Bridging the Medication Adherence Gap from Therapeutic Drug Monitoring: A Bayesian approach for Anti-Seizure Medications

Bridging the Medication Adherence Gap from Therapeutic Drug Monitoring: A Bayesian approach for Anti-Seizure Medications | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Bridging the Medication Adherence Gap from Therapeutic Drug Monitoring: A Bayesian approach for Anti-Seizure Medications Zheng Jiao, Xiao-Qin Liu, Zi-Ran Li, Wei-Wei Lin, Juan Wang, Fu-Qing Gu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5884827/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Adherence to antiseizure medications (ASMs) is crucial for the success of treatment. However, current recommendations for assessing medication adherence through therapeutic drug monitoring (TDM) may overlook individual patient characteristics, potentially leading to misjudgments. This study aims to evaluate the capability of a Bayesian approach in assessing adherence for 14 ASMs using TDM. Method: A Bayesian framework incorporating population pharmacokinetics was used to assess adherence using TDM data. Additionally, the impact of patient characteristics, concomitant medications, sampling times, and prior adherence probability was examined. Results: With essential patient information, such as age, weight, and scheduled dosing regimen, the Bayesian approach effectively assessed recent adherence for all investigated ASMs. The concentration thresholds varied by ASM and were influenced by patients' characteristics. To facilitate individual adherence evaluations, a web-based dashboard was developed. Conclusion: The integration of Bayesian methods with pharmacokinetics significantly enhances the reliability of TDM in assessing adherence to ASMs. Health sciences/Neurology/Neurological disorders/Epilepsy Health sciences/Health care/Patient education anti-seizure medication medication adherence therapeutic drug monitoring Bayesian method population pharmacokinetics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Full Text Additional Declarations There is NO Competing Interest. Supplementary Files nrreportingsummary20250123.pdf reporting summary nreditorialpolicychecklist20250123.pdf policy checklist supplementaryfiles0123.pdf Supplementary files Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5884827","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":407846488,"identity":"f98daebe-e30f-43c0-9b20-ecf101e2fb39","order_by":0,"name":"Zheng Jiao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYBACxmYQ0cDAwA+kDiSAhCSI1SIJxMRpgegDKjc4ANTCQIwW5nbmhw9/7rDJMz7ee+DAwx02DPKzGxg/F+B1GJuxgeSZtGKzM+cSDiSeSWMwuHOAWXoGfr+YSRi2HU7cdiPH4EBi22EGA4kENmYevFrYv0kktv1P3DwDrOU/g/wMglp4zCQOth1I3CAB1nKAgeEGYS3Fho1tyYkzzpwBaUnmMbiR2CyNT4th//GND3+22SX2t/cYghhy8jOSD37Gq6UBTYAHEk14gDxe2VEwCkbBKBgFIAAAWK9Ouwe622wAAAAASUVORK5CYII=","orcid":"","institution":"Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Zheng","middleName":"","lastName":"Jiao","suffix":""},{"id":407846489,"identity":"237caf2e-a00f-43a7-9014-b9e3f8404381","order_by":1,"name":"Xiao-Qin Liu","email":"","orcid":"https://orcid.org/0000-0002-3615-3274","institution":"Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiao-Qin","middleName":"","lastName":"Liu","suffix":""},{"id":407846490,"identity":"636d3155-5b7e-4aa4-9653-59f698ce36a6","order_by":2,"name":"Zi-Ran Li","email":"","orcid":"","institution":"Department of Bioengineering \u0026 Therapeutic Sciences, University of California San Francisco, San Francisco, United States","correspondingAuthor":false,"prefix":"","firstName":"Zi-Ran","middleName":"","lastName":"Li","suffix":""},{"id":407846491,"identity":"f1fcc1b2-d424-47f7-a8c9-3d9fcf12e1b2","order_by":3,"name":"Wei-Wei Lin","email":"","orcid":"","institution":"Department of Pharmacy, The First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wei-Wei","middleName":"","lastName":"Lin","suffix":""},{"id":407846492,"identity":"38102e4b-85a0-4c0a-a5c2-13d074311a4f","order_by":4,"name":"Juan Wang","email":"","orcid":"","institution":"Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"","lastName":"Wang","suffix":""},{"id":407846493,"identity":"503433a6-b0ab-4e38-8e03-c6f2ddc4dc56","order_by":5,"name":"Fu-Qing Gu","email":"","orcid":"","institution":"Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Fu-Qing","middleName":"","lastName":"Gu","suffix":""},{"id":407846494,"identity":"c7ede8b8-8cff-4a44-81d8-3b66bddfe6b7","order_by":6,"name":"Junjie Ding","email":"","orcid":"","institution":"University of Oxford","correspondingAuthor":false,"prefix":"","firstName":"Junjie","middleName":"","lastName":"Ding","suffix":""}],"badges":[],"createdAt":"2025-01-23 04:00:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5884827/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5884827/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75087491,"identity":"061fe887-af4e-4fe9-bc65-819104f63522","added_by":"auto","created_at":"2025-01-30 10:21:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":33244,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe dosing scenarios when the last one, two or three dosing are considered.