Towards early detection of adverse drug reactions: combining pre-clinical drug structures and post-market safety reports

Abstract 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 s...

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Main Authors: Ruoqi Liu, Ping Zhang
Format: Article
Language:English
Published: BMC 2019-12-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Online Access:https://doi.org/10.1186/s12911-019-0999-1
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spelling doaj-4c86224a02d04aa28113c8567b4b7b152020-12-20T12:35:16ZengBMCBMC Medical Informatics and Decision Making1472-69472019-12-011911910.1186/s12911-019-0999-1Towards early detection of adverse drug reactions: combining pre-clinical drug structures and post-market safety reportsRuoqi Liu0Ping Zhang1Department of Computer Science and Engineering, The Ohio State UniversityDepartment of Computer Science and Engineering, The Ohio State UniversityAbstract 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.https://doi.org/10.1186/s12911-019-0999-1Adverse drug reactionsSignal DetectionFDA Adverse Event Reporting SystemDrug similarity
collection DOAJ
language English
format Article
sources DOAJ
author Ruoqi Liu
Ping Zhang
spellingShingle Ruoqi Liu
Ping Zhang
Towards early detection of adverse drug reactions: combining pre-clinical drug structures and post-market safety reports
BMC Medical Informatics and Decision Making
Adverse drug reactions
Signal Detection
FDA Adverse Event Reporting System
Drug similarity
author_facet Ruoqi Liu
Ping Zhang
author_sort Ruoqi Liu
title Towards early detection of adverse drug reactions: combining pre-clinical drug structures and post-market safety reports
title_short Towards early detection of adverse drug reactions: combining pre-clinical drug structures and post-market safety reports
title_full Towards early detection of adverse drug reactions: combining pre-clinical drug structures and post-market safety reports
title_fullStr Towards early detection of adverse drug reactions: combining pre-clinical drug structures and post-market safety reports
title_full_unstemmed Towards early detection of adverse drug reactions: combining pre-clinical drug structures and post-market safety reports
title_sort towards early detection of adverse drug reactions: combining pre-clinical drug structures and post-market safety reports
publisher BMC
series BMC Medical Informatics and Decision Making
issn 1472-6947
publishDate 2019-12-01
description Abstract 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.
topic Adverse drug reactions
Signal Detection
FDA Adverse Event Reporting System
Drug similarity
url https://doi.org/10.1186/s12911-019-0999-1
work_keys_str_mv AT ruoqiliu towardsearlydetectionofadversedrugreactionscombiningpreclinicaldrugstructuresandpostmarketsafetyreports
AT pingzhang towardsearlydetectionofadversedrugreactionscombiningpreclinicaldrugstructuresandpostmarketsafetyreports
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