Developing integrated ethiopian traditional herbal medicine and phytochemicals database (ETM-DB) and identifying herb and phenotype associations from prescription data

Abstract

Recently, there has been an increasing tendency to go back to nature in search of new medicines. To facilitate this, a great deal of effort has been made to compile information on natural products worldwide, and as a result, many ethnic-based traditional medicine databases have been developed. In Ethiopia, there are more than 80 ethnic groups, each having their own indigenous knowledge on the use of traditional medicine. About 80% of the population uses traditional medicine for primary health care. Despite this, there is no structured online database for Ethiopian traditional medicine, which limits natural products based drug discovery researches using natural products from the country. To address this problem, we developed Ethiopian Traditional Herbal Medicine and Phytochemicals Database (ETM-DB) - the largest, freely accessible, web-based integrated database on Ethiopian traditional medicine. It provides traditional herbal medicine entities and their interrelationships in structured forms including reference to the original sources. To develop ETM-DB, online research articles, theses, books, and public databases containing Ethiopian herbal medicine and phytochemicals information were searched. These resources were thoroughly inspected and the necessary data were extracted. Then, we developed a comprehensive online relational database which contains information on 1,054 Ethiopian medicinal herbs with 1,465 traditional therapeutic uses, 573 multi-herb prescriptions, 4,285 compounds, 11,621 human target gene/proteins, covering 5,779 herb-phenotype, 1,879 prescription-herb, 16,426 herb-compound, 105,202 compound-phenotype, 162,632 compound-gene/protein, and 16,584 phenotype-gene/protein relationships. Using various cheminformatics tools, we obtained predicted physicochemical and absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of ETM-DB compounds. We also evaluated drug-likeness properties of these compounds using FAF-Drugs4 webserver. From the 4,285 compounds, 4,080 of them passed the FAF-Drugs4 input data curation stage, of which 876 were found to have acceptable drug-likeness properties. The ETM-DB website interface allows users to search the entities using various options provided by the search menu. The current version of ETM-DB is openly accessible at http://biosoft.kaist.ac.kr/etm. We hope that ETM-DB will expedite drug discovery and development researches from Ethiopian natural products as it contains information on the chemical composition and related human target gene/proteins. In Ethiopia, the use of traditional medicine as multicomponent prescription varies between the practitioners, cultures and geographical locations in the country. Hence, prescription information is enormous and diverse with prevailing redundancies and overlaps. This makes it difficult to manually analyze and discover patterns on their use. Therefore, proper documentation, analysis methods, and tools are needed to improve data interpretation and availability to the scientific community. To tackle this, problem we analyzed Ethiopian traditional medicine prescription data obtained from the book Medicinal Plants and Enigmatic Health Practices of Northern Ethiopia, to reveal and characterize associations between the phenotype symptoms and medicinal materials using a data mining technique. We manually compiled 505 prescriptions from some combinations of 567 medicinal materials for treating 106 phenotypes from the book. Association rule mining algorithm was applied to obtain useful and descriptive relationships between the symptoms and medicinal materials. Network analysis and radar charts were also used to easily view and explore the associations. Identifying relationships between phenotype symptoms and medicinal materials can give important insight for screening new potential therapeutic medicine from Ethiopian traditional medicine. In addition, proper use of the association information will be helpful for traditional medicine practitioners for effective and consistent treatment through shared knowledge and experiences. Researchers can also use the result of the association rule mining to further study and validate the claimed therapeutic effect of the medicinal materials.