Biochemistry · 2025
Biased Signaling and Its Role in the Genesis of Short- and Long-Acting β2-Adrenoceptor Agonists.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Phan Ngan T N, Borrega-Roman Leire, Hoare Bradley L, Harwood Clare R, Geary Natalie, Guba Wolfgang, Han Yongqi, Zenko Vladimirs, Koers Eline J, Rufer Arne C, Grether Uwe, Veprintsev Dmitry B, Sykes David A
Аннотация
Drug discovery is a costly and time-intensive process that is often limited by efficacy issues and unforeseen side effects. GPCR-targeting ligands, which account for one-third of marketed drugs, have been shown to exhibit biased signaling and preferential activation of one signaling pathway over another. While designing biased ligands is a recent advancement, their therapeutic benefits remain uncertain. However, the success of existing drugs raises the following question: do they inherently exhibit signaling bias that enhances efficacy or safety? This study examines the signaling profiles of short- and long-acting β2AR agonists (SABAs and LABAs), key treatments for asthma and COPD, using biosensors to measure G protein and β-arrestin coupling. Older SABAs, such as isoprenaline and isoetharine, show minor G protein bias, while newer agents, such as salbutamol, demonstrate significant G protein bias. Among LABAs, salmeterol shows greater G protein bias compared to that of the more balanced formoterol. This shift toward G protein bias over 50 years reflects efforts to improve asthma treatments. The increased bias results from reduced ligand-receptor residence times and weaker receptor-β-arrestin complex formation, contributing to the enhanced efficacy. Despite the potential advantages, a systematic evaluation of signaling bias remains underutilized in drug development. Early-stage, high-throughput tools to assess signaling profiles could improve candidate selection, reduce late-stage failures, and minimize side effects. We advocate for the routine integration of biosensors for quantifying signaling bias, optimizing compound selection, and enhancing therapeutic outcomes.