Evidence map›Paper›PMID 29520503›Full record

ArticlePharmaceutical research2018

Comparing Mechanistic and Preclinical Predictions of Volume of Distribution on a Large Set of Drugs.

Rosa Chan, Tom De Bruyn, Matthew Wright, Fabio Broccatelli

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in Pharmaceutical research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Methods to Predict Volume of Distribution.Current pharmacology reports · 2019
    Article
  12. 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

4 authors.

Rosa ChanDepartment of Bioengineering and Therapeutic Sciences, School of Pharmacy and Medicine, University of California San Francisco, San Francisco, California, USA.
Tom De BruynDrug Metabolism & Pharmacokinetics Genentech, Inc, 1 DNA Way, South San Francisco, California, 94080-4990, USA.
Matthew WrightDrug Metabolism & Pharmacokinetics Genentech, Inc, 1 DNA Way, South San Francisco, California, 94080-4990, USA.
Fabio BroccatelliDrug Metabolism & Pharmacokinetics Genentech, Inc, 1 DNA Way, South San Francisco, California, 94080-4990, USA. broccatelli@gene.com.ORCID http://orcid.org/0000-0002-0724-9752

Funding

Maximizing Opportunities for Research Excellence - Evaluation Training SupplementR25GM056847 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI DUNCAN, D'ANNE S., SELLO, JASON K · 1998 to 2023
$14.1M
National Institute of General Medical Sciences R25 GM56847
6 · The paper itself

Abstract

purposeVolume of distribution at steady state (Vd

methodsFor all the drugs analyzed in this study experimental in vitro measurements of LogP, plasma protein binding and pKa are used as input for the mechanistic in silico model to predict human Vd

resultsThis assessment indicates that overall the in silico mechanistic model presented by Rodgers and Rowland is comparably accurate or superior to empirical approaches based on the extrapolation of in vivo data from preclinical species.

conclusionsThese results illustrate the great potential of mechanistic in silico models to accurately predict Vd

Indexed as

Computer SimulationDrug Evaluation, PreclinicalModels, BiologicalAbsorption, PhysiologicalDatasets as TopicMetabolic Clearance RatePharmaceutical ResearchSoftwareTissue Distributionmechanistic in silico modelPBPKphysicochemical propertiespreclinicalvolume of distribution (Vdss)

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.