Pharmacophore modeling and virtual screening to identify potential RET kinase inhibitors

Bioorganic & Medicinal Chemistry Letters 21 (2011) 4490–4497

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Bioorganic & Medicinal Chemistry Letters journal homepage: www.elsevier.com/locate/bmcl

Pharmacophore modeling and virtual screening to identify potential RET kinase inhibitors Kuei-Chung Shih a, , Chung-Wai Shiau b, , Ting-Shou Chen c, Ching-Huai Ko c, Chih-Lung Lin c, Chun-Yuan Lin d,e, Chrong-Shiong Hwang f, Chuan-Yi Tang a,g, Wan-Ru Chen f, Jui-Wen Huang f,⇑ a

Department of Computer Science, National Tsing Hua University, Hsinchu 30013, Taiwan Institute of Biopharmaceutical Sciences, National Yang-Ming University, Taipei 11221, Taiwan c Biomedical Technology and Device Research Laboratories, Industrial Technology Research Institute, Chutung 31040, Taiwan d Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan 33302, Taiwan e Research Center for Emerging Viral Infections, Chang Gung University, Taoyuan 33302, Taiwan f Biomedical Technology and Device Research Laboratories, Industrial Technology Research Institute, Hsinchu 30011, Taiwan g Department of Computer Science and Information Engineering, Providence University, Taichung 43301, Taiwan b

a r t i c l e

i n f o

Article history: Received 1 February 2011 Revised 25 May 2011 Accepted 1 June 2011 Available online 12 June 2011 Keywords: RET kinase Pharmacophore Docking NCI database CDOCKER GOLD Molecular modeling Discovery Studio Goodness of hit (GH) test

a b s t r a c t Chemical features based 3D pharmacophore model for REarranged during Transfection (RET) tyrosine kinase were developed by using a training set of 26 structurally diverse known RET inhibitors. The best pharmacophore hypothesis, which identified inhibitors with an associated correlation coefficient of 0.90 between their experimental and estimated anti-RET values, contained one hydrogen-bond acceptor, one hydrogen-bond donor, one hydrophobic, and one ring aromatic features. The model was further validated by a testing set, Fischer’s randomization test, and goodness of hit (GH) test. We applied this pharmacophore model to screen NCI database for potential RET inhibitors. The hits were docked to RET with GOLD and CDOCKER after filtering by Lipinski’s rules. Ultimately, 24 molecules were selected as potential RET inhibitors for further investigation. Ó 2011 Elsevier Ltd. All rights reserved.

The receptor tyrosine kinase RET (REarranged during Transfection) protooncogene plays important roles in survival, development, and regeneration of many neuronal populations, and human cancers.1,2 According to its crystal structure, RET is a transmembrane, multidomain protein with an intracellular tyrosine kinase domain.3,4 Members of the glial cell line-derived neurotrophic factor (GDNF) family activate RET native kinase5 in conjunction with a GDNF family receptor a1–4 (GFRa1–4) by forming a tripartite complex with RET.6–8 Activating mutations and rearrangements in RET have been implicated in the development of various neoplasias, including thyroid cancer,2,3,8–17 breast cancer,18–20 neuroblastoma,21–25 prostate cancer,26 lung cancer,27–29 brain tumor and glioblastoma,30,31 and pancreatic cancer.32,33 Because there is a strong correlation between the RET oncogene and certain types of cancers, numerous small molecule inhibitors

⇑ Corresponding author. Tel.: +886 5743924; fax: +886 5732374.  

E-mail address: [email protected] (J.-W. Huang). These authors contributed equally to this work.

