Recreating chronic respiratory infections in vitro using physiologically relevant models

Lucia Grassi1, Aurélie Crabbé2

  • 1Laboratory of Pharmaceutical Microbiology, Ghent University, Belgium.

Insights

Developing better models for chronic respiratory infections is crucial for new antimicrobial drugs. Advanced cell culture and ex vivo models offer more accurate preclinical testing than standard methods.

Area of Science:

  • Microbiology
  • Pharmacology
  • Translational Medicine

Background:

  • Chronic respiratory infections, often biofilm-associated, lack effective treatments due to limited antimicrobial development.
  • Poorly predictive preclinical models hinder the translation of antimicrobial candidates from research to clinical application.
  • The airway microenvironment significantly influences infection dynamics and antimicrobial efficacy in vivo.

Purpose of the Study:

  • To review advanced cell culture and ex vivo models for simulating chronic airway infections.
  • To highlight the advantages of physiologically relevant models over standard biofilm methods.
  • To discuss challenges and future directions for in vivo-like infection models in drug development.

Main Methods:

  • Overview of air-liquid interface cultures.
  • Description of 3D cultures using rotating-wall vessel bioreactors.
  • Analysis of lung-on-a-chip and ex vivo pig lung models.

Main Results:

  • These advanced models provide greater physiological relevance for studying chronic bacterial infections.
  • Studies using these platforms have explored novel antibiofilm strategies.
  • Physiologically relevant models improve the investigation of antimicrobial activity in simulated airway infections.

Conclusions:

  • Advanced models like ALI cultures, 3D bioreactors, lung-on-a-chips, and ex vivo lungs offer improved preclinical insights.
  • Widespread adoption of these in vivo-like models requires overcoming current challenges in antimicrobial drug development.
  • Informed selection of appropriate models is essential for generating clinically relevant data for airway infections.