Article(id=1309634592252981342, tenantId=1146029695717560320, journalId=1227999776411738115, issueId=1309634582362809158, articleNumber=null, orderNo=null, doi=10.12048/j.issn.1674-229X.2026.04.009, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1748275200000, receivedDateStr=2025-05-27, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790170818626, onlineDateStr=2026-09-23, pubDate=1777046400000, pubDateStr=2026-04-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790170818626, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790170818626, creator=13701087609, updateTime=1790170818626, 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=299, endPage=307, ext={EN=ArticleExt(id=1309634592429142111, articleId=1309634592252981342, tenantId=1146029695717560320, journalId=1227999776411738115, language=EN, title=Model-informed Precision Dosing of Aripiprazole in Chinese Patients With Schizophrenia in Clinical Practice, columnId=null, journalTitle=Pharmacy Today, columnName=null, runingTitle=null, highlight=null, articleAbstract=
OBJECTIVE To establish a new pathway for the external validation of population pharmacokinetics (PPK) models based on real-world data, using aripiprazole in the treatment of schizophrenia as an example, and to explore a novel strategy for the clinical application of model-informed precision dosing (MIPD) and its feasibility.
METHODS A total of 101 patients with schizophrenia (1 178 blood drug concentration samples) were retrospectively enrolled at a class Ⅲ Grade A specialized hospital. Published PPK models of aripiprazole were identified by searching PubMed, Web of Science, and China National Knowledge Infrastructure (CNKI). Candidate models were evaluated using goodness-of-fit (R2) prediction error tests (PE, RPE%) to assess their predictive performance, and the model consistent with the characteristics of the blood concentration data was screened. Based on the selected PPK model, Monte Carlo simulation was performed to predict the steady-state plasma concentration of aripiprazole under different dosing regimens for individualized medication.
RESULTS Three published PPK models of aripiprazole were included in this study. Goodness-of- fit and prediction error analyses showed that Model 3 had the best predictive performance. Based on Monte Carlo simulations with Model 3, plasma concentrations under 10 mg qd, 20 mg qd, and 30 mg qd regimens were ranked as IM > EM >UM in patients with the same body weight. For patients with the same CYP2D6 metabolic phenotype, plasma concentrations decreased with increasing body weight: 30 kg > 50 kg > 70 kg > 90 kg > 110 kg.
CONCLUSION After validation with real-world data, the screened model with satisfactory predictive performance can be directly used in clinical practice to guide individualized medication and achieve model-informed precision dosing.
, authors=Shuyan ZENG
1, 2, Wanting HUANG
1, 2, Hui XIA
1, 2, Zhihao GUO
1, 2, Shanqing HUANG
1, 3, Dewei SHANG
1, 3, *, authorsList=Shuyan ZENG, Wanting HUANG, Hui XIA, Zhihao GUO, Shanqing HUANG, Dewei SHANG, authorCompany=null, correspAuthors=Dewei SHANG, 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=1309634592848572512, articleId=1309634592252981342, tenantId=1146029695717560320, journalId=1227999776411738115, language=CN, title=模型引导的阿立哌唑精准用药在中国精神分裂症患者的临床实践, columnId=1228268588277105398, journalTitle=今日药学, columnName=临床药学, runingTitle=null, highlight=null, articleAbstract=
目的 基于真实世界数据反向验证群体药代动力学（Population pharmacokinetics, PPK）模型的新路径，以阿立哌唑治疗精神分裂症为例，探索模型引导的精准用药（Model-informed precision dosing, MIPD）临床应用的新方法及可行性。
方法 回顾性纳入某三甲专科医院101例精神分裂症患者（1178个血药浓度点），利用PubMed、Web of Science、中国国家知识基础设施（China National Knowledge Infrastructure, CNKI）检索已发表的阿立哌唑PPK模型，通过拟合优度法（R2）和预测误差检验（PE、RPE%）对候选模型进行评价，评估已发表的PPK模型的预测性能，筛选得到符合血药浓度数据特征的模型。基于该PPK模型，应用蒙特卡洛模拟法仿真不同用药方案下阿立哌唑的稳态血药浓度范围，探索个体化用药。
结果 本研究共收集到3篇已发表的阿立哌唑的PPK模型。通过拟合优度法、预测误差检验发现，模型3最终模型的预测能力最佳。基于模型3采用蒙特卡洛模拟不同给药方案后的结果表明，10 mg qd、20 mg qd、30 mg qd方案下，相同体重患者的血药浓度：IM > EM > UM，相同CYP2D6代谢型患者的血药浓度：30 kg > 50 kg > 70 kg > 90 kg > 110 kg。
结论 通过真实世界数据的验证，在模型预测性能较为理想的情况下，能够直接将筛选得到的模型应用于临床实践指导个体化用药，实现模型引导的精准用药。
, authors=曾淑燕
1, 2, 黄婉婷
1, 2, 夏慧
1, 2, 郭徵豪
1, 2, 黄善情
1, 3, 尚德为
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1．广州医科大学附属脑科医院，广东 广州 510000
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1.The Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, Guangdong 510000, China
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1.The Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, Guangdong 510000, China
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1．广州医科大学附属脑科医院，广东 广州 510000
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1, 3, address=
1.The Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, Guangdong 510000, China
3.Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, Guangdong 510000, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1309634595960746119, tenantId=1146029695717560320, journalId=1227999776411738115, articleId=1309634592252981342, authorId=1309634595788779651, language=CN, stringName=黄善情, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=
1, 3, address=
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