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Identification of Novel BCRP Inhibitors by Virtual Screening Yongmei Pan, Paresh P Chothe, and Peter W Swaan Mol. Pharmaceutics, Just Accepted Manuscript • DOI: 10.1021/mp300547h • Publication Date (Web): 18 Feb 2013 Downloaded from http://pubs.acs.org on February 23, 2013

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Molecular Pharmaceutics

Identification of Novel BCRP Inhibitors by Virtual Screening

Yongmei Pan1, Paresh P. Chothe1 and Peter W. Swaan1* 1

Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, 20 Penn Street, Baltimore, MD 21201, USA.

*Corresponding Author Peter W. Swaan, Ph.D., Department of Pharmaceutical Sciences, University of Maryland, 20 Penn Street, HSF2-621, Baltimore, MD 21201 USA Tel: (410) 706-0103 Fax: (410) 706-5017 Email: [email protected]

ACS Paragon Plus Environment

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Table of Contents Graphic


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ABSTRACT

Breast Cancer Resistance Protein (BCRP; ABCG2) is an efflux transporter that plays an important role in multi-drug resistance to antineoplastic drugs. The identification of drugs as BCRP inhibitors could aid in designing better therapeutic strategies for cancer treatment and will be critical for identifying potential drug-drug interactions. In the present study, we applied ligand-based virtual screening combined with experimental testing for the identification of novel drugs that can possibly interact with BCRP. Bayesian and pharmacophore models generated with known BCRP inhibitors were validated with an external test set. The resulting models were applied to predict new potential drug candidates from a database with more than 2000 FDAapproved drugs. Thirty three drugs were tested in vitro for their inhibitory effects on BCRPmediated transport of [3H]-mitoxantrone in MCF-7/AdrVp cells. Nineteen drugs were identified with significant inhibitory effect on BCRP transport function. The combined strategy of computational and experimental approaches in this paper has suggested potential drug candidates and thus represents an effective tool for rational identification of modulators of other proteins.

KEYWORDS BCRP, Virtual screening, SAR models, Transport function, inhibitors and MCF-7/AdrVp.


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INTRODUCTION Breast Cancer Resistant Protein (BCRP; ABCG2) is an efflux transporter that plays an important role in multi-drug resistance to antineoplastic drugs 1. Although originally identified as a placenta-specific ATP-binding cassette transporter (ABCP)2, BCRP was later detected in various other tumor types

3, 4

. In normal human tissues, the protein is located at the apical

membrane of epithelial cells 5 and endothelial cells of the small intestine and blood-brain barrier 6

. Since BCRP is involved in exporting substrates from the cell, the pharmacological efficacy of

drugs that are substrates of BCRP may be compromised 7. Many anti-cancer drugs, including mitoxantrone and camptothecin analogs are substrates of BCRP

4, 8

. Thus, the co-administration of BCRP inhibitors with anti-cancer drugs may result in

increased bioavailability of these drugs. For example, the apparent bioavailability of topotecan increased from 40% to 97% when it was administered with the BCRP inhibitor GF120918; importantly, the improved bioavailability was attributed more to inhibition of BCRP than to other efflux transporters, such as P-gp 9. Gefitinib, a potent inhibitor of BCRP, significantly increased the bioavailability of irinotecan in mice

10

. Thus, the design and discovery of new

BCRP inhibitors and identification of already marketed therapeutic drugs as BCRP substrates could aid in improving drug efficacy as well as identifying potential drug-drug interactions. Previous studies aiming to identify new BCRP inhibitors have primarily utilized cell-based assays

1, 3, 11

inhibitors

to screen chemical libraries or congeneric series of structural analogs of known

8, 12-14

. Although global structure-activity relationship (SAR) models have been

established to identify structural characteristics of BCRP inhibitors

1, 4, 8, 11, 13-18

, these were

largely introspective studies analyzing global BCRP inhibition features of diverse datasets or local features aimed at lead optimization for potent inhibitors. The major limitation of these SAR


4

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models was their lack of extrapolating global pharmacophore features for identifying structurally novel BCRP inhibitors. In the present study, novel BCRP inhibitors were identified among commercially available drugs via a combination of virtual screening and in vitro testing. First, Bayesian SAR models were generated from a training set of 124 BCRP inhibitors and non-inhibitors. Pharmacophore models were also built from a select series of 30 potent BCRP inhibitors. Both models were used for virtual screening of the CDD database with more than 2200 FDA-prescribed drugs. From a list of retrieved compounds, 33 high-scoring molecules were tested using a cell-based assay; of these, 19 compounds were identified as BCRP inhibitors. The successful identification of these BCRP inhibitors indicates that virtual screening based on SAR models is a valuable tool for efficient retrieval of novel drugs that interact with BCRP. Furthermore, the models may help identify potential drug-drug interactions due to predicted BCRP affinity of novel chemical entities.

EXPERIMENTAL SECTION Structure Generation and Validation. The molecular structures of 203 previously published BCRP

inhibitors

and

non-inhibitors

were

obtained

from

either

PubChem

(http://www.ncbi.nlm.nih.gov/pccompound) or through molecular sketching and subsequent energy minimization within SYBYL-X 1.2 (Tripos; St. Louis, MO).

