Abstract
Procedures for inhibiting hormone receptor activation include administering to a subject in need of hormone receptor inhibition a compound having a chemical structure of a molecule that, when docked in the PPIase pocket, could disrupt proline-rich loop conformation and interactions. Procedures for treating prostate cancer or breast cancer include administering to a subject having prostate cancer or breast cancer a compound having a chemical structure of a molecule that, when docked in the PPIase pocket, could disrupt proline-rich loop conformation and interactions.
Claims
1. A method of inhibiting hormone receptor activation, comprising administering to a subject in need of hormone receptor inhibition a compound having a chemical structure of Formula II ##STR00007##
2. The method of claim 1, wherein the hormone receptor is an androgen receptor.
3. The method of claim 1, wherein the subject has hyperplasia.
4. The method of claim 1, wherein the subject has neoplasia.
5. The method of claim 1, wherein the subject has prostate cancer or breast cancer.
6. The method of claim 1, further comprising administering chemotherapy or radiation treatments.
7. A method of treating prostate cancer or breast cancer comprising administering to a subject having prostate cancer or breast cancer a compound having a chemical structure of Formula II ##STR00008##
8. The method of claim 7, further comprising administering chemotherapy or radiation treatments.
9. The method of claim 7, wherein the compound when docked in a PPIase pocket disrupts proline-rich loop conformation and interactions.
10. The method of claim 9, wherein the PPIase pocket includes an FKBP52 PPIase pocket.
11. A method of inhibiting hormone receptor activation, comprising administering to a subject in need of hormone receptor inhibition a compound having a chemical structure of Formula III ##STR00009##
12. The method of claim 11, wherein the hormone receptor is an androgen receptor.
13. The method of claim 11, wherein the subject has hyperplasia.
14. The method of claim 11, wherein the subject has neoplasia.
15. The method of claim 11, wherein the subject has prostate cancer or breast cancer.
16. The method of claim 11, further comprising administering chemotherapy or radiation treatments.
17. A method of treating prostate cancer or breast cancer comprising administering to a subject having prostate cancer or breast cancer a compound having a chemical structure of Formula III ##STR00010##
18. The method of claim 17, further comprising administering chemotherapy or radiation treatments.
19. The method of claim 17, wherein the compound when docked in a PPIase pocket disrupts proline-rich loop conformation and interactions.
20. The method of claim 19, wherein the PPIase pocket includes an FKBP52 PPIase pocket.
Description
BRIEF DESCRIPTION OF THE DRAWINGS
[0030] The following drawings form part of the present specification and are included to further demonstrate certain aspects of the present invention. The invention may be better understood by reference to one or more of these drawings in combination with the detailed description of specific embodiments presented herein.
[0031] FIG. 1 depicts a workflow of an FKBP52 virtual screening pipeline in accordance with an illustrative embodiment.
[0032] FIGS. 2A-2J present structures and pIC.sub.50 values for FKBP52 inhibitors in accordance with an illustrative embodiment.
[0033] FIGS. 3A-3C depict dendograms generated with HCA using each set of descriptors in accordance with an illustrative embodiment. (FIG. 3A) Druglike properties. (FIG. 3B) molecular PubChem fingerprint. (FIG. 3C) Distribution of compounds among training and test sets according to their different chemical representative calculated drug-like properties, PubChem fingerprint clusters as representative of structural diversity and the pIC.sub.50 range representing the biological activity.
[0034] FIG. 4 presents pIC.sub.50 values and properties calculated for 42 FKBP52 inhibitors from PaDEL descriptors in accordance with an illustrative embodiment.
[0035] FIGS. 5A-5B depict an overview of the FKBP52 inhibitors in accordance with an illustrative embodiment. (FIG. 5A) ligand from the crystal structure of FKBP52 (PDB ID: 4LAY) used as a template for the alignment. (FIG. 5B) Final alignment of data set. Compound: From literature cited in manuscript; IC.sub.50: Half maximal inhibitory concentration; pIC.sub.50: −log IC.sub.50; MW: Molecular weight; LogP: Partition coefficient of a molecule between an aqueous and lipophilic phases, normally octanol and water; nHBAcc: Number of hydrogen bond acceptor; nHBDon: Number of hydrogen bond donor; HybRatio: Characterizes molecular complexity in terms of carbon hybridization states; nRotB: number of rotatable bonds; TopoPSA: Topological polar surface area; LogS: Aqueous solubility; PubChemFP: PubChem fingerprint.
