Selectivity of OATs and OATPs for Endogenous Metabolites and Signaling Molecules <i>In Vivo</i>

metabolomics data from OAT and OATP knockout mice, focusing on endogenous metabolites selective for OATs (e.g., OAT1 or SLC22A6; OAT3 or SLC22A8) vs OATPs (including the locus containing Oatp1b2, the closest homologue of human OATP1B1 or SLCO1B1 and OATP1B3 or SLCO1B3). Applying chemoinformatic methods to a data set of 210 metabolites based on knockout mouse metabolomics (92 OAT-selective, 118 OATP-selective), we identified a set of distinguishing molecular properties (e.g., MolLogP, RingCount, NumRotatableBonds). We then used machine learning approaches (e.g., Random Forest, Naive Bayes, Logistic Regression) to classify OAT vs OATP metabolites, achieving over 75% accuracy. These results support the view that transporter knockout mouse metabolomics can help define selectivity of SLC drug transporters for endogenous metabolites, signaling molecules, antioxidants, nutrients, and gut microbiome products. In the context of the Remote Sensing and Signaling Theory, we discuss the implications for understanding organ crosstalk and interorganismal communication as well as drug disposition, drug-metabolite interactions, and metabolite-based drug design.