0960-894X/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.bmcl.2011.06.003

against RET have been developed as potential anti-cancer agents. Two pyrazolo-pyrimidine derivatives, PP1(AGL-1972) and PP2, inhibit RET and Src kinase family.7,34–37 CEP-701 (lestaurtinib) and CEP-751, both indolocarbazoles, not only inhibit RET,38,39 but also target other kinases, such as Trk,40–42 FLT3,43–47 and Jak2.48–51 Other multi-target inhibitors, for example, ZD6474à (vandetanib, an anilinoquinazoline),52–58 SU11248 (sunitinib, an indolin-2-one derivative),3,59–62 BAY 43-9006 (sorafenib, a pyridinyloxyphenylureas),63–65 AMG706 (motesanib, a nicotinamide),3,66–68 and XL184,3,69,70 with good inhibitory activities against RET have been used in clinical trails to treat thyroid cancers.70 Most of these multi-target kinase inhibitors were designed to compete with ATP in a kinase domains in general; thus their selectivities are broad. Only a few structure–activity relationship (SAR) studies39,58,68,71 have been performed that use small molecules crystal structures55,58,68 and quantitative structure–activity relationship (QSAR) analysis58 to à In April 2011, AstraZeneca received approval from the FDA for zactima (ZD6474) for the treatment of unresectable locally advanced or metastatic medullary thyroid cancer.

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Figure 1. Chemical structures of the 26 molecules in training set.

enhance the activities of certain anti-RET indolin-2-one or carbolin1-one class compounds. In this paper, we report the first pharmacophore modeling study of RET inhibitors that incorporates with virtual screening of candidate inhibitors. The main purpose of the research was to develop a workflow to screen for RET inhibitors that integrated pharmacophore modeling and in silico docking. This workflow potentially can aid in the design of new scaffolds for RET inhibitors and the optimization of known anti-RET agents. SAR information about RET inhibitors was collected via the Kinase KnowledgeBase database (Eidogen–Sertanty Inc.). According to their chemical structural variations, and differences in biological activity, we chose 76 inhibitors to generate and then validate the pharmacophore models. The inhibitory activities of the 76 inhibitors were represented as their IC50 values which span six orders of magnitudes (5.3–100,000 nM). These inhibitors were divided into training and testing sets of 26 and 50 members, respectively. The pharmacophore hypotheses were generated using the ‘3DQSAR Pharmacophore Generation’ technology based on HypoGen algorithm72 in Accelrys Discovery Studio 2.1. HypoGen attempts to build hypothesis models from a training set of molecules for which activity values on a target protein have been identified. The structures of 26 inhibitors are shown in Figure 1; their experimental and estimated IC50 values (from Hypo-02) of the 26 RET inhibitors in the training set are listed in Table 1. For the program setting, all training set conformations were generated using the CHARMm-like force field.73 The maximum number of conformers for each molecule was 255, and the conformational space for each molecule was constrained by 20 kcal/mol energy threshold above the global energy minimum. Four features were selected for use in the pharmacophore hypothesis generation process: hydrogenbond acceptors (HBA), hydrogen-bond donors (HBD), hydrophobic groups (HY), and ring aromatic (RA). The minimum and maximum

Table 1 Experimental and estimated anti-RET IC50 values derived from the Hypo-02 hypothesis for the training set molecules Compound

Experimental IC50 (nM)

Estimated IC50 (nM)

Error

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

6 36 50 190 210 410 500 1000 1000 1000 1600 1800 1800 2700 4500 10,000 10,000 10,000 10,000 11,000 55,000 100,000 100,000 100,000 100,000 100,000

4.3 310 230 570 450 520 430 430 2300 1500 17,000 500 620 560 2400 7900 13,000 19,000 14,000 43,000 54,000 31,000 50,000 50,000 16,000 16,000

+1.1 +8.7 +4.6 +3 +2.1 +1.3 +2.3 2.3 +2.3 +1.5 +11 3.6 2.9 4.8 1.9 1.3 +1.3 +1.9 +1.4 +3.9 1 3.2 2 2 6.4 6.2

features count was set to 1 and 3, respectively. The critical parameter, an uncertainty value, was set to 2. All other parameters were set to their default values. Ten pharmacophore hypotheses were generated.

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Table 2 Ten pharmacophores generated by the training set RET inhibitors

a b

Hypo No.

