Subset Analysis for Screening Drug–Drug Interaction Signal Using Pharmacovigilance Database
Many patients require multi-drug combinations, and adverse event profiles reflect not only the effects of individual drugs but also drug–drug interactions. Although there are several algorithms for detecting drug–drug interaction signals, a simple analysis model is required for early detection of ad...
Saved in:
| Published in: | Pharmaceutics Vol. 12; no. 8; p. 762 |
|---|---|
| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Basel
MDPI AG
12.08.2020
MDPI |
| Subjects: | |
| ISSN: | 1999-4923, 1999-4923 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Many patients require multi-drug combinations, and adverse event profiles reflect not only the effects of individual drugs but also drug–drug interactions. Although there are several algorithms for detecting drug–drug interaction signals, a simple analysis model is required for early detection of adverse events. Recently, there have been reports of detecting signals of drug–drug interactions using subset analysis, but appropriate detection criterion may not have been used. In this study, we presented and verified an appropriate criterion. The data source used was the Japanese Adverse Drug Event Report (JADER) database; “hypothetical” true data were generated through a combination of signals detected by three detection algorithms. The accuracy of the signal detection of the analytic model under investigation was verified using indicators used in machine learning. The newly proposed subset analysis confirmed that the signal detection was improved, compared with signal detection in the previous subset analysis, on the basis of the indicators of Accuracy (0.584 to 0.809), Precision (= Positive predictive value; PPV) (0.302 to 0.596), Specificity (0.583 to 0.878), Youden’s index (0.170 to 0.465), F-measure (0.399 to 0.592), and Negative predictive value (NPV) (0.821 to 0.874). The previous subset analysis detected many false drug–drug interaction signals. Although the newly proposed subset analysis provides slightly lower detection accuracy for drug–drug interaction signals compared to signals compared to the Ω shrinkage measure model, the criteria used in the newly subset analysis significantly reduced the amount of falsely detected signals found in the previous subset analysis. |
|---|---|
| AbstractList | Many patients require multi-drug combinations, and adverse event profiles reflect not only the effects of individual drugs but also drug–drug interactions. Although there are several algorithms for detecting drug–drug interaction signals, a simple analysis model is required for early detection of adverse events. Recently, there have been reports of detecting signals of drug–drug interactions using subset analysis, but appropriate detection criterion may not have been used. In this study, we presented and verified an appropriate criterion. The data source used was the Japanese Adverse Drug Event Report (JADER) database; “hypothetical” true data were generated through a combination of signals detected by three detection algorithms. The accuracy of the signal detection of the analytic model under investigation was verified using indicators used in machine learning. The newly proposed subset analysis confirmed that the signal detection was improved, compared with signal detection in the previous subset analysis, on the basis of the indicators of Accuracy (0.584 to 0.809), Precision (= Positive predictive value; PPV) (0.302 to 0.596), Specificity (0.583 to 0.878), Youden’s index (0.170 to 