Towards early detection of adverse drug reactions: combining pre-clinical drug structures and post-market safety reports.
Background: Adverse drug reaction (ADR) is a major burden for patients and healthcare industry. Early and accurate detection of potential ADRs can help to improve drug safety and reduce financial costs. Post-market spontaneous reports of ADRs remain a cornerstone of pharmacovigilance and a series of...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 19; no. 1; pp. 1 - 10 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
BioMed Central
12/18/2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=140420898&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 140420898 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 12/18/2019 vid: 19 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 140420898 140420898 NLM31849321 10.1186/s12911-019-0999-1 NLM31849321 140420898 ppf: 1 ppct: 9 formats: tig: atl: Towards early detection of adverse drug reactions: combining pre-clinical drug structures and post-market safety reports. aug: au: Liu, Ruoqi Zhang, Ping affil: Department of Computer Science and Engineering, The Ohio State University, 2015 Neil Ave, 43210, Columbus, Ohio, USA sug: subj: Drug Evaluation, Preclinical Methods Adverse Drug Event Algorithms Pharmacovigilance Adverse Drug Event Prevention and Control Resource Databases Biological Markers Adverse Drug Event Etiology Product Evaluation Clinical Assessment Tools Scales ab: Background: Adverse drug reaction (ADR) is a major burden for patients and healthcare industry. Early and accurate detection of potential ADRs can help to improve drug safety and reduce financial costs. Post-market spontaneous reports of ADRs remain a cornerstone of pharmacovigilance and a series of drug safety signal detection methods play an important role in providing drug safety insights. However, existing methods require sufficient case reports to generate signals, limiting their usages for newly approved drugs with few (or even no) reports.Methods: In this study, we propose a label propagation framework to enhance drug safety signals by combining drug chemical structures with FDA Adverse Event Reporting System (FAERS). First, we compute original drug safety signals via common signal detection algorithms. Then, we construct a drug similarity network based on chemical structures. Finally, we generate enhanced drug safety signals by propagating original signals on the drug similarity network. Our proposed framework enriches post-market safety reports with pre-clinical drug similarity network, effectively alleviating issues of insufficient cases for newly approved drugs.Results: We apply the label propagation framework to four popular signal detection algorithms (PRR, ROR, MGPS, BCPNN) and find that our proposed framework generates more accurate drug safety signals than the corresponding baselines. In addition, our framework identifies potential ADRs for newly approved drugs, thus paving the way for early detection of ADRs.Conclusions: The proposed label propagation framework combines pre-clinical drug structures with post-market safety reports, generates enhanced drug safety signals, and can potentially help to accurately detect ADRs ahead of time.Availability: The source code for this paper is available at: https://github.com/ruoqi-liu/LP-SDA. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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