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Emerging Strategies to Bridge the Gap Between Pharmacogenomic Research and Its Clinical Implementation

Overview
Journal NPJ Genom Med
Specialty Genetics
Date 2020 Mar 21
PMID 32194983
Citations 19
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Abstract

The genomic inter-individual heterogeneity remains a significant challenge for both clinical decision-making and the design of clinical trials. Although next-generation sequencing (NGS) is increasingly implemented in drug development and clinical trials, translation of the obtained genomic information into actionable clinical advice lags behind. Major reasons are the paucity of sufficiently powered trials that can quantify the added value of pharmacogenetic testing, and the considerable pharmacogenetic complexity with millions of rare variants with unclear functional consequences. The resulting uncertainty is reflected in inconsistencies of pharmacogenomic drug labels in Europe and the United States. In this review, we discuss how the knowledge gap for bridging pharmacogenomics into the clinics can be reduced. First, emerging methods that allow the high-throughput experimental characterization of pharmacogenomic variants combined with novel computational tools hold promise to improve the accuracy of drug response predictions. Second, tapping of large biobanks of therapeutic drug monitoring data allows to conduct high-powered retrospective studies that can validate the clinical importance of genetic variants, which are currently incompletely characterized. Combined, we are confident that these methods will improve the accuracy of drug response predictions and will narrow the gap between variant identification and its utilization for clinical decision-support.

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References
1.
Borden B, Galecki P, Wellmann R, Danahey K, Lee S, Patrick-Miller L . Assessment of provider-perceived barriers to clinical use of pharmacogenomics during participation in an institutional implementation study. Pharmacogenet Genomics. 2018; 29(2):31-38. DOI: 10.1097/FPC.0000000000000362. View

2.
Thomas S, Fossella F, Liu D, Schaerer R, Tsao A, Kies M . Asian ethnicity as a predictor of response in patients with non-small-cell lung cancer treated with gefitinib on an expanded access program. Clin Lung Cancer. 2006; 7(5):326-31. DOI: 10.3816/CLC.2006.n.014. View

3.
Zhou J, Theesfeld C, Yao K, Chen K, Wong A, Troyanskaya O . Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk. Nat Genet. 2018; 50(8):1171-1179. PMC: 6094955. DOI: 10.1038/s41588-018-0160-6. View

4.
Schaller L, Lauschke V . The genetic landscape of the human solute carrier (SLC) transporter superfamily. Hum Genet. 2019; 138(11-12):1359-1377. PMC: 6874521. DOI: 10.1007/s00439-019-02081-x. View

5.
Roscoe B, Thayer K, Zeldovich K, Fushman D, Bolon D . Analyses of the effects of all ubiquitin point mutants on yeast growth rate. J Mol Biol. 2013; 425(8):1363-77. PMC: 3615125. DOI: 10.1016/j.jmb.2013.01.032. View