Interleukin-18 as a drug repositioning opportunity for inflammatory bowel disease: A Mendelian randomization study.

Support from human genetics increases the probability of success in drug development. However, few examples exist of successful genomically-driven drug repositioning. Given that a Mendelian form of severe enterocolitis is due to up-regulation of the interleukin-18 (IL18) signaling pathway, and pharmacologic inhibition of IL18 has been shown to reverse this enterocolitis, we undertook a Mendelian randomization study to test the causal effect of elevated IL18 levels on inflammatory bowel disease susceptibility (IBD) in 12,882 cases and 21,770 controls. Mendelian randomization is an established method to assess the role of biomarkers in disease etiology in a manner that minimizes confounding and prevents reverse causation. Using three SNPs that explained almost 7% of the variance in IL18 level, we found that each genetically predicted standard deviation increase in IL18 was associated with an increase in IBD susceptibility (odds ratio = 1.22, 95% CI = 1.11-1.34, P-value = 6 × 10-5). This association was further validated in 25,042 IBD cases and 34,915 controls (odds ratio = 1.13, 95% CI = 1.05-1.20). Recently, an anti-IL18 monoclonal antibody, which decreased free IL18 levels, was found to be safe, yet ineffective in a phase II trial for type 2 diabetes. Taken together, these genomic findings implicated IBD as an alternative indication for anti-IL18 therapy, which should be tested in randomized controlled trials.

Drug repurposing: progress, challenges and recommendations.

Given the high attrition rates, substantial costs and slow pace of new drug discovery and development, repurposing of 'old' drugs to treat both common and rare diseases is increasingly becoming an attractive proposition because it involves the use of de-risked compounds, with potentially lower overall development costs and shorter development timelines. Various data-driven and experimental approaches have been suggested for the identification of repurposable drug candidates; however, there are also major technological and regulatory challenges that need to be addressed. In this Review, we present approaches used for drug repurposing (also known as drug repositioning), discuss the challenges faced by the repurposing community and recommend innovative ways by which these challenges could be addressed to help realize the full potential of drug repurposing.

Uncovering new disease indications for G-protein coupled receptors and their endogenous ligands.

BACKGROUND: The Open Targets Platform integrates different data sources in order to facilitate identification of potential therapeutic drug targets to treat human diseases. It currently provides evidence for nearly 2.6 million potential target-disease pairs. G-protein coupled receptors are a drug target class of high interest because of the number of successful drugs being developed against them over many years. Here we describe a systematic approach utilizing the Open Targets Platform data to uncover and prioritize potential new disease indications for the G-protein coupled receptors and their ligands. RESULTS: Utilizing the data available in the Open Targets platform, potential G-protein coupled receptor and endogenous ligand disease association pairs were systematically identified. Intriguing examples such as GPR35 for inflammatory bowel disease and CXCR4 for viral infection are used as illustrations of how a systematic approach can aid in the prioritization of interesting drug discovery hypotheses. Combining evidences for G-protein coupled receptors and their corresponding endogenous peptidergic ligands increases confidence and provides supportive evidence for potential new target-disease hypotheses. Comparing such hypotheses to the global pharma drug discovery pipeline to validate the approach showed that more than 93% of G-protein coupled receptor-disease pairs with a high overall Open Targets score involved receptors with an existing drug discovery program. CONCLUSIONS: The Open Targets gene-disease score can be used to prioritize potential G-protein coupled receptors-indication hypotheses. In addition, availability of multiple different evidence types markedly increases confidence as does combining evidence from known receptor-ligand pairs. Comparing the top-ranked hypotheses to the current global pharma pipeline serves validation of our approach and identifies and prioritizes new therapeutic opportunities.

Uncovering novel repositioning opportunities using the Open Targets platform.

