The medical world is currently mainly focusing on circumventing this problem via the discovery of new drugs. It is ambitious to think that one day the occurrence of resistance can be prevented, but it is realistic to try to delay the process.
To come up with new treatment strategies with existing drugs, a good understanding of resistance evolution is required. Therefore, AIMMS researcher Coco van Boxtel and her colleagues investigate the evolution of antibiotic resistance in Escherichia coli, a bacterium that, amongst others, causes urinary tract infections. The researchers are performing evolution experiments in the lab. However, the experimental method that is being used highly affects the conclusions that are drawn: how the probability that bacteria will become resistant, given a certain treatment strategy, is estimated. During her PhD study, Van Boxtel is developing three different experimental methods that each cover a different survival strategy that precedes resistance. Together, these three methods lead to a better risk assessment.
In the past year, Van Boxtel has focused on persister formation, subpopulations that temporarily stop growing and are thereby insensitive to drugs. With this new method, quantifying the number of persisters is much faster, reliable for low numbers (1/50.000), and robust with respect to variation in persister state duration. Currently she is finishing up experiments that illustrate a second method, one in which cells can form niches during resistance evolution so that clonal interference is not very stringent. Figure 1 illustrates this method. Finally, Van Boxtel works in a slower pace on a method that selects for the fastest growing resistant cell, which represents the worst-case scenario for resistance evolution in which there is stringent competition between different resistance mutants.
Figure 1: Evolution of antibiotic resistance in Escherichia coli against ciprofloxacin over 188 hours in a 0.5x0.25 meter device. Black: solid growth medium with liquid top layer that facilitates migration. White: colonizing E. coli populations in top layer. The most left image shows the antibiotic concentration of the connected compartments, defined as the multiple of 1x the Minimal Inhibiting Concentration (35 ng/mL).