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

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.

SUBMITTER: Yin A

PROVIDER: S-EPMC6813171 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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

Yin Anyue A Moes Dirk Jan A R DJAR van Hasselt Johan G C JGC Swen Jesse J JJ Guchelaar Henk-Jan HJ

CPT: pharmacometrics & systems pharmacology 20190809 10

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 ...[more]

PMID: 31250989

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Project description:BackgroundCabozantinib is approved, in various settings, for the treatment of renal cell carcinoma, medullary thyroid cancer, and hepatocellular carcinoma, and it has been investigated for the treatment of other cancers. With the available evidence and the real-world performance of cabozantinib compared with clinical trial data, we performed a systematic review of cabozantinib monotherapy as treatment for solid tumors in adults.MethodsThis study was designed in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses and registered with PROSPERO (CRD42020144680). We searched for clinical and observational studies of cabozantinib monotherapy for solid tumors using Embase, MEDLINE, and Cochrane databases (October 2020), and screened relevant congress abstracts. Eligible studies reported clinical or safety outcomes, or biomarker data. Small studies (n < 25) and studies of cabozantinib combination therapies were excluded. Quality was assessed using National Institute for Health and Care Excellence methodology, and study characteristics were described qualitatively.ResultsOf 2888 citations, 114 were included (52 randomized studies, 29 observational studies, 32 nonrandomized phase I or II studies or pilot trials, and 1 analysis of data from a randomized study and a nonrandomized study). Beyond approved indications, other tumors studied were castration-resistant prostate cancer, urothelial carcinoma, Ewing sarcoma, osteosarcoma, uveal melanoma, non-small-cell lung cancer, Merkel cell carcinoma, glioblastoma, pheochromocytomas and paragangliomas, cholangiocarcinoma, gastrointestinal stromal tumor, colorectal cancer, salivary gland cancer, carcinoid and pancreatic neuroendocrine tumors, and breast, endometrial and ovarian cancers. The most common adverse events were hypertension, diarrhea, and fatigue.ConclusionThe identified evidence demonstrates the positive efficacy/effectiveness of cabozantinib monotherapy in various solid tumor types, with safety findings being consistent with those observed with other VEGFR-targeting tyrosine kinase inhibitors. When available, real-world findings were consistent with the data reported from clinical trials. A limitation of this review is the high proportion of abstracts; however, this allowed us to capture the most up-to-date findings.

Project description:The distribution of nanomedicines inside solid tumors is often restricted to perivascular areas, leaving most distal tumor cells out of reach. This partly explains modest patient benefit of many nanomedicines compared to their free-form counterparts. The objective for this study is to develop a mathematical model to quantitatively analyze this phenomenon and the influencing factors to such perivascular distribution and seek for effective strategies to alleviate this. A spatial tumor distribution model was firstly constructed to mimic the geometrical structure of tumor vessels and the surrounding tumor cells. This tumor model was further integrated with a systemic pharmacokinetics model for nanoparticles. A variety of factors on the tumor spatial distributions of nanomedicines were considered in the model. With the model, we quantified the effect of these influencing factors on tumor delivery efficacy (ID %), the magnitude of heterogeneous distribution (H index), and the effect of enhanced permeability and retention (EPR). In particularly, we compared the spatial distributions of the nanoparticles and the free payloads insides tumors. The model predicted high degrees of distributional heterogeneity for both nanoparticles and free payloads. The degree of heterogeneity and the influencing factors for free payloads were markedly different from those for nanoparticles. We found that nanoparticle diffusion coefficient was the most effective factor in reducing the nanoparticle H index but exerted moderate influence on the free payloads H index. The most effective factor in reducing the H index of free payload was payload diffusion coefficient. The factors that improved free payload distribution were closely associated with higher drug efficacy. In contrast, the factors that improved nanoparticle spatial distributions did not always confer improved anti-tumor efficacy of the delivered drug. These findings highlight the importance of assessing the heterogeneous free payload distribution in tumors for the development of effective nanomedicines.

Dataset Information

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

Publications

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

Similar Datasets

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