ArticleCPT: pharmacometrics & systems pharmacology2016
Quantitative Systems Pharmacology Model to Predict the Effects of Commonly Used Anticoagulants on the Human Coagulation Network.
Article in CPT: pharmacometrics & systems pharmacology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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.
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
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Recent applications of quantitative systems pharmacology and machine learning models across diseases.Journal of pharmacokinetics and pharmacodynamics · 2022Pooled it
- Trends of in vitro pharmacological potency and in vivo pharmacokinetics parameters of modern drugs: Can the therapeutic/subtherapeutic dose be estimated from in vitro KClinical and translational science · 2024Article
- A Review of Quantitative Systems Pharmacology Models of the Coagulation Cascade: Opportunities for Improved Usability.Pharmaceutics · 2023Review
- Simulating clinical trials for model-informed precision dosing: using warfarin treatment as a use case.Frontiers in pharmacology · 2023Article
- Precision sirolimus dosing in children: The potential for model-informed dosing and novel drug monitoring.Frontiers in pharmacology · 2023Review
- Evaluation framework for systems models.CPT: pharmacometrics & systems pharmacology · 2022Article
- Beyond Deterministic Models in Drug Discovery and Development.Trends in pharmacological sciences · 2020Review
- Quantitative Systems Pharmacology Model-Based Predictions of Clinical Endpoints to Optimize Warfarin and Rivaroxaban Anti-Thrombosis Therapy.Frontiers in pharmacology · 2020Article
- Review of quantitative systems pharmacological modeling in thrombosis.Communications in information and systems · 2019Article
- Perspective on the State of Pharmacometrics and Systems Pharmacology Integration.CPT: pharmacometrics & systems pharmacology · 2018Review
- Development and validation of a physiology-based model for the prediction of pharmacokinetics/toxicokinetics in rabbits.PloS one · 2018Article
- Quantitative Systems Pharmacology Model to Predict the Effects of Commonly Used Anticoagulants on the Human Coagulation Network.CPT: pharmacometrics & systems pharmacology · 2016Article
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 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Warfarin is the anticoagulant of choice for venous thromboembolism (VTE) treatment, although its suppression of the endogenous clot-dissolution complex APC:PS may ultimately lead to longer time-to-clot dissolution profiles, resulting in increased risk of re-thrombosis. This detrimental effect might not occur during VTE treatment using other anticoagulants, such as rivaroxaban or enoxaparin, given their different mechanisms of action within the coagulation network. A quantitative systems pharmacology model was developed describing the coagulation network to monitor clotting factor levels under warfarin, enoxaparin, and rivaroxaban treatment. The model allowed for estimation of all factor rate constants and production rates. Predictions of individual coagulation factor time courses under steady-state warfarin, enoxaparin, and rivaroxaban treatment reflected the suppression of protein C and protein S under warfarin compared to rivaroxaban and enoxaparin. The model may be used as a tool during clinical practice to predict effects of anticoagulants on individual clotting factor time courses and optimize antithrombotic therapy.
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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.