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An automated fitting procedure and software for dose-response curves with multiphasic features
In cancer pharmacology (and many other areas), most dose-response curves are satisfactorily described by a classical Hill equation (i.e. 4 parameters logistical). Nevertheless, there are instances where the marked presence of more than one point of inflection, or the presence of combined agonist and...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4589737/ https://www.ncbi.nlm.nih.gov/pubmed/26424192 http://dx.doi.org/10.1038/srep14701 |
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author | Veroli, Giovanni Y. Di Fornari, Chiara Goldlust, Ian Mills, Graham Koh, Siang Boon Bramhall, Jo L Richards, Frances M. Jodrell, Duncan I. |
author_facet | Veroli, Giovanni Y. Di Fornari, Chiara Goldlust, Ian Mills, Graham Koh, Siang Boon Bramhall, Jo L Richards, Frances M. Jodrell, Duncan I. |
author_sort | Veroli, Giovanni Y. Di |
collection | PubMed |
description | In cancer pharmacology (and many other areas), most dose-response curves are satisfactorily described by a classical Hill equation (i.e. 4 parameters logistical). Nevertheless, there are instances where the marked presence of more than one point of inflection, or the presence of combined agonist and antagonist effects, prevents straight-forward modelling of the data via a standard Hill equation. Here we propose a modified model and automated fitting procedure to describe dose-response curves with multiphasic features. The resulting general model enables interpreting each phase of the dose-response as an independent dose-dependent process. We developed an algorithm which automatically generates and ranks dose-response models with varying degrees of multiphasic features. The algorithm was implemented in new freely available Dr Fit software (sourceforge.net/projects/drfit/). We show how our approach is successful in describing dose-response curves with multiphasic features. Additionally, we analysed a large cancer cell viability screen involving 11650 dose-response curves. Based on our algorithm, we found that 28% of cases were better described by a multiphasic model than by the Hill model. We thus provide a robust approach to fit dose-response curves with various degrees of complexity, which, together with the provided software implementation, should enable a wide audience to easily process their own data. |
format | Online Article Text |
id | pubmed-4589737 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-45897372015-10-13 An automated fitting procedure and software for dose-response curves with multiphasic features Veroli, Giovanni Y. Di Fornari, Chiara Goldlust, Ian Mills, Graham Koh, Siang Boon Bramhall, Jo L Richards, Frances M. Jodrell, Duncan I. Sci Rep Article In cancer pharmacology (and many other areas), most dose-response curves are satisfactorily described by a classical Hill equation (i.e. 4 parameters logistical). Nevertheless, there are instances where the marked presence of more than one point of inflection, or the presence of combined agonist and antagonist effects, prevents straight-forward modelling of the data via a standard Hill equation. Here we propose a modified model and automated fitting procedure to describe dose-response curves with multiphasic features. The resulting general model enables interpreting each phase of the dose-response as an independent dose-dependent process. We developed an algorithm which automatically generates and ranks dose-response models with varying degrees of multiphasic features. The algorithm was implemented in new freely available Dr Fit software (sourceforge.net/projects/drfit/). We show how our approach is successful in describing dose-response curves with multiphasic features. Additionally, we analysed a large cancer cell viability screen involving 11650 dose-response curves. Based on our algorithm, we found that 28% of cases were better described by a multiphasic model than by the Hill model. We thus provide a robust approach to fit dose-response curves with various degrees of complexity, which, together with the provided software implementation, should enable a wide audience to easily process their own data. Nature Publishing Group 2015-10-01 /pmc/articles/PMC4589737/ /pubmed/26424192 http://dx.doi.org/10.1038/srep14701 Text en Copyright © 2015, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Veroli, Giovanni Y. Di Fornari, Chiara Goldlust, Ian Mills, Graham Koh, Siang Boon Bramhall, Jo L Richards, Frances M. Jodrell, Duncan I. An automated fitting procedure and software for dose-response curves with multiphasic features |
title | An automated fitting procedure and software for dose-response curves with multiphasic features |
title_full | An automated fitting procedure and software for dose-response curves with multiphasic features |
title_fullStr | An automated fitting procedure and software for dose-response curves with multiphasic features |
title_full_unstemmed | An automated fitting procedure and software for dose-response curves with multiphasic features |
title_short | An automated fitting procedure and software for dose-response curves with multiphasic features |
title_sort | automated fitting procedure and software for dose-response curves with multiphasic features |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4589737/ https://www.ncbi.nlm.nih.gov/pubmed/26424192 http://dx.doi.org/10.1038/srep14701 |
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