Biased Agonism at G Protein-Coupled Receptors: The Promise and the Challenges—A Medicinal Chemistry Perspective Jeremy Shonberg,1 Laura Lopez,2 Peter J. Scammells,1 Arthur Christopoulos,2 Ben Capuano,1 and J. Robert Lane2 1 Medicinal

Chemistry, Monash Institute of Pharmaceutical Sciences, Monash University (Parkville Campus), Parkville, Victoria, Australia 2 Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences, Monash University (Parkville Campus), Parkville, Victoria, Australia Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/med.21318

䉲 Abstract: Historically, determination of G protein-coupled receptor (GPCR) ligand efficacy has often been restricted to identifying the ligand as an agonist or antagonist at a given signaling pathway. This classification was deemed sufficient to predict compound efficacy at all signaling endpoints, including the therapeutically relevant one(s). However, it is now apparent that ligands acting at the same GPCR can stabilize multiple, distinct, receptor conformations linked to different functional outcomes. This phenomenon, known as biased agonism, stimulus bias, or functional selectivity offers the opportunity to separate on-target therapeutic effects from side effects through the design of drugs that show pathway selectivity. However, the medicinal chemist faces numerous challenges to develop biased ligands, including the detection and quantification of biased agonism. This review summarizes the current state of the field of research into biased agonism at GPCRs, with a particular focus on efforts to relate biased agonism to  C 2014 Wiley Periodicals, Inc. Med. Res. Rev., 00, No. 00, 1–45, 2014 ligand structure. Key words: Biased agonism; structure–activity relationships; G protein-coupled receptor; stimulus bias; functional selectivity

1. INTRODUCTION G protein-coupled receptors (GPCRs), also known as 7-transmembrane receptors (7TMRs), are the single largest class of drug targets,1 and have more than 800 members encoded by Contract grant sponsor: NHMRC; Contract grant numbers: 1011920, 1052304, 1055134; Contract grant sponsor: Australian Research Council; Contract grant number: DP110100687. Correspondence to: Dr J. Robert Lane & Dr Ben Capuano, 381 Royal Parade, Parkville, Melbourne, Victoria 3052, Australia. E-mail: [email protected]; [email protected] Medicinal Research Reviews, 00, No. 00, 1–45, 2014  C 2014 Wiley Periodicals, Inc.

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L3

L1 L2

Pathway 2 Pathway 1

Pathway 2 Pathway 1 Pathway 3

Figure 1. Schematic representation of biased agonism, where three different ligands (L1–L3) stabilize distinct receptor conformations that will each promote distinct functional outcomes at three signaling pathways.

the human genome.2, 3 GPCRs have been grouped into five distinct classes on the basis of sequence conservation, where class A (rhodopsin-like) is the largest and most studied.4 Classical approaches to GPCR drug discovery have largely focused on targeting the binding site of the endogenous hormone or neurotransmitter (known as an “orthosteric binding site”) to generate agonists that mimic the response of the endogenous ligand, or antagonists that bind to this site and block the action of the endogenous agonist.5 Furthermore, numerous examples exist for drugs targeting a topographically distinct GPCR binding site (known as the “allosteric binding site”).6, 7 Drugs are typically characterized by two major biological properties: the propensity of the ligand to bind to the receptor (affinity); and, the magnitude of the cellular response generated by the ligand-bound receptor (efficacy).8 Central to efforts of GPCR drug discovery has been the relationship between parameters describing these properties to both structureactivity relationships (SAR) and physiological effects. Affinity relates simply to the avidity with which the drug binds to the target receptor, and can be quantified and compared between different ligands across various receptors. As such, affinity has traditionally been the dominant guide for medicinal chemists in terms of target selectivity of drug-like molecules. Importantly, affinity can be described by a single, robust figure; the dissociation constant of a ligand (KA ). However, the efficacy of a drug will also determine the nature of the physiological response. This parameter has proved far more challenging both to quantify and to relate to ligand structure.9, 10 In particular, it has now become apparent that GPCRs can assume multiple conformational states that differ in ability to couple to various intracellular effectors (see Fig. 1). Furthermore, different ligands acting at the same receptor may stabilize distinct receptor conformations each with different functional outcomes, a phenomenon termed biased agonism, stimulus bias, ligand-directed signaling, or functional selectivity.8, 11–13 Although biased agonism offers the opportunity to generate pathway selective ligands and even to separate therapeutic effects from adverse effects, it also means that the observed efficacy of a ligand at one pathway cannot be used to predict its effect at another. This review will discuss the nature of GPCR ligand efficacy and the challenges involved both in its quantification and, most importantly for medicinal chemistry, relating this parameter to ligand structure. Medicinal Research Reviews DOI 10.1002/med

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2. A SIMPLE VIEW OF GPCR LIGAND EFFICACY: THE TWO-STATE MODEL Agonist-mediated GPCR activation causes conformational changes in the GPCR, resulting in coupling and activation of heterotrimeric G proteins to elicit signaling events ranging from the production of second messengers (e.g., cAMP, calcium), modulation of ion channels, and activation of kinase cascades.14 Upon activation, receptors can recruit GPCR kinases (GRK) that mediate phosphorylation of the receptor, causing β-arrestin binding, desensitization, and ultimately receptor internalization. As such these final regulatory processes influence G protein signal transduction, duration, and sensitivity.15 More recently, it has become apparent that β-arrestin can also act as a scaffolding protein, recruiting its own subset of signaling proteins. Thus GPCR function can be mediated via G protein-dependent mechanisms or through G protein-independent mechanisms, such as those involving β-arrestin.11 Historically, GPCRs were regarded as bimodal switches whereby the binding of an agonist caused a conformational change in the receptor structure, shifting the receptor equilibrium of the system from a predominantly inactive to an active state.16–18 The discovery that many GPCRs elicit constitutive signaling activity in the absence of bound ligand led to the identification of ligands that reduce the signaling output of the receptor below that of the basal state, that is, inverse agonists.19–22 Indeed, a large number of ligands previously labeled as GPCR antagonists, have been reclassified as inverse agonists.23, 24 Within this two-state model, partial agonists are defined as compounds that generate a submaximal response even at concentrations that saturate all receptor sites whereas neutral antagonists would not discriminate between two conformers and thus would block agonist, partial agonist, and inverse agonist activities.25 In most drug development programs, initial evaluations of efficacy typically involve the measurement of a single signaling event downstream of receptor activation, such as the measurement of intracellular calcium mobilization, to identify compounds with the desired pharmacological properties.26 The two-state model posits that activity of a single ligand is determined by the relative ability to stabilize an active over an inactive receptor state, and hence the relative efficacy of a drug should be conserved regardless of the signaling event being measured. Thus, it would be expected that such a measurement could be used to predict efficacy in all systems including the therapeutic one, and a single functional assay would be sufficient to detect the efficacy of a ligand and to predict its action in all tissues at all signaling endpoints. This assumption has underpinned the majority of high-throughput screening approaches and SAR studies in both academia and industry. As such, descriptions of ligand affinity (KA ) are often supplemented with a value of the percent maximal effect relative to a reference ligand and/or the potency (EC50 ) of the ligand in a functional assay. However, it has long been acknowledged that the absolute potency of a GPCR agonist is as much dependent on the properties of the biological assay system, such as receptor density, that is used to determine agonist activity as it is on the molecular properties of affinity and efficacy (this is graphically demonstrated in Fig. 7).16, 27 In a system with high receptor density, two agonists with different levels of intrinsic efficacy may both appear to be full agonists. If the same two agonists are applied to a cell system with a much lower receptor density, one agonist may appear as a partial agonist as compared to the other. As such it is common to compare potency ratios between a group of agonists rather than absolute potencies when assessing novel agonist SAR, since such ratios should be independent from the influence of receptor density and stimulus–response coupling.

3. BEYOND TWO STATES: BIASED AGONISM More recently, it has become obvious that rather than “linear” (sequentially coupled) signaling cascades, GPCRs activate complex ensembles of G protein-dependent and -independent Medicinal Research Reviews DOI 10.1002/med

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signaling pathways.28, 29 Furthermore, evidence from pharmacological, biochemical, and biophysical experiments has demonstrated that different ligands acting at the same receptor can stabilize distinct receptor conformations linked to diverse functional outcomes in a manner that cannot be explained by simple differences in stimulus–response coupling (reviewed in 12, 13, 30 ). This phenomenon has been termed biased agonism, stimulus bias, ligand-directed signaling, or functional selectivity.8, 11–13 As such, receptors must be able to adopt multiple active state conformations, resulting in conformationally driven preference for cellular signaling proteins.10, 31 There is an ever increasing number of studies identifying biased agonists across a range of therapeutically important GPCR targets including 5-HT2 and 5HT1A serotonin receptors,32, 33 μ-opioid receptors (MORs),34, 35 β 2 adrenoceptors,36–39 dopamine D2L and D1 receptors,12, 40–43 chemokine receptor CCR7,44 melanocortin MC4 receptor,45 α 1A adrenoceptor,46 angiotensin type 1A (AT1A ) receptor,47–49 gonadotropin-releasing hormone receptor,50 type I parathyroid hormone receptor (PTH1 R).51 One of the early examples of biased agonism came from studies at serotonin type 2A and 2C receptors by Berg et al.33, 52 who observed that different 5-HT2A/2C receptor agonists could preferentially activate either phospholipase C-mediated inositol phosphate (IP) accumulation or phospholipase A2 -mediated arachidonic acid (AA) release. This was determined by the rank order of relative agonist maximal response (Emax ), which was different at the two pathways measured, whereas the order of potency remained unchanged. As such, they found that agonists lysergic acid diethylamide (LSD), (±)-1-(2,5-dimethoxy-4-iodophenyl)-2-aminopropane (DOI), and bufotenin favored AA release, while 3-trifluoromethylphenylpiperazine (TFMPP) and quipazine preferentially activated the IP pathway. Biased agonism offers the promise of pathway-selective rather than (or in addition to) receptor-selective drugs, particularly appealing if such an approach can separate the therapeutic effect of a drug from an unwanted side effect mediated by the same receptor target. Such an advantage has been suggested for analogues of angiotensin II, an endogenous AT1A receptor agonist. Unlike angiotensin II, the analogues SII (1 Sar, 4 Ile, 8 Ile-angiotensin II),47, 48, 53 and TRV120027 (H-Sar-Arg-Val-Tyr-Ile-His-Pro-D-Ala-OH) induce β-arrestin recruitment, receptor internalization, and β-arrestin-mediated signals without activating G protein coupling.8, 47–49, 53 TRV120027 is now in clinical trials for the treatment of heart failure54 and has shown a beneficial pharmacological profile.11, 55, 56 Such potential has also been highlighted at other GPCRs, notably the β 2 adrenergic receptor (β 2 AR) for the treatment of heart failure,36, 38 and at GPR109A (the target for niacin) for the treatment of dyslipidemia.57, 58 However, rather than being drug design programs designed to exploit therapeutic bias, these latter studies were retrospective, aimed at identifying bias in existing ligands and relating this to the clinical efficacy of existing drugs, such as the β-blocker carvedilol.36 It should also be noted that a biased ligand may not necessarily be therapeutically favorable, and side effects associated with a drug may be conferred by an unfavorable, and perhaps unappreciated, bias profile. Biased agonism also presents a significant challenge to drug discovery programs and the medicinal chemist in particular. Single signaling readouts are not adequately suited to detect the full repertoire of possible responses, and a more complete description of ligand activity should be achieved by using multiple screening assays with different endpoints. Furthermore, for the majority of drug targets, it is not yet clear what the desirable signaling endpoints are in terms of therapeutic efficacy, highlighting the difficulty of selecting the most appropriate endpoint. The particular challenge for the medicinal chemist is to be able to relate biased agonism to ligand structure. Indeed, this is highlighted by the paucity of studies that have attempted to do this, despite the general acceptance within the GPCR field of the existence of biased ligands and the potential that biased agonism presents for the generation of novel, selective GPCR ligands. Outlined below, we describe the studies that have provided some insights into the SAR of biased agonism. It is also clear that for biased targeting of GPCRs to be therapeutically applied in Medicinal Research Reviews DOI 10.1002/med

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the most effective manner, standard SAR must be enriched through parameters that describe the degree of bias. As such we will discuss analytical approaches to quantify biased agonism. Finally, the last 5 years have seen a boom in GPCR structural biology. Both active (agonist bound) and inactive (antagonist bound) structures have provided insights into mechanisms of GPCR activation and, by extension, the features of GPCR ligands that are important for their intrinsic efficacy. We will discuss the impact that such structures may have upon efforts to rationally design biased ligands.

4. STRUCTURAL INSIGHTS INTO EFFICACY: FULL AGONISTS, PARTIAL AGONISTS, AND BIASED AGONISTS A. Comparison of Rhodopsin-Like Class A GPCR Structures Reveals Conserved Structural Motifs All GPCRs have a canonical fold of seven transmembrane (TM) helical segments connected by three loops facing the intracellular environment (ICLs) and three loops facing the extracellular environment (ECLs). As a generalization, the extracellular region modulates ligand access, the core TM region binds ligands and transduces this signal to the intracellular region, and the intracellular region is the interface for interaction with intracellular signaling proteins, including heterotrimeric G proteins. Since 2007, more than 16 different rhodopsin-like class A family GPCR crystal structures have been determined across multiple receptor families,59–70 and the list is growing. For the most part, the structures have been determined cocrystallized with antagonists. However, there has been a more limited number of GPCRs cocrystallized with an agonist and, to date, these are limited to the β 1 adrenergic receptor (β 1 AR),67, 69 the β 2 AR,62, 65 the neurotensin receptor,71 the serotonin receptors 5HT1B and 5HT2B ,72, 73 , the adenosine A2A receptor (A2A R),68, 74 and the M2 muscarinic receptor (M2 mAChR).75 Comparison of these high-resolution crystal structures by Venkatakrishnan et al.76 has revealed conserved features; despite the sequence diversity across the GPCRs for which structures have been determined to date, they identified a consensus network of 24 inter-TM contacts mediated by 36 topologically different amino acids permitting diverse sequences to adopt a similar structure. Furthermore, there is a relatively conserved binding pocket in the extracellular side of the TM bundle in which residues from TM3, TM6, and TM7 typically contact the ligand in all receptors. Residues in the intracellular region and the cytoplasmic ends of TM regions bind downstream signaling effectors such as G proteins, GPCR kinases, and arrestins. Several studies have implicated a short amphipathic helix (often termed helix 8) proximal to the C terminus of TM7 as important for the binding of G proteins. For example, a polybasic motif proximal to helix 8 in many GPCRs has been shown to facilitate G protein precoupling.77 Residues in the C-terminal tail can be posttranslationally modified and that the pattern of this posttranslational modification (mediated by diverse GPCR kinases) can regulate the interaction of the receptor with partners such as β-arrestin.78–80 B. Mechanistic Insights Gained from Comparison of Active and Inactive GPCR Structures When compared with the corresponding inactive antagonist bound GPCR structures (see Fig. 2 for comparative topology of GPCR active and inactive conformations), important insights have been gained into the molecular mechanisms responsible for GPCR activation. There are now significant biochemical, biophysical, and structural data that suggest that GPCRs exist in a dynamic equilibrium between inactive and active states.18, 81 Binding of agonists shifts the equilibrium toward the active state whereas inverse agonists shift the equilibrium toward the inactive Medicinal Research Reviews DOI 10.1002/med

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Figure 2. The superimposed topology of GPCR active and inactive conformations revealed by X-ray crystal structures of the β 2 AR. Shown is the superimposition of β 2 AR inactive conformation (orange) in complex with the antagonist carazolol (2RH1),60 and β 2 AR active conformation (green) in complex with BI-167107 (3SN6).62 ˚ is identified by a gray arrow. For clarity, the extracellular part of The movement of TM6 (approximately 14 A) TM1 and 7, the heterotrimeric G protein (3SN6), the intracellular nanobody (Nb35, 3SN6), and the T4 lysozyme fused to the either the amino terminus of the β 2 AR (3SN6) or within the third intracellular loop (2RH1) have been omitted. The orthosteric binding site (OBS) has been identified as centroid of cocrystallized ligands, carazolol and BI-167107. Proteins aligned using Prime software94 and image generated using PyMol software.183 Adapted from Rasmussen et al.62

states of a receptor. Interestingly, activation of a GPCR appears to involve the communication of relatively modest structural rearrangements within the agonist-binding site to large-scale conformational shifts at the receptor’s intracellular side.62, 65, 82, 83 These crystal structures have also revealed the agonist–receptor interactions that underlie this mechanism and, as such, may provide a looking glass into the molecular determinants of ligand efficacy. Despite key differences within the agonist-binding pocket that confer ligand selectivity, comparison of inactive and active state structures (see Fig. 2) reveal common activation-related features with respect to Medicinal Research Reviews DOI 10.1002/med

