What problem does it solve?
This Skill helps researchers identify drug-related metabolic changes, predict likely metabolic pathways and enzyme interactions, and prioritize putative metabolites from LC‑MS data and molecular structures to accelerate pharmacometabolomics studies.
Core Features & Use Cases
- Paired drug response analysis: statistical comparison of pre-dose and post-dose metabolomics with multiple testing correction and visualization.
- CYP450 substrate prediction: compute RDKit descriptors and apply rule-based heuristics to rank likely metabolizing isoforms.
- ADME estimation and metabolic soft spots: Lipinski/Veber checks, ESOL solubility estimation, and atom-level soft-spot detection for oxidation and dealkylation.
- Phase I/II metabolite search in LC‑MS: compute expected m/z shifts for common biotransformations and search experimental mz/rt tables within ppm tolerance.
- DrugBank and PK/PD integration: retrieve enzyme and metabolite annotations via DrugBank API (requires API key) and correlate PK parameters (AUC, Cmax, Tmax) with metabolite levels.
- R integration with MetaboAnalystR: end-to-end differential analysis, normalization, volcano plots, and PLS-DA for responder vs non-responder studies.
- Use Case: Compare paired metabolomics from a clinical dosing study to detect drug-responsive metabolites, predict responsible CYP isoforms, and validate candidates with MS/MS and DrugBank annotations.
Quick Start
Use the pharmacometabolomics skill to compare pre-dose and post-dose metabolomics CSV files and produce a ranked list of significant drug-associated metabolites.
