Discovery of New non-steroidal selective glucocorticoid receptor agonists

Accepted Manuscript Title: Discovery of New Non-steroidal Selective Glucocorticoid Receptor Agonists Authors: Constantinos Potamitis, Dimitra Siakouli, Konstantinos D. Papavasileiou, Athina Boulaka, Vassiliki Ganou, Marina Roussaki, Theodora Calogeropoulou, Panagiotis Zoumpoulakis, Michael N. Alexis, Maria Zervou, Dimitra J. Mitsiou PII: DOI: Reference:

S0960-0760(18)30097-9 https://doi.org/10.1016/j.jsbmb.2018.10.007 SBMB 5231

To appear in:

Journal of Steroid Biochemistry & Molecular Biology

Received date: Revised date: Accepted date:

15-2-2018 19-9-2018 11-10-2018

Please cite this article as: Potamitis C, Siakouli D, Papavasileiou KD, Boulaka A, Ganou V, Roussaki M, Calogeropoulou T, Zoumpoulakis P, Alexis MN, Zervou M, Mitsiou DJ, Discovery of New Non-steroidal Selective Glucocorticoid Receptor Agonists, Journal of Steroid Biochemistry and Molecular Biology (2018), https://doi.org/10.1016/j.jsbmb.2018.10.007 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Discovery of New Non-steroidal Selective Glucocorticoid Receptor Agonists

Constantinos Potamitisa,#, Dimitra Siakoulia,#, Konstantinos D. Papavasileioua,b, Athina Boulakaa, Vassiliki Ganoua, Marina Roussakia, Theodora Calogeropouloua, Panagiotis Zoumpoulakisa, Michael N. Alexisa, Maria

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Zervoua,*, Dimitra J. Mitsioua,*

Institute of Biology, Medicinal Chemistry and Biotechnology, National Hellenic Research Foundation, 48

Vassileos Constantinou Ave., 116 35 Athens, Greece b

Present address: National Center for Scientific Research “Demokritos”, Institute of Nanoscience and

Nanotechnology, Molecular Thermodynamics and Modelling of Materials Laboratory, GR-15310 Aghia

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C.P. and D.S. contributed equally to this work

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Paraskevi Attikis, Greece

* Corresponding Authors

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M. Zervou, TEL: +30 210 7273870; FAX: +30 210 7273872; E-MAIL: [email protected]; ORCID iD: 0000-

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0001-5963-4954

D.J. Mitsiou, TEL: +30 210 7273741; FAX: +30 210 7273677; E-MAIL: [email protected]; ORCID iD: 0000-

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0001-8521-4216

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Graphical abstract

Highlights

Virtual Screening utilizing a structure-based pharmacophore model of GR-LBD

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Discovery of two 1,3-benzothiazole analogs (1 and 13) acting as genuine SEGRA

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1 and 13 transrepress a subset of NFKB targets genes in a GR-dependent manner

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New proposed binding contacts of 1 and 13 at the extended cavity of GR-LBD

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Abstract

Glucocorticoids (GCs) are widely used as potent anti-inflammatory drugs; however, GC therapy is often

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accompanied by adverse side effects. The anti-inflammatory action of GCs is exerted through the glucocorticoid

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receptor (GR) in part by antagonizing the pro-inflammatory nuclear factor k B (NF-kB) whereas the majority of side effects are assumed to be mediated by transactivation of GR target genes. We set out to identify novel non-

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steroidal selective GR agonists (SEGRA) favoring transrepression of NF-kB target genes over transactivation of genes associated with undesirable effects. Our virtual screening protocol was driven by a pharmacophore model

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based on a pyrrolidinone amide analogue (named as ‘compound 12’ in Biggadike et al 2009, PNAS USA 106, 18114) bound to the extended binding pocket of the GR ligand binding domain (GR-LBD). Ambinter library (7.8 million compounds) was queried by our validated pharmacophore hypothesis and the prioritized compounds were biologically evaluated using a series of well-established screening assays. Two structurally similar hits (1 and 13) were identified that bind to GR, induce its translocation to the nucleus, do not mediate transactivation of GR target genes whereas partially repress a number of pro-inflammatory NF-kB target genes, in a GR-dependent 2

manner. Explanatory molecular dynamics (MD) calculations could detail the per-residue interactions accounting for the binding of 1 and 13 to the extended binding pocket of GR. The discovered 1,3-benzothiazole analogs introduce a new class of genuine SEGRA paving the way for hit-to-lead optimization.

Glucocorticoids

GR

Glucocorticoid Receptor

GRE

Glucocorticoid Responsive Element

SEGRA

Selective Glucocorticoid Receptor Agonist

NF-kB

Nuclear Factor kB

HC

Hydrocortizone

Dex

Dexamethasone

RU486

Mifepristone

TNF

Tumor Necrosis Factor

CCL2

Chemokine (C-C motif) ligand 2

IL6

Interleukin 6

FKBP5

FK506 binding protein 5

IL8

Interleukin 8

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Keywords

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GC

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Abbreviations

Glucocorticoid Receptor; selective agonists; anti-inflammatory action; pharmacophore model; virtual screening;

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MD simulations.

1.

Introduction

Glucocorticoids (GCs) are essential steroid hormones that are secreted by the adrenal cortex. GCs regulate a variety of physiological processes including development, metabolism, homeostasis and apoptosis in a cell3

specific manner. The biological actions of GCs are mediated through the ubiquitously expressed glucocorticoid receptor (GR; NR3C1), a ligand inducible transcription factor that belongs to the nuclear receptor superfamily. GC binding to GR regulates transcription of several hundreds of target genes either through direct binding of activated GR to DNA elements known as glucocorticoid responsive elements (GREs; ‘classical model’ mainly associated with transactivation) or through interaction with other transcription factors including Nuclear factor-

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kappa B (NF-kB; ‘non-classical model’ associated with transrepression) [1-4].

NF-kB is a family of constitutively expressed transcription factors critically involved in many biological

processes such as cell proliferation, development, and inflammatory and immune responses. Inactive NF-kB exists as a dimer (composed mainly of p50 and p65) that remains in the cytosol due to its association with the inhibitory protein I-kB (NF-kB Inhibitor alpha). In response to diverse inflammatory stimuli, I-kB is

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phosphorylated and rapidly degraded thus releasing the NF-kB dimer (activated NF-kB) which translocates to

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the nucleus, binds to NF-kB response elements (NF-kB RE) and regulates gene expression. NF-kB target genes

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encode various proteins such as pro-inflammatory cytokines, chemokines, receptors and adhesion molecules [5].

