Discovery of drug mode of action and drug repositioning from transcriptional responses

Computational drug repositioning: from data to therapeutics

Traditionally, most drugs have been discovered using phenotypic or target-based screens. Subsequently, their indications are often expanded on the basis of clinical observations, providing additional benefit to patients. This review highlights computational techniques for systematic analysis of transcriptomics (Connectivity Map, CMap), side effects, and genetics (genome-wide association study, GWAS) data to generate new hypotheses for additional indications. We also discuss data domains such as electronic health records (EHRs) and phenotypic screening that we consider promising for novel computational repositioning methods.

Selective elimination of human pluripotent stem cells by an oleate synthesis inhibitor discovered in a high-throughput screen

The use of human pluripotent stem cells (hPSCs) in cell therapy is hindered by the tumorigenic risk from residual undifferentiated cells. Here we performed a high-throughput screen of over 52,000 small molecules and identified 15 pluripotent cell-specific inhibitors (PluriSIns), nine of which share a common structural moiety. The PluriSIns selectively eliminated hPSCs while sparing a large array of progenitor and differentiated cells. Cellular and molecular analyses demonstrated that the most selective compound, PluriSIn #1, induces ER stress, protein synthesis attenuation, and apoptosis in hPSCs. Close examination identified this molecule as an inhibitor of stearoyl-coA desaturase (SCD1), the key enzyme in oleic acid biosynthesis, revealing a unique role for lipid metabolism in hPSCs. PluriSIn #1 was also cytotoxic to mouse blastocysts, indicating that the dependence on oleate is inherent to the pluripotent state. Finally, application of PluriSIn #1 prevented teratoma formation from tumorigenic undifferentiated cells. These findings should increase the safety of hPSC-based treatments.

Predicting cancer drug mechanisms of action using molecular network signatures

Molecular signatures are a powerful approach to characterize novel small molecules and derivatized small molecule libraries. While new experimental techniques are being developed in diverse model systems, informatics approaches lag behind these exciting advances. We propose an analysis pipeline for signature based drug annotation. We develop an integrated strategy, utilizing supervised and unsupervised learning methodologies that are bridged by network based statistics. Using this approach we can: 1, predict new examples of drug mechanisms that we trained our model upon; 2, identify "New" mechanisms of action that do not belong to drug categories that our model was trained upon; and 3, update our training sets with these "New" mechanisms and accurately predict entirely distinct examples from these new categories. Thus, not only does our strategy provide statistical generalization but it also offers biological generalization. Additionally, we show that our approach is applicable to diverse types of data, and that distinct biological mechanisms characterize its resolution of categories across different data types. As particular examples, we find that our predictive resolution of drug mechanisms from mRNA expression studies relies upon the analog measurement of a cell stress-related transcriptional rheostat along with a transcriptional representation of cell cycle state; whereas, in contrast, drug mechanism resolution from functional RNAi studies rely upon more dichotomous (e.g., either enhances or inhibits) association with cell death states. We believe that our approach can facilitate molecular signature-based drug mechanism understanding from different technology platforms and across diverse biological phenomena.

Construction of cell type-specific logic models of signaling networks using CellNOpt

Mathematical models are useful tools for understanding protein signaling networks because they provide an integrated view of pharmacological and toxicological processes at the molecular level. Here we describe an approach previously introduced based on logic modeling to generate cell-specific, mechanistic and predictive models of signal transduction. Models are derived from a network encoding prior knowledge that is trained to signaling data, and can be either binary (based on Boolean logic) or quantitative (using a recently developed formalism, constrained fuzzy logic). The approach is implemented in the freely available tool CellNetOptimizer (CellNOpt). We explain the process CellNOpt uses to train a prior knowledge network to data and illustrate its application with a toy example as well as a realistic case describing signaling networks in the HepG2 liver cancer cell line.

Predictive methods in drug repurposing: gold mine or just a bigger haystack?

There are nearly 2000 patent and peer-reviewed literature-based examples of drug repurposing to be found at http://www.drugrepurposing.info/; yet there has recently been a spate of experimental techniques used to predict new drug repurposing opportunities. This review questions whether these new methods - from computerised modelling of drug-target interactions to retrospective analysis of clinical experience - merely add testable hypotheses without addressing their inherent validity, or whether they also partially validate the new uses so that the predictions are more likely to be successfully developed. In addition, ontological methods take existing information and link two known facts to create an unknown association. These can both enhance other methods of repurposing and provide patented, commercial products, including several case historical examples.

A cross-species analysis method to analyze animal models' similarity to human's disease state

Background

Animal models are indispensable tools in studying the cause of human diseases and searching for the treatments. The scientific value of an animal model depends on the accurate mimicry of human diseases. The primary goal of the current study was to develop a cross-species method by using the animal models' expression data to evaluate the similarity to human diseases' and assess drug molecules' efficiency in drug research. Therefore, we hoped to reveal that it is feasible and useful to compare gene expression profiles across species in the studies of pathology, toxicology, drug repositioning, and drug action mechanism.

