Community guidelines for GPCR ligand bias: IUPHAR review 32

GPCRs modulate a plethora of physiological processes and mediate the effects of one‐third of FDA‐approved drugs. Depending on which ligand activates a receptor, it can engage different intracellular transducers. This ‘biased signalling’ paradigm requires that we now characterize physiological signalling not just by receptors but by ligand–receptor pairs. Ligands eliciting biased signalling may constitute better drugs with higher efficacy and fewer adverse effects. However, ligand bias is very complex, making reproducibility and description challenging. Here, we provide guidelines and terminology for any scientists to design and report ligand bias experiments. The guidelines will aid consistency and clarity, as the basic receptor research and drug discovery communities continue to advance our understanding and exploitation of ligand bias. Scientific insight, biosensors, and analytical methods are still evolving and should benefit from and contribute to the implementation of the guidelines, together improving translation from in vitro to disease‐relevant in vivo models.

However, it is still unclear what the precise relationship between conformation and signalling is-at least at the level of detail required to predict such outcomes.  (Sommer et al., 2020). Each section below provides definitions, terminology and recommendations for experiments and reporting (summarized in Data S1, S2, and S3).

| Pathway definition and modulation
A GPCR pathway is here defined by a transducer protein, or family thereof, binding intracellularly to the receptor and eliciting a distinct cellular downstream signalling cascade, trafficking or internalization.
Based on present knowledge, this includes the four G protein families-that is, the G s , G i/o , G q/11 , G 12/13 pathways-and the two GPCR kinase (GRK) and arrestin families (in all six transducer protein families). Typically, there is a sequential engagement of a receptor with a G protein, GRK, and arrestin. However, GRKs and arrestins can be recruited to and bind to GPCRs independently of functionally active G proteins (Grundmann et al., 2018;Hunton et al., 2005;Sauliere et al., 2012;Wehbi et al., 2013). For example, GRK4-6 functions do not appear to require either G proteins or arrestins, as they are not recruited by G βγ but anchored to the plasma membrane via polybasic domains and lipid modification (Komolov & Benovic, 2018).
Ligand bias is not limited to the comparison of these six transducer protein families but can also occur within each such family, that is, across its different members. This extends the number of pathways to 27: 16 Gα proteins, 7 GRKs, and 4 arrestin proteins. For example, G proteins belonging to the same family may differ in their functional outcome due to unique binding kinetics, cellular expression levels, and engagement of different downstream effectors (Anderson et al., 2020;Avet et al., 2021;Ho & Wong, 2001;Jiang & Bajpayee, 2009;Olsen et al., 2020). Similarly, differential recruitment of the two isoforms of β-arrestin (β-arrestin 1-2) can translate to distinct functional outcomes, with respect to regulatory and signalling paradigms (Ghosh et al., 2019;Srivastava, Gupta, Gupta, & Shukla, 2015).
Furthermore, GRK2 and GRK3 have a RGS homology domain (RH) binding to G q/11 to inhibit signalling, and a pleckstrin homology (PH) domain that can bind to G βγ to inhibit its signalling while inducing recruitment of GRK to the receptors (Carman et al., 1999;DebBurman, Ptasienski, Benovic, & Hosey, 1996;Ribas et al., 2007).

Terminology summary A
Transducer: For the purpose of defining biased signalling initiated by the GPCRs, transducers are defined as proteins that bind directly to an activated receptor to initiate, facilitate or modulate downstream events such as signalling, trafficking or internalization. This includes G proteins, GRKs and arrestins. Some also use 'primary' effector to denote a transducer, but this word can be confusing as these proteins typically bind the receptor one after the other.
Effector: It also includes the GPCR kinase (GRK) and arrestin families, which are often but not always engaged following G protein activation.