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The crosstalk between GR and NF-kB signalling has been the major focus of research for many years and the most extensively studied case has been the transrepression of NF-kB by GR initially in model NF-kB RE-

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dependent promoters. Using global analysis of GR-NF-kB crosstalk we have recently shown that co-activation

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of GR and NF-kB leads to their mutual recruitment on novel chromatin binding sites and alters the repertoire of genes regulated by each factor separately [6].

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GCs are potent anti-inflammatory drugs that have been used since the late 1940s for the treatment of inflammatory and autoimmune diseases. However, GC therapy is often accompanied by a wide range of adverse

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side effects, such as metabolic disorders including type-2 diabetes (resulting from hyperglycemia and a decreased carbohydrate tolerance), fat redistribution, osteoporosis, skin and muscle atrophy, hypothalamo-

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pituitary-adrenal (HPA) axis insufficiency and mood disorders [7]. Nevertheless, the therapeutic usage of GCs is continuously rising, as well as the total market size. At the moment, there is an unmet high need for new drugs with the therapeutic advantages of classical GCs, but with a reduced profile of side effects. It is widely accepted that the majority of side effects of GCs are mediated by transactivation whereas the anti-inflammatory effects are mainly due to transrepression, including primarily inhibition of NF-kB action. Therefore, uncoupling anti4

inflammatory effects from side effects by identifying novel GR ligands that preferentially mediate transrepression rather than transactivation (Selective Glucocorticoid Receptor Agonists, SEGRA) could improve the clinical performance of long-term treatments with GCs, where adverse side effects should be taken into consideration. GR has the capacity to bind a wide range of ligands a property that has been exploited for the design of SEGRA.

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The development of new SEGRA is based on the ability of GR to bind both steroidal as well as non-steroidal ligands of impressively different size and structure as compared to dexamethasone (Dex, a synthetic GC).

Crystal structure analysis revealed that deacylcortivazol (DAC, a phenylpyrazolo glucocorticoid) doubled the ligand binding domain (LBD) of GR [8]. Notably, a series of non-steroidal GR agonists bearing an

aminopyrazole moiety with excellent selectivity for GR over other steroid hormone receptors, were also found to

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be well accommodated in the extended GR binding site [9]. A rather large number of putative SEGRA, some

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developed by big pharma teams (e.g. GlaxoSmithKline, Abbott, Bayer Schering Pharma, Bristol-Myers Squibb,

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Merck, Boehringer Ingelheim Pharmaceuticals) have been reported [9-13]. Although none of these compounds

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has reached the clinic yet, these studies substantially contributed to the current knowledge on SEGRA action but also pointed to certain limitations. Furthermore, other discoveries suggest that GR-dependent transactivation of

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anti-inflammatory genes is likely to be an important part of the mechanism of anti-inflammatory action of GCs

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[14, 15]. The ‘transactivation vs transrepression’ concept is probably too simplistic and the molecular mechanisms underlying the putative selective action of SEGRA remains largely unknown and warrants further

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research.

In the present study we set out to discover new non-steroidal SEGRA and to analyse their mechanism of

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function. Towards this goal we initiated a virtual screening (VS) approach, utilizing structure-based pharmacophore modeling, in silico docking and molecular dynamics (MD) simulations. Our pharmacophore

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model was generated based on the bound pose of an aminopyrazole analogue bearing a terminal pyrrolidinone moiety, which was shown in vitro to display the desired biological profile (named as ‘compound 12’ in Biggadike et al [9]). The selected non-steroidal compound is accommodated in the extended binding cavity of the GR LBD which is expanded beyond the typical steroidal A-ring region. Due to the lack of the respective complex crystal structure, the x-ray structure of GR LBD with another aminopyrazole analogue bearing a 5

terminal D-prolinamide moiety (named as ‘compound 11’ [9]; pdb: 3K23) was used as an initial template to dock the pyrrolidinone amide. The derived complex was subsequently subjected to MD simulations in order to define the essential pharmacophore features. The pharmacophore model was optimized and validated by utilizing carefully selected receptor modulators with the desired biological profile. Ambinter database (7.8 million chemical structures) was filtered based on the physicochemical properties of known SEGRAs and screened

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against the optimized pharmacophore model. The best fitted to the model compounds were evaluated for their binding potential to GR LBD through in silico docking by applying algorithms of increasing accuracy. Among the best ranked, 14 compounds were acquired taking into account the crucial binding contacts and the predicted ADME profile. Biological evaluation of the selected compounds showed that none of them mediate GREdependent, GR-mediated transactivation of gene expression while nine of them significantly repress

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transactivation induced by classical GR agonists. Two of these compounds (1 and 13) bind to GR, although with

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lower affinity compared to dexamethasone, thus leading to its nuclear translocation and to repression of NF-kB-

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mediated transactivation of a subset of pro-inflammatory genes in a GR-dependent manner. Our data provide

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evidence that compounds 1 and 13, which share a benzothiazole scaffold linked to a sulfanyl-N-phenylethyl

2.

Materials and Methods

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2.1. Computational Methods.

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acetamide moiety, are genuine non-steroidal SEGRA.

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The crystal structure of GR LBD with a D-prolinamide moiety (named as ‘compound 11’ [9]; pdb: 3K23) has been used as an initial template to dock the pyrrolidinone amide (named as ‘compound 12’ [9]). Docking studies

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were performed using Glide and Induced Fit Docking modules (Schrödinger Suite) [16]. The derived complex was further subjected to MD simulations (10 ns) performed using Schrodinger Desmond [17] by applying

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OPLS3 force field, using explicit solvation (TIP4P water molecules). Production runs were performed in the NPT ensemble at 300 K and 1.01325 bar with periodic boundary conditions. Alanine scanning MD simulations for the complexes of GR-LBD with hit 13 were performed for 50 ns under the same conditions and the MMGBSA score was calculated as the average of one thousand trajectory frames using the python script, thermal_mmgbsa.py as described in the Supplementary Material.

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Pharmacophore model generation and virtual screening was implemented through LigandScout 4.0 advanced software available from InteLigand, GmbH, Vienna, Austria (http://www.inteligand.com/ligandscout) [18]. Explanatory molecular dynamics simulations of the hit compounds (1 and 13) as well as of the pyrrolidinone amide were performed in AMBER12 [19], modelling the protein using the ff99SB force field [20]. GR-LBD complexes were studied by means of four independent 20 ns molecular dynamics (MD) in the NPT ensemble,

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followed by Molecular mechanics Poisson–Boltzmann surface area (MM-PBSA) free energy calculations, over a total of 8,000 frames. Computational methods are described in detail in the Supplementary Material. 2.2. Biological Methods. 2.2.1. Purchased Compounds.

