CPMCP
CPMCP catalogs detailed, standardized data on Chinese patent medicines (CPMs) and ancient Chinese medicine prescriptions (CMPs), including components, indications, contraindications, compatibility mechanisms, and mappings to standardized TCM and modern medicine (MM) symptom vocabularies to support prescription-focused research.
Key Features:
- Comprehensive information: Provides standardized data for CPMs and CMPs including components, indications, and contraindications.
- Symptom associations: Maps prescription functions from ancient texts to standardized TCM symptom vocabularies, yielding 71,414 associations between compound prescriptions and TCM symptoms.
- Modern medicine integration: Establishes links between TCM symptoms and modern medicine (MM) symptoms to connect traditional concepts with contemporary clinical terminology.
- Compatibility mechanism analysis: Summarizes common drug combination principles from existing prescriptions to elucidate compatibility mechanisms.
Scientific Applications:
- TCM modernization and translational research: Enables integration of TCM prescription knowledge into modern medicine frameworks via symptom and indication mappings.
- Drug combination optimization: Supports investigation of compatibility mechanisms to inform optimization of multi-component prescriptions.
- Therapeutic development and clinical research: Provides standardized prescription and symptom association data to support development of prescription-based therapeutic strategies and clinical studies.
Methodology:
Manual mapping of ancient prescription functions to standardized TCM symptom vocabularies and analysis of existing prescriptions to derive and summarize drug combination principles.
Topics
Details
- License:
- Other
- Tool Type:
- web application
- Operating Systems:
- Mac, Linux, Windows
- Added:
- 11/6/2022
- Last Updated:
- 11/6/2022
Operations
Publications
Sun C, Huang J, Tang R, Li M, Yuan H, Wang Y, Wei J, Liu J. CPMCP: a database of Chinese patent medicine and compound prescription. Database. 2022;2022. doi:10.1093/database/baac073. PMID:36006844. PMCID:PMC9408024.