[0036] FIG. 6 presents statistical results for all CoMFA models obtained from the region focusing technique in accordance with an illustrative embodiment. q.sup.2.sub.LOO: Validation coefficient using (leave-one-out); SEP: standard error of prediction; N: number of main components obtained from the PLS technique; r.sup.2: regression coefficient without validation; SEE: standard non-cross validation error; S: steric contribution; E: electrostatic contribution. w=weight; d (Å)=distance between the grid points.
[0037] FIG. 7 presents statistical results for all CoMSIA models obtained from the region focusing technique in accordance with an illustrative embodiment. q.sup.2.sub.LOO: Validation coefficient using (leave-one-out); SEP: standard error of prediction; N: number of main components obtained from the PLS technique; r.sup.2: regression coefficient without validation; SEE: standard non-cross validation error; A: H-bond acceptor contribution; w=weight; d (Å)=distance between the grid points.
[0038] FIG. 8 presents statistical data of the best constructed CoMFA models for FKBP52 inhibitors in accordance with an illustrative embodiment. d, distance factor; w, standard deviation weight factor; q.sup.2, LOO cross-validation correlation coefficient; SEV, standard error of validation; N, optimal number of components; r.sup.2, non-cross-validation correlation coefficient; SEE, standard error of estimation; dq.sup.2/dr.sup.2yy′, sensitivity index from the scrambling test. Field contribution: S, steric; E, electrostatic.
[0039] FIG. 9 presents statistical data of the best constructed CoMSIA models for FKBP52 inhibitors in accordance with an illustrative embodiment. d, distance factor; w, standard deviation weight factor; q.sup.2, LOO cross-validation correlation coefficient; SEV, standard error of validation; N, optimal number of components; r.sup.2, non-cross-validation correlation coefficient; SEE, standard error of estimation; dq.sup.2/dr.sup.2yy′, sensitivity index from the scrambling test. Field contribution: S, steric; A, Hydrogen acceptor.
[0040] FIG. 10 presents validation of the COMFA and CoMSIA models in accordance with an illustrative embodiment. q.sup.2: LOO cross-validation correlation coefficient; r.sub.pred.sup.2: external predictive potential of the model; RMSEP: Root-mean-square error of prediction; r.sub.m.sup.2: external predictive potential of the model modified
[0041] FIGS. 11A-11B and FIGS. 12A-12B present CoMFA contour maps, for steric and electrostatic terms, and CoMSIA contribution maps, highlighting the acceptor and steric contributions in accordance with an illustrative embodiment. Contour maps showed around the compounds 36, 38 and 39. Green contours represent regions where bulky groups increase biological activity while yellow contours indicate areas where bulky groups decrease biological activity. Favorable electrostatic contributions represented in blue, while unfavorable contributions to the biological activity represented in red. Favorable acceptor contributions are highlighted in pink, while unfavorable in grey. Contour maps showed around the compounds less active (6, 32 and 40) and most active (36, 38 and 39). Green contours represent regions where bulky groups increase biological activity while yellow contours indicate areas where bulky groups decrease biological activity in steric contribution.
[0042] FIGS. 13A-13B depict plots of leverage versus studentized residuals for (FIG. 13A) CoMFA and (FIG. 13B) CoMSIA: black dots represent training set and black triangles represent test set in accordance with an illustrative embodiment.
[0043] FIGS. 14A-14C depict experimental and predicted values of pIC.sub.50 for the training and test sets in accordance with an illustrative embodiment. (FIG. 14A) CoMFA model, (FIG. 14B) CoMSIA model and (FIG. 14C) predicted by CoMSIA; black dots represent training set and grey dots represents test set.
[0044] FIG. 15 presents experimental and predicted pIC.sub.50 for test compounds 1-22 in accordance with an illustrative embodiment.
[0045] FIG. 16 presents experimental and predicted pIC.sub.50 for test compounds 2, 4, 7, 11, 22, 34, 39 and 42 in accordance with an illustrative embodiment.