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conformational changes on the intracellular sides of the GPCRs.62, 65, 76 The most pronounced common rearrangement of helices on the intracellular side includes an outward “swinging” motion of TM6 in concert with a movement of TM5. This opens the cleft required for the binding of the G protein. The magnitude of this motion varies across the three different receptors, ˚ in opsin, and ˚ in the A2A R,68 6 A for example, the intracellular tip of TM6 moves 3.5 A 75, 84–86 ˚ At the β 2 AR, cocrystallized with either a camelid nanobody 10 A for the M2 mAChR. ˚ respectively, as a G protein mimetic or the Gs G protein, TM6 moves as much as 11 and 14 A, suggesting that the full movement requires stabilization with the corresponding G protein and that this movement allows both the binding and activation of the G protein.62, 65 TM2 and TM7 also undergo substantial rearrangements during activation.62, 65, 87 In rhodopsin Arg3.50 (where 3.50 refers to Ballesteros–Weinstein nomenclature88 ) at the cytoplasmic end of TM3 forms a salt bridge with Glu6.30 in the inactive state that is broken after activation. However, this ionic lock is not common to the inactive conformation of all GPCRs85 . Comparison of the active states of the β 2 AR bound to a G protein and that of rhodopsin (metarhodopsin II) bound to a peptide that resembles the C-terminal tail of Gt reveals a consensus interface in the TM helices for G protein binding including residues from TM3, TM5, and TM6.62, 76, 89 In the β 2 adrenoceptor G protein complex, ICL2 was observed to interact with the N-terminus of the G alpha subunit and that the structure of ICL2 is crucial to this interaction.62 Finally, the extracellular region of GPCRs also undergoes conformational changes during activation. Solid-state NMR data demonstrated that activation of rhodopsin was accompanied by conformational changes in ECL2.90 Another study utilized 13 C-NMR spectroscopy to measure ligand-specific conformational changes around a salt bridge that connects ECL2 and ECL3 in the β 2 AR.91 Agonists, antagonists, and neutral antagonists were shown to stabilize distinct conformations. Within the binding pocket, comparison of the active structures determined to date, along with the corresponding inactive structures, reveals modest but well-defined changes of binding pocket residues.76 These changes include relocation of TM3 and TM7 and translation/rotation of TM5 and TM6. In all cases, these movements are accompanied by a rearrangement of conserved hydrophobic and aromatic residues deeper in the receptor core. As a result there is a rearrangement at the TM3-TM5 interface and formation of new noncovalent contacts at the TM5-TM6 interface.83 The agonist induced local structural changes are translated into larger scale helix movements through distinct activation pathways. One of the most conserved liganddependent rearrangements in the binding pocket involves a shift of the Trp6.48 residue.83 The A2A R and rhodopsin active structures reveal direct steric interactions of Trp6.48 with agonists that stabilize a shift of Trp6.48 that is accompanied by the swinging movement of TM6. In contrast at the β 2 AR, Trp6.48 seems to have an indirect role in receptor activation.62, 65, 82, 92 Instead, conformational changes that promote TM6 motion in β 2 AR depend largely on agonists engaging in polar interactions with Ser5.42 and Ser5.46, stabilizing an inward shift of the extracellular part of TM5.65 Further details of this conformational change, as resolved in the crystal structures of active β 2 AR, show that this movement in TM5 results in a rotamer switching in Ile3.40, ˚ movement of the Phe6.44 side chain and a correspondwhich is, in turn, coupled with a 4 A 65, 66 Another general site of conformational changes in the binding pocket ing swing of TM6. involves TM3 and TM7. The specifics of the changes in this site, however, vary for different receptors. In rhodopsin, light activation results in disruption of a salt bridge between Glu3.28 and the Lys7.43 Schiff base linked to retinal, and it corresponds to an increase of the distance ˚ 84 In the A2A R, the ribose rings of agonists between TM3 and TM7 by approximately 2–3 A. participate in a strong hydrogen-bonding network with Thr3.36 and Ser7.42/His7.43, decreas˚ compared with A2A R in complex ing the distance between TM3 and TM7 by approximately 2 A with an inverse agonist.68 In the β 2 AR, Asp3.32 and Asn7.39 are bridged by an ethanolamine tail in both agonists and antagonist complexes, and the distance between these residues in β 2 AR does not change substantially between the active- and inactive-state crystal structures.62, 65, 82 Medicinal Research Reviews DOI 10.1002/med

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Prior to the publication of agonist bound crystal structures, experimental evidence from mutagenesis and biophysical studies had given some insight into the molecular determinants of partial as compared to full agonism particularly for aminergic receptors.93–102 For example, using fluorescence spectroscopy to monitor kinetically distinguishable conformational states in combination with radioligand-binding experiments and molecular modeling, the partial agonist, (R)-salbutamol, was found to occupy a slightly different binding orientation within the β 2 AR binding pocket as compared to (R)-isoproterenol.101 The main difference was in terms of interactions at Ser5.42, Ser5.43, and Ser5.46 in the binding site caused by modification of a phenol to benzyl alcohol by an additional methylene unit. It was suggested that this distinct orientation within the binding pocket stabilized a discrete conformational state of the β 2 AR that was less active than that stabilized by (R)-isopreterenol.101 More recently, further insights have been gained into how specific receptor–ligand interactions contribute to ligand efficacy from high-resolution structures of the turkey β 1 AR cocrystallized with both partial agonists ((R)salbutamol and (R)-dobutamine) and full agonists ((R,R)-carmoterol and (R)-isopreterenol), see Figure 3.69 As expected, the amine moieties of all the agonist ligands (plus the β-hydroxyl for all agonists except (R)-dobutamine) form interactions with Asp3.32 and Asn7.39. In addition, all the agonists can form a hydrogen bond with Ser 5.42 and induce the rotamer conformation change of Ser5.43 so that it makes a hydrogen bond with Asn6.55 (as shown in Fig. 3b for (R)-isoproterenol a full agonist and Fig. 3c for (R)-salbutamol a partial agonist). However, the full agonists (Fig. 3b) form a second hydrogen bond with Ser5.46 not seen for partial agonists, as in Figure 3c. Thus, the number of polar interactions formed by the serine residues on TM5 appears to represent a marker of partial versus full agonism (compare Fig. 3b and c). However, it is not clear from these structures how such subtle differences in the binding pocket affect the nature of larger intracellular conformational changes since the receptor construct used in these studies represents one stabilized in a largely inactive conformation.69 C. The Dynamic Nature of GPCRs—Insights into the Molecular Mechanism of Biased Agonism Although crystal structures give a highly detailed picture, it is only representative of a certain energy state or conformation. As such they can only provide limited information about the range of different functional conformations that the receptor can adopt. Unbiased molecular dynamics simulations at approximately 10 ms time scale has allowed for observations of large conformational changes, such as active-to-inactive transitions in β 2 AR as well as prediction of ligand-binding paths and kinetics.81 To gain a more dynamic understanding of GPCR signaling, biophysical approaches have shown utility in assessing local and global conformational changes in GPCRs. These methods include fluorescent spectroscopy,103–106 double electron– electron resonance,84, 107 hydrogen–deuterium exchange coupled with mass spectrometry,108–110 residue-specific stable-isotope labeling followed by mass spectrometry analysis,111 and nuclear magnetic resonance (NMR) spectroscopy.91 In particular, NMR techniques have been useful to assess dynamic ligand-dependent conformational changes in GPCRs. For example, studies have utilized 13 C-NMR spectroscopy to measure ligand-specific conformational changes around a salt bridge that connects ECL2 and ECL3 in the β 2 AR.91 Another NMR technique to probe dynamics of the intracellular changes in β 2 AR has taken advantage of spectral 19 F labels to provide evidence that TM6 and TM7 can each adopt at least two major conformational states that have a slow (>100 ms) exchange rate.112 The conformational equilibrium distribution between these states is controlled by ligand binding: antagonists and inverse agonists keep the β 2 AR in a predominantly inactive state, whereas the binding of partial agonists, and especially full agonists, shifts the conformational equilibrium toward an active state in both TM6 and TM7.112 However, in the case of the β-arrestin-biased β 2 AR ligands carvedilol and Medicinal Research Reviews DOI 10.1002/med

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H N

OH OH OH

(R)-Isoproterenol β1AR agonist

H N

r 9

OH OH

OH (R)-Salbutamol β1AR partial agonist

Figure 3. (a) Overlay of crystal structures of the turkey β 1 AR with stabilizing mutations and bound full agonist (R)-isoproterenol (9, light blue, 2Y03) or partial agonist (R)-salbutamol (claret, 2Y04)69 ; TM4 has been removed for clarity. (b) zoomed section of binding pocket showing (R)-isoproterenol (dark cyan carbons) and hydrogenbonding network (orange dashes); and (c) zoomed section of binding pocket showing (R)-salbutamol (salmon carbons) and hydrogen-bonding network with amino acid residues. A key difference is the orientation of Ser5.46 and its ability to form a hydrogen bond to the full agonist (R)-isoproterenol, but not the partial agonist (R)salbutamol. The ligands differ in chemical structure (d) primarily by the modification of catechol ((R)-isoproterenol) to hydroxymethylphenol ((R)-salbutamol). Images were generated using PyMol software, and adapted from Warne et al.69

isoetharine, an increase in the occupancy of the TM7-activated state was observed compared to that of unbiased ligands, carazolol and isoproterenol. Unlike typical β 2 AR agonists, the biased ligands showed minimal changes in TM6. These results are consistent with a specific association of TM7 changes with β-arrestin-biased signaling of carvedilol and isoetharine. Furthermore, it seems that the conformational changes in TM6 and TM7 can become uncoupled in a Medicinal Research Reviews DOI 10.1002/med

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ligand-dependent manner. Crystal structures of the β 1 AR in complex with the β-arrestin-biased ligands carvedilol and bucindolol demonstrate that these ligands make additional interactions with residues in ECL2 and TM7, which may promote G protein-independent signaling.113 Finally, lanthanide-based resonance energy transfer spectroscopy studies on the vasopressin type 2 receptor showed that G protein-biased agonists stabilize a conformation of TM6 and TM7 that is distinct from that displayed by β-arrestin-biased ligands.103 One recent study to combine both structural biology and analytical pharmacology focused on the action of the hallucinogen LSD and other ergot alkaloids including ergotamine and dihydroergotamine upon the serotonin 5-HT1B and 5-HT2B receptors.72, 73 Using an analytical pharmacology methodology to quantify bias (described in full in the following section), the authors demonstrate that ergotamine displayed bias for recruitment of β-arrestin over G protein signaling at the 5-HT2B receptor but was unbiased at the 5-HT1B receptor.72 Crystal structures revealed key differences in receptor conformation, particularly at the junction between ECL2 and TM5, which may underlie the distinct bias profile of ergotamine at the two receptors (Fig. 4). In the ECL2 of the 5-HT2B receptor structure, an additional helical turn is present, and stabilized by a water molecule. This helical turn shortens the distance between the extracellular tip of TM5 and the conserved disulfide bond that connects ECL2 and TM3 (Fig. 4a and b). This creates a conformational constraint on the position of helix V causing ergotamine to form additional hydrophobic contacts with residues in TM5 and TM6 (Fig. 4c). These additional interactions prevent the rearrangements of TM6 observed in the 5-HT1B receptor and other active state GPCR structures. In contrast TM7, a region implicated in β-arrestin recruitment, was shown to be in intermediate active states in both receptor structures. However, the relationship between biased agonism and patterns of conformational changes within a GPCR are likely to be complex and receptor dependent. With this in mind, although a conformational change in a particular region of a receptor may be identified as being a determinant of a given signaling event, one should be cautious about extrapolating such observations across the entire GPCR family. To investigate conformational changes in the receptor mediated by nine distinct ligands, ranging from inverse agonists, to partial agonist and full agonists, Kahsai et al.111 used a mass spectrometry strategy that measured the specific reactivity of nine amino acids in distinct regions of the β 2 AR. Only two residues displayed patterns of reactivity that tracked with the efficacy of the compounds determined in assays measuring G protein activation, β-arrestin recruitment, and ERK1/2 activation. For the other seven residues, the ligands engendered distinct patterns of reactivity suggesting that there is significant variability in receptor conformations induced by ligands even for those ligands that appear to be functionally similar. This suggests that we are still some way from a molecular understanding of how these conformational changes in the receptor are translated into cellular signaling events. The combination of crystallography, biophysical approaches, and improved computational methods may allow a greater understanding of the molecular mechanisms that underlie biased agonism in the future and ultimately facilitate the rational design of biased ligands. However, such approaches will also rely on appropriate pharmacological profiling of compounds and analytical methods to identify biased ligands and provide parameters that quantify biased agonism.

5. STRUCTURE–ACTIVITY RELATIONSHIPS OF BIASED AGONISM AT GPCRS A. Biased Action of β-Blockers The phenomenon of biased agonism has been accepted for a number of years, yet the majority of studies focusing on ligand-biased signaling have used empirical observations, such as Medicinal Research Reviews DOI 10.1002/med

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Figure 4. (a) Superimposition of the 5-HT1B R (4IAR, blue) and 5-HT2B R (4IB4, yellow) crystal structures in complex with ergotamine (shown in sticks). TM3, TM4, and part of the ECL2 have been removed for clarity. (b) Zoomed cut-away section of the ergotamine binding site showing the additional helical turn in ECL2 of the 5-HT2B R structure, and the associated stabilized water molecule (red sphere), resulting in a different position of ergotamine in the 5-HT2B R binding site. (c) Residues involved in the additional hydrophobic interactions between ergotamine and the 5-HT2B receptor (yellow sticks). Concomitant changes in the ergotamine binding site implicated in directing the biased agonism of ergotamine at the 5-HT2B R but not the 5-HT1B R are highlighted with yellow surfaces. The hydrogen bond between Ser5.44 and Asn6.55 is shown in orange dashes. For the labeling of the residues, the first letter corresponds to 5-HT1B (4IAR) and the second to 5-HT2B (4IB4). Images created using PyMol software,183 (a) and (b) adapted from Wacker et al.72

reversals in agonist potency orders or maximal agonist effects between different pathways, as key indicators of biased agonism. One important example of such observations is the ability of β-blockers, initially described as antagonists at the β-adrenergic receptors, to activate the mitogen-activated protein kinase (MAPK) signaling pathways while antagonizing the cAMP production pathway. In particular, a study from Bouvier and colleagues illustrated this phenomenon using multiple structurally related ligands at two closely related receptor subtypes, and two effector systems.38 Biased agonism was identified empirically through either comparison of rank orders of potency/maximal agonist response, or by comparison of efficacy profiles (agonist and antagonist/inverse agonist at different pathways). A particularly interesting aspect of this study was the ability to correlate relatively minor structural modifications with biased signaling, as seen in Table I. In particular, the efficacy of propranolol was found to be different depending on the pathway studied, whereby it behaved as an inverse agonist for the adenylate cyclase pathway but an agonist of the ERK1/2 pathway for both β 1 AR and β 2 AR. However Medicinal Research Reviews DOI 10.1002/med

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Table I. Chemical Structures and Qualitative Descriptions of Efficacy Profiles of β 1 AR and β 2 AR Ligands, Propranolol, Metoprolol, Bisoprolol, and Atenolol β 1 AR Ligand

Structure

(±)-Propranolol

H N

(±)-Metoprolol

H N

(±)-Bisoprolol

H N

(S)-(-)-Atenolol

H N

β 2 AR

AC

pERK1/2

AC

pERK1/2

Inverse agonist

Agonist

Inverse agonist

Agonist

Inverse agonist

Neutral antagonist

Inverse agonist

Inverse agonist

Inverse agonist

Neutral antagonist

Inverse agonist

Inverse agonist

Inverse agonist

Neutral antagonist

Inverse agonist

Inverse agonist

OH O

OH O

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OH O

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NH2 O

Homologous structural features are highlighted in brown. Modification of naphthyl (biased ligand, propranolol) to 3-substituted phenoxy substituents caused a change in biased agonism at ␤1/2 AR. Ligand efficacy patterns toward AC and pERK1/2 pathways are shown for the ␤1 AR and ␤2 AR. Adapted from Galandrin et al.37

the modification of the lipophilic naphthyl substituent to more polar, 3-substituted phenoxy substituents (metoprolol, bisoprolol, and atenolol) resulted in complete loss of agonism (and bias) at the β 1 AR and β 2 AR, with all compounds displaying activity as antagonists/inverse agonists in both effector pathways. This is relevant as it suggests the role of specific nonpolar interactions with the aromatic region of the ligand to be crucial for signal bias and that one can gain structural insight into the requirements for biased ligands at the β 1 AR and β 2 AR. B. Biased Agonists as a Novel Approach for the Design of Safer Opioid Analgesics Morphine has been used as an analgesic for millenia. The pharmacological action of morphine is mediated by the MOR. A wide range of MOR agonists have been developed to treat severe pain including morphine derivatives such as oxycodone. However, all of these agents suffer from similar adverse effects including constipation, nausea, vomiting, sedation, pruritis, respiratory depression, and the development of tolerance and dependence. Two key observations pointed toward the importance of biased agonism at the MOR. First, morphine shows compromised MOR internalization despite having significant efficacy at mediating G protein activation.34, 35 Second, morphine elicited increased maximal effect and duration of analgesic response with reduced side effects in β-arrestin-2 knockout mice compared to wild-type mice.114 Chen and colleagues of Trevena Inc. set out to identify novel analgesics with the hypothesis that G protein pathway-biased MOR agonists would be more efficacious with reduced adverse effects.115 An initial screening program identified a 4-phenyl-4-(2-benzylaminoethyl)tetrahydropyran analogue novel MOR agonist that displayed lower maximal effect in a β-arrestin recruitment assay as compared to morphine (Fig. 5). Development of this lead revealed distinct SAR for Medicinal Research Reviews DOI 10.1002/med