Hydrocortizone (HC), dexamethasone (Dex), mifepristone (RU486) and TNF were purchased from Sigma-

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Aldrich. The 14 compounds (1-14 in Table 1) selected from the VS approach were from Ambinter.

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2.2.2. Cells, Luciferase Reporter Assay and Assessment of Antagonism, Trypan Blue and Crystal Violet

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Staining Assays and Immunocytochemical analysis by Indirect Immunofluorescence.

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HeLa B2 and HeLa B2 GR_KD cells (HeLa B2 cells lacking GR expression) were established and maintained as previously described [6]. Cells were cultured in DMEM supplemented with 5% charcoal stripped FBS for 48 h

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before subsequent treatments described for each experiment. For the luciferase assay, cells were plated in 96-

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well microplates (8000 cells/well), treated with selected compounds for 20 h and harvested in Glo Lysis Buffer. Luciferase activity was measured using a Steady Glo Luciferase Assay system (Promega Madison) in a TECAN

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Safire 2 microplate reader. Antagonism of the Agonist Effect of HC (AEHC) in the presence of vehicle, test compounds or RU486, expressed as % of the antagonism in the presence of RU486, was calculated by

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[(AEHCvehicle – AEHCcompound) x 100/(ΑΕHCvehicle - AEHCRU486)] as described [21]. Statistically significant antagonisms of AEHC (p < 0.05 vs antagonism in the presence of DMSO, calculated using the Independent-

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Samples T-Test) were classified as full, partial or weak depending on whether they were >66, 33–66 or <33%, respectively, of AEHC by RU486. Cell viability was determined using the trypan blue staining assay. Briefly, cells were plated and treated as described for the luciferase assay and then stained with 0.08% trypan blue solution (Life Technologies) in

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phosphate-buffered saline (PBS 1X) for 2 min. Cells were washed with PBS 1X and cell images were taken by using a Nikon Eclipse TE2000 inverted microscope. Relative cell numbers were determined using the crystal violet staining assay as previously described [22]. Briefly, cells were plated and treated as described for the luciferase assay and then fixed in ice-cold methanol, stained with 0.2% crystal violet (Sigma) and washed with tap water. The dye was extracted with acetic acid and

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the absorbance was measured at 595 nm in a TECAN Safire 2 microplate reader. The difference in optical

density at 595 and 690 nm (reference wavelength) was taken to measure the actual number of cells and was expressed relatively to that of cells treated with vehicle alone.

Immunocytochemical analysis by indirect immunofluorescence (IF) were performed as previously described with minor modifications [23]. Briefly, cells were grown on coverslips in phenol red-free DMEM supplemented

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with 5% charcoal inactivated FBS for 48 h and subsequently treated with selected compounds for 2 h, as

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described for each experiment. Treated cells were fixed in ice-cold methanol/acetone (1/1), permeabilized with

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0.25% Triton X-100 in PBS (PBST) and blocked with 3% BSA in PBST at room temperature (RT). Cells were

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incubated O/N, at 4oC, with a rabbit antiserum against human GR (Ab10-26, dilution 1:500) [24] followed by a 1-h incubation, at RT, with a secondary anti-rabbit IgG (H+L) coupled to Alexa Fluor® 555 Conjugate (Cell

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Signaling, #4413; red) and a final 1-min incubation with Hoechst 33342 (0.5 μg/ml). Coverslips were mounted

confocal microscope.

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in ProLong™ Gold Antifade Mountant (Invitrogen) and IF images were taken by using a Leica TCS SP5

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2.2.3. Fluoresence Polarization (FP) assay. Binding of selected compounds, relative to that of fluorescent hormone (fluormone), to purified recombinant

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human GR was assessed using the PolarScreen™ Glucocorticoid Receptor Competitor Assay Kit (Green) according to manufacturer’s protocol (Life Technologies, USA). Compounds were incubated with purified GR,

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in the presence of fluormone, for 2 h at RT and fluorescence polarization was measured in a TECAN Safire 2 microplate reader as previously described [25]. 2.2.4. RNA Isolation, Reverse Transcription and Real-Time PCR (qPCR). For analysis of mRNA expression, cells were treated with selected compounds for 3 h followed by an additional 1-h incubation in the presence of TNF (10 ng/ml). Total RNA was extracted using the Trizol reagent (Life 8

Technologies, USA) followed by purification using Qiagen RNeasy kit with on-column DNase treatment according to manufacturer’s protocol (Qiagen). Reverse transcription and qPCR was carried out as already described [26]. The comparative Ct method was used to calculate the relative gene expression by the formula 2(ΔCt)

. Expression levels of the genes of interest were normalized to the respective levels of GAPDH. The primer

pairs used in this study are presented in the Supplementary Material.

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2.2.5. Statistics.

The SPSS statistical package and the Independent-Samples T-Test were used to determine statistical significant differences (p < 0.05).

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RESULTS

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3.1. Pharmacophore model generation and validation.

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For the pharmacophore modeling we utilized a pyrrolidinone amide (named as ‘compound 12’ in Biggadike et al

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[9]), which has been shown to selectively mediate transrepression of NF-kB activity but not GRE-dependent

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transactivation. Due to the lack of the respective crystal structure complex with GR LBD, we utilized the crystal complex of the GR LBD bound to an analogue structure, a D-prolinamide derivative (named as ‘compound 11’

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in Biggadike et al [9]; pdb: 3K23; Supplementary Figure S1A). Thus, we docked the pyrrolidinone amide at the

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GR LBD and subjected the complex to MD simulations in order to refine the bound pose and identify the crucial interactions with the binding site residues.

