Predicting Antibiotic Resistance
Scientists at Freie Universität Berlin and ETH Zurich are developing mathematical methods for planning antibiotic therapies
№ 051/2018 from Mar 16, 2018
Scientists at Freie Universität Berlin and the Swiss Federal Institute of Technology (ETH) Zurich have developed a mathematical method for predicting resistance to antibiotics and pharmaceuticals. The procedure was developed by the German-Swiss group led Prof. Dr. Jens Rolff, Department of Biology, Chemistry, and Pharmacy at Freie Universität, and Professor Dr. Roland Regoes, ETH Zurich. It can be applied to a variety of antibiotics. The required data can easily be collected for each antibiotic. The researchers hope that the procedure will provide a further opportunity for the sustainable planning of antibiotic therapies and the development of new antibiotics. The findings appeared in the latest issue of the journal Proceedings of the Royal Society.
Researching and avoiding resistance to antibiotics is one of the biggest medical challenges. One reason is that it is extremely difficult to predict when and how antibiotic resistance will develop. The method now developed relies on the dose-effect of the drugs, which is basically an observation of how large the margin is between a dose that has no effect and a dose that kills all bacteria. Classic antibiotics and a new group of drugs, the antimicrobial peptides, are fundamentally different in their dose-response. The model developed by the team of researchers correctly predicts that resistance to antimicrobial peptides is much more seldom than to traditional antibiotics, which is consistent with empirical data. The new method is based on methods that are widely used. Thus, for the first time, a tool is available that makes it possible to make predictions about the emergence of resistance. The tool can be used in administering and developing new antibiotics, i.e., before resistant strains occur.
More Information
Prof. Dr. Jens Rolff, Department of Biology, Chemistry, Pharmacy, Freie Universität Berlin, Tel.: +49 30 838-54893, Email: jens.rolff@fu-berlin.de
Article
DOI: DOI: 10.1098/rspb.2017.2687