[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-444811-105":59,"doc-detail-444811-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","bayesian-based-pharmacokinetic-framework-integrated-with-therapeutic-drug-monitoring-for-assessing-adherence-to-antiseizure-medications-a-clinical-trial-simulation-study","Bayesian-Based Pharmacokinetic Framework Integrated with Therapeutic Drug Monitoring for Assessing Adherence to Antiseizure Medications: A Clinical Trial Simulation Study","","Adherence to antiseizure medications (ASMs) is central to effective epilepsy management, yet existing therapeutic drug monitoring (TDM) consensus approaches may overlook individual patient characteristics, reducing adherence assessment accuracy. The study proposes a Bayesian-based pharmacokinetic framework that integrates TDM data to evaluate adherence across 14 commonly used ASMs. Trial simulations cover full adherence and multiple nonadherent dosing behaviors, using population PK models, patient demographics, dosing history, prior adherence probabilities, and TDM measurements while assessing covariate effects. The resulting method enables robust retrodiction, drug-specific concentration thresholds, and a real-time web dashboard for practical patient-specific adherence profiling.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/bayesian-based-pharmacokinetic-framework-integrated-with-therapeutic-drug-monitoring-for-assessing-adherence-to-antiseizure-medications-a-clinical-trial-simulation-study/444811/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/bayesian-based-pharmacokinetic-framework-integrated-with-therapeutic-drug-monitoring-for-assessing-adherence-to-antiseizure-medications-a-clinical-trial-simulation-study/444811.png","ImageObject",300,407,{"name":92,"@type":93},"Alexandar","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-29",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does the study address in current TDM-based adherence assessment?","Question",{"text":113,"@type":114},"Current TDM consensus guidelines may neglect individual patient characteristics, which can compromise the accuracy of adherence assessments.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does the proposed framework incorporate patient and dosing information?",{"text":118,"@type":114},"It integrates validated population pharmacokinetic models with patient demographics, dosing history, prior adherence probabilities, and TDM measurements to derive Bayesian posterior probabilities of dosing behaviors.",{"name":120,"@type":111,"acceptedAnswer":121},"What outcomes does the Bayesian-based PK approach aim to deliver in practice?",{"text":122,"@type":114},"It provides accurate retrodiction of recent missed doses under simulations, identifies drug- and patient-dependent concentration thresholds, and supports real-time adherence assessment via a web-based dashboard.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},444811,1790766991,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":145,"read_time":146,"preview_page_count":4},962090768658,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","JOURNAL OF MEDICAL INTERNET RESEARCH Liu et al  \nOriginal Paper  \nBayesian-Based Pharmacokinetic Framework Integrated with Therapeutic Drug Monitoring for Assessing Adherence to Antiseizure Medications: A Clinical Trial Simulation Study  \n\n| Xiao-Qin Liu 1 , PhD; Zi-Ran Li2 , PhD; Wei-Wei Lin3 , PhD; Juan Wang1 , BSc; Fu-Qing Gu 1 , BSc; Jun-Jie Ding4 , PhD; Zheng Jiao 1 , PhD |\n| --- |\n| 1Department of Pharmacy, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China 2Department of Bioengineering & Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, United States 3Department of Pharmacy, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China\u003Cbr>4Center for Tropical Disease and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom\u003Cbr>Corresponding Author:\u003Cbr>Zheng Jiao, PhD\u003Cbr>Department of Pharmacy\u003Cbr>Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine\u003Cbr>241 West Huaihai Road Shanghai 200030 China\u003Cbr>Phone: 86 15502125571\u003Cbr>Email: [jiaozhen@online.sh.cn](jiaozhen@online.sh.cn)\u003Cbr>Abstract |\n| Background: Adherence to antiseizure medications (ASMs) is a cornerstone of effective epilepsy management. However, current consensus guidelines for assessing medication adherence via therapeutic drug monitoring (TDM) may neglect individual patient characteristics, thereby compromising the accuracy of adherence assessments.\u003Cbr>Objective: This study proposed an innovative Bayesian–based pharmacokinetic (PK) framework integrated with TDM data to address the above limitations, with a focus on 14 widely prescribed ASMs, including brivaracetam, carbamazepine, clobazam, eslicarbazepine acetate, lacosamide, lamotrigine, levetiracetam, oxcarbazepine, perampanel, phenobarbital, topiramate, valproic acid, vigabatrin, and zonisamide.\u003Cbr>Methods: Comprehensive clinical trial simulations were conducted to investigate the PK of ASMs in patients with epilepsy under conditions of full adherence and various nonadherent dosing behaviors, including omission of the last dose and consecutive missed doses. Bayesian posterior probabilities of these dosing behaviors were derived by integrating validated population PK models, individual patient demographics (eg, age, weight, creatinine clearance), dosing history, prior adherence probabilities and TDM measurements. Additionally, the influence of covariates on assessment outcomes was systematically evaluated.\u003Cbr>Results: The Bayesian-based PK approach demonstrated robust discriminative ability. Under idealized simulation conditions with minimized variabilities, the approach achieved accurate retrodiction of the last 1 or 2 doses across all 14 ASMs and partial retrodiction of extended nonadherence trajectories for 6 ASMs. Concentration thresholds for adherence classification varied significantly across drugs and are influenced by patient-specific factors, comedications, formulation, sampling time, and prior probability. To translate these insights into practice, an adaptable web-based dashboard was developed using the shiny package in R software to enable precise and real-time assessments of medication adherence.\u003Cbr>Conclusions: This study establishes a Bayesian-based PK approach to enhance the assessment of ASMs adherence. This approach facilitates a paradigm shift from population-based management to patient-specific adherence profiling, offering a practical methodology for the precise evaluation of medication-taking behaviors.\u003Cbr>J Med Internet Res 2026;28:e77917; doi:  10.2196/77917 |\n\n[https://www.jmir.org/2026/1/e77917](https://www.jmir.org/2026/1/e77917) J Med Internet Res 2026 | vol. 28 | e77917 | p. 1  \n(page number not for citation purposes)  \nJOURNAL OF MEDICAL INTERNET RESEARCH Liu et al  \nKeywords: antiseizure medications; medication adherence; therapeutic drug monitoring; Bayesian theory; population pharmacokinetics  \nIntroduction  \nEpilepsy","cbCairieaUqKkFkb","https://ap.wps.com/l/cbCairieaUqKkFkb","pdf",991643,13,"English","# Abstract\n# Introduction\n## Epilepsy burden and the need for adherence assessment\n## Limits of self-reported adherence and role of TDM\n## Consensus TDM guidelines and their constraints","[{\"question\":\"What problem does the study address in current TDM-based adherence assessment?\",\"answer\":\"Current TDM consensus guidelines may neglect individual patient characteristics, which can compromise the accuracy of adherence assessments.\"},{\"question\":\"How does the proposed framework incorporate patient and dosing information?\",\"answer\":\"It integrates validated population pharmacokinetic models with patient demographics, dosing history, prior adherence probabilities, and TDM measurements to derive Bayesian posterior probabilities of dosing behaviors.\"},{\"question\":\"What outcomes does the Bayesian-based PK approach aim to deliver in practice?\",\"answer\":\"It provides accurate retrodiction of recent missed doses under simulations, identifies drug- and patient-dependent concentration thresholds, and supports real-time adherence assessment via a web-based dashboard.\"}]","Bayesian-Based Pharmacokinetic Framework Integrated with Therapeutic Drug Monitoring for Assessing Adherence to Antiseizure Medications: A Clinical Trial Simulation Study | PDF",1790709339,33]