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（DDI）是潜在的令人痛苦的药物干预推论，可能会导致不适，使人衰弱甚至死亡。现有研究主要只考虑单一水平的相互作用。但是，多路径DDI可能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DDI。本文介绍了一种新方法，该方法通过使用语义Web技术实现的基于规则的模型在两个药理学水平（代谢和转运蛋白相互作用）上预测DDI。化疗药物伊立替康作为案例研究证明了这种方法的有效性。从可用资源中提取了机理和相互作用数据，然后将其用于预测伊立替康的相互作用物，包括以前未知机制介导的潜在DDI。这些发现还引起人们对DDI资源之间巨大差异的关注，表明临床实践将从开发基于证据的资源以支持DDI识别中获得重大价值。