Biased ligand quantification in drug discovery: from theory to high throughput screening to identify new biased opioid receptor agonists
Biased ligand quantification in drug discovery: from theory to high throughput screening to identify new biased opioid receptor agonists
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DOI：
10.1111/bph.13441
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发表时间：
2016-04-01
影响因子：
7.3
通讯作者：
Cawkill, Darren
中科院分区：
文献类型：
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作者：
Winpenny, David;Clark, Mellissa;Cawkill, Darren
背景和目的：GPCR配体能够以优先激活不同下游信号的方式与其靶受体结合，并为下一代治疗提供了潜力。然而，体外配体偏差的准确定量是复杂的，并且当前的最佳实践不适合于测试大量化合物。因此，我们试图将配体偏置理论应用于工业规模的筛选活动，以确定新的偏向受体agonists.Experimental Approach受体测定与适当的动态范围开发了G（i）依赖的信号和arrestin 2招聘。log（E-max/EC 50）分析被验证为计算对G（i）依赖性信号传导的途径偏倚的激动作用操作模型的替代。该分析被施加到一个高通量的屏幕，以表征的患病率和性质的途径偏见之间的一组不同的化合物与receptor agonist activity.Key ResultsA高通量筛选活动产生了440命中大于10倍的偏见相对于DAMGO。为了验证这些结果，我们使用激动作用的操作模型量化了命中子集的途径偏倚。这些有偏差的命中之间的高度相关性证实了日志（E-max/EC_（50））是一种合适的方法，用于在大量不同化合物中鉴定真正的偏向性配体。（E-max/EC50），药物发现可以大规模应用有偏配体定量的概念，并加速通过这种复合物作用的新型治疗剂的有意发现药理学
Background and PurposeBiased GPCR ligands are able to engage with their target receptor in a manner that preferentially activates distinct downstream signalling and offers potential for next generation therapeutics. However, accurate quantification of ligand bias in vitro is complex, and current best practice is not amenable for testing large numbers of compound. We have therefore sought to apply ligand bias theory to an industrial scale screening campaign for the identification of new biased receptor agonists.Experimental Approach receptor assays with appropriate dynamic range were developed for both G(i)-dependent signalling and -arrestin2 recruitment. log(E-max/EC50) analysis was validated as an alternative for the operational model of agonism in calculating pathway bias towards G(i)-dependent signalling. The analysis was applied to a high throughput screen to characterize the prevalence and nature of pathway bias among a diverse set of compounds with receptor agonist activity.Key ResultsA high throughput screening campaign yielded 440 hits with greater than 10-fold bias relative to DAMGO. To validate these results, we quantified pathway bias of a subset of hits using the operational model of agonism. The high degree of correlation across these biased hits confirmed that log(E-max/EC50) was a suitable method for identifying genuine biased ligands within a large collection of diverse compounds.Conclusions and ImplicationsThis work demonstrates that using log(E-max/EC50), drug discovery can apply the concept of biased ligand quantification on a large scale and accelerate the deliberate discovery of novel therapeutics acting via this complex pharmacology.