You are not a member of this wiki.
Pages and Files
All pages of this SPL Wiki
- Documents and References
- Training and Seminars
- Industry Survey
All Groups/ Subteams
FAQs / Q and As
DUNS Number FAQ
UNII Codes FAQ
Drug Element Q&A
Drug Listing Q&A
PLR Recent Major Changes
New Materials and Updates
Wikispaces : SPL-work-group - all changes
home: Animal OTC
What are the appropria...
Mar 5, 2014
No. You should update ...
home: Inactive ingredient change and NDC change
Mar 5, 2014
If there's a change in...
Mar 3, 2014
Hello, Is eList same...
home: SPL Submission to OC-Error Message
Feb 24, 2014
Hello SPL-Wor-Group, ...
Feb 19, 2014
...Objective: Address ...
20140115 SPL ERDL Minutes.pdf
Feb 19, 2014
home: Delisting a product
Feb 18, 2014
Could someone tell me ...
Feb 17, 2014
You are welcome. Howard
Feb 17, 2014
Thank you Howard!
Feb 13, 2014
Running into issues wi...
home: Bulk NDC
Feb 13, 2014
Hello, We are importi...
Bibliography - Articles containing SPL
Articles referencing SPL
(2014) 2013 Fung KW et al.
Fung KW, Jao CS, Demner-Fushman D. Extracting drug indication information from structured product labels using natural language processing J Am Med Inform Assoc. 2013 May 1;20(3):482-8. doi: 10.1136/amiajnl-2012-001291. Epub 2013 Mar 9. (PMID:23475786)
Note: It is feasible to use publicly available natural language processing tools to extract indication information from freely available drug labels. Named entity recognition sources (eg, MetaMap) provide reasonable recall. Combination with other data sources provides higher precision.
PMCID: PMC3628062 [Available on 2014/5/8]
2013 Boyce RD et al.
Boyce RD, Freimuth RR, Romagnoli KR, Pummer T, Hochheiser H, Empey PE. Toward Semantic Modeling of Pharmacogenomic Knowledge for Clinical and Translational Decision Support.. 2013 AMIA Summit on Translational Bioinformatics.San Francisco CA 2013. AMIA-027-T2013.R1
Note: Pilot work on a semantic model of the pharmacogenomics information found
in drug product labels.showed potential
make the unstructured pharmacogenomic information currently written in product labeling more accessible and actionable through structured annotations
of pharmacogenomics effects and clinical recommendations.
Model illustration here:
PMCID: 24303292 PubMed Central PMC3814496
2013 Boyce RD et al.
Boyce RD, Horn JR, Hassanzadeh O, de Waard A, Schneider J, Luciano JS, Rastegar-Mojarad M, Liakata M. Dynamic Enhancement of Drug Product Labels to Support Drug Safety, Efficacy, and Effectiveness.
J Biomed Semantics
2013. Jan 26, 4:5
Note: SPL data from content of labeling (clinical studies, drug interactions, and clinical pharmacology sections) for psychotropic drug products was utilized as one of four sources for this study. The other three sources utilized for this study were: LinkedCT, National Drug File-Reference Terminology, and The Drug Interaction Knowledge Base.
2013 Cheng CM et al.
Cheng, Christine M., Colleen DeLizza, and Joan Kapusnik-Uner. "Prevalence and Therapeutic Classifications of FDA-Approved Prescription Drugs With Boxed Warnings." Therapeutic Innovation & Regulatory Science (2013): 2168479013496091.
2013 Li et al.
Li Q, Deleger L, Lingren T, Zhai H, Kaiser M, Stoutenborough L, Jegga AG, Cohen KB, Solti I. Mining FDA drug labels for medical conditions. BMC Med Inform Decis Mak. 2013 Apr 24;13:53. doi: 10.1186/1472-6947-13-53. PubMed PMID:23617267; PubMed Central PMCID: PMC3646673.
Note: Natural-language application was designed to extract medications with their corresponding medical conditions (Indications, Contraindications, Overdosage, and Adverse Reactions) as triples of medication-related information ([(1) drug name]-[(2) medical condition]-[(3) LOINC section header]) for an intelligent database system, in order to improve patient safety and the quality of health care. The Food and Drug Administration’s (FDA) drug labels are used to demonstrate the feasibility of building the triples as an intelligent database system task.
The results demonstrate that (1) medical conditions can be extracted from FDA drug labels with high performance; and (2) it is feasible to develop a framework for an intelligent database system.
2013 Hassanzadeh O et al.
Hassanzadeh O, Zhu Q, Freimuth R, Boyce R. Extending the "Web of Drug Identity" with Knowledge Extracted from United States Product Labels
2013 AMIA Summit on Translational Bioinformatics. 2013.San Francisco CA.
LinkedSPLs, a Linked Data resource that extends the "web of drug identity" using information extracted
from SPLs, to provide a map between SPL active ingredients and DrugBank chemical entities. Comparison of three approaches (comparison, exact string matching based on the chemical name, and automatic (unsupervised) linkage identification. found that, while these three approaches are complementary, the automatic approach performs well in terms of precision and recall.
