Identification of novel therapeutics for complex diseases from genome-wide association data

Repurposing – second life for drugs

Drug repurposing refers to finding new indications for existing drugs. The paradigm shift from traditional drug discovery to drug repurposing is driven by the fact that new drug pipelines are getting dried up because of mounting Research & Development (R&D) costs, long timeline for new drug development, low success rate for new molecular entities, regulatory hurdles coupled with revenue loss from patent expiry and competition from generics. Anaemic drug pipelines along with increasing demand for newer effective, cheaper, safer drugs and unmet medical needs call for new strategies of drug discovery and, drug repurposing seems to be a promising avenue for such endeavours. Drug repurposing strategies have progressed over years from simple serendipitous observations to more complex computational methods in parallel with our ever-growing knowledge on drugs, diseases, protein targets and signalling pathways but still the knowledge is far from complete. Repurposed drugs too have to face many obstacles, although lesser than new drugs, before being successful.

The use of a gene expression signature and connectivity map to repurpose drugs for bipolar disorder

To create a gene expression signature (GES) to represent the biological effects of a combination of known drugs for bipolar disorder (BD) on cultured human neuronal cells (NT2-N) and rat brains, which also has evidence of differential expression in individuals with BD. To use the GES to identify new drugs for BD using Connectivity Map (CMap).Methods: NT2-N (n = 20) cells and rats (n = 8) were treated with a BD drug combination (lithium, valproate, quetiapine and lamotrigine) or vehicle for 24 and 6 h, respectively. Following next-generation sequencing, the differential expression of genes was assessed using edgeR in R. The derived GES was compared to differentially expressed genes in post-mortem brains of individuals with BD. The GES was then used in CMap analysis to identify similarly acting drugs.Results: A total of 88 genes showed evidence of differential expression in response to the drug combination in both models, and therefore comprised the GES. Six of these genes showed evidence of differential expression in post-mortem brains of individuals with BD. CMap analysis identified 10 compounds (camptothecin, chlorambucil, flupenthixol, valdecoxib, rescinnamine, GW-8510, cinnarizine, lomustine, mifepristone and nimesulide) acting similarly to the BD drug combination.Conclusions: This study shows that GES and CMap can be used as tools to repurpose drugs for BD.

Antihypertensive Drugs and Risk of Depression: A Nationwide Population-Based Study

Hypertension, cardiovascular diseases, and cerebrovascular diseases are associated with an increased risk of depression, but it remains unclear whether treatment with antihypertensive agents decreases or increases this risk. The effects of individual drugs are also unknown. We used Danish population-based registers to systematically investigate whether the 41 most used individual antihypertensive drugs were associated with an altered risk of incident depression. Analyses of diuretics were included for comparisons. Participants were included in the study in January 2005 and followed until December 2015. Two different outcome measures were included: (1) a diagnosis of depressive disorder at a psychiatric hospital as an inpatient or outpatient and (2) a combined measure of a diagnosis of depression or use of antidepressants. Continued use of classes of angiotensin agents, calcium antagonists, and β-blockers was associated with significantly decreased rates of depression, whereas diuretic use was not. Individual drugs associated with decreased depression included 2 of 16 angiotensin agents: enalapril and ramipril; 3 of 10 calcium antagonists: amlodipine, verapamil, and verapamil combinations; and 4 of 15 β-blockers: propranolol, atenolol, bisoprolol, and carvedilol. No drug was associated with an increased risk of depression. In conclusion, real-life population-based data suggest a positive effect of continued use of 9 individual antihypertensive agents. This evidence should be used in guiding prescriptions for patients at risk of developing depression including those with prior depression or anxiety and patients with a family history of depression.

Turning genome-wide association study findings into opportunities for drug repositioning

Drug development is a very costly and lengthy process, while repositioned or repurposed drugs could be brought into clinical practice within a shorter time-frame and at a much reduced cost. Numerous computational approaches to drug repositioning have been developed, but methods utilizing genome-wide association studies (GWASs) data are less explored.

