成人患者中达托霉素群体药动学模型的系统评价与研究进展

    Systematic Review and Research Progress of Population Pharmacokinetic Models of Daptomycin in Adult Patients

    • 摘要:
      目的 系统评价已发表的达托霉素群体药动学(population pharmacokinetic,PopPK)模型,总结模型结构、协变量及验证方法,识别影响药动学变异的关键因素,为特殊人群的个体化给药提供理论依据。
      方法 计算机检索PubMed、Embase、Cochrane Library、Web of Science、CNKI、万方和维普等数据库,检索时限从建库至2025年9月4日。由2名研究者独立筛选文献并提取数据,纳入符合标准的成年患者达托霉素PopPK研究,提取相关数据进行分析。
      结果 共纳入24项研究。多数研究采用NONMEM软件(15项)进行建模,模型多为二室模型(18项)。达托霉素清除率(clearance,CL)典型值范围为0.229~1.02 L·h−1,中央室分布容积(volume of central compartment,Vc)为0.95~151 L。CL的主要协变量为肾功能(肌酐清除率或肾小球滤过率)和体质量;Vc的主要协变量包括体质量、性别、体成分(如去脂体质量)和疾病状态。模型多通过Bootstrap、视觉预测检查等方法进行验证。
      结论 肾功能、体质量和疾病状态是达托霉素的药动学变异的主要原因,对于肾功能不全、肥胖、重症等特殊人群,需要采用适宜的模型进行个体化方案制定。未来需扩大样本量、加强多中心验证,并进一步探索特殊人群及遗传因素对药动学行为的影响。

       

      Abstract:
      OBJECTIVE To systematically evaluate published population pharmacokinetic(PopPK) models of daptomycin regarding their structural characteristics, covariate effects, and validation strategies, and identify key factors that affect its pharmacokinetics variability, and to provide a theoretical basis for individualized dosing in special populations.
      METHODS A comprehensive literature search was performed in PubMed, Embase, Cochrane Library, Web of Science, CNKI, WanFang, and VIP databases from inception until September 4, 2025. Two investigators independently screened studies and extracted data from eligible PopPK studies of daptomycin in adults and the extracted data were analyzed.
      RESULTS Twenty-four studies were included. The two-compartment model(n=18) and NONMEM software(n=15) were most frequently used. Typical estimates of daptomycin for clearance(CL) and volume of central compartment(Vc) ranged from 0.229 to 1.02 L·h−1 and 0.95 to 151 L, respectively. Renal function(creatinine clearance or estimated glomerularfiltration rate) and body weight were the primary covariates for CL, while Vc was significantly associated with body weight, sex, body composition(eg. fat-free mass), and disease status. Bootstrap and visual predictive check were common validation methods.
      CONCLUSION Renal function, body weight, and disease status are the primary sources of pharmacokinetic variability for daptomycin. Patients with these factors should receive individualized dosing regimens based on suitable PopPK models. Future efforts should focus on increasing sample sizes, strengthening multi-center validation, and further investigating the impact of genetic factors and special populations on pharmacokinetic behavior.

       

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