Metabolic modeling reveals determinants of prebiotic and probiotic treatment efficacy across multiple human intervention trials.

MCMM-predicted probiotic engraftment largely agreed with measured engraftment, achieving 75%-80% accuracy across two human clinical trial cohorts.

• Accuracy of 75%-80% was achieved across two distinct clinical trial cohorts with different probiotic formulations.
• The first cohort tested a five-strain probiotic combined with the prebiotic inulin designed to improve metabolic health.
• The second cohort tested an eight-strain probiotic designed to treat recurrent Clostridioides difficile infections.
• Engraftment probabilities varied across taxa within the probiotic formulations.

MCMMs captured treatment-driven shifts in predicted short-chain fatty acid (SCFA) production in response to probiotic and prebiotic interventions.

• The models predicted microbiota-mediated SCFA production changes following biotic interventions.
• SCFA production shifts were assessed in the context of both probiotic and prebiotic (inulin) treatment arms.
• The models reflected complex interactions between introduced biotics, the endogenous microbiota, and host diet.

Higher model-predicted growth rates of Akkermansia muciniphila were negatively associated with glucose area under the curve (AUC) in the first clinical trial.

• This association was observed in the trial testing a five-strain probiotic combined with the prebiotic inulin designed to improve metabolic health.
• The finding provided clues about the mechanisms underlying treatment efficacy.
• Glucose AUC was used as a cardiometabolic health outcome measure.

Substantial inter-individual variability in predicted responses to increasing dietary fiber was found in a third human cohort undergoing a healthy diet and lifestyle intervention.

• The models were extended to a third human cohort undergoing a healthy diet and lifestyle intervention.
• Inter-individual variability in predicted fiber responses was significantly associated with baseline-to-follow-up changes in cardiometabolic health markers.
• This finding highlights that individual microbiome composition shapes the metabolic response to dietary interventions.

Simulation results suggested that personalized prebiotic selection may further enhance probiotic efficacy.

• Simulations were used to explore whether matching specific prebiotics to individuals could improve outcomes beyond a standard intervention.
• This finding supports the concept of personalized microbiome-mediated interventions.
• The result was derived from in silico modeling rather than direct experimental validation in the reported cohorts.

Predicting individual-specific success or failure of probiotic and prebiotic therapies is a major challenge driven by complex interactions between introduced biotics, the endogenous microbiota, and host diet.

• Prebiotic, probiotic, and combined (synbiotic) interventions often show variable outcomes across individuals.
• The paper frames this variability as the key motivating problem for applying metabolic modeling approaches.
• MCMMs were leveraged specifically to address this challenge of predicting individual-level response.