The past decade has observed a massive growth in the amount of data from GWAS; the rich information contained in GWAS has great potential to guide drug repositioning or discovery. While multiple tools are available for finding the most relevant genes from GWAS hits, searching for top susceptibility genes is only one way to guide repositioning, which has its own limitations.

Here we provide a comprehensive review of different computational approaches that employ GWAS data to guide drug repositioning. These methods include selecting top candidate genes from GWAS as drug targets, deducing drug candidates based on drug-drug and disease-disease similarities, searching for reversed expression profiles between drugs and diseases, pathway-based methods as well as approaches based on analysis of biological networks. Each method is illustrated with examples, and their respective strengths and limitations are discussed. We also discussed several areas for future research.

New drug candidates for bipolar disorder-A nation-wide population-based study

OBJECTIVE

Drug repurposing is an increasingly promising idea in many fields of medicine. We systematically used Danish nation-wide population-based registers to investigate whether continued use of non-aspirin non-steroidal anti-inflammatory drugs (NSAIDs), low-dose aspirin, high-dose aspirin, statins, allopurinol, and angiotensin agents decrease the rate of incident mania/bipolar disorder.

METHODS

A nation-wide population-based longitudinal study using Poisson regression analyses including all persons in Denmark who purchased the exposure medication of interest and a random sample of 30% of the Danish population. The follow-up period comprised a 10 years period from 2005 to 2015. Two different outcome measures were included, (1) a diagnosis of mania/bipolar disorder at a psychiatric hospital contact as inpatient or outpatient and (2) a combined measure of a diagnosis of mania/bipolar disorder or initiation of lithium use.

RESULTS

A total of 1,605,365 subjects were exposed to one of the six drugs of interest during the exposure period from 2005 to 2015, median age 57 years [quartiles: 43;69], and female proportion of 53.1%. Continued use of low-dose aspirin, statins, and angiotensin agents were associated with decreased rates of incident mania/bipolar disorder on both outcome measures. Continued uses of non-aspirin NSAIDs as well as high-dose aspirin were associated with an increased rate of incident bipolar disorder. There were no statistically significant associations for allopurinol.

CONCLUSIONS

The study supports the potential of agents acting on inflammation and the stress response system in bipolar disorder and illustrates that population-based registers can be used to systematically identify drugs with repurposing potentials.

New drug candidates for depression - a nationwide population-based study

OBJECTIVE

To investigate whether continued use of non-aspirin NSAID, low-dose aspirin, high-dose aspirin, statins, allopurinol and angiotensin agents decreases the rate of incident depression using Danish nationwide population-based registers.

METHODS

All persons in Denmark who purchased the exposure medications of interest between 1995 and 2015 and a random sample of 30% of the Danish population was included in the study. Two different outcome measures were included, (i) a diagnosis of depressive disorder at a psychiatric hospital as in-patient or out-patient and (ii) a combined measure of a diagnosis of depression or use of antidepressants.

RESULTS

A total of 1 576 253 subjects were exposed to one of the six drugs of interest during the exposure period from 2005 to 2015. Continued use of low-dose aspirin, statins, allopurinol and angiotensin agents was associated with a decreased rate of incident depression according to both outcome measures. Continued uses of non-aspirin NSAIDs as well as high-dose aspirin were associated with an increased rate of incident depression.

CONCLUSION

The findings support the potential of agents acting on inflammation and the stress response system in depression as well as the potential of population-based registers to systematically identify drugs with repurposing potential.

Family History as an Important Factor for Stratifying Participants in Genetic Studies of Major Depression

Depression is estimated to affect 350 million people worldwide. The World Mental Health Survey conducted in 17 countries found that, on average, about one in 20 people reported having an episode of depression in the previous year. Although depression has been shown to be moderately heritable by studies conducted in the past, the search for its so-called missing heritability has so far been unsuccessful. The difficulty in identifying common genetic variants predisposing to depression could be due to large sample sizes needed to detect small effects on genetic risk and the heterogeneous nature of major depressive disorder (MDD). The aim of our study was to determine whether there was a connection between a family history of depression in MDD patients and the presence of putative risk variants in the well-studied SLC6A4, COMT and PCLO genes. We analyzed 133 patients with MDD (30.0% with a positive family history for MDD and 70.0% sporadic cases) and compared them to 279 healthy controls. When comparing all the depressed patients to controls, no significant differences in genotype and allele distributions were detected. After stratifying patients according to their family history, the PCLO rs2522833 C allele was shown to be significantly less common in patients with a positive family history (p = 0.001), indicating a possible difference in the genetic structure of MDD between familial and sporadic cases and a less important role of the common genetic risk variants for the development of MDD in familial cases.