[0046] FIG. 17 presents results from the cross-validation (LNO) for the CoMFA model and a plot obtained from robustness test-cross-validated results (LNO). n.sub.CV=number of groups; q.sup.2.sub.CV=average of cross-validated q.sup.2 in accordance with an illustrative embodiment.
[0047] FIG. 18 presents results from the cross-validation (LNO) of the CoMSIA model and a plot obtained from robustness test-cross-validated results (LNO) in accordance with an illustrative embodiment. n.sub.CV=number of groups; q.sup.2.sub.CV=average of cross-validated q.sup.2
[0048] FIGS. 19A-19B depict identification of novel FKBP52-specific hit compounds in accordance with an illustrative embodiment. Structure-based drug design methodology and in silico library screening was used to identify 107 molecules targeting the FKBP52 PPIase pocket. Molecules were assessed for the ability to inhibit AR-mediated luciferase expression at a single high concentration (25 μM) in MDA-kb2 cells. Molecules that showed inhibition at 25 μM were assessed in full dose response curves to determine the IC50. MDA-kb2 cells were treated with 200 pM DHT with a range of derivative concentrations. Molecules in the low μM range will be tested in GR-, PR- and ER-Mediated luciferase assays in order to assess GR-dependent activity, PR-dependent activity and to test the effects of ER-regulated activity. A detailed evaluation of all candidate molecules will be tested in multiple cellular and animal models of prostate cancer.
[0049] FIGS. 20A-20D depict PC257 (ZINC3424402) which inhibits FKBP52-Specific AR, GR and ER-Mediated Activity in accordance with an illustrative embodiment. (FIG. 20A) An in silico screen lead to 107 lead molecules for functional screening that lead to an initial hit molecule PC257 (ZINC3424402). (FIG. 20B) MDA-kb2 cells expressing a stably AR- and GR-response luciferase reporter was treated with 200 pM DHT with a range of PC257 (ZINC3424402) concentrations (0, 0.01, .1, 1, 10, 25, 50, and 100 uM) for 16-18 hours in order to test for AR-dependent activity. The graphs represent an average of 4 independent receptor mediated luciferase receptor experiments. (FIG. 20C) MDA-kb2 cells expressing a stably AR- and GR-responsive luciferase reporter was treated with 50 nM DEX with a range of PC257 (ZINC3424402) concentrations (0, 0.01, .1, 1, 10, 25, 50, and 100 uM) for 16-18 hours in order to test for GR-dependent activity. The graphs represent an average of 4 independent receptor mediated luciferase receptor experiments. (FIG. 20D) T47D-KBluc cells express ERα and ERβ, cells were treated with 10 pM E2 with a single high dose of 100 uM PC257 (ZINC3424402) and vehicle control for 16-18 hours in order to test for effects on ER-regulated activity.
[0050] FIG. 21 depicts that PC257 specifically abrogates AR, GR and PR-dependent reporter gene expression in accordance with an illustrative embodiment.
[0051] FIG. 22 depicts that PC257 preferentially targets FKBP52-regulated receptor activity in accordance with an illustrative embodiment.
[0052] FIGS. 23A-23C depict that PC257 abrogates endogenous AR-dependent gene expression in accordance with an illustrative embodiment.
[0053] FIG. 24 depicts that PC257 Blocks Androgen-Dependent AR Nuclear Translocation in accordance with an illustrative embodiment.