The recently developed Open Targets platform consolidates a wide range of comprehensive evidence associating known and potential drug targets with human diseases. We have harnessed the integrated data from this platform for novel drug repositioning opportunities. Our computational workflow systematically mines data from various evidence categories and presents potential repositioning opportunities for drugs that are marketed or being investigated in ongoing human clinical trials, based on evidence strength on target-disease pairing. We classified these novel target-disease opportunities in several ways: (i) number of independent counts of evidence; (ii) broad therapy area of origin; and (iii) repositioning within or across therapy areas. Finally, we elaborate on one example that was identified by this approach.

In silico prediction of novel therapeutic targets using gene-disease association data.

BACKGROUND: Target identification and validation is a pressing challenge in the pharmaceutical industry, with many of the programmes that fail for efficacy reasons showing poor association between the drug target and the disease. Computational prediction of successful targets could have a considerable impact on attrition rates in the drug discovery pipeline by significantly reducing the initial search space. Here, we explore whether gene-disease association data from the Open Targets platform is sufficient to predict therapeutic targets that are actively being pursued by pharmaceutical companies or are already on the market. METHODS: To test our hypothesis, we train four different classifiers (a random forest, a support vector machine, a neural network and a gradient boosting machine) on partially labelled data and evaluate their performance using nested cross-validation and testing on an independent set. We then select the best performing model and use it to make predictions on more than 15,000 genes. Finally, we validate our predictions by mining the scientific literature for proposed therapeutic targets. RESULTS: We observe that the data types with the best predictive power are animal models showing a disease-relevant phenotype, differential expression in diseased tissue and genetic association with the disease under investigation. On a test set, the neural network classifier achieves over 71% accuracy with an AUC of 0.76 when predicting therapeutic targets in a semi-supervised learning setting. We use this model to gain insights into current and failed programmes and to predict 1431 novel targets, of which a highly significant proportion has been independently proposed in the literature. CONCLUSIONS: Our in silico approach shows that data linking genes and diseases is sufficient to predict novel therapeutic targets effectively and confirms that this type of evidence is essential for formulating or strengthening hypotheses in the target discovery process. Ultimately, more rapid and automated target prioritisation holds the potential to reduce both the costs and the development times associated with bringing new medicines to patients.

Harnessing public domain data to discover and validate therapeutic targets.

INTRODUCTION: Discovering, developing and validating new disease treatments is a challenging and time-consuming endeavor. Successful drug discovery hinges on selecting the best drug targets with relevance to human disease and evidence that modulating them will be beneficial for patients. Open data initiatives are increasingly placing such knowledge into the public domain. Areas covered: In this review, the authors discuss emerging resources such as Open Targets which integrate key information to prioritize target-disease connections. Researchers can use it, along with other resources, to select potential new therapeutic targets to initiate drug discovery projects. They also discuss public resources such as DrugBank and ChEMBL that offer potential tools to interrogate these targets. Expert opinion: In our opinion, publically available resources are democratizing and connecting information, enabling disease experts to access and prioritize targets of interest in ways that were not possible a few years ago. Moreover, there are several modalities in addition to small molecule perturbation to modulate a target's activity. Drug discovery scientists can now utilize these new resources to simultaneously evaluate a much larger number of targets than previously possible.

Systematic Analysis of Drug Targets Confirms Expression in Disease-Relevant Tissues.

It is commonly assumed that drug targets are expressed in tissues relevant to their indicated diseases, even under normal conditions. While multiple anecdotal cases support this hypothesis, a comprehensive study has not been performed to verify it. We conducted a systematic analysis to assess gene and protein expression for all targets of marketed and phase III drugs across a diverse collection of normal human tissues. For 87% of gene-disease pairs, the target is expressed in a disease-affected tissue under healthy conditions. This result validates the importance of confirming expression of a novel drug target in an appropriate tissue for each disease indication and strengthens previous findings showing that targets of efficacious drugs should be expressed in relevant tissues under normal conditions. Further characterization of the remaining 13% of gene-disease pairs revealed that most genes are expressed in a different tissue linked to another disease. Our analysis demonstrates the value of extensive tissue specific expression resources.both in terms of tissue and cell diversity as well as techniques used to measure gene expression.