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) the last one dosing behavior; (B) the last two dosing behavior; (C) the last three dosing behavior.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5884827/v1/32de10dde6aeae020b5db994.png"},{"id":75087494,"identity":"242cf869-b4c1-440b-b171-600998a56426","added_by":"auto","created_at":"2025-01-30 10:21:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":525245,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe workflow of adherence assessment by population pharmacokinetic based Bayesian approach.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5884827/v1/f1bdb44d42dce81914587442.png"},{"id":75088616,"identity":"5c8400fa-3352-41ac-958e-28d6c8089431","added_by":"auto","created_at":"2025-01-30 10:29:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":96310,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThree types of retrodiction when investigating dosing instances prior to sampling.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) complete retrodiction; (B) partial retrodiction; (C) no retrodiction.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5884827/v1/03dc29f2dd8bdf32f8748b33.png"},{"id":75087496,"identity":"23b11ee7-7749-4ed1-bf35-60571a4a7388","added_by":"auto","created_at":"2025-01-30 10:21:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":20616,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExamples of posterior probability-concentration curves when investigating the last one, two and three dosing instances.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5884827/v1/c933d82a424289417a055878.png"},{"id":75087501,"identity":"91dec174-d864-4975-a74e-64ec6b4c6899","added_by":"auto","created_at":"2025-01-30 10:21:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":328628,"visible":true,"origin":"","legend":"\u003cp\u003eThe traceability when recalling the last one, two and three dosing instances prior to sampling.\u003c/p\u003e\n\u003cp\u003eD1, the last dosing instance; D2, the last two dosing instances; D3, the last three dosing instances. CR, complete retrodiction, which is defined as when the maximum posterior probabilities of all dosing events are ≥80%; PR, partial retrodiction, which is defined as when only events which are fully adherent (ω\u003csub\u003e1\u003c/sub\u003e,ω\u003csub\u003e11\u003c/sub\u003e or ω\u003csub\u003e111\u003c/sub\u003e) and fully non-adherent (ω\u003csub\u003e0\u003c/sub\u003e,ω\u003csub\u003e00\u003c/sub\u003e or ω\u003csub\u003e000\u003c/sub\u003e) have a maximum posterior probability ≥ 80%; NR, not retrodiction, which is defined as when only one or no dosing events have a maximum posterior probability ≥ 80%. Adult: 40 years, 70 kg, 180 cm; children: 8 years, 25 kg, 127 cm; pregnant women: 25 years, 70 kg, 160 cm, being 30 weeks pregnant.\u0026nbsp;\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5884827/v1/7b7a16c83896c8bd7da9c05c.png"},{"id":75087498,"identity":"f484cded-660b-4148-b751-73e94520c57c","added_by":"auto","created_at":"2025-01-30 10:21:19","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":38418,"visible":true,"origin":"","legend":"\u003cp\u003eScreenshot of adherence assessment for elderly patient with impaired renal function taking oxcarbazepine 300 mg q12h\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eω\u003csub\u003e00\u003c/sub\u003e: missing two continuous doses before sampling; ω\u003csub\u003e01\u003c/sub\u003e, missing the second-to-last dose but taking the last dose; ω\u003csub\u003e10\u003c/sub\u003e, missing the last dose but taking the second-to-last dose; ω\u003csub\u003e11\u003c/sub\u003e, taking all doses.