Imai Takeshi, Hayakawa Masayo, Ohe Kazuhiko
Center for Disease Biology and Integrative Medicine, Graduate School of Medicine, The University of Tokyo, Japan.
Stud Health Technol Inform. 2013;192:567-71.
Prediction of synergistic or antagonistic effects of drug-drug interaction (DDI) in vivo has been of considerable interest over the years. Formal representation of pharmacological knowledge such as ontology is indispensable for machine reasoning of possible DDIs. However, current pharmacology knowledge bases are not sufficient to provide formal representation of DDI information. With this background, this paper presents: (1) a description framework of pharmacodynamics ontology; and (2) a methodology to utilize pharmacodynamics ontology to detect different types of possible DDI pairs with supporting information such as underlying pharmacodynamics mechanisms. We also evaluated our methodology in the field of drugs related to noradrenaline signal transduction process and 11 different types of possible DDI pairs were detected. The main features of our methodology are the explanation capability of the reason for possible DDIs and the distinguishability of different types of DDIs. These features will not only be useful for providing supporting information to prescribers, but also for large-scale monitoring of drug safety.
多年来,预测体内药物相互作用(DDI)的协同或拮抗作用一直备受关注。诸如本体论之类的药理学知识的形式化表示对于可能的药物相互作用的机器推理是必不可少的。然而,当前的药理学知识库不足以提供药物相互作用信息的形式化表示。在此背景下,本文提出:(1)药效学本体的描述框架;(2)一种利用药效学本体检测不同类型的可能药物相互作用对并提供诸如潜在药效学机制等支持信息的方法。我们还在与去甲肾上腺素信号转导过程相关的药物领域评估了我们的方法,检测到了11种不同类型的可能药物相互作用对。我们方法的主要特点是能够解释可能的药物相互作用的原因,以及区分不同类型的药物相互作用。这些特点不仅有助于为开处方者提供支持信息,也有助于大规模监测药物安全性。