Ingelman-Sundberg Magnus, Molden Espen
Section of Pharmacogenetics, Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden.
Center for Psychopharmacology, Diakonhjemmet Hospital, Oslo, Norway.
Br J Clin Pharmacol. 2025 Jun;91(6):1569-1579. doi: 10.1111/bcp.16048. Epub 2024 Mar 24.
Pharmacokinetics plays a central role in understanding the significant interindividual differences that exist in drug metabolism and response. Effectively addressing these differences requires a multi-faceted approach that encompasses a variety of tools and methods. In this review, we examine three key strategies to achieve this goal, namely pharmacogenomics, therapeutic drug monitoring (TDM) and liquid biopsy-based monitoring of hepatic ADME gene expression and highlight their advantages and limitations. We note that larger cohort studies are needed to validate the utility of liquid biopsy-based assessment of hepatic ADME gene expression, which includes prediction of drug metabolism in the clinical setting. Modern mass spectrometers have improved traditional TDM methods, offering versatility and sensitivity. In addition, the identification of endogenous or dietary markers for CYP metabolic traits offers simpler and more cost-effective alternatives to determine the phenotype. We believe that future pharmacogenomic applications in clinical practice should prioritize the identification of missing heritable factors, using larger, well-characterized patient studies and controlling for confounding factors such as diet, concomitant medication and physical health. The intricate regulation of ADME gene expression implies that large-scale studies combining long-read next-generation sequencing (NGS) of complete genomes with phenotyping of patients taking different medications are essential to identify these missing heritabilities. The continuous integration of such data into AI-driven analytical systems could provide a comprehensive and useful framework. This could lead to the development of highly effective algorithms to improve genetics-based precision treatment by predicting drug metabolism and response, significantly improving clinical outcomes.
药代动力学在理解药物代谢和反应中存在的个体间显著差异方面起着核心作用。有效解决这些差异需要一种多方面的方法，涵盖各种工具和方法。在本综述中，我们研究了实现这一目标的三种关键策略，即药物基因组学、治疗药物监测（TDM）和基于液体活检的肝脏药物吸收、分布、代谢和排泄（ADME）基因表达监测，并强调了它们的优点和局限性。我们指出，需要更大规模的队列研究来验证基于液体活检评估肝脏ADME基因表达的实用性，这包括在临床环境中预测药物代谢。现代质谱仪改进了传统的TDM方法，提供了多功能性和灵敏度。此外，识别细胞色素P450（CYP）代谢特征的内源性或饮食标志物为确定表型提供了更简单、更具成本效益的替代方法。我们认为，未来药物基因组学在临床实践中的应用应优先识别缺失的遗传因素，采用规模更大、特征明确的患者研究，并控制饮食、合并用药和身体健康等混杂因素。ADME基因表达的复杂调控意味着，将完整基因组的长读长下一代测序（NGS）与服用不同药物患者的表型分析相结合的大规模研究对于识别这些缺失的遗传因素至关重要。将这些数据持续整合到人工智能驱动的分析系统中，可以提供一个全面且有用的框架。这可能会导致开发高效算法，通过预测药物代谢和反应来改善基于遗传学的精准治疗，显著改善临床结果。