\u0026nbsp;\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5884827/v1/f3016f5f05e761578114f013.png"},{"id":75087500,"identity":"4a6c70b0-49cc-4299-afa9-bbab36d60fd4","added_by":"auto","created_at":"2025-01-30 10:21:19","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":38075,"visible":true,"origin":"","legend":"\u003cp\u003eScreenshot of adherence assessment for pediatric patient taking valproic acid 500 mg q12h and carbamazepine 100 mg q12h \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eω\u003csub\u003e00\u003c/sub\u003e: missing two continuous doses before sampling; ω\u003csub\u003e01\u003c/sub\u003e, missing the second-to-last dose but taking the last dose; ω\u003csub\u003e10\u003c/sub\u003e, missing the last dose but taking the second-to-last dose; ω\u003csub\u003e11\u003c/sub\u003e, taking all doses. \u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5884827/v1/bb4255b6208ce0aa7b3ba529.png"},{"id":75335059,"identity":"e0fb9bea-6a83-46e7-886a-adebf7f23b8a","added_by":"auto","created_at":"2025-02-03 13:13:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1755622,"visible":true,"origin":"","legend":"","description":"","filename":"maintext.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5884827/v1_covered_bae05ee8-9991-4c1d-8a77-8ea0e9e089b9.pdf"},{"id":75087492,"identity":"7f87f914-25fe-470f-84b4-ec3b74ed5e4e","added_by":"auto","created_at":"2025-01-30 10:21:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":545019,"visible":true,"origin":"","legend":"reporting summary","description":"","filename":"nrreportingsummary20250123.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5884827/v1/ce312a49f681d2d48239a290.pdf"},{"id":75088615,"identity":"acea4d5e-3662-45f4-a83d-458815454f2b","added_by":"auto","created_at":"2025-01-30 10:29:19","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":437741,"visible":true,"origin":"","legend":"policy checklist","description":"","filename":"nreditorialpolicychecklist20250123.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5884827/v1/cd1a9762a33d56e1e6261c7f.pdf"},{"id":75087504,"identity":"19ec5ce4-c432-4216-8cfa-c2f7d0b719c3","added_by":"auto","created_at":"2025-01-30 10:21:19","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":5465003,"visible":true,"origin":"","legend":"Supplementary files","description":"","filename":"supplementaryfiles0123.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5884827/v1/12da6e7f69df962708bf0fb7.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Bridging the Medication Adherence Gap from Therapeutic Drug Monitoring: A Bayesian approach for Anti-Seizure Medications","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"anti-seizure medication, medication adherence, therapeutic drug monitoring, Bayesian method, population pharmacokinetics ","lastPublishedDoi":"10.21203/rs.3.rs-5884827/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5884827/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Adherence to antiseizure medications (ASMs) is crucial for the success of treatment. However, current recommendations for assessing medication adherence through therapeutic drug monitoring (TDM) may overlook individual patient characteristics, potentially leading to misjudgments. This study aims to evaluate the capability of a Bayesian approach in assessing adherence for 14 ASMs using TDM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod: \u003c/strong\u003eA Bayesian framework incorporating population pharmacokinetics was used to assess adherence using TDM data. Additionally, the impact of patient characteristics, concomitant medications, sampling times, and prior adherence probability was examined.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e With essential patient information, such as age, weight, and scheduled dosing regimen, the Bayesian approach effectively assessed recent adherence for all investigated ASMs. The concentration thresholds varied by ASM and were influenced by patients' characteristics. To facilitate individual adherence evaluations, a web-based dashboard was developed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The integration of Bayesian methods with pharmacokinetics significantly enhances the reliability of TDM in assessing adherence to ASMs.\u003c/p\u003e","manuscriptTitle":"Bridging the Medication Adherence Gap from Therapeutic Drug Monitoring: A Bayesian approach for Anti-Seizure Medications","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-30 10:21:14","doi":"10.21203/rs.3.rs-5884827/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6b702e95-6ead-41c0-9657-904c3d00ec2f","owner":[],"postedDate":"January 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":43484066,"name":"Health sciences/Neurology/Neurological disorders/Epilepsy"},{"id":43484067,"name":"Health sciences/Health care/Patient education"}],"tags":[],"updatedAt":"2025-02-03T13:05:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-30 10:21:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5884827","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5884827","identity":"rs-5884827","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}