DETAILED DESCRIPTION
Broader Scale Screen in Silico Screen for Fkbp52 Inhibitors
[0054] While the targeting of the FKBP52 regulatory surface on AR is a promising therapeutic strategy that allows for AR-specific targeting, direct targeting of FKBP52 offers a number of advantages over MJC13 that would lead to a more potent and effective drug. First, the AR BF3 surface represents a less than ideal drug binding site, and, as a result, Applicants have only been able to achieve effective drug concentrations in the low micromolar range. In contrast, the FKBP52 PPIase pocket not only represents an ideal hydrophobic drug binding pocket, but the FKBP PPIase pocket is a known ‘druggable target’ as the immunosuppressive drug Tacrolimus is already FDA approved for use in the clinic. Also, given the conservation within the FKBP PPIase pocket, drugs targeting the FKBP52 PPIase pocket would likely target FKBP52 and the closely related FKBP51 protein simultaneously. While FKBP52, but not FKBP51, is largely considered the relevant steroid hormone receptor regulator, more recent evidence suggests that both FKBP51 and FKBP52 are positive regulators of AR in prostate cancer cells. In addition, FKBP52 is a known positive regulator of AR, GR and PR, and the direct targeting of FKBP52 would target the activity of all three receptors simultaneously. Increasing evidence suggests that many factors (e.g. growth factors, cytokines, and angiogenic factors) implicated in prostate cancer progression are targets of the GR signaling pathway. In addition, recent evidence suggests that GR signaling confers resistance to current antiandrogen treatments. While very little work has been done to characterize a role for PR in prostate cancer, data suggests that PR expression is elevated in metastatic disease, and that PR antagonist are potential treatments for prostate cancer. Finally, based on preliminary data discussed below, targeting FKBP52 proline-rich loop interactions will abrogate β-catenin interaction with AR. Thus, the direct targeting of FKBP52 with small molecules will lead to a more potent drug with the potential to simultaneously hit a variety of targets known to have, or suspected of having, a role in prostate cancer.
[0055] Applicants have conducted a large scale virtual screening using the crystal structure of FKBP52 for the novel hit discovery. A FKBP52 virtual screening pipeline is shown in FIG. 1. A workflow begins with database 110 and proceeds to docking analysis 120. This is followed with a Ph4 filter 130 and then QSAR model 140 analysis resulting in hits 150. However, a high quality ranking tool is needed for good hit selection. Here, Applicants are reporting CoMFA and CoMSIA models for ranking compounds of a FKBP52 virtual screening. In addition, Applicants are reporting the three hit molecules identified in the screen. In addition, Applicants show that the most potent hit, PC257, selectively inhibits the steroid hormone receptors regulated by FKBP52.
CoMFA and CoMSIA models of FKBP52
[0056] Dataset collection: Forty-two inhibitors of pipecolate sulfonamides of the FKBP52 dataset were selected to generate CoMFA and CoMSIA models (FIGS. 2A-2J). Biological activity data were converted into pIC.sub.50 (−log IC.sub.50) values, where the IC.sub.50 of FKBP52 inhibition is represented as molar values (FIGS. 2A-2J).
[0057] Compounds were grouped according to molecular structural diversity, drug-like properties and range of biological activity. Molecular structural diversity and drug-like properties were clustered using hierarchical cluster analysis (HCA) with the complete linkage method and Euclidian distance implemented in Chemoface. Compounds were separated into different groups according to their biological activity range in log unit (FIGS. 3A-3C). Molecular structural information was encoded from PubChem fingerprints (PF), while drug-like properties include descriptors of LogP, number of H-bond acceptors (HBA) and donors (HBD), topological surface area (TPSA), number of rotatable bonds (nRot) and molecular weight (MW) (FIG. 4). PF and descriptors were determined using PaDel descriptors and all data were normalized before HCA.
[0058] The FKBP52 dataset compounds were randomly separated into a training set of 34 compounds and a test set of 8 compounds respectively, which can represent each cluster in the total set as shown in FIGS. 3A-3C. The protonation, ionization and minimization, and flexible alignment of the compound structures were subsequently processed using MOE suite of programs.
Generating the FKBP52 CoMFA and CoMSIA models
[0059] Dataset and alignment: Molecular alignment is the critical step of CoMFA and CoMSIA modelling because the three-dimensional descriptors are evaluated based on a lattice grid. The alignment of the inhibitors of the FKBP52 dataset indicates the importance of the three important rings (FIG. 5A). Ring 1 is a part of pipecolate group with a system of two H-bond donor and three H-bond acceptor which in the dataset the carbon atom between oxygen (orange arrow) was substituted by a sulfur atom (FIG. 5B). The other two aromatic ring are important for hydrophobic contacts (FIG. 5B) in the FKBP52 active site.
[0060] Models were generated using comparative molecular fields' analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) after alignment, respectively, using the partial least-squares (PLS) regression method implemented in Sybyl8.1 from the training set. Seventeen models of CoMFA (FIG. 6) and CoMSIA (FIG. 7) respectively were inferred by varying the standard parameter settings.