| Ligand bias definition and distinction from system bias
This paper focuses on ligand-dependent bias, that is, cases where a receptor's pathway engagement changes when binding a given ligand ( Figure 1). Quantification of bias compares two pathways at a time, typically the pathway with the strongest signalling relative to the most relevant other pathway(s). It also uses a reference ligand and is therefore a comparison of both pathways and ligands (like a quantitative rank order).
In addition to ligand bias, a receptor's response is also determined by system bias, which encompasses all non-ligand molecules involved in signalling. System bias encompasses, for example, the stoichiometry of receptors, transducers, effectors, and modulatory proteins which varies spatially across tissues, cell types, and temporally across physiological states. System bias includes a receptor's G protein or arrestin selectivity. For example, some GPCRs lack the inherent ability to elicit G protein coupling while exhibiting robust arrestin interaction (Meyrath et al., 2020;Pandey et al., 2021;Rajagopal et al., 2010).
Furthermore, system bias depends on the different levels of receptor constitutive activity, and intracellular intra-or inter-pathway feedbacks. System bias affects all ligands equally (including the reference ligand), unless the system itself has an influence on ligand concentration (e.g. by conditions where one of two ligands is metabolized).
Functional selectivity is the combined effect of ligand and system bias (Stallaert, Christopoulos, & Bouvier, 2011). For example, a GPCR can give rise to different physiological effects after activation by alternative endogenous agonists or for the same ligand in different cells/ tissues with differentially expressed signalling components. In drug discovery, achieving functional selectivity by tuning ligand bias on the background of system bias in the tissue of interest provides an opportunity to elicit predominantly the therapeutically beneficial effect of a receptor target.
F I G U R E 1 Ligand, system and observational bias. Ligand and system bias together determine the functional selectivity (see terminology subsection for definitions). Each type of bias is measured relative to a reference. Observational bias is not of biological origin, but a consequence of assay sensitivity/non-linearity and the experimental set-up Biased signalling studies can also suffer from observational bias, which is an artificial bias caused by an inappropriate experimental set-up and therefore not reflecting native biology. An example are time points for data collection that are not relevant for the physiological effect. Another example is if one of the two studied pathways is measured using an assay with insufficient sensitivity or, conversely, has an artificially high level of amplification.
Therefore, 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 signalling partners (alternative).
Moreover, the actual signal plateau may be missed if the signal detection tools saturate prematurely. Observational bias can be avoided by using an independent 'orthogonal' assay to validate each pathway.
Disclaimer: A ligand may be unbiased (relative to the reference ligand) with respect to two particular pathways but may be biased when additional different pathways are studied or if a different reference ligand is used. Hence, the term 'biased ligand' cannot be used as an absolute descriptor for a ligand, but only in the context of receptor, reference ligand, and pathways.

Terminology summary B
Ligand bias: Ligand-dependent preferential receptor activation so that one over other transducer pathways in a given cellular system and relative to a reference ligand is induced (see below).
System bias: Bias due to differences in the cellular system, including so called 'tissue bias'. System bias can, for example, arise from differential expression levels of receptors, transducers, effectors, or modulatory proteins.
Functional selectivity: Functional selectivity is the observed response combining ligand-and system-bias.
Observational bias: An artificial bias introduced by the experimental set-up. This effect can be minimized, for example, by using equally sensitive assays across pathways.

| CHOOSING A REFERENCE LIGAND
3.1 | The choice of reference ligand distinguishes benchmark-, pathway-, or physiology-bias The meaning of ligand bias depends on the choice of reference ligand (Table 1). When the reference ligand is a tool compound, for example, isoprotenerol/isoprenaline for the β 2 -adrenoceptor) or a drug (e.g. to investigate if an agent in a clinical trial is different from this drug), we refer to this as ligand benchmark-bias, as all that can be concluded is how the ligands differ in bias. In contrast, ligand pathway-bias utilizes the reference ligand with the most balanced signalling across pathways, allowing the researcher to identify ligands for or functionally dissect a specific pathway.
Finally, ligand physiology-bias utilizes the principal physiological agonist as the reference ligand and consequently can conclude whether the tested ligands induce 'natural' or 'unnatural' signalling.
Thus, the three types of ligand bias are complementary in that they allow different scientific conclusions to be drawn for the tested ligands.
As an example, the choice of reference ligand changes the preferred pathway of the κ-opioid receptor agonist naphthoylβ-naltrexamine (β-NNTA) (White et al., 2014). Using the endogenous ligand dynorphin-A as the reference, β-NNTA has a 4.3-fold bias for β-arrestin recruitment over G protein activation. However, when using the plant psychedelic salvinorin A, β-NNTA displays a 8.9-fold G protein bias instead. When using an analogue, salvinorin B, β-NNTA still has a G protein bias but less pronounced, only 2-fold.