Synstable Fusion: A Network-Based Algorithm for Estimating Driver Genes in Fusion Structures

Gene fusion structure is a class of common somatic mutational events in cancer genomes, which are often formed by chromosomal mutations. Identifying the driver gene(s) in a fusion structure is important for many downstream analyses and it contributes to clinical practices. Existing computational approaches have prioritized the importance of oncogenes by incorporating prior knowledge from gene networks. However, different methods sometimes suffer different weaknesses when handling gene fusion data due to multiple issues such as fusion gene representation, network integration, and the effectiveness of the evaluation algorithms. In this paper, Synstable Fusion (SYN), an algorithm for computationally evaluating the fusion genes, is proposed. This algorithm uses network-based strategy by incorporating gene networks as prior information, but estimates the driver genes according to the destructiveness hypothesis. This hypothesis balances the two popular evaluation strategies in the existing studies, thereby providing more comprehensive results. A machine learning framework is introduced to integrate multiple networks and further solve the conflicting results from different networks. In addition, a synchronous stability model is established to reduce the computational complexity of the evaluation algorithm. To evaluate the proposed algorithm, we conduct a series of experiments on both artificial and real datasets. The results demonstrate that the proposed algorithm performs well on different configurations and is robust when altering the internal parameter settings.

Drug repositioning: computational approaches and research examples classified according to the evidence level

Increasing need for novel drugs and their application for treating diseases are the main reasons for the development of bioinformatics platforms for drug repositioning. The use of existing approved drugs for treating other diseases reduces cost and time needed for a drug to come to clinical use. Different strategies for drug repositioning have been reported. The use of several omics types is becoming increasingly important in drug repositioning. Although there are several public databases intended for drug repositioning, not many successful cases of novel use of drugs have been reported in the literature and transferred to clinical use. Additionally, the study approaches in published literature are very heterogeneous. A classification scheme - Drug Repositioning Evidence Level (DREL) - for drug repositioning projects, according to the level of scientific evidence has been proposed previously. In the present study, we have reviewed main databases and bioinformatics approaches enabling drug repositioning studies. We also reviewed six published studies and evaluated them according to the DREL classification. The evaluated cases used drug repositioning approach for therapy of rheumatoid arthritis, cancer, coronary artery disease, diabetes, and gulf war illness. The drug repositioning study field could benefit from clearer definition in published articles therefore including drug repositioning DREL classification scheme could be included in published original and review studies. Novel bioinformatics approaches to improve prediction of drug-target interactions, continuous updating of the databases, and development of novel validation techniques are needed to facilitate the development of the drug repositioning field. Although there are still many challenges in drug repositioning and personalized medicine, stratification of patients based on their molecular signatures and testing of signature-targeting drugs should improve drug efficacy in clinical trials.

MeSHDD: Literature-based drug-drug similarity for drug repositioning

OBJECTIVE

Drug repositioning is a promising methodology for reducing the cost and duration of the drug discovery pipeline. We sought to develop a computational repositioning method leveraging annotations in the literature, such as Medical Subject Heading (MeSH) terms.

METHODS

We developed software to determine significantly co-occurring drug-MeSH term pairs and a method to estimate pair-wise literature-derived distances between drugs.

RESULTS

We found that literature-based drug-drug similarities predicted the number of shared indications across drug-drug pairs. Clustering drugs based on their similarity revealed both known and novel drug indications. We demonstrate the utility of our approach by generating repositioning hypotheses for the commonly used diabetes drug metformin.

CONCLUSION

Our study demonstrates that literature-derived similarity is useful for identifying potential repositioning opportunities. We provided open-source code and deployed a free-to-use, interactive application to explore our database of similarity-based drug clusters (available at http://apps.chiragjpgroup.org/MeSHDD/ ).

