Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia.
School of Pharmacy, Fudan University, Shanghai, China.
Nat Rev Mol Cell Biol. 2018 Oct;19(10):638-653. doi: 10.1038/s41580-018-0049-3.
G protein-coupled receptors (GPCRs) are the largest group of cell surface receptors in humans that signal in response to diverse inputs and regulate a plethora of cellular processes. Hence, they constitute one of the primary drug target classes. Progress in our understanding of GPCR dynamics, activation and signalling has opened new possibilities for selective drug development. A key advancement has been provided by the concept of biased agonism, which describes the ability of ligands acting at the same GPCR to elicit distinct cellular signalling profiles by preferentially stabilizing different active conformational states of the receptor. Application of this concept raises the prospect of 'designer' biased agonists as optimized therapeutics with improved efficacy and/or reduced side-effect profiles. However, this application will require a detailed understanding of the spectrum of drug actions and a structural understanding of the drug-receptor interactions that drive distinct pharmacologies. The recent revolution in GPCR structural biology provides unprecedented insights into ligand binding, conformational dynamics and the control of signalling outcomes. These insights, together with new approaches to multi-dimensional analysis of drug action, are allowing refined classification of drugs according to their pharmacodynamic profiles, which can be linked to receptor structure and predictions of preclinical drug efficacy.
G 蛋白偶联受体（GPCRs）是人类细胞表面受体中最大的一类，它们可以响应各种刺激并调节多种细胞过程。因此，它们是主要的药物靶点之一。我们对 GPCR 动力学、激活和信号转导的理解的进展为选择性药物开发开辟了新的可能性。一个关键的进展是偏性激动剂的概念，它描述了在同一 GPCR 上作用的配体通过优先稳定受体的不同活性构象状态来产生不同的细胞信号转导谱的能力。该概念的应用提出了“设计”偏性激动剂作为优化治疗剂的可能性，这些治疗剂具有提高的疗效和/或降低的副作用谱。然而，这一应用将需要对药物作用的范围有详细的了解，并对驱动不同药理学的药物-受体相互作用有结构上的理解。GPCR 结构生物学的最新革命为配体结合、构象动力学和信号转导结果的控制提供了前所未有的见解。这些见解，以及多维药物作用分析的新方法，使我们能够根据药物的药效学特征对药物进行精细分类，这可以与受体结构和临床前药物疗效的预测联系起来。