Next, the protocol

“Calculate Molecular Properties” within Discovery Studio 2.5 (DS 2.5, Accelrys, San Diego, CA) was used for all molecules to generate 62 molecular descriptors (“features”) representing molecular size, solubility, flexibility, polarity, charge, surface area, and hydrogen bond features. A principal component analysis (PCA) plot (using the “Calculate Principal Components” script


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in DS2.5) of all compounds was generated to inspect whether training set and test set compounds covered similar areas of structural features. PCA was also used to compare molecular features of training set and CDD database drugs. Within the PCA protocol variables, the minimum number of components was set to 3, and the minimum variance explained was set to 0.75. Training and test set compounds were selected such that they occupy similar areas within the PCA plot. Building and Validation of Bayesian Models. Bayesian categorization involves simple and straightforward probabilistic classification by evaluating the frequency of structural features associated with a biological phenomenon (e.g. transporter inhibition) 19. The Bayesian modeling method is fast and efficient for large datasets, scaling linearly with respect to the number of molecules; further, the method is not based on fitting, thus enabling modeling structurally dissimilar (non-congeneric) data. The protocol “Create Bayesian Model” in DS2.5 was applied to generate Bayesian models with the number of bins set to 10. Extended-connectivity fingerprints (ECFP) are circular topological fingerprints designed for molecular characterization, similarity searching, and structure-activity modeling. ECFP were calculated with a maximum diameter of 6 (ECFP_6). Next, functional-class fingerprints with a maximum diameter of 6 (FCFP_6)

20

were calculated.

FCFP’s differ from ECFPs in that equivalent molecular groups (e.g. halogen atoms) are treated as functional groups rather than unique atoms, thereby simplifying the resulting models. Compounds were considered BCRP inhibitors when they increased substrate accumulation by more than 1.5-2 fold upon co-administration with a probe substrate in an in vitro screen (see Supporting Information Table S1). Compounds not satisfying these inclusion criteria were considered non-inhibitors for Bayesian modeling. The models were built by using random combinations of different descriptors and cutoff values. Bayesian models were validated with


6

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leave-one-out cross-validation based ‘receiver operator curve’ area under the curve (XV ROC AUC) 21 associated with the training set compounds. The predictive capacity of Bayesian models was validated with an external test set (see Supporting Information Table S2). The activities of the test set compounds were predicted with the “Calculate Molecular Properties” protocol in DS2.5. Common Feature Pharmacophore Generation. In the present work, a pharmacophore model with quantitative predictive ability in terms of BCRP inhibition was derived. A pharmacophore is a collection of chemical features in space that are required for a desired biological activity. These include hydrophobic groups, charged/ionizable groups, hydrogen bond donors/acceptors, and others, properly assembled in three dimensions to reflect structural requirements for interaction with the target. Even when a protein structure-based approach is made possible by knowledge of the structure of the target from crystallography or modeling studies, a ligand-based approach like that for pharmacophores may provide an alternative and complementary tool for drug design. Common feature pharmacophore models were generated with the “Common Feature Pharmacophore Generation” protocol of DS2.5. Only potent and moderately active inhibitors were used for hypothesis generation. Twenty-five BCRP inhibitors with a higher than 5-fold increase of mitoxantrone accumulation in a cell-based assay were taken from a previous publication

1

and five additional well-characterized BCRP inhibitors were included in the

training set (see Supporting Information Table S3). H-bond acceptor, H-bond donor, hydrophobic, negative ionizable and positive charge features were specified in models generated. Ergocristine, chrysin, nicardipine, Ko143, ethinylestradiol and gefitinib were used as reference compounds while other compounds were considered moderately active during “common feature model” generation. The molecular conformations of training and test set compounds were


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generated by the FAST algorithm

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while the maximum number of conformers generated was

set to 255. The test compounds were fit to the resulting pharmacophores with the molecular docking algorithm RIGID 23, which is relatively fast but does not allow for molecular flexibility. Database Screening with Bayesian and Pharmacophore Models. The Collaborative Drug Discovery (CDD) database (Burlingame, CA. www.collaborativedrug.com) is a open-access database containing 2,815 FDA-approved drugs selected from all medications on the market since 1938

24-26

. This database has been used extensively to identify new drug candidates in

previous publications

24, 25

. Drugs from the CDD database were virtually screened with the

“Ligand Profiler” protocol in DS3.0 using the generated pharmacophore models as templates. CDD conformers were generated with the FAST algorithm and RIGID was again used as the molecular overlapping algorithm. To increase selectivity, no molecular feature was allowed to be missed while mapping these ligands to the pharmacophore models. The minimum interfeature distance was set to 0.5Å. Drugs from the CDD database were also screened with the “Calculate Molecular Properties” protocol in DS3.0 based on the Bayesian model generated above. According to Lipinski’s Rule of 5, compounds with high molecular weight, high hydrophobicity, many hydrogen bond donors and acceptors may not be reasonable drug candidates

27

.

Accordingly, retrieved hits with unfavorable physicochemical properties were removed from the list. Drugs that were tested in vitro were selected based on their Bayesian and pharmacophore scores, their therapeutic categories, physicochemical characteristics and their commercial availability. Materials. Fumitremorgin C, Ko143, doxorubicin, verapamil, dicyclomine, nicergoline, papaverine, ezetimibe, estramustine, prochlorperazine, eprosartan, estrone, nisoldipine, donepezil, dextromethorphan, dimenhydrinate, metoclopramide, pimozide, pregnenolone and


8

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promethazine were procured from Sigma (St. Louis, MO). Amlodipine, cilostazol, raloxifene, butoconazole, clomiphene, fosinopril, glimepiride, domeperidone, losartan and thiothixene were purchased from LKT laboratories, Inc. (St. Paul, MN). Mesoridazine, trifluoperazine, pioglitazone, glipizine, flunarizine and thioridazine were procured from ENZO Life Sciences Inc. (Farmingdale, NY). Quinacrine was obtained from EMD Chemicals, Inc. (Gibbstown, NJ). Hanks’ Balanced Salt Solution (HBSS) was obtained from GIBCO (Invitrogen, Grand Island, NY). [3H]-mitoxantrone (12.7 Ci/mmol) was procured from Moravek Biochemicals Inc. (Brea, CA). MCF-7 and MCF-7/AdrVp cell lines were kindly provided by Dr. Douglas D. Ross (School of Medicine, University of Maryland, Baltimore, MD). Cell Culture. MCF-7 breast cancer cell line and doxorubicin-resistant sub-line MCF-7/AdrVp were maintained in Iscove’s modification of Eagle’s medium (IMEM) with 10% FBS, penicillin (100 IU/ml) and streptomycin (100 µg/ml) (Life Technologies, Inc., Rockville, MD). MCF7/AdrVp was cultured in the presence of 0.5 µg/ml doxorubicin and 1.25 µg/ml verapamil

28

.

