Challenges and Opportunities: Interplay between Infectious Disease and Antimicrobial Resistance in Medical Device Surface Applications

Abstract

Antimicrobial resistance (AMR) is a growing silent pandemic driven by multidrug-resistant infections, particularly those associated with medical devices such as dental implants, heart valves, and urinary catheters. This review addresses the urgent need for alternative antimicrobial strategies by exploring the integration of artificial intelligence (AI) in the discovery of antimicrobial peptides (AMPs) and the rational design of bioactive surfaces. We describe how AI-based models accelerate the identification and optimization of peptide candidates with potent antibiofilm activity. Moreover, we examine recent advancements in surface engineering, such as biomimetic coatings, quorum sensing inhibitors, and enzyme-based strategies, that disrupt bacterial colonization and biofilm formation. The novelty of this work lies in its unified perspective that bridges computational prediction, materials science, and microbial pathogenesis to inform the next generation of antimicrobial surfaces. By highlighting innovative AI-assisted approaches and emerging hybrid strategies, this review underscores their potential to mitigate device-associated infections and address the broader challenge of AMR in healthcare settings.

1

Overview of common pathogenic transmission routes and pathogenic determinants. (A) Pathogens are transmitted through various routes, including direct contact (e.g., injection or injury), oral or nasal secretions, vector-borne and zoonotic pathways, nosocomial infections, and ingestion of contaminated food or water. These represent key mechanisms by which microorganisms gain access to the host. (B) Pathogens are classified as cellular (e.g., bacteria, parasites, and fungi) or acellular (e.g., viruses and prions). Cellular pathogens utilize a range of strategies to establish infection, such as biofilm formation, intracellular survival, and metabolic adaptation. (C) Major virulence mechanisms include the secretion of toxins, activation of secretion systems, and tissue invasion, contributing to host colonization and immune evasion. (D) Acellular pathogens, particularly viruses and prions, exhibit high adaptability and play a significant role in emerging infectious diseases due to their rapid evolution and resistance to conventional therapies.

2

Stages of bacterial biofilm development on a surface, illustrating the key virulence factors and biofilm-associated properties. The process begins with attachment of planktonic bacteria via adhesins, flagella, pili/fimbriae, and lipopolysaccharide (LPS). During maturation, bacteria proliferate and produce EPS, facilitating QS and horizontal gene transfer. In the detachment phase, cells disperse through mechanisms, such as erosion, sloughing, enzymatic degradation, or cell lysis, enabling colonization of new niches.

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Schematic representation of bacterial virulence mechanisms facilitated by horizontal gene transfer. (A) Horizontal gene transfer enables the dissemination of key virulence factors among bacterial populations, including adhesins, siderophores, and hemolysins. (B) Adhesins promote bacterial attachment to host epithelial cells, facilitating colonization. (C) Siderophores are secreted to chelate extracellular iron (Fe³⁺), which is then internalized through specific receptors as a siderophore–iron complex. (D) Hemolysins are secreted toxins that disrupt host red blood cells (RBCs), granulocytes, and monocytes, aiding in immune evasion and tissue invasion.

4

Overview of biofilm-forming microorganisms associated with medical device-related infections. This figure highlights the most common bacterial and fungal pathogens found on various types of indwelling medical devices, including dental implants, central venous catheters, cardiovascular implants, orthopedic implants, and urinary catheters. Although many of these microorganisms are endogenous to the host microbiota, they can exploit host vulnerabilities and device surfaces through the expression of virulence factors, such as adhesins and biofilm-promoting structures. These biofilms contribute to persistent infections, impair device functionality, and reduce the lifespan of the implanted material. Associated clinical outcomes range from local inflammation and tissue damage to severe systemic conditions, such as septicemia, endocarditis, and device failure.

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Engineered surface strategies to prevent biofilm formation on medical devices. Various antimicrobial surface modifications have been developed to inhibit microbial adhesion and biofilm maturation, including: (A) metal and metal oxide coatings (e.g., Au, Ag, Pt, Cu, Zn) that exert bactericidal or antiadhesive effects; (B) functionalized polymers that create unfavorable topographies or release antimicrobial agents; (C) QS inhibitors that disrupt bacterial communication necessary for coordinated biofilm development; (D) surface-tethered AMPs that provide direct bactericidal activity; (E) bacteriophages immobilized on surfaces that selectively infect and lyse biofilm-forming bacteria; and (F) biofilm-degrading enzymes that break down the EPS, facilitating biofilm disruption. These surface engineering approaches represent a promising toolbox for combating biofilm-associated infections, particularly in clinical and implantable device contexts.

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Integrated workflow for the discovery and validation of antimicrobial peptides (AMPs) using artificial intelligence and experimental approaches. The process begins with model training, where AI-based machine learning and deep learning algorithms are applied to user-defined protein data sets to identify residues with antimicrobial potential. In the AMP prediction stage, computational proteolysis is used to fragment selected proteins, followed by the curation and identification of candidate AMP fragments. These are then subjected to experimental characterization, including analytical analysis by mass spectrometry and structural assessment via circular dichroism. Finally, experimental validation is performed through in vitro assays (e.g., MIC and cytotoxicity testing) and in vivo testing using mouse models of implant-associated infection. Continuous data collection throughout the pipeline refines the predictive accuracy and informs future AMP design cycles.

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Conceptual workflow for the discovery of novel antimicrobial peptides (AMPs) using deep learning and paleogenomic data. (A) Starting from reconstructed or extant genomic sequences, artificial intelligence (AI) models based on deep neural networks are trained to predict coding regions with potential antimicrobial activity. These predictions guide the translation of genomes to (B) transcriptomes and ultimately to (C) proteomes from both extinct and extant species. (D) Candidate peptide regions are selected through structural and sequence analysis, followed by (E) in vitro antimicrobial screening and (F) in vivo validation in preclinical models.