What question did this study set out to answer?

This research aims to develop a quantitative model for predicting the efficacy of living biotherapeutics in metabolic disorders resulting from gut microbiome dysfunction.

March 25, 2026npj Systems Biology and Applications0 citationsOpen Access

Signed, sealed, delivered: a generalizable model for living biotherapeutic dosing and metabolism

VDVicenzo L. DeVitoFlorida State University BKBhargav R. KaramchedFlorida State University

Key Points

This research aims to develop a quantitative model for predicting the efficacy of living biotherapeutics in metabolic disorders resulting from gut microbiome dysfunction.
Developed the Bacterial Compartment Absorption and Transit (BCAT) model incorporating probiotic transit and microbiome metabolism.
Validated BCAT using clinical data on choline metabolism and SYNB1618 probiotic trials.
Conducted global sensitivity analysis to determine key design parameters for dosing.
Achieved a 5% mean prediction error in dose prediction, significantly better than the ~30% error from previous models.
Predicted that administering ~10^9 CFU of probiotic can reduce systemic trimethylamine by 95% when taken 3-4 hours before meals.
Identified enzyme expression level as the most critical factor for model performance, emphasizing its wide applicability.

Abstract

Abstract Living Biotherapeutic Products (LBPs) offer a promising therapeutic strategy for metabolic disorders rooted in gut microbiome dysfunction, yet quantitative frameworks for predicting their efficacy remain underdeveloped. We introduce the Bacterial Compartment Absorption and Transit (BCAT) model, a pharmacokinetic-pharmacodynamic framework that couples probiotic transit, endogenous microbiome metabolism, and enzymatic transformation within a unified dose-optimization setting. Building on the classical CAT model, BCAT incorporates mechanistically-derived colon compartments and treats dosing time as a control variable. We validate BCAT against clinical data for native choline metabolism and SYNB1618 probiotic trials, achieving 5% mean prediction error compared to ~30% for prior two-compartment models. Applying BCAT to trimethylaminuria (TMAU), we predict that ~10 9 CFU of engineered probiotic, administered 3–4 h before meals, achieves 95% reduction in systemic trimethylamine, matching healthy hepatic clearance. Global sensitivity analysis identifies enzyme expression level as the dominant design parameter, enforcing the broad applicability of this model. The BCAT framework generalizes to any gut microbiome-mediated metabolic disorder and provides quantitative dosing targets to guide live biotherapeutic development.

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