Selective Signal Capture from Multidimensional GPCR Outputs with Biased Agonists: Progress Towards Novel Drug...

G protein coupled receptors (GPCRs) are a superfamily of transmembrane-spanning receptors that are activated by multiple endogenous ligands and are the most common target for agonist or antagonist therapeutics across a broad spectrum of diseases. Initial characterization within the superfamily suggested that a receptor activated a single intracellular pathway, depending on the G protein to which it coupled. However, it has become apparent that a given receptor can activate multiple different pathways, some being therapeutically desirable, while others are neutral or promote deleterious signaling. The activation of pathways that limit effectiveness of a primary pathway or promote unwanted signals has led to abandonment of some GPCRs as drug targets. However, it is now recognized that the conformation of the receptor in its ligand-bound state can be altered by the structure of the agonist or antagonist to achieve pathway selectivity, a property termed biased signaling. Biased ligands could dramatically expand the number of novel drugs acting at GPCRs for new indications. However, the field struggles with the complexity and uncertainty of these structure-functions relationships. In this review we define the theoretical underpinnings of the biased effect, discuss the methods for measuring bias, and the pitfalls that can lead to incorrect assignments of bias. Using the recent elucidation of a β₂-adrenergic receptor agonist that is biased in favor of Gs coupling over β-arrestin binding, we provide an example of how large libraries of compounds that are impartial to preconceived notions of agonist binding can be utilized to discover pathway-specific agonists. In this case, an agonist that lacks tachyphylaxis for the treatment of obstructive lung diseases was uncovered, with a structure that was distinctly different from other agonists. We show how biased characteristics were ascertained analytically, and how molecular modeling and simulations provide a structural basis for a restricted signaling repertoire.