| Problems and pitfalls
• Bias cannot be reported without the use of a reference ligand, as a presumed bias may otherwise in fact be due to other differences in, for example, the baseline, window, and sensitivity of assays ('system bias' or 'observational bias' in Figure 1).
• It cannot be concluded that a tested ligand induces isolated/ specific signalling via one pathway unless a balanced ligand is used as reference. In all other cases, the only conclusion is that the tested ligand differs from the reference. This is because the reference ligand may itself have pathway-bias, making a tested balanced ligand appear biased in comparison. ii. The claims should be restricted to what is supported by the chosen reference ligand (Table 1). Thus, in order to conclude on pathway-and physiology-bias, a balanced and physiological reference ligand, respectively, must be used.
iii. Define the bias type. Instead of just 'ligand bias', we recommend using the specific terms 'ligand benchmark-bias', 'ligand pathway-bias', and 'ligand physiology-bias' when the reference ligand is a drug/probe, balanced and physiological ligand, respectively. Although the person who generated the data may only be interested in one type of bias, the community understands the meaning of bias differently.

| Disclaimer
Assay/system dependence: If the system or assays change, the same ligands, receptor, and pathways can display a different bias. This is because a reference ligand does not automatically equalise an imbalance in pathways but merely moves the baseline. Imbalance in pathways is instead predominantly determined by the choice of systems and assays, which should be as similar as possible.

Terminology summary C
Biased ligand: Ligand preferentially activating one receptor transducer pathway in a given cellular system and relative to a reference ligand. Ligand bias is a property of not just a ligand, but of a ligand, pathway pair and receptor in combination, and only valid within the specific system that is investigated. Therefore, the term 'biased ligand' should only be used if explicitly defined, and not be construed to represent a ligand-only property. A recommended definition is included in Section 8.1, which provides one-sentence and A biased ligand for which the reference ligand was not selected based on specific signalling pathway qualities has bias only relative to the reference ligand, which in turn can elicit any bias.

Ligand pathway-bias
Pathway-balanced ligand Signalling preferentially via one pathway, as the reference ligand approximates a pathway-balanced signal.
A pathway-balanced/unbiased ligand can be physiology-biased, although it is by definition unbiased in the pathway definition. A balanced ligand in one system may not be 'balanced' in another (applies to all types of ligand bias).

Ligand physiology-bias
Principal endogenous agonist Signalling differs from the physiological, as the reference ligand represents the endogenous response of the given receptor and system.
An endogenous agonist can be pathway-biased, although it is by definition unbiased in the physiological definition.
Note: The terms 'ligand pathway-bias' and 'ligand physiology-bias' are recommended when researchers wish to attribute a specific function (in addition to just a difference to the reference ligand employed).

| Ligand pathway-bias (uses a pathwaybalanced reference ligand)
Ligand pathway-bias is the most relevant type of ligand bias when seeking to discover a drug with functional selectivity, as it describes predominant signalling via one pathway. A pathwaybalanced reference ligand (typically a surrogate, but can also be an endogenous ligand) has near equal signalling through compared pathways, meaning that if a tested ligand is biased, it preferentially activates one pathway over the other (Table 1).

