Assiri Abdullah, Noor Adeeb
Department of Clinical Pharmacy, College of Pharmacy, King Khalid University, Abha 62529, Saudi Arabia.
Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 80221, Saudi Arabia.
Saudi Pharm J. 2020 Dec;28(12):1507-1513. doi: 10.1016/j.jsps.2020.09.017. Epub 2020 Sep 29.
Drug-drug interactions (DDIs) are a potentially distressing corollary of drug interventions, and may result in discomfort, debilitating illness, or even death. Existing research predominantly considers only a single level of interaction; however, serious health complications may result from multi-pathway DDIs, and so new methods are needed to enable predicting and preventing complex DDIs. This article introduces a novel method for the prediction of DDIs at two pharmacological levels (metabolic and transporter interactions) by means of a rule-based model implemented with Semantic Web technologies. The chemotherapy agent irinotecan is used as a case study for demonstrating the validity of this approach. Mechanistic and interaction data were mined from available sources and then used to predict interactors of irinotecan, including potential DDIs mediated by previously unidentified mechanisms. The findings also draw attention to the profound variation between DDI resources, indicating that clinical practice would see significant value from the development of an evidence-based resource to support DDI identification.
药物相互作用（DDIs）是药物干预可能令人困扰的一个结果，可能导致不适、使人衰弱的疾病甚至死亡。现有研究主要只考虑单一层次的相互作用；然而，多途径药物相互作用可能会导致严重的健康并发症，因此需要新的方法来预测和预防复杂的药物相互作用。本文介绍了一种通过使用语义网技术实现的基于规则的模型，在两个药理学水平（代谢和转运体相互作用）上预测药物相互作用的新方法。化疗药物伊立替康用作案例研究，以证明该方法的有效性。从现有来源挖掘机制和相互作用数据，然后用于预测伊立替康的相互作用物，包括由先前未确定的机制介导的潜在药物相互作用。这些发现还提请注意药物相互作用资源之间的巨大差异，表明基于证据的资源开发对于支持药物相互作用识别在临床实践中将具有重大价值。
