ReviewYonsei medical journal2017
A Review of Modeling Approaches to Predict Drug Response in Clinical Oncology.
Review in Yonsei medical journal, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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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.
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.
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Who cites it
14 citing papers in PubMed.
- Contribution of machine learning to tumor growth inhibition modeling for hepatocellular carcinoma patients under Roblitinib (FGF401) drug treatment.CPT: pharmacometrics & systems pharmacology · 2022Trial
- Exposure-Response Analysis of Necitumumab Efficacy in Squamous Non-Small Cell Lung Cancer Patients.CPT: pharmacometrics & systems pharmacology · 2017Trial
- Re-Thinking Pharmacokinetics in Ovarian Cancer: What Do Organoids Add?International journal of molecular sciences · 2026Review
- CDCR-Rank: a computational model for predicting drug combination dose response using ranking-based optimization.Bioinformatics advances · 2026Article
- The Three Musketeers in Cancer Therapy: Pharmacokinetics, Pharmacodynamics and Personalised Approach.Journal of personalized medicine · 2025Review
- Coumarin Derivatives as Anticancer Agents: Mechanistic Landscape with an Emphasis on Breast Cancer.Molecules (Basel, Switzerland) · 2025Review
- Stochastic modeling and first-passage-time analysis of oncological time metrics with dynamic tumor barriers.Scientific reports · 2025Article
- From Crypts to Cancer: A Holistic Perspective on Colorectal Carcinogenesis and Therapeutic Strategies.International journal of molecular sciences · 2024Review
- Two-way pharmacodynamic modeling of drug combinations and its application to pairs of repurposed Ebola and SARS-CoV-2 agents.Antimicrobial agents and chemotherapy · 2024Article
- A prognostic baseline blood biomarker and tumor growth kinetics integrated model in paclitaxel/platinum treated advanced non-small cell lung cancer patients.CPT: pharmacometrics & systems pharmacology · 2023Article
- Precision Oncology by Point-of-Care Therapeutic Drug Monitoring and Dosage Adjustment of Conventional Cytotoxic Chemotherapies: A Perspective.Pharmaceutics · 2023Review
- Article
- Multiscale Model Identifies Improved Schedule for Treatment of Acute Myeloid Leukemia In Vitro With the Mcl-1 Inhibitor AZD5991.CPT: pharmacometrics & systems pharmacology · 2020Article
- Mathematical models applied to thyroid cancer.Biophysical reviews · 2019Review
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Model-based approaches have emerged as important tools for quantitatively understanding temporal relationships between drug dose, concentration, and effect over the course of treatment, and have now become central to optimal drug development and tailored drug treatment. In oncology, the therapeutic index of a chemotherapeutic drug is typically narrow and a full dose-response relationship is not available, often because of treatment failure. Noting the benefits of model-based approaches and the low therapeutic index of oncology drugs, in recent years, modeling approaches have been increasingly used to streamline oncologic drug development through early identification and quantification of dose-response relationships. With this background, this report reviews publications that used model-based approaches to evaluate drug treatment outcome variables in oncology therapeutics, ranging from tumor size dynamics to tumor/biomarker time courses and survival response.
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Registered trials
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