0.465), F-measure (0.399 to 0.592), and Negative predictive value (NPV) (0.821 to 0.874). The previous subset analysis detected many false drug–drug interaction signals. Although the newly proposed subset analysis provides slightly lower detection accuracy for drug–drug interaction signals compared to signals compared to the Ω shrinkage measure model, the criteria used in the newly subset analysis significantly reduced the amount of falsely detected signals found in the previous subset analysis. Many patients require multi-drug combinations, and adverse event profiles reflect not only the effects of individual drugs but also drug-drug interactions. Although there are several algorithms for detecting drug-drug interaction signals, a simple analysis model is required for early detection of adverse events. Recently, there have been reports of detecting signals of drug-drug interactions using subset analysis, but appropriate detection criterion may not have been used. In this study, we presented and verified an appropriate criterion. The data source used was the Japanese Adverse Drug Event Report (JADER) database; "hypothetical" true data were generated through a combination of signals detected by three detection algorithms. The accuracy of the signal detection of the analytic model under investigation was verified using indicators used in machine learning. The newly proposed subset analysis confirmed that the signal detection was improved, compared with signal detection in the previous subset analysis, on the basis of the indicators of Accuracy (0.584 to 0.809), Precision (= Positive predictive value; PPV) (0.302 to 0.596), Specificity (0.583 to 0.878), Youden's index (0.170 to 0.465), F-measure (0.399 to 0.592), and Negative predictive value (NPV) (0.821 to 0.874). The previous subset analysis detected many false drug-drug interaction signals. Although the newly proposed subset analysis provides slightly lower detection accuracy for drug-drug interaction signals compared to signals compared to the Ω shrinkage measure model, the criteria used in the newly subset analysis significantly reduced the amount of falsely detected signals found in the previous subset analysis.Many patients require multi-drug combinations, and adverse event profiles reflect not only the effects of individual drugs but also drug-drug interactions. Although there are several algorithms for detecting drug-drug interaction signals, a simple analysis model is required for early detection of adverse events. Recently, there have been reports of detecting signals of drug-drug interactions using subset analysis, but appropriate detection criterion may not have been used. In this study, we presented and verified an appropriate criterion. The data source used was the Japanese Adverse Drug Event Report (JADER) database; "hypothetical" true data were generated through a combination of signals detected by three detection algorithms. The accuracy of the signal detection of the analytic model under investigation was verified using indicators used in machine learning. The newly proposed subset analysis confirmed that the signal detection was improved, compared with signal detection in the previous subset analysis, on the basis of the indicators of Accuracy (0.584 to 0.809), Precision (= Positive predictive value; PPV) (0.302 to 0.596), Specificity (0.583 to 0.878), Youden's index (0.170 to 0.465), F-measure (0.399 to 0.592), and Negative predictive value (NPV) (0.821 to 0.874). The previous subset analysis detected many false drug-drug interaction signals. Although the newly proposed subset analysis provides slightly lower detection accuracy for drug-drug interaction signals compared to signals compared to the Ω shrinkage measure model, the criteria used in the newly subset analysis significantly reduced the amount of falsely detected signals found in the previous subset analysis. |