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The pyrrolidinone amide is accommodated in the expanded binding pocket of the GR LBD encompassing the typical lipophilic steroidal pocket extended beyond the gatekeeper residues Gln570 (helix 3) and Arg611 (helix

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5) (Supplementary Figure S1B). Throughout the MD simulations, the crucial interactions are retained as the typical hydroxyl contact with Asn564 (helix H3) which influences GR activation, the hydrogen bonding to

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Gln570 via the indazole nitrogen and the hydrogen bonding with Arg611 which is now mediated through water, bridging its sidechain with the amide NH. The rigid pyrrolidinone terminal ring is oriented towards Lys667 (helix H8) developing a very stable hydrogen bonding interaction with its lactam oxygen. This interaction was not observed in the crystal complex with the D-prolinamide since its terminal amide is oriented towards Glu540 driven by a hydrogen bond formation. Furthermore, several lipophilic contacts occur during most of the 9

simulation time including the pi-pi interaction of indazole moiety with Phe623 ring (Figure 1A). Interestingly, Phe623 develops aromatic interactions in all GR LBD crystal complexes with non-steroidal ligands as well as in the case of the steroid analogue deacylcortivazol (DAC) (Supplementary Table S1). Thus, our initial pharmacophore hypothesis included the most conserved interactions among the co-crystallized steroidal and non-steroidal GR ligands, namely an H-bond donor to interact with Asn564 and an H-bond

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acceptor to contact Gln570, while additionally introduced an aromatic feature capable of pi-pi interactions with Phe623 and an H-bond acceptor feature able to contact Lys667. The validation procedure was implemented by recovering from ChEMBL database known GR modulators named as ‘active’ compounds and ‘inactive’ ones. The ‘active’ compounds were chosen based on their ability to act as full agonists in the transrepression pathway and affecting the transactivation pathway to a minor extent, while as ‘inactive’ were selected those compounds

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that were incapable of mediating transrepression. Importantly, different chemotypes of ‘active’ compounds were

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included in the library. An iterative procedure included the screening of the validation library against the model

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in every step of its modification and the optimization of its performance based on ROC curve analysis in order to

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increase the ratio of retrieved ‘active’ over ‘inactive’ compounds. The optimized pharmacophore hypothesis (Figure 1B) was set to satisfy at least three out of its four

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pharmacophore features. We applied this approach considering the flexibility of the binding pocket which can

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accommodate ligands of substantially different size. ROC analysis reveals the robustness of the model since it is capable of retrieving 61% of the ‘active’ and only 19% of the ‘inactive’ molecules.

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3.2. Virtual screening protocol.

Ambinter library entries (7.8 million) were initially filtered according to the selected physicochemical properties

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and the "eligible" molecules were screened against the pharmacophore query. Those best fitted to the pharmacophore model (1.8 million) were subsequently docked at the GR LBD by applying docking procedures

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of increasing accuracy. The initial docking filter included Glide SP algorithm by imposing at least two constrained H bond interactions either with Asn564 (sidechain amine and carbonyl groups) or with Asn564 and Gln570 (sidechain amine group). To narrow down the size of the library, a docking score cutoff (Gscore < -10 Kcalmol-1) led to a pool of 5198 compounds which were further subjected to Induced Fit docking (IFD) protocol. A set of 125 compounds were qualified taking into account the well preserved H bonds with Asn564, Gln570 10

and the pi-pi interaction with Phe623 as well as a Gscore < -10.5 Kcalmol-1. Among the best ranked, chemotypes diversity and predicted ADME profiling (high lipophilicity and low number of primary metabolites, Supplementary Table S2) navigated the selection of a first set of 7 compounds (1-7) to be purchased for biological evaluation which led to the discovery of hit 1. Based on this scaffold, we performed a similarity search in Ambinter database using the relevant online tool of the library which yielded a set of similar structures,

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with 13 being qualified from docking studies. The structural difference is focused at the substitution of the 3,5 dichloro-phenol by the naphthol moiety which is well accommodated in a lipophilic cavity of the pocket. This compound along with additional 6 compounds from the pool of the best ranked in our VS pipeline were further acquired for in vitro testing. The total set of purchased compounds (1-14) which were undergone biological evaluation is presented in (Table 1).

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To resume, the selection criteria as depicted in Figure 2 were based on the: i) pharmacophore fit score, ii)

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presence of crucial interactions with binding site residues, iii) docking score, , iv) scaffold variety and v)

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3.3. Biological evaluation and hit identification.

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predicted ADME profiling.

For the biological evaluation we used, as reference compounds, dexamethasone (Dex, synthetic GC of very high

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affinity for GR), hydrocortizone (HC, the human GC), mifepristone (RU486, a known GR antagonist) and the

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steroidal SEGRA 21ΟΗ-6,19OP (21-hydroxy-6,19-oxido-4-pregnene-3,20-dione, 21ΟΗ-6,19OP, 21) [27] (Figure 3A). We synthesized 21 using a novel route as described in the Supplementary Material (Supplementary

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Scheme S1).

3.3.1. Effect on GRE-dependent transactivation and binding to GR.

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In order to evaluate the biological activity of the 14 selected non-steroidal compounds (1-14, Table 1), we first assessed their ability to induce GRE-dependent transactivation of gene expression using HeLa cells stably

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transfected with a reporter plasmid expressing luciferase under the control of 4 GREs of the mouse mammary tumor virus long terminal repeat (HeLa B2 cells). Treatment of HeLa B2 cells with 300 nM of HC induced expression of luciferase 35-fold, while all 14 selected compounds were completely ineffective in this respect up to the concentration of 100 μM, showing that they cannot induce GRE-dependent transactivation of gene expression (Figure 3B). 11

In addition, we tested whether these compounds were able to represses HC-induced GRE-dependent transactivation. Treatment of HeLa B2 cells with 300 nM HC in the presence of increasing concentrations of the 14 compounds showed that nine of them, namely 1, 4, 5, 6, 8, 9, 11, 13 and 14, could inhibit HC-induced luciferase expression more than 50% at a concentration ≤ 100 μM, indicating likely binding of these compounds to GR. RU486 fully repressed HC-induced luciferase expression at 100 nM (Figure 3B). Supplementary Table

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S3 presents the antagonism of the agonistic effect of HC displayed by the 14 compounds at 10, 30 and 100 μM. Evidently, compounds 1, 9 and 13 displayed at 100 μM the highest repression of HC activation of gene

expression (97 ± 2, 103 ± 1 and 98 ± 1 % of that of RU486, respectively). However, compound 9 at 100 μM significantly reduced the number of viable cells whereas none of the other compounds could significantly affect cell viability at any of the concentrations tested (Supplementary Figures S2, S3 and S4).

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Next, using a competitive fluorescence polarization binding assay, we assessed whether the nine compounds that

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inhibited HC-induced transactivation are bona fide GR ligands. Figure 4A shows that binding of the fluorescent

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glucocorticoid (fluormone) to the purified GR was almost completely inhibited by dexamethasone whereas the

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known steroidal SEGRA 21 exhibited only partial inhibition at 10 M. From the nine compounds tested, only 1 and its analog 13 resulted in weak and partial, respectively, inhibition of fluormone binding to GR at 10 M,

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indicating that 1 and 13 bind to GR although with less affinity as compared to dexamethasone. Our data do not

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exclude binding of the other compounds to GR at higher micromolar concentrations; however, we selected

activity.

