期刊
DRUG METABOLISM REVIEWS
卷 53, 期 2, 页码 253-278出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/03602532.2021.1909613
关键词
Pharmacogenomics; precision medicine; biomarkers; clinical implementation; drug development; genetic variants; pharmacogenomic trials
资金
- American Foundation for Pharmaceutical Education Predoctoral Fellowship
- NIGMS [R25 GM56847]
- Louis Zeh Fellowship
- Swedish Research Council [2016-01153, 2016-01154, 201901837]
- Strategic Research Programmes in Diabetes (SFO Diabetes)
- Stem Cells and Regenerative Medicine (StratRegen)
- EU/EFPIA/OICR/McGill/KTH/Diamond Innovative Medicines Initiative 2 Joint Undertaking [EUbOPEN] [875510]
- Merck KGaA
- Eli Lilly and Company
Pharmacogenetic research has identified a range of genetic variants impacting drug response, including common and rare variations. This review discusses how sequencing information can be used for personalized pharmacogenomic advice, and evaluates the current implementation status of pharmacogenetic testing across different fields.
Pharmacogenetic research has resulted in the identification of a multitude of genetic variants that impact drug response or toxicity. These polymorphisms are mostly common and have been included as actionable information in the labels of numerous drugs. In addition to common variants, recent advances in Next Generation Sequencing (NGS) technologies have resulted in the identification of a plethora of rare and population-specific pharmacogenetic variations with unclear functional consequences that are not accessible by conventional forward genetics strategies. In this review, we discuss how comprehensive sequencing information can be translated into personalized pharmacogenomic advice in the age of NGS. Specifically, we provide an update of the functional impacts of rare pharmacogenetic variability and how this information can be leveraged to improve pharmacogenetic guidance. Furthermore, we critically discuss the current status of implementation of pharmacogenetic testing across drug development and layers of care. We identify major gaps and provide perspectives on how these can be minimized to optimize the utilization of NGS data for personalized clinical decision-support.
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