Pɸ-mi-pa loaded PVA/HA electrospun nanofibers from a statistically designed experiment using machine learning algorithms

Abstract

Bacteriophages, which infect bacterial cells, have a unique ability to reduce bacterial colonization, particularly in antibiotic-resistant biofilm infections. This study aims to fabricate optimized bacteriophage-loaded nanofibers composed of polyvinyl alcohol (PVA) and hyaluronic acid (HA) using machine learning techniques. A comparative analysis was conducted to determine the most efficient machine learning algorithm for this purpose. The eXtreme Gradient Boosting (XGBoost) algorithm was used to optimize three sets of bacteriophage-loaded nanofibers-GA1, GA2, and GA3-at varying sonication times. The nanofibers were characterized using Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), X-ray diffraction (XRD), differential scanning calorimetry (DSC), mucoadhesion analysis, thermogravimetric analysis, tensile strength analysis, and a bacteriophage release assay. SEM analysis revealed that GA3 exhibited homogeneous, well-aligned, defect-free polymer fibers with smooth surfaces, whereas GA1 and GA2 contained bead-like structures. Thermal degradation occurred between 300 °C and 368 °C, with GA1 displaying the highest tensile strength. Predicted values for mucin adsorption and bacteriophage release closely aligned with experimental data, confirming the accuracy of the XGBoost model. Finally, the release of MDR-specific phage Pɸ-Mi-Pa from the optimized nanofibers was highest at pH 5, which is similar to vaginal pH. These findings highlight the potential of these nanofibers for phage therapy, particularly in improving the prognosis of antibiotic-resistant biofilm infections.

Keywords:
Artificial neural network; Bacteriophages; Electrospun; Hyaluronic acid; Nanofibers; Polyvinyl alcohol.