Evidence map›Paper›PMID 41146533›Full record

ReviewClinical pharmacology and therapeutics2025

Transforming Pharmacovigilance With Pharmacogenomics: Toward Personalized Risk Management.

Claire Spahn, Nanase Toda, Blaine Groat, Omar Aimer, Sara Rogers, Akinyemi Oni-Orisan, Andrew Monte, Nancy Hakooz, Pharmacogenomics Global Research Network (PGRN) Publications Committee

Abstract readReview
In one paragraph

Review in Clinical pharmacology and therapeutics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Claire SpahnDepartment of Pharmacy, Stanford Health Care, Stanford, California, USA.ORCID 0009-0008-6426-022X
Nanase TodaDepartment of Clinical Pharmacy, University of California, San Francisco, California, USA.ORCID 0009-0001-7774-8178
Blaine GroatPersonalized Medicine, Healthcare Quality and Safety Center of Excellence, United States Pharmacopeia, Rockville, Maryland, USA.ORCID 0000-0002-8632-4887
Omar AimerInnoVigilance, Laval, Québec, Canada.ORCID 0000-0002-0874-486X
Sara RogersDepartment of Translational Medical Sciences, School of Medicine, Texas A&M University, Bryan, Texas, USA.ORCID 0000-0002-9851-4151
Akinyemi Oni-OrisanDepartment of Clinical Pharmacy, University of California, San Francisco, California, USA.ORCID 0000-0002-5897-5543
Andrew MonteRocky Mountain Poison & Drug Safety, Denver Health & Hospital Authority, Denver, Colorado, USA.
Nancy HakoozDepartment of Biopharmaceutics and Clinical Pharmacy, School of Pharmacy, University of Jordan, Amman, Jordan.ORCID 0000-0002-7973-0473
Pharmacogenomics Global Research Network (PGRN) Publications Committee

Funding

Personalizing EmerGency/Acute therapeuticS Utilizing Systems biology (PEGASUS-2)R35GM152157 · NIGMS · UNIVERSITY OF COLORADO DENVER · PI Andrew Albert Monte · 2024 to 2026
$1.2M
NIGMS NIH HHS R35 GM152157NIGMS NIH HHS R35GM152157
6 · The paper itself

Abstract

Pharmacovigilance is a critical component of medication safety. Despite rigorous evaluation of new drugs during clinical trials, some adverse effects might only be identified once pharmaceuticals are used by a larger population for a longer duration. Adverse drug reactions cause negative healthcare outcomes and in severe cases, may lead to hospital admissions, delayed hospital discharges, or deaths. Adverse event reports submitted to pharmacovigilance programs by healthcare professionals and consumers are a key source of information regarding previously unrecognized detrimental effects. Pharmacogenetic markers that indicate how particular genes impact an individual's response to medication can help explain some idiosyncratic adverse reactions. Incorporating pharmacogenomic guidance in prescribing is proven to decrease the incidence of adverse reactions and improve clinical outcomes. However, this information is not yet routinely included in incident reports. In this era of precision medicine, when prescribing can be tailored to the individual, pharmacogenomic test results yield valuable data that can enhance both individual and population health. Furthermore, advanced artificial intelligence (AI) and machine learning (ML) methods facilitate analysis of complex genetic data, revealing insights not previously available. This white paper outlines current pharmacovigilance and pharmacogenomic practices and recommends that pharmacovigilance programs include pharmacogenomics as a crucial data point in their investigations.

Indexed as

Drug-Related Side Effects and Adverse ReactionsPharmacogeneticsPharmacovigilancePrecision MedicineRisk ManagementAdverse Drug Reaction Reporting SystemsHumansMachine LearningPharmacogenomic Testing

Identifiers

PMID41146533
PMCPMC12598900

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.