Results

We developed a cross-species analysis method to analyze animal models' similarity to human diseases and effectiveness in drug research by utilizing the existing animal gene expression data in the public database, and mined some meaningful information to help drug research, such as potential drug candidates, possible drug repositioning, side effects and analysis in pharmacology. New animal models could be evaluated by our method before they are used in drug discovery.

We applied the method to several cases of known animal model expression profiles and obtained some useful information to help drug research. We found that trichostatin A and some other HDACs could have very similar response across cell lines and species at gene expression level. Mouse hypoxia model could accurately mimic the human hypoxia, while mouse diabetes drug model might have some limitation. The transgenic mouse of Alzheimer was a useful model and we deeply analyzed the biological mechanisms of some drugs in this case. In addition, all the cases could provide some ideas for drug discovery and drug repositioning.

Conclusions

We developed a new cross-species gene expression module comparison method to use animal models' expression data to analyse the effectiveness of animal models in drug research. Moreover, through data integration, our method could be applied for drug research, such as potential drug candidates, possible drug repositioning, side effects and information about pharmacology.

In-silico drug screening and potential target identification for hepatocellular carcinoma using Support Vector Machines based on drug screening result

Hepatocellular carcinoma (HCC) is a severe liver malignancy with few drug treatment options. In finding an effective treatment for HCC, screening drugs that are already FDA-approved will fast track the clinical trial and drug approval process. Connectivity Map (CMap), a large repository of chemical-induced gene expression profiles, provides the opportunity to analyze drug properties on the basis of gene expression. Support Vector Machines (SVM) were utilized to classify the effectiveness of drugs against HCC using gene expression profiles in CMap. The results of this classification will help us (1) identify genes that are chemically sensitive, and (2) predict the effectiveness of remaining chemicals in CMap in the treatment of HCC and provide a prioritized list of possible HCC drugs for biological verification. Four HCC cell lines were treated with 146 distinct chemicals, and cell viability was examined. SVM successfully classified the effectiveness of the chemicals with an average Area Under ROC Curve (AUROC) of 0.9. Using reported HCC patient samples, we identified chemically sensitive genes that may be possible HCC therapeutic targets, including MT1E, MYC, and GADD45B. Using SVM, several known HCC inhibitors, such as geldanamycin, alvespimycin (HSP90 inhibitors), and doxorubicin (chemotherapy drug), were predicted. Seven out of the 23 predicted drugs were cardiac glycosides, suggesting a link between this drug category and HCC inhibition. The study demonstrates a strategy of in silico drug screening with SVM using a large repository of microarrays based on initial in vitro drug screening. Verifying these results biologically would help develop a more accurate chemical sensitivity model.

NetwoRx: connecting drugs to networks and phenotypes in Saccharomyces cerevisiae

Drug modes of action are complex and still poorly understood. The set of known drug targets is widely acknowledged to be biased and incomplete, and so gives only limited insight into the system-wide effects of drugs. But a high-throughput assay unique to yeast-barcode-based chemogenomic screens-can measure the individual drug response of every yeast deletion mutant in parallel. NetwoRx (http://ophid.utoronto.ca/networx) is the first resource to store data from these extremely valuable yeast chemogenomics experiments. In total, NetwoRx stores data on 5924 genes and 466 drugs. In addition, we applied data-mining approaches to identify yeast pathways, functions and phenotypes that are targeted by particular drugs, compute measures of drug-drug similarity and construct drug-phenotype networks. These data are all available to search or download through NetwoRx; users can search by drug name, gene name or gene set identifier. We also set up automated analysis routines in NetwoRx; users can query new gene sets against the entire collection of drug profiles and retrieve the drugs that target them. We demonstrate with use case examples how NetwoRx can be applied to target specific phenotypes, repurpose drugs using mode of action analysis, investigate bipartite networks and predict new drugs that affect yeast aging.

Reverse engineering a mouse embryonic stem cell-specific transcriptional network reveals a new modulator of neuronal differentiation

Gene expression profiles can be used to infer previously unknown transcriptional regulatory interaction among thousands of genes, via systems biology ‘reverse engineering’ approaches. We ‘reverse engineered’ an embryonic stem (ES)-specific transcriptional network from 171 gene expression profiles, measured in ES cells, to identify master regulators of gene expression (‘hubs’). We discovered that E130012A19Rik (E13), highly expressed in mouse ES cells as compared with differentiated cells, was a central ‘hub’ of the network. We demonstrated that E13 is a protein-coding gene implicated in regulating the commitment towards the different neuronal subtypes and glia cells. The overexpression and knock-down of E13 in ES cell lines, undergoing differentiation into neurons and glia cells, caused a strong up-regulation of the glutamatergic neurons marker Vglut2 and a strong down-regulation of the GABAergic neurons marker GAD65 and of the radial glia marker Blbp. We confirmed E13 expression in the cerebral cortex of adult mice and during development. By immuno-based affinity purification, we characterized protein partners of E13, involved in the Polycomb complex. Our results suggest a role of E13 in regulating the division between glutamatergic projection neurons and GABAergic interneurons and glia cells possibly by epigenetic-mediated transcriptional regulation.