PMID: 24303301 PMCID: PMC3814463
sample data can be seen at
2013 Rastegar-Mojarad M et al.
Rastegar-Mojarad, Majid, Brian Harrington, and Steven M. Belknap. "Automatic detection of drug interaction mismatches in package inserts." In Proceedings of the 2013 IEEE International Conference on Advances in Computing, Communications and Informatics (ICACCI). 2013. Pages 373-377.
2013 Samwald M et al.
Samwald M, Freimuth R, Luciano JS, Lin S, Powers RL, Marshall MS, Adlassnig KP, Dumontier M, Boyce RD. An RDF/OWL Knowledge Base for Query Answering and Decision Support in Clinical Pharmacogenetics. Stud Health Technol Inform. 2013;192:539-42. PubMed PMID: 23920613.
Link to FDA Pharmacogenetics Reference:
2013 Zhu Q et al.
Zhu Q, Freimuth RR, Pathak J, Durski MJ, Chute CG. Disambiguation of PharmGKB drug-disease relations with NDF-RT and SPL. J Biomed Inform. 2013 Aug;46(4):690-6. doi: 10.1016/j.jbi.2013.05.005. Epub 2013 May 29. PubMed PMID:23727027; PubMed Central PMCID: PMC3746070.
2013 Zhu Q et al.
Zhu Q, Jiang G, Wang L, Chute CG. Standardized drug and pharmacological class network construction. Stud Health Technol Inform. 2013;192:1125. PubMed PMID:23920899.
2012 Zhu et al.
Zhu Q, Jiang G, Chute CG. Profiling structured product labeling with NDF-RT and RxNorm.
J Biomed Semantics.
2012 Dec 20;3(1):16. [Epub ahead of print]
Note: This paper presents a framework to map SPL labels with existing drug ontologies. This type of mapping could provide useful insights on meaningful use of FDA SPL drug labels in clinical applications through standard drug ontologies such as NDF-RT and RxNorm.
2012/2013 Duke et al.
Duke J; Friedlin J and Li X. Consistency in the safety labeling of bioequivalent medications.
Pharmacoepidemiol Drug Saf. 2013 Mar;22(3):294-301. doi: 10.1002/pds.3351. Epub 2012 Oct 8. (PMID 23042584)
Note: SPL data used in assessing Adverse Reactions and Post-Marketing sections of 1095 labels to attempt to determine real-world consistency of electronic labeling for bioequivalent drugs.
2011 Bisgin et al.
Bisgin H, Liu Z, Fang H, Xu X, Tong W. Mining FDA drug labels using an unsupervised learning technique--topic modeling. BMC Bioinformatics. 2011 Oct. 18;12 Suppl 10:S11. doi: 10.1186/1471-2105-12-S10-S11. PubMed PMID: 22166012; PubMed Central PMCID: PMC3236833.
2011 Chen et al.
Chen M, Vijay V, Shi Q, Liu Z, Fang H, Tong W
. FDA-approved drug labeling for the study of drug-induced liver injury. Drug Discov Today. 2011 Aug;16(15-16):697-703. doi: 10.1016/j.drudis.2011.05.007. Epub 2011 May 20. PubMed PMID: 21624500.
Comparative analysis of drugs based on their DILI potential is an effective approach to discover key DILI mechanisms and risk factors. However, assessing the DILI potential of a drug is a challenge with no existing consensus methods. A systematic classification scheme using FDA-approved drug labeling is proposed to assess the DILI potential of drugs, which yielded a benchmark dataset with 287 drugs representing a wide range of therapeutic categories and daily dosage amounts.
2011 Duke et al.
Duke J, Friedlin J, Ryan P. A quantitative analysis of adverse events and "overwarning" in drug labeling.
Arch Intern Med.
2011 May 23;171(10):944-6. doi: 10.1001/archinternmed.2011.182.
Structured product labeling improves detection of drug-intolerance issues.
J Am Med Inform Assoc.
2009 Mar-Apr;16(2):211-9. doi: 10.1197/jamia.M2933. Epub 2008 Oct 24.
This study highlights specificity problems known to trouble drug-intolerance decision support and suggests how terminology and methods of recording drug intolerances could be improved.
2010 Boyce R et al.
Boyce R, Harkema H, Conway M: Leveraging the semantic web and natural language processing to enhance drug-mechanism knowledge in drug product labels. In Proceedings of the First ACM International Health Informatics Symposium: November 11 - 12, 2010. Arlington, VA, USA; 2010::492-496.
Schadow G. Assessing the Impact of HL7/FDA Structured Product Label (SPL) Content for Medication Knowledge Management.
AMIA Annu Symp Proc. 2007; 2007: 646–650.
SPL labels describe 78% of actual community pharmacy dispensed records, and agree well with RxNorm. The author suggests SPL can be used as the primary source of drug information for e-prescribing systems and existing gaps can be temporarily closed with RxNorm or other sources
HL7 Structured Product Labeling – Electronic Prescribing Information for Provider Order Entry Decision Support
AMIA Annu Symp Proc.
help on how to format text
Turn off "Getting Started"