Evidence map›Paper›PMID 25505653›Full record

ReviewIn silico pharmacology2013

Modeling bioavailability to organs protected by biological barriers.

Nadia Quignot

Abstract readReview
In one paragraph

Review in In silico pharmacology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

1 author.

Nadia QuignotBioengineering Department, Chair of Mathematical Modeling for Systems Toxicology, Université de Technologie de Compiègne, Royallieu Research Center, Compiègne, 60200 France ; LA-SER, Strategy and Decision Analytics, 10 place de la Catalogne, Paris, 75014 France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Computational pharmacokinetic (PK) modeling gives access to drug concentration vs. time profiles in target organs and allows better interpretation of clinical observations of therapeutic or toxic effects. Physiologically-based PK (PBPK) models in particular, based on mechanistic descriptions of the body anatomy and physiology, may also help to extrapolate in vitro or animal data to human. Once in the systemic circulation, a chemical has access to the microvasculature of every organ or tissue. However, its penetration in the brain, retina, thymus, spinal cord, testis, placenta,… may be limited or even fully prevented by dynamic physiological blood-tissue barriers. Those barriers are both physical (involving tight junctions between adjacent cells) and biochemical (involving metabolizing enzymes and transporters). On those cases, correct mechanistic characterization of the passage (or not) of molecules through the barrier can be crucial for improved PBPK modeling and prediction. In parallel, attempts to understand and quantitatively characterize the processes involved in drug penetration of physiological barriers have led to the development of several in vitro experimental models. Data from such assays are very useful to calibrate PBPK models. We review here those in vitro and computational models, highlighting the challenges and perspectives for in vitro and computational models to better assess drug availability to target tissues.

Indexed as

BioavailabilityBiological barriersComputational modelPBPK modelingPharmacokinetics

Identifiers

PMID25505653
PMCPMC4230447

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.