A very major determinant of the time until an antimicrobial drug is rendered useless by pathogen evolution - the therapeutically useful lifespan of a drug - is the strength of selection for resistance. This is a consequence of two things: how many infections (patients) are being treated, and how they are being treated. The resistance management philosophy behind most current patient treatment regimes is to make it hard for resistance to arise in the first place by eliminating mutable pathogens as fast as possible. But if resistance has already arisen, rapidly eliminating susceptible parasites is the most efficient way of driving resistance through a population. How these mutational and selection pressures should be best manipulated by choice of patient treatment regimen is not obvious: quite possibly the best resistance management strategy varies among infectious agents and epidemiological circumstances. Regimes aimed at radical pathogen clearance are particularly problematic from a resistance management point of view when susceptible and resistant parasites compete within the same host. There, drug treatment maximally increases the selective advantage of resistance: not only is the fitness of susceptible parasites reduced to zero, the fitness of resistant parasites is increased because their competitors have been removed. We have shown in experimental studies with malaria parasites in an animal model that radical parasite cure very effectively increases the transmission of resistance, and so will very significantly speed the evolution of resistance. Since current drug regimens often continue beyond that required to restore patient health (hence issues of patient compliance), we hypothesize that there are other regimens which are equally effective clinically but which better retard resistance evolution. We propose to test these ideas experimentally and, using epidemiological evolutionary models, to evaluate the consequences of such regimens at a population level. We contend that, for many infectious diseases, there is an important knowledge gap surrounding the evolutionary consequences of patient treatment. Maximizing the useful lifespan of existing and new drugs - drug stewardship - requires an empirical base for evaluating the evolutionary consequences of contrasting drug treatment regimens.
The evolution of drug resistant pathogens significantly impacts on human wellbeing and health budgets. Evolutionary hypotheses suggest drug treatment regimes that contradict conventional wisdom but which could in some cases better retard the spread of drug resistant pathogens. We want to test the resistance management potential of these regimes: they could prolong the useful therapeutic lifespan of drugs we already have, and those currently in the discovery pipeline.
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