Total cost

Cost differencea

Error cost

RMS deviation

Training set (r)

Testing set (r)

Featureb

1 2 3 4 5 6 7 8 9 10

124.34 129.87 134.97 142.88 143.22 143.53 145.56 146.60 148.54 148.79

138.73 133.20 128.10 120.19 119.85 119.54 117.51 116.47 114.53 114.28

105.47 110.15 117.48 128.59 127.25 128.51 130.20 131.82 133.90 134.37

1.52 1.63 1.80 2.02 2.00 2.02 2.05 2.08 2.12 2.13

0.92 0.90 0.88 0.85 0.85 0.85 0.84 0.84 0.83 0.83

0.76 0.89 0.73 0.72 0.64 0.40 0.55 0.86 0.72 0.72

ADHR ADHR ADHR ADHR ADHR ADHR ADHR ADHR ADHR ADHR

(Null cost Total cost), Null cost = 263.072; Fixed cost = 89.623; Configuration cost = 13.02. All costs are in units of bit. A, hydrogen-bond acceptor; D, hydrogen-bond donor; H, hydrophobic; R, ring aromatic.

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Figure 2. Chemical structures of the 50 compounds in the testing set.

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Figure 3. The best pharmacophore hypothesis is Hypo-02. Hypo-02 includes one hydrogen-bond acceptor (HBA, green), one hydrogen-bond donor (HBD, magenta), one hydrophobic (HY, cyan), and one ring aromatic (RA, orange) features. Table 3 Experimental and estimated anti-RET IC50 values derived from Hypo-02 hypothesis for the testing set molecules Compound

Experimental IC50 (nM)

Estimated IC50 (nM)

Error

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 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76

38 39 90 104 106 110 110 111 128 140 150 201 212 218 337 375 410 475 484 537 692 740 753 1000 1000 1000 1000 1000 1073 1100 1117 1227 1230 1232 1262 1268 2990 3230 3721 4246 9204 10,000 10,000 10,000 10,000 10,000 10,000 100,000 100,000 100,000

119 133 80 365 116 121 371 98 115 147 539 92 85 102 524 120 1096 397 521 300 178 2701 204 320 442 355 2374 320 386 3681 439 1404 554 4116 579 3558 2235 1631 1060 12,507 12,967 3642 4171 4312 4241 3763 7349 50,291 50,258 50,153

+3.1 +3.4 1.1 +3.5 +1.1 +1.1 +3.4 1.1 1.1 +1.1 +3.6 2.2 2.5 2.1 +1.6 3.1 +2.7 1.2 +1.1 1.8 3.9 +3.7 3.7 3.1 2.3 2.8 +2.4 3.1 2.8 +3.4 2.5 +1.1 2.2 +3.3 2.2 +2.8 1.3 2.0 3.5 +3.0 +1.4 2.8 2.4 2.3 2.4 2.7 1.4 2.0 2.0 2.0

The null hypothesis total cost value for these 10 hypotheses was 263.072 bits and the fixed hypothesis total cost value was 89.623 bits. The difference between the null and fixed costs was 173.449 bits, thus, the model has >90% probability of representing a true correlation in the data.74 The configuration cost for the pharmacophore hypotheses was 13.0189 bits. The total cost for the worst pharmacophore, Hypo-10, was 148.79 bits. The cost difference (null cost—.total cost) for Hypo-10 was >110 bits; therefore, the experimental and predicted activity data were strongly correlated (>90%). The correlation coefficient r for the 10 pharmacophores hypotheses ranged from 0.83 to 0.92. Information regarding these 10 pharmacophores hypotheses is shown in Table 2. Fifty additional compounds (Fig. 2) with RET inhibitory activities were selected from the Kinase KnowledgeBase database as the testing set to assess the predictive ability of each pharmacophore. The estimated activities of the testing set molecules were scored using the predictive abilities of the 10 pharmacophores. The r value for the computed IC50 values of the molecules in the testing set was calculated from a simple line regression fit. The Hypo-02 pharmacophore model which is comprised of one hydrogen-bond acceptor (HBA), one hydrogen-bond donor (HBD), one hydrophobic (HY), and one ring aromatic (RA) features (Fig. 3), had the best r value, 0.90, which represented the testing set validation. Therefore, the validation results indicate that the Hypo-02 could accurately forecast the RET inhibitory activities of the compounds. The experimental and estimated anti-RET IC50 values from Hypo-02 for the molecules in the testing set is shown in Table 3. The mapping of the most active compound, sorafenib (compound 1), with the Hypo-02 model shows that the compound shared all of the features of the model quite well. As shown in Figure 4, the hydrogen-bond donor (HBD) feature maps onto the N–H group on the urea function group, the hydrogen-bond acceptor (HBA) feature maps onto the carbonyl group of the amide bond, the ring aromatic (RA) feature maps onto the 4-chloride-3-trifluoromethyl benzene ring, and the hydrophobic (HY) feature maps onto the pyridine ring. Figure S1 (Supporting informaion) shows the regression analysis for the actual versus predicted compound activities by the Hypo-02 pharmacophore hypothesis for both the training and testing set molecules. The validity of the Hypo-02 hypothesis was further demonstrated using Fischer’s randomization test.75 The data for the Fischer’s hypotheses were generated via randomly scrambling and then reassigning the activity values from one molecule to the other molecule in the training set. All the parameters associated with the reassigned activity values were kept at the original settings. A total of 99 random spreadsheets were created to gener-

