Aggressive or moderate drug therapy for infectious diseases? Trade-offs between different treatment goals at the individual and population levels

PLoS Comput Biol. 2019 Aug 12;15(8):e1007223. doi: 10.1371/journal.pcbi.1007223. eCollection 2019 Aug.

Abstract

Antimicrobial resistance is one of the major public health threats of the 21st century. There is a pressing need to adopt more efficient treatment strategies in order to prevent the emergence and spread of resistant strains. The common approach is to treat patients with high drug doses, both to clear the infection quickly and to reduce the risk of de novo resistance. Recently, several studies have argued that, at least in some cases, low-dose treatments could be more suitable to reduce the within-host emergence of antimicrobial resistance. However, the choice of a drug dose may have consequences at the population level, which has received little attention so far. Here, we study the influence of the drug dose on resistance and disease management at the host and population levels. We develop a nested two-strain model and unravel trade-offs in treatment benefits between an individual and the community. We use several measures to evaluate the benefits of any dose choice. Two measures focus on the emergence of resistance, at the host level and at the population level. The other two focus on the overall treatment success: the outbreak probability and the disease burden. We find that different measures can suggest different dosing strategies. In particular, we identify situations where low doses minimize the risk of emergence of resistance at the individual level, while high or intermediate doses prove most beneficial to improve the treatment efficiency or even to reduce the risk of resistance in the population.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Anti-Infective Agents / administration & dosage
  • Communicable Diseases / drug therapy*
  • Communicable Diseases / microbiology
  • Communicable Diseases / transmission
  • Computational Biology
  • Computer Simulation
  • Disease Outbreaks / statistics & numerical data
  • Dose-Response Relationship, Drug
  • Drug Resistance, Microbial / genetics
  • Epidemics / statistics & numerical data
  • Goals
  • Host Microbial Interactions
  • Humans
  • Models, Biological
  • Mutation
  • Precision Medicine
  • Probability
  • Systems Analysis
  • Treatment Outcome

Substances

  • Anti-Infective Agents

Grants and funding

HU was supported by the Swiss National Science Foundation http://www.snf.ch/en/Pages/default.aspx (SNF: 155866 to S.B.) and by the European Research Council https://erc.europa.eu/ (ERC: PBDR 268540 to S.B.). NH gratefully acknowledges the financial support of the Swiss National Science Foundation http://www.snf.ch/en/Pages/default.aspx (grant number: 31003A_149769). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.