Evidence map›Paper›PMID 25056496›Full record

ReviewClinical pharmacokinetics2014

Prediction of pharmacokinetics and drug-drug interactions when hepatic transporters are involved.

Rui Li, Hugh A Barton, Manthena V Varma

Abstract readReview
PubMed Publisher
In one paragraph

Review in Clinical pharmacokinetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing 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

27 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Review
  8. Article
  9. Review
  10. How Science Is Driving Regulatory Guidances.Methods in molecular biology (Clifton, N.J.) · 2021
    Article
  11. Review
  12. Article
  13. Article
  14. Use of imaging to assess the activity of hepatic transporters.Expert opinion on drug metabolism & toxicology · 2020
    Review
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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.

Rui LiPfizer Worldwide Research and Development, Department of Pharmacokinetics, Dynamics and Metabolism, Cambridge, MA, USA.
Hugh A Barton
Manthena V Varma

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hepatobiliary transport mechanisms have been identified to play a significant role in determining the systemic clearance for a number of widely prescribed drugs and an increasing number of new molecular entities (NMEs). While determining the pharmacokinetics, drug transporters also regulate the target tissue exposure and play a key role in regulating the pharmacological and/or toxicological responses. Consequently, it is of great relevance in drug discovery and development to assess hepatic transporter activity in regard to pharmacokinetic and dose predictions and to evaluate pharmacokinetic variability associated with drug-drug interactions (DDIs) and genetic variants. Mechanistic predictions utilizing physiological-based pharmacokinetic modeling are increasingly used to evaluate transporter contribution and delineate the transporter-enzyme interplay on the basis of hypothesis-driven functional in vitro findings. Significant strides were made in the development of in vitro techniques to facilitate characterization of hepatobiliary transport. However, challenges exist in the quantitative in vitro-in vivo extrapolation of transporter kinetics due to the lack of information on absolute abundance of the transporter in both in vitro and in vivo situations, and/or differential function in the holistic in vitro reagents such as suspended and plated hepatocytes systems, and lack of complete mechanistic understanding of liver model structure. On the other hand, models to predict transporter-mediated DDIs range from basic models to mechanistic static and dynamic models. While basic models provide conservative estimates and are useful upfront in avoiding false negative predictions, mechanistic models integrate multiple victim and perpetrator drugs parameters and are expected to provide quantitative predictions. The aim of this paper is to review the current state of the model-based approaches to predict clinical pharmacokinetics and DDIs of drugs or NMEs that are substrates of hepatic transporters.

Indexed as

Models, BiologicalPharmacokineticsBiological TransportDose-Response Relationship, DrugDrug DiscoveryDrug InteractionsHumansLiverMembrane Transport ProteinsMetabolic Clearance RatePharmaceutical PreparationsPredictive Value of TestsTime FactorsTissue DistributionMembrane Transport ProteinsPharmaceutical Preparations

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.