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Prediction of broad-spectrum pathogen attachment to coating materials for biomedical devices

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

Authors

Paulius Mikulskis pazpm3@nottingham.ac.uk

ANDREW HOOK ANDREW.HOOK@NOTTINGHAM.AC.UK
Nottingham Research Fellow

DEREK IRVINE derek.irvine@nottingham.ac.uk
Professor of Materials Chemistry

Olutoba Sanni Olutoba.Sanni@nottingham.ac.uk

Daniel Anderson

Robert Langer

PAUL WILLIAMS paul.williams@nottingham.ac.uk
Professor of Molecular Microbiology

David A. Winkler david.winkler@monash.edu



Abstract

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.

Journal Article Type Article
Journal ACS Applied Materials and Interfaces
Print ISSN 1944-8244
Electronic ISSN 1944-8252
Publisher American Chemical Society
Peer Reviewed Peer Reviewed
Volume 10
Issue 1
APA6 Citation Mikulskis, P., Hook, A., Dundas, A., Irvine, D. J., Sanni, O., Anderson, D., …Winkler, D. A. (in press). Prediction of broad-spectrum pathogen attachment to coating materials for biomedical devices. ACS Applied Materials and Interfaces, 10(1), https://doi.org/10.1021/acsami.7b14197
DOI https://doi.org/10.1021/acsami.7b14197
Keywords medical devices; broad spectrum; antimicrobial surfaces; machine learning; polymer arrays
Publisher URL http://pubs.acs.org/doi/10.1021/acsami.7b14197
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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