Bayesian Design for Identifying Cohort-Specific Optimal Dose Combinations Based on Multiple Endpoints: Application to a Phase I Trial in Non-Small Cell Lung Cancer

Immunotherapy and chemotherapy combinations have proven to be a safe and efficacious treatment approach in multiple settings. However, it is not clear whether approved doses of chemotherapy developed to achieve a maximum tolerated dose are the ideal dose when combining cytotoxic chemotherapy with im...

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Autores principales:	Bethany Jablonski Horton, Nolan A. Wages, Ryan D. Gentzler
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Bayesian trial design early phase dose finding treatment combinations optimal dose combination oncology Medicine R
Acceso en línea:	https://doaj.org/article/c21c28f4d5fd4bdf9cf2b2ccf3e2a83f
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spelling	oai:doaj.org-article:c21c28f4d5fd4bdf9cf2b2ccf3e2a83f2021-11-11T16:34:41ZBayesian Design for Identifying Cohort-Specific Optimal Dose Combinations Based on Multiple Endpoints: Application to a Phase I Trial in Non-Small Cell Lung Cancer10.3390/ijerph1821114521660-46011661-7827https://doaj.org/article/c21c28f4d5fd4bdf9cf2b2ccf3e2a83f2021-10-01T00:00:00Zhttps://www.mdpi.com/1660-4601/18/21/11452https://doaj.org/toc/1661-7827https://doaj.org/toc/1660-4601Immunotherapy and chemotherapy combinations have proven to be a safe and efficacious treatment approach in multiple settings. However, it is not clear whether approved doses of chemotherapy developed to achieve a maximum tolerated dose are the ideal dose when combining cytotoxic chemotherapy with immunotherapy to induce immune responses. This trial of a modulated dose chemotherapy and Pembrolizumab, with or without a second immunomodulatory agent, uses a Bayesian design to select the optimal treatment combination by balancing both safety and efficacy of the chemotherapy and immunotherapy agents within each of two cohorts. The simulation study provides evidence that the proposed Bayesian design successfully addresses the primary study aim to identify the optimal dose combination for each of the two independent patient cohorts. This conclusion is supported by the high percentage of simulated trials which select a treatment combination that is both safe and highly efficacious. The proposed trial was funded and was being finalized when the sponsoring company decided not to proceed due to negative findings in another patient population. The proposed trial design will continue to be relevant as multiple chemotherapy and immunotherapy combinations become the standard of care and future research will require evaluating the appropriate doses of various components of multiple drug regimens.Bethany Jablonski HortonNolan A. WagesRyan D. GentzlerMDPI AGarticleBayesian trial designearly phase dose findingtreatment combinationsoptimal dose combinationoncologyMedicineRENInternational Journal of Environmental Research and Public Health, Vol 18, Iss 11452, p 11452 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Bayesian trial design early phase dose finding treatment combinations optimal dose combination oncology Medicine R
spellingShingle	Bayesian trial design early phase dose finding treatment combinations optimal dose combination oncology Medicine R Bethany Jablonski Horton Nolan A. Wages Ryan D. Gentzler Bayesian Design for Identifying Cohort-Specific Optimal Dose Combinations Based on Multiple Endpoints: Application to a Phase I Trial in Non-Small Cell Lung Cancer
description	Immunotherapy and chemotherapy combinations have proven to be a safe and efficacious treatment approach in multiple settings. However, it is not clear whether approved doses of chemotherapy developed to achieve a maximum tolerated dose are the ideal dose when combining cytotoxic chemotherapy with immunotherapy to induce immune responses. This trial of a modulated dose chemotherapy and Pembrolizumab, with or without a second immunomodulatory agent, uses a Bayesian design to select the optimal treatment combination by balancing both safety and efficacy of the chemotherapy and immunotherapy agents within each of two cohorts. The simulation study provides evidence that the proposed Bayesian design successfully addresses the primary study aim to identify the optimal dose combination for each of the two independent patient cohorts. This conclusion is supported by the high percentage of simulated trials which select a treatment combination that is both safe and highly efficacious. The proposed trial was funded and was being finalized when the sponsoring company decided not to proceed due to negative findings in another patient population. The proposed trial design will continue to be relevant as multiple chemotherapy and immunotherapy combinations become the standard of care and future research will require evaluating the appropriate doses of various components of multiple drug regimens.
format	article
author	Bethany Jablonski Horton Nolan A. Wages Ryan D. Gentzler
author_facet	Bethany Jablonski Horton Nolan A. Wages Ryan D. Gentzler
author_sort	Bethany Jablonski Horton
title	Bayesian Design for Identifying Cohort-Specific Optimal Dose Combinations Based on Multiple Endpoints: Application to a Phase I Trial in Non-Small Cell Lung Cancer
title_short	Bayesian Design for Identifying Cohort-Specific Optimal Dose Combinations Based on Multiple Endpoints: Application to a Phase I Trial in Non-Small Cell Lung Cancer
title_full	Bayesian Design for Identifying Cohort-Specific Optimal Dose Combinations Based on Multiple Endpoints: Application to a Phase I Trial in Non-Small Cell Lung Cancer
title_fullStr	Bayesian Design for Identifying Cohort-Specific Optimal Dose Combinations Based on Multiple Endpoints: Application to a Phase I Trial in Non-Small Cell Lung Cancer
title_full_unstemmed	Bayesian Design for Identifying Cohort-Specific Optimal Dose Combinations Based on Multiple Endpoints: Application to a Phase I Trial in Non-Small Cell Lung Cancer
title_sort	bayesian design for identifying cohort-specific optimal dose combinations based on multiple endpoints: application to a phase i trial in non-small cell lung cancer
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/c21c28f4d5fd4bdf9cf2b2ccf3e2a83f
work_keys_str_mv	AT bethanyjablonskihorton bayesiandesignforidentifyingcohortspecificoptimaldosecombinationsbasedonmultipleendpointsapplicationtoaphaseitrialinnonsmallcelllungcancer AT nolanawages bayesiandesignforidentifyingcohortspecificoptimaldosecombinationsbasedonmultipleendpointsapplicationtoaphaseitrialinnonsmallcelllungcancer AT ryandgentzler bayesiandesignforidentifyingcohortspecificoptimaldosecombinationsbasedonmultipleendpointsapplicationtoaphaseitrialinnonsmallcelllungcancer
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Bayesian Design for Identifying Cohort-Specific Optimal Dose Combinations Based on Multiple Endpoints: Application to a Phase I Trial in Non-Small Cell Lung Cancer

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