To determine the best course of therapy for each type of patient and prevent the spread of bacteria, researchers at the Universidad Carlos III de Madrid (UC3M) are analyzing patterns of antibiotic resistance. Together with the Universities of Exeter, Birmingham (both in the United Kingdom), and Westmead Hospital in Sydney, this work was published in the scientific journal Nature Communications (Australia).
Image Credit: Universidad Carlos III de Madrid.
A measurement known as MIC (Minimum Inhibitory Concentration), which is the lowest concentration of antibiotic effective in inhibiting bacterial growth, is utilized to evaluate a bacterial pathogen’s resistance to an antibiotic in clinical contexts. A bacterium’s resistance increases with increasing MIC against an antibiotic.
Yet, the majority of public databases only contain the frequency of resistant pathogens, which is aggregated data calculated from MIC measurements and predefined resistance thresholds.
For example, for a given pathogen, the antibiotic resistance threshold may be 4: if a bacterium has an MIC of 16, it is considered resistant and is counted when calculating the resistance frequency.
Pablo Catalán, Study Author, Lecturer, and Researcher, Department of Mathematics, Universidad Carlos III de Madrid
In this sense, these aggregated resistance frequency statistics are used to compile the resistance reports that are conducted nationally and by organizations like the WHO.
The team’s analysis of a ground-breaking database, which contains raw data on antibiotic resistance, was used to carry out this study. Pfizer oversees this ATLAS database, which has been accessible to the general public since 2018. The UC3M-led working group evaluated the data of 600,000 patients from more than 70 countries and utilized machine learning techniques (a sort of artificial intelligence approach) to discover patterns of resistance evolution.
The study team has learned through analyzing this data that there are patterns of resistance evolution that can be seen when utilizing the raw data (MIC), but not when using the aggregated data.
A clear example of this is a pathogen whose MIC is slowly increasing over time, but below the resistance threshold. Using this frequency data we wouldn’t be able to say anything, since the resistance frequency remains constant. However, by using MIC data we can detect such a case and be on alert. In the paper, we discuss several clinically relevant cases which have these characteristics. Furthermore, we are the first team to describe this database in depth.
Pablo Catalán, Study Author, Lecturer, and Researcher, Department of Mathematics, Universidad Carlos III de Madrid
This research enables the development of antibiotic therapies that are more potent in preventing infections and slowing the emergence of resistance, which leads to a variety of clinical issues. “The research uses mathematical ideas to find new ways of extracting antibiotic resistance patterns from 6.5 million data points,” concludes the research author.
Journal Reference:
Catalán, P., et al. (2022) Seeking patterns of antibiotic resistance in ATLAS, an open, raw MIC database with patient metadata. Nature Communications. doi.org/10.1038/s41467-022-30635-7.
Source: https://www.uc3m.es/Home