Metabolic modeling reveals determinants of synbiotic efficacy in a human intervention trial.

Prebiotic, probiotic, and combined (synbiotic) interventions often show variable outcomes across individuals, driven by complex interactions between introduced biotics, the endogenous microbiota, and the host diet. Predicting individual-specific success or failure of probiotic and prebiotic therapies remains a major challenge. Here, we leverage microbial community-scale metabolic models (MCMMs) to forecast probiotic engraftment and microbiota-mediated short-chain fatty acid (SCFA) production in response to probiotic and prebiotic interventions. Using data from two clinical trials, testing a five-strain probiotic combined with the prebiotic inulin designed to improve metabolic health and an eight-strain probiotic designed to treat recurrent Clostridioides difficile infections, respectively, we show that MCMM-predicted engraftment largely agrees with measurements, achieving 75-80% accuracy. Engraftment probabilities varied across taxa, with Akkermansia muciniphila and Bifidobacterium infantis showing higher predicted and observed engraftment than the clostridial strains. MCMMs captured treatment-driven shifts in predicted SCFA production, and higher model-predicted growth rates of A. muciniphila were negatively associated with glucose AUC in the synbiotic trial, providing clues about the mechanisms underlying treatment efficacy. Finally, extending these models to a longitudinal cohort undergoing a healthy diet and lifestyle intervention revealed substantial inter-individual variability in predicted responses to increasing dietary fiber, which were significantly associated with baseline-to-follow-up changes in cardiometabolic health markers. Finally, our simulation results suggested that personalized prebiotic selection may further enhance probiotic efficacy. Together, these findings demonstrate the potential of metabolic modeling to guide personalized microbiome-mediated interventions.