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Figure 5. Development of novel MOR receptor ligands that display bias toward activation of G protein-coupled pathways as compared to β-arrestin recruitment. An initial hit from a library screen (1) that displayed lower maximal effect than morphine in a β-arrestin recruitment assay was further optimized to yield compounds (R)-6 and (R)-19, compounds with increased potency in a G protein assay (inhibition of cAMP production) and lower maximal effect in the β-arrestin assay. However, although compound (R)-19 displayed potent hERG channel inhibition. Exploration of substitution of the 2-thiphene and 2-furan led to the development of [(3-methoxythiophen2-yl)methyl]({2-[(9R)-9-(pyridin-2-yl)-6-oxaspiro[4.5]decan-9-yl]ethyl})amine (TRV130). This ligand retained the desired in vitro profile of (R)-19, did not cause any proarrythmic effects, and was shown to be potently analgesic in mice and rats with less gastrointestinal side effects as compared to morphine. This ligand is currently being evaluated in human clinical trials for the treatment of acute severe pain (Emax displayed is a percentage of that of morphine at saturation concentrations).115

β-arrestin recruitment as compared to the cAMP assay. Because 2,2-diethyl substitution of the tetrahydropyran functionality retained activity in the cAMP assay, a set of spirocyclic ring analogues were prepared of which a spirocyclopentane analogue was the most potent. Of interest, characterization of the enantiomers of this analogue revealed that not only was the (R)-enantiomer ((R)-6) 16-fold more potent in the cAMP assay as compared to the (S)enantiomer but it also has a much lower maximal effect in the β-arrestin assay. Further optimization yielded compound (R)-19 that displayed relatively potent activity in the cAMP assay but low maximal effect in the β-arrestin assay. Although this compound displayed a favorable in vivo profile in terms of analgesic effect and the adverse affect of constipation as compared to morphine, it also displayed potent hERG channel current inhibition that precluded further preclinical development. Further exploration of substitution of the 2-thiophene and 2-furan resulted in the identification of [(3-methoxythiophen-2-yl)methyl]({2-[(9R)-9-(pyridin-2-yl)-6oxaspiro[4.5]decan-9-yl]ethyl})amine (TRV130) that retained the desired in vitro profile of Medicinal Research Reviews DOI 10.1002/med

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(R)-19 but did not cause any proarrythmic effects. TRV130 was a potent analgesic in mice and rats while causing less gastrointestinal dysfunction and respiratory suppression than morphine at equianalgesic doses.115 C. Biased Agonists as Novel Antipsychotics The dopamine D2 receptor was one of the first targets at which biased agonism was identified, and more recently, biased agonism at the D2 R has been suggested to underlie antipsychotic efficacy.12, 40, 41, 43, 116, 117 Initial studies suggested that the antipsychotic efficacy of aripiprazole was entirely consistent with its action as a partial agonist. However, Mailman and colleagues observed that aripiprazole acted as a functional antagonist at D2L Rs expressed in a CHO cell line in an assay measuring inhibition of forskolin-stimulated cAMP production.118 In contrast, in corresponding experiments performed using a C-6 glioma cell background aripiprazole was shown to act as a potent partial agonist. These data were difficult to rationalize within a simple two-state model since the levels of receptor expression were fourfold higher in the CHO cell line. The authors suggested that biased agonism could explain these data.118 In another study by Mailman and co-workers, dopamine, quinpirole, and (S)-(-)-3PPP displayed higher potencies than aripiprazole in an assay measuring MAPK phosphorylation, whereas a reversal in the order of potencies was observed in an assay measuring AA release in which aripiprazole was the most potent.43 This reversal of the orders of potency can only be reconciled with biased agonism.12 More recently Caron and co-workers reported that while aripiprazole acted as a potent agonist in an assay measuring inhibition of forskolin-stimulated cAMP, it acted as an antagonist in an assay measuring the recruitment of β-arrestin.117 Importantly, these authors have provided evidence that β-arrestin acts as a scaffolding protein to recruit a signaling pathway involving Akt and glycogen synthase kinase-3 that is important for regulation of behavior by dopamine.119 The authors suggest that antagonism of this pathway may be a requisite for antipsychotic efficacy.119–122 However, the reference agonist quinpirole demonstrated tenfold lower potency in the β-arrestin recruitment assay as compared to the cAMP assay, making it unclear whether stimulus coupling efficiency rather than biased agonism may explain the antagonist action of aripiprazole at the β-arrestin recruitment assay. Indeed, such data highlight the utility of more quantitative approaches to identify biased agonists. Until recently, only modest structure–activity relationships for aripiprazole have been reported and such studies were not extended to measurements of biased agonism.123, 124 However, using the hypothesis that D2 R signaling via β-arrestins is important for the therapeutic actions of antipsychotic agents, Allen and co-workers recently used a diversity-orientated modification of the scaffold represented by aripiprazole in an effort to develop ligands that display bias toward recruitment of β-arrestin (Fig. 6a). In terms of the phenylpiperazine core, various mono- and disubstituted phenyl groups (including halo, alkyl, alkoxy, cyano, and trifluoromethyl) were explored in addition to various cyclic amines (piperidine and homopiperazine). Extending away from the privileged phenylpiperazine core, the study explored various linkers (alkyloxy, alkenyloxy, alkynyloxy, (cycloalkyl)alkyloxy, and alkylphenoxy) and bicyclic heterocycles (quinolinone, dihydroisoquinolinone, dihydronaphthyridinone, indazole, benzimidazolone, and benzothiazole).116, 125 Measurements of agonist effect at the D2 R were obtained in assays measuring Gi -regulated cAMP production and the recruitment of β-arrestin-2. In contrast to previous observations, aripiprazole was a partial agonist with similar maximal effect and potency in both the cAMP and β-arrestin-2 recruitment assays, perhaps attributable to the increased sensitivity of the assays used in this latter study to detect β-arrestin recruitment. Overall the electronic nature, steric bulkiness of substituents on the phenyl ring and the various patterns of substitution did not affect patterns of D2 R-biased agonism. However, replacing the piperazine group in the middle amino region of aripiprazole with the more basic piperidine Medicinal Research Reviews DOI 10.1002/med

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Cl 'homopiperazine' analogue

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UNC9975

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Figure 6. (a) Four regions of aripiprazole investigated in terms of biased agonism (termed by the authors structure–functional selectivity relationships) including substitution of the left-hand side (LHS) phenyl moiety, the middle amino region, the central linker region, and the right-hand side (RHS) aromatic region. (b) Modifications to the linker region, the middle amino region, and the RHS bicyclic aromatic region confer bias toward the β-arrestin pathway. (c) The authors combined these observations to generate three compounds UNC006, UNC9975, and UNC99944 as high-affinity β-arrestin-biased ligands at the D2 R.116

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group increased the potency of β-arrestin recruitment as compared to that determined in the cAMP assay. A more significant modification from aripiprazole in terms of bias was changing the 2,3-dichlorophenylpiperazine core to structurally related 2,3-dichlorophenylhomopiperazine or 2,3-dichlorophenylpiperidine. These compounds with the modified middle amino region (Fig. 6b) behaved as partial agonists of β-arrestin-2 recruitment but as antagonists of Gi regulated cAMP production, indicative of biased agonsim as compared to the parent compound. In terms of the central linker region, introducing an acetylene (Fig. 6b), a meta-benzyl group, and/or a para-benzyl group gave compounds that again showed agonist activity but with reduced potency at the β-arrestin-2 recruitment assay, and were also antagonists in the cAMP assay. However, this apparent bias was also accompanied by a loss of affinity for the D2 R. Replacement of the bicyclic aromatic dihydroquinolinone motif of aripiprazole with a dihydroisoquinolinone, dihydronaphthyridinone, indazole, benzimidazolone, and benzothiazole resulted in apparent bias toward β-arrestin-2 recruitment over Gi G protein signaling with the dihydronaphthyridinone motif (Fig. 6b) exhibiting the greatest affinity. Thus the piperidine core, bicyclic aromatic moiety, and central linker that connect them appear to be important for the bias profile of these reported compounds. Finally the authors combined these observations to generate three compounds, UNC0006, UNC9975, and UNC9994 (Fig. 6c) as high-affinity β-arrestin-biased ligands at the D2 R. Importantly, UNC9975 displayed potent antipsychoticlike activity in an animal behavioral model without inducing catalepsy. When the same experiments were performed in β-arrestin-2 knockout mice, the antipsychotic activity was attenuated and the compound induced catalepsy. These results are somewhat difficult to reconcile with the previous findings of Masri et al. that suggested that it was aripiprazole’s ability to antagonize β-arrestin recruitment that confers its antipsychotic efficacy.117 As such, it is clear that further studies are required to elucidate the D2 R-mediated signaling pathways important for the treatment of schizophrenia. It should be noted that, while amphetamine-induced hyperlocomotion is associated with altered mesolimbic dopamine transmission and provides a proxy marker for the positive symptoms of schizophrenia, the ability to reverse this locomotion is not necessarily a predictor of antipsychotic efficacy in the clinic.126 Thus, it will be interesting to see whether β-arrestin-biased ligands such as UNC9975 can be developed into clinically effective antipsychotics. D. The Limitations of Qualitative Approaches to Identify Biased Agonists The qualitative approaches to identify biased agonists described in Section 5, are suboptimal approaches to generate structure–activity relationships of bias. The potency of a ligand toward a given pathway is determined by both its affinity for the receptor state governing that pathway and its intrinsic efficacy for generating stimulus to that pathway. In contrast, maximal effect is only determined by the intrinsic efficacy but not the affinity. Thus unless both potency and maximal effect are incorporated into any analysis of biased signaling, important information will be lost. Second, as discussed above, the potency and maximal effect of a ligand will be influenced by cell-dependent differences in signal amplification, the efficiency of coupling to a particular signaling endpoint, and/or receptor expression. Thus for a high-efficacy agonist (see Fig. 7b), changes in receptor expression cause observed differences only in potency and not efficacy within the range of this simulation, whereas a low-efficacy agonist (Fig. 7c) may appear to be a full agonist or an antagonist depending on the level of receptor expression (compare Fig. 7a and 7c). Of importance, similar effects would be observed when comparing the action of agonists at two signaling pathways of different coupling efficiencies or degree of amplification. Therefore, a methodology is needed to distinguish such “system bias” from biased agonism in cases where such extreme reversals in potency or efficacy are not observed. Medicinal Research Reviews DOI 10.1002/med

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Figure 7. Simulated data demonstrate that receptor density can have a profound effect upon both the potency and maximal effect of an agonist. Using the operational model developed by Black and Leff,127 we performed simulations using GraphPad Prism (version 6.00)184 to assess the effect of up to a 300-fold increase in levels of relative receptor expression ([receptor] = 1–300) upon two ligands with differing intrinsic efficacies. (a) The intrinsic efficacy of ligand A was set to a level 300-fold higher than that of ligand B (KE = 0.033–0.1) and the affinity of both ligands was set to 100 μM (pKA = −4). The Hill slope was set to 1, the basal effect was set at 0, and the maximal effect of the system was set at 100. As receptor levels increase, the high-efficacy agonist gains potency. However, in the case of the low-efficacy agonist, we first observe increases in maximal effect followed by increases in potency. Thus in a system with high receptor expression (b), ligand A appears to have a comparable maximal effect to that of ligand B (i.e., both could be described as full agonists). In contrast, in the system with lowest receptor expression (c), ligand A still gives a robust dose–response curve, whereas ligand B has no effect.

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6. QUANTIFICATION OF BIASED AGONISM CAN FORM AN ESSENTIAL PART OF GPCR-SAR A. The Operational Model of Agonism as an Approach to Quantify Biased Agonism The quantification of parameters that describe the ability of a ligand to bind to or activate its receptor target has been an essential part of drug discovery and drug development. For example, the quantification of a ligand’s dissociation constant (KA ) as a measure of receptor affinity allows one to compare the affinity of different ligands at the same receptor, as well as the affinity of a ligand across a range of different receptor targets. As such, the quantification of a single parameter that describes ligand efficacy at one pathway as compared to another can allow the medicinal chemist to determine how biased a ligand is for one pathway over another, and relate such observations to ligand structure.9 An approach that can relate changes in biased agonism to changes in ligand structure will be far more likely to develop useful SAR around biased ligands. For such a methodology to be useful, it must be applicable to routinely derived concentration–response data. One of the most important analytical tools in pharmacology that satisfies these criteria is the “Operational Model of Agonism” first derived by Black and Leff.127, 128 E = Basal +

(Em − Basal)τ n [A]n τ n [A]n + ([A] + KA )n

(1)

where E is the effect of the ligand, [A] is the concentration of agonist, Em is the maximal response of the system, Basal is the basal level of response in the absence of agonist, KA denotes the functional equilibrium dissociation constant of the agonist, and n is the slope of the transducer function that links occupancy to response. This model treats the entire stimulus–response cascade of a receptor-linked signaling pathway as a virtual enzyme system with a Michaelis–Menten-type constant, KE that defines the intrinsic efficacy of an activated ligand–receptor complex. As discussed above, the observed response will be influenced by the receptor density [Rt ] in the system. Taking the ratio of [Rt ]/KE yields an overall operational efficacy parameter, τ . Thus the equilibrium dissociation constant, that is, affinity, for the receptor (denoted as KA ) coupled to a particular effector protein of signaling pathway, and τ , which encompasses both the intrinsic efficacy of the agonist in activating a particular cellular response pathway and receptor density, are sufficient to quantify the overall activity of an agonist for a given signaling pathway, and can be combined into a “transduction coefficient” (τ /KA ), as seen by dividing both numerator and denominator terms in (1) by KA :9   τ n (Em − Basal) [A]n KA E = Basal +  (2)   n τ n [A] [A]n + +1 KA KA Fitting a family of concentration–response curves for different agonists at the same receptor-mediated signaling pathway can almost always derive the transduction coefficient. As an example,46 concentration–response data derived for a series of phenethlyamine derivatives at the α 1A -AR in assays measuring intracellular Ca2+ mobilization or extracellular acidification rate (ECAR) were analyzed according to this method to derive values of log(τ /KA ) (see Fig. 8b, i).46 However, the magnitude of this coefficient for each agonist will still be dependent upon the coupling efficiency of the signaling pathway. For example, the value of log(τ /KA ) calculated for the reference agonist norepinephrine is significantly higher in the Ca2+ assay as compared Medicinal Research Reviews DOI 10.1002/med

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Figure 8. (a) Chemical structures of a series of phenethylamine derivates targeting the α 1A -AR, including norepinephrine (norepi), epinephrine (epi), methoxamine (methox), and phenylepinephrine (phen).

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to that devised from the ECAR assay (log(τ /KA )Ca2+ = 9.01 as compared to log(τ /KA )ECAR = 7.48, respectively (see Fig. 8b, ii). To eliminate this system bias, the value of log(τ /KA ) for each agonist can be normalized to the value of log(τ /KA ) for the same reference agonist at each pathway, giving log(τ /KA ) (see equation (3), in the case of the example data the reference agonist is norepinephrine (see Fig. 8b, iii).       τ τ τ = log log − log (3) KA KA ligand KA reference agonist This normalized value can then be compared across pathways by subtracting the log(τ /KA ) obtained for an agonist at one pathway from that obtained for the same agonist at another (equation (4) to give log(τ /KA ), which can then be converted into a “bias factor” log(τ /KA ) (equation (5) (see Fig. 8b, iv).       τ τ τ = log log − log (4) KA KA pathway 1 KA pathway 2 log



bias factor = 10

τ KA



(5)

The error associated with this value of bias can be calculated to allow statistical analysis. Of interest, distinct bias profiles were observed within this small set of highly structurally similar compounds (see Fig. 8a), while no significant bias was observed between the two endogenous agonists norepinephrine and epinephrine. However, phenylephrine (which lacks the catecholamine para-hydroxyl group of epinephrine and norepinephrine) shows significant bias toward ECAR as compared to the agonists epinephrine and norepinephine. Importantly, this value is quantitative. The value of log(τ /KA ) = 1.52 for phenylepinephrine means that this agonist is 33-fold biased toward ECAR as compared to norepinephrine. This allows for a single robust and simple number to compare and contrast biased agonists multilaterally, and to assess the statistical significance of this bias. For example, although methoxyamine displays fourfold bias toward ECAR as compared to epinephrine, this bias is not significant. Furthermore, one can start to build SAR, in this case focusing on the absence or presence of the para-hydroxyl group for determination of biased agonism. This approach becomes even more powerful if such quantitative observations can be related to receptor structure. Of interest, a series of partial agonist and full agonist-bound structures of the β 1 AR demonstrated that the para-hydroxyl group of the full agonists only interact with Ser5.46.69 Therefore, interactions with key residues in TM5 may underlie the biased agonism of phenylephrine toward the ECAR pathway. This example also demonstrates how relatively subtle receptor–ligand interactions may engender significantly different bias profiles across a series of structurally similar ligands. In a recent study, we generated SAR based around a novel dopamine D2 receptor partial agonist, tert-butyl (trans-4-(2-(3,4-dihydroisoquinolin-2(1H)-yl)ethyl)cyclohexyl)carbamate (MIPS1026) making use of the transduction coefficient method to quantify bias.129 This ligand shares structural similarity to cariprazine, a drug awaiting FDA approval for the  (b) (i) Concentration–response data derived for compounds at the α 1A -AR in assays measuring intracellular

