Identifying and Measuring Bias
There are simple in vitro techniques that can be used to detect and quantify bias but the most reliable is to simply express two signaling responses for an agonist for the same receptor as functions of each other; this is a ‘bias’ plot and clearly indicates bias without models or equations. For example, Fig 1 shows the obviously different bias of four Κ-opioid agonists for G-protein and β-arrestin response; the data indicate that these agonists stabilize two distinctly different active receptor conformations, one preferring G proteins and one preferring β-arrestin (White et al, 2014). For exploitation of biased signaling in discovery programs, models have been derived to put numbers on the magnitude of bias to allow medicinal chemists to systematically modify chemical scaffolds (Onaran et al, 2017; Kenakin, 2019; Kolb et al, 2022). Basically these procedures identify exemplar agonists as being special, the assumption being that they stabilize a unique active receptor state conformation. When these effects are seen, there are important criteria that must be met before biased signaling is considered seriously.
  1. Lack of response does not necessarily signify bias but rather only that the agonist efficacy is insufficient to demonstrate where along the concentration axis of a dose-response (DR) curve the response occurs in a particular assay. Therefore, a measurable CR curve must be observed to accurately determine bias.
  2. If no response in a particular pathway is observed for a molecule (appearance of ‘perfect’ bias) it should be tested as an antagonist of other molecules that do activate the pathway to assess the interaction with the receptor and determine affinity.
  3. Bias has no translatable meaning in itself but only as a ratio to another agonist, i.e. bias always refers to a comparison to another agonist, usually the natural agonist.
  4. Potency ratios must be used as this cancels measurement bias caused by differences in the sensitivities of different functional assays, i.e. second messenger assays are more sensitive than BRET assays. In this regard, the separation between two pathway DR curves does not designate the relative concentrations producing effect in vivo ; this occurs over the same concentration range but just with different magnitudes.
Finally, it should be recognized that ‘bias’ does not designate whether or not selective agonism will be seen; this is determined by the intrinsic efficacy of the agonist. However, when the system is sensitive enough and/or when the agonist has sufficient efficacy, bias determines the relative strength of signal for a given pathway when agonism is observed.
It should be recognized that assays identifying biased signaling, whether it be therapeutically applicable or not, identify an important agonist species, i.e. an agonist that produces a unique active state receptor of the receptor. This is the important deliverable of anin vitro bias assay but the selected pathways used to detect the receptor conformations may not be the relevant pathways for therapeutic bias. In essence, the in vitro detection bias measurements can be considered a method of detecting a harbinger of complex signaling heterogeneity to come that eventually may culminate in therapeutically beneficial bias. The main utility of bias scores is that they enable classification of new agonists for further testing in vivo and once bias is identified at the level of the receptor, it can be considered a rogue property and a unique uncontrolled signal initiator that then imparts a unique activation pattern on cells.
In terms of thermodynamics, the complete description of a ligand activity on a receptor must include both affinity and efficacy and the operational model index of Log(τ/KA) fulfils that criterion (Kenakin et al, 2012). However, while bias can arise through differences in either affinity or efficacy (or both), the most robust differences come from differences in efficacy (Rajagopal et al, 2011). This is in keeping with the fact that agonism is much more resistant to changes in tissue sensitivity when it is based on high efficacy as opposed to high affinity (Kenakin, 1984).
From it’s inception, bias has been considered a binary readout, i.e. if cyclic AMP and β-arrestin assays are used to discern signaling, compounds would fall into one of two groups (or perhaps a third that did not discern pathways). However, these are crude indicators of a more sophisticated phenomenon, namely the stabilization of a unique receptor active state and this active state could go on to selectively interact with a number of other proteins in the cell to show further differentiations. Therefore, from the first binary test of agonism in two pathways, a further series of experiments could be done to delineate finer texture in the signaling produced by the molecule.
Currently, sophisticated readouts of receptor activation of signaling components are being used to detect and quantify bias in ways that go beyond a binary readout. For example, measuring biased signaling through monitoring G protein subunits allows estimation of imbalances in neurotransmission (Park et al, 2023). New assays have been developed to determine micro-interactions of receptors with the complete complement of G proteins in a cell (Olsen et al, 2020). The application of pathway-selective bioluminescence resonance energy transfer (BRET) biosensors that monitor the engagement and activation of signaling effectors downstream of G proteins (including phospholipase C (PLC), p63RhoGEF, protein kinase C (PKC), and Rho), allow clustering of compounds into different subfamilies of biased ligands. These effects, in turn, show bias among G protein subtypes, in terms of not only functional selectivity between Gαq and β-arrestin but, interestingly, bias through the engagement of different G proteins to activate a common effector. Through a suite of BRET biosensors, a fingerprint of angiotensin receptor agonism has the ability to provide a view of signaling at various levels of GPCR activation – see Fig 2 (Namkung et al, 2018). Nanoluciferase-based complementation assays also have been used to provide textured readouts of biased signaling (Laschet et al, 2019) and genetically-encoded fluorescent biosensors have been employed to illuminate spatiotemporal biased signaling (Kayser et al, 2023).