ArticlePloS one2009
Towards predictive computational models of oncolytic virus therapy: basis for experimental validation and model selection.
Article in PloS one, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
27 citing papers in PubMed, 97 citations in OpenAlex.
- In silico clinical trials of BiTE expression by oncolytic viruses reveal the impact of patient heterogeneity on dosage protocol.PLoS computational biology · 2026Article
- CAR T-cell and oncolytic virus dynamics and determinants of combination therapy success for glioblastoma.Mathematical biosciences · 2025Article
- A comparative analysis of 2D and 3D experimental data for the identification of the parameters of computational models.Scientific reports · 2023Article
- Article
- Modeling Oncolytic Viral Therapy, Immune Checkpoint Inhibition, and the Complex Dynamics of Innate and Adaptive Immunity in Glioblastoma Treatment.Frontiers in physiology · 2020Article
- Backward Hopf bifurcation in a mathematical model for oncolytic virotherapy with the infection delay and innate immune effects.Journal of biological dynamics · 2019Article
- Investigating Macrophages Plasticity Following Tumour-Immune Interactions During Oncolytic Therapies.Acta biotheoretica · 2019Article
- Spatial Model for Oncolytic Virotherapy with Lytic Cycle Delay.Bulletin of mathematical biology · 2019Article
- Imaging, Tracking and Computational Analyses of Virus Entry and Egress with the Cytoskeleton.Viruses · 2018Review
- Developing a Minimally Structured Mathematical Model of Cancer Treatment with Oncolytic Viruses and Dendritic Cell Injections.Computational and mathematical methods in medicine · 2018Article
- Fighting Cancer with Mathematics and Viruses.Viruses · 2017Review
- Article
- Oncolytic potency and reduced virus tumor-specificity in oncolytic virotherapy. A mathematical modelling approach.PloS one · 2017Article
- The Role of the Innate Immune System in Oncolytic Virotherapy.Computational and mathematical methods in medicine · 2017Article
- Computational modeling approaches to the dynamics of oncolytic viruses.Wiley interdisciplinary reviews. Systems biology and medicine · 2016Review
- Article
- Perfusion Pressure Is a Critical Determinant of the Intratumoral Extravasation of Oncolytic Viruses.Molecular therapy : the journal of the American Society of Gene Therapy · 2016Article
- On the laws of virus spread through cell populations.Journal of virology · 2014Article
- Modeling T cell responses to antigenic challenge.Journal of pharmacokinetics and pharmacodynamics · 2014Review
- Evolutionary dynamics of giant viruses and their virophages.Ecology and evolution · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Oncolytic viruses are viruses that specifically infect cancer cells and kill them, while leaving healthy cells largely intact. Their ability to spread through the tumor makes them an attractive therapy approach. While promising results have been observed in clinical trials, solid success remains elusive since we lack understanding of the basic principles that govern the dynamical interactions between the virus and the cancer. In this respect, computational models can help experimental research at optimizing treatment regimes. Although preliminary mathematical work has been performed, this suffers from the fact that individual models are largely arbitrary and based on biologically uncertain assumptions. Here, we present a general framework to study the dynamics of oncolytic viruses that is independent of uncertain and arbitrary mathematical formulations. We find two categories of dynamics, depending on the assumptions about spatial constraints that govern that spread of the virus from cell to cell. If infected cells are mixed among uninfected cells, there exists a viral replication rate threshold beyond which tumor control is the only outcome. On the other hand, if infected cells are clustered together (e.g. in a solid tumor), then we observe more complicated dynamics in which the outcome of therapy might go either way, depending on the initial number of cells and viruses. We fit our models to previously published experimental data and discuss aspects of model validation, selection, and experimental design. This framework can be used as a basis for model selection and validation in the context of future, more detailed experimental studies. It can further serve as the basis for future, more complex models that take into account other clinically relevant factors such as immune responses.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.