| Author | Tachi, Tomoya Teramachi, Hitomi Noguchi, Yoshihiro |
| AuthorAffiliation | 1 Laboratory of Clinical Pharmacy, Gifu Pharmaceutical University, 1-25-4, Daigakunishi, Gifu-shi, Gifu 501-1196, Japan; tachi@gifu-pu.ac.jp 2 Laboratory of Community Healthcare Pharmacy, Gifu Pharmaceutical University, Daigakunishi, Gifu-shi, Gifu 501-1196, Japan |
| AuthorAffiliation_xml | – name: 1 Laboratory of Clinical Pharmacy, Gifu Pharmaceutical University, 1-25-4, Daigakunishi, Gifu-shi, Gifu 501-1196, Japan; tachi@gifu-pu.ac.jp – name: 2 Laboratory of Community Healthcare Pharmacy, Gifu Pharmaceutical University, Daigakunishi, Gifu-shi, Gifu 501-1196, Japan |
| Author_xml | – sequence: 1 givenname: Yoshihiro orcidid: 0000-0002-9110-9604 surname: Noguchi fullname: Noguchi, Yoshihiro – sequence: 2 givenname: Tomoya orcidid: 0000-0002-5380-0218 surname: Tachi fullname: Tachi, Tomoya – sequence: 3 givenname: Hitomi surname: Teramachi fullname: Teramachi, Hitomi |
| BookMark | eNp9ks1q3DAUhUVJadI0j1AwdNPNtPq1JQqFkPRnINDANMsiruVrR4PHmkp2ILu8Q9-wT1I5k0ASSoRAQvrOkXR0X5O9IQxIyFtGPwhh6MftJcQNOJxG7xLjVNOq5C_IATPGLKThYu_BfJ8cpbSmuQnBtDCvyL7gmpaK6QPyazXVCcfieID-OvlUtCEWKxcRBz90xWmcur83f-ahWA4jRnCjD0Ox8l0WFBdphs53twlXvvM9DA6LUxihhoRvyMsW-oRHd-Mhufj65efJ98XZj2_Lk-OzhVNSj4sWWC0a6SpFVd0qoSssneSqoYJxnREoNTVYubo2rqGcUY1G8FLUFLXTrTgky51vE2Btt9FvIF7bAN7eLoTYWYg5qx6tc7LiDWMS20bK1tWASjpRUhSgoNXZ6_POazvVG2wcDmOE_pHp453BX9ouXNlKliVTs8H7O4MYfk-YRrvxyWGfo8EwJculyNFXSs_ouyfoOkwxJ3tLSWVyLzP1aUe5GFKK2FrnR5j_IZ_ve8uonavC_rcqslo9Ud8_5XndP9dSw74 |
| CitedBy_id | crossref_primary_10_1016_j_eplepsyres_2025_107626 crossref_primary_10_3390_ph14040377 crossref_primary_10_1080_17512433_2024_2343875 crossref_primary_10_1177_10600280231168858 crossref_primary_10_3390_pharmaceutics13101531 crossref_primary_10_1007_s40290_022_00441_z crossref_primary_10_3390_ph14010004 crossref_primary_10_1080_14740338_2024_2368817 crossref_primary_10_1007_s40290_023_00465_z crossref_primary_10_1177_10600280231168860 |
| Cites_doi | 10.1080/00031305.1999.10474456 10.1007/s00228-019-02770-6 10.1038/srep26375 10.1097/CAD.0000000000000862 10.1007/s002280050466 10.1016/j.jfda.2015.11.009 10.1007/s11095-020-02801-3 10.3389/fphar.2018.00197 10.3389/fphar.2019.01319 10.1093/bib/bbx010 10.1002/sim.3247 10.1111/epi.16626 10.1002/pds.1001 10.1007/s40264-020-00911-w 10.1002/pds.677 10.1136/amiajnl-2013-001612 10.1002/pds.964 10.2105/AJPH.2007.124537 10.1111/j.1365-2125.2007.02900.x 10.3390/pharmaceutics4040607 10.1007/s00228-017-2233-3 |
| ContentType | Journal Article |
| Copyright | 2020. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2020 by the authors. 2020 |
| Copyright_xml | – notice: 2020. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2020 by the authors. 2020 |
| DBID | AAYXX CITATION 3V. 7XB 8FK 8G5 ABUWG AFKRA AZQEC BENPR CCPQU DWQXO GNUQQ GUQSH M2O MBDVC PHGZM PHGZT PIMPY PKEHL PQEST PQQKQ PQUKI Q9U 7X8 5PM DOA |
| DOI | 10.3390/pharmaceutics12080762 |
| DatabaseName | CrossRef ProQuest Central (Corporate) ProQuest Central (purchase pre-March 2016) ProQuest Central (Alumni) (purchase pre-March 2016) Research Library (Alumni) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials - QC ProQuest Central ProQuest One Community College ProQuest Central Korea ProQuest Central Student ProQuest Research Library Research Library Research Library (Corporate) ProQuest Central Premium ProQuest One Academic ProQuest Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) Directory of Open Access Journals |
