Vol. 25 no. 19 2009, pages 2466–2472
Network analyses in systems pharmacology
Seth I. Berger and Ravi Iyengar∗
Department of Pharmacology and Systems Therapeutics, Mount Sinai School of Medicine, One Gustave Levy Place,
New York, NY 10029, USA
Received on June 9, 2009; revised on July 22, 2009; accepted on July 26, 2009
Advance Access publication July 30, 2009
Associate Editor: Jonathan Wren
Systems pharmacology is an emerging area of pharmacology which
utilizes network analysis of drug action as one of its approaches.
By considering drug actions and side effects in the context of the
regulatory networks within which the drug targets and disease gene
products function, network analysis promises to greatly increase
our knowledge of the mechanisms underlying the multiple actions
of drugs. Systems pharmacology can provide new approaches
for drug discovery for complex diseases. The integrated approach
used in systems pharmacology can allow for drug action to be
considered in the context of the whole genome. Network-based
studies are becoming an increasingly important tool in understanding
the relationships between drug action and disease susceptibility
genes. This review discusses how analysis of biological networks
has contributed to the genesis of systems pharmacology and
how these studies have improved global understanding of drug
targets, suggested new targets and approaches for therapeutics,
and provided a deeper understanding of the effects of drugs. Taken
together, these types of analyses can lead to new therapeutic options
while improving the safety and efficacy of existing medications.
Translational medical sciences aim to convert our understanding
of biological mechanisms into effective ways of treating and
preventing diseases. Such understanding has enabled us to move
orally. For example, the use of surgery to treat peptic ulcers
has greatly declined as use of Histamine-2 receptor blockers
and proton pump inhibitors gained popularity (Towfigh et al.,
2002). As our understanding of complex diseases has grown, it is
becoming increasingly feasible to design orally delivered drugs for
many pathophysiologies. Drug discovery has also been enabled by
combinatorial chemistry that allows for the facile synthesis of many
High-throughput screening technology facilitates the use to these
compound libraries to identify leads that can be further developed
into useful therapeutic agents. As technological limitations in drug
discovery are reduced, there still exist challenges in identifying
appropriate targets for complex diseases. This challenge arises from
∗To whom correspondence should be addressed.
our incomplete knowledge of the functions of biological systems
including those involved in human physiology and disease. New
experimental technologies are generating massive amounts of data
that characterize complex biological systems. Extracting knowledge
from these large datasets requires both bioinformatics and extensive
computational analysis. There are many types of computations that
aid in knowledge extraction. It seems clear that such computational
analysis will be a part of all biomedical sciences.
Systems pharmacology is an emerging field that uses both
experiments and computation to develop an understanding of
drug action across multiple scales of complexity ranging from
molecular and cellular levels to tissue and organism levels. By
integrating multi-faceted approaches, systems pharmacology can
provide mechanistic understanding of both the therapeutic and
adverse effects of drugs. This includes understanding of how drugs
act in different tissues and cell types, as well as the issues of
multiple actions within a single cell type due to the presence of
several interacting pathways. Such studies are important from a
predict adverse events and can improve the safety and efficacy of
existing drugs. These goals have become increasingly important in
the current practice of medicine as many therapeutic challenges deal
with complex diseases such as cancers, psychiatric disorders and the
In previous decades, there has been considerable success in the
development of targeted therapies for diseases with single targets,
such as Fabry’s disease. Additionally, there are many successful
ways to treat some complex diseases such as hypertension and
inflammatory diseases like arthritis. However, such treatments have
been developed empirically and it is not entirely clear why certain
drugs are effective in certain patients. Systems pharmacology, if
successful, should provide for a general understanding of drug
action in individual patients. This includes the balance between
the therapeutic action of a medication and the unintended adverse
A general understanding of drug action requires a systems level
view rooted in the human genome. Implicit in such understanding
of drug action is also the knowledge of how complex diseases
originate in the context of the whole genome of an individual. This
type of understanding will come from various sources of data such
as physiological, biochemical and genomic parameters. Integrating
these datasets requires an array of computational approaches. One
particularly valuable approach is the use of network analysis of
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Network analyses in systems pharmacology
Fig. 1. Different relationships between nodes in networks used in systems pharmacology. In systems pharmacology, networks can be used to understand the
relationship between drugs, their targets and diseases. Nodes can be genes, proteins, small molecules, drugs, diseases or any other biological entity capable