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compounds 1 and 13 that bind to GR at low micromolar concentration for the evaluation of their biological

To extend these data, we assessed the potency of 1, 13 and 21 to repress HC-induced transactivation. Figure 4B

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shows that all three were able to fully repress HC-induced luciferase expression in a dose-dependent manner. Supplementary Table S4 details the antagonistic effect of increasing concentrations of the three compounds.

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Supplementary Figure S5 shows that none of the three compounds affected the number of cells compared to their number in the presence of vehicle at any of the concentration tested. The concentration of 1, 13 and 21 that displayed 50% repression of HC-induced luciferase expression (IC50) was 59 ± 4 μM, 26 ± 1 μM and 34 ± 2 μM, respectively. Our data show that compound 13 and 21 displayed significantly higher potency in repressing HC-

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induced transactivation compared to compound 1, in accordance with the higher affinity of 13 and 21 for GR binding obtained in the fluorescence polarization assay. To corroborate the above data, we next tested weather 1 and 13 are able to induce nuclear translocation of GR using indirect immunofluorescence (IF). Figure 4C shows that unliganded GR resided both in the cytoplasm and in the nucleus of HeLa B2 cells, in agreement with a constant shuttling of GR between these two cellular

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compartments, and that treatment with Dex led to almost complete translocation of GR into the nucleus.

Interestingly, 13 induced nuclear translocation of GR to a similar extent as that observed with Dex and this was also the case with the steroidal SEGRA 21. Treatment with 1 led to nuclear translocation of GR to somewhat lesser extent. In contrast, neither cytoplasmic nor nuclear staining of GR was observed in HeLa B2 cells lacking GR expression (HeLa B2 GR_KD). Our new observations are in agreement with the above fluorescence

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3.3.2. Effect on the expression of NF-kB and GR target genes.

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polarization data and further support the binding of 1 and 13 to GR.

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Finally, we addressed the ability of compounds 1 and 13 to transrepress tumor necrosis factor (TNF)-induced

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activation of NF-kB target genes. To this end, we assessed the effect of 1 and 13 on the expression of four genes encoding proteins with different roles in inflammatory diseases, namely the pro-inflammatory cytokine TNF, the

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cytokine IL6 (interleukin 6) with cell/stimuli-dependent pro- and anti-inflammatory properties and the

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chemokines IL8 (interleukin 8) and CCL2 (chemokine (C-C motif) ligand 2) which are exhibiting proinflammatory and chemotactic functions [28]. Treatment of HeLa B2 cells with TNF significantly induced

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expression of TNF (>120 fold), IL6 (34 ± 3 fold), IL8 (>100 fold) and CCL2 genes (14 ± 3 fold) whereas treatment with dexamethasone or the test compounds alone did not affect expression of any of these genes. Co-

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treatment with TNF and dexamethasone resulted in significant repression of TNF-induced activation of all four genes (Figure 5A-C and Supplementary Figure S6), in agreement with the known anti-inflammatory role of GCs

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through transrepression of NF-kB target genes and our previously published data [6]. Interestingly, Figure 5A shows that 1 and 13 significantly repressed TNF-induced activation of TNF gene, to a similar extent compared to dexamethasone and this was the case also with the steroidal SEGRA 21. To ensure that repression of gene activation is GR-dependent, HeLa B2 GR_KD lacking GR expression were used. Figure 5A shows that neither dexamethasone nor the tested compounds could repress activation of TNF in HeLa B2 GR_KD cells, confirming 13

that the repression is mediated through GR. Similarly to TNF gene, activation of IL8 expression by TNF was repressed by 1, 13 and 21 in a GR-dependent manner (Supplementary Figure S6). In contrast, 1, 13 and 21 did not affect TNF-induced activation of IL6 gene, while dexamethasone was effective in this respect (Figure 5B). In addition, in contrast to both 21 and dexamethasone, 1 and 13 were ineffective in repressing TNF-induced activation of CCL2 gene (Figure 5C). The tendency of all three compounds to enhance the TNF-induced

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expression of all tested genes (TNF, IL6, CCL2 and IL8), in the absence of GR, likely reflects an off-target

effect(s) of these compounds. Taken together, the above data revealed that compounds 1 and 13 repress a subset of NF-kB target genes, in a GR-dependent manner, differently from dexamethasone and to some extent from steroidal SEGRA 21 as well.

We also sought to ensure that 1 and 13 do not mediate GRE-dependent transactivation of endogenous genes. To

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this end, we assessed their effect on the expression of FKBP5 gene, a GRE-dependent direct target of GR [29].

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Figure 5D shows the effect of dexamethasone, 1, 13 and 21 on FKBP5 expression in the presence of TNF.

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Dexamethasone significantly activated FKBP5 gene expression in the presence of TNF, whereas 1, 13 and 21

dependent luciferase expression (Figure 3B).

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did not affect it. These observations are in accordance with our data showing that 1 and 13 do not mediate GRE-

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3.4. In silico binding studies of the hit compounds.

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In silico docking studies revealed that both hits (1 and 13) are smoothly accommodated in the extended cavity of GR LBD (Figure 6). The benzothiazole central scaffold is stabilized by H-bonding and pi-pi interaction with

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Gln570 and Phe623, respectively. The crucial contact with Asn564 is mediated via the –OH group of the compounds, while their phenylethyl fragment is oriented towards the edge of the extended cavity forming pi-pi

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and pi-cation contacts with Trp577 (helix H3) and Lys667 (helix H8) respectively. X-ray data demonstrate that these two residues along with Glu540 (end of helix H1) cap the binding pocket forming a network of intra-

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residual interactions [8]. Interestingly, regarding the role of Arg611, docking poses of 1 and 13 revealed the possibility of electrostatic interactions with the carbonyl group or the terminal benzyl ring, although not consistently in every extracted pose. In line with the higher GR binding affinity (Figure 4A) and the higher potency in repression of HC-mediated transactivation of 13 as compared to 1 (Figure 4B), the in silico binding of 13 is slightly more favored compared 14

to 1 as depicted by the docking scores (Table 1). This difference is mainly due to the lipophilic contributions (13 glide lipo = -7 kcal mol-1, 1 glide lipo = -6 kcal mol-1) and could be attributed to the well fitted naphthyl moiety of 13 into the lipophilic cavity formed by the residues Leu732, Tyr735 and Cys736 of the α helix H10 and Met 601 and Leu603 of the α helix H5.In agreement with the docking results, the binding free energy (ΔGbind) of 13 at GR-LBD as calculated by molecular dynamics simulations emerges slightly lower than the corresponding of