| DatabaseTitle | CrossRef Publicly Available Content Database Research Library Prep ProQuest Central Student ProQuest One Academic Middle East (New) ProQuest Central Basic ProQuest Central Essentials ProQuest One Academic Eastern Edition ProQuest Central (Alumni Edition) ProQuest One Community College Research Library (Alumni Edition) ProQuest Central ProQuest One Academic UKI Edition ProQuest Central Korea ProQuest Research Library ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic Publicly Available Content Database CrossRef |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Pharmacy, Therapeutics, & Pharmacology |
| EISSN | 1999-4923 |
| ExternalDocumentID | oai_doaj_org_article_cc472d114efd44fcbae54c360e3a5af8 PMC7466158 10_3390_pharmaceutics12080762 |
| GroupedDBID | --- 53G 5VS 8G5 AADQD AAYXX ABDBF ABUWG ACGFO ACIHN ACUHS AEAQA AFFHD AFKRA AFZYC ALMA_UNASSIGNED_HOLDINGS AZQEC BENPR BPHCQ CCPQU CITATION DIK DWQXO EBD ESX F5P FD6 GNUQQ GROUPED_DOAJ GUQSH GX1 HH5 HYE IHR KQ8 M2O M48 MK0 MODMG M~E OK1 P6G PGMZT PHGZM PHGZT PIMPY PQQKQ PROAC RNS RPM TR2 TUS 3V. 7XB 8FK MBDVC PKEHL PQEST PQUKI Q9U 7X8 5PM |
| ID | FETCH-LOGICAL-c548t-fa1b3d4c7505bf5387e6c425d03128548a6809e7cbb9cd02108e93263b0e8c8f3 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 12 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000564071100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1999-4923 |
| IngestDate | Fri Oct 03 12:42:28 EDT 2025 Tue Nov 04 01:48:27 EST 2025 Sun Nov 09 12:03:38 EST 2025 Sun Jun 29 12:09:50 EDT 2025 Sat Nov 29 07:13:41 EST 2025 Tue Nov 18 21:12:17 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 8 |
| Language | English |
| License | Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c548t-fa1b3d4c7505bf5387e6c425d03128548a6809e7cbb9cd02108e93263b0e8c8f3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0002-5380-0218 0000-0002-9110-9604 |
| OpenAccessLink | https://doaj.org/article/cc472d114efd44fcbae54c360e3a5af8 |
| PMID | 32806518 |
| PQID | 2434594596 |
| PQPubID | 2032349 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_cc472d114efd44fcbae54c360e3a5af8 pubmedcentral_primary_oai_pubmedcentral_nih_gov_7466158 proquest_miscellaneous_2435187588 proquest_journals_2434594596 crossref_citationtrail_10_3390_pharmaceutics12080762 crossref_primary_10_3390_pharmaceutics12080762 |
| PublicationCentury | 2000 |
| PublicationDate | 20200812 |
| PublicationDateYYYYMMDD | 2020-08-12 |
| PublicationDate_xml | – month: 8 year: 2020 text: 20200812 day: 12 |
| PublicationDecade | 2020 |
| PublicationPlace | Basel |
| PublicationPlace_xml | – name: Basel |
| PublicationTitle | Pharmaceutics |
| PublicationYear | 2020 |
| Publisher | MDPI AG MDPI |
| Publisher_xml | – name: MDPI AG – name: MDPI |
| References | Noguchi (ref_2) 2018; 9 Evans (ref_5) 2001; 10 Sundberg (ref_16) 2008; 27 Vilar (ref_9) 2018; 19 Berlin (ref_1) 2008; 98 Caster (ref_22) 2020; 43 Cheng (ref_20) 2016; 24 Iyer (ref_3) 2013; 21 ref_21 Rothman (ref_6) 2004; 13 Noguchi (ref_14) 2020; 37 Uno (ref_12) 2020; 76 Thakrar (ref_18) 2007; 64 Bate (ref_7) 1998; 54 ref_19 Suling (ref_4) 2012; 4 Susuta (ref_17) 2014; 19 Noguchi (ref_10) 2019; 10 Sanagawa (ref_13) 2020; 31 DuMouchel (ref_8) 1999; 53 Nagashima (ref_11) 2016; 6 Kubota (ref_15) 2004; 13 |