MCF-7/AdrVp cells were seeded in 24 well plate (Costar, Corning, NY) at an initial density of 0.15 × 106 cells/well. Uptake studies were performed on the 3rd day post seeding. Uptake Assay and Inhibition studies. Uptake assays were performed as previously described with some modifications

29

. On the 3rd day post seeding, cells were washed two times with

Dulbecco’s PBS (DPBS, pH 7.4, 37 °C) containing Ca2+ and Mg2+ followed by incubation in uptake medium- Hanks’ Balanced Salt Solution (HBSS pH 7.4) at 37 °C containing 2.5 µM mitoxantrone (spiked with 0.2 µCi [3H]-mitoxantrone) for 15 min. Uptake was stopped by washing the cells three times with ice-cold HBSS (pH 7.4). Cells were then lysed in 0.5 ml of 1% sodium dodecyl sulfate-0.2N NaOH, and cell associated radioactivity was measured using an LS6500 liquid scintillation counter (Beckmann Coulter, Inc., Fullerton, CA). For inhibition


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studies, uptake of [3H]-mitoxantrone was measured in the absence and presence of each drug in HBSS pH 7.4. Calculation of kinetic transport parameters: To calculate and compare the inhibitory effects 30

of 10 compounds on BCRP transport function, a method published by Pavek and colleagues

was used with slight modifications. Potency of individual compounds for the inhibition of BCRP was calculated from increased radioactivity measured in the presence of a test compound related to increased radioactivity measured in presence of the potent BCRP inhibitor FTC according to following formula. % Inhibition = DPM (radioactivity) with test compound – DPM (radioactivity) without test compound x 100 DPM (radioactivity) with FTC - DPM (radioactivity) without FTC

The IC50 (i.e., concentration of tested compounds necessary to cause 50% inhibition of BCRP transport function) was calculated from dose–response experiments using nonlinear regression.

Statistical Analysis. Statistical analysis was performed with the one-way ANOVA followed by Tukey's post-hoc test. Experiments were repeated two times, and measurements were made in duplicate for each experimental condition. Data are presented as the mean ± SEM.

RESULTS Structure Generation and Validation. The PCA plot is a useful tool to predict potential outliers by assessing similarity among training and test set compounds

31

. PCA of 203 training and test

set drugs with at least three principal components was performed based on 62 descriptors. There were 124 and 79 compounds in the training and test sets, respectively (see Supporting


10

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Information tables S1 and S2). The majority of training set compounds were from two previously published studies

1, 4

. The first two components accounted for 71.8% of the total variance,

indicating that these components represented the majority of overall descriptor space occupied by the molecules. Training and test set compounds were chosen such that they shared similar areas on the PCA plot. Figure 1(A) demonstrates that the test set drugs accommodate similar space compared with the training set compounds. Figure 1(B) shows a PCA plot of training set compounds overlaid on a PCA plot of CDD drugs, illustrating that training set compounds cover most of the descriptor space occupied by the compounds featured in the CDD drug database. Building and Validation of Bayesian Models. Bayesian models for BCRP inhibition were developed with a training set of 124 BCRP inhibitors and non-inhibitors (Supporting Information Table S1). 70 structural descriptors as well as structural extended connectivity fingerprints (ECFP_6 or FCFP_6, see Methods) were incorporated for model development. 4 Bayesian models were generated with a combination of fingerprints and in vitro cutoff values to separate inhibitors from non-inhibitors. Thus, a 1 or 2 fold increase in the mitoxantrone accumulation in presence of a drug was used as the cutoff value that discriminated inhibitors from non-inhibitors for model generation. Models ECFP-T1 and ECFP-T2 (Table 1) were obtained with fingerprint ECFP_6 and discriminant cutoff values 1 and 2. The other two models FCFP-T1 and FCFP-T2 were developed based on functional class fingerprints FCFP_6.

Figure 1. PCA plots amongst test set and training set drugs (A), and among training set and CDD database drugs (B). The first two principal components explained 71.8% and 61.6% of variance for plot (A) and (B), respectively.


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The predictive performance of Bayesian models were evaluated by XV ROC AUC based on leave-one-out cross-validation of training set compounds. XV ROC AUC reflects the relationship between sensitivity and specificity, ranging from 0 to 1 with a higher number indicating a better model 21, 32. The Bayesian models were also validated with an external test set consisting of 79 drugs and their predicted performance was established by sensitivity (SE), specificity (SP), overall prediction accuracy (Q) and Matthew’s correlation coefficients (C values; a measure of the quality of binary classifications) calculated from the empirical true positive (TP), true negative (TN), false positive (FP), and false negative (FN) values 33 (Table 1).

