Antibiotics can be used to contain drug-resistant bacteria by maintaining sufficiently large sensitive populations

Standard infectious disease practice calls for aggressive drug treatment
that rapidly eliminates the pathogen population before resistance can
emerge. When resistance is absent, this elimination strategy can lead to
complete cure.  However, when resistance is already present,
removing drug-sensitive cells as quickly as possible removes competitive
barriers that may slow the growth of resistant cells. In contrast to the
elimination strategy, the containment strategy aims to maintain the
maximum tolerable number of pathogens, exploiting competitive suppression
to achieve chronic control. Here we combine in vitro experiments in
computer-controlled bioreactors with mathematical modeling to investigate
whether containment strategies can delay failure of antibiotic treatment
regimens. To do so, we measured the "escape time" required for
drug-resistant E. coli populations to eclipse a threshold density
maintained by adaptive antibiotic dosing. Populations containing only
resistant cells rapidly escape the threshold density, but we found that
matched resistant populations that also contain the maximum possible
number of sensitive cells could be contained for significantly longer. The
increase in escape time occurs only when the threshold density--the
acceptable bacterial burden--is sufficiently high, an effect that
mathematical models attribute to increased competition. The findings
provide decisive experimental confirmation that maintaining the maximum
number of sensitive cells can be used to contain resistance when the size
of the population is sufficiently large.