| Research Article |
Open Access |
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| Computational Strategies for Drug Reprofiling |
| Serban San-Marina1,2*, Rajeesh Gupta2 and Ionel Iosif1 |
| 1University of Toronto, Canada |
| 2BiostatistiX, Canada |
| *Corresponding author: |
Dr. Serban San-Marina
University of Toronto, Canada
E-mail: s.sanmarina@biostatistix.com |
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| Received September 20, 2011; Accepted October 20, 2011; Published October
29, 2011 |
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| Citation: San-Marina S, Gupta R, Iosif I (2011) Computational Strategies for Drug
Reprofiling. J Proteomics Bioinform 4: 242-244. doi:10.4172/jpb.1000196 |
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| Copyright: © 2011 San-Marina S, et al. This is an open-access article distributed
under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the
original author and source are credited. |
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| Abstract |
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| This article introduces some of the recent developments in drug re-profiling with emphasis on how computational
chemistry and biology approaches together with access to public data bases can help generate new leads from
existing drugs. It discusses the drawbacks of high-throughput (HTS) genomics and how the concepts of target polypharmacology
can help speed up delivery of the next generation of drugs. Some of the successful strategies for drug
re-profiling are presented and computational tools are discussed. |
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| The Problem with HTS-Genomics |
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| Completion of the Human Genome Project promised to
dramatically speed up the discovery of new medicines. Knowledge of
the complete sequence of all protein coding genes meant that molecules
could now be designed to target specific amino acid sequences. At
about the same time, advances in combinatorial chemistry made
it possible to generate chemical libraries of increasing complexity
containing small, drug-like molecule. Combined with the pioneering
successes in molecular biology over the previous two decades, it is now
possible to synthesize any protein and pan an entire combinatorial
library against it in the hopes of finding specific, high affinity leads
for the next generation of drugs. When genomics and robotics joined
forces genomics high-throughput screening (genomics-HTS) was born
and quickly became the main thrust for lead development. A variety of
drugs were produced using genomics-HTS including imatinib mesylate
developed for the treatment of chronic myelocytic leukemia by Brian
Drucker at the University of Oregon. Gleevec dramatically changed
the course of a disease with grim prognosis for thousands of people
world-wide. However, as the number of New Drug Application (NDA)
submissions to the US Food and Drug Administration has remained
flat since the introduction of genomics-HTS relative to the period prior
to it these early successes did not translate into increased productivity.
The problem is not with the generation of sufficient well-targeted leads,
but with the high attrition rates of these leads, particularly in the clinic.
Specifically, these targeted molecules repeatedly violate Lipinsky's rule
of five requirements, for example by coming in at higher than optimal
molecular sizes [1]. The failure of HTS-genomics to significantly
increase productivity is only adding to the well-documented drug
pipeline crunch. The quadrupling of R&D expenditures over the last
25 years and the heavy reliance on revenues generated by a handful of
'block-buster' drugs with only a few years left in their patents threaten
dry out pharma pipelines in the near future. With insufficient funds
coming in will pharma afford the hefty R&D bill for new medicines?
Leading researchers suggest that a new generation of selectively
promiscuous drugs could solve the pipeline crisis [2]. Because of
shorter pipeline transit times and lower development costs re-profiling
promiscuous old drugs for new uses is an attractive alternative to the
traditional new chemical entity development. |
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| The re-profiling paradigm |
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| Roll back the clock by 20 years and the idea of drug re-profiling was
every bit of a heresy. Back then the distinction was clear between the
desirable or therapeutic effects of drugs and their undesirable side effects which were thought to be 'non-specific'. Therapeutic effects result when
drug molecules interfere with the activity of specific protein targets,
often through binding with good affinity to amino acid sequences
delineating three-dimensional cavities or pockets in enzymes. In the
absence of a good pharmacological explanation, pharmacokinetics
was blamed for the fact that not all patients with the same condition
benefited from the same drug. The status of the intended targets,
i.e. wild-type or mutated was not considered. Of substantially more
concern was the fact that a percentage of all patients receiving the same
medication experienced adverse side effects. Unfortunately, side effects
were usually coaxed in such broad terms that in general, they did not
suggest the involvement of separate mechanisms and specific targets.