of interacting in the system of interactions being modeled. Edges can be directed or undirected, weighted or not weighted and can represent direct physical
interactions, activation, inhibition, coregulation or any other relationship between the nodes. Using networks centered on dasatinib, a tyrosine kinase inhibitor
used to treat chronic mylogenous leukemia as an example, depicted above are small sub-networks demonstrating different types of nodes, represented by
shaded shapes and edges, represented by lines. (A) Nodes are proteins connected by physical interactions found in literature. Diamond shaped nodes are
annotated as targets of dasatinib and interacting proteins shown as octagons physically interact with at least two of the drug targets. (B) Oval nodes are drugs
connected by shared targets within two steps of dasatinib, shown in a triangle. Dasatinib connects directly to other tyrosine kinase inhibitors. Imatinib also
connects to clusters of drugs which interact with the ABCB1 and ABCG2 drug efflux pumps. (C) Oval nodes are drugs connected by sharing a therapeutic
indication as described by the Anatomical Therapeutic Chemcial Classification System (ATC) third level codes. Within two steps of dasatinib shown in a
triangle, the network forms three connected clusters. The cluster on the left of anti-neoplastic agents is connected to the cluster of anti-inflammatory agents on
the right through celecoxib which has both indications. Both these clusters have connections to drugs above them in which are topical acne treatments. Drug
targets and indication codes were taken from Drugbank (Wishart et al., 2008) and the protein interaction network was taken from Genes2Networks (Berger
et al., 2007). These examples demonstrate different ways that one can study different aspects of the same drug using different types of networks.
cellular systems. In this review, we describe current network-based
approaches for a global understanding of drug action.
2 WHY USE NETWORK ANALYSIS IN SYSTEMS
Network approaches in biology have proven to be useful for
organizing high-dimensional biological datasets and extracting
meaningful information.Anetwork is a way of representing datasets
or any other entity capable of interacting in the system being
modeled, are connected by edges, which represent the nature of
the interaction. Nodes and edges can have various attributes and
annotations. Depending on the nature of the study, interactions can
be experimentally determined physical and chemical interactions,
genetic regulatory interactions, higher order relationships such as
co-expression or some other shared property linking the nodes.
Edges represent these interactions between the nodes and, when
the information is available, edges can have directions, weights
and other attributes that provide information about the hierarchy
of effects. Different relationships between nodes which can be
used to study drug are depicted in Figure 1. These three examples
feature dasatinib, a tyrosine kinase inhibitor used to treat chronic
mylogenous leukemia. A protein interaction-based approach shown
in Figure 1Ashows the relationship between the targets of dasatinib
and their interacting proteins.Anetwork connecting drugs based on
to other tyrosine kinase inhibitors which are also connected to
drugs which interact with efflux pumps such as the multi-drug
resistant transporters that pump drugs out of cells. Figure 1C
shows a different network which connects drugs based on shared
S.I.Berger and R.Iyengar
indications. Dasatinib falls in a cluster of antineoplastic agents. This
cluster is connected to anti-inflammatory agents through celecoxib
which has both indications. Both these clusters have drugs which
share indications with topical acne treatments. These examples
demonstrate how different network views of drug relationships can
highlight different aspects of the same drug.
Network data structures are amenable to many sophisticated
forms of computational analysis which can uncover important, non-
obvious, properties of nodes and the relationships between them.
Networks allow integration of diverse sources of experimental
data and biological knowledge into a framework which provides
new insight into the systems. These approaches can combine
genome scale datasets with information about specific genes and
proteins. In recent years, studies of metabolic networks, gene
regulatory networks, protein–protein interaction networks and other
biological networks have provided insight into the origins of
overall cellular behaviors and evolutionary design principles, as
well as more focused fields of study concerning specific cell
biological processes or diseases. From these analyses, one can
devise experimentally testable hypotheses ranging from prediction
of novel functions of specific genes to genome scale properties
of human cellular networks. In a similar manner, analysis of
networks for pharmacologic studies has the promise of allowing
for the identification of new drug targets for many diseases, better
understanding of what makes a good drug target, improved ability
to predict effective drug combinations and drug adverse events.
These studies contribute to shifting the paradigm of drug action
from a relatively simple cascade of signaling events downstream
of a target to a coordinated response to multiple perturbations of
the cellular network. As illustrated by Figure 2, network studies in
systems pharmacology can be divided into three main categories
based on the type and scale of data being studied and the type of
information sought: networks for global views of drug relationships,
are discussed below.
