中国精准医学中的药物管理模型。
Pharmaceutical care model in precision medicine in China.
发表日期:2023 Aug 17
作者:
Ping Zheng, Liqian Mo, Boxin Zhao, Liren Li, Baihong Cen, Zhongyuan Xu, Yilei Li
来源:
CLINICAL PHARMACOLOGY & THERAPEUTICS
摘要:
药学服务是为患者提供个性化的药物护理,应遵循当前的循证药学,并不断验证证据,产生新的证据。在药物护理中,常常发现药物的疗效和不良反应在个体之间甚至在同一患者身上存在差异,这与患者的遗传学、肝肾功能、疾病状态和药物相互作用密切相关。早在20世纪80年代,治疗药物监测(TDM)已被应用于定期监测接受抗癫痫药物或移植后使用的免疫抑制剂的患者的血药浓度,以提供个性化的剂量建议并积累大量的药代动力学(PK)/药效学(PD)数据。随着个性化药学护理的进行,精准医学的概念结合了循证药学、PK/PD理论和大数据引入到药学服务中,以进一步推进TDM技术和药物,并进行药物基因组学分析。TDM和药物基因组学已逐步应用于抗菌、抗肿瘤、抗精神病药物和免疫抑制剂领域。基于精准药学的概念,我们采用了包括PK/PD、定量药理学、药物人群药代动力学和大数据机器学习等方法,提供更加个性化的药学服务,主要针对特殊患者,如危重患者、存在多种药物相互作用风险的患者、肝肾功能不全的患者、孕妇、儿童和老年患者。随着精准药学服务模式的构建和不断完善,将产生更好的临床实践证据,为患者提供更好的精准药学服务。
版权所有©2023年西班牙医院药学学会(S.E.F.H)。由Elsevier España, S.L.U.出版。保留所有权利。
Pharmacy service is to provide individualized pharmaceutical care for patients, which should follow the current evidence-based pharmacy, and constantly verify the evidence and then produce new evidence. In pharmaceutical care, differences are often found in the efficacy and adverse reactions of drugs among individuals, even within individuals, which are closely related to patients' genetics, liver and kidney functions, disease states, and drug interactions. Back in the 1980s, therapeutic drug monitoring (TDM) has been applied to routinely monitor the blood drug concentration of patients taking antiepileptic drugs or immunosuppressants after transplantation to provide individualized dosage recommendations and accumulate a large amount of pharmacokinetic (PK)/pharmacodynamic (PD) data. As individualized pharmaceutical care proceeds, the concept of precision medicine was introduced into pharmacy services in combination with evidence-based pharmacy, PK/PD theories, and big data to further promote the TDM technology and drugs, and carry out pharmacogenomics analysis. The TDM and pharmacogenomics have been applied gradually to the fields of antimicrobial, antitumor, and antipsychotic drugs and immunosuppressants. Based on the concept of precision pharmacy, we adopted approaches including PK/PD, quantitative pharmacology, population pharmacokinetics, and big data machine learning to provide more personalized pharmacy services, which is mainly for special patients, such as critical patients, patients with interaction risk of multiple drugs, patients with liver and renal insufficiency, pregnant women, children, and elderly patients. As the service pattern of precision pharmacy has been constructed and constantly improved, better evidence in clinical practice will be produced to provide patients with better precision pharmacy service.Copyright © 2023 Sociedad Española de Farmacia Hospitalaria (S.E.F.H). Publicado por Elsevier España, S.L.U. All rights reserved.