ReviewNature reviews. Clinical oncology2016
Computational oncology--mathematical modelling of drug regimens for precision medicine.
Review in Nature reviews. Clinical oncology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 98 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
98 citing papers in PubMed, 221 citations in OpenAlex.
- Heterogenous treatment effect of neuromuscular blocking agents for moderate-to-severe ARDS: a post hoc Markov model re-analysis of the ACURASYS trial.Intensive care medicine · 2025Trial
- Computational reactive-diffusive modeling for stratification and prognosis determination of patients with breast cancer receiving Olaparib.Scientific reports · 2023Trial
- Toward Personalized Computer Simulation of Breast Cancer Treatment: A Multiscale Pharmacokinetic and Pharmacodynamic Model Informed by Multitype Patient Data.Cancer research · 2019Trial
- A computational framework for optimizing radioiodine therapy protocols in metastatic thyroid cancer.Scientific reports · 2026Article
- Cuproptosis meets sonodynamics: a nanomedicine platform for multimodal cancer therapy.RSC advances · 2026Review
- AI accelerate the identification of druggable targets by 3D structures of proteins and compounds.NPJ precision oncology · 2026Review
- Mathematical Oncology: How Modeling Is Transforming Clinical Decision-Making.Cancer research · 2025Review
- Dynamic modelling of cell cycle arrest through integrated single-cell and mathematical modelling approaches.PLoS computational biology · 2025Article
- Mathematical model of tumor-macrophage dynamics in glioma to advance myeloid-targeted therapies.Computers in biology and medicine · 2025Article
- Metabolic reprogramming and prognostic insights in molecular landscapes driven by glycolysis in ovarian cancer.Scientific reports · 2025Article
- Mathematical model of tumor-macrophage dynamics in glioma to advance myeloid-targeted therapies.bioRxiv : the preprint server for biology · 2025Article
- Pattern Formation as a Resilience Mechanism in Cancer Immunotherapy.Bulletin of mathematical biology · 2025Article
- Multi-scale computational modeling towards efficacy in radiopharmaceutical therapies while minimizing side effects: Modeling of amino acid infusion.PLoS computational biology · 2025Article
- A quantitative characterization of the heterogeneous response of glioblastoma U-87 MG cell line to temozolomide.Scientific reports · 2025Article
- Insights from a multiscale framework on metabolic rate variation driving glioblastoma multiforme growth and invasion.Communications engineering · 2024Article
- Understanding patient-derived tumor organoid growth through an integrated imaging and mathematical modeling framework.PLoS computational biology · 2024Article
- To modulate or to skip: De-escalating PARP inhibitor maintenance therapy in ovarian cancer using adaptive therapy.Cell systems · 2024Article
- Nucleic acid-based drugs for patients with solid tumours.Nature reviews. Clinical oncology · 2024Review
- Morphological Stability for in silico Models of Avascular Tumors.Bulletin of mathematical biology · 2024Article
- Simulating BRAFV600E-MEK-ERK signalling dynamics in response to vertical inhibition treatment strategies.NPJ systems biology and applications · 2024Article
38 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
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computational oncology is a generic term that encompasses any form of computer-based modelling relating to tumour biology and cancer therapy. Mathematical modelling can be used to probe the pharmacokinetics and pharmacodynamics relationships of the available anticancer agents in order to improve treatment. As a result of the ever-growing numbers of druggable molecular targets and possible drug combinations, obtaining an optimal toxicity-efficacy balance is an increasingly complex task. Consequently, standard empirical approaches to optimizing drug dosing and scheduling in patients are now of limited utility; mathematical modelling can substantially advance this practice through improved rationalization of therapeutic strategies. The implementation of mathematical modelling tools is an emerging trend, but remains largely insufficient to meet clinical needs; at the bedside, anticancer drugs continue to be prescribed and administered according to standard schedules. To shift the therapeutic paradigm towards personalized care, precision medicine in oncology requires powerful new resources for both researchers and clinicians. Mathematical modelling is an attractive approach that could help to refine treatment modalities at all phases of research and development, and in routine patient care. Reviewing preclinical and clinical examples, we highlight the current achievements and limitations with regard to computational modelling of drug regimens, and discuss the potential future implementation of this strategy to achieve precision medicine in oncology.
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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.