Phase I dose-escalation oncology trials with sequential multiple schedules
Conventional methods for phase I dose-escalation trials in oncology are based on a single treatment schedule only. More recently, however, multiple schedules are more frequently investigated in the same trial. Here, we consider sequential phase I trials, where the trial proceeds with a new schedule...
Gespeichert in:
Veröffentlicht in: | BMC medical research methodology 2021-04, Vol.21 (1), p.69-14, Article 69 |
---|---|
Hauptverfasser: | , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 14 |
---|---|
container_issue | 1 |
container_start_page | 69 |
container_title | BMC medical research methodology |
container_volume | 21 |
creator | Günhan, Burak Kürsad Weber, Sebastian Seroutou, Abdelkader Friede, Tim |
description | Conventional methods for phase I dose-escalation trials in oncology are based on a single treatment schedule only. More recently, however, multiple schedules are more frequently investigated in the same trial.
Here, we consider sequential phase I trials, where the trial proceeds with a new schedule (e.g. daily or weekly dosing) once the dose escalation with another schedule has been completed. The aim is to utilize the information from both the completed and the ongoing schedules to inform decisions on the dose level for the next dose cohort. For this purpose, we adapted the time-to-event pharmacokinetics (TITE-PK) model, which were originally developed for simultaneous investigation of multiple schedules. TITE-PK integrates information from multiple schedules using a pharmacokinetics (PK) model.
In a simulation study, the developed approach is compared to the bridging continual reassessment method and the Bayesian logistic regression model using a meta-analytic-predictive prior. TITE-PK results in better performance than comparators in terms of recommending acceptable dose and avoiding overly toxic doses for sequential phase I trials in most of the scenarios considered. Furthermore, better performance of TITE-PK is achieved while requiring similar number of patients in the simulated trials. For the scenarios involving one schedule, TITE-PK displays similar performance with alternatives in terms of acceptable dose recommendations. The R and Stan code for the implementation of an illustrative sequential phase I trial example in oncology is publicly available ( https://github.com/gunhanb/TITEPK_sequential ).
In phase I oncology trials with sequential multiple schedules, the use of all relevant information is of great importance. For these trials, the adapted TITE-PK which combines information using PK principles is recommended. |
doi_str_mv | 10.1186/s12874-021-01218-9 |
format | Article |
fullrecord | <record><control><sourceid>gale_doaj_</sourceid><recordid>TN_cdi_gale_infotracmisc_A658551909</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A658551909</galeid><doaj_id>oai_doaj_org_article_3ba3de22ca8b4b09b849def7df152e0f</doaj_id><sourcerecordid>A658551909</sourcerecordid><originalsourceid>FETCH-LOGICAL-c563t-86f06f2e751c1e49687023e50ed2d2aec86c3bb7f089f6abf3d4a9a7b8fa37c13</originalsourceid><addsrcrecordid>eNptkl1rFDEUhoNY7If-AS9kwOup-Z7kRiil2pVCvdDrkElOdrPMTNZkRum_b9qttQuSi4ST93045_Ai9J7gc0KU_FQIVR1vMSUtJpSoVr9CJ4R3pKVUqdcv3sfotJQtxqRTTL5Bx4wpwQTTJ-jb940t0Kwanwq0UJwd7BzT1KTJpSGt75o5RzuU5k-cN02BXwtMcy004zLMcTdAU9wG_DJAeYuOQlXCu6f7DP38cvXj8rq9uf26ury4aZ2QbG6VDFgGCp0gjgDXUnWYMhAYPPXUglPSsb7vAlY6SNsH5rnVtutVsKxzhJ2h1Z7rk92aXY6jzXcm2WgeCymvjc1zdAMY1lvmgVJnVc97rHvFtYfQ-UAEBRwq6_OetVv6Ebyrw2U7HEAPf6a4Mev02yjMBceiAj4-AXKquymz2aYlT3V-QwXhXGmG2T_V2tau4hRShbkxFmcupFBCEI11VZ3_R1WPhzG6NEGItX5goHuDy6mUDOG5cYLNQ0TMPiKmRsQ8RsQ8mD68HPnZ8jcT7B6tQrer</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2514489303</pqid></control><display><type>article</type><title>Phase I dose-escalation oncology trials with sequential multiple schedules</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>PubMed Central Open Access</source><source>Springer Nature OA Free Journals</source><source>Springer Nature - Complete Springer Journals</source><source>EZB-FREE-00999 freely available EZB journals</source><source>PubMed Central</source><creator>Günhan, Burak Kürsad ; Weber, Sebastian ; Seroutou, Abdelkader ; Friede, Tim</creator><creatorcontrib>Günhan, Burak Kürsad ; Weber, Sebastian ; Seroutou, Abdelkader ; Friede, Tim</creatorcontrib><description>Conventional methods for phase I dose-escalation trials in oncology are based on a single treatment schedule only. More recently, however, multiple schedules are more frequently investigated in the same trial.
