Evidence map›Paper›PMID 22571482›Full record

ReviewCurrent drug metabolism2012

Physiologically based pharmacokinetic models: integration of in silico approaches with micro cell culture analogues.

A Chen, M L Yarmush, T Maguire

Abstract readReview
In one paragraph

Review in Current drug metabolism, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
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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

3 authors.

A ChenRutgers University, Department of Biomedical Engineering, 599 Taylor Road, Piscataway, NJ 08854, USA.
M L Yarmush
T Maguire

Funding

Bio-MicroElectroMechanical Systems Resource CenterP41EB002503 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI TONER, MEHMET · 2004 to 2018
$17.4M
UMDNJ TRAINING GRANT IN BIOTECHNOLOGYT32GM008339 · NIGMS · RUTGERS THE ST UNIV OF NJ NEW BRUNSWICK · PI YARMUSH, MARTIN L · 1989 to 2019
$6.1M
NIBIB NIH HHS P41 EB002503NIGMS NIH HHS T32 GM008339
6 · The paper itself

Abstract

There is a large emphasis within the pharmaceutical industry to provide tools that will allow early research and development groups to better predict dose ranges for and metabolic responses of candidate molecules in a high throughput manner, prior to entering clinical trials. These tools incorporate approaches ranging from PBPK, QSAR, and molecular dynamics simulations in the in silico realm, to micro cell culture analogue (CCAs)s in the in vitro realm. This paper will serve to review these areas of high throughput predictive research, and highlight hurdles and potential solutions. In particular we will focus on CCAs, as their incorporation with PBPK modeling has the potential to replace animal testing, with a more predictive assay that can combine multiple organ analogs on one microfluidic platform in physiologically correct volume ratios. While several advantages arise from the current embodiments of CCAS in a microfluidic format that can be exploited for realistic simulations of drug absorption, metabolism and action, we explore some of the concerns with these systems, and provide a potential path forward to realizing animal-free solutions. Furthermore we envision that, together with theoretical modeling, CCAs may produce reliable predictions of the efficacy of newly developed drugs.

Indexed as

Animal Testing AlternativesCell Culture TechniquesModels, BiologicalAnimalsComputer SimulationHumansPharmaceutical PreparationsPharmacokineticsPharmaceutical Preparations

Identifiers

PMID22571482
PMCPMC3966908

What OpenQuestion holds

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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.