Splicing imbalances in basal-like breast cancer underpin perturbation of cell surface and oncogenic pathways and are associated with patients' survival

Despite advancements in the use of transcriptional information to understand and classify breast cancers, the contribution of splicing to the establishment and progression of these tumours has only recently starting to emerge. Our work explores this lesser known landscape, with special focus on the basal-like breast cancer subtype where limited therapeutic opportunities and no prognostic biomarkers are currently available. Using ExonArray analysis of 176 breast cancers and 9 normal breast tissues we demonstrate that splicing levels significantly contribute to the diversity of breast cancer molecular subtypes and explain much of the differences compared with normal tissues. We identified pathways specifically affected by splicing imbalances whose perturbation would be hidden from a conventional gene-centric analysis of gene expression. We found that a large fraction of them involve cell-to-cell communication, extracellular matrix and transport, as well as oncogenic and immune-related pathways transduced by plasma membrane receptors. We identified 247 genes in which splicing imbalances are associated with clinical patients’ outcome, whilst no association was detectable at the gene expression level. These include the signaling gene TGFBR1, the proto-oncogene MYB as well as many immune-related genes such as CCR7 and FCRL3, reinforcing evidence for a role of immune components in influencing breast cancer patients’ prognosis.

Finding melanoma drugs through a probabilistic knowledge graph

Metastatic cutaneous melanoma is an aggressive skin cancer with some progression-slowing treatments but no known cure. The omics data explosion has created many possible drug candidates; however, filtering criteria remain challenging, and systems biology approaches have become fragmented with many disconnected databases. Using drug, protein and disease interactions, we built an evidence-weighted knowledge graph of integrated interactions. Our knowledge graph-based system, ReDrugS, can be used via an application programming interface or web interface, and has generated 25 high-quality melanoma drug candidates. We show that probabilistic analysis of systems biology graphs increases drug candidate quality compared to non-probabilistic methods. Four of the 25 candidates are novel therapies, three of which have been tested with other cancers. All other candidates have current or completed clinical trials, or have been studied in in vivo or in vitro. This approach can be used to identify candidate therapies for use in research or personalized medicine.

Update on the genetic architecture of rheumatoid arthritis

Human genetic studies into rheumatoid arthritis (RA) have uncovered more than 100 genetic loci associated with susceptibility to RA and have refined the RA-association model for HLA variants. The majority of RA-risk variants are highly shared across multiple ancestral populations and are located in noncoding elements that might have allele-specific regulatory effects in relevant tissues. Emerging multi-omics data, high-density genotype data and bioinformatic approaches are enabling researchers to use RA-risk variants to identify functionally relevant cell types and biological pathways that are involved in impaired immune processes and disease phenotypes. This Review summarizes reported RA-risk loci and the latest insights from human genetic studies into RA pathogenesis, including how genetic data has helped to identify currently available drugs that could be repurposed for patients with RA and the role of genetics in guiding the development of new drugs.

Gene-set analysis based on the pharmacological profiles of drugs to identify repurposing opportunities in schizophrenia

Genome-wide association studies (GWAS) have identified thousands of novel genetic associations for complex genetic disorders, leading to the identification of potential pharmacological targets for novel drug development. In schizophrenia, 108 conservatively defined loci that meet genome-wide significance have been identified and hundreds of additional sub-threshold associations harbour information on the genetic aetiology of the disorder. In the present study, we used gene-set analysis based on the known binding targets of chemical compounds to identify the 'drug pathways' most strongly associated with schizophrenia-associated genes, with the aim of identifying potential drug repositioning opportunities and clues for novel treatment paradigms, especially in multi-target drug development. We compiled 9389 gene sets (2496 with unique gene content) and interrogated gene-based p-values from the PGC2-SCZ analysis. Although no single drug exceeded experiment wide significance (corrected p<0.05), highly ranked gene-sets reaching suggestive significance including the dopamine receptor antagonists metoclopramide and trifluoperazine and the tyrosine kinase inhibitor neratinib. This is a proof of principle analysis showing the potential utility of GWAS data of schizophrenia for the direct identification of candidate drugs and molecules that show polypharmacy.

ksRepo: a generalized platform for computational drug repositioning

Background

Repositioning approved drug and small molecules in novel therapeutic areas is of key interest to the pharmaceutical industry. A number of promising computational techniques have been developed to aid in repositioning, however, the majority of available methodologies require highly specific data inputs that preclude the use of many datasets and databases. There is a clear unmet need for a generalized methodology that enables the integration of multiple types of both gene expression data and database schema.