It is difficult to pinpoint with certainty when the realization hit that
some of the side effects can be as 'specific' as the primary therapeutic
effects and can create opportunities for re-profiling. The re-profiling
of Viagra is often quoted as an example of how a `bust` was turned
into a `block-buster` but one of the earliest examples of successful reprofiling
was the re-branding of the anti-hypertensive drug Minoxidil
for the treatment of male-pattern baldness. From retinoic acid being
successfully re-profiled for the treatment pro-myelocytic leukemia, to
the use of thalidomide in the treatment of non-Hodgkin's lymphoma,
the rush to re-profile is on. |
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| Re-profiling: Plan A |
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| More recently, formalised approaches to drug re-profiling have
been initiated using computational tools developed to assess drug-drug
and target-target likeness. By analysing existing targets and ligands it
was thought that formal principles can be deduced to help predict the
nature and number of all potential targets and thus determine the size
of the druggable genome. Prior to sequencing the human genome up
to 10,000 drug targets were predicted but a post-sequencing 2002 study
set the number closer to ~3,000 [3]. By contrast, a recent study puts the
number of confirmed drug targets at only 218 [4]. These predictions were based on the amino acid sequences of the three most highly
represented targets used in drug development, namely G proteincoupled
receptors, kinases and nuclear receptors and on the similarities
between the common drug matter that binds to these proteins. In a
different approach taken by Han and colleagues a support vector
machines algorithm was used to map the physico-chemical features of
druggable targets, rather than their amino acid sequences and to derive
a list of predicted targets that are compliant with these features [5]. This
study sets the size of the druggable genome at 3379. What makes these
studies possible is the observation that similar proteins bind closely
related ligands and similar ligands bind to a constant set of targets.
Ligand similarity is routinely determined by the Tanimoto coefficient
which is the ratio of the common and the distinct features of any two
ligands and takes values between 0 (no similarity) and 1 (identity). The
SuperDrug and SuperLigand databases also allow 3D superimpositions
of ligands to determine which of the conserved side groups are
implicated in interactions with a given target. By using protein-protein
and ligand-ligand similarity searches, the existing drug-target (DT)
pharmacological space has been mapped. It displays every interaction
between all known drugs and targets. One remarkable feature of the DT
landscape is the extent of drug promiscuity. For example, the aminergic
GPCR family of D(2) dopamine receptors bind over 8,000 active
compounds, SRC kinases almost 1,800 and Protein Kinase C delta type
almost 200. Paolini and colleagues provide a simplified visual reference
guide to the DT landscape with additional information available by
request from these authors [6] while Yamanishi and colleagues used
the KEGG database to create their own map of the DT landscape [7].
Spreadsheets linking drugs to multiple targets and targets to multiple
drugs are available from these authors by means of a limited license.
The STITCH database is also freely available to the public without
registration and it provides a graphical interface that links multiple
drugs and targets. Another useful resource is the ID Map, a freely
downloadable Java application where MDDR and ASINEX libraries
data on ~600,000 compounds have been linked with assay bioactivity
data. This tool offers access to more than just FDA approved drugs but
does not link compounds to individual targets. |
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| Network poly-pharmacology |
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| Although these resources link drugs to multiple targets, it can
be a daunting task to identify diseases in which the activity of these
targets is up-regulated. With the possible exception of some of the
more important kinases, activity data is generally not available for
proteins across the wide spectrum of human diseases. Fortunately,
extensive experience with gene expression profile analysis over the
last decade shows that in general, gene expression data can be used as
a surrogate for protein activity. Nevertheless, targeting just one gene
product may not bring about the expected therapeutic benefit given
that diseases are rarely caused by a single aberrantly expressed protein
and more often involve a network of interacting proteins. Thus, what is
further required is some knowledge of network pharmacology. Cellular
networks of interacting proteins consist of hubs, which are hotspots
receiving multiple inputs, nodes which are individual non-hub proteins
and vertices that link hubs and nodes. Each hub is characterized by its
degree which refers to the total number of vertices that connect to it.
