Biased Receptor Signaling in Drug Discovery

A. G Proteins

Canonical 7TMR signaling was first described as an interaction with heterotrimeric G proteins (G[α]/G[βγ]) leading to a ligand/GPCR-catalyzed GDP/GTP exchange on the G[α] subunit to induce structural rearrangement or dissociation of G[α]-GTP and G[βγ] (Denis et al., 2012). The numerous G[α] subunits are transducers of adenylyl cyclase (G[α]_s stimulation, G[α]_i inhibition), phospholipase C (PLC) (G[α]_q), and RhoGEFs (G[α]_12/13) (Neer, 1995; Luttrell, 2008; Walther and Ferguson, 2015) to produce a range of intracellular second messengers such as cAMP, inositol triphosphate (IP3), and diacylglycerol (Neves et al., 2002). Receptor G protein–induced production of second messengers such as cAMP also can be temporally and spatially separated from membrane events (Gidon et al., 2014; Luttrell, 2014). First reported in yeast (Slessareva et al., 2006), nonmembrane-based cAMP production has since been reported in mammalian cell systems for dopamine D₁ receptors (Kotowski et al., 2011), β₂-adrenergic receptors (Irannejad et al., 2013), glucagon-like peptide (GLP)-1 receptors (Kuna et al., 2013), pituitary adenylate cyclase-activating polypeptide (PACAP) type 1 receptors (Merriam et al., 2013), thyroid-stimulating hormone receptors (Calebiro et al., 2015), parathyroid hormone (PTH) receptors (Castro et al., 2005; Ferrandon et al., 2009), sphingosine-1-phosphate receptors (Mullershausen et al., 2009), melanocortin-4 receptors (Molden et al., 2015), and vasopressin type 2 receptors (Feinstein et al., 2013). In addition, G[βγ] variants (dissociated from G_a subunits) can function as independent sources of second messengers (Clapham and Neer, 1997; Dupré et al., 2009). It will be seen that this wide array of G protein subunits forms the basis for a diverse potential for biased signaling.

B. β-Arrestins

GPCR activation sequelae lead to phosphorylation of receptor cytoplasmic domains by second messenger–dependent protein kinases or the G protein–coupled receptor kinase (GRK) family (GRK 1–7), a class of seryl-threonyl kinases that phosphorylate the cytoplasmic tail of agonist-occupied receptors (Pitcher et al., 1998; Lefkowitz and Shenoy, 2005; Drake et al., 2006). The phosphorylated receptors then have a high affinity for a family of proteins called arrestins (four isoforms consisting of two “visual” arrestins, arrestin1 and arrestin4, and two ubiquitous cellular arrestins involved in 7TMR signaling, β-arrestin1 and β-arrestin2). The binding of arrestins to receptors, first reported for rhodopsin by Kühn et al. (1984), results in suppression of G protein activation through competition (Wilden et al., 1986; Wilden, 1995). First characterized for their role in receptor desensitization in mammalian systems (Ferguson, 2001), a variety of subsequent studies confirmed the role of β-arrestin in receptor desensitization to G protein signaling (i.e., receptor desensitization; Kohout and Lefkowitz, 2003; Lefkowitz and Whalen, 2004; Shenoy and Lefkowitz, 2005; Moore et al., 2007). These receptor interactions with β-arrestins also cause receptor internalization and vesicular trafficking, routing, and desensitization (Luttrell, 2008; Walther and Ferguson, 2013, 2015). As early as 1999, it was reported that β-arrestin2 bound nonreceptor tyrosine kinase and c-Src recruited β₂-adrenoceptors (Luttrell et al., 1999); subsequent studies revealed that receptor–β-arrestin interactions can lead to the formation of signalsomes (receptorsomes) by functioning as scaffolding proteins for recruitment and further activation of cytoplasmic proteins (Shenoy et al., 2006; Noma et al., 2007; Irannejad et al., 2013). These receptor-bound β-arrestins can interact with extracellular signal-regulated kinase (ERK1/2), protein kinase B, mitogen-activated protein kinase kinase, Raf-1, ubiquitin kinase, and other cytoplasmic proteins (Luttrell et al., 2001; Shenoy et al., 2001; Beaulieu et al., 2005; Del’guidice et al., 2011; Urs et al., 2011; Kuhar et al., 2015), the Src family of kinases (Barlic et al., 2000; DeFea et al., 2000a), E3 ubiquitin ligase Mdm2 (Shenoy et al., 2001), c-Jun N-terminal kinase (JNK) 3 mitogen-activated protein kinase cascades (DeFea et al., 2000b; McDonald et al., 2000; Luttrell et al., 2001), cAMP phosphodiesterases 4D3/5 (Perry et al., 2002), the inhibitor of nuclear factor-κB IκBα (Gao et al., 2004; Witherow et al., 2004), the Ral-GDP dissociation stimulator (Bhattacharya et al., 2002), the actin filament-severing protein cofilin (Zoudilova et al., 2007), diacylglycerol kinase (Nelson et al., 2007), and serine/threonine protein phosphatase 2A (Beaulieu et al., 2004, 2005). In addition, β-arrestins have been shown to potentiate Gα_s activity through a β-arrestin–G[βγ] complex that allows multiple rounds of Gα_s association and dissociation (Wehbi et al., 2013). Receptors can interact with other signaling partners in addition to G protein and β-arrestins to produce cytoplasmic signaling (Ritter and Hall, 2009), including PDZ domains [multi-PDZ domain protein 1, synapse-associated protein 97, postsynaptic density protein 95, sorting nexin family member 27, Na⁺/H⁺ exchange regulatory factors (NHERFs), membrane-associated guanylate kinase] and non-PDZ domain [A-kinase-anchoring proteins, Jak2, 14-3-3]–containing scaffolds (Walther and Ferguson, 2015). Thus, structurally diverse receptorsomes created in different cells can influence pluridimensional efficacy profiles for 7TMR ligands (Maudsley et al., 2011, 2013).

A. Receptor/G Protein Interactions

As discussed, the first and most prominent interaction noted for 7TMRs is their interaction with G proteins. Mammalian genomes code for 16 different G protein Gα subunits capable of interacting with 7TMRs, and it has been suggested that choices between these constitute the largest source of functional selectivity (Hermans, 2003). These 16 types of Gα subunits associate with an equally diverse set of Gβγ subunits (Hermans, 2003; Oldham and Hamm, 2008) made up of six different β-subunits and 12 γ-subunits (Gautam et al., 1998; Vanderbeld and Kelly, 2000) that form a large network of possible signaling units. Whereas Gβγ subunits are thought to be functionally interchangeable (Smrcka, 2008), activated Gα subunits regulate distinct and nonredundant cellular effectors (Wettschureck and Offermanns, 2005; Hubbard and Hepler, 2006). In addition, restricted tissue expression of some of these elements prevents some combinations. Within this context, the potential compartmentalization and concentrations of some of these elements in cells may contribute to cell-based modifications of biased signaling. This diverse system of G proteins is generally classified into four main functional groupings according to the family of Gα subunits that are predominantly activated: namely G_i/o, G_q/11, G_s, and G_12/13. 7TMRs generally can interact with members spanning across these general groupings despite the fact that the G protein functional outcomes can be very different (Michal et al., 2007; Inoue et al., 2012; Saulière et al., 2012). For instance, cannabinoid receptors interact with Gα_z, Gα_q/11, and Gα_12/13 (Diez-Alarcia et al., 2016; Laprairie et al., 2017). Ligand-stabilized receptor conformations drive the selection of G protein interactions and these selections can be discerned within G protein subunit families; for instance, studies with bioluminescence resonance energy transfer (BRET) biosensors have shown that oxytocin analogs differentiate between individual G_i/o family members (Gα_q, Gα_i1, Gα_i2, Gα_i3, Gα_oA, Gα_oB; Busnelli et al., 2012). Ligand bias between subunit members of G protein families is increasingly observed with agonists for μ-opioid receptors (Gα_i1, Gα_oA; Saidak et al., 2006), dopamine receptors (Gα_i1, Gα_i2, Gα_i3, Gα_oA, Gα_oB; Möller et al., 2017), and PTH receptors (Gα_s, Gα_q/11, and Gα_io; Appleton et al., 2013). Signaling also results from activation of Gβ subunits (activation or deactivation of adenylate cyclase, PLCs, phosphatidylinositol 3-kinase) and Gγ subunits (protein kinase D) (Morris and Malbon, 1999; Vanderbeld and Kelly, 2000). Bias between Gα and Gβγ subunit activation also has been observed. For example, the small molecule agonist GUE1654 [7-(methylthio)-2-[(2,2-diphenylacetyl)amino]benzo[1,2-d:4,3-d′]bisthiazole] for G_i/o-coupled oxoeicosanoid receptors produces inhibition of Gβγ otherwise allowing activation of Gα-dependent pathways (Blättermann et al., 2012). This type of bias may be important to drug therapy, as differential activation of specific combinations of Gα and Gβγ subunits is beginning to be recognized in terms of clinical benefits (Piñeyro, 2009; Lin and Smrcka, 2011).

B. Receptor–β-Arrestin Interactions

Another major set of 7TMR-signaling protein interactions occurs with arrestins. In fact, of 350 nonolfactory human 7TMRs, nearly all couple to β-arrestin (Roth and Marshall, 2012; Kroeze et al., 2015). The interaction of receptors with β-arrestins is promoted by receptor activation, phosphorylation of receptors by GRKs (Benovic et al., 1986), and, in certain instances, special interactions and post-translational processes such as palmitoylation (Charest and Bouvier, 2003). Receptor phosphorylation is particularly important to receptor-arrestin interactions (Tobin, 2008; Tobin et al., 2008; Reiter et al., 2012). This phosphorylation of serine/threonine residues produces a distinct pattern at the C terminus or intracellular loops of the receptor referred to as a “barcode” (Tobin et al., 2008; Nobles et al., 2011); different patterns for these have been shown through mass spectrometry proteomics for ligand interactions with β-adrenoceptors (Nobles et al., 2011). Mass spectrometry and receptor phosphorylation-specific antibodies also can be employed to elucidate these unique barcodes (Prihandoko et al., 2015).

In terms of signaling bias, the nature of the ligand-receptor complex has been shown to influence the phosphorylation barcode of the receptor to further influence the signaling outcome of the complex (i.e., signaling bias) (Kim et al., 2005; Tobin et al., 2008; Zidar et al., 2009; Butcher et al, 2011; Nobles et al., 2011; Zhou et al., 2017a). Agonist-specific phosphorylation barcodes have been reported for angiotensin II type I receptors (Xiao et al., 2007; Christensen et al., 2010), opioid receptors (Just et al., 2013), and serotonin 5-HT_2A receptors (González-Maeso et al., 2007). For the β₁-adrenoceptor, structural evidence that this occurs through unique binding modes for carvedilol and bucindolol has been given (Warne et al., 2012) to describe special conformations that go on to provide unique signaling effects (Reiter et al., 2012). Phosphorylation also has been shown to modify G protein interactions for 5-HT₆ receptor changes from ligand-dependent Gα_s coupling to a ligand-independent coupling to Cdc42 (Duhr et al., 2014).

There are at least three major possible outcomes of activated receptor–β-arrestin interaction: cessation of G protein signaling (Lohse et al., 1990), internalization of receptors (Lefkowitz, 1998; Ferguson, 2001; Luttrell, 2008; Ahn et al., 2009; Walther and Ferguson, 2013, 2015), and formation of an intracellular scaffold for internal cellular signaling (Luttrell et al., 1999; DeFea et al., 2000a; Lefkowitz and Shenoy, 2005). These mechanisms have been associated with a wealth of physiologic responses to pharmacological activity, including receptor desensitization (Deshpande et al., 2008; Wang et al., 2009; Whalen et al., 2011), receptor internalization (Ferguson et al., 1996; Goodman et al., 1996; Laporte et al., 1999; Hanyaloglu and von Zastrow, 2008), cell apoptosis (Chen et al., 2009), protein cell synthesis (DeWire et al., 2008; Ahn et al., 2009), central nervous system reward (Bohn et al., 2003), and learning and memory (Poulin et al., 2010).

The dependence of receptor internalization on β-arrestin has been demonstrated in numerous studies but there are a diverse number of mechanisms involved in this process. The most extensively characterized involves the receptor/β-arrestin complex binding to clathrin and its adapter protein AP2 (Wilden et al., 1986; Lohse et al., 1990; Ferguson et al., 1996; Goodman et al., 1996; Laporte et al., 1999), the clathrin heavy chain (Goodman et al., 1996), and E3 ligase Mdm2 (Shenoy et al., 2007) to form clathrin-coated vesicles that traffic to the endosome with subsequent possible transfer to lysosomes for degradation or recycling back to the plasma membrane (Cao et al., 1998). The introduction of green fluorescent protein–tagged receptors has moved these types of experiments forward (Barak et al., 1997). A classification system, based on differential affinity of the receptor for arrestin, has evolved from these studies, with class A 7TMRs (e.g., β₂-adrenoceptors, μ-opioid receptors, dopamine D₁) preferentially binding β-arrestin1 (also known as arrestin2) to be rapidly dephosphorylated and recycled. In contrast, class B 7TMRs (e.g., angiotensin II type A, vasopressin V2, substance P) bind β-arrestin1 and β-arrestin2 (also known as arrestin3) and have equal affinity to form stable complexes that can be retained in the cytosol, recycled, or degraded (Walther and Ferguson, 2013).

