SynthesisJournal of pharmacokinetics and pharmacodynamics2018
Structural identifiability for mathematical pharmacology: models of myelosuppression.
Synthesis in Journal of pharmacokinetics and pharmacodynamics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- PKPD Modeling and Dosing Considerations in Advanced Ovarian Cancer Patients Treated with Cisplatin-Based Intraoperative Intraperitoneal Chemotherapy.The AAPS journal · 2020Trial
- A Nonparametric Approach to Practical Identifiability of Nonlinear Mixed Effects Models.Bulletin of mathematical biology · 2026Article
- Semi-mechanistic population PK/PD model to aid clinical understanding of myelodysplastic syndromes following treatment with Venetoclax and Azacitidine.CPT: pharmacometrics & systems pharmacology · 2025Article
- Classical structural identifiability methodology applied to low-dimensional dynamic systems in receptor theory.Journal of pharmacokinetics and pharmacodynamics · 2024Article
- Analysis of cellular kinetic models suggest that physiologically based model parameters may be inherently, practically unidentifiable.Journal of pharmacokinetics and pharmacodynamics · 2022Article
- Longitudinal and time-to-event modeling for prognostic implications of radical surgery in retroperitoneal sarcoma.CPT: pharmacometrics & systems pharmacology · 2022Article
- How translational modeling in oncology needs to get the mechanism just right.Clinical and translational science · 2022Review
- Using mathematical modeling to estimate time-independent cancer chemotherapy efficacy parameters.In silico pharmacology · 2022Article
- Importance of Stability Analysis When Using Nonlinear Semimechanistic Models to Describe Drug-Induced Hematotoxicity.CPT: pharmacometrics & systems pharmacology · 2020Article
- Mathematical models for cytarabine-derived myelosuppression in acute myeloid leukaemia.PloS one · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Structural identifiability is an often overlooked, but essential, prerequisite to the experiment design stage. The application of structural identifiability analysis to models of myelosuppression is used to demonstrate the importance of its considerations. It is shown that, under certain assumptions, these models are structurally identifiable and so drug and system specific parameters can truly be separated. Further it is shown via a meta-analysis of the literature that because of this the reported system parameter estimates for the "Friberg" or "Uppsala" model are consistent in the literature.
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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.