Strategies for developing pregnane X receptor antagonists: Implications from metabolism to cancer

Abstract

Pregnane X receptor (PXR) is a ligand-activated nuclear receptor (NR) that was originally identified as a master regulator of xenobiotic detoxification. It regulates the expression of drug-metabolizing enzymes and transporters to control the degradation and excretion of endobiotics and xenobiotics, including therapeutic agents. The metabolism and disposition of drugs might compromise their efficacy and possibly cause drug toxicity and/or drug resistance. Because many drugs can promiscuously bind and activate PXR, PXR antagonists might have therapeutic value in preventing and overcoming drug-induced PXR-mediated drug toxicity and drug resistance. Furthermore, PXR is now known to have broader cellular functions, including the regulation of cell proliferation, and glucose and lipid metabolism. Thus, PXR might be involved in human diseases such as cancer and metabolic diseases. The importance of PXR antagonists is discussed in the context of the role of PXR in xenobiotic sensing and other disease-related pathways. This review focuses on the development of PXR antagonists, which has been hampered by the promiscuity of PXR ligand binding. However, substantial progress has been made in recent years, suggesting that it is feasible to develop selective PXR antagonists. We discuss the current status, challenges, and strategies in developing selective PXR antagonists. The strategies are based on the molecular mechanisms of antagonism in related NRs that can be applied to the design of PXR antagonists, primarily driven by structural information.

Keywords: PXR; antagonist; detoxification; small molecule; xenobiotics.

FIGURE 1.

Diversity of reported PXR ligands (agonists and inhibitors). A) Hierarchical clustering of reported PXR ligands. Binary linear fingerprints were generated using Canvas (Schrodinger), and subsequently clustered using the Tanimoto similarity metric. Compounds and their corresponding cluster nodes were then loaded into the R software environment (www.http://.r-project.org). Hierarchical clustering was performed using an average linkage. The dendrogram was plotted using the ggdendrogram package. B) Physiochemical distribution of reported PXR ligands showing the wide range. C) Principal Component Analysis (PCA) of physiochemical properties, illustrating the diverse chemical space of PXR ligands. By using Canvas (Schrodinger), the following physiochemical properties were calculated for each compound: the partition coefficient (AlogP; shown in B), the hydrogen bond acceptors (HBA), the hydrogen bond donors (HBD), the molecular weight (MW; shown in B), the polar surface area (PSA; shown in B), and the polar residues (Polar). These properties were used as input for calculating a two-component PCA. D) Principal Component Analysis of binary fingerprints as an alternative PCA analysis, grouping the ligands into four clusters, represented in red (SJA1, SJA3, SJA5, SJA6, SJA7, SJA8, SJA9, SJA10, SJB4, SJB7, SJB10, SJC6, SJC7, SJC10), orange (SPA70), purple (most of the steroid compounds), and blue (the remaining ligands). Binary and linear fingerprints were generated for all compounds by using Canvas (Schrodinger). These fingerprints were then used as input to calculate a two-component PCA. Clusters were gated into four main groups based on the components. For all graphs, the 164 ligands were prepared using LigPrep (Schrodinger). Each compound was desalted, specified chiralities were retained, and ionization states were not changed. Prepared versions of the compounds were used for all analyses.

FIGURE 2.

Position of the AF-2 helix in the agonist-induced and apo forms. A) When bound to the agonist (green), the AF-2 helix (raspberry red) is positioned in the active configuration favorable for coactivator recruitment, as exemplified by the PXR–SJB7 structure (PDB code 5X0R). B) Surprisingly, most apo-NR structures, including all the reported apo-PXR structures, showed the AF-2 helix being held in the agonist-induced form (PDB code 1ILG). C) Apo-RXRα showing the AF-2 helix extending away from the LBD core, which is unfavorable for coactivator recruitment (PDB code 6HN6). D) Apo-form of TR4 showing the AF-2 helix in an autorepressive position that prevents coactivator or corepressor binding (PDB code 3P0U).

FIGURE 3.

Comparisons of the AF-2 helix position of GR in the agonist- and antagonist-induced states. A) The agonist dexamethasone (green) enhances the positioning of the AF-2 helix (raspberry red) in the active conformation for binding the coactivator TIF2 peptide (yellow). B) Mifepristone (green) was taken from the mifepristone–GR structure (PDB code 3H52) and superimposed on the GR LBD of the dexamethasone–GR structure (PDB code 1M2Z) to demonstrate the clash between mifepristone and the agonist-induced AF-2 helix due to the large moiety. C) The GR LBD, showing the AF-2 helix in the active configuration conducive for coactivator TIF2 binding in the presence of dexamethasone (green). D) The fully pronounced active antagonism state, showing the AF-2 helix in a position favoring binding of the corepressor NCoR peptide (blue) in the presence of mifepristone (green). E) The less pronounced active antagonism state (compare to D). Green: mifepristone. F) The passive antagonism state, showing the AF-2 helix in place of the corepressor NCoR peptide. Green: mifepristone.

FIGURE 4.

Allosteric binding sites of NR antagonists. A) The crystal structure of TRβ in complex with the antagonist HPPE (blue) shows the antagonist residing at the surface area where a coactivator peptide would otherwise engage (PDB code 2PIN). B) For comparison, the coactivator peptide GRIP1 (yellow) is shown positioned at the AF-2 interface of TRβ (PDB code 1BSX). C) Binding of the antagonist 3,3’,5-triiodothyroacetic acid (blue) to the alternative site termed BF-3 (green) on the surface of AR weakens coactivator interaction with AR (PDB code 2PIV). The AF-2 helix is colored raspberry red.

FIGURE 5.

Strategies and considerations in the development of PXR antagonists. A) Inter-species differences should be considered when using non-human models. B) Tissue-dependent levels of coregulatory proteins and ligand distribution can have an effect on NR antagonism. C) Disruption of the AF-2 helix (red) from the agonistic configuration by an antagonist (blue) has been the most studied approach for NR antagonist development. D) Coupling of PXR ligands (blue) to a bait (green) in order to recruit machinery (orange) for degradation or inhibition of PXR (grey), such as the recruitment of E3 ubiquitin ligase by PROTACs. E) Molecules that bind in alternate sites (pink) can be linked to PXR ligands (blue) for enhanced potency and selectivity. All protein and ligand models are hypothetical representations.