Article(id=1309634592739517347, tenantId=1146029695717560320, journalId=1227999776411738115, issueId=1309634582362809158, articleNumber=null, orderNo=null, doi=10.12048/j.issn.1674-229X.2026.04.010, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1770307200000, receivedDateStr=2026-02-06, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790170818742, onlineDateStr=2026-09-23, pubDate=1777046400000, pubDateStr=2026-04-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790170818742, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790170818742, creator=13701087609, updateTime=1790170818742, updator=13701087609, issue=Issue{id=1309634582362809158, tenantId=1146029695717560320, journalId=1227999776411738115, year='2026', volume='36', issue='4', pageStart='241', pageEnd='320', issueExtLink='null', onlineDate='null', pubDate='1777046400000', pubDateStr='2026-04-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790170816268, creator='13701087609', updateTime=1790219015105, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309836742979576497, tenantId=1146029695717560320, journalId=1227999776411738115, issueId=1309634582362809158, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309836742979576498, tenantId=1146029695717560320, journalId=1227999776411738115, issueId=1309634582362809158, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=308, endPage=314, ext={EN=ArticleExt(id=1309634592907289508, articleId=1309634592739517347, tenantId=1146029695717560320, journalId=1227999776411738115, language=EN, title=Individualized Dosing Regimens of Teicoplanin for Patients with Hypoalbuminemia Based on Population Pharmacokinetics, columnId=null, journalTitle=Pharmacy Today, columnName=null, runingTitle=null, highlight=null, articleAbstract=
OBJECTIVE To characterize the pharmacokinetic characteristics of teicoplanin in patients with hypoalbuminemia, explore the target values of pharmacokinetic/pharmacodynamic (PK/PD) parameters, and develop individualized dosing regimens.
METHODS Clinical data of patients with hypoalbuminemia were retrospectively collected, and a population pharmacokinetic analysis was performed using a nonlinear mixed-effects model. Classification and regression tree (CART) analysis and survival analysis were used to explore and verify the impact of the target values of (PK/PD) parameters on therapeutic efficacy. Monte Carlo simulation was applied to develop individualized dosing regimens.
RESULTS A one-compartment population pharmacokinetic model of teicoplanin was established using 328 blood concentration values from 195 patients. The population typical values of clearance and apparent volume of distribution of the model were 0.612 L·h-1 and 91.2 L, respectively. Estimated glomerular filtration rate (eGFR) was included as a covariate for clearance, and albumin and urea were included as covariates for the apparent volume of distribution. The target values of PK/PD parameters were determined as trough concentration ≥15 mg·L-1 and the ratio of 24 hour area under the drug-time curve to minimum inhibitory concentration (AUC24/MIC)≥790. Several individualized dosing regimens were developed based on different levels of the model covariates.
CONCLUSION For patients with hypoalbuminemia receiving teicoplanin, it is necessary to increase the dosage and frequency of the loading dose regimen according to the patients' albumin and eGFR levels, and adjust the maintenance dose regimen according to the MIC of pathogenic bacteria, so as to improve clinical efficacy.
, authors=Jianpeng HE
1, 2, Chen YE
1, Ying WANG
1, Zhanhua HU
1, Zeyuan HE
1, Yuqing CHEN
1, Guocheng LI
1, Min HUANG
2, Xiaoxia YU
1, *, authorsList=Jianpeng HE, Chen YE, Ying WANG, Zhanhua HU, Zeyuan HE, Yuqing CHEN, Guocheng LI, Min HUANG, Xiaoxia YU, authorCompany=null, correspAuthors=Xiaoxia YU, 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=1309634593272193957, articleId=1309634592739517347, tenantId=1146029695717560320, journalId=1227999776411738115, language=CN, title=基于群体药动学的替考拉宁在低白蛋白血症患者中的个体化用药研究, columnId=1228268588277105398, journalTitle=今日药学, columnName=临床药学, runingTitle=null, highlight=null, articleAbstract=
目的 明确替考拉宁在低白蛋白血症患者中的药动学特征，探索药动学/药效学参数的目标值，制定个体化给药方案。
方法 回顾性收集低白蛋白血症患者的临床数据，使用非线性混合效应模型进行群体药动学分析。使用分类与回归树和生存分析，探索并验证药动学/药效学参数的目标值对疗效的影响。使用蒙特卡洛模拟制定个体化给药方案。
结果 使用195例患者的328个血药浓度值，建立了替考拉宁的一房室群体药动学模型。模型的清除率和表观分布容积的群体典型值分别为0.612 L·h-1和91.2 L，纳入肾小球滤过率作为清除率的协变量，白蛋白和尿素作为表观分布容积的协变量。确定谷浓度≥15 mg·L-1和24 h药时曲线下面积与最小抑菌浓度的比值（AUC24/MIC）≥790为药动学/药效学参数的目标值。基于模型协变量的不同水平，制定了多种个体化给药方案。
结论 低白蛋白血症患者使用替考拉宁时，需根据患者的白蛋白和肾小球滤过率水平，提高负荷剂量方案的用量和次数，并根据病原菌的最小抑菌浓度，调整维持剂量方案，以提高临床疗效。
, authors=何健鹏
1, 2, 叶晨
1, 王莹
1, 胡展华
1, 何泽源
1, 陈雨晴
1, 李国成
1, 黄民
2, 余晓霞
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1.Department of Pharmacy, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510120, China
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