Table 1. Predictive performance of Bayesian models with training set (n = 124, leave-one-out cross-validation) and test set (n = 79). name

FCFP-T2

ECFP-T2

FCFP-T1

ECFP-T1

2D-fingerprints

FCFP_6

ECFP_6

FCFP_6

ECFP_6

Cutoff

2

2

1

1


12

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XV ROC AUC (%)a

0.902

0.898

0.774

0.795

TP/FP/TN/FNb

36/3/14/26

32/2/15/30

59/6/11/3

59/5/12/3

Best split c

-2.630

-0.861

-4.871

-4.308

SE (%)b

0.58

0.52

0.95

0.95

SP (%)b

0.82

0.88

0.65

0.71

Q (%)b

0.63

0.59

0.89

0.90

C (%)b

0.33

0.33

0.64

0.69

a

Based on training set compounds.

b

Predictive performance validation by test set compounds. True positive (TP), true negative

(TN), false positive (FP), false negative (FN), sensitivity (SE), specificity (SP), overall prediction accuracy (Q), and Matthew’s correlation coefficient (C)33, 34. SE =TP/(TP + FN), SP = TN/(TN + FP), Q = (TP + TN)/(TP + TN + FP + FN), C c

∗ ∗

Bayesian scores that give the best prediction among training set compounds.

Table 1 shows the AUCs of Bayesian models based on the leave-one-out cross-validation with training set compounds. AUC values range between 0 and 1, with 0.5 indicating 50% correct prediction and 1 indicating a perfect match between observed and predicted data

35

. The AUC

values associated with the 4 individual models indicated good internal consistency and prediction accuracy. The AUC values obtained for single LOO cross-validation were somewhat lower when the compounds were subjected to a more robust 10-fold cross-validation algorithm, but the overall models were highly similar (data not shown) 36. When Bayesian models were generated


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based on cutoff values of 1 and 2 to discriminate between inhibitors and non-inhibitors, distinct differences in prediction properties were observed in terms of their AUC, sensitivity, specificity and overall accuracy (Table 1). Models FCFP-T2 and ECFP-T2 have higher specificity (SP: 0.82 and 0.88) but lower sensitivity (SE: 0.58 and 0.52) compared to models FCFP-T1 and ECFP-T1 (SP: 0.65 and 0.71, SE: both 0.95) , indicating a lower ratio of false positives and higher ratio of false negatives. The fact that models FCFP-T2 and ECFP-T2 identified fewer positives and had lower sensitivity (0.58 and 0.52, respectively) is reasonable, because weak inhibitors could be characterized as negatives by models generated with higher cutoff, but as positives by models with lower cutoff. Compared with T2 models, T1 models recognized more positives, but at the same time displayed a higher ratio of false positives. To minimize the ratio of false positives during experimental test, the ECFP-T1 model, which has the highest sensitivity and good selectivity amongst the four models, was selected for virtual screening to identify novel BCRP inhibitors. Fingerprints can be defined as molecular fragments that characterize the structural features of drug molecules. Figure 2 visualizes the structural fragments that are favorable and unfavorable for inhibitory activity against BCRP by using fingerprints ECFP_6 obtained with model ECFPT1. Structural elements depicted in Figures 2(A) and 2(B) were identified in inhibitors and noninhibitors amongst training set compounds, respectively. Therefore, the identified fingerprints could be helpful in distinguishing inhibitors and non-inhibitors of BCRP amongst novel compounds. Common Feature Pharmacophore Generation and Validation. To extend the Bayesian models, common feature pharmacophore models were generated with 30 potent known BCRP inhibitors (Supporting Information Table S3). Figure 3 illustrates the four pharmacophore models that were


14

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generated with the most favorable ranking scores, primarily characterized by hydrophobes and a hydrogen-bond acceptor oriented in spatially distinct patterns. Ko143, the molecule with best fit values to the individual models is shown within the pharmacophore grid points. Overall, the structural elements of Ko143 correlated well with each identified pharmacophore feature. Although it adopts various conformations matching to each pharmacophore model, identical fragments of this molecule may represent similar features among each individual model. The 2methylpropyl group attached to the pyrido ring and the tert-butyl group of the 1,1-dimethylethyl ester are characterized by hydrophobes in all models; the carbonyl group of the 1,1dimethylethyl ester of Ko143 matches to a H-bond acceptor feature in figures 2(A), (B) and (D); further, a hydrophobe maps to the indole ring in Figures (A), (B) and (D). Figure 3(C) is unique from the other models by featuring a H-bond acceptor located at an oxygen atom of the dioxopyrazino ring of Ko143. These combined pharmacophore features can be used for characterization of BCRP inhibitors. The pharmacophore models were validated with the same test set as the Bayesian models (Table 2). The pharmacophore models demonstrated reasonable prediction capacity, in terms of their specificity (SP), sensitivity (SE), overall accuracy (Q), and Matthew’s correlation coefficient (C). Pharmacophore model Pharm-1, which displays the highest specificity (0.71), sensitivity (0.65), overall accuracy (0.66), and C value (0.29) was selected for virtual screening of a drug database. The acceptable but not excellent prediction performance of the obtained pharmacophore models might be due to the limit of pharmacophore methodology and the structural diversity of training set compounds. Virtual Screening of CDD Database Drugs with Bayesian and Pharmacophore Models. Drugs from the CDD database (>2200 FDA-approved drugs) were virtually screened based on Bayesian


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and pharmacophore models for identification of novel BCRP inhibitors. Bayesian scores are accumulations of scores of properties that are predicted to have a favorable or unfavorable impact on the inhibitory activity of a test molecule based on training set compounds19,

20, 37

.

There is no upper limit to a Bayesian score, wherein the higher the score, the higher chance of a molecule being a positive (inhibitor). However, there is a cutoff value for positives and negatives that gives the best prediction of training set compounds. The cutoff value for model ECFP-T1 is -4.3 (see best split in Table 2). 974 drugs had Bayesian scores higher than -4.3 and were predicted as positives by Bayesian model ECFP-T1. In parallel, there were 834 drugs matched to all features of pharmacophore model Pharm-1. The pharmacophore scores have a range between 0 and 1, where a value of 1 indicates a perfect match to a pharmacophore model. Out of the 834 compounds, 254 had pharmacophore scores above 0.8, representing good match to the pharmacophore features. To narrow down the list of possible candidates for in vitro testing further, drugs were selected that featured LogP < 7.5, molecular weight < 776, number of hydrogen bond acceptors ≤ 8, number of hydrogen bond donors ≤ 6 and number of rotatable bonds ≤ 18. Figure 2. Favorable (A) and unfavorable (B) fingerprints obtained from Bayesian model ECFP2T based on ECFP_6.