3 NETWORKS FOR GLOBAL VIEWS OF DRUG
RELATIONSHIPS: WHAT MAKES A GOOD
Networks provide global views of the relationships between nodes
and thus allow for network properties to be calculated based on a
node’s connections and relationship to the rest of the network. For
with. Studies of many biological networks, often protein–protein
interaction or gene regulatory networks, have focused on the degree
nodes which connect to relatively many other nodes (Barabasi and
Albert 1999; Jeong et al., 2000). These are often important for the
functioning of multiple biological processes (Jeong et al., 2001).
Similarly, several different measures of node centrality have been
network (Jeong et al., 2001; Jovelin and Phillips, 2009; Vinogradov,
2009; Wang et al., 2008). Other studies have looked at network
motifs, small sub-graphs of three to five nodes which have patterns
of connectivity overrepresented in the network (Milo et al., 2002).
These have shed light onto the information processing capabilities
of the network and suggested mechanisms of network evolution
A Networks for Global Views of
B New Drug Target Studies
C Studies of Current Drugs
Fig. 2. Types of network studies in systems pharmacology. Network studies
in systems pharmacology can be grouped into three broad categories.
(A) Global drug network studies that incorporate information about many
types of drugs and biological datasets such as protein–protein interaction
data can generate network properties of drug targets. These properties
give information about historical drug development trends and can suggest
properties of what makes a target druggable. (B) Disease specific network
specific diseases and drugs can identify new indications for drugs, unknown
targets of drugs, and other potentially interesting properties of the drugs.
Wuchty et al., 2003). Networks connecting drug targets based on
the chemical similarity of their ligands have been shown to have
also important topological attributes distinct from the attributes of
networks based on structural similarity of the proteins (Hert et al.,
2008). Properties such as these are interesting as they represent non-
obvious, but intrinsically important, attributes of a gene, protein or
set of proteins, that arise from its interactions and position in the
cellular network topology. Applying studies such as these to drugs
and targets of drugs can allow for more rapid identification of some
of these non-obvious network properties that define potentially good
drug targets, proteins which can interact with a drug to provide a
Several studies have looked at network properties of drug targets.
Ma’ayan et al. constructed a bipartite network connecting drug
Network analyses in systems pharmacology
targets and drugs and subsequently analyzed the targets in the
context of a global protein–protein interaction network (Ma’ayan
et al., 2007). This initial work showed that drug targets are not
randomly distributed throughout the cellular interaction network.
For example, drug-targeted proteins frequently have annotated
localization to the cellular membrane. This is reasonable as this
localization provides easier access to the target for the drug and
demonstrates that importance in a biological process is not the
only property that is required for a good drug target. Yildirim
et al. constructed a similar bipartite network of drugs and targets,
but used it to generate projections (Yildirim et al., 2007). In one
projection, nodes are drugs and are connected if they share a
common target. Analysis of this network as drugs are added in
order of development demonstrated that most new drugs interact
few drugs entering the market with novel targets. In the other
projection, nodes are the drug targets, the cellular components
which interact with a drug. These are connected by edges if they
are affected by a common drug. Overlaying this with a protein–
protein interaction network allowed several observations of network
properties of drug targets. For example, drug targets tend to have
a higher degree than other nodes in the protein–protein interaction
network. This means that drug targets, on average, participate in
to be essential for viability as defined by mouse knockout studies.
Nacher et al. also constructed a bipartite graph, but theirs connected
drugs to their therapeutic indications (Nacher and Schwartz, 2008).
The authors made projections of this graph to make a network
of drugs connected if they are used for similar indications and a
network of diseases connected if they are treated with the same
drugs. From this, the authors found diseases clustered based on their
treatments as well as treatments clustered based on the diseases.
Various measures of network centrality were used to calculate the
topological importance of the drugs in the network and found that
involving drugs was studied by Yeh et al. (2006). In this study, a
network of antibiotics was constructed based on experiments where
edges connected antibiotics that either worked synergistically or
antagonized each other in inhibiting the growth of Escherichea coli.
