Simone BrogiSimone Brogi and Andrea Tafiand Andrea Tafi

Dipartimento Farmaco Chimico Tecnologico, Università degli Studi di SienaDipartimento Farmaco Chimico Tecnologico, Università degli Studi di SienaVia Aldo Moro, I-53100 Siena, ItalyVia Aldo Moro, I-53100 Siena, Italy

We have applied a novel approach to generate a ligand-based pharmacophore model. The pharmacophore was built from a set of 42 compounds showing activity against MCF-7 cell line We have applied a novel approach to generate a ligand-based pharmacophore model. The pharmacophore was built from a set of 42 compounds showing activity against MCF-7 cell line derived from human mammary adenocarcinoma,derived from human mammary adenocarcinoma,11 using the program PHASE, using the program PHASE,2 2 implemented in the Schrödinger suite software package. PHASE is a highly flexible system for common implemented in the Schrödinger suite software package. PHASE is a highly flexible system for common pharmacophore identification and assessment and 3D-database creation and searching. The best pharmacophore hypothesis showed five features: two hydrogen-bond acceptors, one pharmacophore identification and assessment and 3D-database creation and searching. The best pharmacophore hypothesis showed five features: two hydrogen-bond acceptors, one hydrogen-bond donor, and two aromatic rings. The structure–activity relationship (SAR) so acquired was applied within PHASE for molecular alignment in a comparative molecular field hydrogen-bond donor, and two aromatic rings. The structure–activity relationship (SAR) so acquired was applied within PHASE for molecular alignment in a comparative molecular field analysis (CoMFA) 3D-QSAR study.analysis (CoMFA) 3D-QSAR study.33 The 3D-QSAR model yielded a internal test set r The 3D-QSAR model yielded a internal test set r2 2 equal to 0.97 and demonstrated to be highly predictive with respect to an external test set of 18 equal to 0.97 and demonstrated to be highly predictive with respect to an external test set of 18 compounds (rcompounds (r22 =0.93). In summary, in this study we improved a previously developed Catalyst MCF-7 inhibitory pharmacophore, =0.93). In summary, in this study we improved a previously developed Catalyst MCF-7 inhibitory pharmacophore,44 and established a predictive 3D-QSAR model. We have and established a predictive 3D-QSAR model. We have further used this model to detect novel MCF-7 cell line inhibitors through 3D database searchingfurther used this model to detect novel MCF-7 cell line inhibitors through 3D database searching

Pharmacophore generationPharmacophore generation

Acknowledgment: We are grateful to Prof. Vassilios Roussis and co-workers for the chemical entities and the biological assaysAcknowledgment: We are grateful to Prof. Vassilios Roussis and co-workers for the chemical entities and the biological assays

References: References: (1) Soule H. D. (1) Soule H. D. et al.et al. J. Natl Cancer Inst.J. Natl Cancer Inst. 1973 1973 5151 (5) 1409; (2) PHASE 2.5 ( (5) 1409; (2) PHASE 2.5 (Schrödinger, LLC, New York, NYSchrödinger, LLC, New York, NY); (3) ); (3) Dixon S. L. Dixon S. L. et al. J. Comput. Aided Mol. Des. et al. J. Comput. Aided Mol. Des. 2006 2006 2020 (10-11) 647; (4) (10-11) 647; (4) Kladi, M. Kladi, M. et al. J. Nat. Prod. et al. J. Nat. Prod. 20092009 ASAP ASAP DOI: 10.1021/np800481w; (5) DOI: 10.1021/np800481w; (5) PHASE user manual; PHASE user manual; (6) (6) Walters, W. P. Walters, W. P. et al.et al. Adv. Drug Deliv. Rev.Adv. Drug Deliv. Rev. 2002 2002 5454 255 255

PHASE 2.5 implemented in the Maestro 8.0 PHASE 2.5 implemented in the Maestro 8.0

modeling package (Schrmodeling package (Schröödinger, LLC, New dinger, LLC, New

York, NY) was used to generate York, NY) was used to generate

pharmacophore models for MCF-7 cell line pharmacophore models for MCF-7 cell line

inhibitorsinhibitorsSome highly active SERM, were selected Some highly active SERM, were selected

for generating the pharmacophore for generating the pharmacophore

hypotheses hypotheses (Fig. 2)(Fig. 2)Pharmacophore feature sites for the best Pharmacophore feature sites for the best

