The study aims to formulate and optimise topical antibacterial preparation by using Malaysian kelulut honey as the active ingredient and xanthan gum as the polymeric agent. Response surface methodology was used to optimise the preparation. The acidity, honey concentration and xanthan gum concentration were the independent variables. The zone of inhibitions on S. aureus ATCC6538 and E. coli ATCC8739 were the response variables. The optimal preparation was evaluated on its physicochemical properties, viscosity, antibacterial efficacy and stability. The antibacterial efficacy of the optimal preparation was compared to the commercially antibacterial gel (MediHoney™, Comvita). The optimal preparation was formulated at pH of 3.5, honey concentration of 90% (w/v) and xanthan gum concentration of 1.5% (w/v) with the inhibition zones measured on S. aureus ATCC6538 was 16.2 mm and E. coli ATCC8739 was 15.8 mm respectively. The factors of acidity and honey concentration have significantly influenced the inhibition zone on S. aureus ATCC6538 and E. coli ATCC8739. The utilisation of xanthan gum as the polymeric agent was fit for the preparation which showed by adequate physicochemical properties and retained of the antibacterial effects. This was supported by constant viscosity and efficacy of the preparation within the six months of stability study indicating stable and reliable preparation. Xanthan gum is a potential polymeric agent due to its effective use in preparing stable preparation with effective antibacterial properties.
Current in silico modelling techniques, such as molecular dynamics, typically focus on compounds with the highest concentration from chromatographic analyses for bioactivity screening. Consequently, they reduce the need for labour-intensive in vitro studies but limit the utilization of extensive chromatographic data and molecular diversity for compound classification. Compound permeability across the blood-brain barrier (BBB) is a key concern in central nervous system (CNS) drug development, and this limitation can be addressed by applying cheminformatics with codeless machine learning (ML). Among the four models developed in this study, the Random Forest (RF) algorithm with the most robust performance in both internal and external validation was selected for model construction, with an accuracy (ACC) of 87.5% and 86.9% and area under the curve (AUC) of 0.907 and 0.726, respectively. The RF model was deployed to classify 285 compounds detected using liquid chromatography quadrupole time-of-flight mass spectrometry (LCQTOF-MS) in Kelulut honey; of which, 140 compounds were screened with 94 descriptors. Seventeen compounds were predicted to permeate the BBB, revealing their potential as drugs for treating neurodegenerative diseases. Our results highlight the importance of employing ML pattern recognition to identify compounds with neuroprotective potential from the entire pool of chromatographic data.