An Evidence-Based Framework for Evaluating Pharmacogenomics Knowledge for Personalized Medicine.


Journal

Clinical pharmacology and therapeutics
ISSN: 1532-6535
Titre abrégé: Clin Pharmacol Ther
Pays: United States
ID NLM: 0372741

Informations de publication

Date de publication:
09 2021
Historique:
received: 02 04 2021
accepted: 16 06 2021
pubmed: 4 7 2021
medline: 15 9 2021
entrez: 3 7 2021
Statut: ppublish

Résumé

Clinical annotations are one of the most popular resources available on the Pharmacogenomics Knowledgebase (PharmGKB). Each clinical annotation summarizes the association between variant-drug pairs, shows relevant findings from the curated literature, and is assigned a level of evidence (LOE) to indicate the strength of support for that association. Evidence from the pharmacogenomic literature is curated into PharmGKB as variant annotations, which can be used to create new clinical annotations or added to existing clinical annotations. This means that the same clinical annotation can be worked on by multiple curators over time. As more evidence is curated into PharmGKB, the task of maintaining consistency when assessing all the available evidence and assigning an LOE becomes increasingly difficult. To remedy this, a scoring system has been developed to automate LOE assignment to clinical annotations. Variant annotations are scored according to certain attributes, including study size, reported P value, and whether the variant annotation supports or fails to find an association. Clinical guidelines or US Food and Drug Administration (FDA)-approved drug labels which give variant-specific prescribing guidance are also scored. The scores of all annotations attached to a clinical annotation are summed together to give a total score for the clinical annotation, which is used to calculate an LOE. Overall, the system increases transparency, consistency, and reproducibility in LOE assignment to clinical annotations. In combination with increased standardization of how clinical annotations are written, use of this scoring system helps to ensure that PharmGKB clinical annotations continue to be a robust source of pharmacogenomic information.

Identifiants

pubmed: 34216021
doi: 10.1002/cpt.2350
pmc: PMC8457105
doi:

Substances chimiques

Prescription Drugs 0

Types de publication

Journal Article Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

563-572

Subventions

Organisme : NHGRI NIH HHS
ID : U24 HG010615
Pays : United States

Informations de copyright

© 2021 The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.

Références

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Auteurs

Michelle Whirl-Carrillo (M)

Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, California, USA.

Rachel Huddart (R)

Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, California, USA.

Li Gong (L)

Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, California, USA.

Katrin Sangkuhl (K)

Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, California, USA.

Caroline F Thorn (CF)

Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, California, USA.

Ryan Whaley (R)

Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, California, USA.

Teri E Klein (TE)

Department of Biomedical Data Science and Biomedical Informatics Research, School of Medicine, Stanford University, Stanford, California, USA.

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Classifications MeSH