Article(id=1292135579945300747, tenantId=1146029695717560320, journalId=1246415772164075586, issueId=1292135425250976333, articleNumber=null, orderNo=null, doi=10.13699/j.cnki.1001-6821.2026.10.009, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1770566400000, receivedDateStr=2026-02-09, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1785998728977, onlineDateStr=2026-08-06, pubDate=1779897600000, pubDateStr=2026-05-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1785998728977, onlineIssueDateStr=2026-08-06, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1785998728977, creator=13701087609, updateTime=1785998728977, updator=13701087609, issue=Issue{id=1292135425250976333, tenantId=1146029695717560320, journalId=1246415772164075586, year='2026', volume='42', issue='10', pageStart='1351', pageEnd='1500', issueExtLink='null', onlineDate='null', pubDate='1779897600000', pubDateStr='2026-05-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1785998692095, creator='13701087609', updateTime=1786014373634, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1292201198489202995, tenantId=1146029695717560320, journalId=1246415772164075586, issueId=1292135425250976333, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1292201198489202996, tenantId=1146029695717560320, journalId=1246415772164075586, issueId=1292135425250976333, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1403, endPage=1408, ext={EN=ArticleExt(id=1292135580326982412, articleId=1292135579945300747, tenantId=1146029695717560320, journalId=1246415772164075586, language=EN, title=Population pharmacokinetics of duloxetine in patients with depression, columnId=1246531407389020354, journalTitle=Chinese Journal of Clinical Pharmacology, columnName=Pharmacokinetics and Bioequivalence Study, runingTitle=null, highlight=null, articleAbstract=
Objective To establish a population pharmacokinetic (PPK) model of duloxetine (DLXT) in hospitalized patients with depression, to investigate its influencing factors, and to provide reference for individualized medication.
Methods A total of 325 patients who were hospitalized and received oral DLXT treatment at Shenzhen Kangning Hospital from January 2022 to October 2025 were retrospectively included, and their medication information, plasma concentrations, and other clinically relevant data were collected. Nonlinear mixed-effects modeling (NONMEM) was used to establish a PPK model of DLXT concentrations. Goodness-of-fit (GOF) plots, visual predictive check (VPC) and Bootstrap were used to evaluate the stability and predictive performance of the model, respectively. Based on the significant covariates identified in the final model, the steady-state plasma concentrations (Css) of DLXT over time were simulated under different dosage regimens in various populations.
Results A one-compartment model with first-order absorption and elimination was developed using 450 plasma concentration data points from 325 inpatients. The inter-individual variability of apparent clearance (CL/F) was 42.2%, and the proportional residual variability was 27.1%. Gender was identified as a significant covariate affecting CL/F. The final model was expressed as: CL/F(L·h-1)=64.6×(1-0.25×Gender), where Gender is an indicator variable (male=0, female=1). The apparent volume of distribution was 1 530 L, and the absorption rate constant was fixed at 0.168 h-1. Monte Carlo simulations showed that the Css in females was higher than that in males at the same doses.
Conclusion A PPK model for DLXT in hospitalized patients with depression was successfully established. The final model is stable and reliable, and it can be used for individualized dosing regimens in clinical practice.
, authors=Yi-lin ZHONG
1, 2, Huan-si ZENG
2, Cai-ni ZHONG
2, Wei-wei ZENG
1, 3, authorsList=Yi-lin ZHONG, Huan-si ZENG, Cai-ni ZHONG, Wei-wei ZENG, authorCompany=null, correspAuthors=Wei-wei ZENG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1292135580679303949, articleId=1292135579945300747, tenantId=1146029695717560320, journalId=1246415772164075586, language=CN, title=度洛西汀在抑郁患者中的群体药代动力学研究, columnId=1246531407988805834, journalTitle=中国临床药理学杂志, columnName=药代动力学与生物等效性研究, runingTitle=null, highlight=null, articleAbstract=
目的 建立度洛西汀（DLXT）在住院抑郁患者中的群体药代动力学（PPK）模型，考察影响因素，为临床制定个体化给药方案提供参考。
方法 回顾性纳入2022年1月至2025年10月在深圳市康宁医院住院并接受口服DLXT治疗的325例患者，收集其用药情况、血药浓度等临床相关信息。采用非线性混合效应模型法（NONMEM）建立DLXT的PPK模型，采用拟合优度图（GOF）、可视化预测检验（VPC）和自举法（Bootstrap）分别对模型的稳定性和预测性能进行评估。根据最终筛选出的显著协变量，模拟不同给药方案下，不同人群DLXT的稳态血药浓度（Css）随时间变化的情况。
结果 基于325例住院患者的450个血药浓度数据，成功建立了DLXT的一阶吸收和消除的一房室模型。表观清除率（CL/F）的个体间变异为42.2%，比例型残差变异为27.1%，性别是影响CL/F的显著协变量。最终模型表示为：CL/F（L·h-1）=64.6×（1-0.25×Gender）（其中Gender为指示变量：男性=0，女性=1），表观分布容积为1 530 L，吸收速率常数固定为0.168 h-1。蒙特卡洛模拟结果显示，相同给药剂量下女性的Css较男性高。
结论 建立了DLXT在住院抑郁患者中的PPK模型，最终模型具有较好的稳定性和可靠性，可用于临床个体化用药的参考。
, authors=钟易霖
1, 2, 曾环思
2, 钟彩妮
2, 曾位位
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钟易霖（1993-），女，主管药师，主要从事临床药学研究
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