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Recursive model for dose-time responses in pharmacological studies

BACKGROUND: Clinical studies often track dose-response curves of subjects over time. One can easily model the dose-response curve at each time point with Hill equation, but such a model fails to capture the temporal evolution of the curves. On the other hand, one can use Gompertz equation to model t...

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Autores principales: Dhruba, Saugato Rahman, Rahman, Aminur, Rahman, Raziur, Ghosh, Souparno, Pal, Ranadip
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6584530/
https://www.ncbi.nlm.nih.gov/pubmed/31216980
http://dx.doi.org/10.1186/s12859-019-2831-4
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author Dhruba, Saugato Rahman
Rahman, Aminur
Rahman, Raziur
Ghosh, Souparno
Pal, Ranadip
author_facet Dhruba, Saugato Rahman
Rahman, Aminur
Rahman, Raziur
Ghosh, Souparno
Pal, Ranadip
author_sort Dhruba, Saugato Rahman
collection PubMed
description BACKGROUND: Clinical studies often track dose-response curves of subjects over time. One can easily model the dose-response curve at each time point with Hill equation, but such a model fails to capture the temporal evolution of the curves. On the other hand, one can use Gompertz equation to model the temporal behaviors at each dose without capturing the evolution of time curves across dosage. RESULTS: In this article, we propose a parametric model for dose-time responses that follows Gompertz law in time and Hill equation across dose approximately. We derive a recursion relation for dose-response curves over time capturing the temporal evolution and then specify a regression model connecting the parameters controlling the dose-time responses with individual level proteomic data. The resultant joint model allows us to predict the dose-response curves over time for new individuals. CONCLUSION: We have compared the efficacy of our proposed Recursive Hybrid model with individual dose-response predictive models at desired time points. We note that our proposed model exhibits a superior performance compared to the individual ones for both synthetic data and actual pharmacological data. For the desired dose-time varying genetic characterization and drug response values, we have used the HMS-LINCS database and demonstrated the effectiveness of our model for all available anticancer compounds. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-019-2831-4) contains supplementary material, which is available to authorized users.
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spelling pubmed-65845302019-06-26 Recursive model for dose-time responses in pharmacological studies Dhruba, Saugato Rahman Rahman, Aminur Rahman, Raziur Ghosh, Souparno Pal, Ranadip BMC Bioinformatics Research BACKGROUND: Clinical studies often track dose-response curves of subjects over time. One can easily model the dose-response curve at each time point with Hill equation, but such a model fails to capture the temporal evolution of the curves. On the other hand, one can use Gompertz equation to model the temporal behaviors at each dose without capturing the evolution of time curves across dosage. RESULTS: In this article, we propose a parametric model for dose-time responses that follows Gompertz law in time and Hill equation across dose approximately. We derive a recursion relation for dose-response curves over time capturing the temporal evolution and then specify a regression model connecting the parameters controlling the dose-time responses with individual level proteomic data. The resultant joint model allows us to predict the dose-response curves over time for new individuals. CONCLUSION: We have compared the efficacy of our proposed Recursive Hybrid model with individual dose-response predictive models at desired time points. We note that our proposed model exhibits a superior performance compared to the individual ones for both synthetic data and actual pharmacological data. For the desired dose-time varying genetic characterization and drug response values, we have used the HMS-LINCS database and demonstrated the effectiveness of our model for all available anticancer compounds. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-019-2831-4) contains supplementary material, which is available to authorized users. BioMed Central 2019-06-20 /pmc/articles/PMC6584530/ /pubmed/31216980 http://dx.doi.org/10.1186/s12859-019-2831-4 Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Dhruba, Saugato Rahman
Rahman, Aminur
Rahman, Raziur
Ghosh, Souparno
Pal, Ranadip
Recursive model for dose-time responses in pharmacological studies
title Recursive model for dose-time responses in pharmacological studies
title_full Recursive model for dose-time responses in pharmacological studies
title_fullStr Recursive model for dose-time responses in pharmacological studies
title_full_unstemmed Recursive model for dose-time responses in pharmacological studies
title_short Recursive model for dose-time responses in pharmacological studies
title_sort recursive model for dose-time responses in pharmacological studies
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6584530/
https://www.ncbi.nlm.nih.gov/pubmed/31216980
http://dx.doi.org/10.1186/s12859-019-2831-4
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