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Figure 3. The most favorable common feature pharmacophores generated with 30 BCRP inhibitors. Cyan and green spheres represent hydrophobes and hydrogen bond acceptors. The compound mapped to the pharmacophore models is a potent BCRP inhibitor Ko143.


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These cutoff values were determined based on corresponding properties of known BCRP inhibitors. Molecular features were calculated with “Calculate Molecular Properties” in DS3.0 and all compounds that were included in the training and test sets were removed from the database. The final test drugs that were selected for in vitro testing were 1) 57 drugs with Bayesian scores above 1.0 and pharmacophore scores above 0.8 and 2) 109 drugs with Bayesian scores above 5.0. All drug compounds selected for testing were checked for their BCRP interaction and those without previously published affinity were considered for in vitro test. The final selection of test drugs was based on their pharmacophore and Bayesian scores, therapeutic classes and commercial availability. In addition, since two antipsychotics- trifluoperazine and prochlorperazine showed strong BCRP inhibition (see experimental results below), additional antipsychotics and antiemetics were virtually screened and validated experimentally (see Table 3). The final selection included 33 drugs that were tested with cell-based assays.


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Table 2. Validation of pharmacophore models with test set (n = 79). name

Pharm-1

Pharm-2

Pharm-3

Pharm-4

TP/FP/TN/FN a

40/5/12/22

37/5/12/25

36/5/12/26

34/5/12/28

SE (%)a

0.65

0.60

0.58

0.55

SP (%)a

0.71

0.71

0.71

0.71

Q (%)a

0.66

0.62

0.61

0.58

C (%)a

0.29

0.25

0.24

0.21

a

Based on test set compounds.

Table 3. Molecules predicted following CDD database search with Bayesian and pharmacophore models. No

name

Bay score

Phar score

a

b

LogP

MW

category

c

fold increase (Mean ± SE)

1

amlodipine

4.79

0.86

1.58

408.88

antihypertensi ve

2.40 ± 0.01***

2

butoconazole

8.91

0.8

6.5

411.78

antifungal

1.80 ± 0.15d2***

3

clomiphene

15.78

0.75

6.48

405.96

estrogen

1.74 ± 0.12d2***

4

dicyclomine

3.34

0.83

5.11

309.49

antispasmodic

1.91± 0.08**

5

domperidonef

-1.09

0.52

2.45

425.91

antiemetic

1.76 ± 0.05** 1.35 ± 0.04d1*

6

ezetimibe

2.62

0.86

4.63

409.43

antihyperlipid emic

7

flunarizine

7.94

—e

5.94

404.5

vasodilator

1.48 ± 0.06d1** 1.97 ± 0.17***

8

fosinopril

10.89

0.8

5.6

563.66

antihypertensi ve

9

glimepiride

8.19

—

3.78

490.62

antidiabetic

1.42 ± 0.03*

10

mesoridazine

12.77

0.55

4.45

386.57

antipsychotic

1.68 ± 0.15*

d


19


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Page 20 of 43

11

nicergoline

12.67

0.82

3.46

484.39

vasodilator

2.01 ± 0.03***

12

nisoldipine

9.61

0.93

2.96

388.41

antihypertensi ve

2.26 ± 0.23***

13

papaverine

8.34

0.88

3.5

339.39

vasodilator

1.85± 0.06**

14

pimozide

6.82

0.5

5.52

461.55

antipsychotic

1.53 ± 0.05d1*

15

prochlorperazine

14.11

0.28

4.7

373.94

antipsychotic

2.59 ± 0.17***

16

promethazine

4.39

—e

-0.09

284.42

antipsychotic

2.24 ± 0.25***

17

quinacrine

8.31

0.76

5.67

399.96

antimalarial

1.77 ± 0.13**

18

thioridazine

23.22

0.68

5.56

370.58

antipsychotic

1.71 ± 0.02**

19

trifluoperazine

16.42

0.52

4.98

407.5

antipsychotic

2.74 ± 0.31***

20

cilostazol

3.6

0.83

3.23

369.46

antithromboti c

1.43± 0.91d2ns

21

dextromethorpha n

5.8

—

3.96

370.32

antitussive

1.04 ± 0.08ns

22

dimenhydrinate

3.4

0.64

3.96

469.96

antiemetic

1.14 ± 0.03ns

23

donepezil

9.78

0.8

4.57

379.49

nootropic

1.35 ± 0.14ns

24

eprosartan

3.36

0.88

3.68

428.54

antihypertensi ve

1.30 ± 0.09ns

25

estramustine

16.61

—

5.31

520.38

antineoplastic

0.89 ± 0.07ns

26

estrone

5.34

3.94

270.37

estrogen

1.31 ± 0.02ns

27

glipizide

5.01

—

1.9

445.54

antidiabetic

1.23 ± 0.07ns

28

losartan

9.22

0.93

4.24

422.91

antihypertensi ve

0.94 ± 0.72ns

29

metoclopramide,f

-33.6

0.83

1.78

299.8

antiemetic

1.09 ± 0.05ns

30

pioglitazone

1.28

0.85

3.91

356.44

antidiabetic

1.20 ± 0.13d3ns

31

pregnenolone

9.54

—

3.85

416.55

progestogen

1.38 ± 0.04ns

473.58

bone resorption inhibitor

1.09 ± 0.08d1ns

32

raloxifene

13.1

0.8

6.46


20

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33

thiothixene

17.98

0.63

3.53

443.63

antipsychotic

1.17 ± 0.90ns

a

Bayesian scores based on model ECFP-2T. Higher values are more favorable. Pharmacophore scores based on model pharm-1. Higher values are more favorable. c Calculated LogP by DS2.5. d Fold increase in intracellular accumulation of [3H]-mitoxantrone compared to control at a concentration of 100 µM (d110 µM, d225 µM, and d350 µM), e Failed to be matched to pharmacophore model pharm-1. f Antipsychotics and antiemetics with lower bayesian scores than 1.0. ***p < 0.001, **p < 0.01, *p < 0.05 and ns is no significance. b

