Prediction of qualitative antibiofilm activity of antibiotics using supervised machine learning techniques

Although biofilm-specific antibiotic susceptibility assays are available, they are time-consuming and resource-intensive, and hence they are not usually performed in clinical settings. Herein, we introduce a machine learning-based predictive modeling approach that uses routinely available and easily accessible data to qualitatively predict in vitro antibiofilm activity of antibiotics with relatively high accuracy. Three optimized models based on logistic regression, decision tree, and random forest algorithms were successfully developed in this study using data manually collected from published literature. In these models, independent variables that serve as significant predictors of antibiofilm activity are minimum inhibitory concentration, bacterial Gram type, biofilm formation method, in addition to antibiotic's mechanism of action, molecular weight, and pKa. The cross-validation method showed that the optimized models exhibit prediction accuracy of 67% ± 6.1% for the logistic regression model, 73% ± 5.8% for the decision tree model, and 74% ± 5% for the random forest model. However, the one-way ANOVA test revealed that the difference in prediction accuracy between the 3 models is not statistically significant, and hence they can be considered to have comparable performance. The presented modeling approach can serve as an alternative to the resource-intensive biofilm assays to rapidly and properly manage biofilm-associated infections, especially in resource-limited clinical settings.