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Life sciences · Journal article
Cellular and Molecular Bioengineering · October 20, 2026
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Abstract Introduction Engineering microbiome therapies requires an organizing principle for the mechanisms by which microbiome factors impact the host. We previously developed the “pharmacobiome”, a framework that uses mechanisms of drugs to quantify the host impact of microbiome factors. Here, we generalized this framework into a Microbiome Pharmacology Foundation Model (MPFM) for mechanism-informed design of microbiome therapeutics. Methods We used correlation analysis of vaginal multi-omics data to generate host gene signatures for vaginal microbiome factors and compared the signatures to drug treatment host transcriptomics to construct microbiome factor-drug similarity scores. This Vaginal Microbiome Pharmacology database of 6,719,800 training instances enabled us to train the MPFM, a deep autoencoder model, and to derive experimentally testable predictions of individual microbiome factors and designed combination therapeutic properties. Results The MPFM training converged with strong performance (r = 0.57, MSE = 0.0058) and the latent space formed a continuous manifold. We analyzed cisplatin in the MPFM to identify anti-cancer metabolites and experimentally confirmed that taurine was Lactobacillus- derived and cytotoxic against cancer cell lines ( p < 0.0005). We also analyzed metronidazole to identify antibiotic metabolites and experimentally showed hydroxyisocaproate was Lactobacillus- derived and selectively killed vaginal pathogens ( p < 0.05). Finally, we used the model to explore patient-specific microbiome pharmacology and design probiotic combinations to support in vitro fertilization. Conclusions We demonstrated consistent performance of the MPFM and experimentally supported the validity of predictions. Our continued work simulates probiotic, prebiotic, and postbiotic combinations for patient-specific in vivo sample analysis to develop tailored microbiome therapies for gynecologic conditions.