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

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.

医疗环境中的细菌感染常伴随导尿术、手术部位干预等常规诊疗操作发生。随着用于管理慢性疾病、提升生活质量的医用器械数量持续增长，这类感染的影响愈发显著。病原体对多种抗生素的耐药性也不断攀升，进一步加剧了安全有效医疗操作实施的复杂性。降低植入式与留置式医用器械相关感染发生率的一种可行方案，是使用可抑制细菌生物膜形成的聚合物材料。为显著加速这类材料的研发进程，本研究首次展示了如何通过最先进的机器学习方法，在单一模型中实现多种病原体对大型聚合物文库附着情况的定量预测。此类模型可助力设计出对不同细菌物种的病原体附着率极低的聚合物，这类材料可作为导尿管、人工耳蜗植入体、心脏起搏器等植入式或留置式医用器械的候选材料。