Significant obesity-associated gene expression changes occur in the stomach but not intestines in obese mice.

The gastrointestinal (GI) tract can have significant impact on the regulation of the whole-body metabolism and may contribute to the development of obesity and diabetes. To systemically elucidate the role of the GI tract in obesity, we performed a transcriptomic analysis in different parts of the GI tract of two obese mouse models: ob/ob and high-fat diet (HFD) fed mice. Compared to their lean controls, significant changes in the gene expression were observed in both obese mouse groups in the stomach (ob/ob: 959; HFD: 542). In addition, these changes were quantitatively much higher than in the intestine. Despite the difference in genetic background, the two mouse models shared 296 similar gene expression changes in the stomach. Among those genes, some had known associations to obesity, diabetes, and insulin resistance. In addition, the gene expression profiles strongly suggested an increased gastric acid secretion in both obese mouse models, probably through an activation of the gastrin pathway. In conclusion, our data reveal a previously unknown dominant connection between the stomach and obesity in murine models extensively used in research.

The support of human genetic evidence for approved drug indications.

Over a quarter of drugs that enter clinical development fail because they are ineffective. Growing insight into genes that influence human disease may affect how drug targets and indications are selected. However, there is little guidance about how much weight should be given to genetic evidence in making these key decisions. To answer this question, we investigated how well the current archive of genetic evidence predicts drug mechanisms. We found that, among well-studied indications, the proportion of drug mechanisms with direct genetic support increases significantly across the drug development pipeline, from 2.0% at the preclinical stage to 8.2% among mechanisms for approved drugs, and varies dramatically among disease areas. We estimate that selecting genetically supported targets could double the success rate in clinical development. Therefore, using the growing wealth of human genetic data to select the best targets and indications should have a measurable impact on the successful development of new drugs.

Systematic prediction of drug combinations based on clinical side-effects.

Drug co-prescription (or drug combination) is a therapeutic strategy widely used as it may improve efficacy and reduce side-effect (SE). Since it is impractical to screen all possible drug combinations for every indication, computational methods have been developed to predict new combinations. In this study, we describe a novel approach that utilizes clinical SEs from post-marketing surveillance and the drug label to predict 1,508 novel drug-drug combinations. It outperforms other prediction methods, achieving an AUC of 0.92 compared to an AUC of 0.69 in a previous method, on a much larger drug combination set (245 drug combinations in our dataset compared to 75 in previous work.). We further found from the feature selection that three FDA black-box warned serious SEs, namely pneumonia, haemorrhage rectum, and retinal bleeding, contributed mostly to the predictions and a model only using these three SEs can achieve an average area under curve (AUC) at 0.80 and accuracy at 0.91, potentially with its simplicity being recognized as a practical rule-of-three in drug co-prescription or making fixed-dose drug combination. We also demonstrate this performance is less likely to be influenced by confounding factors such as biased disease indications or chemical structures.

Integrative clinical transcriptomics analyses for new therapeutic intervention strategies: a psoriasis case study.

Psoriasis is a chronic inflammatory skin disease with complex pathological features and unmet pharmacotherapy needs. Here, we present a framework for developing new therapeutic intervention strategies for psoriasis by utilizing publicly available clinical transcriptomics data sets. By exploring the underlying molecular mechanisms of psoriasis, the effects of subsequent perturbation of these mechanisms by drugs and an integrative analysis, we propose a psoriasis disease signature, identify potential drug repurposing opportunities and present novel target selection methodologies. We anticipate that the outlined methodology or similar approaches will further support biomarker discovery and the development of new drugs for psoriasis.

Minipig and beagle animal model genomes aid species selection in pharmaceutical discovery and development.

