ArticleCPT: pharmacometrics & systems pharmacology2022
Streamlining physiologically-based pharmacokinetic model design for intravenous delivery of nanoparticle drugs.
Article in CPT: pharmacometrics & systems pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
6 citing papers in PubMed.
- Multifunctional Nano-Contrast Agent Carriers: From Traditional Platforms to Next-Generation Theranostic Applications in Molecular Imaging.Biomedicines · 2026Review
- Mechanistic PBPK/PD modeling of free and nano-liposomal irinotecan reveals formulation-specific determinants of disposition and efficacy.International journal of pharmaceutics · 2026Article
- Advances in Physiologically Based Pharmacokinetic (PBPK) Modeling of Nanomaterials.ACS pharmacology & translational science · 2024Review
- Development of a multi-route physiologically based pharmacokinetic (PBPK) model for nanomaterials: a comparison between a traditional versus a new route-specific approach using gold nanoparticles in rats.Particle and fibre toxicology · 2022Article
- Streamlining physiologically-based pharmacokinetic model design for intravenous delivery of nanoparticle drugs.CPT: pharmacometrics & systems pharmacology · 2022Article
- Machine Learning and Artificial Intelligence in Nanomedicine.Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Physiologically-based pharmacokinetic (PBPK) modeling for nanoparticles elucidates the nanoparticle drug's disposition in the body and serves a vital role in drug development and clinical studies. This paper offers a systematic and tutorial-like approach to developing a model structure and writing distribution ordinary differential equations based on asking binary questions involving the physicochemical nature of the drug in question. Further, by synthesizing existing knowledge, we summarize pertinent aspects in PBPK modeling and create a guide for building model structure and distribution equations, optimizing nanoparticle and non-nanoparticle specific parameters, and performing sensitivity analysis and model validation. The purpose of this paper is to facilitate a streamlined model development process for students and practitioners in the field.
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What 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.