Drug Discovery, Pharmaceuticals, Bayer AG, Berlin, Germany.
Research and Development, Genedata AG, Basel, Switzerland.
Br J Pharmacol. 2019 Dec;176(24):4731-4744. doi: 10.1111/bph.14841. Epub 2019 Dec 26.
Target engagement dynamics can influence drugs' pharmacological effects. Kinetic parameters for drug:target interactions are often quantified by evaluating competition association experiments-measuring simultaneous protein binding of labelled tracers and unlabelled test compounds over time-with Motulsky-Mahan's "kinetics of competitive binding" model. Despite recent technical improvements, the current assay formats impose practical limitations to this approach. This study aims at the characterisation, understanding and prevention of these experimental constraints, and associated analytical challenges.
Monte Carlo simulations were used to run virtual kinetic and equilibrium tracer binding and competition experiments in both normal and perturbed assay conditions. Data were fitted to standard equations derived from the mass action law (including Motulsky-Mahan's) and to extended versions aiming to cope with frequently observed deviations of the canonical traces. Results were compared to assess the precision and accuracy of these models and identify experimental factors influencing their performance.
Key factors influencing the precision and accuracy of the Motulsky-Mahan model are the interplay between compound dissociation rates, measurement time and interval frequency, tracer concentration and binding kinetics and the relative abundance of equilibrium complexes in vehicle controls. Experimental results produced recommendations for better design of tracer characterisation experiments and new strategies to deal with systematic signal decay.
Our data advances our comprehension of the Motulsky-Mahan kinetics of competitive binding models and provides experimental design recommendations, data analysis tools, and general guidelines for its practical application to in vitro pharmacology and drug screening.
药物与靶点的结合动力学可影响药物的药理作用。药物与靶点相互作用的动力学参数通常通过评估竞争结合实验来量化——即随着时间的推移，同时测量标记示踪剂和未标记测试化合物与蛋白质的结合情况——采用 Motulsky-Mahan 的“竞争结合动力学”模型。尽管最近技术有所改进，但当前的测定方法仍然存在实际限制。本研究旨在研究和预防这些实验限制及其相关分析挑战，并对其进行特征描述和理解。
使用蒙特卡罗模拟在正常和受干扰的测定条件下运行虚拟动力学和平衡示踪剂结合与竞争实验。数据采用标准方程进行拟合，这些方程源自质量作用定律（包括 Motulsky-Mahan 的方程）和扩展版本，旨在应对经常观察到的标准曲线偏离情况。结果进行比较，以评估这些模型的精密度和准确性，并确定影响其性能的实验因素。
影响 Motulsky-Mahan 模型精密度和准确性的关键因素包括化合物解离速率、测量时间和间隔频率、示踪剂浓度和结合动力学之间的相互作用，以及载体对照中平衡复合物的相对丰度。实验结果提出了更好地设计示踪剂特征测定实验的建议，并提供了新的策略，以应对系统信号衰减。
我们的数据增进了对 Motulsky-Mahan 竞争结合动力学模型的理解，并提供了实验设计建议、数据分析工具以及其在体外药理学和药物筛选中的实际应用的一般指南。
