Evidence map›Paper›PMID 26143647›Full record

ReviewExpert review of clinical pharmacology2015

Pharmacokinetic modeling of therapies for systemic lupus erythematosus.

Xiaoyan Yang, Catherine M T Sherwin, Tian Yu, Venkata K Yellepeddi, Hermine I Brunner, Alexander A Vinks

Abstract readReview
In one paragraph

Review in Expert review of clinical pharmacology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.3field-weighted citation impact, top 40% 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

5 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Mechanism of Action and Efficacy of Immunosupressors in Lupus Nephritis.International journal of nephrology and renovascular disease · 2021
    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

6 authors at 4 institutions in 1 country.

Xiaoyan Yanga 1 Division of Pharmaceutical Sciences, School of Pharmacy, University of Missouri-Kansas City, Kansas City, MO, USA.
Catherine M T Sherwin
Tian Yu
Venkata K Yellepeddi
Hermine I Brunner
Alexander A Vinks
University of Utah · USCincinnati Children's Hospital Medical Center · USUniversity of Cincinnati Medical Center · USUniversity of Missouri–Kansas City · US

Funding

LCenter for Clinical and Translational Science and TrainingUL1TR001425 · NCATS · UNIVERSITY OF CINCINNATI · PI MEINZEN-DERR, JAREEN, STRAWN, JEFFREY ROBERT · 2015 to 2024
$37.5M
NCATS NIH HHS UL1 TR001425
6 · The paper itself

Abstract

With the increasing use of different types of therapies in treating autoimmune diseases such as systemic lupus erythematosus (SLE), there is a need to utilize pharmacokinetic (PK) strategies to optimize the clinical outcome of these treatments. Various PK analysis approaches, including population PK modeling and physiologically based PK modeling, have been used to evaluate drug PK characteristics and population variability or to predict drug PK profiles in a mechanistic manner. This review outlines the PK modeling of major SLE therapies including immunosuppressants (methotrexate, azathioprine, mycophenolate and cyclophosphamide, among others) and immunomodulators (intravenous immunoglobulin). It summarizes the population PK modeling, physiologically based PK modeling and model-based individualized dosing strategies to improve the therapeutic outcomes in SLE patients.

Indexed as

Models, BiologicalAnimalsHumansImmunologic FactorsImmunosuppressive AgentsLupus Erythematosus, SystemicImmunologic FactorsImmunosuppressive Agentsazathioprinecyclophosphamidemethotrexatemycophenolatepharmacokinetic modelingsystemic lupus erythematosus

Identifiers

PMID26143647
PMCPMC5575755
OpenAlexW1541202942

What OpenQuestion holds

Textmetadata
LicenceTDM
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.