Evidence map›Paper›PMID 25505415›Full record

ArticleFrontiers in pharmacology2014

Applications of linking PBPK and PD models to predict the impact of genotypic variability, formulation differences, differences in target binding capacity and target site drug concentrations on drug responses and variability.

Manoranjenni Chetty, Rachel H Rose, Khaled Abduljalil, Nikunjkumar Patel, Gaohua Lu, Theresa Cain, Masoud Jamei, Amin Rostami-Hodjegan

Open access · goldAbstract read
In one paragraph

Article in Frontiers in pharmacology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
6.9field-weighted citation impact, top 3% of its field
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

24 citing papers in PubMed, 58 citations in OpenAlex.

  1. Review
  2. Review
  3. Article
  4. Article
  5. Article
  6. Process and System Clearances in Pharmacokinetic Models: Our Basic Clearance Concepts Are Correct.Drug metabolism and disposition: the biological fate of chemicals · 2023
    Review
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Bridging the Data Gap FromFrontiers in public health · 2018
    Review
  16. Article
  17. Article
  18. Review
  19. A Tutorial on Pharmacodynamic Scripting Facility in Simcyp.CPT: pharmacometrics & systems pharmacology · 2016
    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

8 authors at 1 institution in 1 country.

Manoranjenni ChettySimcyp Limited (a Certara Company), Blades Enterprise Centre Sheffield, UK.
Rachel H RoseSimcyp Limited (a Certara Company), Blades Enterprise Centre Sheffield, UK.
Khaled AbduljalilSimcyp Limited (a Certara Company), Blades Enterprise Centre Sheffield, UK.
Nikunjkumar PatelSimcyp Limited (a Certara Company), Blades Enterprise Centre Sheffield, UK.
Gaohua LuSimcyp Limited (a Certara Company), Blades Enterprise Centre Sheffield, UK.
Theresa CainSimcyp Limited (a Certara Company), Blades Enterprise Centre Sheffield, UK.
Masoud JameiSimcyp Limited (a Certara Company), Blades Enterprise Centre Sheffield, UK.
Amin Rostami-HodjeganSimcyp Limited (a Certara Company), Blades Enterprise Centre Sheffield, UK ; Manchester Pharmacy School, University of Manchester Manchester, UK.
Simcyp (United Kingdom) · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aimed to demonstrate the added value of integrating prior in vitro data and knowledge-rich physiologically based pharmacokinetic (PBPK) models with pharmacodynamics (PDs) models. Four distinct applications that were developed and tested are presented here. PBPK models were developed for metoprolol using different CYP2D6 genotypes based on in vitro data. Application of the models for prediction of phenotypic differences in the pharmacokinetics (PKs) and PD compared favorably with clinical data, demonstrating that these differences can be predicted prior to the availability of such data from clinical trials. In the second case, PK and PD data for an immediate release formulation of nifedipine together with in vitro dissolution data for a controlled release (CR) formulation were used to predict the PK and PD of the CR. This approach can be useful to pharmaceutical scientists during formulation development. The operational model of agonism was used in the third application to describe the hypnotic effects of triazolam, and this was successfully extrapolated to zolpidem by changing only the drug related parameters from in vitro experiments. This PBPK modeling approach can be useful to developmental scientists who which to compare several drug candidates in the same therapeutic class. Finally, differences in QTc prolongation due to quinidine in Caucasian and Korean females were successfully predicted by the model using free heart concentrations as an input to the PD models. This PBPK linked PD model was used to demonstrate a higher sensitivity to free heart concentrations of quinidine in Caucasian females, thereby providing a mechanistic understanding of a clinical observation. In general, permutations of certain conditions which potentially change PK and hence PD may not be amenable to the conduct of clinical studies but linking PBPK with PD provides an alternative method of investigating the potential impact of PK changes on PD.

Indexed as

CYP P450 genotypes and responseformulation effects on drug responseheart drug concentration and QTcPBPK linked PD modelstarget site concentrations and response

Identifiers

PMID25505415
PMCPMC4244809
OpenAlexW1963931468

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.