Evidence map›Paper›PMID 31028591›Full record

ArticleJournal of pharmacokinetics and pharmacodynamics2019

Two-pore physiologically based pharmacokinetic model with de novo derived parameters for predicting plasma PK of different size protein therapeutics.

Zhe Li, Dhaval K Shah

Abstract read
In one paragraph

Article in Journal of pharmacokinetics and pharmacodynamics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

25 citing papers in PubMed.

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  8. Modeling onActa pharmaceutica Sinica. B · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Zhe LiDepartment of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, The State University of New York at Buffalo, 455 Kapoor Hall, Buffalo, NY, 14214-8033, USA.
Dhaval K ShahDepartment of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, The State University of New York at Buffalo, 455 Kapoor Hall, Buffalo, NY, 14214-8033, USA. dshah4@buffalo.edu.ORCID 0000-0002-0723-6206

Funding

Translational Systems Pharmacokinetic Models of Novel Anticancer BiologicsR01GM114179 · NIGMS · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI SHAH, DHAVAL K · 2015 to 2019
$1.7M
Modulation of antigen pharmacokinetics with pH dependent antibodyR21AI138195 · NIAID · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI PARK, SHELDON · 2018 to 2019
$425k
NIAID NIH HHS R21 AI138195NIGMS NIH HHS R01 GM114179
6 · The paper itself

Abstract

Two-pore PBPK models have been used for characterizing the PK of protein therapeutics since 1990s. However, widespread utilization of these models is hampered by the lack of a priori parameter values, which are typically estimated using the observed data. To overcome this hurdle, here we have presented the development of a two-pore PBPK model using de novo derived parameters. The PBPK model was validated using plasma PK data for different size proteins in mice. Using the "two pore theory" we were able to establish the relationship between protein size and key model parameters, such as: permeability-surface area product (PS), vascular reflection coefficient (σ), peclet number (Pe), and glomerular sieving coefficient (θ). The model accounted for size dependent changes in tissue extravasation and glomerular filtration. The model was able to a priori predict the PK of 8 different proteins: IgG (150 kDa), scFv-Fc (105 kDa), F(ab)

Indexed as

AnimalsAntibodies, MonoclonalHumansKineticsMiceModels, BiologicalProteinsTissue DistributionAntibodies, MonoclonalProteinsAntibody fragmentsDe novo parameter derivationMonoclonal antibody (mAb)Physiologically based pharmacokinetic (PBPK) modelProtein therapeuticsQSPKRTwo-pore theory

Identifiers

PMID31028591
PMCPMC6531344

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Registered trials

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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.