ArticleJournal of developing drugs2013
DDDAS Design of Drug Interventions for the Treatment of Dyslipidemia in ApoE
Article in Journal of developing drugs, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed, 5 citations in OpenAlex.
- Understanding the Causes and Implications of Endothelial Metabolic Variation in Cardiovascular Disease through Genome-Scale Metabolic Modeling.Frontiers in cardiovascular medicine · 2016Review
- BSN723T Prevents Atherosclerosis and Weight Gain in ApoE Knockout Mice Fed a Western Diet.WebmedCentralArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
Computational models of complex systems, such as signaling networks and biological systems, can be used to explain the behavior of such systems under various conditions. The large number of integrated processes and variables, and the nonlinearities inherent in the fundamental processes, make it difficult for scientists unassisted by computer simulations to effectively predict the consequences of a particular intervention. For this reason, computer simulation has become an important tool for generating hypotheses about the behavior of these systems that can then be tested in the laboratory and clinic. A dynamic data-driven application simulation (DDDAS) was designed by Biospherics to model complex metabolic disease pathways by testing potential binary therapies in simulations at various combinations of two points in the pathways. Since DDDAS chooses the most effective pair-wise combinations, this data-driven system allows for the implementation of real-time data to model or predict a measurement or event. By incorporating data dynamically rather than statically, the predictions and measurements become more reliable. Dyslipidemia, a common precursor to atherosclerosis, can be manifested by high triglycerides, increased apolipoprotein (Apo) B, high levels of LDL, and low levels of HDL. SPX106 and D-tagatose is a combination drug therapy composed of a carbohydrate (D-tagatose) and SPX106. D-tagatose has been studied for the treatment of diabetes for several years, and has the ability to lower blood insulin levels and to decrease glycogen formation. SPX106 is a natural substance that accelerates lipid catabolism and inhibits dyslipidemia. In apolipoprotein E knockout mice (ApoE
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Registered trials
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.