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Figure 4. Mapping of compound 1 (sorafenib) with the features of Hypo-02. Features: hydrogen-bond acceptor (HBA, green), hydrogen-bond donor (HBD, magenta), hydrophobic (HY, cyan), and ring aromatic (RA, orange).

220

Total cost values (bits)

200

total Costs random1 random2 random3 random4 random5 random6 random7 random9 random8 random10

180

160

140

120

1

2

3

4

5

6

7

8

9

10

Pharmacophore hypotheses Figure 5. Comparison of the total costs for the original 10 pharmacophores hypotheses (blue line with diamond shaped data markers) with the best 10 randomly generated by Fisher’s randomization test.

Table 4 Statistical results of GH test Serial no.

Parameter

Values

1 2

Total molecules in database (D) Total actives (experimental IC50 <300 nM) in database (A) Total hits (estimated IC50 <300 nM) (Ht) Active hits (experimental and estimated IC50 <300 nM) (Ha) % Ratio of active inhibitors (%A = Ha/A  100) % Yield of active inhibitors (%Y = Ha/Ht  100) False negatives (A Ha) False positives (Ht Ha) GH scorea

90 31

3 4 5 6 7 8 9 a

NCI database

30 25 80.65 83.33 6 5 0.76

GH test score >0.6 indicates a very good model.

260,071 compounds Ligand Pharmacophore Mapping 8,663 compounds Lipinski’s rule 3,019 compounds

Gold docking 845 compounds CDOCKER docking

ate pharmacophore hypotheses that fit a confidence level of 99% (99% = [1 (1 + 0)/(99 + 1)]  100%). The total costs of the initial 10 hypotheses and the total cost results of the 10 lowest runs from

Figure 6. The screening workflow used to identify RET kinase inhibitors in a small molecule database.

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K.-C. Shih et al. / Bioorg. Med. Chem. Lett. 21 (2011) 4490–4497 Table 5 Top 10 hits identified from NCI database according to the screening workflow (the other 14 hits are list as Supplementary data in Table S2) No.

Hits

Structure

EIC50a (nM)

Gold score

CIEb

0.061

59.754

57.724

0.135

67.269

55.721

0.269

81.212

60.842

0.894

65.666

57.951

1.167

60.427

59.34

1.33

63.789

56.95

1.633

56.316

58.321

2.592

75.879

57.558

3.14

61.394

57.566

3.545

77.155

55.512

OH O OH O 1

OH

OH O

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NSC 211983

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NSC 653142

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+

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Estimated IC50 from the ligand pharmacophore mapping. -CDOCKER interaction energy.