Biological network inference for drug discovery

A better understanding of the pathophysiology should help deliver drugs whose targets are involved in the causative processes underlying a disease. Biological network inference uses computational methods for deducing from high-throughput experimental data, the topology and the causal structure of the interactions among the drugs and their targets. Therefore, biological network inference can support and contribute to the experimental identification of both gene and protein networks causing a disease as well as the biochemical networks of drugs metabolism and mechanisms of action. The resulting high-level networks serve as a foundational basis for more detailed mechanistic models and are increasingly used in drug discovery by pharmaceutical and biotechnology companies. We review and compare recent computational technologies for network inference applied to drug discovery.

DvD: An R/Cytoscape pipeline for drug repurposing using public repositories of gene expression data

Summary: Drug versus Disease (DvD) provides a pipeline, available through R or Cytoscape, for the comparison of drug and disease gene expression profiles from public microarray repositories. Negatively correlated profiles can be used to generate hypotheses of drug-repurposing, whereas positively correlated profiles may be used to infer side effects of drugs. DvD allows users to compare drug and disease signatures with dynamic access to databases Array Express, Gene Expression Omnibus and data from the Connectivity Map.

Availability and implementation: R package (submitted to Bioconductor) under GPL 3 and Cytoscape plug-in freely available for download at www.ebi.ac.uk/saezrodriguez/DVD/.

Contact:saezrodriguez@ebi.ac.uk

Supplementary information:Supplementary data are available at Bioinformatics online.

A New Method for Computational Drug Repositioning Using Drug Pairwise Similarity

The traditional de novo drug discovery is known as a high cost and high risk process. In response, recently there is an increasing interest in discovering new indications for known drugs-a process known as drug repositioning-using computational methods. In this study, we present a new systematic approach for identifying potential new indications of an existing drug through its relation to similar drugs. Different from the previous similarity-based methods, we adapted a novel bipartite-graph based method when considering common drug targets and their interaction information. Furthermore, we added drug structure information into the calculation of drug pairwise similarity. In cross-validation experiments, our method achieved a sensitivity of 0.77 and specificity of 0.92 (AUC = 0.888) and compared favorably to the state of the art. When compared with a control group of drug uses, our drug repositioning results were found to be significantly enriched in both the biomedical literature and clinical trials. Our results indicate that combining chemical structure and drug target information results in better prediction performance and that the proposed approach successfully captures the implicit information between drug targets.

In silico drug screen in mouse liver identifies candidate calorie restriction mimetics

Calorie restriction (CR) extends life span in mammals and delays the onset of age-related diseases, including cancer and diabetes. Drugs that target the same genes and pathways as CR may have enormous therapeutic potential. Recently, genome-scale data on the responses of human cell lines to over 1,000 drug treatments have become available. Here we integrate these data with gene expression signatures of CR in mouse liver to generate a prioritized list of candidate CR mimetics. We identify 14 drugs that reproduce the effects of CR at the transcriptional level.

FacPad: Bayesian sparse factor modeling for the inference of pathways responsive to drug treatment

MOTIVATION

It is well recognized that the effects of drugs are far beyond targeting individual proteins, but rather influencing the complex interactions among many relevant biological pathways. Genome-wide expression profiling before and after drug treatment has become a powerful approach for capturing a global snapshot of cellular response to drugs, as well as to understand drugs' mechanism of action. Therefore, it is of great interest to analyze this type of transcriptomic profiling data for the identification of pathways responsive to different drugs. However, few computational tools exist for this task.

RESULTS

We have developed FacPad, a Bayesian sparse factor model, for the inference of pathways responsive to drug treatments. This model represents biological pathways as latent factors and aims to describe the variation among drug-induced gene expression alternations in terms of a much smaller number of latent factors. We applied this model to the Connectivity Map data set (build 02) and demonstrated that FacPad is able to identify many drug-pathway associations, some of which have been validated in the literature. Although this method was originally designed for the analysis of drug-induced transcriptional alternation data, it can be naturally applied to many other settings beyond polypharmacology.

AVAILABILITY AND IMPLEMENTATION

The R package 'FacPad' is publically available at: http://cran.open-source-solution.org/web/packages/FacPad/.

Transcriptional data: a new gateway to drug repositioning?

Recent advances in computational biology suggest that any perturbation to the transcriptional programme of the cell can be summarised by a proper ‘signature’: a set of genes combined with a pattern of expression. Therefore, it should be possible to generate proxies of clinicopathological phenotypes and drug effects through signatures acquired via DNA microarray technology.

Gene expression signatures have recently been assembled and compared through genome-wide metrics, unveiling unexpected drug–disease and drug–drug ‘connections’ by matching corresponding signatures. Consequently, novel applications for existing drugs have been predicted and experimentally validated.

Here, we describe related methods, case studies and resources while discussing challenges and benefits of exploiting existing repositories of microarray data that could serve as a search space for systematic drug repositioning.

