Evidence map›Paper›PMID 18058203›Full record

ArticleJournal of pharmacokinetics and pharmacodynamics2008

Exposure-response modeling using latent variables for the efficacy of a JAK3 inhibitor administered to rheumatoid arthritis patients.

Matthew M Hutmacher, Sriram Krishnaswami, Kenneth G Kowalski

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Article in Journal of pharmacokinetics and pharmacodynamics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing 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

36 citing papers in PubMed.

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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.

Matthew M HutmacherPharmacometrics Group, Pfizer Corporation, Ann Arbor, MI, USA. matt.hutmacher@a2pg.com
Sriram Krishnaswami
Kenneth G Kowalski

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Currently, no general methods have been developed to relate pharmacologically based models, such as indirect response models, to discrete or ordered categorical data. We propose the use of an unobservable latent variable (LV), through which indirect response models can be linked with drug exposure. The resulting indirect latent variable response model (ILVRM) is demonstrated using a case study of a JAK3 inhibitor, which was administered to patients in a rheumatoid arthritis (RA) study. The clinical endpoint for signs and symptoms in RA is the American College of Rheumatology response criterion of 20%--a binary response variable. In this case study, four exposure-response models, which have different pharmacological interpretations, were constructed and fitted using the ILVRM method. Specifically, two indirect response models, an effect compartment model, and a model which assumes instantaneous (direct) drug action were assessed and compared for their ability to predict the response data. In general, different model interpretations can influence drug inference, such as time to drug effect onset, as well as affect extrapolations of responses to untested experimental conditions, and the underlying pharmacology that operates to generate key response features does not change because the response was measured discretely. Consideration of these model interpretations can impact future study designs and ultimately provide greater insight into drug development strategies.

Indexed as

AlgorithmsAnti-Inflammatory Agents, Non-SteroidalArthritis, RheumatoidDose-Response Relationship, DrugEndpoint DeterminationHumansJanus Kinase 3Models, StatisticalNonlinear DynamicsRandomized Controlled Trials as TopicSoftwareAnti-Inflammatory Agents, Non-SteroidalJAK3 protein, humanJanus Kinase 3

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Registered trials

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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.