Intended Audience: Researchers
The NaPDI public portal is intended to serve a diverse group of researchers interested in the assessment of natural product-drug interactions. Potential users include, but are not limited to, drug-drug interaction scientists, clinical pharmacists, and drug-compendium editors.
The public portal and associated data repository intend to provide a reliable resource where researchers can access scientific results, raw data, and recommended approaches to assess the clinical relevance of pharmacokinetic natural product-drug interactions.
|In vitro: Data derived from in vitro systems include parameters describing natural product binding (fu,plasma, fu,mic), metabolism and transport (Km, Vmax), inhibition potency (IC50, Ki), and induction potential (EC50, Emax).||IVIVE: in vitro to in vivo extrapolation approaches encompass numerous mathematical models to predict the magnitude of natural product-drug interactions. Mathematical models include static interaction models, mechanistic static models, and fully mechanistic physiologically-based pharmacokinetic interaction models.||Clinical: Appropriately designed clinical pharmacokinetic interaction studies where the natural product is the precipitant and a drug (or drugs) is the object can provide a definitive assessment of interaction risk. Clinical data include pharmacokinetic outcomes (AUC, Cmax, Tmax, t1/2) describing object drug disposition in the absence and presence of the precipitating natural product.|
All data generated from NaPDI Center activities will be organized and archived into an open-access repository that is publicly accessible through a web-based public portal created and managed by the Informatics Core.
The Center uses ontologies to foster the reuse and interoperability of natural product-drug interaction (NPDI) data stored in the NaPDI data repository. [Judkins et al. 2017] Domain experts have produced a list of terms and definitions used in the repository to represent data from pharmacokinetic NPDI studies. For each term, an ontology expert attempts to map the definitions to existing ontologies. Terms that are not present in existing ontologies are created within the Drug-Drug Interaction and Evidence Ontology (DIDEO). The NaPDI data repository provides links to term definitions through its user interface. Each term definition will have a unique identifying code that researchers and computer programs will be able to use to query the repository.
Currently all published data generated from the in vitro and in vitro studies conducted by the NaPDI Center is available to researchers with no restrictions through the Center’s data repository. Natural product analytical data generated by the Center is also available to researchers upon request with no restrictions including certificate of analysis (CoA)-style reports and spectra from mass spectrometry and 1H NMR.
FAIR Data Principles
The NaPDI team strives to ensure that pharmacokinetic natural product-drug interaction (PK-NPDI) data satisfies the four foundational principles of good data management and stewardship — Findability, Accessibility, Interoperability, and Reusability (FAIR).
- Findability: Each PK-NPDI dataset receives a globally unique and persistent (meta)data identifier. This identifier enables effective registration and indexing of PK-NPDI data for discoverability.
- Accessibility: The NaPDI data repository uses an ontology (DIDEO) to provide clarity on the relationship between PK-NPDI metadata and the datasets described by that metadata. Documentation available through this portal explains how to deposit PK-NPDI data for easy open access retrieval. The data repository makes distinctions between in vitro, in vivo, and analytic data.
- Interoperable: The NaPDI data repository uses ontologies for all PK-NPDI data elements and provides links to term definitions through its user interface. Each term definition will have a unique identifying code that researchers and computer programs will be able to use to query the repository.
- Reusability: Documentation available on this portal provides clarity about data quality assurance and provenance, data usage restrictions, and the algorithms and tools required for reusability
Utecht J, Brochhausen M, Judkins J, Schneider J, Boyce RD. Formalizing Evidence Type Definitions for Drug-Drug Interaction Studies to Improve Evidence Base Curation. Stud Health Technol Inform. 2017;245:960-964. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5765984/
(Editors) Bernadette FL, Burle C., Calegari N. Data on the Web Best Practices. World Wide Web Consortium (W3C) Recommendation. 1/31/2017. https://www.w3.org/TR/dwbp/