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WIREs Syst Biol Med
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In silico methods for drug repurposing and pharmacology

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Data in the biological, chemical, and clinical domains are accumulating at ever‐increasing rates and have the potential to accelerate and inform drug development in new ways. Challenges and opportunities now lie in developing analytic tools to transform these often complex and heterogeneous data into testable hypotheses and actionable insights. This is the aim of computational pharmacology, which uses in silico techniques to better understand and predict how drugs affect biological systems, which can in turn improve clinical use, avoid unwanted side effects, and guide selection and development of better treatments. One exciting application of computational pharmacology is drug repurposing—finding new uses for existing drugs. Already yielding many promising candidates, this strategy has the potential to improve the efficiency of the drug development process and reach patient populations with previously unmet needs such as those with rare diseases. While current techniques in computational pharmacology and drug repurposing often focus on just a single data modality such as gene expression or drug–target interactions, we argue that methods such as matrix factorization that can integrate data within and across diverse data types have the potential to improve predictive performance and provide a fuller picture of a drug's pharmacological action. WIREs Syst Biol Med 2016, 8:186–210. doi: 10.1002/wsbm.1337 This article is categorized under: Analytical and Computational Methods > Computational Methods Translational, Genomic, and Systems Medicine > Translational Medicine
Connecting side effects to diseases. Yang and Agarwal constructed 145 disease‐specific models using drug side effects as predictive features to evaluate each drug's therapeutic potential for the disease. Shown is their predictive model for hypertension, where the association between hypertension and each side effect (quantified by the Matthews correlation coefficient, MCC) is depicted by both color and edge‐thickness. Binary associations between drugs and side effects are shown in grey. Notice that many of the features such as postural hypotension and cold extremities seem reasonable in that they are commonly associated with low blood pressure. Reprinted with permission under the CreativeCommons license: https://creativecommons.org/licenses/by/2.0/.
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Connecting chemical features to side effects. Scheiber et al. used known drug–ADE associations to connect specific chemical features of drugs to various ADE terms using an extension of Naïve Bayes modeling. An example of a resulting model is shown here, associating specific chemical features with QT interval prolongation, which causes cardiac arrhythmia. (Adapted with permission from Ref)
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Computational chemistry approaches for target prediction. (a) Result of a molecular docking simulation. The globular surface of the protein is shown in grey, and its docked ligand is in blue. (b) Example of a pharmacophore model. A pharmacophore model is used to represent the chemical features deemed to be important for interaction with a chosen target. The features are arranged in three dimensions along with some tolerance radius in an attempt to account for dynamic conformational changes of both protein and ligand. A pharmacophore model can be constructed from structural analysis of the target's binding pocket, or can be based on previously known interactions with the target. Compounds can then be aligned and scored against a pharmacophore model in order to prioritize likely interactions. Colors indicate different chemical descriptors such as hydrogen bond donor, or hydrogen bond acceptor, or hydrophobic region. (a) is reproduced from ‘Evolution of Conformational Disorder & Diversity of the P53 Interactome’ by Anne‐Sophie Huart and Ted R. Hupp, under the terms of the Creative Commons Attribution License. (b) is recreated based on a figure by Wikimedia user ‘Dcirovic.’ Licensed under CC0 via Commons:https://commons.wikimedia.org/wiki/File:PharmacophoreModel_example.svg#/media/File:PharmacophoreModel_example.svg.
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Quantifying and representing drug space. (a) Representing chemical structure. A two‐dimensional representation of chemical structure can be processed into line notation such as the SMILES string, or into a binary fingerprint. To construct the SMILES string, first hydrogens are removed and any cycles are broken by removing one edge from each cycle. The SMILES string is then generated by printing the node symbols during a depth‐first tree traversal of the chemical graph, and using parentheses to denote branches of the tree. In the example, the gold‐colored path represents the main backbone for traversal. A binary fingerprint can be generated by pre‐defining chemical features such as the ones shown, and then using a ‘1’ or ‘0’ to indicate the presence or absence of each feature in the chemical structure. (b) Quantifying drug–target interactions. A dose–response curve is shown, plotting the percentage of ligand (drug) bound with a candidate target, as a function of the logarithm of ligand concentration. Since the slope is positive, the inflection point is called the EC50 value (see text). This is a measure of potency, with a lower EC50 corresponding to a more potent effect of the drug on the target. The height of the curve at the inflection point is a measure of the strength of the effect, that is, efficacy. (c) Quantifying drug‐perturbed gene expression. Gene expression can be used to characterize the effect of a drug on a group of cells by comparing expression between treated and untreated samples. The data can be processed into a differential expression profile, or processed further into a signature of up‐ and down‐regulated genes. One could also summarize the perturbation using differential variance or differential coexpression. (d) Representing categorical associations such as side effects, diseases, or therapeutic classes. Categorical metadata can often be mapped to a structured ontology (see text for examples), where the highest level of the tree corresponds to the broadest categorization, and deeper levels divide these into more and more detailed distinctions. A numerical representation can be generated by selecting a level of detail in the ontology tree and indicating presence or absence of a drug's association with each category using a ‘1’ or ‘0.’ The construction of the SMILES string in (a) is modified based on a figure created by the Wikimedia user ‘Fdardel’ and reused according to the Creative Commons Attribution‐Share Alike 2.5 Generic license. (c) is modified from Gaiteri and Ding used with permission.
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Various guilt‐by‐association strategies in computational pharmacology. The top‐left panel could be expressed by the statement ‘similar drugs may have common targets [or side effects or diseases]’; the top‐right panel could be expressed as ‘similar targets may interact with the same drug’ while the bottom‐left panel expresses ‘similar drugs may interact with similar targets.’
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Data can be integrated across compounds and/or across data types. Note that this is a simplified illustration in the sense that both targets and gene expression responses to a compound can vary depending on the biological conditions in which they are assayed, for example, different cell lines, concentrations, etc.
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A visual map of this article. We can discover new associations between drugs and molecular targets, side effects, or diseases, using a variety of techniques. Some of the strategies reviewed in this article are listed in the three segments.
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‘Connectivity mapping’ for drug repurposing. The connectivity mapping approach hypothesizes that a drug and disease with opposing or anti‐correlated expression profiles might be a therapeutic match, reasoning that if gene expression is perturbed in one direction in a diseased state, and in the reverse direction upon exposure of a drug, then perhaps that drug could ‘push’ the disease‐perturbed expression back toward a more normal state, and hence provide therapeutic benefit for the disease.
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Improved performance of data integration. Napolitano et al. demonstrated the benefits of data integration for predicting drug therapeutic class; incorporating gene expression (GEX), chemical structure (CHEM), and target interaction profiles (TAR) into a single drug–similarity matrix that was input to a multiclass SVM classifier. They compared the ROC curve generated using the multisource similarity matrix against curves generated using three different single‐data source similarity matrices, with the former achieving higher accuracy, as shown. (Reprinted with permission according to the Chemistry Central copyright and license agreement.)
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