Examples are not even closer to the genes specifically

Examples as analysis of protein interaction network
for targets of FDA approved drugs and genes related to disease in OMIM revealed
that most drug targets are not even closer to the genes specifically involved
in disease ref 3 and hence reflects the lack of selectivity in traditional
drugs towards the genetic cause. Besides, biasness of literature-mined
interaction sets towards well-known proteins, dependence of current approach on
target profile similarity or identification of shortest path between drug
targets in the interactome ref 6-11 has proved to be less efficient in the analysis of relationship between
drugs and disease ref?.

 

However, an interdisciplinary approach like the
ones used by ref 4, 5 has reflected its efficiency to predict novel targets
and other uses of the existing drugs through network-driven knowledge. In
addition, recent findings have demonstrated that genes associated with a
disease, tend to cluster in a disease module and represent a connected
sub-network within the interactome ref 12, 14. This led us to think, that for
a drug to be efficient enough to cure a disease, it must target proteins within
or in the immediate vicinity of the corresponding disease module formed by the well-associated
genes. Hence, to understand therapeutic action of drugs at different levels of biological
organization, we developed an unsupervised and unbiased network-driven
framework to come-up with a drug-disease proximity measure that would help us to
quantify the therapeutic effect of drugs.

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In this study, we took Picroliv to develop an
initial understanding of the context within which molecular-level drug-target
interactions can lead to distal effectors in a process that result in adverse
phenotypes at the organ and organismal levels. Picroliv, is one of the active compounds
yielded by underground parts of Picrorhiza kurroa, growing at elevations of 3,000-5,000 meters. It is usually a mixture of
kutkoside and picroside-I in 1:1.5 ratio. While the other major product
synthesized by the underground part is kutkin composed of picroside-I and
picroside-II.

The
active principal of Picrorhiza kurroa is kutkin
comprehend kutkoside and the
iridoid glycoside picrosides I, II. Picroside I, also known as 6′-O-cinnamoylcatalpol, forms
a stable mixture with kutkoside to form
kutkin 1. Another
isolated catalpol derivative, identified
as 6-O-vanilloylcatalpol, was named
Picroside II 2. Traditionally
picrorhiza has been used to treat
disorders of the liver and upper respiratory tract, dyspepsia, chronic
diarrhea, and scorpion sting. Studies on picrorhiza
show its crucial role in restoring the
depleted glutathione levels in rats infected with malaria 3. Further studies on picrorhiza reveal its anti-lipid peroxidative
effect 5. Recent
studies show that Picroside II plays a critical role in preventing the
alterations that take place in I/R injury
6. Although the
antihepatitic activity of picrorhiza has
been exploited, its exact molecular mechanisms of actions and related pathways
and targets remains poorly understood.

 

To
achieve the desired therapeutic effect while reducing the risk of unpropitious
conditions, with a known drug, it is imperative to identify the neighborhood of
these targets within which have their action. For example, proceeding with
rosiglitazone and peroxisome proliferator-activated receptor ? (PPAR?) as the drug and target
respectively, we can have prostaglandin synthase 2 (PTGS2), plasminogen
activator inhibitor (SERPINE1), vascular endothelial growth factor A (VEGFA),
mitochondrial translocator protein (TPSO), metalloproteinase 9 (MMP9),
interlukin-6 (IL-6), caspase 3 (CASP3), and carbonic anhydrase 2/4 (CA 2/4) as
a series of important PPAR?-regulated effectors. Several of these effectors are
associated with myocardial infarction and may be responsible for the discovered
relationship between myocardial infarction and rosiglitazone. Consequently,
using information from known drug target and creating networks of associated target
proteins; we can understand how drugs can have beneficial and pernicious
consequences 7.

 

To
decipher the regulatory interactions and underlying mechanistic behavior of picrorhiza, a target-pathway
network construction was performed to discover the relationship between the
drug and its relevant target and pathways. Construction and analysis of such
intricate network not only requires the basic concepts of network biology but
also an understanding of how the interaction between drug and its relevant
target determines regulation of various phenotypic characters in a diseased state. Besides the direct consequences
of the interaction between drug and its target, drug action also depends on the
consequences within the physiological system.

 

As
stated earlier, integration of concepts from various fields can help to reach the
best solution for a given problem. Hence, we integrated advanced application of computational and experimental systems
biology approaches to pharmacology in our work to build networks for analyzing
drug action and to develop poly-pharmacology for complex diseases and predict therapeutic
efficacy and adverse event risk for individuals prior to commencement of
therapy.