PHASE model were: two hydrogen-bond PHASE model were: two hydrogen-bond

acceptors (A3, A5), one hydrogen-bond acceptors (A3, A5), one hydrogen-bond

donor (D6) and two aromatic sites (R9, R10) donor (D6) and two aromatic sites (R9, R10)

(Fig.1)(Fig.1)Common pharmacophore hypotheses were Common pharmacophore hypotheses were

identified, scored and ranked. identified, scored and ranked. The regression The regression

is performed by a partial least squares (PLS) is performed by a partial least squares (PLS)

methodmethodAll the molecules used for QSAR studies All the molecules used for QSAR studies

were aligned to the pharmacophore were aligned to the pharmacophore

hypothesis obtained in PHASEhypothesis obtained in PHASE

Fig. 1 Fig. 1 Superposition of best PHASE model and the most active compound in the set (38Superposition of best PHASE model and the most active compound in the set (38)). . Pharmacophore features are color-coded: cyan for hydrogen bond donor (D), pink for hydrogen Pharmacophore features are color-coded: cyan for hydrogen bond donor (D), pink for hydrogen bond acceptor (A), brown rings for the aromatic features (R)bond acceptor (A), brown rings for the aromatic features (R)

Fig. 2 Fig. 2 SERM derivatives used in this studySERM derivatives used in this study

Fig. 3 Fig. 3 Predicted versus observed value inhibitory activity pICPredicted versus observed value inhibitory activity pIC50 50 (M)(M)

Development of a PHASE 3D-QSAR modelDevelopment of a PHASE 3D-QSAR model

3D-Database searching3D-Database searching

ConclusionConclusion

The PHASE-generated 3D pharmacophore was used as The PHASE-generated 3D pharmacophore was used as

the alignment template for the 3D-QSAR model the alignment template for the 3D-QSAR model (Fig.4-5)(Fig.4-5)33 Phase determines how molecular structure affects drug Phase determines how molecular structure affects drug

activity by dividing space into a fine cubic grid, encoding activity by dividing space into a fine cubic grid, encoding

atom type occupation as numerical information, and atom type occupation as numerical information, and

performing a partial least-squares (PLS) regressionperforming a partial least-squares (PLS) regressionThe independent variables in the QSAR model were The independent variables in the QSAR model were

derived from a regular grid of cubic volume elements that derived from a regular grid of cubic volume elements that

span the space occupied by the training set ligands and span the space occupied by the training set ligands and

biological activities (pICbiological activities (pIC5050 values) were used as dependent values) were used as dependent

variables. In addition to the qvariables. In addition to the q22, the conventional correlation , the conventional correlation

coefficient rcoefficient r22 and its standard errors were also computed and its standard errors were also computed

(Table 1)(Table 1)

Fig. 4 Fig. 4 3D-QSAR model around the 3D-QSAR model around the most active compounds in the set (38)most active compounds in the set (38)

In our study, we built a pharmacophore model by In our study, we built a pharmacophore model by

applying the ligand-based pharmacophore applying the ligand-based pharmacophore

generation approach, using PHASE. Different generation approach, using PHASE. Different

pharmacophore based QSAR models were pharmacophore based QSAR models were

developed by using PLS analysis developed by using PLS analysis The best resulting hypothesis consisted of five The best resulting hypothesis consisted of five

features: two hydrogen bond acceptors, one features: two hydrogen bond acceptors, one

hydrogen-bond donor and two aromatic sites. The hydrogen-bond donor and two aromatic sites. The

alignment rule of the best-fit model was used to alignment rule of the best-fit model was used to

develop ligand-based 3D-QSAR model develop ligand-based 3D-QSAR model The established computational tool endowed with The established computational tool endowed with

high predictive ability and robustness, might be high predictive ability and robustness, might be

useful for the design and optimization of new MCF-7 useful for the design and optimization of new MCF-7

cell line inhibitors cell line inhibitors

Validation of PHASE 3D-QSAR modelValidation of PHASE 3D-QSAR model18 n e w p o t e n t ia l S ER M s w e r e t e s t e d18 n e w p o t e n t ia l S ER M s w e r e t e s t e d

