Effective Treatment of Skin Wounds Co-Infected with Multidrug-Resistant Bacteria with a Novel Nanoemulsion.

Wound infections with methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant enterococci (VRE) are particularly difficult to treat and present a great challenge to clinicians. Nanoemulsions (NE) are novel oil-in-water emulsions formulated from soybean oil, water, solvent, and surfactants such as benzalkonium chloride (BZK). An optimal ratio of those components produces nanometer-sized particles with the positive-charged surfactant at their oil-water interface. We sought to investigate antimicrobial NE as a novel treatment to address wounds co-infected by MRSA and VRE. Swine split-thickness skin wounds were first infected with MRSA and/or VRE, then treated with the nanoemulsion formulation (X-1735) or placebo controls. Bacterial viability after treatment were determined by nutrient agar plates for total, MRSA-specific, and VRE-specific loads. In addition, inflammation indexes were scored by histopathology. When VRE infected wounds were treated with X-1735, they contained 103 lower VRE CFU counts across a 2-week period compared with placebo. Once co-infected MRSA and VRE split-thickness wounds were successfully established, topical treatment of co-infected wounds with X-1735 resulted in a reduction of bacteria by 2 to 3 logs (compared with placebo) at 3- and 14-day postinfection time points. Importantly, X-1735 was effective in significantly alleviating multilevel inflammation in the treated wounds. X-1735 is a new antimicrobial that is safe to apply to open wounds and effectively kills MRSA and VRE. It appears to also reduce inflammation in these co-infected wounds. The data suggest that this approach offers promise as an antimicrobial for open wounds with MRSA and VRE co-infection. IMPORTANCE Infections, specifically polymicrobial, can cause serious consequences when it comes to wound treatment. Prolonged treatment with antibiotics can lead to an increased risk of bacterial resistance; co-infections can complicate treatment options even further. Our research proposes a novel nanoemulsion treatment for two of the most common antibiotic resistant bacteria: methicillin-resistant Staphylococcus aureus (MRSA) and Vancomycin-resistant enterococci (VRE). This optimized topical treatment formulation not only significantly reduces inflammation and infection in MRSA or VRE infected wounds, but also in MRSA and VRE co-infected wounds as well. The work aims to provide an alternative treatment approach for multidrug-resistant organisms and decrease dependence on systemic treatments.

A porcine model of subcutaneous Staphylococcus aureus infection: a pilot study.

In this descriptive pilot study, we aim to establish a porcine Staphylococcus aureus skin infection model by subcutaneous injection (s.c.) of the porcine S54F9 S. aureus strain in the groin area. Six pigs were used in the study: Five pigs were injected with S. aureus, inocula ranging from 7 × 103 to 5 × 107 colony-forming units per kg bodyweight; one pig was injected with saline exclusively. Lesions were recorded up to 6 days postinoculation using clinical evaluation, ultrasound evaluation, microbiology, flow cytometry, and pathology. Inoculation gave rise to lesions ranging from localized skin infection, that is, minute histological changes, intracellular infection, and macroscopic abscess formation with sequestration of soft tissue, to generalized infection and development of disseminated intravascular coagulation necessitating euthanasia only 10 h after inoculation. Ultrasound assessment of maximum width and characteristics was not able to disclose the progress of the local infection. Flow cytometry and immunohistochemistry revealed the participation of γδT cells in the immune response. In conclusion, we did see a graded inflammatory response associated with the dose of s.c. inoculated bacteria, which may be useful for studying, in particular, the interaction of bacteria and inflammatory mononuclear cell populations. It needs to be investigated if the model is discriminatory and robust.