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Prediction of the Tissue-Specificity of Selective Estrogen Receptor Modulators by Using a Single Biochemical Method

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Prediction of the Tissue-Specificity of Selective Estrogen Receptor Modulators by Using a Single Biochemical Method Prediction of the tissue-specificity of selective estrogen receptor modulators by using a single biochemical method Susie Y. Dai*, Michael J. Chalmers*, John Bruning†, Kelli S. Bramlett‡§, Harold E. Osborne‡, Chahrzad Montrose- Rafizadeh‡, Robert J. Barr‡, Yong Wang‡, Minmin Wang‡, Thomas P. Burris‡¶, Jeffrey A. Dodge‡, and Patrick R. Griffin*ʈ** Departments of *Molecular Therapeutics and †Cancer Biology and ʈThe Scripps Research Molecular Screening Center (SRMSC),The Scripps Research Institute, Jupiter, FL 33458; and ‡Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN 46285 Edited by John A. Katzenellenbogen, University of Illinois at Urbana–Champaign, Urbana, IL, and accepted by the Editorial Board March 20, 2008 (received for review November 14, 2007) Here, we demonstrate that a single biochemical assay is able to used to probe receptor–ligand and receptor–coactivator interac- predict the tissue-selective pharmacology of an array of selective tions (2–4). Although it is a powerful technique, this approach has estrogen receptor modulators (SERMs). We describe an approach been limited to the measurement of the dynamics of regions around to classify estrogen receptor (ER) modulators based on dynamics of cysteine 417 and cysteine 530 (located near the C terminus of helix the receptor-ligand complex as probed with hydrogen/deuterium 11). Recently, hydrogen/deuterium exchange (HDX) coupled with exchange (HDX) mass spectrometry. Differential HDX mapping proteolysis and mass spectrometry has evolved as a powerful coupled with cluster and discriminate analysis effectively predicted method for rapid characterization of protein–protein and protein– tissue-selective function in most, but not all, cases tested. We ligand interactions (5–13). Briefly, the local environment of back- demonstrate that analysis of dynamics of the receptor–ligand bone amide hydrogens can be probed by measuring their rates of complex facilitates binning of ER modulators into distinct groups exchange with deuterium. The hydrogen/deuterium (H/D) ex- based on structural dynamics. Importantly, we were able to dif- change kinetics of amide protons vary as a function of hydrogen ferentiate small structural changes within ER ligands of the same bonding and, to a lesser degree, are influenced by solvent accessi- chemotype. In addition, HDX revealed differentially stabilized bility (14). Mass spectrometry (MS) is ideally suited for HDX regions within the ligand-binding pocket that may contribute to measurement because the technology provides high mass accuracy, the different pharmacology phenotypes of the compounds inde- high sensitivity, and is amenable to a high degree of automation. pendent of helix 12 positioning. In summary, HDX provides a Importantly HDX MS allows for measurement of the majority of sensitive and rapid approach to classify modulators of the estrogen the residues within the target protein, a key advantage over the receptor that correlates with their pharmacological profile. site-specific florescence labeling approach. It has been demonstrated that ligand interactions with nuclear discriminate analysis ͉ hydrogen/deuterium exchange ͉ mass spectrometry receptors alter the exchange kinetics of regions of the ligand- binding domain (LBD) directly involved in ligand binding, and in he estrogen receptors (ER␣ and ER␤) are important transcrip- distal regions of the receptor that could not be predicted from Ttional regulators that mediate a number of fundamental pro- cocrystal structures (13, 15). Here, we have applied HDX to study cesses including regulation of the reproductive system and the interactions of a collection of well characterized ER modulators. In maintenance of skeletal and cardiovascular tone. As such, these addition, we have integrated statistical modeling with HDX analysis receptors are the molecular targets of drugs used to treat diseases to classify ER modulators based on the peptide HDX signatures. such as breast cancer and osteoporosis. Both beneficial and detri- We first applied HDX analysis to a series of known ER ligands with mental effects of ER ligands have been demonstrated in target established tissue-selective pharmacological profiles by measuring tissues, thus tissue-selective ER ligands have been developed and the