Toward a Closed Loop, Integrated Biocompatible Biopolymer Wound Dressing Patch for Detection and Prevention of Chronic Wound Infections

Abstract

Chronic wound infections represent a significant burden to healthcare providers globally. Often, chronic wound healing is impeded by the presence of infection within the wound or wound bed. This can result in an increased healing time, healthcare cost and poor patient outcomes. Thus, there is a need for dressings that help the wound heal, in combination with early detection of wound infections to support prompt treatment. In this study, we demonstrate a novel, biocompatible wound dressing material, based on Polyhydroxyalkanoates, doped with graphene platelets, which can be used as an electrochemical sensing substrate for the detection of a common wound pathogen, Pseudomonas aeruginosa. Through the detection of the redox active secondary metabolite, pyocyanin, we demonstrate that a dressing can be produced that will detect the presence of pyocyanin across clinically relevant concentrations. Furthermore, we show that this sensor can be used to identify the presence of pyocyanin in a culture of P. aeruginosa. Overall, the sensor substrate presented in this paper represents the first step toward a new dressing with the capacity to promote wound healing, detect the presence of infection and release antimicrobial drugs, on demand, to optimized healing.

Keywords: Polyhydroxyalkanoates; Pseudomonas aeruginosa; artificial intelligence; biopolymer; electrochemical; graphene; pyocyanin; wound dressing.

FIGURE 1

(A) The production of P(3HO-co-3HD)/graphene films, consisting of mixing the polymer solution with the graphene followed by casting in a glass petri dish. (B) Scanning electron microscopy of the film showing the change in the surface topography of the films with varying amounts of graphene content (both the upper side and the lower side of the films are shown).

FIGURE 2

Bespoke electrode chambers used for measurements with commercial electrodes and MCL-PHA/graphene composite. The chambers were cut from a 15-mm-thick sheet of PTFE and assembled using silicon adhesive sealant. 1 mL of solution was used in each chamber. (A) Photographs of the layers of the assembly. (B) A detailed view of the assembly, showing the two locations where the PHA/graphene composite was inserted.

FIGURE 3

Conductivity measurements of the upper (air) side and lower (glass) side of the composite film. (A) High impedance was observed across all concentrations of graphene, with the lowest observed in the 30% graphene polymer. (B) Measurements performed on the lower side of the polymer indicate that the resistance is several times lower, particularly for the 30% graphene polymer.

FIGURE 4

Pyocyanin standard curve measurements with different carbon electrode materials. (A) Peaks are easily identifiable following the addition of pyocyanin from 15 μM. On standard carbon screen printed electrodes, the peak was found to occur at –265 mV vs. Ag/AgCl; (B) Standard curve measurements at the peak potential for each measurement. C, standard carbon; GPH, graphene; CNT, carbon nanotubes; (C) overnight growth of PA14 shows that pyocyanin production can readily be detected on standard screen-printed carbon electrodes.

FIGURE 5

Electrochemical performance of MCL-PHA/graphene composites. (A) The composites with different concentrations of graphene were measured in a buffer of LB media containing 100 μM pyocyanin. The peak height (indicated in blue) was obtained using the approach described in SI1. (B) A standard curve was produced using the 30 wt/v% the PHA/graphene composite, with pyocyanin added in 5 μM steps up to 95 μM. The blue line represents a linear fit and was used to calculate pyocyanin concentration in the PA14 experiments.

FIGURE 6

Detection of pyocyanin in an overnight culture of PA14. An aliquot of overnight culture was placed onto the graphene doped biopolymer and DPV measurements were performed. These show that a peak is observed in the same location as that seen in experiments with purified pyocyanin, suggesting that it is possible to detect the presence of pyocyanin in an overnight culture of PA14 using the biopolymer as a sensor.

FIGURE 7

The measurement from the electrochemical biopolymer sensor are used as the input to the neural network, a priori trained, to estimate the model uncertainty. The uncertainty in the dynamic model is updated to improve the prediction accuracy of the model. The model predicts changes in the pyocyanin concentration as shown in the graph, where the blue is measured data and the red is the prediction. When the pyocyanin increases above a threshold level, another algorithm would determine optimal time and concentration of the antimicrobial silver required to kill P. aeruginosa.