Evidence map›Paper›PMID 26312163›Full record

ArticleCPT: pharmacometrics & systems pharmacology2015

Using a Systems Pharmacology Model of the Blood Coagulation Network to Predict the Effects of Various Therapies on Biomarkers.

S Nayak, D Lee, S Patel-Hett, D D Pittman, S W Martin, A C Heatherington, P Vicini, F Hua

Open access · goldAbstract read
In one paragraph

Article in CPT: pharmacometrics & systems pharmacology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
1.5field-weighted citation impact, top 18% 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

12 citing papers in PubMed, 19 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Review of quantitative systems pharmacological modeling in thrombosis.Communications in information and systems · 2019
    Article
  10. Article
  11. A Six-Stage Workflow for Robust Application of Systems Pharmacology.CPT: pharmacometrics & systems pharmacology · 2016
    Article
  12. Systems Analysis of Thrombus Formation.Circulation research · 2016
    Review
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.

S NayakPharmacometrics, Global Innovative Pharma Business (GIPB), Pfizer Inc. Cambridge, Massachusetts, USA.
D LeeQuantitative Clinical Sciences, PharmaTherapeutics Clinical R&D, Pfizer Inc. Cambridge, Massachusetts, USA.
S Patel-HettRare Disease Research Unit Pfizer Inc. Cambridge, Massachusetts, USA.
D D PittmanRare Disease Research Unit Pfizer Inc. Cambridge, Massachusetts, USA.
S W MartinPharmacometrics, Global Innovative Pharma Business (GIPB), Pfizer Inc. Cambridge, Massachusetts, USA.
A C HeatheringtonQuantitative Clinical Sciences, PharmaTherapeutics Clinical R&D, Pfizer Inc. Cambridge, Massachusetts, USA.
P ViciniPharmacokinetics, Dynamics and Metabolism, New Biological Entities, Pfizer Inc. Cambridge, Massachusetts, USA.
F HuaQuantitative Clinical Sciences, PharmaTherapeutics Clinical R&D, Pfizer Inc. Cambridge, Massachusetts, USA.
Pfizer (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A number of therapeutics have been developed or are under development aiming to modulate the coagulation network to treat various diseases. We used a systems model to better understand the effect of modulating various components on blood coagulation. A computational model of the coagulation network was built to match in-house in vitro thrombin generation and activated Partial Thromboplastin Time (aPTT) data with various concentrations of recombinant factor VIIa (FVIIa) or factor Xa added to normal human plasma or factor VIII-deficient plasma. Sensitivity analysis applied to the model revealed that lag time, peak thrombin concentration, area under the curve (AUC) of the thrombin generation profile, and aPTT show different sensitivity to changes in coagulation factors' concentrations and type of plasma used (normal or factor VIII-deficient). We also used the model to explore how variability in concentrations of the proteins in coagulation network can impact the response to FVIIa treatment.

Identifiers

PMID26312163
PMCPMC4544053
OpenAlexW1501071350

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.