Evidence map›Paper›PMID 22859316›Full record

ArticleStatistics in medicine2013

Application of fractional factorial designs to study drug combinations.

Jessica Jaynes, Xianting Ding, Hongquan Xu, Weng Kee Wong, Chih-Ming Ho

Open access · greenAbstract read
In one paragraph

Article in Statistics in medicine, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
3.1field-weighted citation impact, top 9% 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

16 citing papers in PubMed, 73 citations in OpenAlex.

  1. Article
  2. Review
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  4. Review
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  6. Synergistic combinations ofFrontiers in pharmacology · 2024
    Article
  7. On efficiency of locallyJournal of applied statistics · 2024
    Article
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  13. Review
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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

5 authors at 1 institution in 1 country.

Jessica JaynesDepartment of Statistics, University of California, Los Angeles, CA 90095, USA.
Xianting Ding
Hongquan Xu
Weng Kee Wong
Chih-Ming Ho
University of California, Los Angeles · US

Funding

The Center for Systemic Control of Cyto-NetworksPN2EY018228 · NEI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI HO, CHIH-MING · 2006 to 2010
$8.2M
NEI NIH HHS PN2 EY018228NEI NIH HHS PN2EY018228
6 · The paper itself

Abstract

Herpes simplex virus type 1 (HSV-1) is known to cause diseases of various severities. There is increasing interest to find drug combinations to treat HSV-1 by reducing drug resistance and cytotoxicity. Drug combinations offer potentially higher efficacy and lower individual drug dosage. In this paper, we report a new application of fractional factorial designs to investigate a biological system with HSV-1 and six antiviral drugs, namely, interferon alpha, interferon beta, interferon gamma, ribavirin, acyclovir, and tumor necrosis factor alpha. We show how the sequential use of two-level and three-level fractional factorial designs can screen for important drugs and drug interactions, as well as determine potential optimal drug dosages through the use of contour plots. Our initial experiment using a two-level fractional factorial design suggests that there is model inadequacy and that drug dosages should be reduced. A follow-up experiment using a blocked three-level fractional factorial design indicates that tumor necrosis factor alpha has little effect and that HSV-1 infection can be suppressed effectively by using the right combination of the other five antiviral drugs. These observations have practical implications in the understanding of antiviral drug mechanism that can result in better design of antiviral drug therapy.

Indexed as

Drug CombinationsModels, StatisticalAcyclovirAntiviral AgentsDrug Resistance, ViralHerpes SimplexHerpesvirus 1, HumanHumansInterferon-alphaInterferon-betaInterferon-gammaRibavirinTumor Necrosis Factor-alphaAcyclovirAntiviral AgentsDrug CombinationsInterferon-alphaInterferon-betaInterferon-gammaRibavirinTumor Necrosis Factor-alpha

Identifiers

PMID22859316
PMCPMC3878161
OpenAlexW2031907936

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.