ArticleJournal of pharmacokinetics and pharmacodynamics2014
Modeling cancer-immune responses to therapy.
Article in Journal of pharmacokinetics and pharmacodynamics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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
25 citing papers in PubMed.
- Beyond tumor biology: nursing interventions for psychological and immune health in cancer patients.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Review
- Decoding the tumor immune landscape: emerging TIL subsets as prognostic biomarkers and therapeutic targets.Naunyn-Schmiedeberg's archives of pharmacology · 2026Review
- Linear and Non-Linear Optimal Control Methods to Determine the Best Chemotherapy Schedule for Most Effectively Inhibiting Tumor Growth.Biomedicines · 2025Article
- Advancing cancer drug development with mechanistic mathematical modeling: bridging the gap between theory and practice.Journal of pharmacokinetics and pharmacodynamics · 2024Review
- Dose optimization of an adjuvanted peptide-based personalized neoantigen melanoma vaccine.PLoS computational biology · 2024Article
- Treatment of evolving cancers will require dynamic decision support.Annals of oncology : official journal of the European Society for Medical Oncology · 2023Review
- In silico cancer immunotherapy trials uncover the consequences of therapy-specific response patterns for clinical trial design and outcome.Nature communications · 2023Article
- Physics-based tissue simulator to model multicellular systems: A study of liver regeneration and hepatocellular carcinoma recurrence.PLoS computational biology · 2023Article
- Mathematical modeling of cancer immunotherapy for personalized clinical translation.Nature computational science · 2022Article
- Cancerous Tumor Controlled Treatment Using Search Heuristic (GA)-Based Sliding Mode and Synergetic Controller.Cancers · 2022Article
- Development of and insights from systems pharmacology models of antibody-drug conjugates.CPT: pharmacometrics & systems pharmacology · 2022Review
- Classical mathematical models for prediction of response to chemotherapy and immunotherapy.PLoS computational biology · 2022Article
- Dose-dependent thresholds of dexamethasone destabilize CAR T-cell treatment efficacy.PLoS computational biology · 2022Article
- Mathematical model of a personalized neoantigen cancer vaccine and the human immune system.PLoS computational biology · 2021Article
- Article
- A Tumor-Immune Interaction Model for Synergistic Combinations of Anti PD-L1 and Ionizing Irradiation Treatment.Pharmaceutics · 2020Article
- An in silico exploration of combining Interleukin-12 with Oxaliplatin to treat liver-metastatic colorectal cancer.BMC cancer · 2020Article
- A QSP Model for Predicting Clinical Responses to Monotherapy, Combination and Sequential Therapy Following CTLA-4, PD-1, and PD-L1 Checkpoint Blockade.Scientific reports · 2019Article
- Mathematical modeling of tumor-immune cell interactions.Journal of theoretical biology · 2019Review
- Quantitative Mechanistic Modeling in Support of Pharmacological Therapeutics Development in Immuno-Oncology.Frontiers in immunology · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer therapies that harness the actions of the immune response, such as targeted monoclonal antibody treatments and therapeutic vaccines, are relatively new and promising in the landscape of cancer treatment options. Mathematical modeling and simulation of immune-modifying therapies can help to offset the costs of drug discovery and development, and encourage progress toward new immunotherapies. Despite advances in cancer immunology research, questions such as how the immune system interacts with a growing tumor, and which components of the immune system play significant roles in responding to immunotherapy are still not well understood. Mathematical modeling and simulation are powerful tools that provide an analytical framework in which to address such questions. A quantitative understanding of the kinetics of the immune response to treatment is crucial in designing treatment strategies, such as dosing, timing, and predicting the response to a specific treatment. These models can be used both descriptively and predictively. In this chapter, various mathematical models that address different cancer treatments, including cytotoxic chemotherapy, immunotherapy, and combinations of both treatments, are presented. The aim of this chapter is to highlight the importance of mathematical modeling and simulation in the design of immunotherapy protocols for cancer treatment. The results demonstrate the power of these approaches in explaining determinants that are fundamental to cancer-immune dynamics, therapeutic success, and the development of efficient therapies.
Indexed as
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25281420What OpenQuestion holds
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.