Functional expression of BCRP in MCF-7/AdrVp cells. Functional expression of BCRP was assessed by using mitoxantrone as a model substrate. Initial uptake of [3H]-mitoxantrone (2.5 µM cold mitoxantrone spiked with 0.2 µCi of [3H]-mitoxantrone) was analyzed at different time points in MCF-7/Adrvp cells. The uptake of [3H]-mitoxantrone was linear up to 15 min (Figure S1, supporting information). To determine the functional expression of BCRP,

uptake of

[3H]mitoxantrone was studied in the absence and presence of different concentrations of known BCRP inhibitors- Ko143 (0.1 – 10 µM) and fumitremorgin C (0.316 – 31.6 µM). Figures 4A and B show dose dependent increase in the intracellular accumulation of mitoxantrone after addition of two inhibitors. Ko143 at 10 µM and fumitremorgin C at 31.6 µM induced the intracellular accumulation of [3H]mitoxantrone by

2.01 ± 0.01 fold and 2.03 ± 0.06 fold respectively

showing significant inhibition of BCRP transport function. Increase in the intracellular accumulation of [3H]mitoxantrone indicates the inhibition of BCRP transport function. IC50 values of Ko143 and fumitremorgin C were 0.07 ± 0.05 µM and 2.56 ± 0.75 µM (Figure 4C and D). In parallel experiment, functional activity of BCRP was also assessed in MCF-7 cells (data not shown) and it was found to be minimal in comparison to MCF-7/AdrVp sub-line showing that BCRP is robustly expressed in MCF-7/AdrVp cells. Screening of compounds for their potential to interact with BCRP. 33 compounds of different therapeutic classes were tested for their potential to interact with BCRP. Initially all compounds were tested at 10 µM and 100 µM unless otherwise stated. The effect of compounds on the


21


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Page 22 of 43

intracellular accumulation of [3H]-mitoxantrone in MCF-7/AdrVp cells was investigated ( Table 3 and Supporting Information Table S4). At a concentration of 100 µM, 15 compounds showed more than 1.4 fold increase in the accumulation of intracellular [3H]-mitoxantrone in MCF7/AdrVp cells compared to control. Some of the compounds were not tested at 100 µM because of their limited solubility in HBSS buffer. However, flunarizine and pimozide at 10 µM, and cilostazol, butoconazole, clomiphene at 25 µM showed more than 1.4 fold increase in the intracellular accumulation of [3H]-mitoxantrone. The structures of these drugs are shown in Figure 5.

Figure 4. Functional expression of BCRP in MCF-7/AdrVp cells. Uptake of [3H]-mitoxantrone (2.5 µM cold mitoxantrone spiked with 0.2 µCi of [3H]-mitoxantrone) was measured for 15 min in the absence and presence of BCRP inhibitors, A. Ko143 (0.1 – 10 µM) and B. Fumitremorgin C (3.16 – 31.6 µM), in HBSS pH 7.4 in MCF-7/AdrVp cells. Data are presented as a fold induction in comparison to control uptake. The difference in uptake in the absence and presence of BCRP inhibitors was statistically significant as indicated (** P < 0.01 and *** P < 0.001). Percent inhibition of BCRP transport function by C. Ko143 and D. Fumitremorgin C.


22

Page 23 of 43

100 D

% Inhibition (% of control)

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


80 60 40 20 0 -1.0

-0.5

0.0

0.5

1.0

1.5

2.0

FTC µM (Log[conc])

Dose response studies. In the initial screening we found 19 compounds which significantly inhibited BCRP transport function. To further confirm the ability of these compounds to inhibit BCRP function, we selected 10 strong inhibitors out of 19 compounds (amlodipine, nicergoline, nisoldipine, fosinopril, papaverine, prochlorperazine, trifluoperazine, promethazine thioridazine and domperidone) for dose response studies. Figure 6 shows that all 10 tested compounds increased intracellular accumulation of [3H]-mitoxantrone in a dose dependent manner showing dose-dependent inhibition of BCRP function. IC50 values yielded for each compound is shown in table 4.


23


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Page 24 of 43

Figure 5. Structures of identified drugs that significantly inhibited BCRP transport function.


24

Page 25 of 43

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Figure 6. Dose response studies of compounds for BCRP inhibition in MCF-7/AdrVp cells. Uptake of [3H]-mitoxantrone (2.5 µM) was measured for 15 min in the absence and presence of increasing concentrations (5–100 µM) of each compound (A; amlodipine, nicergoline, nisoldipine, fosinopril and papaverine, B; prochlorperazine, trifluoperazine, promethazine, thioridazine and domperidone) in HBSS pH 7.4 in MCF-7/AdrVp cells. The difference in uptake in the absence and presence of BCRP inhibitors was statistically significant as indicated (* P < 0.05, ** P < 0.01 and *** P < 0.001).