Here, we consider sequential phase I trials, where the trial proceeds with a new schedule (e.g. daily or weekly dosing) once the dose escalation with another schedule has been completed. The aim is to utilize the information from both the completed and the ongoing schedules to inform decisions on the dose level for the next dose cohort. For this purpose, we adapted the time-to-event pharmacokinetics (TITE-PK) model, which were originally developed for simultaneous investigation of multiple schedules. TITE-PK integrates information from multiple schedules using a pharmacokinetics (PK) model.
In a simulation study, the developed approach is compared to the bridging continual reassessment method and the Bayesian logistic regression model using a meta-analytic-predictive prior. TITE-PK results in better performance than comparators in terms of recommending acceptable dose and avoiding overly toxic doses for sequential phase I trials in most of the scenarios considered. Furthermore, better performance of TITE-PK is achieved while requiring similar number of patients in the simulated trials. For the scenarios involving one schedule, TITE-PK displays similar performance with alternatives in terms of acceptable dose recommendations. The R and Stan code for the implementation of an illustrative sequential phase I trial example in oncology is publicly available ( https://github.com/gunhanb/TITEPK_sequential ).
In phase I oncology trials with sequential multiple schedules, the use of all relevant information is of great importance. For these trials, the adapted TITE-PK which combines information using PK principles is recommended.</description><identifier>ISSN: 1471-2288</identifier><identifier>EISSN: 1471-2288</identifier><identifier>DOI: 10.1186/s12874-021-01218-9</identifier><identifier>PMID: 33853539</identifier><language>eng</language><publisher>England: BioMed Central Ltd</publisher><subject>Bayes Theorem ; Bayesian statistics ; Cancer therapies ; Clinical trials ; Computer Simulation ; Dose-response relationship (Biochemistry) ; Dose-Response Relationship, Drug ; Drug dosages ; Humans ; Maximum Tolerated Dose ; Medical Oncology ; Medical research ; Multiple treatment schedules ; Neoplasms - drug therapy ; Oncology ; Patients ; Pharmacokinetics ; Phase I dose-escalation trials ; PK models ; Probability ; Research Design ; Research methodology ; Schedules</subject><ispartof>BMC medical research methodology, 2021-04, Vol.21 (1), p.69-14, Article 69</ispartof><rights>COPYRIGHT 2021 BioMed Central Ltd.</rights><rights>2021. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>The Author(s) 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c563t-86f06f2e751c1e49687023e50ed2d2aec86c3bb7f089f6abf3d4a9a7b8fa37c13</citedby><cites>FETCH-LOGICAL-c563t-86f06f2e751c1e49687023e50ed2d2aec86c3bb7f089f6abf3d4a9a7b8fa37c13</cites><orcidid>0000-0001-5347-7441 ; 0000-0002-7454-8680</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8045405/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8045405/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,724,777,781,861,882,2096,27905,27906,53772,53774</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33853539$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Günhan, Burak Kürsad</creatorcontrib><creatorcontrib>Weber, Sebastian</creatorcontrib><creatorcontrib>Seroutou, Abdelkader</creatorcontrib><creatorcontrib>Friede, Tim</creatorcontrib><title>Phase I dose-escalation oncology trials with sequential multiple schedules</title><title>BMC medical research methodology</title><addtitle>BMC Med Res Methodol</addtitle><description>Conventional methods for phase I dose-escalation trials in oncology are based on a single treatment schedule only. More recently, however, multiple schedules are more frequently investigated in the same trial.
Here, we consider sequential phase I trials, where the trial proceeds with a new schedule (e.g. daily or weekly dosing) once the dose escalation with another schedule has been completed. The aim is to utilize the information from both the completed and the ongoing schedules to inform decisions on the dose level for the next dose cohort. For this purpose, we adapted the time-to-event pharmacokinetics (TITE-PK) model, which were originally developed for simultaneous investigation of multiple schedules. TITE-PK integrates information from multiple schedules using a pharmacokinetics (PK) model.