Results

ksRepo eliminates the need for a single microarray platform as input and allows for the use of a variety of drug and chemical exposure databases. We tested ksRepo’s performance on a set of five prostate cancer datasets using the Comparative Toxicogenomics Database (CTD) as our database of gene-compound interactions. ksRepo successfully predicted significance for five frontline prostate cancer therapies, representing a significant enrichment from over 7000 CTD compounds, and achieved specificity similar to other repositioning methods.

Conclusions

We present ksRepo, which enables investigators to use any data inputs for computational drug repositioning. ksRepo is implemented in a series of four functions in the R statistical environment under a BSD3 license. Source code is freely available at http://github.com/adam-sam-brown/ksRepo. A vignette is provided to aid users in performing ksRepo analysis.

Large-Scale Predictive Drug Safety: From Structural Alerts to Biological Mechanisms

The recent explosion of data linking drugs, proteins, and pathways with safety events has promoted the development of integrative systems approaches to large-scale predictive drug safety. The added value of such approaches is that, beyond the traditional identification of potentially labile chemical fragments for selected toxicity end points, they have the potential to provide mechanistic insights for a much larger and diverse set of safety events in a statistically sound nonsupervised manner, based on the similarity to drug classes, the interaction with secondary targets, and the interference with biological pathways. The combined identification of chemical and biological hazards enhances our ability to assess the safety risk of bioactive small molecules with higher confidence than that using structural alerts only. We are still a very long way from reliably predicting drug safety, but advances toward gaining a better understanding of the mechanisms leading to adverse outcomes represent a step forward in this direction.

Novel therapeutics for coronary artery disease from genome-wide association study data

Background

Coronary artery disease (CAD), one of the leading causes of death globally, is influenced by both environmental and genetic risk factors. Gene-centric genome-wide association studies (GWAS) involving cases and controls have been remarkably successful in identifying genetic loci contributing to CAD. Modern in silico platforms, such as candidate gene prediction tools, permit a systematic analysis of GWAS data to identify candidate genes for complex diseases like CAD. Subsequent integration of drug-target data from drug databases with the predicted candidate genes can potentially identify novel therapeutics suitable for repositioning towards treatment of CAD.

Methods

Previously, we were able to predict 264 candidate genes and 104 potential therapeutic targets for CAD using Gentrepid (http://www.gentrepid.org), a candidate gene prediction platform with two bioinformatic modules to reanalyze Wellcome Trust Case-Control Consortium GWAS data. In an expanded study, using five bioinformatic modules on the same data, Gentrepid predicted 647 candidate genes and successfully replicated 55% of the candidate genes identified by the more powerful CARDIoGRAMplusC4D consortium meta-analysis. Hence, Gentrepid was capable of enhancing lower quality genotype-phenotype data, using an independent knowledgebase of existing biological data. Here, we used our methodology to integrate drug data from three drug databases: the Therapeutic Target Database, PharmGKB and Drug Bank, with the 647 candidate gene predictions from Gentrepid. We utilized known CAD targets, the scientific literature, existing drug data and the CARDIoGRAMplusC4D meta-analysis study as benchmarks to validate Gentrepid predictions for CAD.

Results

Our analysis identified a total of 184 predicted candidate genes as novel therapeutic targets for CAD, and 981 novel therapeutics feasible for repositioning in clinical trials towards treatment of CAD. The benchmarks based on known CAD targets and the scientific literature showed that our results were significant (p < 0.05).

Conclusions

We have demonstrated that available drugs may potentially be repositioned as novel therapeutics for the treatment of CAD. Drug repositioning can save valuable time and money spent on preclinical and phase I clinical studies.