Biased signaling involving β-arrestins can be extremely complex due to the fact that arrestins have so many consequential actions in the cell with respect to receptor disposition and signaling and also because there are a number of alternative activities that can be exploited therapeutically. For instance, biased signaling toward β-arrestin–receptor interaction has alternately been suggested to be beneficial for some receptors, such as promotion of antipsychotic dopamine D₂ receptor activity (Lawler et al., 1999; Mailman and Murthy, 2010; Allen et al., 2011) and angiotensin-mediated cardioprotection (Violin et al., 2010), or detrimental in conditions such as κ-opioid receptor–mediated dysphoria (White et al., 2014). In terms of internal signaling, receptor-arrestin complexes have been reported to interact with the Src family of tyrosine kinases (Luttrell et al., 1999), a number of mitogen-activated protein kinases (Chavkin et al., 2014), ERK1/2 and JNK3, and p38 (Seo et al., 2011). These effectors can use β-arrestin/receptor complexes as scaffolds to form different signalsomes (Peterson and Luttrell, 2017) to produce long-lasting signals in the cytosol (Luttrell et al., 1999; McDonald et al., 2000; Gong et al., 2008; Song et al., 2009). For some specific receptors, β-arrestin has been shown to scaffold AKT (Schmid and Bohn, 2010; Schmid et al., 2013), PI3K, and phosphodiesterase 4 (DeWire et al., 2007) and also to produce mediation of nuclear signaling such as microRNA processing after β₁-adrenoceptor activation (Kim et al., 2014). In addition, β-arrestin complexes have been implicated in ubiquitination (Shenoy et al., 2001) of receptors. Finally, although β-arrestin2 has received the most attention in the literature for these activities, increasing studies with β-arrestin1 have revealed a further variety of cellular effects (Srivastava et al., 2015). For example, GLP-1 receptors interact with β-arrestin1 to promote insulin release in the pancreatic β cell (with application to the treatment of diabetes; Sonoda et al., 2008). Biased signaling through selective arrestin coupling has also been observed for δ-opioid receptors. Specifically, whereas the highly internalizing δ-opioid agonist SNC80 [(+)-4-[(αR)-α-((2S,5R)-4-allyl-2,5-dimethyl-1-piperazinyl)-3-methoxybenzyl]-N,N-diethylbenzamide] initiates receptor interaction with β-arrestin1, the lower internalizing agonists ARM930 and JNJ2078860 preferentially recruit β-arrestin2 (Pradhan et al., 2016).

In terms of receptor internalization, the ligand-receptor conformational complex may or may not code for internalization; for instance, although agonists such as quinpirole promote dopamine D₂ receptor internalization, the biased ligand aripiprazole does not (Allen et al., 2011). Receptor internalization can occur with subsequent rapid recycling or with receptor degradation (Tsao et al., 2001; Whistler et al., 2002). For instance, for GLP-1 receptors, the agonist GLP-1 mediates a faster recycling rate than do the synthetic agonists exendin-4 and liraglutide, leading to temporal differences in levels of activation (Roed et al., 2014). There can be even more diverse trafficking ligand effects as in the case of CCR5. This receptor mediates human immunodeficiency virus (HIV)-1 infection, and one therapeutic approach to limiting HIV-1 infection is to internalize the receptor so that the gp120 protein on the HIV viral coat cannot bind. Two chemokines have very different effects: RANTES (regulated on activation normal T cell expressed and secreted; CCL5) promotes rapid internalization of receptor with rapid recycling, whereas the analog AOP-RANTES promotes rapid internalization but with much less and slower recycling of the receptor to the cell surface (Mack et al., 1998).

C. Other Receptor Signaling Partners and Receptor Behaviors

Some 7TMRs express an endogenous PDZ domain at their distal carboxyl termini (Bockaert et al., 2003), allowing them to interact with PDZ domain proteins (Bockaert et al., 2004). These domains generally consist of 80 to 100 residues forming six β-strands and two α-helices. The carboxyl-terminal tail of the receptor can then interact with an elongated surface groove that is situated between the second β-strand and the second α-helix. Three classes of PDZ ligands have been described: class I (-E-S/T-xV/I), class II (-ϕ-ϕ-), and class III (ψ -x-ϕ-), in which ψ represents an acidic residue and ϕ represents a hydrophobic residue (Sheng and Sala, 2001). Many such partners have been proposed (C kinase 1 protein, Golgi reassembly stacking protein, glutamate receptor-interacting protein, and nebulin molecules) but the most extensively characterized interaction is with NHERF1 and NHERF2. For instance, PTH receptor 1 facilitates PLC signaling while inhibiting Gα_s/cAMP signaling (Mahon et al., 2002; Mahon and Segre, 2004) through interactions with NHERF1 and NHERF2. In general, these interactions result in effects on transcriptional regulation, intracellular trafficking, and cell growth.

With increasing assay technology has come an appreciation of the wealth of receptor behaviors practiced by 7TMRs during cell signaling and function. In addition to receptor phosphorylation, coupling of receptors to G proteins, β-arrestins and other cytosolic proteins, and ligands may change other behaviors as well. For instance, the μ-opioid receptor agonists DAMGO ([d-Ala²,Nme-Phe⁴,Gly-ol⁵]-enkephalin) and morphine increase (whereas endomorphin-2 decreases) lateral mobility of μ-opioid receptors in the cell membrane; the interaction of these effects with membrane cholesterol content produces variable signaling and this suggests a possible mechanism of bias variation with cell type (Melkes et al., 2016).

In addition, 7TMRs are known to oligomerize with other receptors to form homodimers and heterodimers (Gomes et al., 2016). Through such activity, receptor signaling can be increased (dopamine D₁-D₃ heteromer; Fiorentini et al., 2008), diminished (adenosine A_2A-dopamine D₂ heteromer; Strömberg et al., 2000), or completely changed (dopamine D₁-D₂ heteromer; Rashid et al., 2007). In some systems (notably chemokine receptors), receptor dimerization has been proposed as a natural mechanism required for activation (Rodríguez-Frade et al., 1999; Vila-Coro et al., 1999; Trettel et al., 2003; Hernanz-Falcón et al., 2004). In fact, some receptors such as C-X-C chemokine receptor CXCR4 are proposed to function constitutively as dimers (Babcock et al., 2003). Given that oligomerization theoretically offers a new ligand target and/or choices for ligand efficacy, the possibility of bias in such systems would be predicted (Zhou and Giraldo, 2018). Although there are currently little data to definitively suggest that dimerization is involved in biased signaling, a recent study with melanocortin receptors suggests that this may be a fruitful line of research for selective receptor activation (Lensing et al., 2019).

D. Internal Signaling

Finally, a relatively new receptor behavior has been described, whereby receptor–G protein signaling complexes translocate to the endoplasmic reticulum, Golgi apparatus, and nucleus (Revankar et al., 2005; Re et al., 2010) and continue to signal (Castro et al., 2005; Hein et al., 2006; Boivin et al., 2008). In fact, such intracellular signaling has been described in unique terms therapeutically in treatments for multiple sclerosis (Mullershausen et al., 2009), pain (Geppetti et al., 2015; Cahill et al., 2017), and nociception (Jensen et al., 2017). The vast array of interactions possible within receptor systems offers the opportunity for natural fine-tuning of cell signaling.

A. Assessing the Impact of Biased Signaling on Natural Physiology

One of the main tools available to pharmacologists in the interpretation of in vitro bias in terms of what it may mean in vivo is the genetic knockout system (i.e., a frequent approach is to produce genetic knockouts for β-arrestin to assess the importance of this signaling system). Although deletion of both nonvisual arrestin isoforms is lethal, individual deletion of β-arrestin1 or β-arrestin2 can be studied (Kohout et al., 2001). Thus, it can be seen that opioid analgesics such as morphine produce less respiratory depression in β-arrestin knockout mice (compared with wild-type mice) (Raehal et al., 2005), leading to the hypothesis that μ-opioid agonists with less propensity to induce receptor–β-arrestin interaction might offer a better margin of analgesia (over respiratory depression; e.g., see data with TRV027; Violin et al., 2014). Mice devoid of β-arrestin2 (but not β-arrestin1) have demonstrated altered behavioral responses to addicting drugs such as morphine (Bohn et al., 2003; Urs and Caron, 2014), amphetamine (Urs and Caron, 2014), and alcohol (Li et al., 2013). In general, the use of transgenic mice has identified β-arrestin2 as a clear mediator of unwanted μ-opioid receptor agonism (Raehal et al., 2005). Similarly, the fact that PTH analogs do not effectively produce bone in β-arrestin knockout mice leads to the hypothesis that G protein–biased PTH agonists could offer better profiles for therapy in osteoporosis (Ferrari et al., 2005). There are cases in which genetic modification of systems can be linked to actual therapeutically relevant drug profiles. For instance, the biased ligand UNC9975 (7-[4-[4-(2,3-dichlorophenyl)-1,4-diazepan-1-yl]butoxy]-1,2,3,4-tetrahydro-1,8-naphthyridin-2-one) displays potent antipsychotic-like activity without induction of motoric side effects in inbred C57BBL/6 mice. Furthermore, genetic deletion of β-arrestin2 attenuates antipsychotic activity, thus transforming UNC9975 from an atypical to a typical antipsychotic (Allen et al., 2011).

Technological advances have enabled genetic knockout systems to be made (e.g., Rohrer and Kobilka, 1998) and these have enabled the study of physiologic systems without selected components in studies to determine the importance of those components to the physiology. In general, knockout animals have been instrumental in identifying physiologically relevant pathways for drug candidates for dopamine D₁ receptors (Xu et al., 1994a,b), metabotropic glutamate 1 receptors (Aiba et al., 1994), 5-HT_2B receptors (Saudou et al., 1994), angiotensin 1A receptors (Ito et al., 1995; Coffman, 1997), μ-opioid receptors (Sora et al., 1997), α_2b-adrencoceptors, (Link et al., 1996; MacMillan et al., 1996), α_1b-adrenoceptors (Cavalli et al., 1997), β₁/β₂-adrenoceptors (Rohrer et al., 1999), β₃-adrenoceptors (Susulic et al., 1995), and muscarinic M₃ receptors (Duttaroy et al., 2004). Similar outcomes have been observed through ablation of receptor effects through application of RNA-guided CRISPR/Cas9 endonucleases (Naylor et al., 2016).

Complimentary data are obtained from knock-in studies whereby the endogenous GPCR gene is replaced with a gene for a mutant receptor and the expression of that mutant is driven by the wild-type promoter; the aim of this approach is to express the mutant receptor in the same tissue types and at the same receptor levels as the wild-type receptor. An example of the application of this technology is found in the elucidation of the relative contributions of δ- and μ-opioid receptors in the sensation of mechanical and heat pain with enhanced green fluorescent protein δ-opioid receptors (Scherrer et al., 2006, 2009; Pradhan et al., 2009; Shenoy and Lefkowitz, 2011; Faget et al., 2012). Similarly, the impact of muscarinic M₃ receptor phosphorylation on learning, glucose tolerance, and insulin release has been studied with phosphorylation-deficient M₃ receptors (Kong et al., 2010; Poulin et al., 2010). These phosphorylation-deficient muscarinic M₃ receptors do not internalize but couple normally to G protein–dependent signaling such as PLC/calcium mobilization mechanisms (Budd et al., 2001; Urban and Roth, 2015).

Inserting a coding sequence of a mutant receptor that is only activated by a synthetic ligand [to code for a designer receptor exclusively activated by designer drugs (DREAD); Conklin et al., 2008; Urban and Roth, 2015] is a powerful technology whereby the relevance of certain signaling to cognate physiology can be assessed (Peng et al., 2008). The first application of this approach was made with the κ-opioid receptor modified to contain the second extracellular loop of the δ-opioid receptor (Coward et al., 1998) to yield a receptor with a 200-fold reduction in the binding of the endogenous opioid agonist dynorphin (and reductions in the binding of 21 other opioid peptides) but maintained binding and activation for the synthetic agonist spiradoline. This receptor was given the name RASSL for “receptor activated solely by a synthetic ligand.” A problem with early studies with RASSLs was that the retention of activity of the synthetic ligand for native receptors caused concomitant activation of native receptors in the transgenically modified animals in addition to the RASSLs (Redfern et al., 1999). In addition, RASSLs often have a high level of constitutive activity, further complicating interpretation of experimental data (Hsiao et al., 2008). These drawbacks led to the development of second-generation RASSLs named DREADs, in which the agonist (clozapine-N-oxide) has no other activating properties for native receptors (Armbruster et al., 2007; Conklin et al., 2008; Giguere et al., 2014; Urban and Roth, 2015). Using this technology, the role of M₃ receptor signaling (Armbruster et al., 2007; Dong et al., 2010) and free fatty acid receptor-2 signaling (Hudson et al., 2012) has been explored. DREADs have been used to evaluate the importance of biased signaling in different cell types as in studies on the cell type–specific expression of a muscarinic M₃ receptor DREAD mutationally modified to not interact with β-arrestin but rather only G_q/11 proteins (Hu et al., 2016). A related approach allows the activation of the mutant receptor optically through light (Levitz et al., 2013), a new technology as yet to be applied to the study of signaling bias.

Finally, there are obvious caveats to the interpretation of these studies to human therapeutics. Differences in animal versus human signaling can confuse conclusions from the data. For instance, it has been suggested that cannabinoid CB₁ receptor signaling differs between humans and rodents (Straiker et al., 2012). In addition, the known changes in signaling preferences of receptors with receptor mutation (vide infra), as well as the expectation of signaling signatures different from natural ones with synthetic agonists such as clozapine-N-oxide, raises the specter that DREADDs will give misleading signaling profiles in natural physiology. At present, studies to assess this are consistent with this not being a tangible problem (Alvarez-Curto et al., 2011).