[0061] Normally, the quality of the models can be evaluated by correlation coefficients: (q.sup.2 and cross-validation: r.sup.2), number of principal components (PC) and others parameters such as standard error estimate (SEE) and contribution of force fields. The optimal CoMFA and CoMSIA models are the ones with minimal PC determined by cross-validation PLS regression, which are used to generate the contour maps. The intermediate models were inferred by varying the standard parameter settings as weight (0.3 to 1.5) and distance (1 to 4 Å) between the grid points. A positively charged sp.sup.3 carbon was used as the probe atom to calculate molecular interaction fields (CoMFA), and a positively charged sp.sup.3 hybridized carbon probe atom to calculate a range of different similarity indices (CoMSIA); and the molecular alignment of training set molecules with an initial grid spacing of 2 Å and an energy cut-off of 30 kcal/mol was used to generate the CoMFA and CoMSIA models. The molecular interaction field was calculated using the probe atom, and the steric and electrostatic interactions with training set compounds were calculated using Lennard-Jones and Coulomb energy terms of the CoMFA model. The different combinations of similarity indices of steric, electrostatic, hydrophobic, H-bond donor and H-bond acceptor were calculated in the CoMSIA model, and the best combination for the best model was determined when the highest q.sub.LOO.sup.2 among the pairs of indices was further optimized using focus approach that changes either the grid spacing from 1 to 4 Å by a step of 0.5 multiplied by the original distance or the weight factor from 0.3 to 1.5 by a step of 0.2 multiplied by the standard deviation (SD) of the original model.
[0062] Varied combinations of weight factor and grid spacing were employed to generate the intermediate models which were ranked by Q.sub.LOO.sup.2 values to obtain the best model. The maximum number of principal components (PCs) used in both the CoMFA and the CoMSIA models respected the size of the dataset (42 compounds) that each intermediate model takes the least number of PCs sufficient to explain the variability of the system (FIGS. 6 and 7).
[0063] The best models of CoMFA and CoMSIA respectively were selected by the internal robustness (q.sub.LOO.sup.2>0.6) and external robustness (Q.sub.F2.sup.2 and Q.sub.F3.sup.2>0.7), which were used to generate contribution and contour maps for the most and least active and selective compounds. Additional external validation metrics of r.sub.m.sup.2, which compares the correlation coefficients in the prediction of the test set when passing through the origin (r.sub.0.sup.2) were evaluated to assess the model's predictability. Detailed description of these metrics can be found in a comprehensive review. The sensitivity index (dq.sup.2/dr.sup.2yy′) was generated by 50 runs of progressive scrambling CoMFA and CoMSIA, the values of what should be between 0.8 and 1.2 (FIGS. 8 and 9). Applicability domain in FIGS. 13A-13B showed that 93% of the training and test set compounds are inside the predictability domain of the left-bottom dashed-lined quadrant of leverage and studentized residual.
Validation of Models
[0064] The selected CoMFA and CoMSIA models need to be cross-validated for the activity prediction of new compounds such as filter in virtual screening. CoMFA have steric (S) and electrostatic (E) fields while CoMSIA presents additional contributions of hydrogen bonds (donor (D) and acceptor (A)) and hydrophobic (H) fields, which provide more information about structural modification. In relation to force fields calculated by CoMFA and CoMSIA and to the combination of CoMFA and CoMSIA, the CoMFA model and CoMSIA model should be built by partial least square (PLS) and validated by cross validation.
[0065] Normally, the optimal models are determined by the internal correlation coefficients of q.sup.2 and cross-validation r.sup.2 and the number of principle components (NP). Other parameters of a model can be calculated, such as standard error estimate (SEE) and contribution of force fields. Thus, the best models are constructed with optimal NP by cross-validation PLS regression, which are used to generate the contour maps.
[0066] After that, the contour maps of the models are analyzed and the biological activities of the training and test sets are predicted. In addition, the Y-randomization is applied to ensure the robustness of the models to repeat the model training procedure several times by randomly shuffling the activities in the training set. The lowest q.sup.2 and r.sup.2 values built with randomized activities indicate that the constructed models are acceptable and reliable.
