One strategy, multiple pathogens Antimicrobial peptide therapeutics to combat bacterial, fungal, and mixed infections

Antimicrobial resistance (AMR) is an escalating global health crisis that demands innovative therapeutic strategies beyond conventional antibiotics. Antimicrobial peptides (AMPs) are promising alternatives because of their broad-spectrum activity and low propensity to induce resistance. However, clinical translation has been hindered by several limitations, including poor stability, reduced efficacy in complex infection environments, and challenges with effective delivery. This thesis aimed to overcome these limitations through an integrated approach that combines machine learning (ML)-guided peptide discovery, evaluation in clinically relevant infection models, peptide stabilization, and biomaterial-based delivery systems.
An ML-driven framework, Guided Designed Smart Antimicrobial Therapeutics (GDST), identified two lead antimicrobial peptides (AMPs) with potent activity against multidrug-resistant bacteria and Candida species, including multidrug-resistant Candidozyma auris, while preventing biofilm formation and showing no detectable resistance evolution in vitro. However, fungal aspartic proteases reduced their efficacy in complex, polymicrobial biofilm models, underscoring the limitations of traditional mono-species assays. Protease-resistant retro-inverso variants of AMPs partially overcome this neutralization in a sequence-dependent manner. Furthermore, immobilization of the AMP SAAP-148 into a photo-crosslinkable hydrogel created a highly stable, non-leaching, and long-lasting antimicrobial material. This advanced delivery method successfully minimized cytotoxicity while fully sustaining therapeutic potency.
Overall, this thesis demonstrates that successful AMP development requires not only the identification of potent antimicrobial sequences but also optimization for biological complexity, protease resistance, and appropriate drug delivery. These findings provide a framework for the next generation of AMP therapeutics and support the use of clinically relevant infection models to improve translational success.