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Step‐by‐step comparison of ordinary differential equation and agent‐based approaches to pharmacokinetic‐pharmacodynamic models
Mathematical models in oncology aid in the design of drugs and understanding of their mechanisms of action by simulation of drug biodistribution, drug effects, and interaction between tumor and healthy cells. The traditional approach in pharmacometrics is to develop and validate ordinary differentia...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8846629/ https://www.ncbi.nlm.nih.gov/pubmed/34399036 http://dx.doi.org/10.1002/psp4.12703 |
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author | Truong, Van Thuy Baverel, Paul G. Lythe, Grant D. Vicini, Paolo Yates, James W. T. Dubois, Vincent F. S. |
author_facet | Truong, Van Thuy Baverel, Paul G. Lythe, Grant D. Vicini, Paolo Yates, James W. T. Dubois, Vincent F. S. |
author_sort | Truong, Van Thuy |
collection | PubMed |
description | Mathematical models in oncology aid in the design of drugs and understanding of their mechanisms of action by simulation of drug biodistribution, drug effects, and interaction between tumor and healthy cells. The traditional approach in pharmacometrics is to develop and validate ordinary differential equation models to quantify trends at the population level. In this approach, time‐course of biological measurements is modeled continuously, assuming a homogenous population. Another approach, agent‐based models, focuses on the behavior and fate of biological entities at the individual level, which subsequently could be summarized to reflect the population level. Heterogeneous cell populations and discrete events are simulated, and spatial distribution can be incorporated. In this tutorial, an agent‐based model is presented and compared to an ordinary differential equation model for a tumor efficacy model inhibiting the pERK pathway. We highlight strengths, weaknesses, and opportunities of each approach. |
format | Online Article Text |
id | pubmed-8846629 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-88466292022-02-25 Step‐by‐step comparison of ordinary differential equation and agent‐based approaches to pharmacokinetic‐pharmacodynamic models Truong, Van Thuy Baverel, Paul G. Lythe, Grant D. Vicini, Paolo Yates, James W. T. Dubois, Vincent F. S. CPT Pharmacometrics Syst Pharmacol Tutorials Mathematical models in oncology aid in the design of drugs and understanding of their mechanisms of action by simulation of drug biodistribution, drug effects, and interaction between tumor and healthy cells. The traditional approach in pharmacometrics is to develop and validate ordinary differential equation models to quantify trends at the population level. In this approach, time‐course of biological measurements is modeled continuously, assuming a homogenous population. Another approach, agent‐based models, focuses on the behavior and fate of biological entities at the individual level, which subsequently could be summarized to reflect the population level. Heterogeneous cell populations and discrete events are simulated, and spatial distribution can be incorporated. In this tutorial, an agent‐based model is presented and compared to an ordinary differential equation model for a tumor efficacy model inhibiting the pERK pathway. We highlight strengths, weaknesses, and opportunities of each approach. John Wiley and Sons Inc. 2022-02-01 2022-02 /pmc/articles/PMC8846629/ /pubmed/34399036 http://dx.doi.org/10.1002/psp4.12703 Text en © 2022 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://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 | Tutorials Truong, Van Thuy Baverel, Paul G. Lythe, Grant D. Vicini, Paolo Yates, James W. T. Dubois, Vincent F. S. Step‐by‐step comparison of ordinary differential equation and agent‐based approaches to pharmacokinetic‐pharmacodynamic models |
title | Step‐by‐step comparison of ordinary differential equation and agent‐based approaches to pharmacokinetic‐pharmacodynamic models |
title_full | Step‐by‐step comparison of ordinary differential equation and agent‐based approaches to pharmacokinetic‐pharmacodynamic models |
title_fullStr | Step‐by‐step comparison of ordinary differential equation and agent‐based approaches to pharmacokinetic‐pharmacodynamic models |
title_full_unstemmed | Step‐by‐step comparison of ordinary differential equation and agent‐based approaches to pharmacokinetic‐pharmacodynamic models |
title_short | Step‐by‐step comparison of ordinary differential equation and agent‐based approaches to pharmacokinetic‐pharmacodynamic models |
title_sort | step‐by‐step comparison of ordinary differential equation and agent‐based approaches to pharmacokinetic‐pharmacodynamic models |
topic | Tutorials |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8846629/ https://www.ncbi.nlm.nih.gov/pubmed/34399036 http://dx.doi.org/10.1002/psp4.12703 |
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