B. Assessing the Impact of Biased Signaling from Known Ligands

Retrospective analyses have provided insights into how some uniquely beneficial currently used therapeutic drugs achieve their favorable profiles through biased signaling. Thus, the beneficial effects of carvedilol, a nonselective β-adrenoceptor inverse agonist for Gα_s-mediated cAMP production in congestive heart failure, have been attributed to its β-arrestin–mediated partial agonist activity for activation of ERK1/2 (Wisler et al., 2007; Kim et al., 2008). Similar signaling profiles have been associated with nebivolol (Erickson et al., 2013), alprenolol (Kim et al., 2008), and propranolol (Azzi et al., 2003; Baker et al., 2003). The diminished respiratory depression potential of the opioid analgesic levorphanol (over morphine) has been attributed to its biased signaling profile (lack of β-arrestin2 recruitment) (Le Rouzic et al., 2019). In fact, the dependence liability of oxycodone, hydrocodone/paracetamol, and hydromorphone has been attributed to biased signaling (toward G protein vs. β-arrestin) (Johnson et al., 2017). The cardioprotective and cardiac fibrosis–modulating properties of the adenosine agonist capadenoson (currently in clinical trials) have been attributed to its biased cAMP activity through adenosine 2b receptor activity (Baltos et al., 2017). The biased activation of G_s protein (over nonspecific dual G_s and G_i activation) for fenoterol has been proposed as a favorable property for this bronchodilator (Jozwiak et al., 2010). Similarly, the tolerance seen with morphine, as opposed to other μ-opioid receptor agonists, has been attributed to this agonist’s selective signaling through β-arrestin2 as opposed to β-arrestin1 (Raehal and Bohn, 2011).

Irrespective of novel therapeutics, biased ligands can be valuable probes of physiologic processes and disease states. For example, PAR₂, which is highly expressed in HT-29 colorectal carcinoma cells, is implicated in cancer (Elste and Petersen, 2010). Through observation of the effects of newly developed PAR₂ biased agonists, the relative importance of ERK1/2 versus calcium signaling in human cancer through this receptor has been studied (Jiang et al., 2017). Similarly, comparison of nonpeptide biased agonists of the nociception/orphanin FQ receptor has been applied to study the pharmacology of nociceptin orphanin activation in disease states (Ferrari et al., 2017). Elegant studies with a range of biased PTH analogs have been valuable in elucidating the complicating bone-building and bone resorption effects of PTH for therapy of osteoporosis (Luttrell et al., 2018). The study of the biased κ-opioid receptor antagonist norbinaltorphimine has enabled linkage of JNK signaling to long-term blockade of antinociception (with no ERK activity) and selective long-term effects on regulation of κ-opioid receptors (Jamshidi et al., 2016). A novel application of bias in the delineation of the role of β-arrestin signaling in cardiac β-adrenoceptor function has been suggested in the use of a biased pepducin, (ICL)-1-9 [TAIAKFERLLQTVTNYFIT], to decouple β-arrestin signaling from occupation of the receptor (Carr et al., 2016). Biased ligands have been especially valuable in the study of systems where there appears to be duplication and crossover between ligands and receptors such as the chemokine receptor system (Amarandi et al., 2016; Milanos et al., 2016a).

For peptide receptors, truncation of the natural peptide can lead to biased analogs of value in the delineation of physiologic pathways. For example, a biased analog of human neuropeptide S, hNPS-(1-10) lacks 10 residues from the C terminus of the natural peptide and preferentially activates Gα_q-mediated calcium mobilization with less activity at Gα_s (compared with the natural peptide); this analog produces no physiologic effect in vivo, providing a unique probe of the physiology and therapeutic potential of hNPS-directed signaling (Liao et al., 2016). Biased agonists can dissect complex signaling patterns of endogenous agonists to determine dominant signaling. For instance, the dependence on various physiologic endpoints of free fatty acid receptor-2 stimulation on different signaling was determined through studies with the biased agonist AZ1729 (N-[3-(2-carbamimidamido-4-methyl-1,3-thiazol-5-yl)phenyl]-4-fluoro benzamide), which predominantly activates only G_i (not G_q/G₁₁) signaling (Bolognini et al., 2016).

Beyond using biased ligands to probe natural physiology, this idea has been advanced as a strategy for the design of better (i.e., more selective) drug therapy with fewer side effects. There are basically four rationales for this approach: 1) emphasis of a therapeutically favorable signal (i.e., PTH in osteoporosis; Gesty-Palmer and Luttrell, 2011; Gesty-Palmer et al., 2013), 2) de-emphasis of an unfavorable signal (respiratory depression for opioid agonists; Raehal et al., 2005; Kelly, 2013; Koblish et al., 2017), 3) production of limited signaling to allow prosecution of otherwise forbidden drug targets (i.e., κ-opioid receptors; White et al., 2014; Brust et al., 2016), and 4) emphasis of a favorable signal and prevention of the natural system production of an unfavorable signal (i.e., angiotensin in heart failure; Violin et al., 2006, 2010). Based on these general ideas, Table 2 shows a sampling of receptors and therapeutic applications of biased signaling that have been proposed in the literature as possible avenues toward better drug therapy.

One important consideration in the evaluation of biased signaling in therapeutics is the difference between natural nondiseased systems that are used for ligand characterization and pathologically modified systems in therapy (Insel et al., 2015). In disease states, the relative stoichiometry or components and sensitivities of cells are known to vary. For example, GRK2 is upregulated in heart failure, leading to an increased phosphorylation of β-adrenoceptors and downregulation of receptors (Casey et al., 2010). In hypertrophic myocytes from mice with heart failure, levels of G protein were found to be upregulated (i.e., Gα_o, 7.5-fold; and Gα₁₁, 12.5-fold) leading to differences in β-adrenoceptor agonist biased signaling (Onfroy et al., 2017). Similarly, the apelin pathway is known to be downregulated in heart failure (Yang et al., 2015). The natural signaling bias of the calcium-sensing receptor is also altered in disease states (for review, see Leach et al., 2015). Models of dystonia (a common movement disorder) involving mutation of the protein torsinA demonstrate a pathologic increase in cholinergic tone to affect dopamine interneurons, and there is a change in dopamine signaling polarity and a bias introduced into dopamine signaling from primarily G_i/o to noncanonical β-arrestin signaling (Scarduzio et al., 2017). In general, biased signaling is an obvious mechanism to exploit for drug therapy and the existing data with characterized biased ligands certainly show different phenotypical signaling profiles in vivo. What is lacking at this time is a systematic linkage between in vitro profiles of biased signaling and the translation to in vivo systems.

A. Deviations from Monotonic Signaling

Biased signaling is detected by measuring deviations from a pattern and they are quantified by measuring the degree of that deviation. As discussed in section I, all systems are biased by physiology in terms of the needs of the organ system. Thus, receptor levels, relative stoichiometry of receptors to signaling proteins, and efficiency of stimulus-response coupling are unique in every organ system and dictate the sensitivity of those organ systems to endogenous agonism. Much of the challenge of translating in vitro effects to in vivo systems is due to this customization of organ sensitivity in the body.

The molecular mechanisms involved in the production of biased receptor signals were suggested from the early work describing deviations from predictions of monotonic (unbiased) receptor signaling. As first presented, agonist efficacy was a monotonic signal emanating from the receptor upon binding of the agonist to the receptor (Ariens, 1954; Stephenson, 1956; Furchgott, 1972; Mackay and Van Rossum, 1977). This signal was assumed to be homogeneous in nature, varying only in signal strength; the assumption for this was rooted in the lack of knowledge of the nature of cellular receptor signals and the fact that only a single readout of drug response was normally available in the assays used for characterizing agonism. For example, Stephenson (1956) characterized ligands with efficacy as those producing contraction of guinea pig ileum in functional in vitro experiments. This concept led to the derivation of one of the most useful tools in quantitative pharmacology for the classification of therapeutic agonists, namely the agonist potency ratio (PR). Through application of null experiments and the assumption that agonists produce a homogeneous signal of activation from the receptor termed “stimulus” (Stephenson, 1956), the idea emerged that equiactive concentrations of agonists could be used to derive a ratio of potency that would be receptor agonist specific and thus transcend the test system in which it was measured. This would be the case since the cellular system translating the receptor stimulus would serve only as an amplifier that does not change the nature of the signal. Under these circumstances, PRs of agonists could be derived in test systems and used to predict relative agonist potency in all systems including the therapeutic one; this is an enormously useful idea since agonists are usually developed in test systems, not the therapeutic one. The system independence of PR values can be illustrated through examination of agonist response through the Black/Leff operational model (Black and Leff, 1983) (eq. 2):(2)where τ is efficacy and K_A is the equilibrium dissociation constant of the agonist-receptor complex. Equation 2 predicts that potency as quantified by the EC₅₀ of the agonist (concentration producing the half-maximal effect), which is given by eq. 3:(3)For full agonists where τ > >1, the EC₅₀ becomes K_A/τ, an expression derived from the ratio of the affinity and efficacy of the agonist. It follows that the PR of two agonists (A₁ and A₂) measured in the same functional system is calculated as shown in eq. 4:(4)As defined by Black and Leff (1983), τ is a term formally identical to Stephenson’s efficacy term, in that it contains elements related strictly to the agonist and also the test system in which it measured. However, ratios of τ cancel the tissue-related elements and leave a ratio of strictly agonist-related efficacy. Therefore, ratios of τ/K_A values for two agonists become tissue-independent measures of the relative power of the two agonists to induce response in any system.

Although PR values were and are important quantitative parameters for the measurement of agonist in pharmacology, their intrinsic value is predicated on the assumption that the nature of the signal produced by the two agonists at the level of the receptor is identical and that the processing of that signal is a monotonic function (one y for every x in a Cartesian system of stimulus to response) by the cell. It follows that deviations of PR values seen experimentally, presuming they are not due to experimental error, furnish evidence negating the monotonic assumption of signal processing. The key to determining such heterogeneity in signaling is the availability of multiple measures of agonist response (i.e., the delineation of the nature of agonist response into its elements at the level of the receptor-signaling protein interface). If independent observation of two signals from the same agonist-receptor interaction can be obtained, then a direct test of the monotonic nature of stimulus-response coupling can be made. This was predicted in theoretical terms for a system of receptor interaction with two G proteins (Kenakin and Morgan, 1989). In this study, the effect of the production of different active states of the receptor with two agonists on the resulting affinity of interaction of that receptor with two G proteins on the observed potency of the agonists was simulated. Assuming that different active states (i.e., different tertiary conformations of the receptor) will have correspondingly different affinities for the two G proteins in the system, it is predicted that differences in the relative potency of the agonists will be observed. Thus, variation in PR values would constitute evidence of the production of different receptor active states by the agonists (i.e., biased agonism would be predicted) (Kenakin and Morgan, 1989).

As agonists were tested in more and different functional systems, reports increasingly cited instances where the simple relationship between occupancy and response predicted by monotonic efficacy systems were not verified (for example, see Roth and Chuang, 1987; Mottola et al., 1991; Roerig et al., 1992; Fisher et al., 1993; Gurwitz et al., 1994; Lawler et al., 1994, 1999; Ward et al., 1995; Heldman et al., 1996; Mailman et al., 1998). Incontrovertible evidence of deviation from monotonic signaling was furnished for the PACAP receptor in a system whereby cAMP and IP signaling could be monitored from the same receptor as a function of activation by two agonists, PACAP_1-27 and PACAP_1-38 (Spengler et al., 1993). These experiments showed a reversal of relative potency of PACAP_1-27 and PACAP_1-38 for these two pathways, indicating undisputable variation in affinity and/or efficacy of the agonists as the receptor interacted with different signaling pathways. Specifically, as shown by eq. 4, reversed PR values can only occur through different values of K_A and τ and this most likely is the result of different receptor conformations stabilized by PACAP_1-27 and PACAP_1-38. These and other data led to a formal declaration of the stabilization of different receptor active states by different agonists as the source of observed biased agonism (Kenakin, 1995).

As a preface to discussion of the various methods available to detect and quantify biased signaling, it is important to consider an important mechanism of apparent deviation from signaling pattern that is not due to bias. Specifically, where the strength of agonist signal is not adequate to produce response but rather converts the ligand from an agonist to partial agonist or antagonist; this is due to the interaction of the strength of the magnitude of efficacy and the sensitivity of the system.

B. Biased Agonism, Antagonism, and Strength of Signal

Historically, ligands that block responses to agonists and produce no further direct effect have been termed antagonists with the assumption that these molecules simply occupy the agonist binding site to prevent activation and thus block functional response. A well known phenomenon in pharmacology is the observed range of behaviors of low-efficacy ligands from showing agonism in sensitive functional systems to antagonism in systems of low sensitivity. For example, the low-efficacy β-adrenoceptor ligand prenalterol is nearly a full agonist in the thyroxine-treated guinea pig right atria to a complete antagonist in the guinea pig extensor digitorum longus muscle (Kenakin, 1985). This indicates that prenalterol produces a change in receptor conformation even though low-sensitivity systems cannot reveal positive efficacy. Low-efficacy ligands can be classified as partial agonists or antagonists depending on the sensitivity of the functional assay used to measure effects. This has been shown in studies with some classic antipsychotic drugs for dopamine D₂/β receptors that are reported to be partial agonists in some functional assays (Allen et al., 2011) and silent antagonists in other cell systems (Masri et al., 2008). Similarly, the chemokine ligands (CCR2 and CCR5) J113863 [1,4-cis-1-(1-cycloocten-1-ylmethyl)-4-[[(2,7-dichloro-9H-xanthen-9-yl)carbonyl]amino]-1-ethylpiperidinium iodide] and UCB35625 [1,4-trans-1-(1-cycloocten-1-ylmethyl)-4-[[(2,7-dichloro-9H-xanthen-9-yl)carbonyl]amino]-1-ethylpiperidinium iodide] can function as antagonists, partial agonists, or full agonists depending on the receptor and signaling pathway being observed (Corbisier et al., 2017). When such changes in system sensitivity occur with different signaling from the same receptor, it can take on the profile of biased signaling.

