Selectivity of OATs and OATPs for Endogenous Metabolites and Signaling Molecules In Vivo

Organic anion transporters
(OATs, SLC22) in the kidney
and organic
anion-transporting polypeptides (OATPs, SLCO) in the liver play crucial
roles in the disposition of small molecule drugs that are organic
anions. According to the Remote Sensing and Signaling Theory, these
multispecific “drug” transporters are also central to
crosstalk between the liver, kidney, and other organs via endogenous
small molecules (e.g., metabolites, signaling molecules, gut microbiome
products). These multispecific drug transporters govern access of
small molecules with high informational content across multiple scales
(organism to organelle). Previous chemoinformatic and machine learning
methods have proven useful for identifying molecular properties of
organic anion drugs that predispose them to handling by the OAT (renal)
and the OATP (hepatic) transporters. This is important for understanding
pharmacokinetics (ADME) in the context of chronic kidney disease (CKD)
and liver disease. Given that OATs and OATPs are involved in many
metabolic diseases, we sought to determine whether molecular properties
could be identified for distinguishing OAT- versus OATP-interacting
endogenous metabolites in vivo. This is essential
for understanding endogenous small molecule communication between
the kidney proximal tubule and hepatocytes in a larger Remote Sensing
and Signaling System. We analyzed in vivo 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.