This network allowed drugs to be grouped into classes that were
either mutually synergistic or antagonistic and showed that drugs
with similar mechanisms could be grouped into classes using this
From these analyses, one can begin to formulate a set of network
criteria that define a good drug target and allow for selection of new
drug targets from the network. For example, measures of network
centrality can relate to the importance of a node and thus ability
to disrupt a biological process of interest. However, since too much
properties of a node can also suggest whether or not a protein may
serve as a good drug target. Hwang et al. propose an approach for
identifying ‘bridging nodes’as potential drug targets (Hwang et al.,
2008b). These node are not core regulators of important pathways
and thus not likely to have major global effects and drug side effects
when targeted. However, they are situated in the network topology
modules of interest and thus specifically target the disease process
effectively. Given that nodes in networks can thus be identified as
potential drug targets, ideally networks for specific diseases must be
constructed to find targets useful in treatment of these diseases.
4 NEW DRUG TARGETS: IDENTIFICATION AND
In addition to global cellular network studies, network analysis has
proven useful for more focused studies investigating a particular
disease or pathophysiology. It has been shown that proteins which
interact with each other are frequently involved in a common
biological process (Luo et al., 2007). Similarly, interacting proteins
can be involved in a disease process (Goh et al., 2007; Ozgur et al.,
2008). Approaches based on this idea have led to several candidate
gene prediction tools which use the relative location in a network
and node network properties to suggest proteins topologically
related to a set of disease genes (Berger et al., 2007; Chen et al.,
2009; Kohler et al., 2008). From a pharmacological point of view,
any new candidate disease gene, which is suspected of playing
a role in a pathophysiological process, can also be considered a
candidate drug target for modifying that disease process. To this
end, specific networks around several disease processes have been
constructed. These include asthma, schizophrenia, cardiovascular
disease, various cancers and various infectious diseases (Camargo
et al., 2007; Chu and Chen, 2008; Durmus et al., 2009; Fatumo
et al., 2009; Hwang et al., 2008a; Hyduke et al., 2007; Lim et al.,
2006; Raman and Chandra, 2008; Tanaka et al., 2008). Whether
these are constructed by careful literature review, generated from
high-throughput experiments or computationally extracted from a
larger network, these disease centered networks provide insights
into the specific disease processes and therefore can suggest novel
Often disease or pathogen-related networks are amenable to
properties of druggable targets. Singh et al. examined the metabolic
network of E.histolytica, a pathogen causing amoebiasis (Singh
et al., 2007). They performed choke point analysis, an approach
which identifies enzymes that either uniquely produce or consume a
cause a lethal inability to produce an essential metabolite or toxic
accumulation of another metabolite in the pathogenic organism.
While without detailed kinetic information a true rate limiting step
can not be identified, this analysis suggests that network topology
alone is enough to propose reasonable drug targets. Sridhar et al.
have suggested several other approaches to identifying drug targets
in metabolic networks with a focus on minimizing potential side
effects (Sridhar et al., 2007, 2008). They developed an algorithm
for identifying a set of enzymes, which when inhibited blocks
production of a desired set of target metabolites while minimizing
effects on other metabolites. Similar approaches have been applied
by Ruths et al. on biological signaling networks (Ruths et al., 2006).
an algorithmic solution, they identify all the proteins affected by
targeting a specified node in the network. This allows one to find
nodes that inhibit a specified biological process while leaving others
decreasing the risk of undesired side effects.
When a particular potential target is identified in a biological
network, there are often multiple different approaches to targeting it
therapeutically. For example, a drug binding a specific conformation
S.I.Berger and R.Iyengar
conformation. While a purely interaction-based network does not
contain the information to make this distinction, incorporation of
rate constants and other kinetic information can allow more detailed
analysis. Stites et al. performed dynamical model analysis of the
Ras signaling network in cancer and wild-type cells (Stites et al.,
2007). Their ordinary differential equation model was then used to
compare the effect of hypothetical drugs which inhibits the GTP-
bound Ras, GDP-bound Ras or both forms of Ras.The results of this
demonstrated that only the hypothetical drug which preferentially
binds GDP-bound Ras would have the desired effect of decreasing
Ras effector activity in cancer cells more than in the healthy
Another promising area in network-based prediction of drug
targets is the ability to predict combinations of targets, or protein
complexes, which will prove to be most efficacious and safe
when targeted together. Since networks emphasize the relationships
between proteins, they can suggest pairs or groups of drug targets
that can work well together to treat a disease. Ruths et al. formulate
this as the minimum knockout problem where they identify the
set of nodes that are required to be removed from a signaling
downstream effectors (Ruths et al., 2006). Similarly, Dasika et al.
formulate a solution to the Min-Interference problem (Dasika et al.,
nodes needed to be disrupted in order to block an undesired process,
but it also aims to preserve desired ones. These approaches will
allow combinations of drugs against these targets to be developed
where neither drug alone would block a process, but together the
drugs would work. The global understanding of diseases and drug
effects based on network relationships between drug targets can
also allow repurposing of existing drugs with known targets for
use in combination therapies for different diseases. Such reuse of
existing drugs has the advantage that their individual safety has
already been established. So, combination therapy using previously
approved drugs could lead to safer and more efficient treatments.