[0067] It has been shown that CoMFA and CoMSIA have been used to investigate the SAR. Therefore, Applicants have constructed CoMFA and CoMSIA models of FKBP52, and generated the counter maps of CoMFA and CoMSIA that can be used for hit selection of FKBP52 VS after docking.
External Validation and Model Selection
[0068] The CoMFA and CoMSIA models were satisfactory with values within the specifications and according to OECD guidelines. The models are good as indicated by their r.sub.Pred.sup.2 values of >0.7 (FIGS. 14A and 14B, as a graphical representation) and low root-mean-square error of prediction (RMSEP) rates (FIG. 10). Thus another external test set of 22 compounds of pipecolate derivatives were curated and their activities were predicted by CoMSIA model (FIG. 14C and FIG. 15).
[0069] The high Q.sub.F2.sup.2 and Q.sub.F3.sup.2 values suggest that the CoMSIA model has high predictability of FKBP52 inhibition. Additionally, the small discrepancy between predicted and observed activity can be demonstrated by r.sub.m.sup.2, which is also bigger than 0.60. Residuals were always smaller than 1 and showed no correlation with predicted values (FIG. 16).
[0070] In order to test the robustness and stability of the models against variation of the training set composition, Applicants also performed a leave-N-out (LNO) validation (FIGS. 17 and 18), with cross-validation group numbers varying from 5 to 50 and the average q.sup.2 values are bigger than 0.8 indicating a great internal consistency.
Physicochemical Interpretation of Models
[0071] Although CoMFA and COMSIA models complement each other, CoMSIA often behaves better than CoMFA, because CoMSIA model is trained with a lot more chemical information of the training dataset. To evaluate the quality of CoMFA and CoMSIA models, it is necessary to perform both internal and external validation. In particular, if hydrophobic, acceptor and donor contributions are important for the dataset, it is more likely that CoMSIA performs better than CoMFA which only considers two descriptors of electrostatic and steric.
[0072] The internal and external validation results in FIG. 10 of FKBP52 showed that CoMSIA is more predictive than the CoMFA, because acceptor of the data set (FIG. 9) is a strong contribution. Thus, the CoMSIA contour map in FIGS. 11A-11B and 12A-12B should be used to evaluate different chemical cores and substitutions to optimize or select FKBP52 inhibitors.
[0073] The hydrogen acceptor maps in FIGS. 11A-11B and 12A-12B show that the bottom small purple volume of the carboxylic acid of compound 36 and of the dichlorophenol of compounds 38 and 39 reinforces the importance of the hydrogen acceptor that interacts with the active site. In contrast, the large green maps around the morpholine group of compounds 36, 38 and 39 reveal the importance of the bulky hydrophobic groups; and the purple maps highlight the favorable hydrogen acceptor contributions to the potential hydrogen acceptors of the binding site.
[0074] The yellow maps of the phenoxyacetic acid in compound 40 and of the phenoxyacetic acid and benzothiophene of compound 6 in FIGS. 11A-11B and 12A-12B suggest unfavorable steric clash with the binding site. The yellow maps of the pyrrolidine group of compound 32 also suggests that the mitigation of the steric clash is critical to boost the biological activity.
[0075] By the CoMFA model (FIGS. 11A-11B and 12A-12B), only small blue contour maps present in all compounds, which suggest just favorable contributions of the electrostatic contributions which can be explained by a low number electrostatic contribution E of 0.207 in FIG. 8. From a complementary CoMSIA field analysis, substitutions of hydrogen-bond acceptor in the blue map regions should enhance biological activity.
[0076] Since the FKBP52 CoMSIA model is highly predictive, Applicants have applied the model to rank the docking-predicted FKBP52 binding poses of ZINC15 compounds. 106 hits were selected from the VS by the FKBP52 CoMSIA ranking and visual check. Seven active compounds have been confirmed. The most active compound found has a IC.sub.50 of approximately 1 μM, which is a magnitude better than the co-crystalized ligand (IC.sub.50=10.5 μm ).sup.9.
[0077] The CoMFA and CoMSIA results showed that CoMSIA model is more predictive than CoMFA, which provides a good ranking tool to select FKBP52 VS hits.