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1, while both of them are predicted to exhibit higher binding free energy as compared to the pyrrolidinone amide (Table 2). Specifically, MM-PBSA analysis performed on the equilibrated system trajectories (Supplementary Figure S7) revealed that the van der Waals contribution (ΔEvdW) is the most significant one for the formation of each GR-LBD complex with the ligands 1, 13 as well as the pyrrolidinone amide analogue, followed by the electrostatic component (ΔEelec). Consideration of the electrostatic contribution to solvation (ΔGPB) yields

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positive total electrostatic contributions (ΔGelec,(tot)) in both GR-LBD-1 and GR-LBD-13 which is also the case of

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the GR-LBD-pyrrolidinone amide complex, thus suggesting that electrostatics disfavor the binding to the GR-

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LBD. Therefore, complex formation in cases of compounds 1 and 13 as well as of the pyrrolidinone amide, is

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driven by the van der Waals contribution and the nonpolar contribution to solvation ΔGNP (Table 2). In addition, per-residue decomposition analysis provided useful insight on individual contributions to binding of

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1, 13 and the pyrrolidinone amide to GR-LBD (Supplementary Figure S8). In all complexes, common important

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residues that favor binding (with more than 1 kcal mol-1 in binding enthalpy) are the hydrophobic residues Leu563, Leu566, Gly567 and Met604, hence the significant van der Waals contributions to binding. These

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residues are in the vicinity of the central scaffold of the studied ligands (benzothiazole of 1 and 13 and indazole of the pyrrolidinone amide). In addition, complexation in all compounds is favored by the Glu540 and the

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Trp577 amino acids, at the edge of the extended binding cavity. Regarding the flexible residues Arg611 and Lys667, although exhibiting favorable electrostatic interactions in the gas phase for all complexes, they are

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counterbalanced by the contribution of the solvation energy calculated by the PB approach, yielding positive total electrostatics. Moreover, the sidechain of Arg611 emerges more flexible in the case of 1 and 13 as compared to the pyrrolidinone amide as shown by the RMSF of selected terminal atoms, while the terminal sidechain of Lys667 undergoes similar fluctuations in all cases (Supplementary Table S6). The role of the local water environment was evaluated by means of radial (RDF) and spatial (SDF) distribution functions revealing 15

that the RMS fluctuations of the side chains atoms of Arg611 and Lys667 are related to their hydration numbers, as water infiltrates between these residues and 1, 13 and pyrrolidinone amide compounds (Supplementary Figure S9). These two amino acids may guide the hit-to lead optimization through the development of stable contacts with their sidechains. We attempted to investigate further the significance of Trp577 and Lys667 in ligand binding since they have not

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been observed to participate in any interaction at the available crystal complexes. Towards this aim, those

residues were mutated to alanine and MD simulations (50 ns) were carried out for the complexes GR-LBD-13, GR-LBD(W577A)-13 and GR-LBD(K667A)-13. The corresponding simulation interaction diagrams depicting the frequency of occurrence of the interactions throughout the trajectory for the studied systems are shown in the Supplementary Figure S10. At the complex GR-LBD- 13, the crucial H bonds with Asn564 and Gln570 are

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retained almost throughout the simulation time. Compound 13 is further stabilized by the pi-pi stacking of the

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benzothiazole scaffold with Phe623 and the contacts of the carbonyl moiety with the side chain of Arg611 (H-

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bond or ionic). The proposed new contacts at the edge of the extended binding cavity developed by the phenyl

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ring (pi-pi interaction with Trp577 and pi-cation with Lys667) are retained for more than one third of the simulation time thus supporting our suggestion that these two residues could be exploited further towards more

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stable contacts.

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In the mutated systems, the crucial H-bond with Asn564 is the only retained interaction throughout the simulation time. As it is expected, in both cases the mutated residues lose their interactions with the ligand.

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Furthermore, the K667A mutant results in significant reduction of the time-preserved interactions with Gln570 and Arg611 as compared to the W577A mutant. The impact of alanine mutations is also reflected at the stronger

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binding affinity for the wild type complex as estimated by the difference of MM-GBSA scores between mutant and wild type complexes, ΔΔG= 5.68 Kcal mol-1 (SD for mean difference=0.29) for the W577A mutant and

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ΔΔG= 0.80 Kcal mol-1 (SD for mean difference=0.24) for the K667A mutant.

4.

Discussion and conclusion

In the present study we implemented a pharmacophore-based VS coupled with molecular docking approach in order to identify novel non-steroidal SEGRA. Our VS protocol designated a series of chemotypes as potential 16

ligands and the biological evaluation revealed two hits (1 and 13) which share a 1,3-benzothiazole scaffold linked to a sulfanyl-N-phenylethyl acetamide moiety at position 2. The predicted binding poses of 1 and 13 lay at the extended cavity of GR LBD contacting crucial residues (Asn564, Gln570, Phe623, Arg611). Moreover, the discovered hits suggest new binding contacts at the edge of the extended cavity (Trp577 and Lys667) which could be exploited further as possible key residues in hit

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optimization. This specific binding motif along with their high predicted lipophilicity (clogP 1= 5.9, clogP 13= 5.8) could contribute significantly to their biological effectiveness. The potent role of these two residues (Trp577 and Lys667) has been further highlighted by alanine scanning MD studies. Hit 13 is predicted to bind with

slightly higher affinity at GR-LDB as compared to hit 1 (ΔGbind and Docking score), favored by the observed embracement of the naphthyl moiety into the cavity formed by the lipophilic residues of the α helices H5 and

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H10.

