Synthetic Repurposing of Drugs in Hypertension: a Datamining Method Based on Association Rules and a Novel Discrete Algorithm

Abstract Background: Drug repurposing aims to detect new benefits of the existing drugs and to reduce the time and cost of drug development projects. Although synthetic repurposing of drugs may be more useful than single repurposing in terms of reducing toxicity and enhancing efficacy, the researchers have not taken it into account. To address the issue, a novel datamining method is introduced and applied to the repositioning of drugs in hypertension (HT). This disease is a complex one and needs to efficient treatment plans to cure it better.Methods: A novel two-step data mining method, which is based on the If-Then association rules and a novel discrete optimization algorithm, is proposed and applied to the synthetic repurposing of drugs in HT. The required data are extracted from DruhBank, KEGG, and DrugR+ databases. Results: The outcomes presented that the proposed method outperforms other state-of-the-art approaches in terms of different statistical criteria. Since different methods failed to discover the list for some datasets, our method could suggest a combination of drugs for all the datasets. Conclusion: Due to using a minimum dosage of medicines, the synthetic method may revive some failed drug development projects and maybe a suitable plan for curing orphan and rare diseases. Also, to achieve better outcomes, it is essential to use efficient computational methods.