With the ability to measure multiple signaling from receptors has come the realization that ligands can have multiple efficacies (i.e., “pluridimensional efficacy”; Galandrin and Bouvier, 2006). In addition, the stabilization of unique receptor conformations by antagonists opens possibilities that these conformations may confer signaling under some conditions. For example, the PTH receptor ligand [d-Trp¹²,Tyr³⁴]-bPTH(7-34) is a neutral antagonist of calcium signaling and an inverse agonist for cAMP production but a positive partial agonist for ERK1/2 (Luttrell et al., 2018); the simple labels of “antagonist” and “agonist” fail to capture the pharmacology of ligands in a biased conformational world (Kenakin, 2008).

The sensitivity of the functional assay used to identify signaling can have profound effects on the determination of bias and/or efficacy. A great deal of research on signaling bias involves G protein versus β-arrestin signaling. However, it is very important to consider that well coupled sensitive assays such as second messenger assays are generally more sensitive than β-arrestin assays (which are not normally amplified), leading to apparent bias toward weak agonists producing second messenger responses without concomitant β-arrestin responses. This gives the illusion of “perfect bias” in that no response in one of the pathways is observed, further implying no interaction of the receptor with that signaling protein. However, experiments in which the “perfectly biased” ligand is used as an antagonist indicate that an interaction between the receptor and β-arrestin actually is taking place but no overt agonism can be displayed (Kenakin, 2015b; Stahl et al., 2015). A classic example of this is shown with the angiotensin biased ligand TRV120027, where the lack of G_q protein response is further shown to be a competitive antagonism of angiotensin G_q-mediated responses through Schild analysis (Violin et al., 2010). Similarly, the dopamine D₂ biased ligand MLS1547 [5-chloro-7-[[4-(2-pyridinyl)-1-piperazinyl]methyl]-8-quinolinol] activates cAMP but does not promote receptor β-arrestin BRET signaling at any concentrations ranging from 1 pM to 100 μM, thereby giving the illusion of perfect bias. However, further experimentation reveals that MLS1547 is an antagonist of dopamine/β-arrestin effects, with an IC₅₀ of 1 μM (Free et al., 2014). This dependence on assay sensitivity can be demonstrated within G proteins as well. For example, a lack of G_q protein activation and β-arrestin activation for the biased angiotensin agonist SII was reported in early studies (Wei et al., 2003), but subsequent work with more sensitive G protein assays in fact did demonstrate the G protein activation by SII (Saulière et al., 2012). Thus, the observation of “perfect bias” (i.e., where no signaling is observed in one signaling pathway) is not necessarily evidence of bias until the second pathway response can be observed and quantified in another assay.

A major way in which cells control their signaling is through variation of the relative stoichiometry of signaling elements, both receptors and signaling proteins. Experimentally, it can be shown that changes in cell surface receptor levels result in concomitant changes in sensitivity to agonists. For example, the β₃-adrenoceptor ligand SR592230A [3-(2-ethylphenoxy)-1-[(1,S)-1,2,3,4-tetrahydronaph-1-ylamkino]-2S-2-propanol oxalate] is normally an antagonist but it produces agonism for cAMP formation in high-density β₃-adrenoceptor cells (Sato et al., 2007). Moreover, for pleiotropically coupled receptors, receptor levels are linked to the actual quality of signal through recruitment of signaling pathways with increasing receptor levels. Thus, it has been shown that increased expression of α₂-C10-adrenoceptor levels in transfected Chinese hamster ovary (CHO) cells show a pattern of initial coupling to G_i protein (the most sensitively linked pathway), followed by a recruitment of G_s signaling at higher receptor levels (Eason et al., 1992). Similar effects have been shown with the human calcitonin receptor, with initial G_s coupling evolving into mixed G_s and G_q coupling with increasing receptor expression (Kenakin, 1997). These types of effects can produce fine-tuning of response to low-efficacy agonists in vivo and can also lead to discontinuities in the translation of biased effects from simple in vitro assays to in vivo systems. These effects also can confound predictions of in vivo effects from in vitro profiles in pathologic systems, as changes in sensitivity in these latter tissues can occur due to altered stoichiometry between receptors and signaling elements. For example, downregulation of β-adrenoceptors and Gα_i proteins (Eschenhagen et al., 1992), Gα_s proteins (Longabaugh et al., 1988), and even adenylyl cyclases V/VI (Ishikawa et al., 1994) has been seen in congestive heart failure and these effects are known to change receptor signaling (Bristow et al., 1982). Altered G protein levels also have been noted in Parkinson disease (Corvol et al., 2004). In cancer, huge overexpression of receptors (notably of vasoactive intestinal peptide receptors) has been noted (for review, see Kenakin, 2001). The fact that very low-efficacy ligands can primarily function as antagonists raises the possibility that, like biased agonists, the same mechanisms may lead to biased antagonism. At this point, it is worth considering the evidence available to suggest that receptor conformational states can contribute to differential affinity for different ligands and signaling pathways.

C. Biased Antagonism

Since affinity and efficacy are both components of biased signaling, it is logical to consider that the same mechanisms may cause biased antagonism as well. There are a number of biased agonists that are of very low efficacy and thus function as antagonists of some signaling pathways. The mechanism of stabilization of unique receptor conformations can lead to differences in receptor affinity as well as efficacy, an effect shown in the observed affinity of weak partial agonists. Specifically, the EC₅₀ of a partial agonist is a good approximation of the affinity (K_A, equilibrium dissociation constant of the partial agonist-receptor complex) through the relationship EC₅₀ = K_A/(1 + τ) (Black et al., 1985), where τ is efficacy. When efficacy is low (τ → 0), the EC₅₀ → K_A (Bdioui et al., 2018). There are systems in which a weak partial agonist produces submaximal concentration-response curves for both pathways and where it is clear that a single estimate of affinity of the partial agonist for the receptor cannot be used to fit the curves (Kenakin, 2014). For example, extremely divergent EC₅₀ values indicate variation in ligand affinity for different signaling pathways for adenosine A_2B receptors where it is impossible to fit the curves for the partial agonist BAY 60-6583 (2-[(6-amino-3,5-dicynao-4-[4-(cyclopropulmethoxy)phenyl]-2-pyridinyl]thio]-acetamide) with a single value for receptor affinity (see Fig. 6). In fact, a 19-fold difference in the affinity of BAY 60-6583 is required to fit BAY 60-6583 curves in IP1 versus ERK assays (Baltos et al., 2017). A similar effect is seen with dopamine D₂ receptors. Specifically, the partial agonist 16c is 700-fold biased toward Gα_oA over Gα_i2 (compared with the agonist quinpirole) but this effect cannot be accounted for completely by selective efficacy. This is because the concentration-response curves to 16c cannot be fit to the operational model assuming the same affinity differing only in efficacy. The fact that there are divergent EC₅₀ values for 16c for these two G protein pathways indicates a 158-fold difference in the affinity of an agonist for the dopamine D₂ receptor when the receptor binds to Gα_oA versus Gα_i2 protein (Möller et al., 2017).

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Fig. 6.

Concentration-response curves for adenosine A_2B receptor agonists NECA and BAY 60-6583 for IP1 metabolism and ERK1/2 activation. (A) BAY 60-6583 is a partial agonist for IP1 metabolism (filled circles) and for ERK1/2 activation (open circles) with significantly different EC₅₀ values for each pathway. (B) DR curves for IP1 metabolism for NECA (filled circles) and BAY 60-6583 (open circles). Fitting the Black/Leff operational model yields values for affinity (K_A) and relative efficacy (τ_BAY/τ_NECA) for these agonists activating this pathway. (C) DR curves for NECA (filled circles) and BAY 60-6583 (open circles) with values for relative affinity and efficacy as in (B) but for the ERK1/2 pathway. Fitting the Black/Leff operational model yields estimates of the affinity of BAY 60-6583 for the adenosine A_2B receptor differing by a factor of 19 (K_A-IP1 = 15 μM, K_A-ERK= 0.8 μM). DR, dose response. Data are redrawn from Baltos et al. (2017).

Divergent pathway-selective affinity is consistent with the allosteric nature of receptor systems. Specifically, receptors alter their properties when a second body is bound to them and among those properties is the affinity for ligands through a cooperativity factor imposed on the receptor by the cobinding protein (denoted with the parameter α) (Ehlert, 2005; Kenakin, 2005; Price et al., 2005). Allosteric modulators can have varying effects on different molecules interacting with the receptor proteins to either increase or decrease the affinity of the receptor for ligands. As shown by Staus et al. (2016), the nanobody Nb80 enhances affinity 1000-fold, whereas the nanobody Nb60 reduces the affinity of noradrenaline for β₂-adrenoceptors by a factor of 100.

The power of allosteric mechanisms to alter receptor antagonist activity is demonstrated in natural physiology. Thus, cellular coexpression of receptor activity-modifying proteins (RAMPs) produces a wealth of different phenotypes for the calcitonin/calcitonin gene-related peptide (CGRP) family of peptides that include calcitonin, α- and β-CGRP, amylin, adrenomedullin, and adrenomedullin 2/intermedin (Hay et al., 2018). For example, coexpression of calcitonin receptors and RAMP3 yields a receptor with a selectively high affinity for amylin. In cells containing RAMP3, the calcitonin receptor forms a complex that has completely different pharmacology compared with cells not containing RAMP3 and this leads to a selective change in antagonist potency of peptide antagonists such as AC66 (Armour et al., 1999). Without RAMP3, AC66 has a K_B of 0.25 nM for blockade of responses to amylin and calcitonin; with coexpressed RAMP3, there is a selective 7-fold decrease in potency of AC66 for blockade of amylin responses (potency = 1.8 nM) and no concomitant change in potency for calcitonin responses. In this regard, the presence or absence of RAMPs in various cell types produces induced bias for ligands (i.e., amylin and human calcitonin) much like what is seen with synthetic positive allosteric modulators (PAMs) (vide infra).

Biased agonists stabilize different receptor active-state conformations and telegraph their selective conformations through distinct signaling patterns. Stabilization of unique receptor conformations by biased antagonists may not be as evident if an assay is not available to detect the conformational change. For example, the G_q protein assay denoting the competitive angiotensin antagonism by TRV120027 belies any production of a new receptor conformation until it is revealed through the β-arrestin assay (Violin et al., 2010). This type of dissimulation has been demonstrated for inverse agonists as well; for a selection of 380 apparently silent simple competitive antagonists for 73 receptors, 322 (85%) were found to actually be inverse agonists when tested in a constitutively active system (Kenakin, 2004). Thus, the stabilization of an inactive state receptor was not evident until the appropriate assay (constitutive receptor activity) was in place to detect it.

The profile of G_q protein blockade and β-arrestin agonism seen with TRV120027 highlights the essentially semantic issues with the nomenclature. From the point of view of β-arrestin effect, TRV120027 is a biased agonist; in terms of G_q protein signaling, it is an antagonist. It should be stressed that this is not because of differences in signal strength, as the bias is calculated with respect to a reference agonist and strength of signal is canceled. In the case of TRV120027, the antagonism is not selective (i.e., the EC₅₀ for β-arrestin effect is equal to the pK_B for G_q protein blockade). However, there are cases of ligand-directed selective antagonism whereby stabilization of unique receptor conformations results in differences of affinity. For example, the PACAP receptor antagonist PACAP_1-6 has varying potency when blocking different PACAP agonists. Specifically, PACAP_1-27 and PACAP_1-38 produce elevation of cAMP but PACAP_1-6 is significantly more potent in blocking the effects of PACAP_1-27 than the effects of PACAP_1-38 (Walker et al., 2014). A similar effect is seen with the β-blocking drug propranolol, which produces blockade of conventional agonists such as isoproterenol but less antagonism of the β-adrenoceptor agonist CGP-12177 (4-[3-[(1,1-dimethylethyl)amino]-2-hydroxypropoxy]-1,3-dihydro-2H-benzimidazol-2-one hydrochloride) (Konkar et al., 2000; Baker and Hill, 2007).

Similar arguments to those proposing therapeutic advantage for biased agonism can be put forward for biased antagonism as well. In terms of therapeutics, biased antagonism would differ from biased agonism, in that selective signaling would emerge through blockade of endogenous agonists and the quality of endogenous efficacy would change with the biased antagonist. In this case, the nature of the agonist would dictate the degree of blockade by the antagonist (true agonist-mediated biased antagonism), and this would allow for preferential blockade of some endogenous agonists and not others or selective blockade of some signaling pathways but not others. Historical data suggest that biased antagonism can be of therapeutic benefit. Thus, the β₁-adrenoceptor–mediated β-arrestin signaling effects leading to cardioprotective transactivation of epidermal growth factor receptors have been proposed as the reason carvedilol and alprenolol provide a survival advantage over 18 other drugs in heart failure (Wisler et al., 2007). Specific cases can be made for biased antagonism as well for future work. For example, the natural agonist endothelin 1 activates endothelin A receptors to cause oncogenic (Gα_q-coupled and/or β-arrestin signaling; Spinella et al., 2004; Rosanò et al., 2013; Teoh et al., 2014) and tumor-suppressive (Gα_s-signaling; Takahashi et al., 2009; Follin-Arbelet et al., 2013; Teoh et al., 2014) effects. On balance, patients with ovarian cancer and high levels of endothelin A receptors were shown to have a low survival rate (Follin-Arbelet et al., 2013), yet endothelin receptor antagonists appeared to be ineffective as a cancer treatment (Cognetti et al., 2013). This has led researchers to postulate that nonspecific endothelin A receptor blockade appears to inhibit both the oncogenic and tumor-suppressive effects to negate a beneficial effect. This further suggests that a biased antagonist of the Gα_q and/or β-arrestin effects of endothelin 1 would be beneficial (Bologna et al., 2017).