| Problems and pitfalls
Lacking reference ligands: When no balanced ligand is available, bias can still be measured but not interpreted as pathway-bias.
In these cases, it can be useful to visualize the relative pathway preferences of the most interesting tested ligands in a bias plot ( Figure 2).

| Recommendations for experimental design
a. Determine a pathway-balanced reference ligand in a bias plot.
A balanced ligand can be defined based on a bias plot, which is an equimolar comparison of induced pathway activities ( Figure 2). A balanced (least biased) ligand has a slope of (close to) 1.

| Reporting recommendations
i. Document the pathway-balance of the reference ligand. For example, present a bias plot (Figure 2) or reference a previous supporting study, if available.

| Disclaimer
Context specific bias: Many researchers argue, rightly so, that a ligand referred to as balanced will not be balanced in another system or assay. Therefore, any claim of a ligand being balanced and tested ligands exhibiting pathway-bias is always system-specific (which is true for all ligand bias studies). See also the Section 5.1.

Terminology summary D
Balanced ligand: Has indistinguishable or very similar signalling through compared pathways ( Figure 2).
Ligand pathway-bias: Ligand bias that is measured relative to a balanced reference ligand and therefore has the meaning that signalling is predominant via one pathway.

| Ligand physiology-bias (uses the principal endogenous ligand as reference)
When using an endogenous ligand as a reference ligand, the statement that a tested ligand is biased carries the meaning that its signalling differs from the physiological one (Table 1). This is valuable, for example, when seeking to map the relationships between pathways and physiological effects. Whether this entails a response through a single pathway or not will therefore depend on the extent to which the endogenous ligand engages different transducer pathways or not, in the first place.

| Problems and pitfalls
• The same receptor often has multiple endogenous agonists and depending on which one is used as the reference for a ligand physiology-bias study, the measured bias may change substantially.
• A secondary endogenous agonist can be biased relative to the principal endogenous agonist.
• Ligand physiology-bias cannot be determined for 'orphan' GPCRs, as they have no known endogenous ligand.
• Endogenous ligands with low efficacy cannot be quantified for bias (see Section 7.1).
F I G U R E 2 Bias plot of an equimolar comparison of ligandinduced activities in two pathways. The plot is adapted from White et al., (2014)

| Problems and pitfalls
• If a single representative transducer is used, it may not be representative for the signalling behaviour of the entire transducer family (e.g. G q , G 11 , G 14 , and G 15 in the G q/11 family). Differential activation or recruitment of individual transducer family members has been shown both for G protein families (Avet et al., 2021;Inoue et al., 2019;Namkung et al., 2018;Olsen et al., 2020) and the arrestin family (Avet et al., 2021;Srivastava, Gupta, Gupta, & Shukla, 2015).
• Different cellular systems can express transducers differentially.
Hence, even measurements of transducer-levels, albeit devoid of signalling amplification, can differ across cells and tissues.
• Assays are often only referred to by their detection method, for example, 'BRET assay'. This is insufficient, as the same detection technique can be used to measure fundamentally different molecules and processes, such as GPCR-G protein binding/coupling or   to distinguish such signalling processes is visualized in Figure 3a and tabulated in Table 2 along with example assay principles.

| Ligand bias measured downstream of distinct transducers
ii. Report the measured molecules. To provide clarity on what has been measured, we recommend that the pathways are defined not only for the upstream GPCR-binding transducer but also for the downstream measured molecule or molecule pair ( can also lead to the generation of these second messengers and activation of this kinase (Dorn, Oswald, McCluskey, Kuhel, & Liggett, 1997).
• The use of recombinant and/or overexpressed receptor, transducer, or effector proteins may not reflect the ligand bias in a native system. Insofar as ligand bias quantification can be carried out in a recombinant system, it serves to identify bias as a property of the molecule (not the system) and still serves as an identifier for molecular mechanisms to be elucidated further in natural systems.
• Some signalling pathways are difficult to evaluate in tissues (i.e. arrestin recruitment) as many assays require genetically modified tags to be applied to receptors and/or effectors. If modifications are made to proteins in endogenous systems, those modifications must be clearly indicated, and interpretations should consider the possible effects of overexpression and modification on the system.