Shape-based reprofiling of FDA-approved drugs for the H₁ histamine receptor

Reprofiling of existing drugs to treat conditions not originally targeted is an attractive means of addressing the problem of a decreasing stream of approved drugs. To determine if 3D shape similarity can be used to rationalize an otherwise serendipitous process, we employed 3D shape-based virtual screening to reprofile existing FDA-approved drugs. The study was conducted in two phases. First, multiple histamine H(1) receptor antagonists were identified to be used as query molecules, and these were compared to a database of approved drugs. Second, the hits were ranked according to 3D similarity and the top drugs evaluated in a cell-based assay. The virtual screening methodology proved highly successful, as 13 of 23 top drugs tested selectively inhibited histamine-induced calcium release with the best being chlorprothixene (IC(50) 1 nM). Finally, we confirmed that the drugs identified using the cell-based assay were all acting at the receptor level by conducting a radioligand-binding assay using rat membrane.

Getting personalized cancer genome analysis into the clinic: the challenges in bioinformatics

Progress in genomics has raised expectations in many fields, and particularly in personalized cancer research. The new technologies available make it possible to combine information about potential disease markers, altered function and accessible drug targets, which, coupled with pathological and medical information, will help produce more appropriate clinical decisions. The accessibility of such experimental techniques makes it all the more necessary to improve and adapt computational strategies to the new challenges. This review focuses on the critical issues associated with the standard pipeline, which includes: DNA sequencing analysis; analysis of mutations in coding regions; the study of genome rearrangements; extrapolating information on mutations to the functional and signaling level; and predicting the effects of therapies using mouse tumor models. We describe the possibilities, limitations and future challenges of current bioinformatics strategies for each of these issues. Furthermore, we emphasize the need for the collaboration between the bioinformaticians who implement the software and use the data resources, the computational biologists who develop the analytical methods, and the clinicians, the systems' end users and those ultimately responsible for taking medical decisions. Finally, the different steps in cancer genome analysis are illustrated through examples of applications in cancer genome analysis.

Prediction of chemical-protein interactions network with weighted network-based inference method

Chemical-protein interaction (CPI) is the central topic of target identification and drug discovery. However, large scale determination of CPI is a big challenge for in vitro or in vivo experiments, while in silico prediction shows great advantages due to low cost and high accuracy. On the basis of our previous drug-target interaction prediction via network-based inference (NBI) method, we further developed node- and edge-weighted NBI methods for CPI prediction here. Two comprehensive CPI bipartite networks extracted from ChEMBL database were used to evaluate the methods, one containing 17,111 CPI pairs between 4,741 compounds and 97 G protein-coupled receptors, the other including 13,648 CPI pairs between 2,827 compounds and 206 kinases. The range of the area under receiver operating characteristic curves was 0.73 to 0.83 for the external validation sets, which confirmed the reliability of the prediction. The weak-interaction hypothesis in CPI network was identified by the edge-weighted NBI method. Moreover, to validate the methods, several candidate targets were predicted for five approved drugs, namely imatinib, dasatinib, sertindole, olanzapine and ziprasidone. The molecular hypotheses and experimental evidence for these predictions were further provided. These results confirmed that our methods have potential values in understanding molecular basis of drug polypharmacology and would be helpful for drug repositioning.

Network biology methods integrating biological data for translational science

The explosion of biomedical data, both on the genomic and proteomic side as well as clinical data, will require complex integration and analysis to provide new molecular variables to better understand the molecular basis of phenotype. Currently, much data exist in silos and is not analyzed in frameworks where all data are brought to bear in the development of biomarkers and novel functional targets. This is beginning to change. Network biology approaches, which emphasize the interactions between genes, proteins and metabolites provide a framework for data integration such that genome, proteome, metabolome and other -omics data can be jointly analyzed to understand and predict disease phenotypes. In this review, recent advances in network biology approaches and results are identified. A common theme is the potential for network analysis to provide multiplexed and functionally connected biomarkers for analyzing the molecular basis of disease, thus changing our approaches to analyzing and modeling genome- and proteome-wide data.

Network-based characterization of drug-regulated genes, drug targets, and toxicity

Proteins do not exert their effects in isolation of one another, but interact together in complex networks. In recent years, sophisticated methods have been developed to leverage protein-protein interaction (PPI) network structure to improve several stages of the drug discovery process. Network-based methods have been applied to predict drug targets, drug side effects, and new therapeutic indications. In this paper we have two aims. First, we review the past contributions of network approaches and methods to drug discovery, and discuss their limitations and possible future directions. Second, we show how past work can be generalized to gain a more complete understanding of how drugs perturb networks. Previous network-based characterizations of drug effects focused on the small number of known drug targets, i.e., direct binding partners of drugs. However, drugs affect many more genes than their targets - they can profoundly affect the cell's transcriptome. For the first time, we use networks to characterize genes that are differentially regulated by drugs. We found that drug-regulated genes differed from drug targets in terms of functional annotations, cellular localizations, and topological properties. Drug targets mainly included receptors on the plasma membrane, down-regulated genes were largely in the nucleus and were enriched for DNA binding, and genes lacking drug relationships were enriched in the extracellular region. Network topology analysis indicated several significant graph properties, including high degree and betweenness for the drug targets and drug-regulated genes, though possibly due to network biases. Topological analysis also showed that proteins of down-regulated genes appear to be frequently involved in complexes. Analyzing network distances between regulated genes, we found that genes regulated by structurally similar drugs were significantly closer than genes regulated by dissimilar drugs. Finally, network centrality of a drug's differentially regulated genes correlated significantly with drug toxicity.