D. Assay Effects in Biased Signaling Measurement

Pharmacological assays are the “eyes to see” the physiologic effects of ligands and having optimal assays is crucial to the successful detection and quantification of signaling bias. Historically, the paucity of pharmacological assays made drug response a simple dependent variable, usually the physiologic effect from an isolated tissue. In some cases, texture for these responses could be obtained as in the measurement of cardiac inotropy (force of contraction) and lusitropy (myocardial relaxation) from a single cardiac preparation (Kenakin et al., 1991) but in general, a single output for response (and therefore efficacy) was used to quantify and classify drug effect. In this type of environment, signaling bias was moot since there were few choices for signal measurement. Beginning in the 1980s, increasing technology made evident through different functional assays showed that agonists have many efficacies. More than any single factor, the availability of multiple measures of agonist response from a single receptor led to the discovery of signaling bias. It is worth considering the characteristics of these assays and how these characteristics affect the measurement of signaling.

One well known factor contributing to heterogeneity in observed agonist effect is variation in levels of receptor expression (Zhu et al., 1994; Nasman et al., 2001). Thus, low sensitivity resulting from low receptor expression leads to an absence of measured response for weak agonists and differential sensitivity of different assays (i.e., second messenger assays vs. β-arrestin complementation; Rajagopal et al., 2010) contributes to system bias. The effects of assay sensitivity are illustrated with the determination of receptor G protein signaling. The most widely applied method to determine G protein activation has been stimulation of 5′-O-(3-[³⁵S]thio)triphosphate ([³⁵S]GTPγS) binding, a method that is relatively insensitive to weak stimulation and is restricted mainly to the Gα_i/o family (cannot readily distinguish Gα_i/o isoforms) (Denis et al., 2012). Vast improvements in G protein signaling detection have been made with new BRET-based biosensors, which can directly measure activation of all G proteins in living cells (Lohse et al., 2012). There are indirect methods available as well (e.g., for G_q protein activation, via IP1 metabolism or calcium release) (Trinquet et al., 2006, 2011). In fact, changes in assay sensitivity have led to the observation of more subtle variation in signaling for the angiotensin ligand SII. As noted earlier, the lack of G protein signaling effect for this molecule with GTPγS or IP3 radioactive assays led to its classification as a β-arrestin agonist and G_q protein antagonist (Wei et al., 2003). Application of a more sensitive IP1-homogeneous time-resolved fluorescence-based assay by the same group (Strachan et al., 2014) confirmed a different classification for SII (Saulière et al., 2012), namely one suggesting that SII does couple to some G proteins. BRET- and fluorescence resonance energy transfer–based biosensors have been used to monitor rearrangement of Gαβγ subunits (Galés et al., 2005; Nikolaev et al., 2006; Audet et al., 2008; Masuho et al., 2015), receptors and G protein (Galés et al., 2005; Audet et al., 2008), receptors and β-arrestin (Zimmerman et al., 2012), and G proteins and downstream effectors (Riven et al., 2006; Richard-Lalonde et al., 2013). Single platform optical formats utilizing BRET signals generated by dissociated βγ G protein subunits and GRK further increase the power of these assays to differentiate G protein receptor interactions (Masuho et al., 2015). In fact, the development of genetically encoded fluorescence-based biosensors has led to an explosion in terms of the observation of signaling events in living cells (Oldach and Zhang, 2014; Miyawaki and Niino, 2015; Galandrin et al., 2016b). This strategy has been extended to probe ligand-induced changes in β-arrestin conformation through intramolecular BRET-based biosensors (Charest et al., 2005). BRET-based biosensors have been used to study complex signaling for neurotensin type 1 receptors (Besserer-Offroy et al., 2017), angiotensin AT1 receptors (Saulière et al., 2012; Devost et al., 2017), κ-opioid receptors (Rives et al., 2012), a mutant somatostatin 5 receptor (Peverelli et al., 2013), and oxytocin receptors (Busnelli et al., 2012).

Assay sensitivity can affect observed system bias as seen in the highly amplified second messenger versus nonamplified β-arrestin signals, but more subtle effects can also change the magnitude of in vitro bias. As shown in Fig. 7, in some cases reversals in the relative potency of agonists are observed with varying assay format assessment of a single pathway, namely β-arrestin activity. Thus, a BRET method versus an enzyme complementation assay for dopamine D₂ receptor/β-arrestin2 interactions (Path-Hunter) indicates differences in the bias of some agonists when comparing G_i protein versus β-arrestin interactions (relative to the reference quinpirole) due to the nature of the assay format (Möller et al., 2017). Finally, one experimental strategy that can be employed to expand detection capability is to manipulate assay sensitivity. For example, the usually low sensitivity of β-arrestin assays can be increased through coexpression of GRKs (phosphorylation of receptors increases their sensitivity to β-arrestin) (Urs et al., 2016).

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Fig. 7.

Bias activity for dopamine D₂ receptor Gα_i1 activation/β-arrestin interaction for 10 agonists measured either by BRET or enzyme complementation. Data are redrawn from Möller et al. (2017).

It is important to note that the assay used to quantify biased effects must accurately reflect the agonist activity; this is illustrated by the dissimulation of G_q protein-activating effects of the muscarinic PAM-agonist BQCA [1-(4-methoxybenzyl)-4-oxo-1,4-dihydroquinoline-3-carboxylic acid]. Specifically, agonism determined with calcium transient responses (a hemi-equilibrium assay) indicates an apparent 253-fold bias toward IP1 metabolism over calcium (compared with acetylcholine). However, the known inability of calcium assays to accurately reflect the response to slow-acting agonists (Unett et al., 2013), coupled with the fact that the agonism for BQCA cannot be reconciled with its allosteric receptor occupancy with the calcium assay (Bdioui et al., 2018), indicates that the agonism observed with the hemi-equilibrium calcium assay yields an erroneous location parameter for the agonist concentration-response curve and a concomitant erroneous estimate of bias. These effects illustrate the importance of the assay in bias measurements.

A valuable addition to the study of biased signaling has been made with label-free assays. In these experiments, the cellular responses to agonists are measured either as changes in dynamic mass redistribution (Fang and Ferrie, 2008; Kebig et al., 2009; Deng et al., 2013) or cellular impedance (Peters and Scott, 2009). These assays are highly sensitive and yield textured nuances in drug effect due to the fact that the complex signaling components of the cell (i.e., Gα_s-, Gα_i- and Gβγ-dependent signaling events, including activation canonical cAMP and ERK1/2 pathways) contribute to the final magnitude of response. In addition, virtually any cell type may be used, thereby allowing the testing of biased signaling in a variety of cell backgrounds and contexts (Ferrie et al., 2011; Stallaert et al., 2012; Deng et al., 2013; Morse et al., 2013).

E. Methods to Quantify Biased Signaling

If biased signaling is considered to be a valuable therapeutic property of drug candidate molecules, then a continuous scale of bias is required to allow medicinal chemists to optimize it. In general, a quantitative method for quantifying biased signaling should have the following properties:

• Sensitive/system independent: The method must provide an index of activity that is scaled to a reference within the series of measurements made and becomes an independent inner scale that can then be compared across assays to gauge differential signaling (bias).

• Quantifiable (have a scale): The method must provide a continuous numerical scale reflecting the degree of differential signaling (i.e., rank order is insufficient).

• Theoretically sound (relevant pharmacological parameters): The method must be grounded in mass action receptor models to relate the biased measurements to a molecular interaction between the ligand and the receptor.

• High throughput (or at least suitable for multiple molecules): For active SAR medicinal chemistry programs aimed at optimizing biased effects, the method should be amenable to rapid comparison of multiple molecules (null methods comparing two agonists in a single functional experiment are rigorous but not amenable to multiple-molecule SARs).

• Statistically verifiable/bounded: The method should ideally furnish statistical estimates of variability leading to assessment of significant difference (i.e., 95% confidence limits).

There are a number of methods proposed for the quantification of bias in the literature; an excellent discussion of these is given by Onaran et al. (2017).

5. Double-Reciprocal Null Comparison of Agonism

Barlow et al. (1967) published a method to compare agonism of full and partial agonists, which (like the RA method) does not require prior knowledge of the affinity and/or efficacy of the agonists. Rather, reciprocals of equiactive concentrations of the agonists are used to construct a double-reciprocal plot, the slope of which is an estimate of the logarithm of the efficacy/affinity ratio [i.e., Log(τ/K_A)] according to eq. 5:(5)where agonists A₁ and A₂ have respective efficacies of τ_A1 and τ_A2 and respective affinities K_A-1 and K_A-2. An example of this procedure is shown in Fig. 8, where the relative power of the chemokines CCL3-like 1 and CCL5 are compared in an assay measuring CCR5 receptor internalization (data redrawn from Kenakin et al., 2012). Although this method is theoretically sound, there are some practical issues with applying it—namely that the concentration-response curves must diverge (see Fig. 8A) to allow convergence of the plot. There are also serious statistical limitations with the use of double-reciprocal plots, which can skew errors and emphasize certain regions of the data set over others (see Fig. 8B). In addition, like RA, this is a null method requiring the comparison of two agonists in the same assays, thereby limiting analyses of large data sets for SARs.

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Fig. 8.

Determination of the relative Log(τ/K_A) values for CCL3-like 1 (CCL3L1) and CCL5 mediation of internalization of the CCR5 receptor. (A and B) Reciprocals of equiactive concentrations of agonist (a–d) (A) are used to construct a linear regression (B) according to eq. 4. Data are redrawn from Kenakin et al. (2012).

6. Reference Intrinsic Activity Trajectory and Rank Order Method

A model-free method of assessing possible signaling bias with two variations was presented by Onaran et al. (2017). This method is based on the fact that system bias tightly links the maximal responses of agonists of different efficacy into a regular pattern, usually a hyperbolic relationship. It should be noted that there are no assumptions about a model determining these effects; rather, the method is based on what is found in experimental pharmacology. A plot of the maximal responses to a range of agonists obtained in one pathway as a function of the maximal response found in another will be a singular continuous function if the activation of the two pathways by the agonists has the same mechanism; in essence, a trajectory of intrinsic activities (relative maximal responses) will be defined by a ratio of the maximal responses to the agonists (see Fig. 9). This defines the system bias for the two pathways and will be referred to as a “reference I.A. trajectory.” In addition, another method of depicting such a relationship is to plot the rank order of the magnitude of the maximal effects for both pathways. If the rank order in each pathway is the same (this will yield a straight-line correlation of unit slope), this would reflect system bias but suggest no ligand bias (see inset in Fig. 9). If a given agonist departs from this trajectory (i.e., the ratio of maximal responses deviates from the pattern), this would suggest ligand bias for that agonist. A classic example and one that furnished among the first pieces of evidence to support ligand bias was published by Berg et al. (1998) for 5-HT_2A receptor agonists. Specifically, these studies revealed agonists that actually reverse their efficacy for different signaling pathways (IP1 metabolism vs. arachidonate release) upon receptor activation. This is clear evidence of a reversal of efficacy for these pathways by these agonists that is shown in departures from the reference I.A. trajectory for these two signaling pathways (Fig. 10) and also rank order of intrinsic activities (inset in Fig. 10). The I.A. trajectory and rank order methods are essentially identical to the Log(τ) method; as such, they are solely based on differences in efficacy and ignore any effects on affinity. One of the main disadvantages of the I.A. trajectory (and rank order) method(s) is that a series of agonists with a wide range of intrinsic efficacies is needed to define the trajectory; if this is not available in a synthetic program, then the control (system) bias cannot be determined.

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Fig. 9.

Concentration-response curves for agonists with a constant ratio of efficacies for two signaling pathways (denoted by solid and dotted curves in 1–6). The panels are shown for agonists of descending efficacies from highest (1) to lowest (6). The ratios of the intrinsic activities (maximal responses) for each pathway are plotted to produce a trajectory depicting the system bias for the two assays with no ligand-based bias operative. Ratios of intrinsic efficacies that lie on this trajectory would simply indicate system bias and not ligand bias. The inset ranks the intrinsic activities of the agonists from highest to lowest for each pathway. A linear regression with no deviations indicates no ligand bias.

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Fig. 10.

Relative intrinsic activities for 5-HT_2A receptor agonists for two responses linked to the 5-HT_2A receptor: arachidonate release and IP1 accumulation. Whereas LSD, DOI, bufotenin, and 5-HT_2A lie on a single I.A. trajectory (system bias), TFMPP and quipazine clearly deviate, indicating biased signaling. This is due to a difference in efficacy for these pathways as the intrinsic activities actually reverse. This bias is also reflected in the rank order inset plot. AA, arachidonic acid; DOI, (6)-1-(2,5-dimethoxy-4iodophenyl)-2-aminopropane; LSD, lysergic acid diethylamide; TFMPP, 3-trifluoromethylphenyl-piperazine. Data redrawn from Berg et al. (1998).

In general, it can be seen that there are numerous methods proposed to detect and, in some cases, quantify signaling bias. These methods, along with their advantages and disadvantages, are summarized in Table 4. Analysis of bias estimates with these different methods unveils systematic and nonsystematic deviation of actual bias values (Onaran et al., 2017), which is a problem for exact application of the bias scale numbers. However, it will be seen that these in vitro assays essentially identify bias and rank compounds in terms of magnitude of bias, with less emphasis on the expectation that these actual numbers will accurately translate to in vivo therapy. This is due to the fact that there are a number of factors that can change these in vitro bias numbers as the ligands activate receptors in vivo (vide infra). This being the case, the in vitro assays should be considered only in terms of how serviceable they are to yield scales for the sorting of compounds (and optimize SARs) for further testing and not sources of immutable numbers. Thus, features such as high-throughput capability, continuity of the scale, and amenability to statistical analysis become the most important features of an in vitro method to quantify signaling bias.

