Selectivity of OATs and OATPs for Endogenous Metabolites in Knockout Mice

Anisha K. Nigam published a dataset on figshare in 2026 containing 210 endogenous metabolites analyzed from OAT and OATP transporter knockout mice. The data includes molecular properties for 92 OAT-selective and 118 OATP-selective metabolites, used to train machine learning classifiers. The work supports the Remote Sensing and Signaling Theory for understanding organ crosstalk and drug disposition.

Use Cases

• Classifying OAT vs. OATP metabolite selectivity based on molecular properties like MolLogP and RingCount.
• Training machine learning models (e.g., Random Forest) for transporter-metabolite interaction prediction.
• Investigating organ crosstalk and interorganismal communication within the Remote Sensing and Signaling Theory framework.
• Informing metabolite-based drug design by understanding transporter selectivity for endogenous molecules.

Strengths

• Dataset contains 210 specifically annotated metabolites (92 OAT-selective, 118 OATP-selective).
• Includes molecular properties derived from chemoinformatic methods, such as MolLogP and NumRotatableBonds.
• Machine learning models trained on the data achieved over 75% classification accuracy.

Limitations

• Row count for the underlying data is unknown, limiting suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Data is derived from a specific mouse model, which may limit direct translation to human biology.

Provenance

Source

figshare, author Anisha K. Nigam.

Collection Method

Analysis of in vivo metabolomics data from OAT and OATP knockout mice.

Freshness

Last updated 2026-04-16 18:07:58.