHASEPHASE--generatedgenerated 3D 3D pharmacophorepharmacophore waswas usedused asas the the alignmentalignment templatetemplate forfor the 3Dthe 3D--QSAR model QSAR model (Fig.4(Fig.4--5)5)33

��Phase determines how molecular structure affects dru g Phase determines how molecular structure affects dru g activity by dividing space into a fine cubic grid, encoding atomactivity by dividing space into a fine cubic grid, encoding atomtype occupation as numerical information, and perfo rming a type occupation as numerical information, and perfo rming a partial leastpartial least --squares (PLS) regressionsquares (PLS) regression��The independent variables in the QSAR model were der ived The independent variables in the QSAR model were der ived from a regular grid of cubic volume elements that s pan the from a regular grid of cubic volume elements that s pan the space occupied by the training set ligands and biol ogical space occupied by the training set ligands and biol ogical activities (pICactivities (pIC 5050 values) were used as dependent variables. In values) were used as dependent variables. In addition to the Qaddition to the Q 22, the conventional correlation coefficient r, the conventional correlation coefficient r 22

and its standard errors were also computedand its standard errors were also computed (Table 1)(Table 1)

ConclusionConclusion

��In In ourour studystudy , , wewe builtbuilt a a pharmacophorepharmacophore model model bybyapplyingapplying the the ligandligand --basedbased pharmacophorepharmacophore generation generation approachapproach , , usingusing PHASE. PHASE. DifferentDifferent pharmacophorepharmacophore basedbasedQSAR QSAR modelsmodels werewere developeddeveloped byby usingusing PLS PLS analysisanalysis��The best The best resultingresulting hypothesishypothesis consistedconsisted of of fivefive featuresfeatures : : twotwo hydrogenhydrogen bond bond acceptorsacceptors , one , one hydrogenhydrogen --bondbond donor donor and and twotwo aromaticaromatic sitessites . The . The alignmentalignment rulerule of the of the bestbest --fitfitmodel model waswas usedused toto developdevelop ligandligand --basedbased 3D3D--QSAR model QSAR model ��The The establishedestablished computationalcomputational tooltool endowedendowed withwith high high predictivepredictive abilityability and and robustnessrobustness , , mightmight bebe usefuluseful forfor the the design and design and optimizationoptimization of new MCFof new MCF --7 7 cellcell line line inhibitorsinhibitors��To improve the probability of identifying suitable hit To improve the probability of identifying suitable hit candidatescandidates , , wewe coupled coupled pharmacophorepharmacophore --based virtual based virtual screening with docking filtrationscreening with docking filtration��These promising results encourage us to continue These promising results encourage us to continue pursuing our target prioritization research program . pursuing our target prioritization research program . Expansion of this method to predict bioactivity on t he Expansion of this method to predict bioactivity on t he basis of relationship between activities and chemic al basis of relationship between activities and chemic al structures is expected to direct compounds isolated in structures is expected to direct compounds isolated in limited amounts towards targeted pharmacological limited amounts towards targeted pharmacological testing, thereby accelerating the hit discovery pro cesstesting, thereby accelerating the hit discovery pro cess

>20012

>20013

>20014

184.6 ± 27.511

181.4 ± 22.810

171.8 ± 23.59

158.6 ± 23.48

151.2 ± 19.57

148.5 ± 14.26

122.6 ± 17.95

107.8 ± 13.74

65.4 ± 14.73

38.5 ± 6.32

30.2 ± 4.61

Observed Observed Activity(Activity( µµM)M)

AsinexAsinexCompoundsCompounds

TableTable 2 2 BiolocicalBiolocical evaluationevaluation of the of the AsinexAsinex compoundcompound selectedselected after after VirtualVirtual ScreeningScreening

0.56R

0.76Q2

1.384RMSE

6.731 e-16

P

261.4F

0,97r2

0.327SD

ValueValueStatisticalStatisticalparameterparameter

TableTable 11 StatisticalStatistical parameterparameterof PHASE 3Dof PHASE 3D--QSAR QSAR modelsmodels. . Descriptor of the QSAR Descriptor of the QSAR results: results: SDSD Standard deviation Standard deviation of the regression. of the regression. rr22: Value of r: Value of r22

for the regression. for the regression. FF Variance Variance ratio. ratio. PP Significance level of Significance level of variance ratio. variance ratio. RMSERMSE RootRoot--meanmean--square error. square error. QQ22 value of value of QQ22 for the predicted activities. for the predicted activities. RR rr--Pearson valuePearson value

Fig. 2 Fig. 2 Superposition of best PHASE model and Superposition of best PHASE model and one of highly active compound in the set (38one of highly active compound in the set (38)). . PharmacophorePharmacophore features are features are colorcolor--coded: cyan coded: cyan for hydrogen bond donor (D), pink for hydrogen for hydrogen bond donor (D), pink for hydrogen bond acceptor (A), brown rings for the aromatic bond acceptor (A), brown rings for the aromatic features (R)features (R)

Fig. 5Fig. 5 3D3D--QSAR model for an active ligand (left) and an inactive ligand (rQSAR model for an active ligand (left) and an inactive ligand (right); ight); coloredcolored accordingaccording toto the the signsign of of theirtheir coefficientcoefficientvaluesvalues: blue : blue forfor positive positive coefficientscoefficients and and redred forfor negative negative coefficientscoefficients. Positive . Positive coefficientscoefficients indicate indicate anan increaseincrease in in activityactivity, negative , negative coefficientscoefficients a a decreasedecrease..

Fig. 4 Fig. 4 3D3D--QSAR model around the compound QSAR model around the compound 3838

Fig. 3 Fig. 3 Calculated versus observed Calculated versus observed value inhibitory activity pICvalue inhibitory activity pIC5050

Fig. 1 Fig. 1 SERM derivatives used in this studySERM derivatives used in this study

Fig.Fig. 6 6 MolecularMolecular DockingDocking: a) : a) AsinexAsinex compoundcompound 1; b) 1; b) AsinexAsinex compoundcompound 22

HisHis --524524

ArgArg --394394 ArgArg --394394

HisHis --524524