Development of an online tracking tool for antibiotic resistance

Abstract

Background: By 2050, drug resistance is projected to kill 10 million people, surpassing the amount of people killed by cancer annually. Recent reports from the World Health Organization revealed that microbes responsible for human diseases such as tuberculosis, pneumonia, malaria and salmonella, have become increasingly resistant to commonly prescribed antibiotics. Critically, these resistances have spread globally and are particularly rampant in developing countries due to the indiscriminate use of antibiotics in human‐related activities such as food processing and agriculture. Currently, the lack of accessibility to localized as well as drug‐ and organism‐specific information of antibiotic resistance has hampered efforts to combat the global spread of drug resistance. Here, we introduce Resistance Tracker (RT), which is a web‐portal that tracks reports of antibiotic resistance across different organisms and sources.

Methods: The RT web server was built using topic‐specific knowledge bases and interactive networks generated through text mining from online repositories. PubMed‐indexed articles were filtered through pre‐determined keywords and matching articles were downloaded as MySQL database using the Medical Reference software. Duplicated articles were removed by a second screen with PubMed IDs. The RT database currently includes dictionaries of ‘Antibiotic Resistance’, ‘Bacteria’, ‘Animals’, ‘Human Body’ and ‘Locations’ built by surveying >70 000 PubMed‐indexed articles.

Results: The RT web server is available at https://resistancetracker.com without registration or license. The tool allows users to search for the occurrence of resistance to a particular drug in one or more of the following fields: types of organisms which are further divided into bacteria, animals and humans, as well as geographical locations. The user first enters the type of resistance in the search box before selecting the desired fields. The web server then returns results containing resistance to the selected drugs in specified organisms or body parts if ‘human’ is selected as well as frequencies in selected geographical locations. Results are presented as ‘RT values’ which correspond with the number of articles that satisfy user‐selected fields.

Conclusion: In summary, we present the development of a new online tool that allows the tracking of resistance to 1) selected drugs in 2) specific organisms and at 3) selected geographical locations. This web server provides quick and convenient access to processed information of drug resistance thus enabling medical practitioners and the general public to make informed decisions. Given the increasing connectivity especially in developing countries, our web server contributes to the broader goal of combating antibiotic resistance. This tool is currently under development and will be revised based on user feedbacks. Please address all suggestions and comments to alwong@kean.edu.

Acknowledgement: This work was supported by the Student partnering with Faculty (SpF) research programs of Wenzhou‐Kean University (Grant No. WKU201617002 and WKU201718009).