Ca2+ mobilization or extracellular acidification rate (ECAR); (ii) combination of the intrinsic efficacy of the agonist and receptor density quantified to give the overall activity of an agonist for a given signaling pathway, “transduction coefficient” (τ /KA ); (iii) to eliminate system bias, the value of log(τ /KA ) for each agonist normalized to the value of log(τ /KA ) for the same reference agonist at each pathway, giving log(τ /KA ); (iv) comparison across pathways by subtraction of the log(τ /KA ) obtained for an agonist at one pathway from that obtained at another to give a “bias factor” log(τ /KA ). *Significant changes in bias, one-way ANOVA with Tukey’s posttest, P < 0.05. Data have been adapted from Evans et al.46

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Figure 9. Comparison of the structures of MIPS1026 and cariprazine reveals structural similarities in the cyclohexylene “spacer” group but key differences in the head group (tetrahydroisoquinoline vs. 2,3dichlorophenylpiperazine) and tail group (tert-butyl carbamate vs. dimethyl urea). Cariprazine displays 218-fold bias toward the cAMP pathway as compared to dopamine and 68-fold as compared to MIPS1026. Replacement of the dimethyl urea group of cariprazine with the tert-butyl carbamate group of MIPS1026 to give the compound MIPS1277 decreases bias toward the cAMP pathway 42-fold. *Significant changes in bias, one-way ANOVA with Tukey’s posttest, P < 0.05. Adapted from Shonberg et al.134

treatment of schizophrenia. However, MIPS1026 and cariprazine display significantly different (60-fold) bias profiles between an assay measuring cAMP, and that measuring phosphorylation of ERK1/2, with cariprazine demonstrating 230-fold bias toward the cAMP pathway as compared to dopamine. We synthesized a number of derivatives of MIPS1026 with subtle structural modifications, including incorporation of cariprazine fragments. By combining pharmacological profiling with analytical methodology to identify and quantify bias, we have demonstrated that efficacy and biased agonism can be finely tuned by minor structural modifications to the head group containing the tertiary amine, a tail group that extends away from this moiety and the orientation and length of a spacer region between these two moieties. For example, although both compounds have a conserved spacer group they differ in their tail group (MIPS1026; tert-butyl carbamate, cariprazine; N,N-dimethylurea). Exchange of the N,N-dimethylurea of cariprazine for tert-butyl carbamate caused a 42-fold decrease in bias (MIPS1277, Fig. 9). Recent reports from Gmeiner and co-workers130, 131 have also combined medicinal chemistry with analytical pharmacology, using an operational model of agonism to quantify biased Medicinal Research Reviews DOI 10.1002/med

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(a) Bias: cAMP - pERK1/2 O

O O

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FAUC335 ΔΔlog(τ/KA) = -0.43

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Figure 10. (a) Chemical structures of structurally related phenylpiperazine derivatives that acted unbiased and biased agonists at the D2 R. The ligands (FAUC346, FAUC335, FAUC321, and FAUC350) were compared in their ability to activate ERK1/2 phosphorylation and inhibit cAMP production and bias was quantified using the transduction coefficient method (log(τ /KA )), where positive values indicate bias toward cAMP and negative values indicate bias toward pERK1/2 relative to quinpirole.131 A bias value could not be calculated for FAUC350 since this compound did not display activity in the cAMP assay. (b) Enantiomers of (N-(4-(4-phenylbenzoylamino)butyl)N-propyl-5-amino-4,5,6,7-tetrahydropyrazolo[1,5-a]pyridine (5a) display distinct bias profiles at the D2 R. The ligands were compared in their ability to stimulate [3 H]thymidine incorporation and inhibit cAMP production. Bias was quantified using the transduction coefficient method (log(τ /KA )), where positive values indicate bias toward [3 H]thymidine incorporation and negative values indicate bias toward cAMP relative to quinpirole.132

agonism in terms of transduction coefficients to understand the molecular mechanisms responsible for biased agonism at the D2 R, as seen in Figure 10. In one study, structurally related phenylpiperazines were compared in their ability to inhibit cAMP accumulation and to stimulate ERK1/2 phosphorylation. As compared to quinpirole or dopamine, aripiprazole and the structurally related compound FAUC346 did not show any detectable bias, while FAUC335, displayed modest biased agonism (Fig. 10a). However, FAUC321 and FAUC350 showed significant biased agonism toward ERK1/2 phosphorylation and in the latter case there was no detectable agonism in the cAMP assay. From a medicinal chemistry perspective, it is of interest to note the minor structural modifications that engendered significant changes in bias. Most notably, modification of the ortho-methoxy group (FAUC321) upon the phenyl ring of the phenylpiperzine core to a methylsulfide substituent (FAUC335) caused an eightfold decrease in bias toward the pERK1/2 pathway. Cyclization of the ortho-methoxy Medicinal Research Reviews DOI 10.1002/med

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group to give the dihyrofuran fused system generated FAUC350 as a biased ligand with no activity in the cAMP assay. Interestingly, the mutation of His6.55 to alanine, a residue predicted to interact with the phenylpiperazine core, essentially removed any biased agonism for all tested molecules as compared to quinpirole. In a second study, Tschammer et al.131 explored 5-aminotetrahydroprazolopyridines in terms of dopamine receptor subtype selectivity and biased agonism (Fig. 10b). Enantiomers of one compound (N-(4-(4-phenylbenzoylamino)butyl)N-propyl-5-amino-4,5,6,7-tetrahydropyrazolo[1,5-a]pyridine, denoted as compound 5a in the original paper) displayed distinct bias profiles at the dopamine D2 receptor with the (S)enantiomer displaying 140-fold bias toward stimulation of [3 H]thymidine incorporation over the inhibition of cAMP production whereas the (S)-enantiomer displayed tenfold bias toward the cAMP assay. Such methodology can also be applied retrospectively to previously published data. The Mailman group has provided pioneering studies identifying biased agonism at the D2 R.40, 41, 132, 133 Dihydrexidine (DHX) a full D1 receptor (D1 R) agonist, shows variable functional properties mediated by D2 -like receptors.40, 132 DHX and its congener N-propyl-DHX were initially characterized as full agonists at the D2 R because they were as efficacious as dopamine in inhibiting cAMP synthesis, inhibiting prolactin release in vivo, and stimulating GTPγ S binding in rat substantia nigral tissue.40 However, neither DHX nor N-propylDHX inhibited the synthesis and release of dopamine in rat striatum or the firing of nigral dopaminergic neurons, effects in all of these functional assays would be expected for typical D2 R agonists. These studies were extended in a study in which a series of rigid D2 R agonists were characterized at the D2L R (expressed in Chinese hamster ovary cells) in three functional endpoints—inhibition of cAMP synthesis, stimulation of MAPK phosphorylation, and activation of G protein-coupled inwardly rectifying potassium channels. The agonists chosen (see Fig. 11a) were (S)-N-propylnorapomorphine (SNPA), (R)-N-propylnorapomorphine (RNPA), dinoxyline (DNX), DHX, and dinapsoline (DNS), in addition to the prototypical full agonist quinpirole. The authors observed distinct orders of potency of these compounds across the three different assays.41 Although these observations are clearly consistent with biased effects, the focus on changes in potency only means that information regarding the different efficacies of the ligands at the two different pathways is incomplete. To illustrate the utility of applying the log(τ /KA ) scale of bias, values of log(τ /KA ) were determined for this data set (see Fig. 11b). From our analysis it is clear that the three different chemical scaffolds have distinct bias profiles. Relative to quinpirole, the aporphinoids display no significant bias (although a trend of bias towards the cAMP pathway is observed), whereas DHX and DNS display sixfold and fivefold bias toward the MAPK pathway. However, DNX does not show significant bias to either pathway as compared to quinpirole. DNX and DNS differ in structure by a single replacement of ether (DNX) for a methylene bridge (DNS), thus it is tempting to relate this subtle change in bias profile to this relatively discrete structural change. By using a scale of bias, rather than rely upon empirical markers such as loss of activity at one pathway or a reversal of potency rank orders, we can identify subtle changes that would otherwise have been lost. Moreover, these subtle changes can be related to ligand structure to allow construction of a SAR for biased agonism just as for SARs for ligand affinity have been based upon changes in dissociation constants. These studies were recently extended by the Mailman group to include an analysis of the specific binding interactions that cause D2 R-biased agonism.134 The DHX analogues, DNS and DNX, were again used as probes in that study along with reference agonists, dopamine and quinpirole. Given the minor structural differences between DNX and DNS, specific modifications of three TM5 serines (Ser5.42, Ser5.43, and Ser5.46) highlighted differential ligand–receptor interactions across the range of compounds that may underlie bias. Quinpirole was unaffected by all mutations, whereas mutation of Ser5.42 and Ser5.46 to alanine abolished the activity of DHX analogues and dopamine. In contrast, Medicinal Research Reviews DOI 10.1002/med

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Figure 11. (a) Chemical structures of the full agonist quinpirole, and D2 R ligands (S)-N-propylnorapomorphine (SNPA), (R)-N-propylnorapomorphine (RNPA), dinoxyline (DNX), dihydrexidine (DHX), and dinapsoline (DNS) as examples of biased agonists at the D2 R. (b) Quantification of biased agonism at MAPK versus cAMP assays as determined by the log(τ /KA ) scale. *Significant changes in bias, one-way ANOVA with Tukey’s posttest, P < 0.05.

mutation of Ser5.43 caused complete loss of agonist effect for dopamine, DHX, and DNS in an assay measuring AA release, but not at the other signaling pathways measured.134 As such, the sensitivity of different agonists to mutation of various residues highlights the different receptor–ligand interactions that may contribute to their distinct bias profiles. Medicinal Research Reviews DOI 10.1002/med

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B. Alternative Approaches to Quantify Biased Agonism A number of methods to quantify bias have been proposed and published in recent times.30, 135–137 Another useful measurement for describing agonist effect is the “relative-activity scale.”135 This scale uses the maximum response divided by the EC50 and thus takes into account the agonist potency and maximal response produced by an agonist in a given system. This approach has the advantage that it is simple to calculate but requires that the slope of the concentration–response curves is not significantly different from unity. However, particular attention has been given to the relative merits of the transduction ratio method as compared to the “sigma” method proposed by Rajagopal et al.30, 136, 137 Both methods are based on the operational model of agonism, however the difference between the two methods is that the sigma methodology fixes the values of functional affinity (KA ) to that determined biochemically in a radioligand-binding assay and, as such, this value of affinity is assumed to be the same for a single ligand at all pathways measured. In contrast the “transduction coefficient” method derives the KA of partial agonists directly from the fitting of the dose–response curves to the operational model of agonism. This operationally derived KA represents the functional affinity of an agonist associated with that particular signaling pathway. The relative merits of the two methods remain the subject of debate,137–139 reflecting the nascent status of quantification of biased agonism in the field, but are likely to yield similar results in most instances. It is noteworthy that in our recent study of bias at the dopamine D2 receptor, the biochemical affinity determined in radioligand-binding experiments was found to be significantly different to that determined in one or both of the functional assays 11 of 26 times (42%).129 Furthermore, there was no pattern to the distribution of the values of pKA that differ significantly from the corresponding value of affinity determined by radioligand binding, with examples in both functional assays. As such, in this situation, fixing the KA to that determined in a radioligand-binding assay would introduce significant error into estimates of transduction coefficients, and ultimately identification of ligand bias.

C. The Utility of Analytical Approaches to Quantify Bias Is Limited by the Size of the Data Set Analytical approaches on their own may not be sufficient to draw out useful SAR information, particularly if the ligand set is limited. Indeed, a recent example highlights the challenge of relating quantitative measures of bias to ligand structure. The human histamine H4 receptor (hH4 R) represents a promising therapeutic target for pathologies such as airway inflammation, inflammatory bowel disease, and atopic dermatitis.140 Recently, JNJ7777120, initially described as an antagonist, was reclassified as a biased ligand that selectively recruits β-arrestin in a Gα i independent manner.141 To build on this discovery, Nijmeijer et al. evaluated the efficacy of 31 known hH4 R ligands in their ability to induce β-arrestin-2 recruitment or Gα i G protein signaling (in the form of a CRE luciferase assay).142 These ligands were taken from nine distinct chemical classes to include histamine analogues, triazoles, guanidines, isothioureas, dibenzodiazepines, aminopyrimidines, indole-carboxamides, quinoxalines, and quinazoline sulfonamides. Data from these experiments were analyzed using the Black and Leff operational model of agonism to generate “bias factors.” A general trend could not be identified in the nine chemical classes apart from the indole-carboxamides that all induced β-arrestin-2 recruitment but not Gα i G protein signaling, and the quinazoline sulfonamides that were antagonists. For example, the isothioureas were able to give a broad spectrum of efficacies upon relatively modest structural changes (see Fig. 12), from Gα i G protein-biased (VUF5222) to β-arrestin-2-biased (VUF5223) to nonbiased antagonist (VUF9107).142 It should be noted that the authors followed the sigma methodology and, rather than determining functional affinities, fixed the pKA of their ligands to that determined in a Medicinal Research Reviews DOI 10.1002/med

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Figure 12. Chemical structures of analogues with a broad spectrum of efficacy targeting the hH4 R. Subtle modification of aromatic substitution pattern from nonhalogenated (VUF9107, nonbiased antagonist), to mchlorinated (VUF5222, Gα i G protein-biased agonist), to o-chlorinated (VUF5223, β-arrestin-2-biased) resulted in diverse bias profiles.142

radioligand-binding assay. As discussed above, it is possible that adding such constraints within the model may underlie errors in the estimation of transduction coefficients for these ligands. However, their observations may also highlight the challenges facing the medicinal chemist in terms of the rational design of biased ligands even with the application of the analytical methods to quantify biased agonism described above. Indeed, from the examples listed above it is clear that structural changes that engender significant changes in bias within are often relatively subtle. This is not surprising if one considers the relatively modest changes in the orthosteric binding site of active agonist-bound receptor crystal structures, for example those of the β 2 -adrenoceptor, when compared to the corresponding antagonist bound structures.62, 65, 82 Such minor movements are coupled to major structural movements at the cytosolic face of the receptor, allowing signaling proteins such as G proteins to bind. Indeed, the structural difference between GPCR agonists and antagonists can also be subtle.101 Accordingly, we can expect that structural changes that determine differential agonist action at one pathway as compared to another will also be subtle. As such the SAR around bias represents a challenge, but the use of the operational model to identify and quantify changes in bias profile and relate them to such subtle changes is a valuable approach to meet this challenge.

7. BIASED AGONISM BY DESIGN—SIMULTANEOUS TARGETING OF ALLOSTERIC AND ORTHOSTERIC SITES THROUGH “BITOPIC” LIGANDS It is not only ligands that target the binding site of the endogenous ligand that can display biased agonism. Targeting a topographically distinct site (allosteric site) on a GPCR provides another approach to develop selective GPCR ligands, not least because this allosteric site will be less evolutionarily conserved across a receptor family as compared to the orthosteric site. Small Medicinal Research Reviews DOI 10.1002/med

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molecules that target the allosteric site are termed “allosteric modulators”6, 143 due to their ability to stabilize distinct receptor conformations resulting in the modulation of orthosteric ligand affinity and/or efficacy. Such ligands may also display efficacy in their own right. Given that allosteric modulators promote a conformational change in GPCR structure, these compounds may engender biased agonism, either by themselves or by modulating the actions of the orthosteric ligand in a pathway-biased manner.6, 144 Such pathway-biased modulation has been observed for small molecule modulators of a number of GPCR targets to include the muscarinic M4 receptor,145 the glucagon-like peptide receptor 1,146 the calcium-sensing receptor,147 and the tachykinin NK2 receptor.148 Over the past 5 years a number of groups have explored the advantages of targeting both the orthosteric site and the allosteric site on a GPCR simultaneously by generating dual allosteric/orthosteric ligands, termed bitopic ligands.149–152 The predominant aim of such an approach was the generation of more subtype selective, higher affinity ligands. Of interest, a number of bitopic ligands have displayed biased agonism. For the successful generation of a bitopic ligand, the allosteric site must be both accessible from, and proximal to, the orthosteric site. As such this requires knowledge about the location and to a certain extent the nature of the allosteric binding site. One of the best-characterized GPCR subfamilies in this regard is the mAChR family, not least due to the recent determination of the antagonist bound “inactive” structure of the M2 mAChR.153 Furthermore, there is substantial evidence to show that at least one allosteric site, comprising extracellular-facing residues, is close to the orthosteric site.75, 153 Not surprisingly, these receptors were the first to be targeted with a rationally designed bitopic ligand. This well-characterized allosteric site allows validation of allosteric and bitopic modes of ligand interaction via mutagenesis studies.154, 155 One pioneering study by Mohr and colleagues pursued a bitopic approach at the muscarinic M2 receptor (M2 mAChR) by linking a potent orthosteric agonist derived from oxotremorine (iperoxo) with a M2 mAChRselective bis(amino)alkane-type allosteric fragment (allosteric inhibitor W84, and allosteric enhancer naphmethonium).155 Not only did such an approach engender subtype selectivity for the M2 mAChR but the authors also used a “label-free” dynamic mass redistribution assay to suggest that these compounds displayed distinct pathway selectivity as compared to either acetylcholine or the orthosteric moiety iperoxo.156 Mohr and colleagues suggested that the binding of a bitopic ligand extends the area of the ligand–receptor interface, thus increasing the chance of exploiting subtle differences in receptor architecture, resulting in subtype selectivity and biased agonism.155 Further validation of a bitopic mode of interaction for their rationally designed hybrid ligands was provided by perturbing either the orthosteric or allosteric site using judiciously chosen point mutations. In each instance, the bitopic ligands displayed either purely orthosteric or purely allosteric behavior upon mutation of the allosteric or orthosteric site, respectively.155 More recently the Mohr group extended these findings aided by molecular modeling to design bitopic ligands that target a specific allosteric site within the extracellular domain and explore the role of this domain in controlling the nature of the agonist response (see Fig. 13).157 Again iperoxo was linked to either a phthalimide allosteric building block or a more bulky naphthalimide building block via a hexamethylene or octamethylene middle chain. These allosteric moieties have been shown to interact with Trp422(7.35) at the beginning of TM7 and to Tyr177(5.32) in extracellular loop 2 of the M2 AChR. The authors hypothesized that if this interaction caused biased agonism, enlargement of the substituent from phthalimide to naphthalimide (see Fig. 13a) should confer even greater agonist bias. Finally, elongation of the middle chain of the naphthalimide derivative was hypothesized to shift this substituent outward so it was unable to make an interaction with Trp422 and Tyr177 and as such this compound would display no bias relative to iperoxo. These ligands were characterized using both a labelfree technology (dynamic mass redistribution) and more conventional assays measuring Gi Medicinal Research Reviews DOI 10.1002/med