Functional Screening of Virtual Hits: Identification PC257, 615, and 892 as Next Generation FKBP52 Inhibitors
[0078] An in silico structure-based drug design identified 107 hits for functional screening. As detailed in FIGS. 19A-19B, Applicants first screened these molecules at a single high dose (25 μM) for inhibition of AR activity in MDA-kb2 cell reporter assays. Any analogs that inhibited AR activity by 75% or more were then screened on full dose response curves to determine the IC50. From these data, Applicants identified 3 hits that displayed inhibition of AR activity in the low micromolar range, with PC257 (ZINC3424402) being the most potent with an IC50 of 2 μM. As previously mentioned FKBP52 functionally potentiates AR, GR and PR activities but does not potentiate ER. This is the result of the cochaperones proline-rich loop overhanging the PPIase catalytic pocket in the FK1 domain which is responsible for the regulation of receptor activity.
[0079] As detailed in FIGS. 20A-20D, Applicants predict that PC257 (ZINC3424402) binds to the FKBP52 pocket resulting in a conformational change of the proline-rich loop disrupting its interaction with receptors, in which case displaying FKBP52-specific inhibition of AR, GR and PR but not ER activity. Applicants previously demonstrated that PC257 (ZINC3424402) displayed inhibition of AR activity in the low micromolar range. As a result, Applicants wanted to show that PC257 (ZINC3424402) displayed inhibition of GR activity but does not show inhibition of ER activity. Further testing will be conducted in order to show that PC257 (ZINC3424402) inhibits PR activity. The other two hits PC892 (ZINC457474880) and PC615 (ZINC161085867) will go through the same process as PC257.
[0080] Referring to FIG. 21, hormone-induced, receptor-dependent luciferase reporter gene expression was assessed in the presence of a range of PC257 concentrations for androgen receptor (AR) and glucocorticoid receptor (GR) in MDAkb2 cells, and for progesterone receptor (PR) and estrogen receptor (ER) in T47D cells. The IC50 values for AR, GR and PR inhibition are shown. These data indicate that PC257 specifically abrogates AR, GR and PR activity, which are known to be regulated by FKBP52, but has no inhibitory activity on ER; a receptor that is not regulated by FKBP52. This strongly suggests that PC257 directly targets the FKBP52 protein.
[0081] Referring to FIG. 22, dihydrotestosterone (DHT)-induced, androgen receptor-dependent luciferase reporter gene expression was assessed in the presence of a range of PC257 concentrations in the presence or absence of exogenous FKBP52 expression in fkbp52-deficient 22Rv1, HeLa, and mouse embryonic fibroblast cells. The IC50 values are indicated. These data indicate that PC257 preferentially targets FKBP52-regulated AR activity with increased potency. PC257 is anticipated to target the PPIase pocket, a highly conserved enzymatic pocket among the FKBP family of proteins. Thus, it is likely that PC257 targets a variety of family members including FKBP51 (FKBP5), which has also been shown to regulate AR activity in some prostate cancer cell lines.
[0082] Referring to FIG. 23, hormone-dependent FKBP51 and/or PSA protein levels were assessed by Western blot and densitometry in the indicated cell lines in the presence of a range of PC257 concentrations. GAPDH was used as a loading control and the densitometry data were normalized to GAPDH.
[0083] Referring to FIG. 24, 22Rv1 prostate cancer cells were treated with or without 75 μM PC257 in the presence of hormone and AR and FKBP52 cellular localization was assessed by fluorescence microscopy. These data indicate that PC257 significantly inhibits AR nuclear translocation.
[0084] All of the methods disclosed and claimed herein can be made and executed without undue experimentation in light of the present disclosure. While the compositions and methods of this invention have been described in terms of preferred embodiments, it will be apparent to those of skill in the art that variations may be applied to the methods and in the steps or in the sequence of steps of the method described herein without departing from the concept, spirit and scope of the invention. More specifically, it will be apparent that certain agents which are both chemically and physiologically related may be substituted for the agents described herein while the same or similar results would be achieved. All such similar substitutes and modifications apparent to those skilled in the art are deemed to be within the spirit, scope and concept of the invention as defined by the appended claims.
