Incidence of Exposure of Patients in the United States to Multiple Drugs for Which Pharmacogenomic Guidelines Are Available
Pre-emptive pharmacogenomic (PGx) testing of a panel of genes may be easier to implement and more cost-effective than reactive pharmacogenomic testing if a sufficient number of medications are covered by a single test and future medication exposure can be anticipated. We analysed the incidence of ex...
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| Published in: | PloS one Vol. 11; no. 10; p. e0164972 |
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| Main Authors: | , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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20.10.2016
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| ISSN: | 1932-6203, 1932-6203 |
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| Abstract | Pre-emptive pharmacogenomic (PGx) testing of a panel of genes may be easier to implement and more cost-effective than reactive pharmacogenomic testing if a sufficient number of medications are covered by a single test and future medication exposure can be anticipated. We analysed the incidence of exposure of individual patients in the United States to multiple drugs for which pharmacogenomic guidelines are available (PGx drugs) within a selected four-year period (2009-2012) in order to identify and quantify the incidence of pharmacotherapy in a nation-wide patient population that could be impacted by pre-emptive PGx testing based on currently available clinical guidelines. In total, 73 024 095 patient records from private insurance, Medicare Supplemental and Medicaid were included. Patients enrolled in Medicare Supplemental age > = 65 or Medicaid age 40-64 had the highest incidence of PGx drug use, with approximately half of the patients receiving at least one PGx drug during the 4 year period and one fourth to one third of patients receiving two or more PGx drugs. These data suggest that exposure to multiple PGx drugs is common and that it may be beneficial to implement wide-scale pre-emptive genomic testing. Future work should therefore concentrate on investigating the cost-effectiveness of multiplexed pre-emptive testing strategies. |
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| AbstractList | Pre-emptive pharmacogenomic (PGx) testing of a panel of genes may be easier to implement and more cost-effective than reactive pharmacogenomic testing if a sufficient number of medications are covered by a single test and future medication exposure can be anticipated. We analysed the incidence of exposure of individual patients in the United States to multiple drugs for which pharmacogenomic guidelines are available (PGx drugs) within a selected four-year period (2009-2012) in order to identify and quantify the incidence of pharmacotherapy in a nation-wide patient population that could be impacted by pre-emptive PGx testing based on currently available clinical guidelines. In total, 73 024 095 patient records from private insurance, Medicare Supplemental and Medicaid were included. Patients enrolled in Medicare Supplemental age > = 65 or Medicaid age 40-64 had the highest incidence of PGx drug use, with approximately half of the patients receiving at least one PGx drug during the 4 year period and one fourth to one third of patients receiving two or more PGx drugs. These data suggest that exposure to multiple PGx drugs is common and that it may be beneficial to implement wide-scale pre-emptive genomic testing. Future work should therefore concentrate on investigating the cost-effectiveness of multiplexed pre-emptive testing strategies. |
| Audience | Academic |
| Author | Blagec, Kathrin Ryan, Patrick Boyce, Richard D. Samwald, Matthias Malone, Daniel C. Hofer, Sebastian Ahmed, Seid Mussa Xu, Hong Empey, Philip E. |
| AuthorAffiliation | 5 Janssen Research and Development, Titusville, New Jersey, United States of America 4 Department of Pharmacy, College of public health and medical sciences, Jimma University, Jimma, Ethiopia 2 Department of Pharmacy and Therapeutics, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America 7 Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America Case Western Reserve University, UNITED STATES 1 Section for Artificial Intelligence and Decision Support; Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria 6 Observational Health Data Sciences and Informatics, New York, New York, United States of America 3 College of Pharmacy, University of Arizona, Tucson, Arizona, United States of America |
| AuthorAffiliation_xml | – name: 1 Section for Artificial Intelligence and Decision Support; Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria – name: 2 Department of Pharmacy and Therapeutics, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America – name: 3 College of Pharmacy, University of Arizona, Tucson, Arizona, United States of America – name: Case Western Reserve University, UNITED STATES – name: 6 Observational Health Data Sciences and Informatics, New York, New York, United States of America – name: 4 Department of Pharmacy, College of public health and medical sciences, Jimma University, Jimma, Ethiopia – name: 7 Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America – name: 5 Janssen Research and Development, Titusville, New Jersey, United States of America |
| Author_xml | – sequence: 1 givenname: Matthias orcidid: 0000-0002-4855-2571 surname: Samwald fullname: Samwald, Matthias – sequence: 2 givenname: Hong surname: Xu fullname: Xu, Hong – sequence: 3 givenname: Kathrin surname: Blagec fullname: Blagec, Kathrin – sequence: 4 givenname: Philip E. surname: Empey fullname: Empey, Philip E. – sequence: 5 givenname: Daniel C. surname: Malone fullname: Malone, Daniel C. – sequence: 6 givenname: Seid Mussa surname: Ahmed fullname: Ahmed, Seid Mussa – sequence: 7 givenname: Patrick surname: Ryan fullname: Ryan, Patrick – sequence: 8 givenname: Sebastian surname: Hofer fullname: Hofer, Sebastian – sequence: 9 givenname: Richard D. surname: Boyce fullname: Boyce, Richard D. |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Competing Interests: PR is an employee of Janssen Research and Development. This does not alter our adherence to PLOS ONE policies on sharing data and materials. Conceptualization: MS RDB. Formal analysis: MS HX KB PEE DCM RDB. Funding acquisition: MS RDB. Methodology: MS HX KB PEE DCM PR SH RDB. Project administration: MS RDB. Software: MS HX SH RDB. Writing – original draft: MS KB RDB. Writing – review & editing: MS HX KB PEE DCM SMA PR SH RDB. |
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| Title | Incidence of Exposure of Patients in the United States to Multiple Drugs for Which Pharmacogenomic Guidelines Are Available |
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