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的重大挑战。尽管在药物开发和临床试验中越来越多地采用下一代测序（NGS），但是将获得的基因组信息转化为可行的临床建议仍然滞后。主要原因是缺乏足够有力的试验来量化药物遗传学检测的附加值，以及大量的药物遗传学复杂性和数百万种罕见的变体，导致功能不清楚。由此产生的不确定性反映在欧洲和美国的药物基因组药物标签不一致中。在这篇综述中，我们讨论了如何缩小将药物基因组学桥接到临床领域的知识差距。第一，新兴的方法可以对药物基因组变异进行高通量实验表征，并结合新颖的计算工具，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库，可以开展高效的回顾性研究，这些研究可以验证目前尚未完全表征的遗传变异的临床重要性。结合起来，我们相信这些方法将提高药物反应预测的准确性，并缩小变体识别及其在临床决策支持中的利用之间的差距。挖掘治疗药物监测数据的大型生物库可进行高性能的回顾性研究，这些研究可验证基因变异的临床重要性，目前尚未完全表征。结合起来，我们相信这些方法将提高药物反应预测的准确性，并缩小变体识别及其在临床决策支持中的利用之间的差距。挖掘治疗药物监测数据的大型生物库可进行高性能的回顾性研究，这些研究可验证基因变异的临床重要性，目前尚未完全表征。结合起来，我们相信这些方法将提高药物反应预测的准确性，并缩小变体识别及其在临床决策支持中的利用之间的差距。