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TABLE 4

Methods to quantify biased signaling

F. Temporal Effects on Measured Biased Signaling

The real-time kinetics of the development and maintenance of cellular signaling can affect the estimation of biased signaling of ligands when snapshots in time are taken to make the measurements. As shown in Fig. 11, the relative potency of three agonists that have different real-time kinetics of response production varies with the actual time that the response is measured (“snapshot” format of response measurement). An obvious dichotomy exists between the kinetics of G protein activation (rapid and transient) and β-arrestin signaling (slow and sustained). Kinetic differences can occur due to differences in on and off rates of agonists with the receptor (differences in drug-target residence time; Strasser et al., 2017). For instance, while PTH(1-34) and parathyroid hormone–related peptide PTHrP(1-36) both elevate cAMP, PTH(1-34) has a rapid onset and slow offset compared with PTHrP(1-36), leading to a selectively prolonged activation for the latter. Similarly, the kinetics of cAMP production differs depending on whether the bulk of the signal comes from membrane or endosomal (intracellular) sources. Although PTH and PTHrP both elevate cAMP, only PTH produces sustained cAMP elevation due to production from early endosomes (Ferrandon et al., 2009; Feinstein et al., 2011). In general, agonist-dependent sensitivity of disruption of the G protein complex by GTP (resulting in divergent agonist-dependent receptor-residency times for the G protein heterotrimer complex and effectors) has been correlated with agonist bias (Furness et al., 2016).

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Fig. 11.

Time course for production of response by three agonists (denoted by different solid and broken lines); measurement of response at three time points (T1, T2, T3) yields three concentration-response curves for the agonists with varying relative potencies.

Since biased signaling is basically a comparison of relative potency for different signaling pathways, temporal differences can translate to differences in bias estimates (Klein Herenbrink et al., 2016). In many cases, this results in modification of actual indices of bias but usually not in general gross direction of bias. However, there are cases in which temporal effects can be critical to the determination of signaling bias. For instance, norepinephrine and oxymetazoline both induce α_1A-adrenoceptor phosphorylation and internalization. Whereas the effects of oxymetazoline are rapid, the effect of norepinephrine requires long time periods; therefore, when measurements of internalization are made at 30 minutes, oxymetazoline produces full agonism, whereas norepinephrine is inactive (apparently perfect bias) (Akinaga et al., 2013).

G. Representations of Biased Signaling Profiles

Depending on the number of pathways, ligands, and systems involved in biased signaling measurement, there are various methods employed to display the results. The main aim of these representations is to view a totality of data to draw general conclusions regarding the structure of ligands and physiologic selectivity. As a general rule, the farther the vantage point for measurement from the ligand-receptor interaction, the more complex (and textured) the pattern of biased signaling will be. There are a number of systems that have been employed to depict these patterns. As discussed earlier, the most straightforward method is to construct a bias plot where the responses to an agonist for one pathway are plotted as a function of the responses to the same agonist concentration in another pathway (see Fig. 1). While bias plots make evident signaling bias in agonists, they do not furnish a quantitative scale to judge the degree to which ligand bias exceeds system bias.

If a quantitative scale of bias is employed then a simple correlation of ΔΔLog(τ/K_A) values can be useful. Figure 12 shows ΔΔLog(τ/K_A) values for a series of bitopic adenosine A₁ receptor agonists (referenced to NECA [1-(6-amino-9H-purin-9-yl)-1-deoxy-N-ethyl-β-d-ribofuranuronamide]) for calcium and cAMP responses; it can be seen that while eight compounds correlate fairly well [uniform ΔΔLog(τ/K_A) values], the eight compounds in red are biased toward cAMP over calcium (Aurelio et al., 2018). Other methods are used if more than one signaling pathway is involved. A common method of representing multiple activities (i.e., efficacies) in molecules is with radar plots; for example, the efficacy of β-adrenoceptor ligands is displayed on multiple intersecting axes to yield a “web of efficacy” (Evans et al., 2010). Radar plots can be used to depict actual differences in agonist potency [i.e., Log(τ/K_A) or Log(max/EC₅₀) values; see Figs. 17, A and C, 18, and 19], or relative agonist activity compared with a reference agonist [ΔLog(τ/K_A) or ΔLog(max/EC₅₀) values; e.g., see Figs. 13, 15, 16, 17, B and D, and 20–22]. Finally, actual differences in bias can be depicted with radar plots of ΔΔLog(τ/K_A) or ΔΔLog(max/EC₅₀) (i.e., “webs of bias”; Baltos et al., 2016a,b). In general, radar plots are used as representations of departures from system and measurement bias.

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Fig. 12.

Measurements of bias [as ΔΔLog(τ/K_A) values] for biotopic adenosine A₁ receptor agonists (referenced to NECA) for calcium (abscissae) and cAMP (ordinates) responses. Agonists denoted with black solid circles show uniform activation of both pathways (no bias), whereas circles in red indicate bias toward cAMP signaling over calcium. Data redrawn from Aurelio et al. (2018).

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Fig. 13.

Relative agonist activity of three ghrelin agonists for the ghrelin receptor mediating various signaling pathways in HEK293T cells. (A) Radar plot based on agonist type for G protein and β-arrestin signaling [ΔLog(τ/K_A) values] shown relative to ghrelin as the reference agonist. (B) More textured depiction of G protein selectivity. Radar plot based on G protein type showing ΔLog(τ/K_A) values (with ghrelin as the reference agonist); the filled pentagon shows activity equal to ghrelin. Data are shown for agonists MK0677 and JMV1843. Redrawn from M’Kadmi et al. (2015). JMV1843, 2-methylalanyl-N-[(1R)-1-(formylamino)-2-(1H-indol-3-yl)ethyl]-D-tryptophanamide; MK0677, 2-amino-2-methyl-N-[(2R)-1-(1-methylsulfonylspiro[2H-indole-3,4′-piperidine]-1′-yl)-1-oxo-3-phenylmethoxypropan-2-yl]propanamide.

Although a great deal of literature is available to report receptor biased signaling between the G protein and β-arrestin pathways, the largest source of functional selectivity, in light of the G protein diversity available in the genome, may well be selective activation of G protein subunits (Hermans, 2003). Despite the technical challenge of measuring low-level G protein activation through GTPγS measurements, G protein selectivity has been determined with this method for some receptors. For example, dopamine receptor selectivity between Gα_i1, Gα_i2, Gα_i3, Gα_o, plus Gβ₁ and Gγ₂ has been reported using GTPγS assays (Gazi et al., 2003). Similarly, selectivity within Gα_i1, Gα_i2, Gα_i3, Gα_o, Gα_z, Gα_q/11, and Gα_12/13 has been reported with cannabinoid receptor agonism (Diez-Alarcia et al., 2016). Recently, the application of sensitive BRET probes to directly measure G protein activation in living cells has been applied to the measurement of G protein subunit selectivity (Lohse et al., 2012; Saulière et al., 2012). These new methods allow delineation of particularly difficult to distinguish G protein subunit isoforms within the G_i/o family (Saulière et al., 2012; Bellot et al., 2015). BRET technology has been applied to the study of multiple Gα subunit interactions (Soto et al., 2015) with receptors. Specifically, multiple G protein subunit interactions with oxytocin receptors (Busnelli et al., 2012), κ-opioid receptors (Rives et al., 2012), and ghrelin receptors (M’Kadmi et al., 2015) have been described. This latter study shows how a simple profile for ghrelin receptor agonists indicating G protein bias (over β-arrestin 2) can be further expanded to show a diverse texture of bias among G protein subunits (see Fig. 13). Recently, an all-inclusive single platform optical method to directly monitor G protein activation in live cells, to yield signal magnitude, activation rates, and varying efficacy and kinetics of ligand activator in real time to provide efficacy fingerprints, has been reported (Masuho et al., 2015).

More complex texture in signaling data can be presented as heat maps in which multiple responses are compared (i.e., Soethoudt et al., 2017). Added value to such heatmaps can be gained if the resulting arrays are statistically clustered; this can yield useful similarity data for SARs (Huang et al., 2009; Stallaert et al., 2012; Kenakin, 2015c). An example of this technique is shown in Fig. 14, where Log(max/EC₅₀) values for 15 opioid agonists in six signaling assays are clustered for similarity to produce nine groups linked by their unique signaling pattern in these assays (Kenakin, 2015c). The clusters obtained from signaling profiles often link structurally diverse molecules (i.e., see dynorphin A and morphine in Fig. 14).

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Fig. 14.

Cluster analysis of 15 μ-opioid agonists in six different functional assays (data from Thompson et al., 2015). The gene cluster program GENE-E groups the agonists according to their Log(max/EC₅₀) values in each assay. Shown is a grouping of two agonists (morphine and dynorphin A) of very different chemical structure. Analysis and data are redrawn from Kenakin (2015c).

The idea that receptor signaling becomes more complex and textured as measurements are made further from the ligand-receptor interaction leads to the demonstration that ligands produce differences in these patterns not evident at the cell membrane. One window into such patterns is receptor-mediated global changes in protein phosphorylation. For example, it has been shown that a 5-minute exposure of osteoblastic cells in vitro to PTH(1-34) leads to changes in the phosphorylation of 224 distinct proteins in the cell (Williams et al., 2016). One of the most complex “fingerprints” of biased signaling involving both ligand bias and cell background is obtained through transcriptome analysis, namely the comparison of differential mRNA expression from tissue cDNA (Maudsley et al., 2011, 2013; Chen et al., 2013b). In vitro studies from mouse calvarial bone cells indicate that bPTH(7-34) regulates 192 genes (47 upregulated and 145 downregulated) to yield a unique fingerprint for PTH receptor activation (Gesty-Palmer et al., 2013). Similarly, in bone tissue from wild-type and congenic β-arrestin2^−/− mice, dramatic differences in the fingerprints are seen with hPTH(1-34) and [d-Trp¹²,Tyr³⁴]-bPTH(7-34) (Maudsley et al., 2015).

H. Biased Signaling in Screening and Lead Optimization

Since pharmacological assays can be used to detect, measure, and quantify ligand-directed biased signaling, it follows that medicinal chemistry can be used to optimize desirable biased profiles. In fact, the detection of chemical scaffolds that demonstrate biased signaling can be achieved at the screening step in the drug discovery process. Thus, once molecules have been screened in one signaling format, a broad selection of the “hits” can then be cross-screened in another signaling format to produce a range of activity; that is, it is nearly guaranteed that a lack of uniformity in activity in the two assays will be seen. At this stage, an informed decision can be made to progress molecules known to be different (i.e., known to stabilize different receptor active states) to a secondary assay rather than only the most potent hits from the initial screening assay. This should, in turn, ensure the optimal opportunity for the discovery of unique therapeutic phenotypes in secondary assays (Kenakin, 2011). Application of such parallel primary screening has been applied to the detection of apelin receptor hits for cardioprotection (McAnally et al., 2017). The utilization of whole cell response assays for such screening optimizes for detection of activity (most sensitive) and biased signaling diversity (differences in pathway activation) (Kenakin, 2012). In this regard, label-free assays can be used for effective screening of biased molecules (Peters and Scott, 2009; Hou et al., 2016). Newer technologies such as those employing sensor surface-immobilized receptors also have been described as systems for the detection of biased ligands (Kumari et al., 2015). In addition, virtual screening techniques have been applied to the detection of biased molecules (Li et al., 2015; Manglik et al., 2016).

The ability to quantify biased signaling to a scale also powers SARs and enables a rational alteration of the effect through medicinal chemistry. Modifications of ligand-receptor function lead to changes in allosteric interaction and these can lead to precipitous changes in activity with small changes in ligand structure; this has been noted in the SAR for negative allosteric modulators (NAMs) and PAMs. For example, very subtle changes in chemical structures are known to radically convert a PAM to a NAM in a 5-(phenylethynyl)pyrimidine scaffold (Sharma et al., 2008); this phenomenon has been given the term “activity switching.” Since biased signaling is no more than the imposition of selective PAM effects for different signaling proteins, this suggests that changes in bias could be equally sensitive to changes in ligand structure. Alteration of biased signaling has been aided in many cases by linking effects with the structure of the receptor. For instance, the 5-HT_2A and β-adrenoceptor crystal structures have identified transmembrane domains specifically linked to biased ligand interaction (Warne et al., 2012; Wacker et al., 2013). Similarly, protein mutation has been employed to generate SAR data for biased signaling (G protein receptor 183, Daugvilaite et al., 2017; GLP-1, Wootten et al., 2016). Chemical scaffolds yielding biased molecules range from modification of natural endogenous agonists to natural products (Gupta et al., 2016). In general, there has been a steady increase in the number of studies on the structural features of molecules required for biased signaling (Tan et al., 2018; and see Table 5).

I. Applications of Biased Scales in Pharmacology

Quantitative scales for biased signaling, such as ΔΔLog(τ/K_A) and ΔΔLog(max/EC₅₀), can be used for a variety of purposes in pharmacology to gauge ligand selectivity and their impact on physiologic changes in systems. Just as these scales quantify agonist intracellular selectivity (biased signaling), they also can be used to quantify extracellular (receptor) selectivity. The inherent advantage of this is the ability to compare full and partial agonism, something that is not possible with simple potency ratios (Kenakin, 2017). By comparing ΔLog(τ/K_A) or ΔLog(max/EC₅₀) values, the sensitivity of each test system for the receptor type can be accounted for (by comparison with a reference agonist) to reveal the selectivity of test agonists. Figure 15A shows ΔLog(max/EC₅₀) values for eight agonists for three 5-HT receptor subtypes illustrating the application of this method (lorcaserin is 5-HT_2C selective) (Zhang et al., 2017).