| References_xml | – volume: 53 start-page: 177 year: 1999 ident: ref_8 article-title: Bayesian Data Mining in Large Frequency Tables, with an Application to the FDA Spontaneous Reporting System publication-title: Am. Stat. doi: 10.1080/00031305.1999.10474456 – volume: 76 start-page: 117 year: 2020 ident: ref_12 article-title: Drug interactions between tacrolimus and clotrimazole troche: A data mining approach followed by a pharmacokinetic study publication-title: Eur. J. Clin. Pharmacol. doi: 10.1007/s00228-019-02770-6 – volume: 6 start-page: 26375 year: 2016 ident: ref_11 article-title: Prevention of antipsychotic-induced hyperglycaemia by vitamin D: A data mining prediction followed by experimental exploration of the molecular mechanism publication-title: Sci. Rep. doi: 10.1038/srep26375 – volume: 31 start-page: 183 year: 2020 ident: ref_13 article-title: Tumor lysis syndrome associated with bortezomib: A post-hoc analysis after signal detection using the US Food and Drug Administration Adverse Event Reporting System publication-title: Anti-Cancer Drugs doi: 10.1097/CAD.0000000000000862 – volume: 54 start-page: 315 year: 1998 ident: ref_7 article-title: A Bayesian neural network method for adverse drug reaction signal generation publication-title: Eur. J. Clin. Pharmacol. doi: 10.1007/s002280050466 – volume: 24 start-page: 427 year: 2016 ident: ref_20 article-title: Correlation between drug-drug interaction-induced Stevens-Johnson syndrome and related deaths in Taiwan publication-title: J. Food Drug Anal. doi: 10.1016/j.jfda.2015.11.009 – volume: 37 start-page: 86 year: 2020 ident: ref_14 article-title: Comparison of signal detection algorithms based on frequency statistical model for drug-drug interaction using spontaneous reporting systems publication-title: Pharm. Res. doi: 10.1007/s11095-020-02801-3 – volume: 9 start-page: 197 year: 2018 ident: ref_2 article-title: A New Search Method Using Association Rule Mining for Drug-Drug Interaction Based on Spontaneous Report System publication-title: Front. Pharmacol. doi: 10.3389/fphar.2018.00197 – volume: 10 start-page: 1319 year: 2019 ident: ref_10 article-title: Review of Statistical Methodologies for Detecting Drug-Drug Interactions Using Spontaneous Reporting Systems publication-title: Front. Pharmacol. doi: 10.3389/fphar.2019.01319 – volume: 19 start-page: 863 year: 2018 ident: ref_9 article-title: Detection of drug-drug interactions through data mining studies using clinical sources, scientific literature and social media publication-title: Brief. Bioinform. doi: 10.1093/bib/bbx010 – volume: 27 start-page: 3057 year: 2008 ident: ref_16 article-title: A statistical methodology for drug-drug interaction surveillance publication-title: Stat. Med. doi: 10.1002/sim.3247 – volume: 19 start-page: 39 year: 2014 ident: ref_17 article-title: Safety risk evaluation methodology in detecting the medicine concomitant use risk which might cause critical drug rash publication-title: Jpn. J. Pharmacoepidemiol. – ident: ref_21 doi: 10.1111/epi.16626 – volume: 13 start-page: 519 year: 2004 ident: ref_6 article-title: The reporting odds ratio and its advantages over the proportional reporting ratio publication-title: Pharmacoepidemiol. Drug Saf. doi: 10.1002/pds.1001 – volume: 43 start-page: 479 year: 2020 ident: ref_22 article-title: Disproportionality Analysis for Pharmacovigilance Signal Detection in Small Databases or Subsets: Recommendations for Limiting False-Positive Associations publication-title: Drug Saf. doi: 10.1007/s40264-020-00911-w – volume: 10 start-page: 483 year: 2001 ident: ref_5 article-title: Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports