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

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.

Keywords: antimicrobial surfaces; broad spectrum; machine learning; medical devices; polymer arrays.

Figure 1

Schematic of the processes employed in the microarray fabrication, pathogen screening, modeling of data, and prediction of pathogen attachment for new polymers.

Figure 2

Measured and predicted attachment (estimated using the log of the GFP fluorescence, log F) of the multipathogen attachment model employing computed molecular descriptors.

Figure 3

Weights of the descriptors in the linear multipathogen attachment model. The error bars represent the standard errors in the parameter estimations from the MLR model. See Table S2 for an explanation of these descriptors and Discussion for the relevance to the model.

Figure 4

Measured and predicted attachment (estimated using the log of the GFP fluorescence, log F) of the multipathogen attachment model employing ToF-SIMS ion peak features and WCA from experiments as descriptors.

Figure 5

Weights of the descriptors in the linear multipathogen attachment model (different scales for the ToF-SIMS ion peaks, WCA, and the indicator variables for the three pathogens). The error bars represent the standard errors in the parameter estimations from the MLR model.

Figure 6

Standard errors of prediction values for the test set for single pathogen versus multipathogen models generated using molecular descriptors or experimental surface analytical ToF-SIMS descriptors.

Figure 7

Test set SEP values for the four pathogen attachment models for the two types of descriptors. The experimental descriptors were dominated by ToF-SIMS ion intensities, and the WCA did not play a significant role.