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Fig. 15.

Agonist selectivities depicted with radar plots. (A) Receptor selectivity [as ΔLog(max/EC₅₀) values] compared with 5-HT for agonists activating three 5-HT receptor subtypes. Data are redrawn from Zhang et al. (2017). (B) Selectivity of dopamine agonists [as ΔLog(max/EC₅₀) values] from label-free activation of dopamine D₂ receptor in two different cell lines. Data indicate a cell-type specificity for A-77636. DHX, dihydrexidine; SKF38393, (±)-1-phenyl-2,3,4,5-tetrahydro-(1H)-3-benzazepine-7,8-diol hydrobromide. Data are redrawn from Peters and Scott (2009).

Basically, any type of selectivity can be quantified with the same techniques used to quantify biased signaling. For example, cell type can confer selectivity on agonists (actually it can modify pathway-selective effects seen in in vitro assays; vide infra). This can readily be seen with label-free assays in which the agonist activity can be measured and compared as ΔLog(τ/KA) or ΔLog(max/EC₅₀) values. Figure 15B shows ΔLog(max/EC₅₀) values for five agonists on dopamine D₂ receptors measured in two cell types with a label-free assay (Peters and Scott, 2009). It can be seen that A-77636 [(1)-(1R,3S)-3-adamantyl-1-(aminomethyl)-3,4-dihydro-5,6-dihydroxy-1H-2-benzopyran hydrochloride hydrate] is selectively 11-fold more potent in U-2 cells (over SK-N-MC cells), thereby identifying this agonist as cell type specific. This type of analysis could be applied to identifying selectivity for pathologic systems over normal physiologic systems (i.e., cancer vs. healthy host, normal cardiac vs. cells from heart failure models, etc.) to identify disease-selective therapeutic molecules (Kenakin, 2015b).

Finally, biased signaling scales can be used to characterize and quantify physiology in a system-dependent manner as in the characterization of the effect of mutation on receptor function. Figure 16 shows ΔLog(max/EC₅₀) values for ghrelin activity on wild-type and mutated analogs of the ghrelin receptor (Mokrosiński et al., 2012). Ideally, ΔLog(τ/K_A) or ΔLog(max/EC₅₀) values should be calculated for two agonists to account for differences in receptor expression (Tschammer et al., 2011; Kenakin, 2017). While conventional analyses of biased signaling for new agonists compares the synthetic agonist profile to the endogenous agonist (to gain an insight into the new signaling that might be expected with the synthetic agonist), in mutational studies the question more often relates to what effect a receptor mutation will have on the endogenous signaling in a pathologic condition. Therefore, the analysis usually uses a synthetic agonist as the reference to control for differences in receptor expression and centers on the impact of the mutation on the endogenous agonist. Ideally, specific residues may be identified in such studies to guide medicinal chemists to regions of the receptor that can change ligand selectivity (e.g., see Daugvilaite et al., 2017).

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Fig. 16.

Effect of ghrelin receptor mutation on ΔLog(max/EC₅₀) values for ghrelin. (A) Mutations made in the second extracellular loop of the receptor. (B) The shaded area represents normal wild-type receptor activity for ghrelin, and the red line indicates departure of activity made by mutation. No large differences in receptor expression were seen in these experiments. WT, wild type. Data are redrawn from Mokrosiński et al. (2012).

J. Allosterically Induced Bias

In view of the fact that allosteric modulators can completely change the receptor through stabilization of different global conformations, there is no a priori reason to believe that the allosterically modified receptor should retain its preallosteric modified signaling bias after binding the allosteric ligand. This being the case, the modulator can induce a new bias to the agonist-receptor system; this will be referred to as allosterically induced bias. With the activation of multiple signaling pathways by an agonist will come a change in the efficacy fingerprint of that agonist. Induced bias in normal physiology has been documented. For instance, the accessory protein RAMP2 binds to the vasoactive intestinal polypeptide-pituitary adenylyl cyclase-activating peptide 1 receptor to selectively enhance G_q/11 signaling (receptor-mediated phosphoinositide turnover) without altering G_s-coupled cAMP production (Christopoulos et al., 2003). Similarly, neurochondrin interaction with the melanin-concentrating hormone receptor 1 leads to inhibition of agonist-induced G_i/o and G_q/11 signaling with no concomitant internalization (Francke et al., 2006).

Induced bias is increasingly seen with synthetic drug-like allosteric modulators. For example, the agonist for the metabotropic glutamate receptor 5(S)-3,5-dihydroxyphenylglycine produces activation of calcium, IP1, and phosphor-ERK1/2 signaling in mouse cortical neurons (Sengmany et al., 2017). Values of Log(τ/K_A) for activation of these pathways in the absence and presence of a high concentration of PAM agonists for this receptor indicate that the pattern of activation of these three pathways changes significantly after allosteric modification (see Fig. 17). Thus, the PAM-agonist VU0360172 [N-cyclobutyl-6-((3-flurophenyl)ethynyl) picolinamide] produces a 12-fold bias toward calcium, while the PAM-agonist DPFE [1-(4-(2,4-difluorophenyl) piperazin-1-yl)-2-((4-fluorobenzyl)oxy)ethanone] produces a 10-fold bias toward calcium. Induced bias has been observed for NAMs of NK2 receptors (Maillet et al., 2007), prostaglandin D₂ receptors (Mathiesen et al., 2005), lysophosphatidic acid receptors (Shimizu and Nakayama, 2017), and calcium-sensing receptors (Davey et al., 2012; Cook et al., 2015). The effect has also been reported for PAMs for GLP-1 receptors (Koole et al., 2010), adenosine A₁ receptors (Aurelio et al., 2009), muscarinic M₁ receptors (Davoren et al., 2016), and metabotropic glutamic acid 5 receptors (Bradley et al., 2011). In addition, induced bias has also been seen with other types of allosteric modulators such as PAM antagonists (Kenakin and Strachan, 2018); for example, this has been documented for the cannabinoid CB₁ molecule Org27569 (5-Chloro-3-ethyl-N-[2-[4-(1-piperidinyl)phenyl]ethyl-1H-indole-2-carboxamide), which blocks cAMP production but not ERK1/2 phosphorylation for some cannabinoid orthosteric agonists (Khajehali et al., 2015). In keeping with the cell-dependent modification of measured biased signaling, cell-dependent modulator-induced bias also has been reported for the follicle-stimulating hormone receptor, where differential effects on follicle-stimulating hormone receptor–stimulated steroidogenesis and ovulation in murine Leydig tumor cell line-1 and rat primary Leydig cells are observed (Ayoub et al., 2016).

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Fig. 17.

Allosterically induced bias. (A) Log(τ/K_A) values for the metabotropic glutamate receptor 5 agonist DHPG for the signaling pathways in mouse cortical neurons. (B) The solid line represents control values; dotted line values in presence of the PAM-agonist VU0360172. Data in (A) are presented as ratios of control Log(τ/K_A) values [ΔLog(τ/K_A) values] and values after allosteric modulation. The shaded triangle indicates the native receptor signaling before allosteric modification, and the solid line indicates the change in this signaling in the presence of VU0360172. Values extending beyond the shaded triangle indicate increased signaling in the presence of the allosteric modulator and inside the triangle a reduced signaling. (C) Log(τ/K_A) control values are indicated as a solid line; values in the presence of the PAM-agonist DPFE are shown as a dotted line. (D) Data in (C) are presented as ratios of control Log(τ/K_A) values [ΔLog(τ/K_A) values]. The shaded triangle indicates the native receptor signaling before allosteric modification, and the solid line indicates the change in this signaling in the presence of DPFE. Values extending beyond the shaded triangle indicate increased signaling in the presence of the allosteric modulator and inside the triangle a reduced signaling. DHPG, 5-(S)-3,5-dihydroxyphenylglycine; VU0360172, N-cyclobutyl-6-((3-flurophenyl)ethynyl) picolinamide. Figure drawn from data recalculated from Sengmany et al. (2017).

A. Translation from Pathways to Whole Cells

Although measurements of signaling bias can be made with simplified in vitro assay systems (e.g., biosensor interactions of receptors and effectors), ultimately the relevant outcome is the in vivo overall cellular effect of the biased agonist. There are a number of factors that contribute to the modification of simple dual pathway estimates of bias by the cell. Convergence of signaling pathways occurring in cells can change the overall outcome of signals initiated with different relative strengths of signal (Alvarez et al., 2002; Ostrom et al., 2012; Charfi et al., 2014). Initial events at the cell membrane may also have differential consequences for the whole cell. For example, while the cannabinoid CB₂ agonists CP55,940 [(−)-cis-3-[2-hydroxy-4-(1,1-dimethylheptyl)phenyl]-trans-4-(3-hydroxypropyl)cyclohexanol] and WIN55,212-2 both promote β-arrestin2 recruitment at the cell membrane, only CP55,940 induces receptor internalization (Atwood et al., 2012). Similarly, seemingly identical cellular outcomes may result from different intracellular signaling pathway activation. For example, in microphysiometry studies (measurement of extracellular acidification, a whole cell response), it was observed that two β₃-adrenoceptor agonists, CL316,243 and SR59,230A produce full agonism. However, when studied further, it was seen that whereas CL316,243 produces response through dual activation of cAMP and p38, SR59,230A produces the same end organ response through sole activation of p38 (Sato et al., 2007). Similarly, a range of PTH receptor agonists activate cAMP and ERK1/2 signaling and also produce anabolic bone response in vivo; paradoxically, the ligand (d-Trp¹²,Tyr³⁴)-bPTH(7-34) has a dramatically different in vitro signaling profile (inverse agonism for cAMP and ERK1/2) but still produces positive anabolic bone responses (Appleton et al., 2013).

Possible further complication may result from different agonist-stabilized receptor active conformations interacting with different conformations of effector; for example, two angiotensin β-arrestin–biased agonists produce two different and distinct patterns of downstream signaling in cells by stabilization of different conformations of β-arrestin (Santos et al., 2015). As conformationally coded receptors interact with various components of the cell cytosol, the relative stoichiometry of those components becomes a factor in the overall impact of any given pathway. Data from intramolecular-bioluminescence resonance energy experiments indicate that different ligands binding to the same receptor can produce unique population average conformational signatures for arrestins (Lee et al., 2016; Nuber et al., 2016). These signatures become instrumental in the assembly of distinct signaling β-arrestin “signalsomes” (Peterson and Luttrell, 2017).

The stoichiometry of interacting components, namely receptors and signaling proteins, is known to vary between different cell types (Kenakin, 1997; Newman-Tancredi et al., 1997, 2000). Proteomic techniques using mass spectrometry show marked differences in the expression level of proteins in 11 different human cell lines (Geiger et al., 2012) and quantification of mRNA showed large differences in receptors and signaling proteins in cell lines often used in pharmacologic experiments, specifically human embryonic kidney 293 (HEK293), AtT20, BV2, and N18 cells (Atwood et al., 2011). Differences in receptor/G protein stoichiometry have been shown to directly influence estimates of signaling bias (for review, see Gurevich and Gurevich, 2018). Figure 18A shows the variation in bias measured for β-adrenoceptor agonists under conditions of varying Gα protein subunit expression (Onfroy et al., 2017). Similarly, the relative stoichiometry of Gα_s protein and calcitonin receptors has been shown to reverse the relative order of potency for calcitonin agonists (see Fig. 18B) (Watson et al., 2000). Theoretical modeling confirms that variation in the relative quantity of different signaling proteins in cells can lead to variation in the whole cell estimate of biased signaling for biased agonists (Kenakin, 2016). In fact, experimentally demonstrated effects have been shown to translate to whole cell differences in experimental cell systems (Masuho et al., 2015). In addition, cell background context (HEK293 vs. rat vascular smooth muscle cells) has been seen to affect basal angiotensin AT1 receptor conformation as detected by receptor conformationally sensitive biosensors (Devost et al., 2017), consistent with the reciprocal allosteric effects of receptors and G proteins. These data show that signaling partners such as G proteins influence the agonist-induced conformational changes and modify them when they are present in the membrane (or deleted with CRISPR). For instance, the absence of G_q/11 confers sensitivity onto the biosensor at intracellular loop 2P2, which then becomes sensitive to angiotensin II and III. Additionally, the nature of the host cell can modify both the basal conformation of the receptor and the changes induced by agonists. This latter effect is demonstrated by the different conformations (as sensed through ΔBRET changes in the second and third intracellular loops of the receptor) produced by two agonists, namely angiotensin II and the biased agonist SI. The effects of these agonists are of different magnitude and, notably, vary in the direction of conformation change when measured in HEK cells versus vascular smooth muscle cells (Devost et al., 2017). These data indicate that the cell type can change the conformational ensemble of the receptor and the nature and magnitude of agonist-induced signaling bias. Similarly, the lipid environment of the receptor has long been postulated to affect receptor function; a recent report utilizing mass spectrometry on preferential binding of phosphatidylinositol-4,5-bisphosphate to adenosine A_2A receptor– and β₁-adrenoceptor–G protein complexes shows variation in receptor–G protein coupling efficiency, thereby illustrating the influence of lipids (and therefore cell type) on receptor function (Yen et al., 2018). Variation in the stoichiometry of intracellular signaling modulators also can have profound effects on responses to agonists in terms of G protein selectivity. For example, Fig. 19 shows measurements of the maximal muscarinic M₃ receptor and selective G protein response produced by acetylcholine in control HEK293T/17 cells and in the presence of a cotransfected regulator of G protein signaling (RGS8) and presence of a cotransfected activator of G protein signaling (AGS1); it can be seen that the interaction of the receptor with individual G protein subtypes changes considerably (Masuho et al., 2015).

