Literature DB >> 33579994

Emerging strategies to bridge the gap between pharmacogenomic research and its clinical implementation.

Volker M Lauschke1, Magnus Ingelman-Sundberg2.   

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.

Year:  2020        PMID: 33579994     DOI: 10.1038/s41525-020-0119-2

Source DB:  PubMed          Journal:  NPJ Genom Med        ISSN: 2056-7944            Impact factor:   8.617


  58 in total

1.  Assessment of provider-perceived barriers to clinical use of pharmacogenomics during participation in an institutional implementation study.

Authors:  Brittany A Borden; Paige Galecki; Rebecca Wellmann; Keith Danahey; Sang Mee Lee; Linda Patrick-Miller; Matthew J Sorrentino; Rita Nanda; Jay L Koyner; Tamar S Polonsky; Walter M Stadler; Cathleen Mulcahy; Robert T Kavitt; Mark J Ratain; David O Meltzer; Peter H O'Donnell
Journal:  Pharmacogenet Genomics       Date:  2019-02       Impact factor: 2.089

Review 2.  Clinical Implementation of Pharmacogenomics for Personalized Precision Medicine: Barriers and Solutions.

Authors:  Michelle E Klein; Md Masud Parvez; Jae-Gook Shin
Journal:  J Pharm Sci       Date:  2017-06-13       Impact factor: 3.534

Review 3.  Rare-variant association analysis: study designs and statistical tests.

Authors:  Seunggeung Lee; Gonçalo R Abecasis; Michael Boehnke; Xihong Lin
Journal:  Am J Hum Genet       Date:  2014-07-03       Impact factor: 11.025

4.  Quantifying rare, deleterious variation in 12 human cytochrome P450 drug-metabolism genes in a large-scale exome dataset.

Authors:  Adam S Gordon; Holly K Tabor; Andrew D Johnson; Beverly M Snively; Themistocles L Assimes; Paul L Auer; John P A Ioannidis; Ulrike Peters; Jennifer G Robinson; Lara E Sucheston; Danxin Wang; Nona Sotoodehnia; Jerome I Rotter; Bruce M Psaty; Rebecca D Jackson; David M Herrington; Christopher J O'Donnell; Alexander P Reiner; Stephen S Rich; Mark J Rieder; Michael J Bamshad; Deborah A Nickerson
Journal:  Hum Mol Genet       Date:  2013-11-26       Impact factor: 6.150

5.  Genetic variability and population diversity of the human SLCO (OATP) transporter family.

Authors:  Boyao Zhang; Volker M Lauschke
Journal:  Pharmacol Res       Date:  2018-10-22       Impact factor: 7.658

6.  Genetic variation in the human cytochrome P450 supergene family.

Authors:  Kohei Fujikura; Magnus Ingelman-Sundberg; Volker M Lauschke
Journal:  Pharmacogenet Genomics       Date:  2015-12       Impact factor: 2.089

7.  A systematic survey of loss-of-function variants in human protein-coding genes.

Authors:  Daniel G MacArthur; Suganthi Balasubramanian; Adam Frankish; Ni Huang; James Morris; Klaudia Walter; Luke Jostins; Lukas Habegger; Joseph K Pickrell; Stephen B Montgomery; Cornelis A Albers; Zhengdong D Zhang; Donald F Conrad; Gerton Lunter; Hancheng Zheng; Qasim Ayub; Mark A DePristo; Eric Banks; Min Hu; Robert E Handsaker; Jeffrey A Rosenfeld; Menachem Fromer; Mike Jin; Xinmeng Jasmine Mu; Ekta Khurana; Kai Ye; Mike Kay; Gary Ian Saunders; Marie-Marthe Suner; Toby Hunt; If H A Barnes; Clara Amid; Denise R Carvalho-Silva; Alexandra H Bignell; Catherine Snow; Bryndis Yngvadottir; Suzannah Bumpstead; David N Cooper; Yali Xue; Irene Gallego Romero; Jun Wang; Yingrui Li; Richard A Gibbs; Steven A McCarroll; Emmanouil T Dermitzakis; Jonathan K Pritchard; Jeffrey C Barrett; Jennifer Harrow; Matthew E Hurles; Mark B Gerstein; Chris Tyler-Smith
Journal:  Science       Date:  2012-02-17       Impact factor: 47.728

8.  Rare genetic variants in cellular transporters, metabolic enzymes, and nuclear receptors can be important determinants of interindividual differences in drug response.

Authors:  Mikael Kozyra; Magnus Ingelman-Sundberg; Volker M Lauschke
Journal:  Genet Med       Date:  2016-04-21       Impact factor: 8.822

9.  Genetic variation among 82 pharmacogenes: The PGRNseq data from the eMERGE network.

Authors:  W S Bush; D R Crosslin; A Owusu-Obeng; J Wallace; B Almoguera; M A Basford; S J Bielinski; D S Carrell; J J Connolly; D Crawford; K F Doheny; C J Gallego; A S Gordon; B Keating; J Kirby; T Kitchner; S Manzi; A R Mejia; V Pan; C L Perry; J F Peterson; C A Prows; J Ralston; S A Scott; A Scrol; M Smith; S C Stallings; T Veldhuizen; W Wolf; S Volpi; K Wiley; R Li; T Manolio; E Bottinger; M H Brilliant; D Carey; R L Chisholm; C G Chute; J L Haines; H Hakonarson; J B Harley; I A Holm; I J Kullo; G P Jarvik; E B Larson; C A McCarty; M S Williams; J C Denny; L J Rasmussen-Torvik; D M Roden; M D Ritchie
Journal:  Clin Pharmacol Ther       Date:  2016-06-01       Impact factor: 6.875

10.  Analysis of population-specific pharmacogenomic variants using next-generation sequencing data.

Authors:  Eunyong Ahn; Taesung Park
Journal:  Sci Rep       Date:  2017-09-04       Impact factor: 4.379

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