Such an ability to better understand and predict synergistic effects
of drug combinations is one of the more powerful goals of systems
5 CURRENT DRUGS: IMPROVED
UNDERSTANDING OF THEIR USE AND
effects of clinically used pharmaceuticals. Various approaches can
identify previously unknown targets of the drug, pathways affected
by the drug and pharmacogenomic factors affecting the usage of the
drug. These can in turn be used to explain off-target effects, adverse
events or suggest additional indications or contraindications for the
usage of a drug.
While many drugs have known therapeutic targets, many drugs
that are currently used work through unknown mechanisms.
Furthermore, even drugs with a known target often have ‘off-target’
effects. These are effects, often undesirable, of a drug which can
not be explained through its interaction with its primary targets. For
example, many drugs can cause cardiac arrhythmias by blocking
target or indication for these drugs (Hoffmann and Warner, 2006).
Network studies of drugs have allowed identification of some of
these secondary targets of drugs. Campillos et al. constructed a
similarity and similar side effect profiles (Campillos et al., 2008).
By identifying pairs of drugs in this network that have distinct
targets, the authors were able to assign the targets of one drug to
the drugs it was connected to and subsequently validate the binding
of the drug to its predicted secondary target. In another network
study with similar goals performed by Iorio et al., a network of
drugs was connected based on similarity of gene expression profiles
after treatment of cells with drugs (Iorio et al., 2009). They were
able to identify regions in this drug network which consisted of
medications with similar modes of action. Another approach to
identifying drug modes of action using expression datasets was
diverse set of gene expression datasets to create a network model of
the regulator influences between genes. Using microarray data from
test treatments with a drug, their network model can distinguish the
targets of the treatment from changes downstream of these targets.
Another way drug targets can be linked together into a network
involves a chemoinformatic approach. Keiser et al. developed
a method of scoring the similarity between the sets of ligands
for different receptor. The authors use this score to construct a
network of receptors connected together if they bind structurally
similar ligands (Keiser et al., 2007). This analysis showed
that many biologically related drug targets clustered together
by ligand similarity even though the targets themselves have
minimal sequence similarity. Using this approach, the authors made
specific predictions such as suggesting that methadone interacts
with Muscarinic-3 receptors and experimentally validated the
predictions. This type of analysis based on the ability of different
targets to bind the same ligand allowed for the identification of
off-targets for certain drugs.
Drug action is not only related to the targets of the drug, but
can also be affected by variations in metabolic enzymes, transporter
proteins and downstream effects of drug action. The field of
pharmacogenomics identifies genetic variations that can change
drug effects. Network analyses can contribute to identification
of such pharmacogenes, genes which modulate the response to
a drug. Hansen et al. constructed a classifier which considered
each gene in the context of a gene–gene–drug interaction network
(Hansen et al., 2009). Physical protein–protein interactions were
used to approximate functional modules. The physical and genetic
interaction of genes with drugs, along with the structural and
indication similarities between drugs, were used to rank genes
based on their likelihood of affecting the pharmacology of a
drug of interest. This allowed prediction of known and novel
In addition to identifying unknown targets of drugs and
pharmacogenes, network-based approaches can suggest potential
alternative uses of drugs. Macpherson et al. (2009) developed
software to examine networks in context of various annotations.
