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Open AccessJournal ArticleDOI

A Review of Mathematical Models for Tumor Dynamics and Treatment Resistance Evolution of Solid Tumors

TLDR
The opportunities of a model‐based approach as discussed in this review can be of great benefit for future optimizing and personalizing anticancer treatment.
Abstract
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.

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Tumor Growth Dynamic Modeling in Oncology Drug Development and Regulatory Approval: Past, Present, and Future Opportunities.

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Treatment-driven tumour heterogeneity and drug resistance: Lessons from solid tumours.

TL;DR: In this paper , the authors focused on tumour heterogeneity and its relation to resistance to targeted-therapy, based on treatment selective pressure across different tumour types, including lung, colorectal, prostate, breast cancer and melanoma.
References
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Journal ArticleDOI

PKPD Modeling of VEGF, sVEGFR-2, sVEGFR-3, and sKIT as Predictors of Tumor Dynamics and Overall Survival Following Sunitinib Treatment in GIST

TL;DR: Based on the proposed modeling framework to link longitudinal biomarker data with overall survival using pharmacokinetic–pharmacodynamic models, sVEGFR‐3 demonstrated the greatest predictive potential for overall survival following sunitinib treatment in GIST.
Journal ArticleDOI

Using pharmacokinetic and pharmacodynamic modeling and simulation to evaluate importance of schedule in topotecan therapy for pediatric neuroblastoma.

TL;DR: The present mathematical model gave an innovative approach to determining relevant topotecan schedules for possible evaluation in the clinic, which could lead to improved tumor response with minimized toxicities.
Journal ArticleDOI

Modeling multi-drug chemotherapy: tailoring treatment to individuals.

TL;DR: The KITT model suggests that including cytostatic drugs like tamoxifen and herceptin during treatment with cytotoxic drugs substantially increases the probability of cure and prolongs patient life, and suggests that altering drug scheduling may be more effective but not more toxic than dose escalation.
Journal ArticleDOI

Model-based clinical drug development in the past, present and future: a commentary

TL;DR: A historical account of the use of model‐based drug development in clinical drug development, the current challenges and further opportunities for its application in the pharmaceutical industry are presented.
Journal ArticleDOI

Personalized Radiotherapy Planning Based on a Computational Tumor Growth Model

TL;DR: Two methods to derive the radiotherapy prescription dose distribution are introduced, which are based on minimizing integral tumor cell survival using the maximum a posteriori or the expected tumor cell density and it is shown how the method allows the user to compute a patient specific radiotherapy planning conformal to the tumor infiltration.
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