ArticleJournal of pharmacokinetics and pharmacodynamics2016
Determination of a suitable voriconazole pharmacokinetic model for personalised dosing.
Article in Journal of pharmacokinetics and pharmacodynamics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Population Pharmacokinetic Models of Tacrolimus in Adult Transplant Recipients: A Systematic Review.Clinical pharmacokinetics · 2020Pooled it
- Towards Model-Informed Precision Dosing of Voriconazole: Challenging Published Voriconazole Nonlinear Mixed-Effects Models with Real-World Clinical Data.Clinical pharmacokinetics · 2023Article
- Article
- Voriconazole: A Review of Population Pharmacokinetic Analyses.Clinical pharmacokinetics · 2019Review
- A Personalized CYP2C19 Phenotype-Guided Dosing Regimen of Voriconazole Using a Population Pharmacokinetic Analysis.Journal of clinical medicine · 2019Article
- Pharmacokinetic Modeling of Voriconazole To Develop an Alternative Dosing Regimen in Children.Antimicrobial agents and chemotherapy · 2018Observational
- Optimizing Antipsychotic Patient Management Using Population Pharmacokinetic Models and Point-of-Care Testing.CPT: pharmacometrics & systems pharmacology · 2017Article
- The Impact of Model-Misspecification on Model Based Personalised Dosing.The AAPS journal · 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Model based personalised dosing (MBPD) is a sophisticated form of individualised therapy, where a population pharmacokinetic (PK) or pharmacodynamic model is utilised to estimate the dose required to reach a target exposure or effect. The choice of which model to implement in MBPD is a subjective decision. By choosing one model, information from the remaining models is ignored, as well as the rest of the literature base. This manuscript describes a methodology to develop a 'hybrid' model for voriconazole that incorporated information from prior models in a biologically plausible manner. Voriconazole is a triazole antifungal with difficult to predict PK, although it does have a defined exposure-response relationship. Nine population PK models of voriconazole were identified from the literature. The models differed significantly in structural components. The hybrid model contained a two-compartment disposition model with mixed linear and nonlinear time-dependent clearance. The parameters for the hybrid model were determined using simulation techniques. Validation of the hybrid model was assessed via visual predictive checks, which indicated the majority of the variability in the literature models was captured by the hybrid model. The predictive performance was assessed using four different sampling strategies of limited concentrations from ten richly PK sampled subjects to predict future concentrations. Overall, the hybrid model predicted future concentrations with good precision. Further prospective and retrospective validation of the hybrid model is required before it could be used in clinical practice.
Indexed as
Identifiers
26676909What OpenQuestion holds
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.