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Figure 13. (a) Chemical structures of the M2 AChR orthosteric ligand iperoxo (blue); the chain extended Iper-6; and M2 AChR bitopic ligands: Iper-6-phth, Iper-6-naph, Iper-8-phth, and Iper-8-naph. (b) Ligands were tested in a [35 S]GTPγ S assay as a measure of Gα i G protein activation and a cAMP assay as a measure of Gα s G protein activation. Bias was quantified using the transduction coefficient method where log(τ /KA ) values compensate for system bias and represent ligand bias relative to acetylcholine (ACh). Positive values of log(τ /KA ) indicate Gi bias and negative values indicate Gs bias. Data adapted from Bock et al.157

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and Gs G protein activation. Ligand bias was quantified using the transduction coefficient (log(τ /KA )) scale of bias that enabled these changes in ligand structure to be related back to graded changes in biased agonism (see Fig. 13b). As such the observed pattern was consistent with the original pattern predicted by the authors. This example illustrates that the combination of GPCR structural approaches with analytical methods to quantify bias and bitopic targeting of orthosteric and allosteric sites within the same receptor, is a viable approach toward the rational design of biased ligands. A similar approach has generated ligands that may have a biased action at the adenosine A1 receptor.149, 158 Such studies highlight the possibility that biased ligands may achieve bias through an unappreciated bitopic mechanism. This is exemplified by another M2 mAChR functionally selective ligand McN-A-343. In this case, rather than conjugating allosteric and orthosteric pharmacophores, a high-efficacy orthosteric agonist (tetramethylammonium) fragment and a negative allosteric modulator (3-chlorophenylcarbamate—DDBL-4) were isolated by generating progressively truncated derivatives of the parent compound. The presence of these two pharmacophores was consistent with a bitopic mode of interaction and was confirmed through mutation of key residues within the allosteric site. When combined the two pharmacophores recapitulated the pharmacology of the partial agonist McN-A-343.159 As such this highlights the possibility that other biased GPCR ligands may have an unappreciated bitopic mode of interaction. The validation of bitopic modes of action for such ligands, as detailed above, may provide the starting point to develop SAR of biased agonism around these ligands.

8. THE PROMISE AND CHALLENGES OF BIASED AGONISM—IMPLICATIONS FOR DRUG DISCOVERY A. Assay Technologies for the Detection of Biased Agonism The increasing number of examples of biased agonists at different GPCR targets can largely be attributed to the increasing ease with which one can measure activity at multiple signaling pathways. A significant challenge associated with biased agonism is that in the majority of cases it is not clear which signaling endpoints are therapeutically important for a particular GPCR target, highlighting the need to profile activity at multiple signaling endpoints. As such the “wrong” selection of pathways may even result in an inability to detect therapeutically relevant biased agonism. Although second messenger assays, such as the measurement of cytosolic Ca2+ levels, have dominated screening programs due to their ease of application and low cost, they fail to capture all aspects of receptor signaling. For example, the scaffolding protein β-arrestin not only has an important role in receptor regulation but can also mediate G protein-independent signaling responses—with both of these functions being important as predictors of ligand effect.28 There are clear advantages for the development of single assay platforms that are capable of detecting multiple signaling endpoints for the identification of biased agonists. Tractable technologies include reporter gene assays,160 high-content imaging,161 and bioluminescence resonance energy transfer (BRET) biosensors of signaling events including G protein activation.162–164 However, many of these technologies require either biologically engineering the 7TM GPCR or other proteins within the signaling cascade rather than looking at the cells “native” components. One approach to address this challenge is an integrative method that captures the global signaling profile of a ligand in a single assay using label-free cell-based technologies to monitor real-time changes in higher order cellular events such as morphology, viability, adhesion, and mass distribution.37, 165 In a recent study, Stallaert et al. used impedance measurements to monitor global cellular activity, and differentiate ligands with distinct signaling profiles at the Medicinal Research Reviews DOI 10.1002/med

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β 2 AR.37 Stimulation of the β 2 AR in living cells by each ligand generated a complex, multifeatured impedance response over time. By clustering ligands by the shape of this impedance response (i.e., by the presence or absence of different kinetic phases within this response) they were able to subclassify ligands according to their different signaling profiles (Fig. 14). The catechol full agonists, isopreterenol and epinephrine, were clustered into Group 1 whereas the partial agonists salbutamol and salmeterol were clustered into Group 2. Alprenolol, bucindolol, labetalol, and pindolol were clustered into Group 3 and propranolol and carvedilol were clustered into Group 4. Finally, Group 5 ligands, which all displayed negative values in impedance response, included metoprolol, timolol, atenolol, and ICI-118,551. This approach has provided evidence that such ligands may have a much more complex efficacy at the β 2 AR than previously thought. As an example, propanolol and ICI-118,551 were identified as biased agonists at the β 2 AR, able to stimulate pERK1/2 phosphorylation but acting as antagonists in assays measuring the accumulation of cAMP.36, 38, 39 However, these two ligands display distinct impedance response profiles indicating that there is likely to be further functional differences between these two biased ligands beyond this description of bias. As such this added “texture” to our understanding of the function of these ligands highlights the utility of such label-free technologies. Larger ligand sets will reveal if impedance responses can be related to ligand structure. The use of such label-free technologies offers an attractive approach to detect a whole cell response to a certain ligand, not least since these technologies can be applied to more therapeutically relevant systems such as primary cells. However, as one moves toward more global responses there will be a corresponding increase in the complexity of the cellular signals measured. This, in turn, may make the identification of a specific profile of a ligand that confers a therapeutic advantage a significant challenge. Although the deconvolution of such complex signals may allow a particular profile to be related to a signaling event such profiles appear to be cell-background dependent.37, 165

B. Ligand Binding Kinetics and Assay Time Points A number of studies have described a relationship between differential agonist-binding kinetics and agonist efficacy.166, 167 Differential binding kinetics may mean that the effect observed for a particular pathway for a given agonist will depend on the incubation time used for the assay. This, in turn, may influence a ligand’s perceived profile of biased agonism. Indeed, differential agonist-binding kinetics have been suggested to underlie a poor correlation between agonist potencies of ergot derivatives at the 5HT2B receptor in assays measuring calcium flux (a time scale of seconds) and IP accumulation (2 hr stimulation).168 One may also consider the possibility that an agonist may not engender coupling to a certain signaling pathway due to the duration of receptor occupancy being insufficient to recruit this pathway. This latter possibility may be considered an example of biased agonism where the underlying mechanism is kinetic rather than conformational. To our knowledge, no study to date has provided evidence of such a mechanism.

C. Cell Background and Engineered Signaling Proteins As discussed above, a number of assay technologies make use of engineered receptors and/or overexpression of engineered cell signaling proteins to allow measurement of certain cell signaling events. For example, the protein–protein interaction between receptor and β-arrestin construct can be monitored using BRET (requiring both receptor and arrestin luciferase or fluorescent protein fusion constructs),169 enzyme fragment complementation (receptor and arrestin fusion constructs with the fragments of the enzyme),170, 171 or protease-activated transcriptional Medicinal Research Reviews DOI 10.1002/med

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Figure 14. Chemical structures of ligands assessed using impedance measurements to monitor global cellular activity, and differentiate ligands with distinct signaling profiles at the β 2 AR. Ligands were clustered according to their impedance signature.37

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reporter gene assays172, 173 (requiring receptor-transcription factor and arrestin-protease fusion proteins). Given that it has been shown that single point mutations of GPCRs can alter the bias profiles of ligands,130, 174–177 it follows that such modified receptors may display distinct patterns of ligand bias as compared to the wild-type receptor, and efforts should be made to confirm bias profiles using multiple assay technologies. Careful interpretation of data is required for studies in which different cell backgrounds are used. Upon changing cell background, one may change the signaling protein content of the cell and/or the stoichiometry of signaling proteins. As such, it may be misleading to quantify the degree of bias observed between an agonist acting at one pathway in one cell background as compared to another pathway in different cell background. If one considers that the overexpression of receptors and/or signaling proteins essentially changes the cellular background in which the assay is performed, this may have significant effects upon the observed efficacy of a ligand at different signaling pathways.178 As for studies that use different cell backgrounds, careful consideration is required to interpret these data. It is also important to consider whether the assay system used to identify biased ligands is predictive of activity in the therapeutically relevant tissue. If there are cytosolic differences between the test and therapeutically relevant cell line then the relevant biased agonist profile may be missed. For example, morphine is known to induce cell background dependent internalization of MORs causing internalization of receptors in striatal neurons but not dorsal root ganglia or locus coeruleus.179 Morphine does not cause internalization of the MOR in HEK293T cells unless GRK2 and/or β-arrestin-2 are overexpressed.179 Hence, the HEK293T system may not be a good predictor of the action of opioid ligands at the MOR expressed in striatal neurons. A similar observation was made in a study designed to identify biased agonists at the dopamine D2 R focusing on the ability of agonists to stimulate β-arrestin-2 recruitment versus inhibition of cAMP production mediated by Gi G proteins.116, 180 None of the tested compounds, including aripiprazole, induced β-arrestin-2 recruitment to the D2 R expressed in HEK293T cells without either the coexpression of GRK2 or the use of more sensitive assay technologies to measure these signaling events. It is not clear which of the above systems is most predictive of the physiological activity of the tested compounds, although selected compounds from this study displayed in vivo efficacy in a β-arrestin-2-dependent manner.116 D. Biased Antagonism If, through the allosteric nature of GPCRs, agonists can display distinct affinities for a receptor coupled to different signaling effectors,30, 137 then the same is theoretically true for antagonists. If these differences in affinity are significant, then such antagonists could be termed “biased antagonists,” blocking distinct pathways to different extents. The most likely mechanism by which biased antagonism can occur is through allosteric modulation whereby a ligand acting at a topographically distinct site to the orthosteric agonist would block some pathways while sparing others.144 As an example, the allosteric modulator 1-(4-ethoxyphenyl)-5-methoxy-2methylindole-3-carboxylic acid has no effect upon the G protein-mediated signaling of the PTGDR2 stimulated by prostaglandin D2 but acts as an antagonist of G protein-independent β-arrestin recruitment.181 This highlights the allosteric nature of GPCRs, a concept that lies at the heart of biased agonism and that give the potential for the medicinal chemist to sculpt a desired functional response from a GPCR through the design of small molecules. E. How Prevalent Are Biased Agonists? From a theoretical point of view, if the efficacy of a ligand relates to its ability to stabilize an ensemble of receptor conformations that, in turn, represent a fraction of all possible Medicinal Research Reviews DOI 10.1002/med

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conformations, then it would be highly unlikely that any two ligands stabilize the same ensemble of conformations.8, 182 Such different ensembles of conformations could differentially engage signaling proteins, meaning that all ligands would be expected show varying degrees of bias toward different signaling proteins. However in many cases, this bias may not be significant in terms of physiological outcome or indeed our ability to detect it. Even novel “label-free” technologies are unlikely to capture all of the functional consequences of these different conformational ensembles. One would expect the identification of biased ligands to increase with both the availability of technologies that allow the readout of multiple signaling endpoints and the number of programs that are focused discovering biased agonists. In this regard, we can draw interesting parallels with the historical identification of inverse agonists. When first observed, inverse agonism was thought to be a relatively rare phenomenon even though theoretically all antagonists would be predicted to be either weak efficacy positive or inverse agonists.25 In agreement with this theoretical prediction, a literature review found that 85% of antagonists at 73 GPCR targets (10 years ago) displayed inverse agonism.25 As with inverse agonists, the more significant challenge may not be the identification of biased ligands but the demonstration of their therapeutic advantages. There are a growing number of published studies that describe programs aimed at discovering biased agonists for a particular therapeutic endpoint. This is exemplified by two drug discovery efforts undertaken by Trevena Inc., one focused on the MOR for the development of novel analgesics115 and the other focused on the angiotensin 1 receptor for the treatment of acute heart failure.49 Both of these programs now have molecules in clinical development. It is noteworthy that the different signaling pathways activated by these targets and their therapeutic importance have been relatively well characterized. This knowledge has guided both programs in terms the selection of pathways used to assess agonist efficacy and is likely a major factor in the success of these programs to date.

9. CONCLUSIONS Biased agonism offers the possibility of developing pathway-selective in addition to receptorselective drugs. By designing appropriately biased agonists, one may be able to modulate a pathway related to a pathology without affecting physiological functions unrelated to the disease mediated by the same receptor target. However as this review has illustrated, to date, the identification of biased agonist ligands has largely relied upon serendipitous discovery rather than rational design. Furthermore, there have been few attempts to develop full SAR around such biased ligands. Underlying this paucity of examples are some significant challenges to medicinal chemistry programs including the selection of the most appropriate signaling pathways (and assay technologies) that will identify biased agonists with the correct therapeutic profile and the correct identification of biased ligands through appropriate pharmacological analysis and the quantification of biased agonism. In this review we have highlighted analytical methodologies that may allow one to relate ligand structure to a measurement of how biased a ligand is for one pathway over another (or others) but also will distinguish biased agonism from effects that can simply be attributed to different coupling efficiencies in two different signaling pathways. The last 5 years has seen the GPCR drug discovery landscape change with the publication of an ever-increasing list of GPCR crystal structures. Efforts to relate GPCR structure to biased agonism will give mechanistic insights and hopefully aid the development of biased ligands. One approach to the design of biased agonists that has already indicated the utility of such combined approaches is the generation of dual orthosteric/allosteric ligands. However, it remains to be seen how applicable such an approach is across the GPCR family. Medicinal Research Reviews DOI 10.1002/med

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ACKNOWLEDGMENTS J.R.L. is a Monash University Larkins Fellow and a RD Wright Biomedical Career Development Fellow of the National Health and Medical Research Council (NHMRC). A.C. is an NHMRC Principal Research Fellow. Work cited from the author’s laboratories was funded in part by NHMRC Project Grants 1011920 and 1052304, NHMRC Program Grant 1055134, and Discovery Grant DP110100687 of the Australian research Council. J.S. acknowledges an Australian Postgraduate Award.

