计算机药效学:h2 -抗组胺药不良反应与组胺n-甲基转移酶结合电位的相关性。

P K Vinod, Badireenath Konkimalla, Nagasuma Chandra
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引用次数: 5

摘要

在目前的临床实践中,大多数药物都表现出不良反应,其原因往往尚不清楚。在这个后基因组时代,生物信息学在理解药物作用机制和设计改进药物方面具有解决几个问题的潜力。本研究通过采用系统生物学方法的基本原则,描述了抗组胺药阻断组胺H(2)受体(H(2)-抗组胺药)可能的药效学行为分析。可以形成适当子系统的不同组件被识别出来,从而提供了一个系统景观。将选定的抗组胺药与这些成分对接和分析,结果确定组胺n -甲基转移酶(HNMT)是H(2)-抗组胺药的潜在非预期靶标。与文献中现有实验数据的相关性表明,抑制HNMT可能是这些药物表现出不良反应的原因。讨论了设计更安全的H(2)-抗组胺药的意义。本文报道的方法有可能作为理解药物作用的一般策略应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
In-silico pharmacodynamics: correlation of adverse effects of H2-antihistamines with histamine N-methyl transferase binding potential.

Adverse effects are exhibited by most drugs in current clinical practice, the causes for which are often not known. In this post genomic era, bioinformatics has the potential to address several issues in understanding the mechanism of drug action and in designing improved drugs. This study describes the analysis of the possible pharmacodynamic behaviour of antihistamines blocking the histamine H(2) receptor (H(2)-antihistamines), by adopting the basic tenets of a systems biology approach. The different components that could form an appropriate sub-system are identified, thus providing a system landscape. Docking and analysis of the chosen antihistamines into each of these components resulted in identifying histamine N-methyl transferase (HNMT) as a potential unintended target for H(2)-antihistamines. Correlation with experimental data available from the literature indicates the inhibition of HNMT to be a possible cause for the adverse effects exhibited by these drugs. Implications for design of safer H(2)-antihistamines are discussed. The method reported here has the potential for application as a general strategy in understanding drug effects.

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