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Fig. 18.

Variation in receptor/G protein stoichiometry can change bias estimates. (A) Log(max/EC₅₀) values for three β-adrenoceptor agonists in HEK293 cells varying in levels of G protein expression, drawn from Onfroy et al. (2017). (B) Effect of coexpression of Gα_s protein into HEK cells on calcitonin receptor agonist Log(max/EC₅₀) values, drawn from Watson et al. (2000). EPI, epinephrine; ISO, isoproterenol; NE, norepinephrine.

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Fig. 19.

Maximal effect of muscarinic M₃ receptor activation with different Gα subunits in HEK293T/17 cells (control) and cells cotransfected with regulator of G protein signaling RGS8 protein (blue) and activator of G protein signaling AGS1 (red). Data redrawn from Masuho et al. (2015).

Yet another whole cell variable may involve the phosphorylation of receptors (Tobin, 2008; Tobin et al., 2008) referred to as receptor “barcoding” (Tobin et al., 2008; Zidar et al., 2009; Nobles et al., 2011). It has been established that different receptor ligands can induce different phosphorylation barcodes (Butcher et al., 2011; Just et al., 2013) to induce different signaling outcomes (Tobin et al., 2008). This may be the result of specific phosphorylation-dependent interactions of receptors with β-arrestins (DeWire et al., 2007) that change from an inactive to active form upon interaction with the phosphorylated receptor (Xiao et al., 2004; Nobles et al., 2007; Shukla et al., 2013). Whole cell modification of signaling bias can be affected, as phosphorylation has been shown to be tissue specific (Torrecilla et al., 2007).

Finally, other points of cellular control that can influence agonist whole cell response have been described. For example, the β₂-adrenoceptor agonists clenbuterol, cimaterol, procaterol, and terbutaline were shown to be biased toward cAMP production (with weak activity on ERK phosphorylation) in HEK293 cells only in adherent cells with 100% confluence; reduction in cell-cell contact by either decreasing the cell density or bringing the cells into suspension abolished this bias (Kaya et al., 2012). Temporal encoding, in which each of these components brings with it a real-time kinetic element, can change the emphasis of various pathways in whole cell response in cell-dependent ways (Grundmann and Kostenis, 2017). In addition, cell-dependent expression of receptor splice variants (as in the case of CXCR3 receptors) also has been shown to produce cell-dependent differences in biased signaling (Berchiche and Sakmar, 2016).

In view of the various points of possible variation in biased signaling measurements to be found in different cell types, it is not surprising that bias measurements have been seen to vary with cell host. For example, variation in cAMP production and receptor internalization has been seen for opioid receptor ligands when measured in CHO versus AtT20 cells (Thompson et al., 2015, 2016). Similarly, the array of signaling pathways activated by relaxin has been shown to markedly differ in seven different cell types, an effect possibly related to varying levels of Gα_i3 and Gα_o (Halls et al., 2009). The fact that label-free assays use total cellular response as the output for agonism makes this format ideal to detect cell type variation (Peters and Scott, 2009). For example, seven muscarinic M₃ receptor agonists have been shown to produce diverse signaling profiles in six different cell lines through label-free assay technology (Deng et al., 2013). In keeping with the idea that more complex outputs of agonist response increase the capability to detect diversity in signaling, mRNA microarrays also offer a useful technology to detect cell type variation in agonist signaling (Atwood et al., 2011).

Cell type differences also extend to comparisons of recombinant versus natural cell systems and even between different recombinant systems utilizing different host cells (Christmanson et al., 1994). In this case, the expression of human calcitonin receptors into COS and CHO cells yields strikingly different relative potency ratios, indicating a clear cellular control of biased signaling for these agonists. When comparing recombinant versus natural cell lines, it has been shown that opioid and cannabinoid receptors access the same pool of G proteins in recombinant cell systems but mediate signals through distinctly different pools in endogenous natural cell systems (Shapira et al., 2000). Similarly, while protein kinase C and GRK2 are important for δ-opioid receptor internalization in cortical neurons, they are less so in HEK293 cells (Charfi et al., 2014). Different pharmacology of PACAP agonists PACAP-27 and PACAP-38 has been reported between transfected PAC_ln cells and natural trigeminal neurons and glia (Walker et al., 2014). In general, beyond variation in the relative stoichiometry of signaling components in recombinant systems, variation in the integration of the transgene in stable transfection systems can permanently alter the cell genotype/phenotype (Lin et al., 2014). However, it should not be assumed that all recombinant versus natural cell systems show divergence. For instance, the G protein bias of the κ-opioid receptor agonist 6′-guanidinonaltrindole originally detected in HEK cells (Rives et al., 2012) retains this profile in primary neuronal cultures where κ-opioid receptors are expressed endogenously (Schmid et al., 2013).

In view of the fact that bias measurements made in recombinant systems often differ from those seen in natural systems and also that cell type can change estimates of bias, it is useful to quantitatively compare different functional systems and measurements of bias. Figure 20 shows data for allosteric agonists of the metabotropic glutamate receptor 5 for functional responses (calcium, IP1, ERK) when the receptor is transfected into HEK cells versus that endogenously found in cultured cortical neurons (Sengmany et al., 2017). These data show a trend of greater agonist activity and increased bias [ΔΔLog(max/EC₅₀) values] in HEK cells versus cortical neurons. This shows that these agonists appear to be more biased in the recombinant system over the natural system.

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Fig. 20.

Bias measured in HEK cells and cultured cortical neurons. The left panel shows ΔLog(max/EC₅₀) values for allosteric agonist activation of metabotropic glutamate receptor 5 (DHPG as the reference agonist) for VU0409551, DFPE, and CDPPB for calcium, IP1, and ERK responses in HEK293A cells. The right panel shows the same responses in cultured cortical neurons. These radar plots indicate that bias is more pronounced in HEK cells than natural cultured cortical neurons. CDPPB, 3-cyano-N-(1,3-diphenyl-1H-pyrazol-5-yl)benzamide; DFPE, 1-(4-(2,4-difluorophenyl)piperazin-1-yl)-2-((4fluorobenzyl)oxy)ethanone; DHPG, 5-(S)-3,5-dihydroxyphenylglycine; VU0409551, ((4-fluorophenyl) (2-(phenoxymethyl)-6,7dihydrooxazolo[5,4-c]pyridin-5(4H)-yl) methanone); VU29, N-(1,3-diphenyl-1H-pyrazolo-5-yl)-4-nitrobenzamide. Figure drawn from data recalculated from Sengmany et al. (2017).

It is interesting to see how the vantage point for observing response can change the magnitude of measured bias. Figure 21 shows how increases in cAMP mediated by dopamine D₂ receptor agonism translate to label-free whole cell response with the resulting changes in quantitative biased signaling (Peters et al., 2007). A more extensive analysis of the changes occurring throughout the stimulus-response cascades controlling whole cell response are shown in Fig. 22 (Klein Herenbrink et al., 2016). Specifically, it can be seen that the part of the overall pathway chosen to make the bias measurements produces different estimates of the relative bias of the agonists.

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Fig. 21.

Concentration-response curves from dopamine D₂ receptor agonists showing relative potency at the point of receptor/G protein interaction (cAMP) and whole cell end-organ response (cellular impedance in label-free format). The center radar plot shows ΔLog(max/EC₅₀) values from both vantage points, indicating differences in bias estimates. Data calculated from Peters et al. (2007).

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Fig. 22.

Different relative agonist activity measurements [ΔLog(max/EC₅₀) vs. dopamine] for seven dopamine D₂ receptor agonists with estimates made at various points along the stimulus-response pathway in Flp-In-CHO cells. The left panel shows ΔLog(max/EC₅₀) values for the agonists measured at six points along the stimulus-response cascade (beginning with receptor/G protein and β-arrestin interaction to whole cell impedance response). Of note are the different patterns of agonism, indicating that bias estimates at each of these points are different, as shown in the right panel. S-3PPP, [3-(3-hydroxyphenyl)-N-propylpiperidine]. Data calculated from Klein Herenbrink et al. (2016).

B. Translation to In Vivo Therapeutic Systems

2. Affinity-Dominant versus Efficacy-Dominant Biased Signaling

In cases where in vivo agonism may be relevant to therapeutic effect, it is important to note that ligand efficacy and not bias is the important factor. While bias determines the relative concentrations at which two responses will appear (in relation to each other) when the responses actually do appear, whether any response occurs at all remains in the realm of the ligand efficacies for that pathway and the sensitivity of the tissue involved. As illustrated by eq. 3, agonist potency depends on both affinity and efficacy; thus, high potency can be achieved through high affinity or high efficacy. However, the dependence of agonism on tissue sensitivity differs depending on the dominating factor for agonist potency. If agonist potency depends largely on affinity (“affinity-dominant” agonists), then decreases in tissue sensitivity will have much more effect on whether agonism is produced than if the potency depends on efficacy (“efficacy-dominant” agonists) (Kenakin, 1984). In general, affinity-dominant agonists are much more prone to show no agonism in less sensitive tissues than efficacy-dominant agonists that produce agonism in all tissues. For example, decreases in α-adrenoceptor density through chemical alkylation depress the response to the more potent affinity-dominant agonist oxymetazoline to a much greater extent than the responses to the less potent but efficacy-dominant agonist norepinephrine (Kenakin, 1984). Since bias is simply a ratio of efficacy to affinity for two pathways, bias can also be affinity dominant or efficacy dominant. As seen in Fig. 23, two κ-opioid agonists, ICI204448 [(RS)[3-[1-[[3,4-dichlorophenyl)acetyl]methylamino]-2-(1-pyrrolidinyl)ethyl]phenoxy]acetic acid hydrochloride] and RB-59, have the same intrinsic activities for cAMP and β-arrestin responses but very different relative potencies (White et al., 2014). The fact that both agonists lie on the same intrinsic activity reference trajectory indicates that efficacy alone cannot account for the bias of these agonists (RB-59 is 147-fold biased toward G protein compared with ICI204448 using the reference agonist salvanorin); rather, differences in affinity play a key role in determining the bias. Parenthetically, this illustrates a major shortcoming of utilizing methods that only involve efficacy for the quantification of bias, such as Log(τ), I.A. reference trajectory, and rank order (see section VII.E). As with agonism, efficacy-dominant bias will be more robust in terms of activating biased signaling pathways in a wider range of tissues in vivo than will affinity-dominant bias. This is demonstrated in Fig. 24, which shows the response to two identically biased agonists in a range of tissues with varying levels of receptors. It can be seen from this figure that there is a greater range of tissues (as seen by a greater range of receptor densities) that will demonstrate biased agonism (as opposed to antagonism) in vivo (in the presence of an EC₅₀ of endogenous agonist response) for the efficacy-dominant biased agonist than for the affinity-dominant biased agonist. These simulations underscore the importance of efficacy, as opposed to only bias, in the prediction of agonist response in vivo.

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Fig. 23.

Regression of the relative intrinsic activities of κ-opioid receptor agonists for G protein/β-arrestin responses on the bias of these agonists for each pathway. It can be seen that there are agonists with identical bias values but differing relative efficacies (reflected as intrinsic activity values) for each pathway and vice versa (identical maximal intrinsic activities but varying bias). This latter effect is demonstrated by RB-59 and ICI204448, which have very different bias values but essentially identical efficacies for each pathway (would lie on the same I.A. trajectory). This illustrates the importance of variable affinity with signaling pathways. Data redrawn from White et al (2014).

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Fig. 24.

Agonist and antagonist effects of two identically biased agonists in a range of tissues with varying sensitivities (receptor densities). (A–D) The agonist in (A) and (C) achieves bias through selective efficacy, whereas the agonist in (B) and (D) is biased because of selective affinity. It can be seen that in high-sensitivity tissues (A and B), both agonists produce identical profiles of biased effect. However, in low-sensitivity tissues (low receptor density), the response in the preferred pathway (red curve) is selectively depressed for affinity-dominant bias (D) when compared with efficacy-dominant bias (C). In such tissues, this agonist will function as an antagonist. The right panel depicts the observed response to these agonists over a range of tissue sensitivities. It can be seen that with low receptor density, no agonist response will be produced and the ligands will function as antagonists. With increasing tissue sensitivity, the direct agonist effect of the agonists emerges. However, the range of tissues (as indicated by the range in receptor densities) demonstrating agonism is far greater for the efficacy-dominant agonist than it is for the affinity-dominant agonist (i.e., biased agonism is more robust in vivo for efficacy-dominant bias).

The complex interplay of bias and efficacy in the production of therapeutically relevant responses is illustrated by the effects of dopaminergic ligands for the treatment of schizophrenia, a disease postulated to be caused by striatal hyperdopaminergic and cortical hypodopaminergic activity. Partial agonists such as aripiprazole, developed to treat this disease, still are insufficient to correct cortical function; this profile is corrected with β-arrestin2 biased partial agonists that enhance firing of cortical fast-firing neurons (Urs et al., 2016). In general, the profile of striatal antagonism and cortical agonism leads to an improved antipsychotic profile, in that the lack of G protein signaling (bias) of these compounds coupled to the elevated expression of β-arrestin2 and GRK2 in the cortex (vs. striatum) produces the optimal conditions for the biased effect. It is proposed that the interplay of agonism and antagonism present in β-arrestin2 biased partial agonists such as the aripiprazole-derived analog compound 94A is critical to the favorable therapeutic potential for the treatment of schizophrenia (Allen et al., 2011; Urs et al., 2014, 2016).