25


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Page 26 of 43

Table 4. IC50 values of compounds for the inhibition of BCRP function in MCF-7/Adrvp cells No

Name

IC50 (µM)

1

amlodipine

15.68 ± 1.33

2

domperidone

11.15 ± 1.75

3

fosinopril

11.56 ± 1.25

4

nicergoline

37.06 ± 1.16

5

nisoldipine

25.93 ± 1.42

6

papaverine

55.08 ± 1.21

7

promethazine

89.87 ± 1.52

8

thioridazine

35.68 ± 1.34

9

trifluoperazine

7.56 ± 1.46

10

prochloroperazine

12.44 ± 1.28

DISCUSSION The Need for Integrated Application of Computational and Experimental Approaches. The integrated application of ligand-based virtual screening and experimental test has proven to be an effective means for identification of novel substrates, inhibitors, and modulators of nuclear receptors and transporters

16, 37-40

. QSAR and pharmacophore models to identify structural

features of drugs has greatly advanced our mechanistic understanding of drug inhibition against BCRP

1, 4, 8, 11-14, 16-18

, but application of these models to identify novel BCRP inhibitors had not

been explored previously. Previous SAR models were mainly applied for characterization of BCRP inhibitors 16-18

1, 4, 8, 11-14,

without leading to newly identified BCRP inhibitors. To overcome limitations of previous

studies, our present work applied virtual screening of FDA-approved drug database based on Bayesian and pharmacophore models generated with structurally diverse compounds. Bayesian


26

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models were generated with known BCRP inhibitors and non-inhibitors with combinations of structural descriptors. The efficacy of Bayesian and pharmacophore models were validated by test set compounds, with the values of sensitivity, specificity and overall accuracy indicating that the models were able to successfully distinguish BCRP inhibitors from noninhibitors. The Bayesian and pharmacophore models with the highest specificity were selected for virtual screening of CDD drug database. The retrieved hits were ranked in terms of their Bayesian and pharmacophore scores, and those with unfavorable physicochemical properties were removed from in vitro test. The combined computational and experimental approaches could be used for the identification of ligands for other proteins. Newly Identified Drugs with BCRP Interaction. In the present study we tested 33 compounds against BCRP transport function. Screening of all 33 compounds was performed in MCF7/Adrvp cell line. BCRP is overexpressed in these cells due to continuous exposure to Adriamycin and verapamil

41

. Honjo and colleagues have shown that during the drug selection

process, there is an acquired mutation (R482T) in BCRP in these cells 42. They demonstrated that this acquired mutation has significant impact on cross-resistance profile and substrate specificity of BCRP e. g. rhodamine and doxorubicin. Both are transportable substrates of BCRP mutant (R482T) but not wild type BCRP. These differences might be attributed to the effect of R482T mutation on structural features of transmembrane domain 3 of BCRP, where Arg482 is located, causing altered substrate specificity. This effect was obviously noticed in the observed IC50 values of already known potent BCRP inhibitors as Ko143 and fumitremorgin C. The observed IC50 values for Ko143 and fumitremorgin C (0.7 ± 0.05 µM and 2.84 ± 0.09 µM respectively) are significantly higher than IC50 values already reported (0.2 µM and 1 µM for Ko143 and fumitremorgin C respectively) 1, 43. It is possible that observed inhibitory effect of 19 compounds


27


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Page 28 of 43

tested in the present study might vary for wild type BCRP depending upon altered affinity of BCRP (due to mutation) for each compound. However, the rank order of their inhibition would likely be similar. Further studies are needed to confirm the potential influence of R482T mutation on possible interaction of BCRP with these compounds. Gupta and colleagues reported the comparison of inhibitory effects of HIV protease inhibitors for the inhibition of WT BCRP, R482T and R482G

44

, demonstrating that HIV protease inhibitors were more potent in the

inhibition of WT than of either mutant. Therefore, it can be speculated that the compounds which showed BCRP inhibition in the present study might inhibit WT BCRP with more potency and hence might have significant impact on BCRP-mediated drug-drug interaction. In the present study we have used a cell based assay to screen compounds for their potential interaction with BCRP. Nineteen out of 33 compounds (~ 58%) significantly inhibited BCRP transport activity. Among them, amlodipine and nisoldipine were not published as BCRP inhibitors prior to review of our study but were confirmed as inhibitors afterwards; however, IC50 data were not provided 45

. The remaining 16 drugs were newly identified BCRP inhibitors according to most recent

literature search. These identified BCRP inhibitors belong to different therapeutic classes and have diverse structures. 5 out of 19 compounds- prochlorperazine, trifluoperazine, promethazine, thioridazine and mesoridazine are phenothiazines- a class of antipsychotic agents. It was shown previously46 that antipsychotics may inhibit BCRP function. Although there is no published interaction of phenothiazines with BCRP (except thioridazine), they have been shown to modulate other efflux transporters such as P-glycoprotein (P-gp) and MRP1

47-49

. Other than

phenothiazines, amlodipine, domperidone, pimozide, flunarizine and quinacrine also significantly interact with BCRP. All of them have previous record of interaction with P-gp and


28

Page 29 of 43

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MRP1

50-59

These compounds serve as a substrate for P-gp and MRP and they have also been

shown to reverse drug resistance mediated by P-gp and MRP. Amlodipine and nisoldipine are dihydropyridine analogs, and other therapeutics in this class have been reported to inhibit BCRP transport function

12, 15

. For example, nicardipine,

nitrendipine and nimodipine have effectively inhibited mitoxantrone efflux in BCRPoverexpressing human embryonic kidney cells

12

. Among them, nitrendipine is structurally

similar to nisoldipine (ethyl group of nitrendipine changed to isobutyl group in nisoldipine). Additionally, synthetic dihydropyridine derivatives were also shown to significantly inhibit BCRP function 60. Next, clomiphene and glimepiride showed significant interaction with BCRP. Clomiphene is an estrogen modulator and glimepiride is sulfonylurea derivative. Some estrogen modulators with a molecular scaffold similar to clomiphene, such as tamoxifen and toremifene were shown to induce reversal of drug resistance mediated by BCRP