CONFLICT OF INTEREST The authors declare no conflict of interest. REFERENCES 1. Ma, P, Zemmel R. Value of novelty? Nat Rev Drug Discov 2002;1(8):571–572. 2. Lagerstrom MC, Schioth HB. Structural diversity of G protein-coupled receptors and significance for drug discovery. Nat Rev Drug Discov 2008;7(4):339–357. 3. Stevens RC, Cherezov V, Katritch V, Abagyan R, Kuhn P, Rosen H, Wuthrich K. The GPCR network: A large-scale collaboration to determine human GPCR structure and function. Nat Rev Drug Discov 2013;12(1):25–34. ¨ MC, Lundin L-G, Schioth ¨ HB. The G-protein-coupled receptors in the 4. Fredriksson R, Lagerstrom human genome form five main families. Phylogenetic analysis, paralogon groups, and fingerprints. Mol Pharmacol 2003;63(6):1256–1272. 5. Christopoulos A. Allosteric binding sites on cell-surface receptors: Novel targets for drug discovery. Nat Rev Drug Discov 2002;1(3):198–210. 6. May LT, Leach K, Sexton PM, Christopoulos A. Allosteric modulation of G protein–coupled receptors. Annu Rev Pharmacol Toxicol 2007;47(1):1–51. 7. Keov P, Sexton PM, Christopoulos A. Allosteric modulation of G protein-coupled receptors: A pharmacological perspective. Neuropharmacology 2011;60(1):24–35. 8. Kenakin T. Functional selectivity and biased receptor signaling. J Pharmacol Exp Ther 2011;336(2):296–302. 9. Kenakin T, Watson C, Muniz-Medina V, Christopoulos A, Novick S. A simple method for quantifying functional selectivity and agonist bias. ACS Chem Neurosci 2011;3(3):193–203. 10. Kenakin T. Collateral efficacy in drug discovery: Taking advantage of the good (allosteric) nature of 7TM receptors. Trends Pharmacol Sci 2007;28(8):407–415. 11. Violin JD, Lefkowitz RJ. β-Arrestin-biased ligands at seven-transmembrane receptors. Trends Pharmacol Sci 2007;28(8):416–422. 12. Urban JD, Clarke WP, von Zastrow M, Nichols DE, Kobilka B, Weinstein H, Javitch JA, Roth BL, Christopoulos A, Sexton PM, Miller KJ, Spedding M, Mailman RB. Functional selectivity and classical concepts of quantitative pharmacology. J Pharmacol Exp Ther 2007;320(1): 1–13. 13. Stallaert W, Christopoulos A, Bouvier M. Ligand functional selectivity and quantitative pharmacology at G protein-coupled receptors. Expert Opin Drug Discov 2011;6(8):811–825. 14. Marinissen MJ, Gutkind JS. G-protein-coupled receptors and signaling networks: Emerging paradigms. Trends Pharmacol Sci 2001;22(7):368–376. 15. Luttrell LM, Lefkowitz RJ. The role of β-arrestins in the termination and transduction of G-proteincoupled receptor signals. J Cell Sci 2002;115(3):455–465. 16. Kenakin T. Efficacy at G-protein-coupled receptors. Nat Rev Drug Discov 2002;1(2):103–110. Medicinal Research Reviews DOI 10.1002/med

BIASED AGONISM AT G PROTEIN-COUPLED RECEPTORS

r 35

17. Kobilka BK, Deupi X. Conformational complexity of G-protein-coupled receptors. Trends Pharmacol Sci 2007;28(8):397–406. 18. Park PS-H, Lodowski DT, Palczewski K. Activation of G protein–coupled receptors: Beyond twostate models and tertiary conformational changes. Annu Rev Pharmacol Toxicol 2008;48(1):107– 141. 19. Milligan G. Constitutive activity and inverse agonists of G protein-coupled receptors: A current perspective. Mol Pharmacol 2003;64(6):1271–1276. 20. Soudijn W, Wijngaarden Iv, IJzerman AP. Structure–activity relationships of inverse agonists for G-protein-coupled receptors. Med Res Rev 2005;25(4):398–426. 21. Samama P, Cotecchia S, Costa T, Lefkowitz RJ. A mutation-induced activated state of the beta 2-adrenergic receptor. Extending the ternary complex model. J Biol Chem 1993;268(7):4625–4636. 22. Samama P, Pei G, Costa T, Cotecchia S, Lefkowitz RJ. Negative antagonists promote an inactive conformation of the beta 2-adrenergic receptor. Mol Pharmacol 1994;45(3):390–394. 23. Bond RA, Leff P, Johnson TD, Milano CA, Rockman HA, McMinn TR, Apparsundaram S, Hyek MF, Kenakin TP, Allen LF, Lefkowitz RJ. Physiological effects of inverse agonists in transgenic mice with myocardial overexpression of the β 2 -adrenoceptor. Nature 1995;374(6519):272–276. 24. Kenakin T. Inverse, protean, and ligand-selective agonism: Matters of receptor conformation. FASEB J 2001;15(3):598–611. 25. Kenakin T. Efficacy as a vector: The relative prevalence and paucity of inverse agonism. Mol Pharmacol 2004;65(1):2–11. 26. Strange PG. Agonist binding, agonist affinity and agonist efficacy at G protein-coupled receptors. Br J Pharmacol 2008;153(7):1353–1363. 27. Kenakin T. Drug efficacy at G protein-coupled receptors. Annu Rev Pharmacol Toxicol 2002;42(1):349–379. 28. Shukla AK, Xiao K, Lefkowitz RJ. Emerging paradigms of beta-arrestin-dependent seven transmembrane receptor signaling. Trends Biochem Sci 2011;36(9):457–469. 29. Luttrell LM. Transmembrane signaling by G protein-coupled receptors. Methods Mol Biol 2006;332:3–49. 30. Kenakin T, Christopoulos A. Signalling bias in new drug discovery: Detection, quantification and therapeutic impact. Nat Rev Drug Discov 2013;12(3):205–216. 31. Onaran HO, Costa T. Agonist efficacy and allosteric models of receptor action. Ann NY Acad Sci 1997;812(1):98–115. 32. Newman-Tancredi A, Martel JC, Assie MB, Buritova J, Lauressergues E, Cosi C, Heusler P, Bruins Slot L, Colpaert FC, Vacher B, Cussac D. Signal transduction and functional selectivity of F15599, a preferential post-synaptic 5-HT1A receptor agonist. Br J Pharmacol 2009;156(2):338–353. 33. Berg KA, Maayani S, Goldfarb J, Scaramellini C, Leff P, Clarke WP. Effector pathway-dependent relative efficacy at serotonin type 2A and 2C receptors: Evidence for agonist-directed trafficking of receptor stimulus. Mol Pharmacol 1998;54(1):94–104. 34. Sternini C, Spann M, Anton B, Keith DE, Jr., Bunnett NW, von Zastrow M, Evans C, Brecha NC. Agonist-selective endocytosis of mu opioid receptor by neurons in vivo. Proc Natl Acad Sci USA 1996;93(17):9241–9246. 35. Keith DE, Murray SR, Zaki PA, Chu PC, Lissin DV, Kang L, Evans CJ, von Zastrow M. Morphine activates opioid receptors without causing their rapid internalization. J Biol Chem 1996;271(32):19021–19024. 36. Wisler JW, DeWire SM, Whalen EJ, Violin JD, Drake MT, Ahn S, Shenoy SK, Lefkowitz RJ. A unique mechanism of β-blocker action: Carvedilol stimulates β-arrestin signaling. Proc Natl Acad Sci USA 2007;104(42):16657–16662. 37. Stallaert W, Dorn JF, van der Westhuizen E, Audet M, Bouvier M. Impedance responses reveal β 2 -adrenergic receptor signaling pluridimensionality and allow classification of ligands with distinct signaling profiles. PLoS One 2012;7(1):e29420. Medicinal Research Reviews DOI 10.1002/med

36

r SHONBERG ET AL.

38. Galandrin S, Bouvier M. Distinct signaling profiles of β 1 and β 2 adrenergic receptor ligands toward adenylyl cyclase and mitogen-activated protein kinase reveals the pluridimensionality of efficacy. Mol Pharmacol 2006;70(5):1575–1584. ˜ 39. Azzi M, Charest PG, Angers S, Rousseau G, Kohout T, Bouvier M, Pineyro G. β-Arrestin-mediated activation of MAPK by inverse agonists reveals distinct active conformations for G protein-coupled receptors. Proc Natl Acad Sci USA 2003;100(20):11406–11411. 40. Mottola DM, Kilts JD, Lewis MM, Connery HS, Walker QD, Jones SR, Booth RG, Hyslop DK, Piercey M, Wightman RM, Lawler CP, Nichols DE, Mailman RB. Functional selectivity of dopamine receptor agonists. I. Selective activation of postsynaptic dopamine D2 receptors linked to adenylate cyclase. J Pharmacol Exp Ther 2002;301(3):1166–1178. 41. Gay EA, Urban JD, Nichols DE, Oxford GS, Mailman RB. Functional selectivity of D2 receptor ligands in a Chinese hamster ovary hD2L cell line: Evidence for induction of ligand-specific receptor states. Mol Pharmacol 2004;66(1):97–105. 42. Ryman-Rasmussen JP, Nichols DE, Mailman RB. Differential activation of adenylate cyclase and receptor internalization by novel dopamine D1 receptor agonists. Mol Pharmacol 2005;68(4):1039– 1048. 43. Urban JD, Vargas GA, von Zastrow M, Mailman RB. Aripiprazole has functionally selective actions at dopamine D2 receptor-mediated signaling pathways. Neuropsychopharmacology 2007;32(1):67– 77. 44. Kohout TA, Nicholas SL, Perry SJ, Reinhart G, Junger S, Struthers RS. Differential desensitization, receptor phosphorylation, β-arrestin recruitment, and ERK1/2 activation by the two endogenous ligands for the CC chemokine receptor 7. J Biol Chem 2004;279(22):23214–23222. 45. Nickolls SA, Fleck B, Hoare SRJ, Maki RA. Functional selectivity of melanocortin 4 receptor peptide and nonpeptide agonists: Evidence for ligand-specific conformational states. J Pharmacol Exp Ther 2005;313(3):1281–1288. 46. Evans BA, Broxton N, Merlin J, Sato M, Hutchinson DS, Christopoulos A, Summers RJ. Quantification of functional selectivity at the human α 1A -adrenoceptor. Mol Pharmacol 2011;79(2):298–307. 47. Wei H, Ahn S, Shenoy SK, Karnik SS, Hunyady L, Luttrell LM, Lefkowitz RJ. Independent β-arrestin 2 and G protein-mediated pathways for angiotensin II activation of extracellular signalregulated kinases 1 and 2. Proc Natl Acad Sci USA 2003;100(19):10782–10787. 48. Ahn S, Shenoy SK, Wei H, Lefkowitz RJ. Differential kinetic and spatial patterns of β-arrestin and G protein-mediated ERK activation by the angiotensin II receptor. J Biol Chem 2004;279(34):35518– 35525. 49. Violin JD, DeWire SM, Yamashita D, Rominger DH, Nguyen L, Schiller K, Whalen EJ, Gowen M, Lark MW. Selectively engaging β-arrestins at the angiotensin II type 1 receptor reduces blood pressure and increases cardiac performance. J Pharmacol Exp Ther 2010;335(3):572–579. 50. McArdle CA. Gonadotropin-releasing hormone receptor signaling: Biased and unbiased. Mini Rev Med Chem 2012;12(9):841–850. 51. Bohinc BN, Gesty-Palmer D. Biased agonism at the parathyroid hormone receptor: A demonstration of functional selectivity in bone metabolism. Mini Rev Med Chem 2012;12(9):856–865. 52. Berg KA, Clarke WP. Functional selectivity at serotonin receptors. In: Neve KA, Ed. Functional Selectivity of G Protein-Coupled Receptor Ligands: New Opportunities for Drug Discovery, The Receptors. New York, NY: Humana Press; 2009. p 155–176. 53. Kim J, Ahn S, Rajagopal K, Lefkowitz RJ. Independent β-arrestin2 and Gq/protein kinase Cζ pathways for ERK stimulated by angiotensin type 1A receptors in vascular smooth muscle cells converge on transactivation of the epidermal growth factor receptor. J Biol Chem 2009;284(18):11953–11962. 54. Boerrigter G, Lark MW, Whalen EJ, Soergel DG, Violin JD, Burnett JC. Cardiorenal actions of TRV120027, a novel β-arrestin–biased ligand at the angiotensin II type I receptor, in healthy and heart failure canines/clinical perspective. Circulation 2011;4(6):770–778. 55. Boerrigter G, Soergel DG, Violin JD, Lark MW, Burnett JC. TRV120027, a novel β-arrestin biased ligand at the angiotensin II type I receptor, unloads the heart and maintains renal function when Medicinal Research Reviews DOI 10.1002/med

BIASED AGONISM AT G PROTEIN-COUPLED RECEPTORS

56.

57.

58.

59.

60.

61.

62.

63.

64.

65.

66.

67.

68. 69.

70.

r 37

added to furosemide in experimental heart failure / clinical perspective. Circulation 2012;5(5):627– 634. Rajagopal K, Whalen EJ, Violin JD, Stiber JA, Rosenberg PB, Premont RT, Coffman TM, Rockman HA, Lefkowitz RJ. β-Arrestin2-mediated inotropic effects of the angiotensin II type 1A receptor in isolated cardiac myocytes. Proc Natl Acad Sci USA 2006;103(44):16284–16289. Walters RW, Shukla AK, Kovacs JJ, Violin JD, DeWire SM, Lam CM, Chen JR, Muehlbauer MJ, Whalen EJ, Lefkowitz RJ. β-Arrestin1 mediates nicotinic acid–induced flushing, but not its antilipolytic effect, in mice. J Clin Invest 2009;119(5):1312–1321. Richman JG, Kanemitsu-Parks M, Gaidarov I, Cameron JS, Griffin P, Zheng H, Guerra NC, Cham L, Maciejewski-Lenoir D, Behan DP, Boatman D, Chen R, Skinner P, Ornelas P, Waters MG, Wright SD, Semple G, Connolly DT. Nicotinic acid receptor agonists differentially activate downstream effectors. J Biol Chem 2007;282(25):18028–18036. Wu H, Wacker D, Mileni M, Katritch V, Han GW, Vardy E, Liu W, Thompson AA, Huang X-P, Carroll FI, Mascarella SW, Westkaemper RB, Mosier PD, Roth BL, Cherezov V, Stevens RC. Structure of the human κ-opioid receptor in complex with JDTic. Nature 2012;485(7398):327–332. Cherezov V, Rosenbaum DM, Hanson MA, Rasmussen SG, Thian FS, Kobilka TS, Choi HJ, Kuhn P, Weis WI, Kobilka BK, Stevens RC. High-resolution crystal structure of an engineered human β 2 -adrenergic G protein-coupled receptor. Science 2007;318(5854):1258–1265. Jaakola VP, Griffith MT, Hanson MA, Cherezov V, Chien EYT, Lane JR, Ijzerman AP, Stevens RC. The 2.6 angstrom crystal structure of a human A2A adenosine receptor bound to an antagonist. Science 2008;322(5905):1211–1217. Rasmussen SGF, DeVree BT, Zou Y, Kruse AC, Chung KY, Kobilka TS, Thian FS, Chae PS, Pardon E, Calinski D, Mathiesen JM, Shah STA, Lyons JA, Caffrey M, Gellman SH, Steyaert J, Skiniotis G, Weis WI, Sunahara RK, Kobilka BK. Crystal structure of the β 2 adrenergic receptor-Gs protein complex. Nature 2011;477(7366):549–555. Chien EYT, Liu W, Zhao Q, Katritch V, Won Han G, Hanson MA, Shi L, Newman AH, Javitch JA, Cherezov V, Stevens RC. Structure of the human dopamine D3 receptor in complex with a D2 /D3 selective antagonist. Science 2010;330(6007):1091–1095. Dor´e Andrew S, Robertson N, Errey James C, Ng I, Hollenstein K, Tehan B, Hurrell E, Bennett K, Congreve M, Magnani F, Tate Christopher G, Weir M, Marshall Fiona H. Structure of the adenosine A2A receptor in complex with ZM241385 and the xanthines XAC and caffeine. Structure 2011;19(9):1283–1293. Rasmussen SGF, Choi H-J, Fung JJ, Pardon E, Casarosa P, Chae PS, DeVree BT, Rosenbaum DM, Thian FS, Kobilka TS, Schnapp A, Konetzki I, Sunahara RK, Gellman SH, Pautsch A, Steyaert J, Weis WI, Kobilka BK. Structure of a nanobody-stabilized active state of the β 2 adrenoceptor. Nature 2011;469(7329):175–180. Rosenbaum DM, Zhang C, Lyons JA, Holl R, Aragao D, Arlow DH, Rasmussen SGF, Choi H-J, DeVree BT, Sunahara RK, Chae PS, Gellman SH, Dror RO, Shaw DE, Weis WI, Caffrey M, Gmeiner P, Kobilka BK. Structure and function of an irreversible agonist-β 2 adrenoceptor complex. Nature 2011;469(7329):236–240. Warne T, Serrano-Vega MJ, Baker JG, Moukhametzianov R, Edwards PC, Henderson R, Leslie AGW, Tate CG, Schertler GFX. Structure of a β 1 -adrenergic G-protein-coupled receptor. Nature 2008;454(7203):486–491. Xu F, Wu H, Katritch V, Han GW, Jacobson KA, Gao Z-G, Cherezov V, Stevens RC. Structure of an agonist-bound human A2A adenosine receptor. Science 2011;332(6027):322–327. Warne T, Moukhametzianov R, Baker JG, Nehme R, Edwards PC, Leslie AGW, Schertler GFX, Tate CG. The structural basis for agonist and partial agonist action on a β 1 -adrenergic receptor. Nature 2011;469(7329):241–244. Wu B, Chien EYT, Mol CD, Fenalti G, Liu W, Katritch V, Abagyan R, Brooun A, Wells P, Bi FC, Hamel DJ, Kuhn P, Handel TM, Cherezov V, Stevens RC. Structures of the CXCR4 chemokine GPCR with small-molecule and cyclic peptide antagonists. Science 2010;330(6007):1066–1071. Medicinal Research Reviews DOI 10.1002/med

38

r SHONBERG ET AL.

