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Review
. 2015 Sep;88(3):579-88.
doi: 10.1124/mol.115.099630. Epub 2015 Jul 1.

Fulfilling the Promise of "Biased" G Protein-Coupled Receptor Agonism

Louis M Luttrell  1 , Stuart Maudsley  2 , Laura M Bohn  2
Affiliations
Review

Fulfilling the Promise of "Biased" G Protein-Coupled Receptor Agonism

Louis M Luttrell et al. Mol Pharmacol. 2015 Sep.

Abstract

The fact that over 30% of current pharmaceuticals target heptahelical G protein-coupled receptors (GPCRs) attests to their tractability as drug targets. Although GPCR drug development has traditionally focused on conventional agonists and antagonists, the growing appreciation that GPCRs mediate physiologically relevant effects via both G protein and non-G protein effectors has prompted the search for ligands that can "bias" downstream signaling in favor of one or the other process. Biased ligands are novel entities with distinct signaling profiles dictated by ligand structure, and the potential prospect of biased ligands as better drugs has been pleonastically proclaimed. Indeed, preclinical proof-of-concept studies have demonstrated that both G protein and arrestin pathway-selective ligands can promote beneficial effects in vivo while simultaneously antagonizing deleterious ones. But along with opportunity comes added complexity and new challenges for drug discovery. If ligands can be biased, then ligand classification becomes assay dependent, and more nuanced screening approaches are needed to capture ligand efficacy across several dimensions of signaling. Moreover, because the signaling repertoire of biased ligands differs from that of the native agonist, unpredicted responses may arise in vivo as these unbalanced signals propagate. For any given GPCR target, establishing a framework relating in vitro efficacy to in vivo biologic response is crucial to biased drug discovery. This review discusses approaches to describing ligand efficacy in vitro, translating ligand bias into biologic response, and developing a systems-level understanding of biased agonism in vivo, with the overall goal of overcoming current barriers to developing biased GPCR therapeutics.

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Figures

None
Graphical abstract
Fig. 1.
Fig. 1.
Example of ligand bias described using the operational model. Transduction efficiencies [Δlog(τ/KA)] for five different assays of κ opioid receptor signaling were used to calculate bias factors (ΔΔlog(τ/KA)j1–j2) that are presented in a multiaxial graphic format. The κ opioid receptor-selective agonist, U69,593 [(+)-(5α,7α,8β)-N-methyl-N-[7-(1-pyrrolidinyl)-1-oxaspiro[4.5]dec-8-yl]-benzeneacetamide], was used as the reference ligand, hence its bias factor conforms to unity in all assays. The pathways represented are: membrane [35S]GTPγS binding (mG protein); βarr2 enzyme fragment complementation (βarr2 EFC), βarr2 fluorescence imaging (βarr2 imaging), cellular impedance (impedance), and whole cell [35S]GTPγS binding (wcG protein). As depicted, the two test compounds, 2-(4-(furan-2-ylmethyl)-5-((4-methyl-3-(trifluoromethyl)benzyl)thio)-4H-1,2,4-triazol-3-yl)pyridine (1.1) and 2-(2-fluorobenzyl)-N-(4-methyl-3-(trifluoromethyl)phenyl)-1-oxo-octahydroisoquinoline-8-carboxamide (2.1), display bias for G protein signaling over arrestin recruitment. This research was originally published in Zhou et al. (2013).
Fig. 2.
Fig. 2.
Systems level characterization of conventional and arrestin biased PTH1R agonism in vivo. (A) Omnimorph structures depicting a high-dimensionality z score representation of the transcriptional responses to hPTH(1-34) (panels 1 and 2, WT; panels 3 and 4, Arrb2 KO) or (d-Trp12,Tyr34)-bPTH(7-34) (panels 5 and 6, WT; panels 7 and 8, Arrb2 KO) in wild-type (WT) and arrestin3 null (Arrb2 KO) murine kidney. (B) Significantly-populated canonical signaling pathways induced by (D-Trp12,Tyr34)-bPTH(7-34) (red bars) or by hPTH(1-34) (black bars) in murine calvarial bone identified using Ingenuity Systems Pathways Analysis. Each histogram bar represents the signaling pathway score. Signaling pathways within the yellow block represent coherently regulated common pathways between the two ligands. The associated Venn diagram indicates the functional signaling separation between hPTH(1-34) and (d-Trp12,Tyr34)-bPTH(7-34). (C) Word cloud interpretation of the individual Textrous! output (dismantled noun-phrases) performed using the most cross-tissue conserved (d-Trp12,Tyr34)-bPTH(7-34) regulated transcripts. (D) Word cloud interpretation of the collective Textrous! output performed using the most cross-tissue conserved (d-Trp12,Tyr34)-bPTH(7-34) regulated transcripts. Textrous! is a latent semantic indexing–based analytical tool that correlates the strength of association between specific genes in a dataset with user-defined interrogation terms, in this case biomedical words and noun-word phrases extracted from PubMed, Online Mendelian Inheritance in Man, and the Mammalian Phenotypes Database at the Jackson Laboratories Mouse Genomic Informatics portal (Chen et al., 2013a). Research originally published in Maudsley et al. (2015a).
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