Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Predicting drug resistance evolution: antimicrobial peptides vs. antibiotics

Guozhi Yu, Desiree Y Baeder, Roland R Regoes, Jens Rolff
doi: https://doi.org/10.1101/138107
Guozhi Yu
aEvolutionary Biology, Institut fuer Biologie, Freie Universitaet Berlin, Berlin, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Desiree Y Baeder
bInstitute of Integrative Biology, ETH Zurich, Zurich, Switzerland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Roland R Regoes
bInstitute of Integrative Biology, ETH Zurich, Zurich, Switzerland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: roland.regoes@env.ethz.ch jens.rolff@fu-berlin.de
Jens Rolff
aEvolutionary Biology, Institut fuer Biologie, Freie Universitaet Berlin, Berlin, Germany
cBerlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: roland.regoes@env.ethz.ch jens.rolff@fu-berlin.de
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Antibiotic resistance constitutes one of the most pressing public health concerns. Antimicrobial peptides are considered part of a solution to this problem, because they are new agents that add to our repertoire. Importantly, antimicrobial peptides differ fundamentally from antibiotics in their pharmacodynamic characteristics. Here we implement these differences within a theoretical framework to predict the evolution of resistance against antimicrobial peptides and compare it to antibiotic resistance. Our analysis of resistance evolution finds that pharmacodynamic differences all combine to produce a much lower probability that resistance will evolve against antimicrobial peptides. The finding can be generalized to all drugs with pharmacodynamics similar to AMPs. Pharmacodynamic concepts are familiar to most practitioners of medical microbiology, and data can be easily obtained for any drug or drug combination. Our theoretical and conceptual framework is therefore widely applicable and can help avoid resistance evolution if implemented in antibiotic stewardship schemes or the rational choice of new drug candidates.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted May 23, 2017.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Predicting drug resistance evolution: antimicrobial peptides vs. antibiotics
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Predicting drug resistance evolution: antimicrobial peptides vs. antibiotics
Guozhi Yu, Desiree Y Baeder, Roland R Regoes, Jens Rolff
bioRxiv 138107; doi: https://doi.org/10.1101/138107
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Predicting drug resistance evolution: antimicrobial peptides vs. antibiotics
Guozhi Yu, Desiree Y Baeder, Roland R Regoes, Jens Rolff
bioRxiv 138107; doi: https://doi.org/10.1101/138107

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Evolutionary Biology
Subject Areas
All Articles
  • Animal Behavior and Cognition (4224)
  • Biochemistry (9101)
  • Bioengineering (6749)
  • Bioinformatics (23935)
  • Biophysics (12086)
  • Cancer Biology (9491)
  • Cell Biology (13728)
  • Clinical Trials (138)
  • Developmental Biology (7614)
  • Ecology (11656)
  • Epidemiology (2066)
  • Evolutionary Biology (15476)
  • Genetics (10615)
  • Genomics (14292)
  • Immunology (9456)
  • Microbiology (22773)
  • Molecular Biology (9069)
  • Neuroscience (48840)
  • Paleontology (354)
  • Pathology (1479)
  • Pharmacology and Toxicology (2562)
  • Physiology (3822)
  • Plant Biology (8307)
  • Scientific Communication and Education (1467)
  • Synthetic Biology (2289)
  • Systems Biology (6170)
  • Zoology (1297)