Walkup Grant K, You Zhiping, Ross Philip L, Allen Eleanor K H, Daryaee Fereidoon, Hale Michael R, O'Donnell John, Ehmann David E, Schuck Virna J A, Buurman Ed T, Choy Allison L, Hajec Laurel, Murphy-Benenato Kerry, Marone Valerie, Patey Sara A, Grosser Lena A, Johnstone Michele, Walker Stephen G, Tonge Peter J, Fisher Stewart L
Infection Innovative Medicines Unit, AstraZeneca Research and Development, Waltham, Massachusetts, USA.
Institute for Chemical Biology and Drug Discovery, Department of Chemistry, Stony Brook University, Stony Brook, New York, USA.
Nat Chem Biol. 2015 Jun;11(6):416-23. doi: 10.1038/nchembio.1796. Epub 2015 Apr 20.
Many drug candidates fail in clinical trials owing to a lack of efficacy from limited target engagement or an insufficient therapeutic index. Minimizing off-target effects while retaining the desired pharmacodynamic (PD) response can be achieved by reduced exposure for drugs that display kinetic selectivity in which the drug-target complex has a longer half-life than off-target-drug complexes. However, though slow-binding inhibition kinetics are a key feature of many marketed drugs, prospective tools that integrate drug-target residence time into predictions of drug efficacy are lacking, hindering the integration of drug-target kinetics into the drug discovery cascade. Here we describe a mechanistic PD model that includes drug-target kinetic parameters, including the on- and off-rates for the formation and breakdown of the drug-target complex. We demonstrate the utility of this model by using it to predict dose response curves for inhibitors of the LpxC enzyme from Pseudomonas aeruginosa in an animal model of infection.
许多候选药物在临床试验中失败，原因是靶点结合有限导致疗效不足或治疗指数不够。对于那些表现出动力学选择性的药物，即药物-靶点复合物的半衰期比非靶点-药物复合物更长，通过减少其暴露量，可以在保留所需药效学（PD）反应的同时将脱靶效应降至最低。然而，尽管慢结合抑制动力学是许多上市药物的一个关键特征，但缺乏能将药物-靶点驻留时间整合到药物疗效预测中的前瞻性工具，这阻碍了药物-靶点动力学在药物发现流程中的整合。在此，我们描述了一个机制性的PD模型，该模型包含药物-靶点动力学参数，包括药物-靶点复合物形成和解离的结合和解离速率。我们通过在感染动物模型中使用该模型预测铜绿假单胞菌LpxC酶抑制剂的剂量反应曲线，证明了该模型的实用性。
