Evidence map›Paper›PMID 34168949›Full record

ArticleCurrent pharmacology reports2019

Methods to Predict Volume of Distribution.

Kimberly Holt, Swati Nagar, Ken Korzekwa

Abstract read
In one paragraph

Article in Current pharmacology reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Article
  2. Article
  3. Bioinformatics and biology insights · 2026
    Article
  4. Article
  5. Physiologically based pharmacokinetic modeling of small molecules: How much progress have we made?Drug metabolism and disposition: the biological fate of chemicals · 2025
    Review
  6. Review
  7. Review
  8. Predicting Pharmacokinetics of Active Constituents inPharmaceuticals (Basel, Switzerland) · 2024
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Oncology reports · 2022
    Article
  14. 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.

Kimberly Holt *Department of Pharmaceutical Sciences, Temple University School of Pharmacy, 3307 N. Broad Street, Philadelphia, PA 19140, USA.
Swati Nagar *Department of Pharmaceutical Sciences, Temple University School of Pharmacy, 3307 N. Broad Street, Philadelphia, PA 19140, USA.
Ken KorzekwaDepartment of Pharmaceutical Sciences, Temple University School of Pharmacy, 3307 N. Broad Street, Philadelphia, PA 19140, USA.

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

purpose of reviewPrior to human studies, knowledge of drug disposition in the body is useful to inform decisions on drug safety and efficacy, first in human dosing, and dosing regimen design. It is therefore of interest to develop predictive models for primary pharmacokinetic parameters, clearance, and volume of distribution. The volume of distribution of a drug is determined by the physiological properties of the body and physiochemical properties of the drug, and is used to determine secondary parameters, including the half-life. The purpose of this review is to provide an overview of current methods for the prediction of volume of distribution of drugs, discuss a comparison between the methods, and identify deficiencies in current predictive methods for future improvement. RECENT

findingsSeveral volumes of distribution prediction methods are discussed, including preclinical extrapolation, physiological methods, tissue composition-based models to predict tissue:plasma partition coefficients, and quantitative structure-activity relationships. Key factors that impact the prediction of volume of distribution, such as permeability, transport, and accuracy of experimental inputs, are discussed. A comparison of current methods indicates that in general, all methods predict drug volume of distribution with an absolute average fold error of 2-fold. Currently, the use of composition-based PBPK models is preferred to models requiring in vivo input. SUMMARY: Composition-based models perfusion-limited PBPK models are commonly used at present for prediction of tissue:plasma partition coefficients and volume of distribution, respectively. A better mechanistic understanding of important drug distribution processes will result in improvements in all modeling approaches.

Indexed as

DistributionMembranepartitioningPredictionmodelsTissue:plasmapartitioncoefficientsVolumeofdistribution

Identifiers

PMID34168949
PMCPMC8221585

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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.