综合药学和精准医学

Cambridge prisms, Precision medicine Pub Date : 2023-03-10 eCollection Date: 2023-01-01 DOI:10.1017/pcm.2023.10
Kenji Fujita, Nashwa Masnoon, John Mach, Lisa Kouladjian O'Donnell, Sarah N Hilmer
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引用次数: 0

摘要

精准医学是一种通过考虑相关的人口统计学、临床、基因组学和环境因素来制定治疗决策,最大限度地提高疾病治疗和预防效果,并最大限度地减少药物伤害的方法。精准医学是复杂的,甚至是针对单一疾病的单一药物的强制决策,因为它需要仔细考虑影响药代动力学和药效学的多种可测量因素,以及许多患者特异性变量。鉴于患有多种疾病和药物的患者数量不断增加,有必要应用从单一治疗和单一疾病管理中获得的精确医学经验来优化多种药物治疗。然而,precisionmedicineforoptimisa-tionofpolypharmacyisparticularlychallengingbecauseofthevastnumberofinteractingfactors thatinfluencedruguseandresponse。Inthisnarrativereview, weaimtoprovideandapplythelatest researchfindingstoachieveprecisionmedicineinthecontextofpolypharmacy。具体地说,这个reviewaimsto (1) summarisechallengesinachievingprecisionmedicinespecifictopolypharmacy;(2)综合当前多药联合用药的精准用药方法;(3)对未知药-药相互作用(DDI)预测领域的文献进行综述;(4)提出一种为多药联合用药患者提供精准用药的新方法。为了使我们提出的模型在常规临床实践中得以实施,需要使用生物信息学方法对广泛的数据进行综合干预,以预测多种药物治疗方案对个体的影响,并将其整合到电子病历中。此外,临床医生需要接受培训,以解释来自药物基因组学测试、DDI预测和生理-药代动力学-药效学等来源的数据结果
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Polypharmacy and precision medicine.

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.

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