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

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

Abstract

© 2017 American Chemical Society. 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.

Citation

Dundas, A. A., Mikulskis, P., Hook, A., Dundas, A., Irvine, D., Sanni, O., …Winkler, D. A. (2018). Prediction of Broad-Spectrum Pathogen Attachment to Coating Materials for Biomedical Devices. ACS Applied Materials and Interfaces, 10(1), 139-149. https://doi.org/10.1021/acsami.7b14197

• Journal Article Type: Article
• Acceptance Date: Dec 1, 2017
• Online Publication Date: Jan 2, 2018
• Publication Date: Jan 10, 2018
• Deposit Date: Dec 11, 2017
• Publicly Available Date: Jan 3, 2019
• 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
• Pages: 139-149
• DOI: https://doi.org/10.1021/acsami.7b14197
• Keywords: medical devices; broad spectrum; antimicrobial surfaces; machine learning; polymer arrays
• Public URL: https://nottingham-repository.worktribe.com/output/898149
• Publisher URL: http://pubs.acs.org/doi/10.1021/acsami.7b14197