| Recommendations for experimental design
a. Where possible, use primary and/or disease-specific cells and evaluate potential system bias.
b. Where possible, validate the effect in a model organism using an appropriate model of efficacy and/or genetic engineering to confirm target and pathway specificity.

| Reporting recommendations
i. Where a non-native system has been used, conclusions should be stated carefully such that a ligand inducing bias in this system may or may not have a beneficial effect in a therapeutically relevant system in vivo.

| Problems and pitfalls
• Ligand bias often depends on the time points of data collection. • A ligand's effect is time-dependent, as signalling efficacy changes (e.g. due to desensitization), which can be pathway-dependent (Stout, Clarke, & Berg, 2002

| Reporting recommendations
i. Report the chosen time points and the reason(s) why they were chosen.
ii. Provide data on the complete time course, if available.

| Reporting recommendations
i. Report the biosensors and tags used for monitoring compartment-specific signalling.
ii. Report the cell types used in assays, as some cell types lack transporters, such as OCT3/SLC22A3, required for the trafficking of hydrophilic small-molecule ligands that cannot cross the plasma membrane (Irannejad et al., 2017).
iii. Report ligands with altered characteristics, for example, permeability, through chemical modification (Jensen et al., 2017). 6 | HOW TO QUANTIFY LIGAND BIAS

| Many models exist to quantify ligand bias
The classical theory of receptor action (Furchott, 1966;Stephenson, 1956) aims to separate ligand-and system-dependent parameters and forms a basis of strategies to quantify ligand bias. Many methods to quantify ligand bias are based on the null principle of equi-effective concentrations of agonist producing equal responses, first described by Barlow, Scott, and Stephenson (1967) and theoretically justified within the framework of the Black-Leff operational model (Black & Leff, 1983

| Problems and pitfalls
• Quantification of ligand bias with different methods can in some cases lead to different conclusions on the biased/unbiased nature of a ligand (and system) (Onaran et al., 2017;Rajagopal et al., 2011) or to a different relative bias rank order of ligands.
• No model can provide an absolute bias value of a given ligand at a given receptor. Only bias values relative to a reference ligand are accessible with current quantification techniques.
6.1.2 | Recommendations for experimental design a. It is not possible to recommend a single, best-practice, quantification method due to the lack of consensus in the community, with new or refined methods still being proposed.
b. Results are more definitive when bias is quantified using multiple models.
c. Irrespective of the model, error propagation and statistics must be handled appropriately.
Further reading: We may refer readers to Table 4 in Kenakin (2019) for an overview of methods to quantify ligand bias, and Onaran and Costa (2021) for a critical review of the detailed principles, on which specific implementations are based.

| A minimal and a refined model to calculate a ligand bias factor
The two most commonly used models to quantify ligand bias are relative-relative Log (E max /EC 50 ) (Ehlert, 2008) and relative-relative Log(τ/K A ) (Box 1, Kenakin, 2015b c. Confirm that the calculated bias factors are consistent with a bias plot ( Figure 2). There is unlikely to be bias in a system if it cannot be visualized in a bias plot. Although a bias plot only provides a qualitative assessment of bias, it does not have the weakness of relying on fit parameters that can have artificially low errors from fitting routines.

