Enhanced formulation of precision probiotics through active machine learning

The human gut microbiome is crucial to health, with dysbiosis increasingly linked to disease. Precision probiotics offer a promising approach to restoring microbial balance, but ensuring probiotic viability through gastrointestinal transit remains a challenge. This study applies an advanced active machine learning (ML) approach to predict how excipients affect the growth of Lactobacillus plantarum, a commonly used probiotic. State-of-the-art experiments were carried out to complement the ML study. Starting with five known excipient-probiotic interactions, we apply active ML over three rounds to predict the effects of 116 excipients, iteratively refining model certainty and accuracy. Five ML models-neural networks, gradient boosting, logistic regression, random forest, and support vector machines-were trained and evaluated, with the final model achieving certainty levels close to 90%. Unlike previous methods, which retrained new models per iteration, our approach continuously optimized a single model, enhancing prediction stability and reducing uncertainty spread. These results highlight the potential of active ML to support accurate excipient selection in probiotic formulations.