71. White JF, Noinaj N, Shibata Y, Love J, Kloss B, Xu F, Gvozdenovic-Jeremic J, Shah P, Shiloach J, Tate CG, Grisshammer R. Structure of the agonist-bound neurotensin receptor. Nature 2012;490(7421):508–513. 72. Wacker D, Wang C, Katritch V, Han GW, Huang X-P, Vardy E, McCorvy JD, Jiang Y, Chu M, Siu FY, Liu W, Xu HE, Cherezov V, Roth BL, Stevens RC. Structural features for functional selectivity at serotonin receptors. Science 2013;340(6132):615–619. 73. Wang C, Jiang Y, Ma J, Wu H, Wacker D, Katritch V, Han GW, Liu W, Huang X-P, Vardy E, McCorvy JD, Gao X, Zhou XE, Melcher K, Zhang C, Bai F, Yang H, Yang L, Jiang H, Roth BL, Cherezov V, Stevens RC, Xu HE. Structural basis for molecular recognition at serotonin receptors. Science 2013;340(6132):610–614. 74. Lebon G, Warne T, Edwards PC, Bennett K, Langmead CJ, Leslie AGW, Tate CG. Agonistbound adenosine A2A receptor structures reveal common features of GPCR activation. Nature 2011;474(7352):521–525. 75. Kruse AC, Ring AM, Manglik A, Hu J, Hu K, Eitel K, Hubner H, Pardon E, Valant C, Sexton PM, Christopoulos A, Felder CC, Gmeiner P, Steyaert J, Weis WI, Garcia KC, Wess J, Kobilka BK. Activation and allosteric modulation of a muscarinic acetylcholine receptor. Nature 2013;504:101–106. 76. Venkatakrishnan AJ, Deupi X, Lebon G, Tate CG, Schertler GF, Babu MM. Molecular signatures of G-protein-coupled receptors. Nature 2013;494(7436):185–194. 77. Qin K, Dong C, Wu G, Lambert NA. Inactive-state preassembly of G(q)-coupled receptors and G(q) heterotrimers. Nat Chem Biol 2011;7(10):740–747. 78. Butcher AJ, Prihandoko R, Kong KC, McWilliams P, Edwards JM, Bottrill A, Mistry S, Tobin AB. Differential G-protein-coupled receptor phosphorylation provides evidence for a signaling bar code. J Biol Chem 2011;286(13):11506–11518. 79. Tobin AB, Butcher AJ, Kong KC. Location, location, location . . . site-specific GPCR phosphorylation offers a mechanism for cell-type-specific signalling. Trends Pharmacol Sci 2008;29(8): 413–420. 80. Nobles KN, Xiao K, Ahn S, Shukla AK, Lam CM, Rajagopal S, Strachan RT, Huang TY, Bressler EA, Hara MR, Shenoy SK, Gygi SP, Lefkowitz RJ. Distinct phosphorylation sites on the beta(2)adrenergic receptor establish a barcode that encodes differential functions of beta-arrestin. Sci Signal 2011;4(185):ra51. 81. Dror RO, Arlow DH, Maragakis P, Mildorf TJ, Pan AC, Xu H, Borhani DW, Shaw DE. Activation mechanism of the β 2 -adrenergic receptor. Proc Natl Acad Sci USA 2011;108(46):18684–18689. 82. Rosenbaum DM, Cherezov V, Hanson MA, Rasmussen SGF, Thian FS, Kobilka TS, Choi H-J, Yao X-J, Weis WI, Stevens RC, Kobilka BK. GPCR engineering yields high-resolution structural insights into β 2 -adrenergic receptor function. Science 2007;318(5854):1266–1273. 83. Deupi X, Standfuss J. Structural insights into agonist-induced activation of G-protein-coupled receptors. Curr Opin Struct Biol 2011;21(4):541–551. 84. Standfuss J, Edwards PC, D’Antona A, Fransen M, Xie G, Oprian DD, Schertler GFX. The structural basis of agonist-induced activation in constitutively active rhodopsin. Nature 2011;471(7340):656–660. 85. Katritch V, Cherezov V, Stevens RC. Structure-function of the G protein–coupled receptor superfamily. Annu Rev Pharmacol Toxicol 2013;53(1):531–556. 86. Haga K, Kruse AC, Asada H, Yurugi-Kobayashi T, Shiroishi M, Zhang C, Weis WI, Okada T, Kobilka BK, Haga T, Kobayashi T. Structure of the human M2 muscarinic acetylcholine receptor bound to an antagonist. Nature 2012;482(7386):547–551. 87. Rosenbaum DM, Rasmussen SGF, Kobilka BK. The structure and function of G-protein-coupled receptors. Nature 2009;459(7245):356–363. 88. Ballesteros JA, Weinstein H. Integrated methods for the construction of three-dimensional models and computational probing of structure-function relations in G protein-coupled receptors. In: Stuart CS, Ed. Methods in Neurosciences, Vol. 25. San Diego, CA: Academic Press; 1995. p 366–428. Medicinal Research Reviews DOI 10.1002/med

BIASED AGONISM AT G PROTEIN-COUPLED RECEPTORS

r 39

89. Choe HW, Kim YJ, Park JH, Morizumi T, Pai EF, Krauss N, Hofmann KP, Scheerer P, Ernst OP. Crystal structure of metarhodopsin II. Nature 2011;471(7340):651–655. 90. Ahuja S, Crocker E, Eilers M, Hornak V, Hirshfeld A, Ziliox M, Syrett N, Reeves PJ, Khorana HG, Sheves M, Smith SO. Location of the retinal chromophore in the activated state of rhodopsin*. J Biol Chem 2009;284(15):10190–10201. 91. Bokoch MP, Zou Y, Rasmussen SG, Liu CW, Nygaard R, Rosenbaum DM, Fung JJ, Choi HJ, Thian FS, Kobilka TS, Puglisi JD, Weis WI, Pardo L, Prosser RS, Mueller L, Kobilka BK. Ligand-specific regulation of the extracellular surface of a G-protein-coupled receptor. Nature 2010;463(7277):108–112. 92. Shi L, Liapakis G, Xu R, Guarnieri F, Ballesteros JA, Javitch JA. β 2 Adrenergic receptor activation: Modulation of the proline kink in transmembrane 6 by a rotamer toggle switch. J Biol Chem 2002;277(43):40989–40996. 93. Isogaya M, Yamagiwa Y, Fujita S, Sugimoto Y, Nagao T, Kurose H. Identification of a key amino acid of the beta2-adrenergic receptor for high affinity binding of salmeterol. Mol Pharmacol 1998;54(4):616–622. 94. Wieland K, Zuurmond HM, Krasel C, Ijzerman AP, Lohse MJ. Involvement of Asn-293 in stereospecific agonist recognition and in activation of the beta 2-adrenergic receptor. Proc Natl Acad Sci USA 1996;93(17):9276–9281. 95. Kikkawa H, Isogaya M, Nagao T, Kurose H. The role of the seventh transmembrane region in high affinity binding of a beta 2-selective agonist TA-2005. Mol Pharmacol 1998;53(1):128–134. 96. Liapakis G, Ballesteros JA, Papachristou S, Chan WC, Chen X, Javitch JA. The forgotten serine. A critical role for Ser-2035.42 in ligand binding to and activation of the beta 2-adrenergic receptor. J Biol Chem 2000;275(48):37779–37788. 97. Sato T, Kobayashi H, Nagao T, Kurose H. Ser203 as well as Ser204 and Ser207 in fifth transmembrane domain of the human beta2-adrenoceptor contributes to agonist binding and receptor activation. Br J Pharmacol 1999;128(2):272–274. 98. Strader CD, Candelore MR, Hill WS, Sigal IS, Dixon RA. Identification of two serine residues involved in agonist activation of the beta-adrenergic receptor. J Biol Chem 1989;264(23):13572– 13578. 99. Strader CD, Sigal IS, Candelore MR, Rands E, Hill WS, Dixon RA. Conserved aspartic acid residues 79 and 113 of the beta-adrenergic receptor have different roles in receptor function. J Biol Chem 1988;263(21):10267–10271. 100. Suryanarayana S, Kobilka BK. Amino acid substitutions at position 312 in the seventh hydrophobic segment of the beta 2-adrenergic receptor modify ligand-binding specificity. Mol Pharmacol 1993;44(1):111–114. 101. Swaminath G, Deupi X, Lee TW, Zhu W, Thian FS, Kobilka TS, Kobilka B. Probing the β 2 adrenoceptor binding site with catechol reveals differences in binding and activation by agonists and partial agonists. J Biol Chem 2005;280(23):22165–22171. 102. Swaminath G, Xiang Y, Lee TW, Steenhuis J, Parnot C, Kobilka BK. Sequential binding of agonists to the β 2 adrenoceptor. Kinetic evidence for intermediate conformational states. J Biol Chem 2004;279(1):686–691. 103. Rahmeh R, Damian M, Cottet M, Orcel H, Mendre C, Durroux T, Sharma KS, Durand G, Pucci B, Trinquet E, Zwier JM, Deupi X, Bron P, Ban`eres J-L, Mouillac B, Granier S. Structural insights into biased G protein-coupled receptor signaling revealed by fluorescence spectroscopy. Proc Natl Acad Sci USA 2012;109(17):6733–6738. 104. Granier S, Kim S, Shafer AM, Ratnala VR, Fung JJ, Zare RN, Kobilka B. Structure and conformational changes in the C-terminal domain of the β 2 -adrenoceptor: Insights from fluorescence resonance energy transfer studies. J Biol Chem 2007;282(18):13895–13905. 105. Ghanouni P, Gryczynski Z, Steenhuis JJ, Lee TW, Farrens DL, Lakowicz JR, Kobilka BK. Functionally different agonists induce distinct conformations in the G protein coupling domain of the β 2 adrenergic receptor. J Biol Chem 2001;276(27):24433–24436. Medicinal Research Reviews DOI 10.1002/med

40

r SHONBERG ET AL.

106. Gether U, Lin S, Kobilka BK. Fluorescent labeling of purified β 2 adrenergic receptor: Evidence for ligand-specific conformational changes. J Biol Chem 1995;270(47):28268–28275. 107. Altenbach C, Kusnetzow AK, Ernst OP, Hofmann KP, Hubbell WL. High-resolution distance mapping in rhodopsin reveals the pattern of helix movement due to activation. Proc Natl Acad Sci USA 2008;105(21):7439–7444. 108. Lodowski DT, Palczewski K, Miyagi M. Conformational changes in the G protein-coupled receptor rhodopsin revealed by histidine hydrogen−deuterium exchange. Biochemistry 2010;49(44):9425– 9427. 109. West Graham M, Chien Ellen YT, Katritch V, Gatchalian J, Chalmers Michael J, Stevens Raymond C, Griffin Patrick R. Ligand-dependent perturbation of the conformational ensemble for the GPCR β 2 adrenergic receptor revealed by HDX. Structure 2011;19(10):1424–1432. 110. Zhang X, Chien EY, Chalmers MJ, Pascal BD, Gatchalian J, Stevens RC, Griffin PR. Dynamics of the β 2 -adrenergic G-protein coupled receptor revealed by hydrogen-deuterium exchange. Anal Chem 2010;82(3):1100–1108. 111. Kahsai AW, Xiao K, Rajagopal S, Ahn S, Shukla AK, Sun J, Oas TG, Lefkowitz RJ. Multiple ligand-specific conformations of the beta2-adrenergic receptor. Nat Chem Biol 2011;7(10):692–700. ¨ 112. Liu JJ, Horst R, Katritch V, Stevens RC, Wuthrich K. Biased signaling pathways in β 2 -adrenergic receptor characterized by 19 F-NMR. Science 2012;335(6072):1106–1110. 113. Warne T, Edwards PC, Leslie AG, Tate CG. Crystal structures of a stabilized β 1 -adrenoceptor bound to the biased agonists bucindolol and carvedilol. Structure 2012;20(5):841–849. 114. Raehal KM, Walker JK, Bohn LM. Morphine side effects in beta-arrestin 2 knockout mice. J Pharmacol Exp Ther 2005;314(3):1195–1201. 115. Chen XT, Pitis P, Liu G, Yuan C, Gotchev D, Cowan CL, Rominger DH, Koblish M, Dewire SM, Crombie AL, Violin JD, Yamashita DS. Structure-activity relationships and discovery of a G protein biased mu opioid receptor ligand, [(3-methoxythiophen-2-yl)methyl]({2-[(9R)-9-(pyridin-2yl)-6-oxaspiro-[4.5]decan- 9-yl]ethyl})amine (TRV130), for the treatment of acute severe pain. J Med Chem 2013;56:8016–8031. 116. Allen JA, Yost JM, Setola V, Chen X, Sassano MF, Chen M, Peterson S, Yadav PN, Huang X-p, Feng B, Jensen NH, Che X, Bai X, Frye SV, Wetsel WC, Caron MG, Javitch JA, Roth BL, Jin J. Discovery of β-arrestin–biased dopamine D2 ligands for probing signal transduction pathways essential for antipsychotic efficacy. Proc Natl Acad Sci USA 2011;108(45):18488–18493. 117. Masri B, Salahpour A, Didriksen M, Ghisi V, Beaulieu J-M, Gainetdinov RR, Caron MG. Antagonism of dopamine D2 receptor/β-arrestin 2 interaction is a common property of clinically effective antipsychotics. Proc Natl Acad Sci USA 2008;105(36):13656–13661. 118. Lawler CP, Prioleau C, Lewis MM, Mak C, Jiang D, Schetz JA, Gonzalez AM, Sibley DR, Mailman RB. Interactions of the novel antipsychotic aripiprazole (OPC-14597) with dopamine and serotonin receptor subtypes. Neuropsychopharmacology 1999;20(6):612–627. 119. Beaulieu J-M, Sotnikova TD, Marion S, Lefkowitz RJ, Gainetdinov RR, Caron MG. An Akt/βarrestin 2/PP2A signaling complex mediates dopaminergic neurotransmission and behavior. Cell 2005;122(2):261–273. 120. Emamian ES, Hall D, Birnbaum MJ, Karayiorgou M, Gogos JA. Convergent evidence for impaired AKT1-GSK3β signaling in schizophrenia. Nat Genet 2004;36(2):131–137. 121. Beaulieu J-M, Sotnikova TD, Yao W-D, Kockeritz L, Woodgett JR, Gainetdinov RR, Caron MG. Lithium antagonizes dopamine-dependent behaviors mediated by an AKT/glycogen synthase kinase 3 signaling cascade. Proc Natl Acad Sci USA 2004;101(14):5099–5104. 122. Beaulieu J-M, Gainetdinov RR, Caron MG. The Akt–GSK-3 signaling cascade in the actions of dopamine. Trends Pharmacol Sci 2007;28(4):166–172. 123. Oshiro Y, Sato S, Kurahashi N, Tanaka T, Kikuchi T, Tottori K, Uwahodo Y, Nishi T. Novel antipsychotic agents with dopamine autoreceptor agonist properties: Synthesis and pharmacology of 7-[4-(4-phenyl-1-piperazinyl)butoxy]-3,4-dihydro-2(1H)-quinolinone derivatives. J Med Chem 1998;41(5):658–667. Medicinal Research Reviews DOI 10.1002/med

BIASED AGONISM AT G PROTEIN-COUPLED RECEPTORS

r 41

124. Kikuchi T, Tottori K, Uwahodo Y, Hirose T, Miwa T, Oshiro Y, Morita S. 7-(4-[4(2,3-Dichlorophenyl)-1-piperazinyl]butyloxy)-3,4-dihydro-2(1H)-quinolinone (OPC-14597), a new putative antipsychotic drug with both presynaptic dopamine autoreceptor agonistic activity and postsynaptic D2 receptor antagonistic activity. J Pharmacol Exp Ther 1995;274(1):329–336. 125. Chen X, Sassano MF, Zheng L, Setola V, Chen M, Bai X, Frye SV, Wetsel WC, Roth BL, Jin J. Structure–functional selectivity relationship studies of β-arrestin-biased dopamine D2 receptor agonists. J Med Chem 2012;55(16):7141–7153. 126. Pratt J, Winchester C, Dawson N, Morris B. Advancing schizophrenia drug discovery: Optimizing rodent models to bridge the translational gap. Nat Rev Drug Discov 2012;11(7):560–579. 127. Black JW, Leff P. Operational models of pharmacological agonism. Proc R Soc Lond B Biol Sci 1983;220(1219):141–162. 128. Black JW, Leff P, Shankley NP, Wood J. An operational model of pharmacological agonism: The effect of E/[A] curve shape on agonist dissociation constant estimation. Br J Pharmacol 2010;160:S54–S64. 129. Shonberg J, Herenbrink CK, Lopez L, Christopoulos A, Scammells PJ, Capuano B, Lane JR. A structure-activity analysis of biased agonism at the dopamine d2 receptor. J Med Chem 2013;56(22):9199–9221. 130. Tschammer N, Bollinger S, Kenakin T, Gmeiner P. Histidine 6.55 is a major determinant of ligandbiased signaling in dopamine D2L receptor. Mol Pharmacol 2011;79(3):575–585. 131. Tschammer N, Elsner J, Goetz A, Ehrlich K, Schuster S, Ruberg M, Kühhorn J, Thompson D, Whistler J, Hübner H, Gmeiner P. Highly potent 5-aminotetrahydropyrazolopyridines: Enantioselective dopamine D3 receptor binding, functional selectivity, and analysis of receptor−ligand interactions. J Med Chem 2011;54(7):2477–2491. 132. Kilts JD, Connery HS, Arrington EG, Lewis MM, Lawler CP, Oxford GS, O’Malley KL, Todd RD, Blake BL, Nichols DE, Mailman RB. Functional selectivity of dopamine receptor agonists. II. Actions of dihydrexidine in D2L receptor-transfected MN9D cells and pituitary lactotrophs. J Pharmacol Exp Ther 2002;301(3):1179–1189. 133. Mailman RB, Gay EA. Novel mechanisms of drug action: Functional selectivity at D2 dopamine receptors. Med Chem Res 2004;13(1):115–126. 134. Fowler JC, Bhattacharya S, Urban JD, Vaidehi N, Mailman RB. Receptor conformations involved in dopamine D2L receptor functional selectivity induced by selected transmembrane 5 serine mutations. Mol Pharmacol 2012;81(6):820–831. 135. Ehlert FJ. On the analysis of ligand-directed signaling at G protein-coupled receptors. Naunyn Schmiedebergs Arch Pharmacol 2008;377(4–6):549–577. 136. Rajagopal S, Ahn S, Rominger DH, Gowen-MacDonald W, Lam CM, Dewire SM, Violin JD, Lefkowitz RJ. Quantifying ligand bias at seven-transmembrane receptors. Mol Pharmacol 2011;80(3):367–377. 137. Kenakin T, Christopoulos A. Measurements of ligand bias and functional affinity. Nat Rev Drug Discov 2013;12(6):483. 138. Rajagopal S. Quantifying biased agonism: Understanding the links between affinity and efficacy. Nat Rev Drug Discov 2013;12(6):483. 139. Kenakin T, Christopoulos A. Signalling bias in new drug discovery: Detection, quantification and therapeutic impact. Nat Rev Drug Discov 2013;12(3):205–216. 140. Zampeli E, Tiligada E. The role of histamine H4 receptor in immune and inflammatory disorders. Br J Pharmacol 2009;157(1):24–33. 141. Rosethorne EM, Charlton SJ. Agonist-biased signaling at the histamine H4 receptor: JNJ7777120 recruits β-arrestin without activating G proteins. Mol Pharmacol 2011;79(4):749–757. 142. Nijmeijer S, Vischer HF, Rosethorne EM, Charlton SJ, Leurs R. Analysis of multiple histamine H4 receptor compound classes uncovers Gα i protein- and β-arrestin2-biased ligands. Mol Pharmacol 2012;82(6):1174–1182.