| Reporting recommendations
i. Report ligand E max , EC 50 , and τ/K A values and system maximum response, Em. We recommend authors to tabulate the underlying quantitative values of the pharmacological parameters.
Reporting these values will improve clarity, increase transparency and future-proof cross-study comparisons. It also allows the calculation and presentation of multiple models of bias or to change the choice of reference ligand to facilitate comparison across studies.
ii. Report the ligand concentration-response curve slope factors (Hill coefficients). This information is critical, especially when ΔΔLog (E max /EC 50 ) is used to assess bias, as slope factors deviating from 1 indicate the extent to which system bias contributes to the calculated bias factors.
iii. Report a bias plot for biased ligands. This will serve to validate quantified ligand bias in a qualitative and visualized manner.
Caution: A large degree of caution is advisable for describing ligands with only weak bias (low bias factor) or absolute efficacy (low E max ), as these compounds are more likely to produce system-dependent bias effects (see Section 7.1). Such agonists are therefore more likely to be spuriously identified as biased, as both methods outlined above rely on best-fit parameters. Weak partial agonists will result in relatively poor fits (but still with excellent R 2 ) with E max /EC 50 or τ/K A values that grossly underestimate the errors of the derived bias factors. One can use a bias plot to confirm non-quantitatively that bias exists between two compounds, but one should never rely on bias factors alone.
6.3 | Comparing ligand bias across studies and systems (use of rank orders)

| Problems and pitfalls
Bias values obtained from different experimental systems are not comparable on a quantitative level. For example, a bias value above 2.0 in one system may be below 2.0 for the same pathways when studied in another system differing by, for example, cell line, measured molecules or process (Figure 3). Achieving a more consistent assessment of which ligand is the most biased towards a given pathway is important to identify functionally selective probes that can be used to dissect a distinct effect. This provides information about which pathways should be targeted or avoided in the design of drugs with higher efficacy and fewer side effects.
6.3.2 | Recommendations for experimental design a. We recommend using ligand rank orders of bias factors (rather than quantitative bias values) for comparisons of ligand bias across studies using different experimental systems. Only the pathways studied in both studies/systems can be compared, and the pathway ranks must be identical (i.e. the strongest, second strongest, etc. pathway).
Disclaimer: The relative ligand bias rank orders may also differ across systems ( Figure 3b). However, they differ less than detailed quantitative values.

| Low efficacy agonists
Ligand bias quantification should be considered together with the quantified strength of intrinsic efficacy of ligands in the relevant signalling pathways. This is because organ sensitivity and relative intrinsic efficacy differences have a large influence on a low-efficacy (for some pathways) ligand, which may appear to be very functionally selective in some organs. However, a signal may 'appear' in a more sensitive organ, or in the same organ at a different physiological state.
7.1.1 | Problems and pitfalls a. Too low efficacy in one pathway will result in bias factors with very large errors.
b. The response may be stronger in more sensitive tissues or assays. acts as an inverse agonist in two pathways compared for bias, a bias factor can, in theory, be calculated in the same way as for agonists, but using an inverse agonist reference. As most endogenous ligands are agonists, this typically precludes quantification of ligand physiology-bias (exceptions exist, for example, agouti is a rare example of an endogenous inverse agonist).
Further reading: Methods to quantify bias for inverse agonists are beyond the scope of this paper, as they involve differences in agonist-mediated and constitutive activity-mediated efficacy (Ehlert, Suga, & Griffin, 2011). It is known that constitutively active receptors themselves possess an efficacy that can be different from agonist-mediated efficacy. This is manifested as the phenomenon of protean agonism whereby a low efficacy partial agonist demonstrates positive agonism in quiescent systems and inverse agonism in constitutively active systems (Chidiac, Nouet, & Bouvier, 1996;Kenakin, 1997). This is because the agonist-mediated active state is of lower efficacy than the constitutively active state. Such phenomena must be considered to ascribe an efficacy to an inverse agonist.

| Problems and pitfalls
When agonism and no agonism (neutral antagonism or inverse agonism), respectively, are observed in two pathways compared, it is not possibly to quantify bias using the above models. This is because calculation of a quantitated bias factor requires two concentration-response curves with the same modality (agonism or inverse agonism).

