ArticleProceedings of the National Academy of Sciences of the United States of America2017
Evaluating optimal therapy robustness by virtual expansion of a sample population, with a case study in cancer immunotherapy.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- In silico clinical trials of BiTE expression by oncolytic viruses reveal the impact of patient heterogeneity on dosage protocol.PLoS computational biology · 2026Article
- A Step-by-Step Workflow for Performing In Silico Clinical Trials With Nonlinear Mixed Effects Models.CPT: pharmacometrics & systems pharmacology · 2025Article
- Hydrogel Micro/Nanostructures for the Delivery of Oncolytic Viruses: Overcoming Limitations and Improving Efficacy.Advanced pharmaceutical bulletin · 2025Review
- Trajectory-matching ABC-MCMC for simulating heterogeneous dynamics in mechanistic models.bioRxiv : the preprint server for biology · 2025Article
- Validating the predictions of mathematical models describing tumor growth and treatment response.ArXiv · 2025Article
- Treatment of evolving cancers will require dynamic decision support.Annals of oncology : official journal of the European Society for Medical Oncology · 2023Review
- A practical guide for the generation of model-based virtual clinical trials.Frontiers in systems biology · 2023Article
- Mathematical modeling of cancer immunotherapy for personalized clinical translation.Nature computational science · 2022Article
- From Fitting the Average to Fitting the Individual: A Cautionary Tale for Mathematical Modelers.Frontiers in oncology · 2022Article
- Impact of sex and pathophysiology on optimal drug choice in hypertensive rats: quantitative insights for precision medicine.iScience · 2021Article
- In silico trials predict that combination strategies for enhancing vesicular stomatitis oncolytic virus are determined by tumor aggressivity.Journal for immunotherapy of cancer · 2021Article
- Spatial Model for Oncolytic Virotherapy with Lytic Cycle Delay.Bulletin of mathematical biology · 2019Article
- Optimization of combination therapy for chronic myeloid leukemia with dosing constraints.Journal of mathematical biology · 2018Article
- Chaos synchronization and Nelder-Mead search for parameter estimation in nonlinear pharmacological systems: Estimating tumor antigenicity in a model of immunotherapy.Progress in biophysics and molecular biology · 2018Article
- How to design preclinical studies in nanomedicine and cell therapy to maximize the prospects of clinical translation.Nature biomedical engineering · 2018Article
- CancerInSilico: An R/Bioconductor package for combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer.PLoS computational biology · 2018Article
- Towards personalized computational oncology: from spatial models of tumour spheroids, to organoids, to tissues.Journal of the Royal Society, Interface · 2018Review
- Developing a Minimally Structured Mathematical Model of Cancer Treatment with Oncolytic Viruses and Dendritic Cell Injections.Computational and mathematical methods in medicine · 2018Article
- Modeling three-dimensional invasive solid tumor growth in heterogeneous microenvironment under chemotherapy.PloS one · 2018Article
- Fighting Cancer with Mathematics and Viruses.Viruses · 2017Review
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4 authors.
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Abstract
Cancer is a highly heterogeneous disease, exhibiting spatial and temporal variations that pose challenges for designing robust therapies. Here, we propose the VEPART (Virtual Expansion of Populations for Analyzing Robustness of Therapies) technique as a platform that integrates experimental data, mathematical modeling, and statistical analyses for identifying robust optimal treatment protocols. VEPART begins with time course experimental data for a sample population, and a mathematical model fit to aggregate data from that sample population. Using nonparametric statistics, the sample population is amplified and used to create a large number of virtual populations. At the final step of VEPART, robustness is assessed by identifying and analyzing the optimal therapy (perhaps restricted to a set of clinically realizable protocols) across each virtual population. As proof of concept, we have applied the VEPART method to study the robustness of treatment response in a mouse model of melanoma subject to treatment with immunostimulatory oncolytic viruses and dendritic cell vaccines. Our analysis (
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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.