A generalizable pre-clinical research approach for orphan disease therapy

With the advent of next-generation DNA sequencing, the pace of inherited orphan disease gene identification has increased dramatically, a situation that will continue for at least the next several years. At present, the numbers of such identified disease genes significantly outstrips the number of laboratories available to investigate a given disorder, an asymmetry that will only increase over time. The hope for any genetic disorder is, where possible and in addition to accurate diagnostic test formulation, the development of therapeutic approaches. To this end, we propose here the development of a strategic toolbox and preclinical research pathway for inherited orphan disease. Taking much of what has been learned from rare genetic disease research over the past two decades, we propose generalizable methods utilizing transcriptomic, system-wide chemical biology datasets combined with chemical informatics and, where possible, repurposing of FDA approved drugs for pre-clinical orphan disease therapies. It is hoped that this approach may be of utility for the broader orphan disease research community and provide funding organizations and patient advocacy groups with suggestions for the optimal path forward. In addition to enabling academic pre-clinical research, strategies such as this may also aid in seeding startup companies, as well as further engaging the pharmaceutical industry in the treatment of rare genetic disease.

DTome: a web-based tool for drug-target interactome construction

BACKGROUND

Understanding drug bioactivities is crucial for early-stage drug discovery, toxicology studies and clinical trials. Network pharmacology is a promising approach to better understand the molecular mechanisms of drug bioactivities. With a dramatic increase of rich data sources that document drugs' structural, chemical, and biological activities, it is necessary to develop an automated tool to construct a drug-target network for candidate drugs, thus facilitating the drug discovery process.

RESULTS

We designed a computational workflow to construct drug-target networks from different knowledge bases including DrugBank, PharmGKB, and the PINA database. To automatically implement the workflow, we created a web-based tool called DTome (Drug-Target interactome tool), which is comprised of a database schema and a user-friendly web interface. The DTome tool utilizes web-based queries to search candidate drugs and then construct a DTome network by extracting and integrating four types of interactions. The four types are adverse drug interactions, drug-target interactions, drug-gene associations, and target-/gene-protein interactions. Additionally, we provided a detailed network analysis and visualization process to illustrate how to analyze and interpret the DTome network. The DTome tool is publicly available at http://bioinfo.mc.vanderbilt.edu/DTome.

CONCLUSIONS

As demonstrated with the antipsychotic drug clozapine, the DTome tool was effective and promising for the investigation of relationships among drugs, adverse interaction drugs, drug primary targets, drug-associated genes, and proteins directly interacting with targets or genes. The resultant DTome network provides researchers with direct insights into their interest drug(s), such as the molecular mechanisms of drug actions. We believe such a tool can facilitate identification of drug targets and drug adverse interactions.

Comprehensive data-driven analysis of the impact of chemoinformatic structure on the genome-wide biological response profiles of cancer cells to 1159 drugs

Background

Detailed and systematic understanding of the biological effects of millions of available compounds on living cells is a significant challenge. As most compounds impact multiple targets and pathways, traditional methods for analyzing structure-function relationships are not comprehensive enough. Therefore more advanced integrative models are needed for predicting biological effects elicited by specific chemical features. As a step towards creating such computational links we developed a data-driven chemical systems biology approach to comprehensively study the relationship of 76 structural 3D-descriptors (VolSurf, chemical space) of 1159 drugs with the microarray gene expression responses (biological space) they elicited in three cancer cell lines. The analysis covering 11350 genes was based on data from the Connectivity Map. We decomposed the biological response profiles into components, each linked to a characteristic chemical descriptor profile.

Results

Integrated analysis of both the chemical and biological space was more informative than either dataset alone in predicting drug similarity as measured by shared protein targets. We identified ten major components that link distinct VolSurf chemical features across multiple compounds to specific cellular responses. For example, component 2 (hydrophobic properties) strongly linked to DNA damage response, while component 3 (hydrogen bonding) was associated with metabolic stress. Individual structural and biological features were often linked to one cell line only, such as leukemia cells (HL-60) specifically responding to cardiac glycosides.

Conclusions

In summary, our approach identified several novel links between specific chemical structure properties and distinct biological responses in cells incubated with these drugs. Importantly, the analysis focused on chemical-biological properties that emerge across multiple drugs. The decoding of such systematic relationships is necessary to build better models of drug effects, including unanticipated types of molecular properties having strong biological effects.

