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分析药理学:数字如何引领药物发现

Analytical Pharmacology: How Numbers Can Guide Drug Discovery.

作者信息

Kenakin Terry

机构信息

Department of Pharmacology, University of North Carolina School of Medicine Chapel Hill, Chapel Hill, North Carolina 27516, United States.

出版信息

ACS Pharmacol Transl Sci. 2019 Jan 3;2(1):9-17. doi: 10.1021/acsptsci.8b00057. eCollection 2019 Feb 8.

Abstract

The unique ways in which pharmacological data compares to mathematical models are described. Examples show that insights into agonist action (prediction of agonism ) and antagonist mechanism of action (orthosteric vs allosteric) can be gained that assist in the candidate selection process for new drugs in drug discovery and development efforts. In addition, the impact of component processes on complex physiological systems can be delineated, such as the effects of the hepatic system on whole body clearance in pharmacokinetics and prediction of drug-drug interactions. Finally, models are instrumental in the procurement of universal drug parameters that can be used in medicinal chemistry-based structure-activity relationships. The revitalization of these ideas under the banner of "Analytical Pharmacology" may serve to re-emphasize these concepts over qualitative description and lead to a better foundation for drug discovery.

摘要

文中描述了药理学数据与数学模型进行比较的独特方式。实例表明,通过这些方式可以深入了解激动剂作用(激动作用预测)和拮抗剂作用机制(正构与变构),这有助于在药物研发过程中进行新药候选物的筛选。此外,还可以描绘组成过程对复杂生理系统的影响,例如肝脏系统在药代动力学中对全身清除率的影响以及药物相互作用的预测。最后,模型有助于获取通用的药物参数,这些参数可用于基于药物化学的构效关系研究。以“分析药理学”之名对这些理念的复兴,可能有助于在定性描述之上再次强调这些概念,并为药物发现奠定更好的基础。

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