The authors tested this software by studying a network consisting of
interactions between human proteins and HIV infections as well as
drugs and their targets. Analysis of this network suggested possible
mechanisms by which several different types of drugs, ranging from
antineoplastic and immunosuppressive drugs to statins, might be
useful in treating HIV-1 infections. Qu et al. (2009) developed
a technique to integrate information about drugs, treatments and
Network analyses in systems pharmacology
Classic View of Drug Action
Systems Pharmacology View of Drug Action
Fig. 3. Changing views of drug actions. Network studies in systems
pharmacology are changing the way we think about the actions of drugs. (A)
The classic view of drug action has a drug that interacts with a therapeutic
target which transduces its signal through its effector pathway to mediate a
therapeutic effect and certain side effects.Additional off-targets can mediate
effects through distinct effector pathways to lead to other side effects and
other adverse events. (B) In a systems pharmacology view of drug action,
a drug interacts with multiple primary and secondary targets. These targets
exist within a complex network which can mediate the response to the drugs
leading to both the therapeutic and adverse effects.
diseases into a Disease–Drug Correlation Ontology. By querying
the complex network structure surrounding the disease Systemic
Lupus Erythematosus, they predicted which drugs might modify
the course of the disease. Their top prediction, the selective
estrogen receptor modulator Tamoxifen, is an experimentally
studied, promising candidate for the treatment of Lupus. Another
study, by Bromberg et al. (2008) investigated the transcription
factors activated downstream of cannabinoid receptor 1. A network
was constructed between this receptor, which is a known target
of certain analgesics, antiemetics, as well as drugs of abuse and
downstream activated transcription factors involved in neuronal
differentiation. This network allowed key regulators of the pathway
to be elucidated and suggested additional targets which might
synergize with effects of cannabinoid agonists to have potential
therapeutic effects such as a role in neuroregeneration.
Systems pharmacology studies are changing the way we think
about drug actions as shown in Figure 3. Whereas originally
medications were thought to hit a specific target and modulate
effects through pathways downstream of that target, we now know
that many drugs hit multiple targets, each of which exist within
a complex network. The effects of the drug, both therapeutic
actions and adverse events, are therefore a result of perturbation
of the complex network landscape. This was suggested in a study
by Xie et al. (2009) where structural homology between protein
drug binding sites was used to predict potential off targets of
Cholesteryl Ester Transfer Protein Inhibitors. Incorporating these
predicted off-targets into a network of metabolic, signaling and gene
regulatory pathways explained the different side-effect profiles of
drugs in this class. Variations from person to person in the cellular
networks due to environmental factors and genetic variation will
and susceptibility to adverse events and side effects. Furthermore,
interacts with its targets, and later effects as downstream signaling
adjusts transcriptional regulation and leads to network rewiring
resulting in delayed physiological action.
Network approaches allow biomedical researchers to rapidly
organize current knowledge by integrating different types of large
datasets. Applying such approaches to problems in pharmacology
can allow systems level descriptions of drug action, rapid
identification of novel therapeutic strategies and potentially safer
and more effective drugs development and prescription. Network
complex diseases which can not be understood in terms of a single
target or single pathway model. Furthermore, it allows for the
prediction and explanation of unexpected effects of medications
and suggests factors influencing drug efficacy and safety. Currently,
network approaches in systems pharmacology are in their infancy.
be generated. These will incorporate not only additional drug target
relationships, gene expression relationships and physical interaction
and protein localization. Each type of information incorporated into
these networks will allow new predictions and understanding of
different aspect of pharmaceuticals.
The future of systems pharmacology will require more than
additional analysis of existing and updated datasets. New
experimental approaches to study drug action broadly at the
biochemical as well as physiological levels are needed. Current
datasets used to build networks need to be greatly improved with
the addition of data specifying tissue expression levels of proteins,
localization information, as well as directionality and strength of
interactions. Quantitative experimental techniques, such a surface
plasmon resonance-based technologies, will facilitate these studies.
targets at a time. A new wave of technology development is needed
to move quantitative drug–target analysis into high-throughput
formats. Moreover, in order to become a truly translational field, the
results of network studies need to find a path to clinical relevance.
to enhance post market drug surveillance data to better understand
and predict drug adverse events. In the end, the true impact of
network analyses will be measured by how information gleaned
through systems pharmacology analyses will lead to improved
drug discovery, safer and more effective drug prescriptions, and
ultimately lead to safe, cost effective and effectual patient care.
S.I.Berger and R.Iyengar Download full-text
We thank Avi Ma’ayan for relevant discussions and Simon Hardy,
Aislyn Wist, and the anonymous reviewers for comments.
Funding: National Institutes of Health (grants DK038761); Systems
Biology Center New York (grant P50GM07558). Pharmacological
Sciences Training (grant GM062754 to S.B.).
Conflict of Interest: none declared.
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