| Reporting recommendations
i. Ligand bias with opposite modalities across pathways can be described as a non-quantitative term, 'modality bias'. In this case, there is no need to quantify bias to claim bias.
ii. Alternatively, it can be approximated by measuring an affinity to limit bias or describe it in a 'bias is larger than' relationship (Kenakin, 2015a;Stahl, Ehlert, & Bohn, 2019;Stahl, Zhou, Ehlert, & Bohn, 2015). Specifically, the affinity (determined from functional antagonism) is used to determine receptor occupancy and a very low level of efficacy is assumed to generate a simulated curve (i.e. maximal response of 5%) which is then used to calculate bias. This yields the lowest possible bias (it could be greater than this if the efficacy is lower than the assumed one giving 5% maximal response).
Caution: Although a very low ligand efficacy cannot be detected in one system, it may be detected in another functional system with higher sensitivity. Hence, the statement should be understood as a practical qualification, in the sense that the efficacy of the ligand is close to zero within the detection limits of the given system. That is, some partial agonists may appear to be neutral antagonists.

Terminology summary I
Modality bias: Ligand with efficacy in only one of the compared pathways (neutral antagonist or inverse agonist in others). As for all biased ligands, this refers to a particular system and in another more sensitive system, there may be efficacy at several compared pathways allowing the determination of concentration-response curves. We discourage the use of the previous terms 'full bias' or 'perfect bias', as they can misleadingly give the impression that the ligand always has full efficacy in the preferred pathway. In fact, a ligand with modality bias may give a weaker activation than other agonists for which bias can be quantified (as they induce agonism in at least two pathways).

| Allosteric modulators affecting ligand bias
Pure allosteric modulators will modulate the orthosteric ligand binding and/or efficacy, whereas ago-allosteric modulators also induce signal- All the above information is necessary to interpret ligand bias correctly and to separate it from system bias ( Figure 1). Hence, the mention of a 'biased' or 'unbiased' ligand as short terms are only meaningful after the prior definition of this information.

| Problems and pitfalls
Ligand bias will not be reproducible and understood consistently without sufficient information about the ligands, pathways and systems.
Such ambiguity hampers consensus and advances in the field.

| CONCLUSION AND OUTLOOK
Adopting the guidelines presented here will facilitate consensus and advances in the GPCR field through increased quality, reproducibility and clarity of ligand-induced bias. More precise experiments will improve individual studies and make them more useful for others, as data become more comparable. Furthermore, the unambiguous and structured reporting of bias data will be directly reflected in more relevant databases and literature reviews.
To unlock the rational targeting of GPCRs by exploiting ligand bias to achieve functional selectivity, we must first map individual or combined pathways to their distinct effects. This requires identification of many more probes inducing bias, pharmacological assays, animal models, and so on. This represents a tremendous challenge, but one with extraordinary potential. The recommendations herein can contribute to this massive endeavour by better description of such probes and effects. We have deliberately left the door open to include additional intracellular transducers, effectors, or modulators involved in GPCR signalling, and our definitions should therefore be future-proofed, in the sense that they can be applied also to signalling proteins that have yet to be discovered.
Ligand-dependent biased function is also being investigated for other protein classes, for example, receptor TKs (Karl, Paul, Pasquale, & Hristova, 2020), nuclear receptors (Heidari et al., 2019), monoamine transporters (Hasenhuetl, Bhat, Freissmuth, & Sandtner, 2019), Tolllike receptors (unpublished), and cytochrome P450 enzymes (Jensen et al., 2021). Many of the recommendations brought forward in this paper are also applicable to these protein families, which would in time benefit from publication of additional dedicated guidelines.

| Nomenclature of targets and ligands
Key protein targets and ligands in this article are hyperlinked to corresponding entries in https://www.guidetopharmacology.org, and are permanently archived in the Concise Guide to PHARMACOLOGY 2021/22 . ii. If possible, dissect upstream transducer contributions using other assays.
Considering cellular system, kinetics and spatial bias 5.1 Bias may not translate across in vitro, physiological, and therapeutic systems a. Where possible, use primary and/or disease-specific cells and evaluate potential system bias. b. Where possible, validate the effect in a model organism using an appropriate model of efficacy, and/or genetic engineering to confirm target and pathway specificity.

