Antimicrobial spectrum against wound pathogens and cytotoxicity of star-arranged poly-l-lysine-based antimicrobial peptide polymers.

As growing numbers of patients are at higher risk of infection, novel topical broad-spectrum antimicrobials are urgently required for wound infection management. Robust pre-clinical studies should support the development of such novel antimicrobials. To date, evidence of robust investigation of the cytotoxicity and antimicrobial spectrum of activity of antimicrobial peptides (AMP)s is lacking in published literature. Using a more clinical lens, we address this gap in experimental approach, building on our experience with poly-l-lysine (PLL)-based AMP polymers. To evaluate the bactericidal activity and cytotoxicity of a PLL-based 16-armed star AMP polymer, designated 16-PLL, as a novel candidate antimicrobial. Antimicrobial susceptibilities of clinical isolates and reference strains of ESKAPE ( spp., , spp.) pathogens, to 16-PLL were investigated. Human erythrocyte haemolysis and keratinocyte viability assays were used to assess toxicity. Modifications were made to 16-PLL and re-evaluated for improvement. Minimum bactericidal concentration of 16-PLL ranged from 1.25 µM to ≥25 µM. At 2.5 µM, 16-PLL was broadly bactericidal against ESKAPE strains/wound isolates. Log-reduction in colony forming units (c.f.u.) per millilitre after 1 h, ranged from 0.3 () to 5.6 (). At bactericidal concentrations, 16-PLL was toxic to human keratinocyte and erythrocytes. Conjugates of 16-PLL, Trifluoroacetylated (TFA)-16-PLL, and Poly-ethylene glycol (PEG)ylated 16-PLL, synthesised to address toxicity, only moderately reduced cytotoxicity and haemolysis. Due to poor selectivity indices, further development of 16-PLL is unlikely warranted. However, considering the unmet need for novel topical antimicrobials, the ease of AMP polymer synthesises/modification is attractive. To support more rational development, prioritising clinically relevant pathogens and human cells, to establish selective toxicity profiles , is critical. Further characterisation and discovery utilising artificial intelligence and computational screening approaches can accelerate future AMP nanomaterial development.