ReviewBiomedical microdevices2019
Mathematical modeling in cancer nanomedicine: a review.
Review in Biomedical microdevices, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
64 citing papers in PubMed.
- Preclinical Pharmacokinetic-Pharmacodynamic Modeling of Antibody Nanoconjugates for Breast Cancer Treatment.CPT: pharmacometrics & systems pharmacology · 2026Article
- Tumor microenvironment-specific nanomedicine: from biology-driven to multi-omics-guided precision engineering.Journal of hematology & oncology · 2026Review
- TheMaterials today. Bio · 2026Review
- Drug-loaded nanoparticles for cancer therapy: A high-throughput multicellular agent-based modeling study.Journal of theoretical biology · 2026Article
- Toward oral nanomaterial-based drug delivery systems for hepatocellular carcinoma therapy: evidence mapping, route-specific validation, and translational challenges.Frontiers in pharmacology · 2026Review
- Informing development of brain cancer therapies within "preclinical trials" using ex vivo patient tumors.Advanced drug delivery reviews · 2026Review
- Prediction of silica nanoparticle biodistribution using a calibrated physiologically based model: Unbound fraction and elimination rate constants for the kidneys and phagocytosis identified as major determinants.International journal of clinical pharmacology and therapeutics · 2025Article
- Rational Design of Safer Inorganic Nanoparticles via Mechanistic Modeling-Informed Machine Learning.ACS nano · 2025Article
- Rational Design of Safer Inorganic Nanoparticles via Mechanistic Modeling-informed Machine Learning.Research square · 2025Article
- Review
- Translational modeling-based evidence for enhanced efficacy of standard-of-care drugs in combination with anti-microRNA-155 in non-small-cell lung cancer.Molecular cancer · 2024Article
- Multiscale physics-basedFrontiers in drug delivery · 2024Review
- Patient-specific prostate tumour growth simulation: a first step towards the digital twin.Frontiers in physiology · 2024Article
- Development of an ultrasound-mediated nano-sized drug-delivery system for cancer treatment: from theory to experiment.Nanomedicine (London, England) · 2024Article
- Multi-Scale and Multi-Physics Models of the Transport of Therapeutic/Diagnostic Cancer Agents.Cancers · 2023Article
- Hybrid Cellular Automata Modeling Reveals the Effects of Glucose Gradients on Tumour Spheroid Growth.Cancers · 2023Article
- Multivariate Analysis of Protein-Nanoparticle Binding Data Reveals a Selective Effect of Nanoparticle Material on the Formation of Soft Corona.Nanomaterials (Basel, Switzerland) · 2023Article
- Bacillus anthracis spores are internalized in human lung epithelial cells by Rab GTPase-supported macropinocytosis.Microbial pathogenesis · 2023Article
- Therapeutic Strategies to Overcome Fibrotic Barriers to Nanomedicine in the Pancreatic Tumor Microenvironment.Cancers · 2023Review
- Examining the efficacy of localised gemcitabine therapy for the treatment of pancreatic cancer using a hybrid agent-based model.PLoS computational biology · 2023Article
4 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Cancer continues to be among the leading healthcare problems worldwide, and efforts continue not just to find better drugs, but also better drug delivery methods. The need for delivering cytotoxic agents selectively to cancerous cells, for improved safety and efficacy, has triggered the application of nanotechnology in medicine. This effort has provided drug delivery systems that can potentially revolutionize cancer treatment. Nanocarriers, due to their capacity for targeted drug delivery, can shift the balance of cytotoxicity from healthy to cancerous cells. The field of cancer nanomedicine has made significant progress, but challenges remain that impede its clinical translation. Several biophysical barriers to the transport of nanocarriers to the tumor exist, and a much deeper understanding of nano-bio interactions is necessary to change the status quo. Mathematical modeling has been instrumental in improving our understanding of the physicochemical and physiological underpinnings of nanomaterial behavior in biological systems. Here, we present a comprehensive review of literature on mathematical modeling works that have been and are being employed towards a better understanding of nano-bio interactions for improved tumor delivery efficacy.
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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.