Cargando…
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...
Autores principales: | , , , , |
---|---|
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 |
_version_ | 1783428528389226496 |
---|---|
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. |
format | Online Article Text |
id | pubmed-6584530 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT dhrubasaugatorahman recursivemodelfordosetimeresponsesinpharmacologicalstudies AT rahmanaminur recursivemodelfordosetimeresponsesinpharmacologicalstudies AT rahmanraziur recursivemodelfordosetimeresponsesinpharmacologicalstudies AT ghoshsouparno recursivemodelfordosetimeresponsesinpharmacologicalstudies AT palranadip recursivemodelfordosetimeresponsesinpharmacologicalstudies |