Prediction of Broad-Spectrum Pathogen Attachment to Coating Materials for Biomedical Devices

Paulius Mikulskis, Andrew Hook, Adam A. Dundas, Derek Irvine, Olutoba Sanni, Daniel Anderson, Robert Langer, Morgan R. Alexander, Paul Williams, David A. Winkler

Research output: Contribution to journalArticleResearchpeer-review

18 Citations (Scopus)


Bacterial infections in healthcare settings are a frequent accompaniment to both routine procedures such as catheterization and surgical site interventions. Their impact is becoming even more marked as the numbers of medical devices that are used to manage chronic health conditions and improve quality of life increases. The resistance of pathogens to multiple antibiotics is also increasing, adding an additional layer of complexity to the problems of employing safe and effective medical procedures. One approach to reducing the rate of infections associated with implanted and indwelling medical devices is the use of polymers that resist the formation of bacterial biofilms. To significantly accelerate the discovery of such materials, we show how state of the art machine learning methods can generate quantitative predictions for the attachment of multiple pathogens to a large library of polymers in a single model for the first time. Such models facilitate design of polymers with very low pathogen attachment across different bacterial species that will be candidate materials for implantable or indwelling medical devices such as urinary catheters, cochlear implants, and pacemakers.

Original languageEnglish
Pages (from-to)139-149
Number of pages11
JournalACS Applied Materials & Interfaces
Issue number1
Publication statusPublished - 10 Jan 2018


  • antimicrobial surfaces
  • broad spectrum
  • machine learning
  • medical devices
  • polymer arrays

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