Scientists have developed a mathematical model that predicts how the number and effects of bacterial mutations that lead to drug resistance will affect the success of antibiotic treatments. Their model, which is described today in the journal ‘e Life’, provides new insights into the development of drug resistance in the clinical setting and provides advice on how novel treatment strategies can be developed to prevent the occurrence of this resistance. Antibiotic resistance is a major public health challenge caused by changes in bacterial cells that allow them to survive drugs designed to kill them. Resistance often arises from new mutations in bacteria that arise during the treatment of an infection. It is important to understand how this resistance develops and spreads through bacterial populations, which in turn prevent treatment failure.
First author Claudia Igler, postdoc at ETH Zurich, Switzerland explained that the mathematical models are a crucial tool for examining the outcome of a drug treatment and for assessing the risk of developing antibiotic resistance. These models typically look at a single mutation that leads to total drug resistance, but there can be multiple mutations that increase antibiotic resistance in bacteria. For their study, Igler and his team collected experimental evidence that the development of drug resistance follows these two patterns: a single mutation and multiple mutations and used this information to create an informed modeling framework that illustrates the development of resistance versus multilevel resistance in bacterial cells in response to the type of drug predicts in the body and treatment strategies.
They looked at how the risk of treatment failure changes when multiple mutation steps are taken into account and how many different strains (mutations) of bacteria are used. The team found that the development of drug resistance is considerably limited when the bacterium requires over and above two mutations. In addition, the extent of this limitation, and hence the likelihood of treatment failure, is highly dependent on the combination of drug type and route of administration, such as oral or intravenous infusion.
This work can be termed as a decisive step towards understanding the development of antibiotic resistance in clinically relevant treatment settings. It underscores the importance of measuring the level of mutation-imparted antibiotic resistance to support effective antimicrobial treatment strategies.
