ArticlePloS one2015
Predictive Modeling of Drug Response in Non-Hodgkin's Lymphoma.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 25 citations in OpenAlex.
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- Article
- Multicompartment modeling of protein shedding kinetics during vascularized tumor growth.Scientific reports · 2020Article
- Mathematical Modeling to Address Challenges in Pancreatic Cancer.Current topics in medicinal chemistry · 2020Article
- Development of a Physiologically-Based Mathematical Model for Quantifying Nanoparticle Distribution in Tumors.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2019Article
- Mathematical modeling in cancer nanomedicine: a review.Biomedical microdevices · 2019Review
- Predicting breast cancer response to neoadjuvant chemotherapy based on tumor vascular features in needle biopsies.JCI insight · 2019Article
- Dynamic Targeting in Cancer Treatment.Frontiers in physiology · 2019Article
- Design Optimization of Tumor Vasculature-Bound Nanoparticles.Scientific reports · 2018Article
- Establishing the effects of mesoporous silica nanoparticle properties on in vivo disposition using imaging-based pharmacokinetics.Nature communications · 2018Article
- Pharmacokinetic/pharmacodynamic modeling of combination-chemotherapy for lung cancer.Journal of theoretical biology · 2018Article
- Understanding the Connection between Nanoparticle Uptake and Cancer Treatment Efficacy using Mathematical Modeling.Scientific reports · 2018Article
- Automated analysis of co-localized protein expression in histologic sections of prostate cancer.PloS one · 2017Article
- Simulation of the Protein-Shedding Kinetics of a Fully Vascularized Tumor.Cancer informatics · 2015Article
Corrections and comments
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Authors and funding
15 authors at 7 institutions in 2 countries.
Funding
Abstract
We combine mathematical modeling with experiments in living mice to quantify the relative roles of intrinsic cellular vs. tissue-scale physiological contributors to chemotherapy drug resistance, which are difficult to understand solely through experimentation. Experiments in cell culture and in mice with drug-sensitive (Eµ-myc/Arf-/-) and drug-resistant (Eµ-myc/p53-/-) lymphoma cell lines were conducted to calibrate and validate a mechanistic mathematical model. Inputs to inform the model include tumor drug transport characteristics, such as blood volume fraction, average geometric mean blood vessel radius, drug diffusion penetration distance, and drug response in cell culture. Model results show that the drug response in mice, represented by the fraction of dead tumor volume, can be reliably predicted from these inputs. Hence, a proof-of-principle for predictive quantification of lymphoma drug therapy was established based on both cellular and tissue-scale physiological contributions. We further demonstrate that, if the in vitro cytotoxic response of a specific cancer cell line under chemotherapy is known, the model is then able to predict the treatment efficacy in vivo. Lastly, tissue blood volume fraction was determined to be the most sensitive model parameter and a primary contributor to drug resistance.
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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.