Improving drug attrition remains a challenge in pharmaceutical discovery and development. A major cause of early attrition is the demonstration of safety signals which can negate any therapeutic index previously established. Safety attrition needs to be put in context of clinical translation (i.e. human relevance) and is negatively impacted by differences between animal models and human. In order to minimize such an impact, an earlier assessment of pharmacological target homology across animal model species will enhance understanding of the context of animal safety signals and aid species selection during later regulatory toxicology studies. Here we sequenced the genomes of the Sus scrofa Göttingen minipig and the Canis familiaris beagle, two widely used animal species in regulatory safety studies. Comparative analyses of these new genomes with other key model organisms, namely mouse, rat, cynomolgus macaque, rhesus macaque, two related breeds (S. scrofa Duroc and C. familiaris boxer) and human reveal considerable variation in gene content. Key genes in toxicology and metabolism studies, such as the UGT2 family, CYP2D6, and SLCO1A2, displayed unique duplication patterns. Comparisons of 317 known human drug targets revealed surprising variation such as species-specific positive selection, duplication and higher occurrences of pseudogenized targets in beagle (41 genes) relative to minipig (19 genes). These data will facilitate the more effective use of animals in biomedical research.

Data mining the human gut microbiota for therapeutic targets.

It is well known that microbes have an intricate role in human health and disease. However, targeted strategies for modulating human health through the modification of either human-associated microbial communities or associated human-host targets have yet to be realized. New knowledge about the role of microbial communities in the microbiota of the gastrointestinal tract (GIT) and their collective genomes, the GIT microbiome, in chronic diseases opens new opportunities for therapeutic interventions. GIT microbiota participation in drug metabolism is a further pharmaceutical consideration. In this review, we discuss how computational methods could lead to a systems-level understanding of the global physiology of the host-microbiota superorganism in health and disease. Such knowledge will provide a platform for the identification and development of new therapeutic strategies for chronic diseases possibly involving microbial as well as human-host targets that improve upon existing probiotics, prebiotics or antibiotics. In addition, integrative bioinformatics analysis will further our understanding of the microbial biotransformation of exogenous compounds or xenobiotics, which could lead to safer and more efficacious drugs.

Electronic health records: Implications for drug discovery.

Electronic health records (EHRs) have increased in popularity in many countries. Pushed by legal mandates, EHR systems have seen substantial progress recently, including increasing adoption of standards, improved medical vocabularies and enhancements in technical infrastructure for data sharing across healthcare providers. Although the progress is directly beneficial to patient care in a hospital or clinical setting, it can also aid drug discovery. In this article, we review three specific applications of EHRs in a drug discovery context: finding novel relationships between diseases, re-evaluating drug usage and discovering phenotype-genotype associations. We believe that in the near future EHR systems and related databases will impact significantly how we discover and develop safe and efficacious medicines.

The role of translational bioinformatics in drug discovery.

The application of translational approaches (e.g. from bed to bench and back) is gaining momentum in the pharmaceutical industry. By utilizing the rapidly increasing volume of data at all phases of drug discovery, translational bioinformatics is poised to address some of the key challenges faced by the industry. Indeed, computational analysis of clinical data and patient records has informed decision-making in multiple aspects of drug discovery and development. Here, we review key examples of translational bioinformatics approaches to emphasize its potential to enhance the quality of drug discovery pipelines, reduce attrition rates and, ultimately, lead to more effective treatments.

Computational biology approaches for selecting host-pathogen drug targets.

The proliferation of genomic platform data, ranging from silencing RNAs through mRNA and microRNA expression to proteomics, is providing new insights into the interplay between human and pathogen genes during infection: the so-called 'host-pathogen interactome'. Exploiting the interactome for novel human drug targets could provide new therapeutic avenues towards the treatment of infectious disease, which could ameliorate the growing clinical challenge of drug-resistant infections. Using the hepatitis C virus interactome as an example, here we suggest a computational biology framework for identifying and prioritizing potential human host targets against infectious diseases.