ReviewEuropean journal of clinical pharmacology2016
Pharmacodynamic modeling of adverse effects of anti-cancer drug treatment.
Review in European journal of clinical pharmacology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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.
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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
15 citing papers in PubMed.
- Label-free, real-time monitoring of cytochrome C drug responses in microdissected tumor biopsies with a multi-well aptasensor platform.Science advances · 2024Article
- Exploring the Potential Role of Phytopharmaceuticals in Alleviating Toxicities of Chemotherapeutic Agents.Current protein & peptide science · 2024Review
- A comprehensive regulatory and industry review of modeling and simulation practices in oncology clinical drug development.Journal of pharmacokinetics and pharmacodynamics · 2023Review
- Plant-Derived Extracellular Vesicles and Their Exciting Potential as the Future of Next-Generation Drug Delivery.Biomolecules · 2023Review
- Pd(II) and Pt(II) Trinuclear Chelates with Spermidine: Selective Anticancer Activity towards TNBC-Sensitive and -Resistant to Cisplatin.Pharmaceutics · 2023Article
- Myelotoxicity of Temozolomide Treatment in Patients with Glioblastoma Is It Time for a More Mechanistic Approach?Cancers · 2023Article
- Pharmacometric modeling of drug adverse effects: an application of mixture models in schizophrenia spectrum disorder patients treated with clozapine.Journal of pharmacokinetics and pharmacodynamics · 2023Article
- Article
- The Effect of CHIR 99021, a Glycogen Synthase Kinase-3β Inhibitor, on Transforming Growth Factor β-Induced Tenon Fibrosis.Investigative ophthalmology & visual science · 2021Article
- Quantification of the pharmacokinetic-toxicodynamic relationship of oral docetaxel co-administered with ritonavir.Investigational new drugs · 2020Article
- Therapeutic Apheresis, Circulating PLD, and Mucocutaneous Toxicity: Our Clinical Experience through Four Years.Pharmaceutics · 2020Review
- Machine Learning Approach to Forecast Chemotherapy-Induced Haematological Toxicities in Patients with Rhabdomyosarcoma.Cancers · 2020Article
- Modeling and Simulation of Pretomanid Pharmacodynamics in Pulmonary Tuberculosis Patients.Antimicrobial agents and chemotherapy · 2019Article
- Mathematical modeling identifies optimum lapatinib dosing schedules for the treatment of glioblastoma patients.PLoS computational biology · 2018Article
- Towards Quantitative Systems Pharmacology Models of Chemotherapy-Induced Neutropenia.CPT: pharmacometrics & systems pharmacology · 2017Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeAdverse effects related to anti-cancer drug treatment influence patient's quality of life, have an impact on the realized dosing regimen, and can hamper response to treatment. Quantitative models that relate drug exposure to the dynamics of adverse effects have been developed and proven to be very instrumental to optimize dosing schedules. The aims of this review were (i) to provide a perspective of how adverse effects of anti-cancer drugs are modeled and (ii) to report several model structures of adverse effect models that describe relationships between drug concentrations and toxicities.
methodsVarious quantitative pharmacodynamic models that model adverse effects of anti-cancer drug treatment were reviewed.
resultsQuantitative models describing relationships between drug exposure and myelosuppression, cardiotoxicity, and graded adverse effects like fatigue, hand-foot syndrome (HFS), rash, and diarrhea have been presented for different anti-cancer agents, including their clinical applicability.
conclusionsMathematical modeling of adverse effects proved to be a helpful tool to improve clinical management and support decision-making (especially in establishment of the optimal dosing regimen) in drug development. The reported models can be used as templates for modeling a variety of anti-cancer-induced adverse effects to further optimize therapy.
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