perturbations in hydrogen exchange of the ER␣LBD on ligand are termed selective estrogen receptor modulators (SERMs). Tra- binding. These ligands were then classified based on cluster analysis ditional drug discovery programs for ER modulators most often of their respective HDX peptide signatures. In the second step, we involve the use of a receptor-binding assay as a primary screen to evaluated ER ligands within the same structural chemotype (ben- identify high-affinity ligands, followed by the use of in vitro cell- zothiophene) that contained subtle molecular differences. For the based assays to determine the functional activity of a given ligand (1). Compounds with the desired intrinsic properties for affinity and selective functional response are then evaluated for in vivo efficacy Author contributions: S.Y.D., M.J.C., T.P.B., J.A.D., and P.R.G. designed research; S.Y.D., in animal models of the targeted disease. Although this drug- M.J.C., K.S.B., H.E.O., C.M.-R., R.J.B., Y.W., and M.W. performed research; J.B., K.S.B., H.E.O., R.J.B., Y.W., and M.W. contributed new reagents/analytic tools; S.Y.D., M.J.C., H.E.O., discovery paradigm has been used successfully to identify most of C.M.-R., Y.W., M.W., T.P.B., J.A.D., and P.R.G. analyzed data; and S.Y.D., M.J.C., T.P.B., the clinically-relevant SERMs discovered to date, the ability of in J.A.D., and P.R.G. wrote the paper. vitro biochemical and cell-based functional assays to translate to in The authors declare no conflict of interest. vivo tissue selectivity has been limited. Cofactor recruitment assays This article is a PNAS Direct Submission. J.A.K. is a guest editor invited by the Editorial Board. have proven to be a useful tool to detect ligand-induced confor- Data deposition: The atomic coordinates have been deposited in the Protein Data Bank, mational changes for many nuclear receptors but can be less www.pdb.org (PDB ID codes 2R6W and 2R6Y). effective for profiling SERMs because the key coactivator interac- §Present address: Research and Development, Ambion, Inc., Austin, TX 78744. tion surface (AF-2) has been blocked by the ligand-induced repo- ¶Present address: Nuclear Receptor Biology Laboratory, Pennington Biomedical Research sitioning of helix 12. Center, Louisiana State University, Baton Rouge, LA 70808. Classical approaches for structural analysis of receptor–ligand **To whom correspondence should be addressed at: The Scripps Research Institute, Scripps interaction involve the use of x-ray crystallography or NMR spec- Florida, 5353 Parkside Drive, Jupiter, FL 33458. E-mail: pgriffi[email protected]. troscopy. The importance of studying changes to protein dynamics This article contains supporting information online at www.pnas.org/cgi/content/full/ BIOPHYSICS during ER modulation has been demonstrated by Tamrazi et al. (2). 0710802105/DCSupplemental. In a series of experiments, site-specific fluorescence labeling was © 2008 by The National Academy of Sciences of the USA www.pnas.org͞cgi͞doi͞10.1073͞pnas.0710802105 PNAS ͉ May 20, 2008 ͉ vol. 105 ͉ no. 20 ͉ 7171–7176 Downloaded by guest on October 2, 2021 Table 1. Activity of ER ligands Receptor affinity ER-dependent cell assays MCF-7 Ishikawa Inhibition, Relative Stimulation, Compound Ki ␣,nM (IC50), nM (IC50), nM % (EC50), nM % 4-hydroxytamoxifen 0.20 0.63 340.95 55.8 2.45 131.3 (0.17, n ϭ 4) (0.06, n ϭ 2) (706, n ϭ 295) (20.8, n ϭ 417) (6.6, n ϭ 404) (33.3, n ϭ 407) Lasofoxifene 0.34 0.68 6.97 87.5 0.22 65.96 (0.18, n ϭ 5) (0.01, n ϭ 2) (1.9, n ϭ 6) (8.5, n ϭ 6) (0.08, n ϭ 6) (17.5, n ϭ 6) LY165176 0.21 6.1 233.6 61.5 0.28 128.7 (0.08, n ϭ 6) (n ϭ 1) (123.5, n ϭ 2) (6.3, n ϭ 2) (0.25, n ϭ 2) (48.6, n ϭ 2) LY156681 0.44 2.89 NA 33.85 4.67 164.4 (0.05, n ϭ 3) (1.7, n ϭ 3) (3.5, n ϭ 4) (0.89, n ϭ 4) (30, n ϭ 4) Raloxifene (38) 0.37 0.37 4.32 96.8 NA 28.6 (0.09, n ϭ 3) (0.03, n ϭ 2) (1.69, n ϭ 8) (7.0, n ϭ 8) (8.5, n ϭ 8) Bazedoxifene 0.65 0.47 3.6 99.1 NA 7.6 (0.17, n ϭ 3) (n ϭ 1) (1.8, n ϭ 274) (8.7, n ϭ 276) (8.8, n ϭ 267) LY117018 0.32 1.05 3.42 83.3 0.39 37.2 (0.07, n ϭ 3) (0.55, n ϭ 34) (0.25, n ϭ 2) (2.1, n ϭ 2) (0.19, n ϭ 2) (17.6, n ϭ 2) Estradiol 0.16 NA NA NA 0.81 581.2 (0.07, n ϭ 380) (1.13, n ϭ 383) (273, n ϭ 383) DES 0.09 NA NA NA 0.11 410.7 (0.05, n ϭ 3) (0.01, n ϭ 2) (136.4, n ϭ 2) LY88074 0.67 NA NA 20.1 105.7 179.8 (0.25, n ϭ 3) (7.4, n ϭ 2) (40.2, n ϭ 2) (34.5, n ϭ 2) ICI182780 3.0 0.29 0.58 101.18 NA 7.8 (0.23, n ϭ 4) (0.20, n ϭ 3) (0.5, n ϭ 383) (7.06, n ϭ 386) (10.8, n ϭ 375) MCF-7 values are half-maximal inhibition concentrations (nM) that block stimulation by 10 pM estradiol.
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