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the 99 randomized runs are shown in Figure 5. This figure shows that the total cost of the original hypotheses (blue line with diamond shaped data markers) are lower than those of the corresponding ones generated using the 99 random spreadsheets. In addition, the GH (goodness of hit) test method was applied to screen the database of known inhibitors to further validate the quality of our RET pharmacophore model.76 The GH test score ranges from 0 to 1, where a score close to 0 indicates a null model and a score close to 1 indicates an ideal model. For a good screening model, the value of the GH score must be P0.5. To calculate the GH test score, a set of parameters are needed, described as follows: D, the number of inhibitors in the database; A, the number of active inhibitors in the database; Ht, the number of hit inhibitors in the database; Ha, the number of active inhibitors in the hit list; %A, percentage of active inhibitors in the hit list; %Y, number of active percent of yield; and GH, value of the GH test score. The equation which was used to calculate the GH test score is listed below. GH = [Ha(3A + Ht)/4HtA]  [1 (Ht Ha)/(D A)]. Ninety compounds were applied for this GH test, including the 26 molecules in the training set, 50 molecules in the testing set, and another 14 RET inhibitors from the literature.77 The experimental and estimated anti-RET IC50 values derived from Hypo-02 model and the chemical structures of the 14 compounds from this literature are shown in the Supplementary data (Table S1). Table 4 presents the GH test validation results for our RET pharmacophore model. Among the 90 compounds in the database, 30 RET inhibitors were predicted to be active hits (Ht, estimated IC50 <300 nM), including 25 actual active inhibitors (Ha, both estimated and experimental IC50 <300 nM) and five false positive inhibitors. The prediction accuracy is 83.33%, and the GH test score is 0.76. The correlation coefficient of testing set, Fischer’s validation, and GH test provide strong confidence on our pharmacophore model. Given the foregoing result, Hypo-02 showed superior recognition ability for RET kinase inhibitors. Consequently, Hypo-02 hypothesis was used to screen the National Cancer Institute (NCI) database, which contained at the time of our study 260,071 compounds. The screening workflow is shown in Figure 6. The first part was ligand pharmacophore mapping. We applied the chemical features of the pharmacophore hypothesis to a 3D geometric search of the database. When a database compound had all the features of the pharmacophore, and its fit value was >9 bits, it was selected as a RET inhibitor candidate. In all, 8663 compounds fit the criteria. We further examined these hits by Lipinski’s Rule-of-Five.78 The rules are widely accepted by the pharmaceutical industry as a guideline to evaluate the drug-like properties of small molecules. The second part was to confirm the interaction between each compound and RET binding site using docking method. The 3019 hits passed Lipinski’s Rule-of-Five filtration were first subjected to Gold (Cambridge Crystallographic Data Centre, Cambridge, U.K.) technology in Accelrys Discovery Studio 2.1 (Discovery Studio, version 2.1; Accelrys, San Diego, CA, USA, 2008). Gold docking79–81 used a genetic algorithm to dock ligands into the receptor with flexible state. Optionally, GoldScore fitness score functions can be evaluated for a set of previously docked poses. We used the RET kinase crystal structure (PDB entry code 2IVU) for the docking calculation. The position of each complexed inhibitor was set as the center for a defined binding site within 9Å. We used the GOLD fitness score of kinase/inhibitor complexes as a filter. There were 845 hits for which the GOLD fitness scores were >55 and these hits were further examined by CDOCKER82 docking program. The ‘Dock Ligands (CDOCKER)’ technology in Accelrys Discovery Studio 2.1 was used to dock each of 845 compounds into the RET kinase domain (PDB entry code 2IVU). CDOCKER uses molecular dynamics (MD) with CHARMm force field scheme to dock ligands into a receptor binding site. The high-temperature MD is used to generate random ligand conformations, and then

