Quantification of Ligand Bias for Clinically Relevant β2-Adrenergic Receptor Ligands: Implications for Drug Taxonomy

The concepts of functional selectivity and ligand bias are becoming increasingly appreciated in modern drug discovery programs, necessitating more informed approaches to compound classification and, ultimately, therapeutic candidate selection. Using the beta 2-adrenergic receptor as a model, we present a proof of concept study that assessed the bias of 19 beta-adrenergic ligands, including many clinically used compounds, across four pathways [cAMP production, extracellular signal-regulated kinase 1/2 (ERK1/2) activation, calcium mobilization, and receptor endocytosis] in the same cell background (human embryonic kidney 293S cells). Efficacy-based clustering placed the ligands into five distinct groups with respect to signaling signatures. In some cases, apparent functional selectivity originated from offtarget effects on other endogenously expressed adrenergic receptors, highlighting the importance of thoroughly assessing selectivity of the responses before concluding receptor-specific ligand-biased signaling. Eliminating the nonselective compounds did not change the clustering of the 10 remaining compounds. Some ligands exhibited large differences in potency for the different pathways, suggesting that the nature of the receptor-effector complexes influences the relative affinity of the compounds for specific receptor conformations. Calculation of relative effectiveness (within pathway) and bias factors (between pathways) for each of the compounds, using an operational model of agonism, revealed a global signaling signature for all of the compounds relative to isoproterenol. Most compounds were biased toward ERK1/2 activation over the other pathways, consistent with the notion that many proximal effectors converge on this pathway. Overall, we demonstrate a higher level of ligand texture than previously anticipated, opening perspectives for the establishment of pluridimensional correlations between signaling profiles, drug classification, therapeutic efficacy, and safety.