Ferdousi Reza, Jamali Ali Akbar, Safdari Reza
Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran.
Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran.
Bioimpacts. 2020;10(2):97-104. doi: 10.34172/bi.2020.12. Epub 2019 Nov 2.
Drug-drug interactions (DDIs) are the main causes of the adverse drug reactions and the nature of the functional and molecular complexity of drugs behavior in the human body make DDIs hard to prevent and threat. With the aid of new technologies derived from mathematical and computational science, the DDI problems can be addressed with a minimum cost and effort. The Market Basket Analysis (MBA) is known as a powerful method for the identification of co-occurrence of matters for the discovery of patterns and the frequency of the elements involved. In this research, we used the MBA method to identify important bio-elements in the occurrence of DDIs. For this, we collected all known DDIs from DrugBank. Then, the obtained data were analyzed by MBA method. All drug-enzyme, drug-carrier, drug-transporter and drug-target associations were investigated. The extracted rules were evaluated in terms of the confidence and support to determine the importance of the extracted bio-elements. The analyses of over 45000 known DDIs revealed over 300 important rules from 22 085 drug interactions that can be used in the identification of DDIs. Further, the cytochrome P450 (CYP) enzyme family was the most frequent shared bio-element. The extracted rules from MBA were applied over 2000000 unknown drug pairs (obtained from FDA approved drugs list), which resulted in the identification of over 200000 potential DDIs. The discovery of the underlying mechanisms behind the DDI phenomena can help predict and prevent the inadvertent occurrence of DDIs. Ranking of the extracted rules based on their association can be a supportive tool to predict the outcome of unknown DDIs.
药物相互作用（DDIs）是药物不良反应的主要原因，而药物在人体内行为的功能和分子复杂性使得药物相互作用难以预防和应对。借助数学和计算科学衍生的新技术，可以以最低的成本和精力解决药物相互作用问题。市场篮子分析（MBA）是一种强大的方法，用于识别事物的共现情况，以发现模式和所涉及元素的频率。在本研究中，我们使用MBA方法来识别药物相互作用发生过程中的重要生物元素。为此，我们从DrugBank收集了所有已知的药物相互作用。然后，使用MBA方法对获得的数据进行分析。研究了所有药物 - 酶、药物 - 载体、药物 - 转运体和药物 - 靶点关联。根据置信度和支持度对提取的规则进行评估，以确定提取的生物元素的重要性。对超过45000个已知药物相互作用的分析揭示了来自22085种药物相互作用的300多条重要规则，这些规则可用于识别药物相互作用。此外，细胞色素P450（CYP）酶家族是最常见的共享生物元素。将从MBA提取的规则应用于超过200万个未知药物对（从FDA批准的药物列表中获得），结果识别出超过20万个潜在的药物相互作用。药物相互作用现象背后潜在机制的发现有助于预测和预防药物相互作用的意外发生。根据其关联性对提取的规则进行排序可以成为预测未知药物相互作用结果的辅助工具。
