Fujita Kenji, Masnoon Nashwa, Mach John, O'Donnell Lisa Kouladjian, Hilmer Sarah N
Departments of Clinical Pharmacology and Aged Care, Kolling Institute, Faculty of Medicine and Health, The University of Sydney and the Northern Sydney Local Health District, Sydney, NSW, Australia.
Camb Prism Precis Med. 2023 Mar 10;1:e22. doi: 10.1017/pcm.2023.10. eCollection 2023.
Precision medicine is an approach to maximise the effectiveness of disease treatment and prevention and minimise harm from medications by considering relevant demographic, clinical, genomic and environmental factors in making treatment decisions. Precision medicine is complex, even for decisions about single drugs for single diseases, as it requires expert consideration of multiple measurable factors that affect pharmacokinetics and pharmacodynamics, and many patient-specific variables. Given the increasing number of patients with multiple conditions and medications, there is a need to apply lessons learned from precision medicine in monotherapy and single disease management to optimise polypharmacy. However, precision medicine for optimisation of polypharmacy is particularly challenging because of the vast number of interacting factors that influence drug use and response. In this narrative review, we aim to provide and apply the latest research findings to achieve precision medicine in the context of polypharmacy. Specifically, this review aims to (1) summarise challenges in achieving precision medicine specific to polypharmacy; (2) synthesise the current approaches to precision medicine in polypharmacy; (3) provide a summary of the literature in the field of prediction of unknown drug-drug interactions (DDI) and (4) propose a novel approach to provide precision medicine for patients with polypharmacy. For our proposed model to be implemented in routine clinical practice, a comprehensive intervention bundle needs to be integrated into the electronic medical record using bioinformatic approaches on a wide range of data to predict the effects of polypharmacy regimens on an individual. In addition, clinicians need to be trained to interpret the results of data from sources including pharmacogenomic testing, DDI prediction and physiological-pharmacokinetic-pharmacodynamic modelling to inform their medication reviews. Future studies are needed to evaluate the efficacy of this model and to test generalisability so that it can be implemented at scale, aiming to improve outcomes in people with polypharmacy.
精准医学是一种通过在制定治疗决策时考虑相关的人口统计学、临床、基因组和环境因素，来最大化疾病治疗和预防效果并最小化药物伤害的方法。精准医学很复杂，即使是针对单一疾病的单一药物决策也是如此，因为它需要专家考虑影响药代动力学和药效动力学的多个可测量因素以及许多患者特异性变量。鉴于患有多种疾病并使用多种药物的患者数量不断增加，有必要将从精准医学在单药治疗和单一疾病管理中获得的经验教训应用于优化多药治疗。然而，由于影响药物使用和反应的相互作用因素众多，优化多药治疗的精准医学尤其具有挑战性。在这篇叙述性综述中，我们旨在提供并应用最新研究结果，以在多药治疗的背景下实现精准医学。具体而言，本综述旨在：（1）总结多药治疗中实现精准医学所特有的挑战；（2）综合多药治疗中精准医学的当前方法；（3）提供未知药物 - 药物相互作用（DDI）预测领域的文献综述；（4）提出一种为多药治疗患者提供精准医学的新方法。为了使我们提出的模型能够在常规临床实践中实施，需要使用生物信息学方法将一个综合干预包整合到电子病历中，利用广泛的数据来预测多药治疗方案对个体的影响。此外，临床医生需要接受培训，以解释来自包括药物基因组学检测、DDI预测和生理 - 药代动力学 - 药效动力学建模等来源的数据结果，为他们的用药评估提供依据。需要未来的研究来评估该模型的疗效并测试其普遍性，以便能够大规模实施，旨在改善多药治疗患者的治疗效果。
