Tremmel Roman, Pirmann Sebastian, Zhou Yitian, Lauschke Volker M
Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany.
University of Tübingen, Tübingen, Germany.
Br J Clin Pharmacol. 2025 Feb;91(2):252-263. doi: 10.1111/bcp.15915. Epub 2023 Oct 16.
The rapid development of sequencing technologies during the past 20 years has provided a variety of methods and tools to interrogate human genomic variations at the population level. Pharmacogenes are well known to be highly polymorphic and a plethora of pharmacogenomic variants has been identified in population sequencing data. However, so far only a small number of these variants have been functionally characterized regarding their impact on drug efficacy and toxicity and the significance of the vast majority remains unknown. It is therefore of high importance to develop tools and frameworks to accurately infer the effects of pharmacogenomic variants and, eventually, aggregate the effect of individual variations into personalized drug response predictions. To address this challenge, we here first describe the technological advances, including sequencing methods and accompanying bioinformatic processing pipelines that have enabled reliable variant identification. Subsequently, we highlight advances in computational algorithms for pharmacogenomic variant interpretation and discuss the added value of emerging strategies, such as machine learning and the integrative use of omics techniques that have the potential to further contribute to the refinement of personalized pharmacological response predictions. Lastly, we provide an overview of experimental and clinical approaches to validate in silico predictions. We conclude that the iterative feedback between computational predictions and experimental validations is likely to rapidly improve the accuracy of pharmacogenomic prediction models, which might soon allow for an incorporation of the entire pharmacogenetic profile into personalized response predictions.
在过去20年中，测序技术的快速发展提供了多种方法和工具，用于在群体水平上研究人类基因组变异。众所周知，药物基因具有高度多态性，并且在群体测序数据中已经鉴定出大量的药物基因组变异。然而，到目前为止，这些变异中只有少数在功能上被表征了它们对药物疗效和毒性的影响，而绝大多数变异的意义仍然未知。因此，开发工具和框架以准确推断药物基因组变异的影响，并最终将个体变异的影响汇总到个性化药物反应预测中至关重要。为了应对这一挑战，我们首先在这里描述技术进展，包括能够实现可靠变异鉴定的测序方法和伴随的生物信息学处理流程。随后，我们强调药物基因组变异解释的计算算法的进展，并讨论新兴策略（如机器学习和组学技术的综合应用）的附加值，这些策略有可能进一步有助于完善个性化药理反应预测。最后，我们概述了验证计算机预测的实验和临床方法。我们得出结论，计算预测和实验验证之间的迭代反馈可能会迅速提高药物基因组预测模型的准确性，这可能很快允许将整个药物遗传谱纳入个性化反应预测中。
