Evidence map›Paper›PMID 35579201›Full record

ArticleClinical and translational science2022

Using partition analysis as a facile method to derive net clearances.

Ken Korzekwa, Jaydeep Yadav, Swati Nagar

Abstract read
In one paragraph

Article in Clinical and translational science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Process and System Clearances in Pharmacokinetic Models: Our Basic Clearance Concepts Are Correct.Drug metabolism and disposition: the biological fate of chemicals · 2023
    Review
  5. Article
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

3 authors.

Ken KorzekwaDepartment of Pharmaceutical Sciences, Temple University, Philadelphia, Pennsylvania, USA.ORCID 0000-0002-7119-9200
Jaydeep YadavDepartment of Pharmaceutical Sciences, Temple University, Philadelphia, Pennsylvania, USA.
Swati NagarDepartment of Pharmaceutical Sciences, Temple University, Philadelphia, Pennsylvania, USA.ORCID 0000-0003-2667-7063

Funding

Predicting Intracellular Drug Concentrations In The Presence Of TransportersR01GM104178 · NIGMS · TEMPLE UNIV OF THE COMMONWEALTH · PI Kenneth Ray Korzekwa, Swati Nagar · 2013 to 2026
$4.0M
Improving prediction of drug interactions mediated by time-dependent inhibitorsR01GM114369 · NIGMS · TEMPLE UNIV OF THE COMMONWEALTH · PI KORZEKWA, KENNETH RAY, NAGAR, SWATI · 2016 to 2023
$3.2M
NIGMS NIH HHS R01 GM104178NIGMS NIH HHS R01 GM114369
6 · The paper itself

Abstract

Partition analysis has been described previously by W.W. Cleland to derive net rate constants and simplify the derivation of enzyme kinetic equations. Here, we show that partition analysis can be used to derive elimination and transfer (distribution) net clearances for use in pharmacokinetic models. For elimination clearances, the net clearance approach is exemplified with a mammillary two-compartment model with peripheral elimination, and the established well-stirred and full hepatic clearance models. The intrinsic hepatic clearance associated with an observed average hepatic clearance can be easily calculated with net clearances. Expressions for net transfer clearances are easily derived, including models with explicit membranes (e.g., monolayer permeability and blood-brain barrier models). Together, these approaches can be used to derive equations for physiologically based and hybrid compartmental/ physiologically based models. This tutorial describes how net clearances can be used to derive relationships for simple models as well as increasingly complex models, such as inclusion of active transport and target mediated processes.

Indexed as

LiverModels, BiologicalBiological TransportHumansKinetics

Identifiers

PMID35579201
PMCPMC9372430

What OpenQuestion holds

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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.