Abstract
The complexity of modern-day diseases often requires drug treatment therapies consisting of multiple pharmaceutical interventions, which can lead to adverse drug reactions for patients. A priori prediction of these reactions would not only improve the quality of life for patients but also save both time and money in regards to pharmaceutical research. Consequently, the drug-gene-pathway (DRUGPATH) meta-database was developed to map known interactions between drugs, genes, and pathways among other information in order to easily identify potential adverse drug events. DRUGPATH utilizes expert-curated sources such as PharmGKB, DrugBank, and the FDA’s NDC database to identify known as well as previously unknown/overlooked relationships, and currently contains 12,940 unique drugs, 3933 unique pathways, 5185 unique targets, and 3662 unique genes. Moreover, there are 59,561 unique drug-gene interactions, 77,808 unique gene-pathway interactions, and over 1 million unique drug-pathway interactions.
Highlights
The concept of “one disease, one drug” is no longer relevant when applied to complex chronic diseases where drug combination treatments can lead to adverse drug-drug interactions [1,2]
Prior analysis of gene expression in peripheral blood mononuclear cells [8] as well as the dynamic regulatory modeling of the neuro-endocrine-immune response to an exercise challenge [7,9] predicted that an initial inhibition of Th1 cytokines, followed by glucocorticoid receptor (GCR) inhibition, would bring patients from the chronic homeostatic regulation induced by neurotoxin exposure back to a more healthy, stable homeostatic regulatory state [7,9,10]
DRUGPATH is a meta-database that consolidates expert-curated sources in order to map the interactions between drugs, genes, targets, and pathways, of which there are currently ≈60,000 drug-gene interactions, ≈94,000 gene-pathway interactions, and over 1.2 million drug-pathway interactions
Summary
The concept of “one disease, one drug” is no longer relevant when applied to complex chronic diseases where drug combination treatments can lead to adverse drug-drug interactions [1,2]. The ability to predict all interactions of a given drug combination therapy would allow for more effective treatment regimens with fewer side effects, leading to better outcomes towards patient health. One example of a combination therapy is the use of enbrel and mifepristone as treatment course proposed by our group for Gulf War Illness (GWI) [5,6]. While subsequent analysis [5] suggested that the best treatment course may be accomplished using enbrel, a tumor necrosis factor alpha (TNFα) inhibitor [11], in combination with the antiglucocorticoid mifepristone [12], great precaution must be used due to the novelty of this treatment regimen as well as the chemical sensitivity found in patients with GWI
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