publication-title: Pharmacoepidemiol. Drug Saf. doi: 10.1002/pds.677 – volume: 21 start-page: 353 year: 2013 ident: ref_3 article-title: Mining clinical text for signals of adverse drug-drug interactions publication-title: J. Am. Med. Inf. Assoc. doi: 10.1136/amiajnl-2013-001612 – volume: 13 start-page: 387 year: 2004 ident: ref_15 article-title: Comparison of data mining methodologies using Japanese spontaneous reports publication-title: Pharmacoepidemiol. Drug Saf. doi: 10.1002/pds.964 – volume: 98 start-page: 1366 year: 2008 ident: ref_1 article-title: Adverse event detection in drug development: Recommendations and obligations beyond phase 3 publication-title: Am. J. Public Health doi: 10.2105/AJPH.2007.124537 – volume: 64 start-page: 489 year: 2007 ident: ref_18 article-title: Detecting signals of drug-drug interactions in a spontaneous reports database publication-title: Br. J. Clin. Pharmacol. doi: 10.1111/j.1365-2125.2007.02900.x – volume: 4 start-page: 607 year: 2012 ident: ref_4 article-title: Signal Detection and Monitoring Based on Longitudinal Healthcare Data publication-title: Pharmaceutics doi: 10.3390/pharmaceutics4040607 – ident: ref_19 doi: 10.1007/s00228-017-2233-3 |
| SSID | ssj0000331839 |
| Score | 2.2489324 |
| Snippet | Many patients require multi-drug combinations, and adverse event profiles reflect not only the effects of individual drugs but also drug–drug interactions.... Many patients require multi-drug combinations, and adverse event profiles reflect not only the effects of individual drugs but also drug-drug interactions.... |
| SourceID | doaj pubmedcentral proquest crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Enrichment Source Index Database |
| StartPage | 762 |
| SubjectTerms | Algorithms Clinical trials Datasets Drug interactions drug-drug interaction Expected values Marketing signal detection algorithms spontaneous reporting systems Studies subset analysis |
| SummonAdditionalLinks | – databaseName: ProQuest Central dbid: BENPR link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1La9wwEBZt0kMvbfoim0dRoeQUN7Yk2_Kp5EkPZVmatORSjCRLG0PwbtfeQG79D_2H_SWdsbXeGkp7KBgMlmQJNG-NviHkbRHZ0DhtA84KF4hIAUtlqQvA10idkZkxLTr_l4_peCyvr7OJD7jVPq1yJRNbQV3MDMbIj5jgIs7gSd7PvwVYNQpPV30JjYdkE5HKgM43T87Hk099lCXkSLNZd3WHg39_NL9Zh4rriIHBlCZsoJRa7P6BwTlMl_xN_1w8_d-Vb5En3vKkxx2pPCMPbPWcHEy6hd4f0qv1Taz6kB7QyRrU-v4F-YoSxjZ0BWJCwdillwazdkD50bPFcvrz-w980TbI2N2XoJflFCdtExP6P96V0_IWqY2eqUahHn1JPl-cX51-CHxphsCAi9METkWaF8KAvRFrB0IztYkB9i9ARuCdTKkSGWY2NVpnpkC_Ulq0FLkOrTTS8Vdko5pVdptQFnOWuUwXiIQjrJY2VBGQi3ZSS62TERGrvcmNxy3H8hm3OfgvuKX5H7d0RN71w-YdcMe_BpzgxvedEXe7_TBbTHPPxrkxImUF-JDWFUI4o5WNheFJaLmKlZMjsreigdwLgzpfE8CIvOmbgY3xbEZVdrZs-8QR-I4SfpEOyG2woGFLVd60gOCpACsrljt_n3yXPGYYLEA8X7ZHNprF0u6TR-auKevFa885vwBO1yuY priority: 102 providerName: ProQuest |
| Title | Subset Analysis for Screening Drug–Drug Interaction Signal Using Pharmacovigilance Database |
| URI | https://www.proquest.com/docview/2434594596 https://www.proquest.com/docview/2435187588 https://pubmed.ncbi.nlm.nih.gov/PMC7466158 https://doaj.org/article/cc472d114efd44fcbae54c360e3a5af8 |
| Volume | 12 |