Network analysis has diverse roles in drug discovery

Computational biologists use network analysis to uncover relationships between various data types of interest for drug discovery. For example, signalling and metabolic pathways are commonly used to understand disease states and drug mechanisms. However, several other flavours of network analysis techniques are also applicable in a drug discovery context. Recent advances include networks that encompass relationships between diseases, molecular mechanisms and gene targets. Even social networks that mirror interactions within the scientific community are helping to foster collaborations and novel research. We review how these different types of network analysis approaches facilitate drug discovery and their associated challenges.

Determination of minimal transcriptional signatures of compounds for target prediction

The identification of molecular target and mechanism of action of compounds is a key hurdle in drug discovery. Multiplexed techniques for bead-based expression profiling allow the measurement of transcriptional signatures of compound-treated cells in high-throughput mode. Such profiles can be used to gain insight into compounds' mode of action and the protein targets they are modulating. Through the proxy of target prediction from such gene signatures we explored important aspects of the use of transcriptional profiles to capture biological variability of perturbed cellular assays. We found that signatures derived from expression data and signatures derived from biological interaction networks performed equally well, and we showed that gene signatures can be optimised using a genetic algorithm. Gene signatures of approximately 128 genes seemed to be most generic, capturing a maximum of the perturbation inflicted on cells through compound treatment. Moreover, we found evidence for oxidative phosphorylation to be one of the most general ways to capture compound perturbation.

The human tripeptide GHK-Cu in prevention of oxidative stress and degenerative conditions of aging: implications for cognitive health

Oxidative stress, disrupted copper homeostasis, and neuroinflammation due to overproduction of proinflammatory cytokines are considered leading causative factors in development of age-associated neurodegenerative conditions. Recently, a new mechanism of aging—detrimental epigenetic modifications—has emerged. Thus, compounds that possess antioxidant, anti-inflammatory activity as well as compounds capable of restoring copper balance and proper gene functioning may be able to prevent age-associated cognitive decline and ward off many common neurodegenerative conditions. The aim of this paper is to bring attention to a compound with a long history of safe use in wound healing and antiaging skin care. The human tripeptide GHK was discovered in 1973 as an activity in human albumin that caused old human liver tissue to synthesize proteins like younger tissue. It has high affinity for copper ions and easily forms a copper complex or GHK-Cu. In addition, GHK possesses a plethora of other regenerative and protective actions including antioxidant, anti-inflammatory, and wound healing properties. Recent studies revealed its ability to up- and downregulate a large number of human genes including those that are critical for neuronal development and maintenance. We propose GHK tripeptide as a possible therapeutic agent against age-associated neurodegeneration and cognitive decline.

Prediction of drug-target interactions and drug repositioning via network-based inference

Drug-target interaction (DTI) is the basis of drug discovery and design. It is time consuming and costly to determine DTI experimentally. Hence, it is necessary to develop computational methods for the prediction of potential DTI. Based on complex network theory, three supervised inference methods were developed here to predict DTI and used for drug repositioning, namely drug-based similarity inference (DBSI), target-based similarity inference (TBSI) and network-based inference (NBI). Among them, NBI performed best on four benchmark data sets. Then a drug-target network was created with NBI based on 12,483 FDA-approved and experimental drug-target binary links, and some new DTIs were further predicted. In vitro assays confirmed that five old drugs, namely montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole, showed polypharmacological features on estrogen receptors or dipeptidyl peptidase-IV with half maximal inhibitory or effective concentration ranged from 0.2 to 10 µM. Moreover, simvastatin and ketoconazole showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays. The results indicated that these methods could be powerful tools in prediction of DTIs and drug repositioning.

Drug repositioning for personalized medicine

Human diseases can be caused by complex mechanisms involving aberrations in numerous proteins and pathways. With recent advances in genomics, elucidating the molecular basis of disease on a personalized level has become an attainable goal. In many cases, relevant molecular targets will be identified for which approved drugs already exist, and the potential repositioning of these drugs to a new indication can be investigated. Repositioning is an accelerated route for drug discovery because existing drugs have established clinical and pharmacokinetic data. Personalized medicine and repositioning both aim to improve the productivity of current drug discovery pipelines, which expend enormous time and cost to develop new drugs, only to have them fail in clinical trials because of lack of efficacy or toxicity. Here, we discuss the current state of research in these two fields, focusing on recent large-scale efforts to systematically find repositioning candidates and elucidate individual disease mechanisms in cancer. We also discuss scenarios in which personalized drug repositioning could be particularly rewarding, such as for diseases that are rare or have specific mutations, as well as current challenges in this field. With an increasing number of drugs being approved for rare cancer subtypes, personalized medicine and repositioning approaches are poised to significantly alter the way we diagnose diseases, infer treatments and develop new drugs.

