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A General Framework for Assessing In vitro/In vivo Correlation as a Tool for Maximizing the Benefit‐Risk Ratio of a Treatment Using a Convolution‐Based Modeling Approach

The net benefit of a treatment can be defined by the relationship between clinical improvement and risk of adverse events: the benefit‐risk ratio. The optimization of the benefit‐risk ratio can be achieved by identifying the most adequate dose (and/or dosage regimen) jointly with the best‐performing...

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Autores principales: Gomeni, Roberto, Fang, Lanyan (Lucy), Bressolle‐Gomeni, Françoise, Spencer, Thomas J., Faraone, Stephen V., Babiskin, Andrew
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6389349/
https://www.ncbi.nlm.nih.gov/pubmed/30659771
http://dx.doi.org/10.1002/psp4.12378
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author Gomeni, Roberto
Fang, Lanyan (Lucy)
Bressolle‐Gomeni, Françoise
Spencer, Thomas J.
Faraone, Stephen V.
Babiskin, Andrew
author_facet Gomeni, Roberto
Fang, Lanyan (Lucy)
Bressolle‐Gomeni, Françoise
Spencer, Thomas J.
Faraone, Stephen V.
Babiskin, Andrew
author_sort Gomeni, Roberto
collection PubMed
description The net benefit of a treatment can be defined by the relationship between clinical improvement and risk of adverse events: the benefit‐risk ratio. The optimization of the benefit‐risk ratio can be achieved by identifying the most adequate dose (and/or dosage regimen) jointly with the best‐performing in vivo release properties of a drug. A general in silico tool is presented for identifying the dose, the in vitro and the in vivo release properties that maximize the benefit‐risk ratio using convolution‐based modeling, an exposure‐response model, and a surface response analysis. A case study is presented to illustrate how the benefit‐risk ratio of methylphenidate for the treatment of attention deficit hyperactivity disorder can be maximized using the proposed strategy. The results of the analysis identified the characteristics of an optimized dose and in vitro/in vivo release suitable to provide a sustained clinical response with respect to the conventional dosage regimen and formulations.
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spelling pubmed-63893492019-03-07 A General Framework for Assessing In vitro/In vivo Correlation as a Tool for Maximizing the Benefit‐Risk Ratio of a Treatment Using a Convolution‐Based Modeling Approach Gomeni, Roberto Fang, Lanyan (Lucy) Bressolle‐Gomeni, Françoise Spencer, Thomas J. Faraone, Stephen V. Babiskin, Andrew CPT Pharmacometrics Syst Pharmacol Research The net benefit of a treatment can be defined by the relationship between clinical improvement and risk of adverse events: the benefit‐risk ratio. The optimization of the benefit‐risk ratio can be achieved by identifying the most adequate dose (and/or dosage regimen) jointly with the best‐performing in vivo release properties of a drug. A general in silico tool is presented for identifying the dose, the in vitro and the in vivo release properties that maximize the benefit‐risk ratio using convolution‐based modeling, an exposure‐response model, and a surface response analysis. A case study is presented to illustrate how the benefit‐risk ratio of methylphenidate for the treatment of attention deficit hyperactivity disorder can be maximized using the proposed strategy. The results of the analysis identified the characteristics of an optimized dose and in vitro/in vivo release suitable to provide a sustained clinical response with respect to the conventional dosage regimen and formulations. John Wiley and Sons Inc. 2019-02-05 2019-02 /pmc/articles/PMC6389349/ /pubmed/30659771 http://dx.doi.org/10.1002/psp4.12378 Text en © 2019 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of the American Society for Clinical Pharmacology and Therapeutics. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
spellingShingle Research
Gomeni, Roberto
Fang, Lanyan (Lucy)
Bressolle‐Gomeni, Françoise
Spencer, Thomas J.
Faraone, Stephen V.
Babiskin, Andrew
A General Framework for Assessing In vitro/In vivo Correlation as a Tool for Maximizing the Benefit‐Risk Ratio of a Treatment Using a Convolution‐Based Modeling Approach
title A General Framework for Assessing In vitro/In vivo Correlation as a Tool for Maximizing the Benefit‐Risk Ratio of a Treatment Using a Convolution‐Based Modeling Approach
title_full A General Framework for Assessing In vitro/In vivo Correlation as a Tool for Maximizing the Benefit‐Risk Ratio of a Treatment Using a Convolution‐Based Modeling Approach
title_fullStr A General Framework for Assessing In vitro/In vivo Correlation as a Tool for Maximizing the Benefit‐Risk Ratio of a Treatment Using a Convolution‐Based Modeling Approach
title_full_unstemmed A General Framework for Assessing In vitro/In vivo Correlation as a Tool for Maximizing the Benefit‐Risk Ratio of a Treatment Using a Convolution‐Based Modeling Approach
title_short A General Framework for Assessing In vitro/In vivo Correlation as a Tool for Maximizing the Benefit‐Risk Ratio of a Treatment Using a Convolution‐Based Modeling Approach
title_sort general framework for assessing in vitro/in vivo correlation as a tool for maximizing the benefit‐risk ratio of a treatment using a convolution‐based modeling approach
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6389349/
https://www.ncbi.nlm.nih.gov/pubmed/30659771
http://dx.doi.org/10.1002/psp4.12378
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