| WOSCitedRecordID | wos000564071100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1999-4923 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331839 issn: 1999-4923 databaseCode: DOA dateStart: 20100101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1999-4923 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331839 issn: 1999-4923 databaseCode: M~E dateStart: 20090101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1999-4923 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331839 issn: 1999-4923 databaseCode: BENPR dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 1999-4923 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331839 issn: 1999-4923 databaseCode: PIMPY dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVPQU databaseName: Research Library customDbUrl: eissn: 1999-4923 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331839 issn: 1999-4923 databaseCode: M2O dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/pqrl providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1La9wwEBZt2kMvJemDbpMsKpSc4saWZEs6Ni9aaLamScv2UIwkSxtDcMKuN5Bb_0P_YX9JRrL3YSjkUjA26C3NjDQjaz4h9L5MbGycthElpYtYokCkJHcR2BrcGSGNCej8P77w0UiMxzJfu-rLnwlr4YHbgTswhnFSgtZuXcmYM1rZlBmaxZaqVLng5htzuWZMhTmYel6VrcsOBbv-4OZytUU8SwgoSjwjvcUoYPb3FM3-Mcm1ded0Ez3vFEb8sW3oFnpk6xdoL2_rudvHFysHqtk-3sP5Cov67iX65ScG2-AF9ggGHRWfG3_YBtYsfDydT_7-_uM_OOwNtm4O-Lya-ErDeYJlibfVpLryTIKPVaP88vcKfT89uTj6FHU3KkQGLJMmcirRtGQG1IRUO5jruM0MSG0Jou1dKYXKRCwtN1pLU3pzUFiv4FEdW2GEo6_RRn1d2zcIk5QS6aQuPYANs1rYWCVAZe2EFlpnA8QWQ1uYDm7c33pxVYDZ4SlS_JMiA_Rhme2mxdt4KMOhp9sysYfLDgHAREXHRMVDTDRAOwuqF50MzwrCKEslPNCXd8tokD7_S0XV9noe0qQJmHwCiuA9buk1qB9TV5cBx5szUI5S8fZ_9GAbPSN-J8CD9ZIdtNFM53YXPTW3TTWbDtFjPhZD9OTwZJR_GwZRgfcZ-Qph-eez_Oc9ErEiww |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3NbtNAEF6VFAku5V-kFFgk6Kmm9u7aXh8QAkLVqGkUqQG1B2S86900UpWEOCnKjXfgPXgonoQZ_wVLCE49IFmyZK_Xf9_MzszufEPI89QzrrbKOJyl1hFeAiIVhdYBXyO0WkZa5-z8H3thvy9PT6PBBvlR5cLgsspKJ-aKOp1qjJHvM8GFH8EWvJ59cbBqFM6uViU0ClgcmdVXcNmyV90O_N8XjB28H747dMqqAo4G63zh2MRTPBUahkpfWZD30AQakJsCvDGdUCaBdCMTaqUinaJLJA0aOVy5RmppOfR7jWwKBHuLbA66x4OzOqrjcpSRqEgV4jxy92fn69B05jEw0MKANQbBvFZAw8BtLs_8bbw7uPW_fanbZKu0rOmbQhTukA0zuUt2B8WHWe3R4TrTLNuju3SwJu1e3SOfUIOaBa1IWigY8_RE46okGNxpZ74c_fz2HXc0D6IW-SD0ZDzCm-YLL-oeL8ej8QVKE-0kiwTthPvkw5W8-gPSmkwn5iGhzOcsspFKkelHGCWNm3ggDspKJZUK2kRUWIh1ycuO5UEuYvDPEELxHyHUJi_ry2YFMcm_LniLQKsbI694fmA6H8Wlmoq1FiFLwUc2NhXCapUYX2geuIYnfmJlm-xUmItLZZfFa8C1ybP6NKgpnHtKJma6zNv4HvjGEroIG_BuPFDzzGR8nhOehwKsSF9u__3mT8mNw-FxL-51-0ePyE2GgRHkLmY7pLWYL81jcl1fLsbZ_EkptZR8vmr4_wLsIIdZ |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEF6VFCEuvBGBAosEPdXE3l3b6wNCQIiIWiJLLag9IONd76aWqiTkUZQb_4F_w8_hlzDjV7CE4NQDkiVLfqxf38x-M54HIU8zz7jaKuNwlllHeCmIVBRaB2yN0GoZaV1U5_94EI5G8vg4irfIjzoXBsMqa51YKOpsqtFH3mOCCz-CJejZKiwi7g9ezr442EEK_7TW7TRKiOyb9Vcw3xYvhn341s8YG7w9evPOqToMOBqY-tKxqad4JjRMm76yIPuhCTSgOAOoY2qhTAPpRibUSkU6Q_NIGiQ8XLlGamk5jHuJbAMlF6xDtuPh-_ik8fC4HOUlKtOGOI_c3ux046ZeeAzIWhiw1oRY9A1okd12qOZvc9_g-v_81m6QaxXjpq9KEblJtszkFtmNy5e03qNHmwy0xR7dpfGmmPf6NvmEmtUsaV28hQLJp4cao5Vg0qf9-Wr889t3XNHCuVrmidDDfIwXLQIymhHP83F-hlJG--kyRf5wh3y4kEe_SzqT6cTcI5T5nEU2UhlWABJGSeOmHoiJslJJpYIuETUuEl3Va8e2IWcJ2G0Ip-SPcOqS581ps7Jgyb9OeI2gaw7GeuPFhul8nFTqK9FahCwD29nYTAirVWp8oXngGp76qZVdslPjL6mU4CLZgK9LnjS7QX3hP6l0Yqar4hjfA5tZwhBhC-qtG2rvmeSnRSH0UAC79OX9v1_8MbkCmE8OhqP9B-QqQ38JljRmO6SznK_MQ3JZny_zxfxRJcCUfL5o9P8CnqeQGQ |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Subset+Analysis+for+Screening+Drug%E2%80%93Drug+Interaction+Signal+Using+Pharmacovigilance+Database&rft.jtitle=Pharmaceutics&rft.au=Noguchi%2C+Yoshihiro&rft.au=Tachi%2C+Tomoya&rft.au=Teramachi%2C+Hitomi&rft.date=2020-08-12&rft.issn=1999-4923&rft.eissn=1999-4923&rft.volume=12&rft.issue=8&rft.spage=762&rft_id=info:doi/10.3390%2Fpharmaceutics12080762&rft.externalDBID=n%2Fa&rft.externalDocID=10_3390_pharmaceutics12080762 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1999-4923&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1999-4923&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1999-4923&client=summon |