The so-called bottleneck hubs funnel the flow of network information
through a single connection to the hub of an adjacent network, while
non-bottleneck hubs have multiple connections to neighbouring
networks. Disrupting bottleneck hubs is the best strategy for disrupting a network. Having decided how to select which type of networks to
target for disruption how can one find disease networks to use for drug
re-profiling? To construct a network one would need to download
experimental microarray data. The NIH's Gene Expression Omnibus
(GEO) and the EMBL-EBI's Array Express contain the majority of
all publically available microarray data. Free gene expression analysis
software can be obtained from The Institute for Genomic Research
or similar organizations. The goal of the gene expression analysis
is to identify the set of up-regulated targets which will contain the
"druggable' genes. Once these genes are identified, programs such as
HiMap or STITCH can be accessed free of charge to build the network
of interacting genes. |
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| Re-profiling: Plan B |
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| Another way of using gene expression data is to convert the
information contained in microarray experiments into a set of
"standards" that will allow navigation of the genomic landscape for
the drug to be re-profiled by testing it against other existing profiles.
Profiles available in public databases include FDA-approved drugs,
other investigational compounds, knock-down siRNA experiments as
well as chronic diseases including most of the common malignancies.
Tools such as EXALT allow to test a query signature against all
signatures deposited in the NIH GEO public data base. Another
excellent tool is Cmap2 at the Broad Institute which compares an
uploaded profile to over 7,000 other profiles generated using FDAapproved
as well as some other chemicals commonly used in cellular
experimentation. The value of these tools consists in the fact that the
drug to be re-profiled is evaluated in terms of how similar it is to other
drugs, whose mechanisms of action are well understood and for which
the targets are well defined. In some cases however, the price paid for
being able to query this massive volume of microarray data is that some
of the matches may not be particularly informative. For example, it is
difficult to interpret the significance of a match between the drug of
interest and a subset of samples exhibiting a clinical condition or an
experimental alteration, such as hypoxia. Therefore, whenever possible
it is a good idea to compile a proprietary library of expression profiles
against which to pan the expression data for the re-profiled drug,
provided of course that a suitable algorithm to enable the comparison
is first selected. In short, similar gene expression profiles suggest
similar mechanisms of action and similar targets. Likewise, if the gene
expression data for the re-profiled drug matches the signature of a
knock-down experiment, the knocked down protein may constitute
a valid target for the drug. Using an algorithm based on correlation
analysis has the added advantage that positive as well as negative
correlations can be established. For example, if the re-profiled drug is
tested against a panel of human diseases, a high negative correlation
value (closer to -1) would suggest that the drug might be able to reverse
some of the symptoms associated with the disease. |
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| Conclusion |
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| Drugs helped us discover how the living body works and in turn
this knowledge has made the next generation of drugs more specific
and less dangerous. Each cycle of re-invention also absorbed the leading
scientific ideas of the time. Bioinformatics, computational chemistry,
network poly-pharmacology and drug promiscuity concepts could
transform drug research to the point that twenty years from now a
scientist looking back at drug development might just wonder how
"one gene, one drug, one disease" has dominated so much of twentieth
century pharmacology. |
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| References |
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- Mencher SK, Wang LG (2005) Promiscuous drugs compared to selective drugs
(promiscuity can be a virtue). BMC Clin Pharmacol 5:3.
- Hopkins AL (2008) Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol 4:682-690.
- Hopkins AL, Groom CR (2002) The druggable genome. Nat Rev Drug Discov
1:727-730
- Imming P, Sinning C, Meyer A (2006) Drugs, their targets and the nature and number of drug targets. Nat Rev Drug Discov 5:821-834.
- Han LY, Zheng CJ, Xie B, Jia J, Ma XH, et al. (2007) Support vector machines
approach for predicting druggable proteins: recent progress in its exploration
and investigation of its usefulness. Drug Discov Today 12:304-313.
- Paolini GV, Shapland RH, van Hoorn WP, Mason JS, Hopkins AL (2006)
Global mapping of pharmacological space. Nat Biotechnol 24 :805-815.
- Yamanishi Y, Araki M, Gutteridge A, Honda W, Kanehisa M (2008) Prediction of
drug-target interaction networks from the integration of chemical and genomic
spaces. Bioinformatics 24:i232-i240.
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