Abstract:Despite advances in modern medicine that led to improvements in cardiovascular outcomes, cardiovascular disease (CVD) remains the leading cause of mortality and morbidity globally. Thus, there is an urgent need for new approaches to improve CVD drug treatments. As the development time and cost of drug discovery to clinical application are excessive, alternate strategies for drug development are warranted. Among these are included computational approaches based on omics data for drug repositioning, which have a… Show more
“…The data suggest high reproducibility of transcriptomics-derived disease module proximity and traditional gene-based approaches. 24 , 25 , 35 , 36 We next discuss several candidate drug classes. Figure 3 Network medicine applied to AF drug repurposing (A) A subnetwork is shown to highlight the 54 candidate drugs associated with AF DEGs and their associated targets.…”
Section: Resultsmentioning
confidence: 99%
“… 21 , 58 A strength of our study is the biorepository of LA tissue cohort of patients with AF and control individuals used to generate the AF disease module compared with traditional disease-associated gene approaches in the literature. 36 , 58 …”
“…The data suggest high reproducibility of transcriptomics-derived disease module proximity and traditional gene-based approaches. 24 , 25 , 35 , 36 We next discuss several candidate drug classes. Figure 3 Network medicine applied to AF drug repurposing (A) A subnetwork is shown to highlight the 54 candidate drugs associated with AF DEGs and their associated targets.…”
Section: Resultsmentioning
confidence: 99%
“… 21 , 58 A strength of our study is the biorepository of LA tissue cohort of patients with AF and control individuals used to generate the AF disease module compared with traditional disease-associated gene approaches in the literature. 36 , 58 …”
“…For example, as shown in Figure 11, pitolisant, a drug approved for use in narcolepsy, has a drug target, the voltage‐activated potassium channel or hERG channel (KCNH2), which is contained within both the non‐ischemic cardiomyopathy disease module but also in the QT prolongation toxicity module. This shared target for (and implied proximity of) these modules would limit the likelihood that the drug could be repurposed for non‐ischemic cardiomyopathy 27 …”
Section: Network Medicine and Drug Target Identificationmentioning
confidence: 99%
“…This shared target for (and implied proximity of) these modules would limit the likelihood that the drug could be repurposed for non-ischemic cardiomyopathy. 27 One final note relates to identifying individual variations in disease modules that can inform more precise drug target identification. We addressed this issue in detail in a study of hypertrophic cardiomyopathy (HCM) patients.…”
Section: Network Medicine and Drug Target Identificationmentioning
Conventional drug discovery requires identifying a protein target believed to be important for disease mechanism and screening compounds for those that beneficially alter the target's function. While this approach has been an effective one for decades, recent data suggest that its continued success is limited largely owing to the highly prevalent irreducibility of biologically complex systems that govern disease phenotype to a single primary disease driver. Network medicine, a new discipline that applies network science and systems biology to the analysis of complex biological systems and disease, offers a novel approach to overcoming these limitations of conventional drug discovery. Using the comprehensive protein–protein interaction network (interactome) as the template through which subnetworks that govern specific diseases are identified, potential disease drivers are unveiled and the effect of novel or repurposed drugs, used alone or in combination, is studied. This approach to drug discovery offers new and exciting unbiased possibilities for advancing our knowledge of disease mechanisms and precision therapeutics.
“…To identify novel drug-repurposing opportunities in the pursuit of unconventional but more efficacious MS treatments, promising insights come from the emerging field of system network theory and its application to medicine, known as network medicine [ 25 , 26 , 27 ]. As computational methods evolve, network medicine increases its capability of capturing the genetic and molecular intricacy of human diseases, and dissecting how such complexity rules disease manifestations, prognosis and, importantly, therapy [ 28 , 29 , 30 ].…”
Multiple sclerosis is an autoimmune disease with a strong neuroinflammatory component that contributes to severe demyelination, neurodegeneration and lesions formation in white and grey matter of the spinal cord and brain. Increasing attention is being paid to the signaling of the biogenic amine histamine in the context of several pathological conditions. In multiple sclerosis, histamine regulates the differentiation of oligodendrocyte precursors, reduces demyelination, and improves the remyelination process. However, the concomitant activation of histamine H1–H4 receptors can sustain either damaging or favorable effects, depending on the specifically activated receptor subtype/s, the timing of receptor engagement, and the central versus peripheral target district. Conventional drug development has failed so far to identify curative drugs for multiple sclerosis, thus causing a severe delay in therapeutic options available to patients. In this perspective, drug repurposing offers an exciting and complementary alternative for rapidly approving some medicines already approved for other indications. In the present work, we have adopted a new network-medicine-based algorithm for drug repurposing called SAveRUNNER, for quantifying the interplay between multiple sclerosis-associated genes and drug targets in the human interactome. We have identified new histamine drug-disease associations and predicted off-label novel use of the histaminergic drugs amodiaquine, rupatadine, and diphenhydramine among others, for multiple sclerosis. Our work suggests that selected histamine-related molecules might get to the root causes of multiple sclerosis and emerge as new potential therapeutic strategies for the disease.
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