Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 80221, Saudi Arabia.
Department of Clinical Pharmacy, College of Pharmacy, King Khalid University, Abha 62529, Saudi Arabia.
Comput Math Methods Med. 2022 Aug 17;2022:9093262. doi: 10.1155/2022/9093262. eCollection 2022.
As more drugs are developed and the incidence of polypharmacy increases, it is becoming critically important to anticipate potential DDIs before they occur in the clinic, along with those for which effects might go unobserved. However, traditional methods for DDI identification are unable to coalesce interaction mechanisms out of vast lists of potential or known DDIs, much less study them accurately. Computational methods have great promise but have realized only limited clinical utility. This work develops a rule-based inference framework to predict DDI mechanisms and support determination of their clinical relevance. Given a drug pair, our framework interrogates and describes DDI mechanisms based on a knowledge graph that integrates extensive available biomedical resources through semantic web technologies and backward chaining inference, effectively identifying facts within the graph that prove and explain the mechanisms of the drugs' interaction. The framework was evaluated through a case study combining a chemotherapy agent, irinotecan, and a widely used antibiotic, levofloxacin. The mutual interactions identified indicate that our framework can effectively explore and explain the mechanisms of potential DDIs. This approach has the potential to improve drug discovery and design and to support rapid and cost-effective identification of DDIs along with their putative mechanisms, a key step in determining clinical relevance and supporting clinical decision-making.
随着越来越多的药物被开发出来，同时伴随药物的联合使用（polypharmacy）的发生率增加，在药物在临床上出现潜在的药物相互作用（DDI）之前，预测这些相互作用变得至关重要，包括那些可能未被观察到的相互作用。然而，传统的 DDI 识别方法无法从大量潜在或已知的 DDI 列表中整合相互作用机制，更不用说准确地研究它们了。计算方法具有很大的潜力，但仅实现了有限的临床实用性。这项工作开发了一个基于规则的推理框架，以预测 DDI 机制，并支持确定其临床相关性。给定一对药物，我们的框架通过一个知识图来询问和描述 DDI 机制，该知识图通过语义 Web 技术和反向推理整合了广泛的可用生物医学资源，有效地在图中识别出证明和解释药物相互作用机制的事实。该框架通过一个案例研究进行了评估，该研究结合了一种化疗药物伊立替康和一种广泛使用的抗生素左氧氟沙星。确定的相互作用表明，我们的框架可以有效地探索和解释潜在 DDI 的机制。这种方法有可能改进药物发现和设计，并支持快速和具有成本效益的识别 DDI 及其潜在机制，这是确定临床相关性和支持临床决策的关键步骤。