translated into the binding site. Each inhibitor was generated random conformation using high-temperature CHARMm-based molecular dynamics, and then docked into the RET ATP binding site. Like the docking method in GOLD, the position of each complexed inhibitor was set as the center of a defined binding site within 9 Å. We use the negative of CDOCKER interaction energy of complex inhibitor as a filter. Twenty-four hits whose negative CDOCKER interaction energy >55 were finally selected. The structures, estimated anti-RET IC50 value, GOLD score, and negative CDOCKER interaction energy of top 10 hits among these 24 compounds are shown in Table 5. The information of the remaining 14 compounds is shown in Table S2. In conclusion, a chemical feature-based-pharmacophore model for RET tyrosine kinase inhibitors was developed by a ligand-based computational approach. The best pharmacophore model with an associated correlation coefficient of 0.90 between its experimental and estimated anti-RET IC50 values, and possesses four features, including one hydrogen-bond acceptor, one hydrogen-bond donor, one hydrophobic group, and one ring aromatic features. The pharmacophore model was validated using the testing set, Fischer’s randomization test, and GH test. The model is a powerful tool for database searching to retrieve potential RET kinase inhibitors. We designed a workflow that integrates pharmacophore modeling, in silico docking, and Lipinski’s Rule-of-Five to search for potential RET inhibitors in the NCI database. Twenty-four molecules from the NCI database were ultimately selected as potential RET inhibitors for further investigation. This workflow can be used to design new scaffold for RET inhibitors and structurally optimize of known anti-RET agents. Acknowledgments This work was supported by the National Science Council (Grant No. NSC98-2320-B-010-005-My3) and by the grant from Ministry of Economic Affairs, Taiwan. We are grateful to the National Center for High-Performance Computing for computer time and facilities. Kinase Knowledge Base is the property of EidogenSertanty Inc. The Gold computation was conducted at Industrial Technology Research Institute, Taiwan. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.bmcl.2011.06.003. References and notes 1. Schuchardt, A.; D’Agati, V.; Larsson-Blomberg, L.; Costantini, F.; Pachnis, V. Nature 1994, 367, 380. 2. Jhiang, S. M. Oncogene 2000, 19, 5590. 3. Lanzi, C.; Cassinelli, G.; Nicolini, V.; Zunino, F. Biochem. Pharmacol. 2009, 77, 297. 4. Anders, J.; Kjaer, S.; Ibanez, C. F. J. Biol. Chem. 2001, 276, 35808. 5. Sariola, H.; Saarma, M. J. Cell Sci. 2003, 116, 3855. 6. Airaksinen, M. S.; Saarma, M. Nat. Rev. Neurosci. 2002, 3, 383. 7. Knowles, P. P.; Murray-Rust, J.; Kjaer, S.; Scott, R. P.; Hanrahan, S.; Santoro, M.; Ibanez, C. F.; McDonald, N. Q. J. Biol. Chem. 2006, 281, 33577. 8. Santoro, M.; Carlomagno, F.; Melillo, R. M.; Fusco, A. Cell. Mol. Life Sci. 2004, 61, 2954. 9. Arighi, E.; Borrello, M. G.; Sariola, H. Cytokine Growth Factor Rev. 2005, 16, 441. 10. Koga, K.; Hattori, Y.; Komori, M.; Narishima, R.; Yamasaki, M.; Hakoshima, M.; Fukui, T.; Maitani, Y. Cancer Sci. 2010, 101, 941. 11. Rusciano, M. R.; Salzano, M.; Monaco, S.; Sapio, M. R.; Illario, M.; De Falco, V.; Santoro, M.; Campiglia, P.; Pastore, L.; Fenzi, G.; Rossi, G.; Vitale, M. Endocr. Relat. Cancer 2010, 17, 113. 12. Ameur, N.; Lacroix, L.; Roucan, S.; Roux, V.; Broutin, S.; Talbot, M.; Dupuy, C.; Caillou, B.; Schlumberger, M.; Bidart, J. M. Endocr. Relat. Cancer 2009, 16, 1261. 13. Coulpier, M.; Anders, J.; Ibanez, C. F. J. Biol. Chem. 2002, 277, 1991. 14. Durand, S.; Ferraro-Peyret, C.; Joufre, M.; Chave, A.; Borson-Chazot, F.; SelmiRuby, S.; Rousset, B. Endocr. Relat. Cancer 2009, 16, 467. 15. Wells, S. A.; Santoro, M. Clin. Cancer Res. 2009, 15, 7119.

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