Medicinal Research Reviews DOI 10.1002/med

42

r SHONBERG ET AL.

143. Christopoulos A, Kenakin T. G protein-coupled receptor allosterism and complexing. Pharmacol Rev 2002;54(2):323–374. 144. Leach K, Sexton PM, Christopoulos A. Allosteric GPCR modulators: Taking advantage of permissive receptor pharmacology. Trends Pharmacol Sci 2007;28(8):382–389. 145. Valant C, Felder CC, Sexton PM, Christopoulos A. Probe dependence in the allosteric modulation of a G protein-coupled receptor: Implications for detection and validation of allosteric ligand effects. Mol Pharmacol 2012;81(1):41–52. 146. Koole C, Wootten D, Simms J, Valant C, Sridhar R, Woodman OL, Miller LJ, Summers RJ, Christopoulos A, Sexton PM. Allosteric ligands of the glucagon-like peptide 1 receptor (GLP-1R) differentially modulate endogenous and exogenous peptide responses in a pathway-selective manner: Implications for drug screening. Mol Pharmacol 2010;78(3):456–465. 147. Davey AE, Leach K, Valant C, Conigrave AD, Sexton PM, Christopoulos A. Positive and negative allosteric modulators promote biased signaling at the calcium-sensing receptor. Endocrinology 2012;153(3):1232–1241. 148. Maillet EL, Pellegrini N, Valant C, Bucher B, Hibert M, Bourguignon JJ, Galzi JL. A novel, conformation-specific allosteric inhibitor of the tachykinin NK2 receptor (NK2R) with functionally selective properties. FASEB J 2007;21(9):2124–2134. 149. Lane JR, Sexton PM, Christopoulos A. Bridging the gap: Bitopic ligands of G-protein-coupled receptors. Trends Pharmacol Sci 2013;34(1):59–66. 150. Valant C, Lane JR, Sexton PM, Christopoulos A. The best of both worlds? Bitopic orthosteric/ allosteric ligands of G protein–coupled receptors. Annu Rev Pharmacol Toxicol 2012;52(1):153–178. 151. Valant C, Sexton PM, Christopoulos A. Orthosteric/allosteric bitopic ligands: Going hybrid at GPCRs. Mol Interv 2009;9(3):125–135. 152. Mohr K, Tr¨ankle C, Kostenis E, Barocelli E, De Amici M, Holzgrabe U. Rational design of dualsteric GPCR ligands: Quests and promise. Br J Pharmacol 2010;159(5):997–1008. 153. Haga K, Kruse AC, Asada H, Yurugi-Kobayashi T, Shiroishi M, Zhang C, Weis WI, Okada T, Kobilka BK, Haga T, Kobayashi T. Structure of the human M2 muscarinic acetylcholine receptor bound to an antagonist. Nature 2012;482(7386):547–551. 154. Valant C, Gregory KJ, Hall NE, Scammells PJ, Lew MJ, Sexton PM, Christopoulos A. A novel mechanism of G protein-coupled receptor functional selectivity. Muscarinic partial agonist McNA-343 as a bitopic orthosteric/allosteric ligand. J Biol Chem 2008;283(43):29312–29321. 155. Antony J, Kellershohn K, Mohr-Andr¨a M, Kebig A, Prilla S, Muth M, Heller E, Disingrini T, ¨ Dallanoce C, Bertoni S, Schrobang J, Tr¨ankle C, Kostenis E, Christopoulos A, Holtje H-D, Barocelli E, De Amici M, Holzgrabe U, Mohr K. Dualsteric GPCR targeting: A novel route to binding and signaling pathway selectivity. FASEB J 2009;23(2):442–450. 156. Kebig A, Kostenis E, Mohr K, Mohr-Andr¨a M. An optical dynamic mass redistribution assay reveals biased signaling of dualsteric GPCR activators. J Recept Signal Transduct Res 2009;29(3– 4):140–145. 157. Bock A, Merten N, Schrage R, Dallanoce C, Batz J, Klockner J, Schmitz J, Matera C, Simon K, Kebig A, Peters L, Muller A, Schrobang-Ley J, Trankle C, Hoffmann C, De Amici M, Holzgrabe U, Kostenis E, Mohr K. The allosteric vestibule of a seven transmembrane helical receptor controls G-protein coupling. Nat Commun 2012;3(1044):1–11. 158. Narlawar R, Lane JR, Doddareddy M, Lin J, Brussee J, IJzerman AP. Hybrid ortho/allosteric ligands for the adenosine A1 receptor. J Med Chem 2010;53(8):3028–3037. 159. Valant C, Gregory KJ, Hall NE, Scammells PJ, Lew MJ, Sexton PM, Christopoulos A. A novel mechanism of G protein-coupled receptor functional selectivity. J Biol Chem 2008;283(43):29312– 29321. 160. van der Westhuizen ET, Christopoulos A, Sexton PM, Wade JD, Summers RJ. H2 relaxin is a biased ligand relative to H3 relaxin at the relaxin family peptide receptor 3 (RXFP3). Mol Pharmacol 2010;77(5):759–772.

Medicinal Research Reviews DOI 10.1002/med

BIASED AGONISM AT G PROTEIN-COUPLED RECEPTORS

r 43

161. Ghosh RN, DeBiasio R, Hudson CC, Ramer ER, Cowan CL, Oakley RH. Quantitative cell-based high-content screening for vasopressin receptor agonists using transfluor technology. J Biomol Screen 2005;10(5):476–484. 162. Sauliere A, Bellot M, Paris H, Denis C, Finana F, Hansen JT, Altie MF, Seguelas MH, Pathak A, Hansen JL, Senard JM, Gales C. Deciphering biased-agonism complexity reveals a new active AT1 receptor entity. Nat Chem Biol 2012;8(7):622–630. 163. Denis C, Sauliere A, Galandrin S, Senard JM, Gales C. Probing heterotrimeric G protein activation: Applications to biased ligands. Curr Pharm Des 2012;18(2):128–144. 164. van der Westhuizen ET, Breton B, Christopoulos A, Bouvier M. Quantification of ligand bias for clinically relevant beta2-adrenergic receptor ligands: Implications for drug taxonomy. Mol Pharmacol 2014;85(3):492–509. 165. Schroder R, Janssen N, Schmidt J, Kebig A, Merten N, Hennen S, Muller A, Blattermann S, MohrAndra M, Zahn S, Wenzel J, Smith NJ, Gomeza J, Drewke C, Milligan G, Mohr K, Kostenis E. Deconvolution of complex G protein-coupled receptor signaling in live cells using dynamic mass redistribution measurements. Nat Biotechnol 2010;28(9):943–949. 166. Sykes DA, Dowling MR, Charlton SJ. Exploring the mechanism of agonist efficacy: A relationship between efficacy and agonist dissociation rate at the muscarinic M3 receptor. Mol Pharmacol 2009;76(3):543–551. 167. Guo D, Mulder-Krieger T, AP IJ, Heitman LH. Functional efficacy of adenosine A(2)A receptor agonists is positively correlated to their receptor residence time. Br J Pharmacol 2012;166(6):1846– 1859. 168. Unett DJ, Gatlin J, Anthony TL, Buzard DJ, Chang S, Chen C, Chen X, Dang HT, Frazer J, Le MK, Sadeque AJ, Xing C, Gaidarov I. Kinetics of 5-HT2B receptor signaling: Profound agonistdependent effects on signaling onset and duration. J Pharmacol Exp Ther 2013;347(3):645–659. 169. Hamdan FF, Audet M, Garneau P, Pelletier J, Bouvier M. High-throughput screening of G proteincoupled receptor antagonists using a bioluminescence resonance energy transfer 1-based betaarrestin2 recruitment assay. J Biomol Screen 2005;10(5):463–475. 170. Olson KR, Eglen RM. Beta galactosidase complementation: A cell-based luminescent assay platform for drug discovery. Assay Drug Dev Technol 2007;5(1):137–144. 171. Zhao X, Jones A, Olson KR, Peng K, Wehrman T, Park A, Mallari R, Nebalasca D, Young SW, Xiao SH. A homogeneous enzyme fragment complementation-based beta-arrestin translocation assay for high-throughput screening of G-protein-coupled receptors. J Biomol Screen 2008;13(8):737–747. 172. Barnea G, Strapps W, Herrada G, Berman Y, Ong J, Kloss B, Axel R, Lee KJ. The genetic design of signaling cascades to record receptor activation. Proc Natl Acad Sci USA 2008;105(1):64–69. 173. Verkaar F, van Rosmalen JW, Blomenrohr M, van Koppen CJ, Blankesteijn WM, Smits JF, Zaman GJ. G protein-independent cell-based assays for drug discovery on seven-transmembrane receptors. Biotechnol Annu Rev 2008;14:253–274. 174. Leach K, Wen A, Cook AE, Sexton PM, Conigrave AD, Christopoulos A. Impact of clinically relevant mutations on the pharmacoregulation and signaling bias of the calcium-sensing receptor by positive and negative allosteric modulators. Endocrinology 2013;154(3):1105–1116. 175. Leach K, Wen A, Davey AE, Sexton PM, Conigrave AD, Christopoulos A. Identification of molecular phenotypes and biased signaling induced by naturally occurring mutations of the human calcium-sensing receptor. Endocrinology 2012;153(9):4304–4316. 176. Gregory KJ, Sexton PM, Tobin AB, Christopoulos A. Stimulus bias provides evidence for conformational constraints in the structure of a G protein-coupled receptor. J Biol Chem 2012;287(44):37066– 37077. 177. Gregory KJ, Hall NE, Tobin AB, Sexton PM, Christopoulos A. Identification of orthosteric and allosteric site mutations in M2 muscarinic acetylcholine receptors that contribute to ligand-selective signaling bias. J Biol Chem 2010;285(10):7459–7474. 178. Langemeijer EV, Verzijl D, Dekker SJ, IJzerman AP. Functional selectivity of adenosine A1 receptor ligands? Purinergic Signal 2013;9(1):91–100. Medicinal Research Reviews DOI 10.1002/med

44

r SHONBERG ET AL.

179. Williams JT, Ingram SL, Henderson G, Chavkin C, von Zastrow M, Schulz S, Koch T, Evans CJ, Christie MJ. Regulation of mu-opioid receptors: Desensitization, phosphorylation, internalization, and tolerance. Pharmacol Rev 2013;65(1):223–254. 180. Allen JA, Roth BL. Strategies to discover unexpected targets for drugs active at G protein–coupled receptors. Annu Rev Pharmacol Toxicol 2011;51(1):117–144. 181. Mathiesen JM, Ulven T, Martini L, Gerlach LO, Heinemann A, Kostenis E. Identification of indole derivatives exclusively interfering with a G protein-independent signaling pathway of the prostaglandin D2 receptor CRTH2. Mol Pharmacol 2005;68(2):393–402. 182. Deupi X, Kobilka BK. Energy landscapes as a tool to integrate GPCR structure, dynamics, and function. Physiology (Bethesda) 2010;25(5):293–303. ¨ 183. The PyMOL Molecular Graphics System, Version 1.5.0.4 Schrodinger, LLC. 184. GraphPadSoftware. GraphPad Prism for Windows 6.00. La Jolla, CA.

Jeremy Shonberg completed his Ph.D. in 2013 at Monash University (Melbourne, Australia) in the Department of Medicinal Chemistry. He focused on the design, synthesis, and pharmacological evaluation of novel ligands targeting the dopamine D2 receptor, with a particular focus on bivalent, bitopic, allosteric, and biased ligands. He is currently working as a Postdoctoral Fellow in the Lane & Capuano labs continuing on the discoveries developed throughout his Ph.D. Laura L´opez obtained her Ph.D. in 2010 at the University Pompeu Fabra. Her research is focused on the study of the structure and function of G protein-coupled receptors. Her work relies on a multidisciplinary approach that combines 3D protein modeling, docking, and molecular dynamics with experimental results to lead with ligand and structure-based drug design. Her postdoctoral work at the Monash Institute of Pharmaceutical Sciences, Melbourne, Australia, involved the study of drug–receptor interactions in the context of allosteric modulators and biased agonists. Prof. Peter Scammells completed a PhD at Griffith University in 1991 under the supervision of Prof. Ronald J. Quinn. After a postdoctoral fellowship at the University of South Florida and an Alexander von Humboldt Fellowship at the Technische Universit¨at Darmstadt, he took up a lectureship at Deakin University in 1993. He was appointed as Professor of Medicinal Chemistry at Monash University in 2001 and is currently the Medicinal Chemistry Theme Leader at the Monash Institute of Pharmaceutical Sciences. Prof. Scammells’ research interests include the medicinal chemistry of G protein-coupled receptor ligands acting at adenosine, opiate, muscarinic acetylcholine and dopamine receptors. Arthur Christopoulos is a world leader in the detection and quantification of allosteric drugs and biased agonists at G protein-coupled receptors. His research crosses academic and industry boundaries, and incorporates computational and mathematical modeling, medicinal chemistry, structural biology, cellular biochemistry, and signal transduction, and in vivo animal models of behavior. He is an author of over 190 scientific articles, is the recipient of numerous prizes and awards for his research, serves/has served on the Editorial Board of eight international journals, and is/has been a Principal Investigator on numerous grant/contracts from various sources. He is a Consultant for a number of large pharma and biotechnology companies, and is a member of the Nomenclature Committee of the International Union of Basic and Clinical Pharmacology (NC-IUPHAR). Medicinal Research Reviews DOI 10.1002/med

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Ben Capuano is a highly experienced synthetic medicinal chemist specializing in the synthesis, structural characterization, and biological evaluation of small biomolecules targeting dopaminerelated disease states such as schizophrenia and Parkinson’s disease. He completed a PhD at Monash University in 2000 under the supervision of Dr Edward J. Lloyd and Dr Ian T. Crosby. Much of his research has been centered on the atypical antipsychotic clozapine, which has culminated in the synthesis and biological evaluation of the first clozapine-based homobivalent ligand with nanomolar affinity for the dopamine D2 receptor. He has published extensive medicinal chemistry-related papers in the area of schizophrenia research. Dr Capuano currently holds an academic position at Monash University and continues to undertake medicinal chemistry research in the area of small-molecule ligands targeting G protein-coupled receptors implicated in psychological and neurological disorders. J. Robert Lane completed his PhD in 2008 at Glasgow University under the supervision of Prof. Graeme Milligan. Following a post doctoral fellowship in the department of Medicinal Chemistry at the University of Leiden, Dr. Lane took up his current position an R.D. Wright Biomedical Research Fellow (NHMRC, Australia) at the Monash Institute of Pharmaceutical Sciences, Melbourne, Australia. His research interests center on the molecular pharmacology of G proteincoupled receptors and in particular the paradigms of allosteric modulation, ligand-biased signaling, and bitopic (dual orthosteric/allosteric) ligands. His current research focus is applying these paradigms to develop novel approaches for the treatment of schizophrenia. Dr. Lane has a particular interest in the pharmacology of dopamine, serotonin and muscarinic receptors.

Medicinal Research Reviews DOI 10.1002/med

Biased agonism at G protein-coupled receptors: the promise and the challenges--a medicinal chemistry perspective.

Historically, determination of G protein-coupled receptor (GPCR) ligand efficacy has often been restricted to identifying the ligand as an agonist or ...
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