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MD results further revealed the importance of van der Waals interactions for the binding and per-residue

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decomposition analysis highlighted the significant role of certain hydrophobic residues (Leu563, Leu566,

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Gly567 and Met604) in the vicinity of the ligands’ central benzothiazole. Moreover, it was found that the infiltrated in the pocket water molecules, intervene between the ligands and the labile amino acids Arg611 and

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Lys 667. Thus, the development of stable interactions with the sidechains of Arg611 and Lys667 could

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substantially contribute to increased binding affinity Biological evaluation (overviewed in Supplementary Figure S11) revealed that the two hit compounds, 1 and 13,

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cannot mediate GRE-dependent transactivation, as shown for the GRE-dependent luciferase reporter and endogenous FKBP5 genes, and therefore they are not classical GCs. However, they were able to inhibit HC-

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mediated transactivation by binding to GR and displacement of HC. These data are in agreement with previous observations showing that the steroidal SEGRA 21 partially displaces dexamethasone from rat GR and inhibits

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dexamethasone-induced transactivation of a GRE-dependent reporter gene whereas it cannot activate expression of tyrosine aminotransferase (TAT), a GRE-dependent gene in rat hepatocytes [30, 31]. Displacement of HC by binding of 1 and 13 to GR was supported by the fluorescence polarization data showing that compounds 1 and 13 bind to GR although with less affinity as compared to dexamethasone. The lack of binding to GR at 10 M for seven compounds that inhibit HC-mediated transactivation may be due, at least in part, to differences in the 17

critical conditions of each assay but it can also imply that (some of) the other compounds bind to GR at higher concentrations. However, for the biological evaluation we selected compounds 1 and 13 that bind to GR at low micromolar concentration. Moreover, binding of 1 and 13 to GR is corroborated by GR nuclear translocation data. Localization of the unliganded GR both in the cytoplasm and in the nucleus of HeLa B2 cells is in agreement with the shuttling of GR between these two cellular compartments [reviewed in 32]. Induction of GR

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nuclear localization by 13 and 21 to an extent similar to that observed upon treatment with dexamethasone and the nuclear localization partially induced by 1 are in agreement with the higher GR-binding affinity and the higher potency in the inhibition of hydrocortizone-mediated transactivation of 13 and 21 as compared to 1. This was also predicted by our in silico data thus enhancing the level of confidence of our theoretical calculations. Taken together, our data provide evidence for binding of 1 and 13 to GR.

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The effect of the selected compounds on NF-kB transrepression was assessed using the well-studied HeLa B2

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cell line that expresses endogenous GR and NF-kB family members and responds properly to glucocorticoids

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and pro-inflammatory cytokines (such as TNF). Our data revealed that the two hit compounds, 1 and 13, repress

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a subset of NF-kB target genes differently from dexamethasone and to some extent from steroidal SEGRA 21 as well. Previous studies have shown that GR uses a variety of distinct mechanisms to repress (pro)-inflammatory

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genes in e.g. different stages of the transcription cycle (initiation vs elongation) and that the corepressor GRIP1

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significantly contributes to this regulation [33]. The differential effect of 21 as well as 1 and 13, as compared to Dex, on the expression of NF-kB target genes probably implies that binding of different SEGRA induces

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different GR conformational changes and recruitment of specific coregulators, thus leading to differential regulation of gene expression depending on the inherent characteristics of each (class of) target gene(s). Notably,

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we have previously shown that co-activation of GR and NF-kB results in extensive and highly significant reprogramming of their target genes and this appeared to be orchestrated to a large extent by significant changes

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in GR and NF-kB chromatin binding patterns [6]. It is anticipated that activation of GR by SEGRA may change receptor crosstalk with NF-kB and lead to altered chromatin binding and transcriptome profiles as compared to classical GCs. Therefore, and given research findings suggesting that transactivation of anti-inflammatory genes by GR is essential to resolve inflammation [15] and that GR mediates both pro- and anti-inflammatory activities

18

[34, 35], global analysis of transcriptome profiles of any lead SEGRA is required in order to reveal truly selective agonists and to resolve their mechanism of action. In conclusion, our data provide evidence that compounds 1 and 13 are genuine non-steroidal SEGRA that i) bind to GR and induce its translocation to the nucleus, ii) do not mediate GRE-dependent transactivation of gene expression, and iii) repress NF-kB-mediated transactivation of pro-inflammatory genes, in a GR-dependent

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manner. 1 and 13 regulate a subset of NF-kB target genes different from classical GCs or known steroidal

SEGRA. The discovered 1,3-benzothiazole based scaffold constitutes a novel pharmacophore useful for SEGRA development and paves the way for hit-to-lead optimization.

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Declarations of interest: None

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Funding

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This work was supported by STHENOS and STHENOS- projects (GRANTS: MIS 447985 and MIS 5002398,

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respectively) funded by the Operational Programme "Competitiveness, Entrepreneurship and Innovation" (NSRF 2007-2013 and NSRF 2014-2020) and co-financed by Greece and the European Union (European Regional

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Acknowledgements

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Development Fund).

We thank Dr. MG Papadopoulos for providing advice and support for the molecular dynamics calculations.

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The GPU accelerated MD simulations were performed at the LinkSCEEM Cy-Tera GPU cluster, supported by the LinkSCEEM-2 project, funded by the European Commission under the 7th Framework Programme through

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Capacities Research Infrastructure, INFRA-2010-1.2.3 Virtual Research Communities, Combination of Collaborative Project and Coordination and Support Actions (CP-CSA) under grant agreement no RI-261600. The MM-PBSA calculations were supported by computational time granted from the Greek Research & Technology Network (GRNET) in the National HPC facility - ARIS - under project ID pr001017.

19

Supplementary Material Supplementary Material is a pdf file that includes supplementary methods, tables, figures and schemes referring to i) the virtual screening pipeline, ii) the molecular dynamics simulations, iii) the chemistry of the steroidal

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SEGRA 21 and iv) the biological evaluation.

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127:1136-1145. https://doi.org/10.1172/JCI88886

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Figure 1: Pharmacophore model generation and validation.

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(A) MD simulations monitored the most conserved interactions of the pyrrolidinone amide (named as

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‘compound 12’ in Biggadike et al, PNAS 2009) bound at the GR LBD in order to guide the pharmacophore model generation. The ligand interactions diagram depicts the interactions that occurred more than 30% of the

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simulation time. (B) The optimized pharmacophore model utilized for virtual screening for GR modulators is

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mapped to the pyrrolidinone amide analogue. The model comprises four features, namely two H-bond acceptors (red arrows), one H-bond donor (green arrow), one aromatic feature (blue ring) and a number of exclusion

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volumes (grey spheres). The ROC curve of the pharmacophore model depicts its ability to retrieve 61% of the active and only 19% of the inactive molecules. The vertical slope at the beginning of the curve indicates that only active candidates are encountered among the ~20% best-fitted molecules.

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Figure 2: Applied pharmacophore based virtual screening protocol.

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The funnel approach filtered Ambinter library entries according to their physicochemical properties,

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pharmacophore fit and docking score cut-off values, interactions with crucial binding site amino acids and ADME profiling. The applied protocol prioritized compounds (1-14) for supply and biological evaluation. Two

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structurally similar hits (1 and 13) were identified as genuine non-steroidal SEGRA.

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Figure 3: Compounds 1-14 do not induce GRE-dependent transactivation but 9 of them inhibit hydrocortizone-

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mediated transactivation.