Systematic identification of genomic markers of drug sensitivity in cancer cells

Clinical responses to anticancer therapies are often restricted to a subset of patients. In some cases, mutated cancer genes are potent biomarkers for responses to targeted agents. Here, to uncover new biomarkers of sensitivity and resistance to cancer therapeutics, we screened a panel of several hundred cancer cell lines--which represent much of the tissue-type and genetic diversity of human cancers--with 130 drugs under clinical and preclinical investigation. In aggregate, we found that mutated cancer genes were associated with cellular response to most currently available cancer drugs. Classic oncogene addiction paradigms were modified by additional tissue-specific or expression biomarkers, and some frequently mutated genes were associated with sensitivity to a broad range of therapeutic agents. Unexpected relationships were revealed, including the marked sensitivity of Ewing's sarcoma cells harbouring the EWS (also known as EWSR1)-FLI1 gene translocation to poly(ADP-ribose) polymerase (PARP) inhibitors. By linking drug activity to the functional complexity of cancer genomes, systematic pharmacogenomic profiling in cancer cell lines provides a powerful biomarker discovery platform to guide rational cancer therapeutic strategies.

The pharmacogenetic background of hepatitis C treatment

Insufficiently treated hepatitis C virus (HCV) infection remains a major healthcare issue. Individual therapy responses vary considerably from spontaneous clearing of the virus to lethal conditions. Host genetics currently receives a major scientific and clinical interest as an important source of interindividual variability in treatment. Mainly the associations of interleukin 28B gene (IL28B) variants with decreased HCV clearance under standard therapy are considered as "state of the art" of hepatitis C pharmacogenetics. However, a search in PubMed identified 41 genes reportedly modulating the individual therapy response, e.g., genes coding for major histocompatibility complex (HLA), the tumor necrosis factor (TNF), interleukin 10 (IL10), other interferon coding genes than IL28B (e.g., IFNAR1, IFNAR2, IFNG), several components of downstream interferon signaling as well as genes modulating side effects of current anti-HCV therapeutics (e.g., SLC28A3, ITPA involved in ribavirin associated hemolytic effects or SLC6A4 and HTR1A involved in serotonin associated psychiatric side effects). Applying knowledge discovery methods from the area of data mining and machine-learning to this comprehensive set of HCV therapy modulating genes, relating the HCV genes to the world wide knowledge on genes given in the form of the Gene Ontology (GO) knowledge base, found that the relevant genes belong to the GO subcategories of "inflammatory response" and "immune response" and "response to virus". This complex approaches to the pharmacogenomics of HCV may serve to identify future candidates for a personalization of HCV therapy and structured approach to possible new therapeutic targets for the control of hepatitis C virus.

The impact of network biology in pharmacology and toxicology

With the need to investigate alternative approaches and emerging technologies in order to increase drug efficacy and reduce adverse drug effects, network biology offers a novel way of approaching drug discovery by considering the effect of a molecule and protein's function in a global physiological environment. By studying drug action across multiple scales of complexity, from molecular to cellular and tissue level, network-based computational methods have the potential to improve our understanding of the impact of chemicals in human health. In this review we present the available large-scale databases and tools that allow integration and analysis of such information for understanding the properties of small molecules in the context of cellular networks. With the recent advances in the omics area, global integrative approaches are necessary to cope with the massive amounts of data, and biomedical researchers are urged to implement new types of analyses that can lead to new therapeutic interventions with increased safety and efficacy compared with existing medications.

Identification of links between small molecules and miRNAs in human cancers based on transcriptional responses

The use of small molecules to target miRNAs is a new type of therapy for human diseases, particularly cancers. We proposed a novel high-throughput approach to identify the biological links between small molecules and miRNAs in 23 different cancers and constructed the Small Molecule-MiRNA Network (SMirN) for each cancer to systematically analyze the properties of their associations. In each SMirN, we partitioned small molecules (miRNAs) into modules, in which small molecules (miRNAs) were connected with one miRNA (small molecule). Almost all of the miRNA modules comprised miRNAs that had similar target genes and functions or were members of the same miRNA family. Most of the small molecule modules involved compounds with similar chemical structures, modes of action, or drug interactions. These modules can be used to identify drug candidates and new indications for existing drugs. Therefore, our approach is valuable to drug discovery and cancer therapy.

Using functional signatures to identify repositioned drugs for breast, myelogenous leukemia and prostate cancer

The cost and time to develop a drug continues to be a major barrier to widespread distribution of medication. Although the genomic revolution appears to have had little impact on this problem, and might even have exacerbated it because of the flood of additional and usually ineffective leads, the emergence of high throughput resources promises the possibility of rapid, reliable and systematic identification of approved drugs for originally unintended uses. In this paper we develop and apply a method for identifying such repositioned drug candidates against breast cancer, myelogenous leukemia and prostate cancer by looking for inverse correlations between the most perturbed gene expression levels in human cancer tissue and the most perturbed expression levels induced by bioactive compounds. The method uses variable gene signatures to identify bioactive compounds that modulate a given disease. This is in contrast to previous methods that use small and fixed signatures. This strategy is based on the observation that diseases stem from failed/modified cellular functions, irrespective of the particular genes that contribute to the function, i.e., this strategy targets the functional signatures for a given cancer. This function-based strategy broadens the search space for the effective drugs with an impressive hit rate. Among the 79, 94 and 88 candidate drugs for breast cancer, myelogenous leukemia and prostate cancer, 32%, 13% and 17% respectively are either FDA-approved/in-clinical-trial drugs, or drugs with suggestive literature evidences, with an FDR of 0.01. These findings indicate that the method presented here could lead to a substantial increase in efficiency in drug discovery and development, and has potential application for the personalized medicine.

