ABSTRACT
The US Food and Drug Administration (FDA) and the National Center for Advancing Translational Sciences (NCATS) have collaborated to publish rigorous scientific descriptions of substances relevant to regulated products. The FDA has adopted the global ISO 11238 data standard for the identification of substances in medicinal products and has populated a database to organize the agency's regulatory submissions and marketed products data. NCATS has worked with FDA to develop the Global Substance Registration System (GSRS) and produce a non-proprietary version of the database for public benefit. In 2019, more than half of all new drugs in clinical development were proteins, nucleic acid therapeutics, polymer products, structurally diverse natural products or cellular therapies. While multiple databases of small molecule chemical structures are available, this resource is unique in its application of regulatory standards for the identification of medicinal substances and its robust support for other substances in addition to small molecules. This public, manually curated dataset provides unique ingredient identifiers (UNIIs) and detailed descriptions for over 100 000 substances that are particularly relevant to medicine and translational research. The dataset can be accessed and queried at https://gsrs.ncats.nih.gov/app/substances.
Subject(s)
Databases, Chemical , Databases, Factual , Databases, Pharmaceutical , Public Health/legislation & jurisprudence , Biological Products/chemistry , Biological Products/classification , Datasets as Topic , Drugs, Investigational/chemistry , Drugs, Investigational/classification , Humans , Internet , Nucleic Acids/chemistry , Nucleic Acids/classification , Polymers/chemistry , Polymers/classification , Prescription Drugs/chemistry , Prescription Drugs/classification , Proteins/chemistry , Proteins/classification , Public Health/methods , Small Molecule Libraries/chemistry , Small Molecule Libraries/classification , Software , United States , United States Food and Drug Administration , Xenobiotics/chemistry , Xenobiotics/classificationABSTRACT
BabelMeSH is a multilanguage search for MEDLINE/PubMed. We created a database of Arabic translations of MeSH terms and other medical terms using MySQL and developed a Web interface for searching MEDLINE/PubMed in Arabic. We evaluated the accuracy of BabelMeSH using a list of medical terms from BMJ Clinical Evidence. The accuracy was 58% (machine scoring) and 65% human review.) The result obtained may be explained by variations in expressing medical terms in Arabic.
