Evidence map›Paper›PMID 27431401›Full record

ArticleDrug delivery and translational research2016

Computational pharmacokinetic rationale for intra-arterial delivery to the brain.

Johann N R Cooke, Jason A Ellis, Shaolie Hossain, Juliane Nguyen, Jeffrey N Bruce, Shailendra Joshi

Abstract read
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In one paragraph

Article in Drug delivery and translational research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–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

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Review
  5. Review
  6. Review
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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

6 authors.

Johann N R CookeDepartment of Anesthesiology, Columbia University Medical Center, 630 West 168th Street, P&S Box 46, New York, NY, 10032, USA.
Jason A EllisDepartment of Neurological Surgery, Columbia University Medical Center, New York, NY, USA.
Shaolie HossainDepartment of Molecular Cardiology, Texas Heart Institute, Houston, TX, USA.
Juliane NguyenDepartment of Pharmaceutical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA.
Jeffrey N BruceDepartment of Neurological Surgery, Columbia University Medical Center, New York, NY, USA.
Shailendra JoshiDepartment of Anesthesiology, Columbia University Medical Center, 630 West 168th Street, P&S Box 46, New York, NY, 10032, USA. sj121@cumc.columbia.edu.

Funding

Optical Imaging of Chemotherapy for Brain TumorsR01CA138643 · NCI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI JOSHI, SHAILENDRA · 2011 to 2015
$2.4M
NCI NIH HHS R01 CA138643
6 · The paper itself

Abstract

Intra-arterial (IA) drug delivery has been proposed for the treatment of a wide range of brain diseases, including malignant brain tumors. However, pharmacokinetic optimization for IA drug delivery to the brain remains a challenge. In this report, we apply and expand the well-established Dedrick model of IA drug delivery to the brain and test the effects of modifying drug and delivery parameters. These simulations show that altering the properties of candidate drugs and physiological variables can have profound effects on regional deposition after IA injections. We show that drug and physiological optimization aimed at rapid drug extraction and sustained retention is necessary to maximize regional deposition after of IA injections.

Indexed as

Drug Delivery SystemsModels, BiologicalAntineoplastic AgentsBrainComputer SimulationHumansInjections, Intra-ArterialAntineoplastic AgentsBlood-brain barrierBrain tumorChemotherapyGlioma

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.