The effective drug of a given disease is aimed to bring abnormal functions associated with disease back to the normal state. Using expression profile as the surrogate marker of the cellular function, we introduce a novel procedure to identify candidate therapeutics by searching for those bioactive compounds that either down-regulate abnormally over-expressed genes, or up-regulate those that are abnormally under-expressed. We show that the approach detects a pool of plausible candidates as repositioning/new drugs. In contrast to previous studies, our approach uses a variable big number of genes and/or gene combinations as a representation of functional signatures to identify bioactive compounds that modulate a given disease, irrespective of the particular genes that contribute to the cellular functions; therefore it covers potential drugs with heterogeneous properties. The method may also have potential application for the personalized medicine.

The emergence of genome-based drug repositioning

In a pair of papers in this issue of Science Translational Medicine, Butte et al. provide a concrete example of how reinterpreting and comparing genome-wide metrics allows us to effectively hypothesize which drugs from one disease-indication can be repurposed for another disease. The shift toward integrative genome-wide computational approaches has precedence in insightful scalar theories of biological information. Here, we discuss how this recent work in drug repurposing adheres to and takes advantage of the concepts surrounding this information paradigm.

A novel method of transcriptional response analysis to facilitate drug repositioning for cancer therapy

Little research has been done to address the huge opportunities that may exist to reposition existing approved or generic drugs for alternate uses in cancer therapy. In addition, there has been little work on strategies to reposition experimental cancer agents for testing in alternate settings that could shorten their clinical development time. Progress in each area has lagged, in part, because of the lack of systematic methods to define drug off-target effects (OTE) that might affect important cancer cell signaling pathways. In this study, we addressed this critical gap by developing an OTE-based method to repurpose drugs for cancer therapeutics, based on transcriptional responses made in cells before and after drug treatment. Specifically, we defined a new network component called cancer-signaling bridges (CSB) and integrated it with a Bayesian factor regression model (BFRM) to form a new hybrid method termed CSB-BFRM. Proof-of-concept studies were conducted in breast and prostate cancer cells and in promyelocytic leukemia cells. In each system, CSB-BFRM analysis could accurately predict clinical responses to more than 90% of drugs approved by the U.S. Food and Drug Administration and more than 75% of experimental clinical drugs that were tested. Mechanistic investigation of OTEs for several high-ranking drug-dose pairs suggested repositioning opportunities for cancer therapy, based on the ability to enforce retinoblastoma-dependent repression of important E2F-dependent cell-cycle genes. Together, our findings establish new methods to identify opportunities for drug repositioning or to elucidate the mechanisms of action of repositioned drugs.

Prediction of drug combinations by integrating molecular and pharmacological data

Combinatorial therapy is a promising strategy for combating complex disorders due to improved efficacy and reduced side effects. However, screening new drug combinations exhaustively is impractical considering all possible combinations between drugs. Here, we present a novel computational approach to predict drug combinations by integrating molecular and pharmacological data. Specifically, drugs are represented by a set of their properties, such as their targets or indications. By integrating several of these features, we show that feature patterns enriched in approved drug combinations are not only predictive for new drug combinations but also provide insights into mechanisms underlying combinatorial therapy. Further analysis confirmed that among our top ranked predictions of effective combinations, 69% are supported by literature, while the others represent novel potential drug combinations. We believe that our proposed approach can help to limit the search space of drug combinations and provide a new way to effectively utilize existing drugs for new purposes.

Systematic drug repositioning based on clinical side-effects

Drug repositioning helps fully explore indications for marketed drugs and clinical candidates. Here we show that the clinical side-effects (SEs) provide a human phenotypic profile for the drug, and this profile can suggest additional disease indications. We extracted 3,175 SE-disease relationships by combining the SE-drug relationships from drug labels and the drug-disease relationships from PharmGKB. Many relationships provide explicit repositioning hypotheses, such as drugs causing hypoglycemia are potential candidates for diabetes. We built Naïve Bayes models to predict indications for 145 diseases using the SEs as features. The AUC was above 0.8 in 92% of these models. The method was extended to predict indications for clinical compounds, 36% of the models achieved AUC above 0.7. This suggests that closer attention should be paid to the SEs observed in trials not just to evaluate the harmful effects, but also to rationally explore the repositioning potential based on this “clinical phenotypic assay”.

Drug discovery in the age of systems biology: the rise of computational approaches for data integration

The increased availability of large-scale open-access resources on bioactivities of small molecules has a significant impact on pharmacology facilitated mainly by computational approaches that digest the vast amounts of data. We discuss here how computational data integration enables systemic views on a drug's action and allows to tackle complex problems such as the large-scale prediction of drug targets, drug repurposing, the molecular mechanisms, cellular responses or side effects. We particularly focus on computational methods that leverage various cell-based transcriptional, proteomic and phenotypic profiles of drug response in order to gain a systemic view of drug action at the molecular, cellular and whole-organism scale.