Lauschke Volker M, Ingelman-Sundberg Magnus
Department of Physiology and Pharmacology, Karolinska Institutet, SE-171 77 Stockholm, Sweden.
NPJ Genom Med. 2020 Mar 5;5:9. doi: 10.1038/s41525-020-0119-2. eCollection 2020.
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.
基因组个体间的异质性仍然是临床决策和临床试验设计面临的重大挑战。尽管下一代测序(NGS)在药物开发和临床试验中越来越多地得到应用,但将获得的基因组信息转化为可行的临床建议却滞后了。主要原因是缺乏足够大规模的试验来量化药物遗传学检测的附加价值,以及存在大量功能后果不明的罕见变异所导致的相当大的药物遗传学复杂性。由此产生的不确定性反映在欧洲和美国药物基因组学药物标签的不一致上。在本综述中,我们讨论了如何缩小药物基因组学与临床应用之间的知识差距。首先,新兴的方法能够对药物基因组变异进行高通量实验表征,并结合新型计算工具,有望提高药物反应预测的准确性。其次,利用治疗药物监测数据的大型生物样本库可以进行大规模回顾性研究,从而验证目前特征不完全明确的基因变异的临床重要性。综合来看,我们相信这些方法将提高药物反应预测的准确性,并缩小变异鉴定与将其用于临床决策支持之间的差距。