ArticleCPT: pharmacometrics & systems pharmacology2024
Cluster Gauss-Newton method for a quick approximation of profile likelihood: With application to physiologically-based pharmacokinetic models.
Article in CPT: pharmacometrics & systems pharmacology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Characterizing Apixaban Pharmacokinetics Through Physiologically-Based Pharmacokinetic Modeling: Critical Role of Biliary Secretion and Enterohepatic Circulation in Humans.CPT: pharmacometrics & systems pharmacology · 2026Article
- Simultaneous Target-Mediated Drug Disposition-Pharmacodynamic (TMDD-PD) Modeling of Finasteride and Dutasteride: Impact of Target Binding and Turnover on Non-linear Pharmacokinetics.The AAPS journal · 2025Article
- Elucidating Contributions of Drug Transporters/Enzyme to Nonlinear Pharmacokinetics of Grazoprevir by PBPK Modeling With a Cluster Gauss-Newton Method.CPT: pharmacometrics & systems pharmacology · 2025Article
- Refined target-mediated drug disposition modeling of the anti-tissue factor pathway inhibitor antibody MG1113 in cynomolgus monkeys and rabbits.Frontiers in pharmacology · 2025Article
- Elucidation of DPP-4 involvement in systemic distribution and renal reabsorption of linagliptin by PBPK modeling with a cluster Gauss-Newton method.Clinical and translational science · 2024Article
- Elucidating nonlinear pharmacokinetics of telmisartan: Integration of target-mediated drug disposition and OATP1B3-mediated hepatic uptake in a physiologically based model.CPT: pharmacometrics & systems pharmacology · 2024Article
- Kinetic modeling of the plasma pharmacokinetic profiles of ADAMTS13 fragment and its Fc-fusion counterpart in mice.Frontiers in pharmacology · 2024Article
- Cluster Gauss-Newton method for a quick approximation of profile likelihood: With application to physiologically-based pharmacokinetic models.CPT: pharmacometrics & systems pharmacology · 2024Article
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2 authors.
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Abstract
Physiologically-based pharmacokinetic (PBPK) models can be challenging to work with because they can have too many parameters to identify from observable data. The profile likelihood method can help solve this issue by determining parameter identifiability and confidence intervals, but it involves repetitive parameter optimizations that can be time-consuming. The Cluster Gauss-Newton method (CGNM) is a parameter estimation method that efficiently searches through a wide range of parameter space. In this study, we propose a method that approximates the profile likelihood by reusing intermediate computation results from CGNM, allowing us to obtain the upper bounds of the profile likelihood without conducting additional model evaluation. This method allows us to quickly draw approximate profile likelihoods for all unknown parameters. Additionally, the same approach can be used to draw two-dimensional profile likelihoods for all parameter combinations within seconds. We demonstrate the effectiveness of this method on three PBPK models.
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