Kinetics and choosing measurement time points
a. When possible, complete time courses and endpoint measurements should be made.
b. Single time points should be the physiologically most relevant or the measure maximum effect.
How to quantify ligand bias 6.1 Many models exist to quantify ligand bias a. It is not possible to recommend a single best practice quantification method.
b. Results are more definitive when bias is quantified using multiple models.
c. Irrespective of the model, error propagation and statistics must be handled appropriately.
6.2 A minimal and a refined model to calculate a ligand bias factor a. ΔΔLog (Emax/EC 50 ) should not be used when ligand concentration-response-curve slope factors (Hill coefficients) are not close to 1.
c. Confirm that the calculated bias factors are consistent with a bias plot.
6.3 Comparing ligand bias across studies and systems (use of rank orders) a. We recommend using ligand rank orders of bias factors (rather than quantitative bias values) for comparisons of ligand bias across studies using different experimental systems.
Special recommendations for 'tricky' ligands 7.1 Low efficacy agonists a. Use ligand pathway rank orders.
b. If quantifying bias, use another reference agonist for Emax.

Inverse agonists
• Use another inverse agonist as the reference ligand.
7.4 Allosteric modulators affecting ligand bias a. Quantification of allosteric modulator bias should make use of an extended operational model of allosterism.

APP E NDIX B : Reporting recommendation checklist
Choosing a reference ligand 3.1 The choice of reference ligand distinguishes benchmark-, pathway-or physiology-bias i. Report the reference ligand along with a motivation to why it was chosen.
ii. The claims should be restricted to what is supported by the chosen reference ligand.
iii. Define the bias type.
3.2 Ligand pathway-bias (uses a pathway-balanced reference ligand) i. Document the pathway-balance of the reference ligand.
Measuring at the transducer or downstream 4.1 Ligand bias measured at the transducer level i. Modifications of receptors, transducers or effectors must be clearly defined (e.g., tags, mutations, and chimeras). 4.2 Ligand bias measured downstream of distinct transducers i. Report measured processes.
ii. Report the measured molecules.
4.3 Ligand bias measured downstream of converged transducer pathways i. When pathways are truly inseparable and their contributions cannot be dissected using upstream assays, the bias may be described as a type of 'effector bias' (instead of pathway-bias) accounting for the net pathway contributions.
ii. Interpret findings in light of their relative strength for the given receptor and ligand.
Considering cellular system, kinetics and spatial bias 5.1 Bias may not translate across in vitro, physiological, and therapeutic systems • Where a non-native system has been used, conclusions should be stated carefully.

Kinetics and choosing measurement time points
i. Report the chosen time points and the reason(s) why they were chosen.
ii. Provide data on the complete time course, if available.

Spatial bias: differing signaling efficacies across cellular compartments
i. Report the biosensors and tags used for monitoring compartment-specific signaling.
ii. Report the cell types used in assays.
iii. Report ligands with altered characteristics, e.g., from chemical modification.
How to quantify ligand bias 6.2 A minimal and a refined model to calculate a ligand bias factor i. Report ligand Emax, EC 50 and τ/K A values and system maximum response, Em.
ii. Report the ligand concentration-response-curve slope factors (Hill coefficients).
iii. Report a bias plot for biased ligands.
Special recommendations for 'tricky' ligands 7.3 Agonist and antagonist across pathways ('modality bias') i. Ligand bias with opposite modalities across pathways can be described as a non-quantitative term, 'modality bias'.
ii. Alternatively, it can be approximated by measuring an affinity to limit bias or describe it in a 'bias is larger than' relationship.

Allosteric modulators affecting ligand bias
i. Report both the allosteric modulator and orthosteric ligand.
Publication and database deposition 8.1 Unambiguous description of ligand bias i. Define the ligand evoking bias and its system in a clear sentence.
ii. Tabulate the experimental details required for unambiguous description (Table 3) iii. Deposit biased ligands in a database.