A Review of Mathematical Models for Tumor Dynamics and Treatment Resistance Evolution of Solid Tumors
Increasing knowledge of intertumor heterogeneity, intratumor heterogeneity, and cancer evolution has improved the understanding of anticancer treatment resistance. A better characterization of cancer evolution and subsequent use of this knowledge for personalized treatment would increase the chance...
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2019-10-01
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Series: | CPT: Pharmacometrics & Systems Pharmacology |
Online Access: | https://doi.org/10.1002/psp4.12450 |
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doaj-89c1a9f57a404ae5b2b62f80c693a8fc2020-11-25T01:19:22ZengWileyCPT: Pharmacometrics & Systems Pharmacology2163-83062019-10-0181072073710.1002/psp4.12450A Review of Mathematical Models for Tumor Dynamics and Treatment Resistance Evolution of Solid TumorsAnyue Yin0Dirk Jan A.R. Moes1Johan G.C. vanHasselt2Jesse J. Swen3Henk‐Jan Guchelaar4Department of Clinical Pharmacy and Toxicology Leiden University Medical Center Leiden The NetherlandsDepartment of Clinical Pharmacy and Toxicology Leiden University Medical Center Leiden The NetherlandsDivision of Systems Biomedicine and Pharmacology Leiden Academic Center for Drug ResearchLeiden University Leiden The NetherlandsDepartment of Clinical Pharmacy and Toxicology Leiden University Medical Center Leiden The NetherlandsDepartment of Clinical Pharmacy and Toxicology Leiden University Medical Center Leiden The NetherlandsIncreasing knowledge of intertumor heterogeneity, intratumor heterogeneity, and cancer evolution has improved the understanding of anticancer treatment resistance. A better characterization of cancer evolution and subsequent use of this knowledge for personalized treatment would increase the chance to overcome cancer treatment resistance. Model‐based approaches may help achieve this goal. In this review, we comprehensively summarized mathematical models of tumor dynamics for solid tumors and of drug resistance evolution. Models displayed by ordinary differential equations, algebraic equations, and partial differential equations for characterizing tumor burden dynamics are introduced and discussed. As for tumor resistance evolution, stochastic and deterministic models are introduced and discussed. The results may facilitate a novel model‐based analysis on anticancer treatment response and the occurrence of resistance, which incorporates both tumor dynamics and resistance evolution. The opportunities of a model‐based approach as discussed in this review can be of great benefit for future optimizing and personalizing anticancer treatment.https://doi.org/10.1002/psp4.12450 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Anyue Yin Dirk Jan A.R. Moes Johan G.C. vanHasselt Jesse J. Swen Henk‐Jan Guchelaar |
spellingShingle |
Anyue Yin Dirk Jan A.R. Moes Johan G.C. vanHasselt Jesse J. Swen Henk‐Jan Guchelaar A Review of Mathematical Models for Tumor Dynamics and Treatment Resistance Evolution of Solid Tumors CPT: Pharmacometrics & Systems Pharmacology |
author_facet |
Anyue Yin Dirk Jan A.R. Moes Johan G.C. vanHasselt Jesse J. Swen Henk‐Jan Guchelaar |
author_sort |
Anyue Yin |
title |
A Review of Mathematical Models for Tumor Dynamics and Treatment Resistance Evolution of Solid Tumors |
title_short |
A Review of Mathematical Models for Tumor Dynamics and Treatment Resistance Evolution of Solid Tumors |
title_full |
A Review of Mathematical Models for Tumor Dynamics and Treatment Resistance Evolution of Solid Tumors |
title_fullStr |
A Review of Mathematical Models for Tumor Dynamics and Treatment Resistance Evolution of Solid Tumors |
title_full_unstemmed |
A Review of Mathematical Models for Tumor Dynamics and Treatment Resistance Evolution of Solid Tumors |
title_sort |
review of mathematical models for tumor dynamics and treatment resistance evolution of solid tumors |
publisher |
Wiley |
series |
CPT: Pharmacometrics & Systems Pharmacology |
issn |
2163-8306 |
publishDate |
2019-10-01 |
description |
Increasing knowledge of intertumor heterogeneity, intratumor heterogeneity, and cancer evolution has improved the understanding of anticancer treatment resistance. A better characterization of cancer evolution and subsequent use of this knowledge for personalized treatment would increase the chance to overcome cancer treatment resistance. Model‐based approaches may help achieve this goal. In this review, we comprehensively summarized mathematical models of tumor dynamics for solid tumors and of drug resistance evolution. Models displayed by ordinary differential equations, algebraic equations, and partial differential equations for characterizing tumor burden dynamics are introduced and discussed. As for tumor resistance evolution, stochastic and deterministic models are introduced and discussed. The results may facilitate a novel model‐based analysis on anticancer treatment response and the occurrence of resistance, which incorporates both tumor dynamics and resistance evolution. The opportunities of a model‐based approach as discussed in this review can be of great benefit for future optimizing and personalizing anticancer treatment. |
url |
https://doi.org/10.1002/psp4.12450 |
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