(A) Chemical structures of hydrocortizone, dexamethasone, mifepristone and the steroidal SEGRA 21-hydroxy-

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6,19-oxido-4-pregnene-3,20-dione (21). (B) HeLa B2 cells were treated with vehicle (0.1% DMSO), the selected

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test compounds at increasing concentrations (10, 30 or 100 μΜ) or RU486 (0.1 μΜ) in the absence or presence of HC (300 nM). Luciferase expression was measured and expressed relative to that in the presence of vehicle

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(set equal to 1). 50% of luciferase expression in the presence of HC is shown by a straight line. Values (mean ±

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SEM) are from a representative experiment carried out in triplicate. HC, hydrocortizone; RU486, mifepristone.

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Figure 4: Compounds 1 and 13 bind to GR and induce its nuclear translocation.

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(A) Compounds 1 and 13 bind to recombinant GR in vitro. Pure recombinant GR was incubated with a

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fluorescent glucocorticoid in the presence of vehicle (0.1% DMSO), Dex (1 μM), 21 (10 μM) or the indicated compounds (10 μM). Fluorescence polarization was expressed relative to that in the presence of vehicle (set

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equal to 100). Fluorescence polarization in the presence of Dex is shown by a straight line. Arrows show

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displacement of the fluormone from the GR complex by compounds 1, 13 and 21. Values (mean ± SEM) are

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from two independent experiments. (B) Compound 13 displays higher potency in repressing hydrocortizoneinduced transactivation compared to compound 1. HeLa B2 cells were treated with vehicle (0.1% DMSO),

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RU486 (0.1 μΜ), or the indicated concentrations of 1, 13 or 21, in the presence of HC (300 nM). Luciferase expression was measured and expressed as in Figure 3B. Luciferase expression in the presence of RU486 is

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shown by a straight line. Values (mean ± SEM) are from a representative experiment carried out in triplicate. (C) Compounds 1 and 13 induce GR nuclear translocation. Indirect immunofluorescence analysis of HeLa B2 and HeLa B2_GR KD cells treated with vehicle (0.1% DMSO), Dex (1 μM) or 1, 13 or 21 (10 μM), probed with an antiserum against GR followed by an Alexa Fluor® 555-labeled anti-rabbit secondary antibody and finally with Hoechst 33346. Magnification: x400. Dex, dexamethasone; RU486, mifepristone.

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Figure 5: Compounds 1 and 13 mediate transrepression of NF-kB target genes but not transactivation of GREdependent genes.

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HeLa B2 (WT) and HeLa B2 GR_KD cells were treated with vehicle (0.1% DMSO), Dex (1 μM) or 1, 13 or 21

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(10 μM) in the presence or absence of TNF. TNF, IL6, CCL2 and FKBP5 mRNA levels were expressed as percentage of their levels in the presence of TNF alone (set equal to 100). Values (mean ± SEM) are from at

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least three independent experiments. *p < 0.05 vs TNF alone for HeLa B2 cells and #p < 0.05 vs TNF alone for HeLa B2 GR_KD cells (t-test). Dex, dexamethasone; TNF, tumor necrosis factor; IL6, interleukin 6; CCL2,

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chemokine (C-C motif) ligand; FKBP5, FK506 binding protein 5.

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Figure 6: New binding contacts predicted for the discovered SEGRA.

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Proposed binding poses and 2D ligand interaction diagrams of 1 (A) and 13 (B) at GR LBD. The suggested new contacts at the edge of the extended binding cavity with Trp577 and Lys667 could further guide the hit

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optimization process.

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Docking Score

Virtual Screening potential hits

Docking Score

1

-11,66

9

-11,57

2

-13,37

10

3

-12,84

11

4

-13,52

12

5

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Virtual Screening potential hits

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Tables Table 1: Selected compounds from virtual screening along with their docking scores

-11,74

-12,74

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U

-11,90

13

-12,43

14

-13,65

TE

D

-12,37

-13,64

A

7

CC

EP

6

8

-13,53

-16,52 pyrrolidinone amide analog (compound 129)

-14,40

31

Table 2: MM–PBSA binding free energy (ΔGbind) calculations of the GR-LBD complexes with the pyrrolidinone amide analogue and with the hits 1 and 13 (units are in kcal mol-1). -1

Energy (kcal mol )

pyrrolidinone amide (compound 12a) -88.1 ± 0.1

ΔEelec

13

b

-72.6 ± 0.1

-70.7 ± 0.1

-21.3 ± 0.1

-15.9 ± 0.1

-10.5 ± 0.1

-109.4 ± 0.1

-88.6 ± 0.1

59.7 ± 0.1

51.3 ± 0.1

38.4 ± 0.1

35.3 ± 0.1

ΔGNP

-6.4 ± 0.0

-5.3 ± 0.0

ΔGsolv

53.3 ± 0.1

46.0 ± 0.1

-56.2 ± 0.1

-42.6 ± 0.1

SC RI PT

ΔEvdW

1

-TΔS

31.6 ± 0.8

26.6 ±0.7

ΔGbind

-24.6 ± 0.5

ΔEMM,gas ΔGPB

34.7 ± 0.1

-5.5 ± 0.0

39.7 ± 0.1

U

-41.5 ± 0.1

d

-16.0 ± 0.4

24.8 ± 0.8 -16.7 ± 0.4

M

A

ΔH(MM+solv)

45.2 ± 0.1

N

ΔGelec(tot)

c

-81.2 ± 0.1

Enthalpy (ΔH) values correspond to calculations performed on a total of 8,000 frames of 4×20 ns trajectories,

D

while entropy (–TΔS) was calculated for 200 frames of the same trajectories. Total binding energy (ΔGbind) and

TE

standard error of the mean values are also provided. Specific energetic contributions are defined as: ΔEvdW: van der Waals non-bonded energy contributions, ΔEelec: electrostatic non-bonded contributions, ΔEMM, gas: internal

EP

energetic contributions from molecular mechanics (i.e. bonds, angles and dihedrals), ΔGsolv: total solvation energy, ΔGPB: electrostatic contribution to the solvation energy calculated by the PB approach, ΔGelec(tot): total

a

Reference [9]





Standard error of the mean, s  standard deviation

N , where N is the number of trajectory frames used

A

b

CC

electrostatic energy and ΔGNP: non polar contribution to the solvation energy.

in calculations (8,000 for enthalpy ( n1 ) and 1,000 for entropy ( n2 ), respectively). c

ΔGelec(tot)= ΔEelec + ΔGPB

d

2 2 Pooled standard error of the mean, S   n1  1 s1    n2  1 s2  





32

 n1  n2  2 .