In a simulation study, the developed approach is compared to the bridging continual reassessment method and the Bayesian logistic regression model using a meta-analytic-predictive prior. TITE-PK results in better performance than comparators in terms of recommending acceptable dose and avoiding overly toxic doses for sequential phase I trials in most of the scenarios considered. Furthermore, better performance of TITE-PK is achieved while requiring similar number of patients in the simulated trials. For the scenarios involving one schedule, TITE-PK displays similar performance with alternatives in terms of acceptable dose recommendations. The R and Stan code for the implementation of an illustrative sequential phase I trial example in oncology is publicly available ( https://github.com/gunhanb/TITEPK_sequential ).
In phase I oncology trials with sequential multiple schedules, the use of all relevant information is of great importance. For these trials, the adapted TITE-PK which combines information using PK principles is recommended.</description><subject>Bayes Theorem</subject><subject>Bayesian statistics</subject><subject>Cancer therapies</subject><subject>Clinical trials</subject><subject>Computer Simulation</subject><subject>Dose-response relationship (Biochemistry)</subject><subject>Dose-Response Relationship, Drug</subject><subject>Drug dosages</subject><subject>Humans</subject><subject>Maximum Tolerated Dose</subject><subject>Medical Oncology</subject><subject>Medical research</subject><subject>Multiple treatment schedules</subject><subject>Neoplasms - drug therapy</subject><subject>Oncology</subject><subject>Patients</subject><subject>Pharmacokinetics</subject><subject>Phase I dose-escalation trials</subject><subject>PK models</subject><subject>Probability</subject><subject>Research Design</subject><subject>Research methodology</subject><subject>Schedules</subject><issn>1471-2288</issn><issn>1471-2288</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>DOA</sourceid><recordid>eNptkl1rFDEUhoNY7If-AS9kwOup-Z7kRiil2pVCvdDrkElOdrPMTNZkRum_b9qttQuSi4ST93045_Ai9J7gc0KU_FQIVR1vMSUtJpSoVr9CJ4R3pKVUqdcv3sfotJQtxqRTTL5Bx4wpwQTTJ-jb940t0Kwanwq0UJwd7BzT1KTJpSGt75o5RzuU5k-cN02BXwtMcy004zLMcTdAU9wG_DJAeYuOQlXCu6f7DP38cvXj8rq9uf26ury4aZ2QbG6VDFgGCp0gjgDXUnWYMhAYPPXUglPSsb7vAlY6SNsH5rnVtutVsKxzhJ2h1Z7rk92aXY6jzXcm2WgeCymvjc1zdAMY1lvmgVJnVc97rHvFtYfQ-UAEBRwq6_OetVv6Ebyrw2U7HEAPf6a4Mev02yjMBceiAj4-AXKquymz2aYlT3V-QwXhXGmG2T_V2tau4hRShbkxFmcupFBCEI11VZ3_R1WPhzG6NEGItX5goHuDy6mUDOG5cYLNQ0TMPiKmRsQ8RsQ8mD68HPnZ8jcT7B6tQrer</recordid><startdate>20210414</startdate><enddate>20210414</enddate><creator>Günhan, Burak Kürsad</creator><creator>Weber, Sebastian</creator><creator>Seroutou, Abdelkader</creator><creator>Friede, Tim</creator><general>BioMed Central Ltd</general><general>BioMed Central</general><general>BMC</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-5347-7441</orcidid><orcidid>https://orcid.org/0000-0002-7454-8680</orcidid></search><sort><creationdate>20210414</creationdate><title>Phase I dose-escalation oncology trials with sequential multiple schedules</title><author>Günhan, Burak Kürsad ; Weber, Sebastian ; Seroutou, Abdelkader ; Friede, Tim</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c563t-86f06f2e751c1e49687023e50ed2d2aec86c3bb7f089f6abf3d4a9a7b8fa37c13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Bayes Theorem</topic><topic>Bayesian statistics</topic><topic>Cancer therapies</topic><topic>Clinical trials</topic><topic>Computer Simulation</topic><topic>Dose-response relationship (Biochemistry)</topic><topic>Dose-Response Relationship, Drug</topic><topic>Drug dosages</topic><topic>Humans</topic><topic>Maximum Tolerated Dose</topic><topic>Medical Oncology</topic><topic>Medical research</topic><topic>Multiple treatment schedules</topic><topic>Neoplasms - drug therapy</topic><topic>Oncology</topic><topic>Patients</topic><topic>Pharmacokinetics</topic><topic>Phase I dose-escalation trials</topic><topic>PK models</topic><topic>Probability</topic><topic>Research Design</topic><topic>Research methodology</topic><topic>Schedules</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Günhan, Burak Kürsad</creatorcontrib><creatorcontrib>Weber, Sebastian</creatorcontrib><creatorcontrib>Seroutou, Abdelkader</creatorcontrib><creatorcontrib>Friede, Tim</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>BMC medical research methodology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Günhan, Burak Kürsad</au><au>Weber, Sebastian</au><au>Seroutou, Abdelkader</au><au>Friede, Tim</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Phase I dose-escalation oncology trials with sequential multiple schedules</atitle><jtitle>BMC medical research methodology</jtitle><addtitle>BMC Med Res Methodol</addtitle><date>2021-04-14</date><risdate>2021</risdate><volume>21</volume><issue>1</issue><spage>69</spage><epage>14</epage><pages>69-14</pages><artnum>69</artnum><issn>1471-2288</issn><eissn>1471-2288</eissn><abstract>Conventional methods for phase I dose-escalation trials in oncology are based on a single treatment schedule only. More recently, however, multiple schedules are more frequently investigated in the same trial.
