Terminology summary
Transducer: For the purpose of defining biased signaling initiated by the GPCRs, transducers are defined as proteins that bind directly to an activated receptor to initiate downstream signaling events. This includes G proteins, GRKs and arrestins. Some also use ‘primary’ effector to denote a transducer, although this word can be confusing for these protein families as they typically are engaged consecutively in a signaling cascade (although all bind to the receptor).
Effector: Downstream protein that is a node of the pathway/signaling cascade, i.e., following the transducer. Some also use ‘secondary’ transducer to denote an effector.
Modulator: Proteins or molecules that interact with the receptor, transducer or effectors to modify the signaling response without mediating it. Examples include RAMPs, GEFs, GAPs, RGSs, NO, cholesterol, other lipids etc.
Second messenger: Small molecules or ions directly controlled by the effectors. Changes in second messenger homeostasis mediate cellular responses and can serve as a quantifiable measurement of GPCR activation. Examples include cAMP, calcium, etc.
Pathway: A pathway is named after a transducer protein, or family thereof, that binds to GPCRs and elicits a distinct downstream signaling cascade or cellular response profile. This includes G proteins and their families – i.e., the Gs, Gi/o, Gq/11, and G12/13. It also includes the arrestin and GPCR kinase (GRK) families, which can be recruited to activated GPCRs either dependent or independent of functionally active G protein heterotrimers.

Ligand bias definition and distinction from receptor and system bias

This paper focuses on ‘ligand-dependent bias’ i.e., cases where a receptor’s signaling pathway engagement changes as a function of the addition of a given ligand. Quantification of bias typically compares only two transducer pathways at a time and includes the pathway with the strongest signaling. An exhaustive quantitative comparison of all pathways would therefore be constituted by a profile of pairwise comparisons. Quantified bias measures the change in transducer-pathway preference relative to a reference ligand (Table 1) and is therefore a comparison of both pathways and ligands (like a quantitative rank order). In contrast, ‘non-quantitative bias’ (previously termed ‘perfect bias’ or ‘full bias’) entails a single ligand’s selective signaling through one pathway while the other pathway(s) display no detectable signaling or signaling with another modality (see section “Special recommendation for agonism versus antagonism…”).
All ligand-independent mechanisms that may result in functional selectivity are covered by the term “system bias” . Functional selectivity due to system bias is independent from the specific identity of the ligand (i.e. applies equally to all ligands of the same modality), but depends on the properties of the system (i.e. experimental setup, cell type, tissue, receptor reserve etc.). System differences span e.g., constitutive selectivity of receptors for different transducers, spatiotemporal expression levels of signaling proteins (including receptor, transducers, effectors and other members of the signaling pathways), presence or absence of proteins acting on the receptor as allosteric modulators (like RAMPs (Hay & Pioszak, 2016) or other modulators like kinases (Strachan, Sciaky, Cronan, Kroeze & Roth, 2010), and finally, presence of intra- or inter-pathway feedbacks are all determinants of system bias. The impact of signaling efficiency of different pathways, on the manifestation of full or partial agonism with agonists of different efficacy is another example of system bias.
In general, ‘functional selectivity’ is a combination of ligand and system bias. Physiologically, this is exemplified by an endogenous agonist regulating alternative physiological functions in different cells/tissues often differentially expressing signaling components. Some GPCRs lack the inherent capability to elicit G protein coupling while exhibiting robust arrestin interaction (Rajagopal et al., 2010; Shubhi Pandey, 2021). This gives all ligands functional selectivity towards arrestin responses through system bias rather than ligand bias. In drug discovery, this provides an opportunity to elicit predominantly one of several physiological effects that a given receptor can mediate by designing drugs that are transducer- or pathway-selective (i.e. adjusting ligand bias on the background of system bias in the tissues of interest) (Figure 1).
Experimental studies can suffer from so called ‘observational bias’ , which is an artificial bias introduced by the experimental setup. For example, stronger signal amplification in one of two compared pathways when measuring different signaling processes at different levels (Figure 1). Another example would be the use of different cells with different protein expression. Another reason of observation bias is that the readout signals of the studied ligand are below the assay’s sensitivity for one pathway, but detectable in the other pathway. This case may be overcome by using more sensitive assays or by increasing expression levels of the involved signaling partners, if feasible. Moreover, the actual signal plateau may be missed if the signal detection tools saturate prematurely or if the measurement time point does not match the ligand binding kinetics. This gives rise to an assay-dependent (nonlinear) amplification in the observed signal(s). Nevertheless, the latter effect is taken care of by bias-quantitation strategies in exactly the same way as the “system-bias” is handled. To test for observational bias, it is recommendable to use an independent ‘orthogonal’ assay to validate each pathway. Furthermore, it is necessary to ensure that at least one assay for each pathway has sufficient sensitivity (preferred) or to increase expression levels of the involved signaling partners (alternative) to overcome sensitivity problems of a particular assay.
Disclaimer: In some cases, it can be difficult to cleanly separate ligand bias and system bias. Furthermore, the use of recombinant and/or overexpressed receptor, transducer or effector proteins may not fully reflect the bias in a native system.