Drugs that target pathogen public goods are robust against evolved drug resistance

Pathogen drug resistance is a central problem in medicine and public health. It arises through somatic evolution, by mutation and selection among pathogen cells within a host. Here, we examine the hypothesis that evolution of drug resistance could be reduced by developing drugs that target the secreted metabolites produced by pathogen cells instead of directly targeting the cells themselves. Using an agent‐based computational model of an evolving population of pathogen cells, we test this hypothesis and find support for it. We also use our model to explain this effect within the framework of standard evolutionary theory. We find that in our model, the drugs most robust against evolved drug resistance are those that target the most widely shared external products, or ‘public goods’, of pathogen cells. We also show that these drugs exert a weak selective pressure for resistance because they create only a weak correlation between drug resistance and cell fitness. The same principles apply to design of vaccines that are robust against vaccine escape. Because our theoretical results have crucial practical implications, they should be tested by empirical experiments.

[1]  K. Cowan,et al.  Drug Resistance and Its Clinical Circumvention , 2003 .

[2]  M. Wade Soft Selection, Hard Selection, Kin Selection, and Group Selection , 1985, The American Naturalist.

[3]  D. Hocquet,et al.  Cumulative Effects of Several Nonenzymatic Mechanisms on the Resistance of Pseudomonas aeruginosa to Aminoglycosides , 2006, Antimicrobial Agents and Chemotherapy.

[4]  A. Griffin,et al.  Social semantics : altruism , cooperation , mutualism , strong reciprocity and group selection , 2007 .

[5]  I. Fong,et al.  Reemergence of Established Pathogens in the 21st Century , 2013, Emerging Infectious Diseases of the 21st Century.

[6]  George R. Price,et al.  Selection and Covariance , 1970, Nature.

[7]  Robert O'Connor,et al.  Drug resistance in cancer – searching for mechanisms, markers and therapeutic agents , 2007, Expert opinion on drug metabolism & toxicology.

[8]  Jean-Baptiste André,et al.  Multicellular organization in bacteria as a target for drug therapy , 2005 .

[9]  S. Okasha The “averaging fallacy” and the levels of selection , 2004 .

[10]  Philip S. Stewart,et al.  Diffusion in Biofilms , 2003, Journal of bacteriology.

[11]  J. Pepper,et al.  THEORY FOR THE EVOLUTION OF DIFFUSIBLE EXTERNAL GOODS , 2010, Evolution; international journal of organic evolution.

[12]  G. Price,et al.  Extension of covariance selection mathematics , 1972, Annals of human genetics.

[13]  J. Pepper Defeating Pathogen Drug Resistance: Guidance from Evolutionary Theory , 2008, Evolution; international journal of organic evolution.

[14]  Robert T. Pennock Models, simulations, instantiations, and evidence: the case of digital evolution , 2007, J. Exp. Theor. Artif. Intell..

[15]  John W. Pepper,et al.  Simple Models of Assortment through Environmental Feedback , 2007, Artificial Life.

[16]  J. Collins,et al.  Bacterial charity work leads to population-wide resistance , 2010, Nature.

[17]  Christoph Adami,et al.  Experiments in Digital Evolution (Editors' Introduction to the Special Issue) , 2004, Artificial Life.

[18]  W. Hamilton Innate social aptitudes of man: an approach from evolutionary genetics , 1975 .