Here, we consider sequential phase I trials, where the trial proceeds with a new schedule (e.g. daily or weekly dosing) once the dose escalation with another schedule has been completed. The aim is to utilize the information from both the completed and the ongoing schedules to inform decisions on the dose level for the next dose cohort. For this purpose, we adapted the time-to-event pharmacokinetics (TITE-PK) model, which were originally developed for simultaneous investigation of multiple schedules. TITE-PK integrates information from multiple schedules using a pharmacokinetics (PK) model.
In a simulation study, the developed approach is compared to the bridging continual reassessment method and the Bayesian logistic regression model using a meta-analytic-predictive prior. TITE-PK results in better performance than comparators in terms of recommending acceptable dose and avoiding overly toxic doses for sequential phase I trials in most of the scenarios considered. Furthermore, better performance of TITE-PK is achieved while requiring similar number of patients in the simulated trials. For the scenarios involving one schedule, TITE-PK displays similar performance with alternatives in terms of acceptable dose recommendations. The R and Stan code for the implementation of an illustrative sequential phase I trial example in oncology is publicly available ( https://github.com/gunhanb/TITEPK_sequential ).
In phase I oncology trials with sequential multiple schedules, the use of all relevant information is of great importance. For these trials, the adapted TITE-PK which combines information using PK principles is recommended.</abstract><cop>England</cop><pub>BioMed Central Ltd</pub><pmid>33853539</pmid><doi>10.1186/s12874-021-01218-9</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0001-5347-7441</orcidid><orcidid>https://orcid.org/0000-0002-7454-8680</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1471-2288 |
ispartof | BMC medical research methodology, 2021-04, Vol.21 (1), p.69-14, Article 69 |
issn | 1471-2288 1471-2288 |
language | eng |
recordid | cdi_gale_infotracmisc_A658551909 |
source | MEDLINE; DOAJ Directory of Open Access Journals; PubMed Central Open Access; Springer Nature OA Free Journals; Springer Nature - Complete Springer Journals; EZB-FREE-00999 freely available EZB journals; PubMed Central |
subjects | Bayes Theorem Bayesian statistics Cancer therapies Clinical trials Computer Simulation Dose-response relationship (Biochemistry) Dose-Response Relationship, Drug Drug dosages Humans Maximum Tolerated Dose Medical Oncology Medical research Multiple treatment schedules Neoplasms - drug therapy Oncology Patients Pharmacokinetics Phase I dose-escalation trials PK models Probability Research Design Research methodology Schedules |
title | Phase I dose-escalation oncology trials with sequential multiple schedules |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-21T01%3A45%3A51IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Phase%20I%20dose-escalation%20oncology%20trials%20with%20sequential%20multiple%20schedules&rft.jtitle=BMC%20medical%20research%20methodology&rft.au=G%C3%BCnhan,%20Burak%20K%C3%BCrsad&rft.date=2021-04-14&rft.volume=21&rft.issue=1&rft.spage=69&rft.epage=14&rft.pages=69-14&rft.artnum=69&rft.issn=1471-2288&rft.eissn=1471-2288&rft_id=info:doi/10.1186/s12874-021-01218-9&rft_dat=%3Cgale_doaj_%3EA658551909%3C/gale_doaj_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2514489303&rft_id=info:pmid/33853539&rft_galeid=A658551909&rft_doaj_id=oai_doaj_org_article_3ba3de22ca8b4b09b849def7df152e0f&rfr_iscdi=true |