-7 a g a in s t M C F c e lls a n d t h e n u s e d a s a n -7 a g a in s t M C F c e lls a n d t h e n u s e d a s a n

3 - e x t e r n a l t e s t s e t f o r P H AS E D Q S AR 3 - e x t e r n a l t e s t s e t f o r P H AS E D Q S AR

. m o d e l v a lid a t io n s f o r p r e d ic t iv e a b il it y . m o d e l v a lid a t io n s f o r p r e d ic t iv e a b il it y

Th e p r e d ic t io n r e s u lt s o f t h is e x t e r n a l Th e p r e d ic t io n r e s u lt s o f t h is e x t e r n a l

t e s t s e t a r e s h o w in t e s t s e t a r e s h o w in 3F ig u r e 3F ig u r eThe large value of variance ratio (F) indicates a The large value of variance ratio (F) indicates a

statistically significant regression model, which is statistically significant regression model, which is

supported by the small value of the significance level of supported by the small value of the significance level of

variance ratio (P), an indication of a high degree of variance ratio (P), an indication of a high degree of

confidence. The qconfidence. The q22 value suggesting the model is robust value suggesting the model is robust

(Table 1)(Table 1)5 5

Therefore, the correlation between the actual and Therefore, the correlation between the actual and

predicted values of activities suggested that the PHASE 3D-predicted values of activities suggested that the PHASE 3D-

QSAR model was reliable. The steric, electrostatic, and QSAR model was reliable. The steric, 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

3D-Database searching is a powerful tool to discover new 3D-Database searching is a powerful tool to discover new

structures and design new ligands of a biological target structures and design new ligands of a biological target In our study, the computational 3D-QSAR model In our study, the computational 3D-QSAR model

developed by PHASE was used to search Asinex chemical developed by PHASE was used to search Asinex chemical

databases (about 250,000 structurally diversified small databases (about 250,000 structurally diversified small

molecules) for new chemical structures active against molecules) for new chemical structures active against

MCF-7 cell lineMCF-7 cell line Compounds with a predicted activity cutoff value of 0.5 Compounds with a predicted activity cutoff value of 0.5

(pIC(pIC5050 µµM) were selected. Other filters were applied to M) were selected. Other filters were applied to

identify entries against MCF-7 cell line: the compounds identify entries against MCF-7 cell line: the compounds

must satisfy the Lipiniski's rule of fivemust satisfy the Lipiniski's rule of fiveThe query identified 19 top-ranking compounds with high The query identified 19 top-ranking compounds with high

predicted activity against MCF-7 cell line. These molecules predicted activity against MCF-7 cell line. These molecules

were considered likely to be well-absorbed because they were considered likely to be well-absorbed because they

satisfied Lipiniski's rule of fivesatisfied Lipiniski's rule of five66 These 19 top-ranking compounds will be submitted to These 19 top-ranking compounds will be submitted to

biological evaluationbiological evaluation

0.81R

0.74Q2

1.384RMSE

6.731 e-16P

261.4F

0,97r2

0.327SD

valuevalueStatistical Statistical parameterparameter

Table 1 Statistical parameter of PHASE 3D-QSAR models.Descriptor of the QSAR results: SD Standard deviation of the regression. r2: Value of r2 for the regression. F Variance ratio. PSignificance level of variance ratio. RMSE Root-mean-square error. Q2 value of Q2 for the predicted activities. R r-Pearson value

Fig. 5Fig. 5 3D-QSAR model for an active ligand (left) and an inactive ligand (right); 3D-QSAR model for an active ligand (left) and an inactive ligand (right); colored according to the sign colored according to the sign of their coefficient values: blue for positive coefficients and red for negative coefficients. Positive coefficients of their coefficient values: blue for positive coefficients and red for negative coefficients. Positive coefficients indicate an increase in activity, negative coefficients a decreaseindicate an increase in activity, negative coefficients a decrease