61, 62

demonstrated that glyburide, a sulfonylurea analog, is a substrate of BCRP

. Previous studies

63-65

but inhibitor of

MRP1 66. Although glimepiride has inhibited BCRP transport function further studies are needed to show whether glimepiride is an inhibitor or substrate of BCRP. Other drugs which inhibited BCRP transport activity include dicyclomine, nicergoline, papaverine, fosinopril and butoconazole. None of these drugs has previous published interaction with efflux transporters P-gp, MRP and BCRP. Thus, these can be considered new additions to drugs classes that potentially interact with these transporters. It should be noted that some of the identified BCRP inhibitors, such as amlodipine, nisoldipine, clomiphene, and glimepiride have analogs that were previously published BCRP interaction, and some of these analogs are included in training set. Thus, retrieval of analogs of


29


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Page 30 of 43

known BCRP substrates or inhibitors can be partially attributed to the fragment-based fingerprints that were used for characterization of BCRP inhibitors by Bayesian models. Clinical Implications of BCRP-Mediated Drug-Drug Interaction. Since the identified drugs have inhibition effects on BCRP transport, they might be involved in drug-drug interactions mediated by BCRP. Next, we retrieved their clinical pharmacokinetic data from http://www.rxlist.com. Previous studies showed that nisoldipine has a 30% to 45% increased bioavailability (AUC) with concomitant administration of cimetidine, a well-known substrate of BCRP and P-gp 67-70. Thus, the observed increase in nisoldipine concentration could be attributed to BCRP or P-gp, but further studies are needed to investigate whether it is a substrate or an inhibitor of BCRP. Thioridazine has a demonstrated drug interaction with fluvoxamine and co-administration of the two drugs leads to three-fold increases in clinical plasma concentrations. Fluvoxamine has not been shown to interact with BCRP, even though it is a P-gp substrate 55, 71 and an inhibitor of CYP1A2

72

and CYP3A

73

. The drug-drug interaction between fluvoxamine and thioridazine

could be mediated either by efflux transporters or cytochrome P450 system or both. Since CYP1A2 is one of the cytochrome P450 isoforms that metabolize thioridazine 74, 75, the reduced clearance of thioridazine through fluvoxamine could be attributed to CYP1A2

76

. The

involvement of P-gp in the drug-drug interaction between fluvoxamine and thioridazine needs to be confirmed by thioridazine interaction with P-gp. In 2012, the US Food and Drug Administration revised its guidance for drug-drug interaction studies and provided decision trees to determine whether an investigational drug is an inhibitor of P-gp/BCRP and whether further in vivo drug interaction studies are necessary. According to this new guidance, if the ratio [I]2/IC50 (where [I]2 is dose of inhibitor (in mol)/250 ml and IC50 is the concentration of an investigational drug needed for 50% inhibition of BCRP activity) is ≥ 10, then


30

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further in vivo drug interaction studies are recommended for an investigational drug. Consequently, we calculated [I]2/IC50 ratio for 10 drugs which showed maximum inhibition of BCRP function (Supporting Information Table S5).

[I]2/IC50 ratio for domperidone, fosinopril, nisoldipine,

papaverine and thioridazine was higher than 10, thereby ndicating that these drugs may need to be tested for possible in vivo clinical drug interaction study using BCRP substrate such as mitoxantrone. Although amlodipine, nicergoline, promethazine and trifluoperazine showed [I]2/IC50 ≤ 10, it is possible that these drugs might turn out to have significant role in BCRP-mediated drug-drug interaction for two reasons: first, these drugs might prove to have greater inhibitory effect on wild type BCRP and second, [I]2/IC50 ratio for these compounds may increase with higher dose.

The possible involvement of BCRP or other efflux transporters in metabolism-related drugdrug interactions remains to be explored due to incomplete pharmacokinetic data. Currently, the influence of cytochrome P450 inhibitors is the primary focus for potential drug-drug interactions caused by changes in drug metabolism and disposition. Since efflux transport is an important pathway for drug disposition, P-gp and BCRP inhibition should be included in clinical development. In conclusion, the integrative application of ligand-based virtual screening and experimental tests has led to identification of 19 FDA-approved drugs that inhibited BCRP transport function. Ligand-based Bayesian and pharmacophore models generated with known BCRP inhibitors and non-inhibitors were validated with external test set and were successfully applied to predict new potential drug candidates that were confirmed with cell-based assays. The newly identified drugs with BCRP inhibition could be co-administered drugs for antineoplastic treatment and will aid the design of new clinical drug-drug interaction studies. The combined strategies of computational and experimental approaches in this paper have suggested potential drug


31


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Page 32 of 43

candidates and thus represent an effective tool for rational identification of modulators of other proteins.

AUHTOR INFORMATION Corresponding Author * Department of Pharmaceutical Sciences, University of Maryland, 20 Penn Street, HSF2-621, Baltimore,

MD 21201

USA.

Tel:

(410)

706-0103

Fax:

(410) 706-5017

E-mail:

[email protected] Author contributions The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. Funding sources This study was supported by research fund from Eli Lilly and Company 10002688. Note Authors declare no competing financial interest

ACKNOWLEDGMENT


32

Page 33 of 43

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We thank Dr. Alex MacKerell (University of Maryland) for making Discovery Studio available to our laboratory. We also thank Dr. Douglas D. Ross for providing us with MCF7/AdrVp cells. ABBREVIATIONS BCRP, Breast Cancer Resistance Protein; XV ROC AUC, Leave-one-out cross-validation based ‘receiver operator curve’ area under the curve. REFERENCES

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(BCRP) Inhibitors by Virtual Screening - ACS Publications - American

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