The use of genomic variants to drive drug repurposing for chronic hepatitis B

BackgroundOne of the main challenges in personalized medicine is to establish and apply a large number of variants from genomic databases into clinical diagnostics and further facilitate genome-driven drug repurposing. By utilizing biological chronic hepatitis B infection (CHB) risk genes, our study proposed a systematic approach to use genomic variants to drive drug repurposing for CHB.MethodThe genomic variants were retrieved from the Genome-Wide Association Study (GWAS) and Phenome-Wide Association Study (PheWAS) databases. Then, the biological CHB risk genes crucial for CHB progression were prioritized based on the scoring system devised with five strict functional annotation criteria. A score of ≥ 2 were categorized as the biological CHB risk genes and further shed light on drug target genes for CHB treatments. Overlapping druggable targets were identified using two drug databases (DrugBank and Drug-Gene Interaction Database (DGIdb)).ResultsA total of 44 biological CHB risk genes were screened based on the scoring system from five functional annotation criteria. Interestingly, we found 6 druggable targets that overlapped with 18 drugs with status of undergoing clinical trials for CHB, and 9 druggable targets that overlapped with 20 drugs undergoing preclinical investigations for CHB. Eight druggable targets were identified, overlapping with 25 drugs that can potentially be repurposed for CHB. Notably, CD40 and HLA-DPB1 were identified as promising targets for CHB drug repurposing based on the target scores.ConclusionThrough the integration of genomic variants and a bioinformatic approach, our findings suggested the plausibility of CHB genomic variant-driven drug repurposing for CHB.     

Drug repurposing in oncology: Compounds,pathways, phenotypes and computational approaches for colorectal cancer

The strategy of using existing drugs originally developed for one disease to treat other indications has found success across medical fields. Such drug repurposing promises faster access of drugs to patients while reducing costs in the long and difficult process of drug development. However, the number of existing drugs and diseases, together with the heterogeneity of patients and diseases, notably including cancers, can make repurposing time consuming and inefficient. The key question we address is how to efficiently repurpose an existing drug to treat a given indication. As drug efficacy remains the main bottleneck for overall success, we discuss the need for machine-learning computational methods in combination with specific phenotypic studies along with mechanistic studies, chemical genetics and omics assays to successfully predict disease-drug pairs. Such a pipeline could be particularly important to cancer patients who face heterogeneous, recurrent and metastatic disease and need fast and personalized treatments. Here we focus on drug repurposing for colorectal cancer and describe selected therapeutics already repositioned for its prevention and/or treatment as well as potential candidates. We consider this review as a selective compilation of approaches and methodologies, and argue how, taken together, they could bring drug repurposing to the next level.     

Pharmacologically active metabolites,combination screening and target identification-driven drug repositioning in antituberculosis drug discovery

There has been renewed interest in alternative strategies to address bottlenecks in antibiotic development. These include the repurposing of approved drugs for use as novel anti-infective agents, or their exploitation as leads in drug repositioning. Such approaches are especially attractive for tuberculosis (TB), a disease which remains a leading cause of morbidity and mortality globally and, increasingly, is associated with the emergence of drug-resistance. In this review article, we introduce a refinement of traditional drug repositioning and repurposing strategies involving the development of drugs that are based on the active metabolite(s) of parental compounds with demonstrated efficacy. In addition, we describe an approach to repositioning the natural product antibiotic, fusidic acid, for use against Mycobacterium tuberculosis. Finally, we consider the potential to exploit the chemical matter arising from these activities in combination screens and permeation assays which are designed to confirm mechanism of action (MoA), elucidate potential synergies in polypharmacy, and to develop rules for drug permeability in an organism that poses a special challenge to new drug development.     

Substrate-Driven Mapping of the Degradome by Comparison of Sequence Logos

Sequence logos are frequently used to illustrate substrate preferences and specificity of proteases. Here, we employed the compiled substrates of the MEROPS database to introduce a novel metric for comparison of protease substrate preferences. The constructed similarity matrix of 62 proteases can be used to intuitively visualize similarities in protease substrate readout via principal component analysis and construction of protease specificity trees. Since our new metric is solely based on substrate data, we can engraft the protease tree including proteolytic enzymes of different evolutionary origin. Thereby, our analyses confirm pronounced overlaps in substrate recognition not only between proteases closely related on sequence basis but also between proteolytic enzymes of different evolutionary origin and catalytic type. To illustrate the applicability of our approach we analyze the distribution of targets of small molecules from the ChEMBL database in our substrate-based protease specificity trees. We observe a striking clustering of annotated targets in tree branches even though these grouped targets do not necessarily share similarity on protein sequence level. This highlights the value and applicability of knowledge acquired from peptide substrates in drug design of small molecules, e.g., for the prediction of off-target effects or drug repurposing. Consequently, our similarity metric allows to map the degradome and its associated drug target network via comparison of known substrate peptides. The substrate-driven view of protein-protein interfaces is not limited to the field of proteases but can be applied to any target class where a sufficient amount of known substrate data is available.     

SWEETLEAD: an In Silico Database of Approved Drugs,Regulated Chemicals,and Herbal Isolates for Computer-Aided Drug Discovery

In the face of drastically rising drug discovery costs, strategies promising to reduce development timelines and expenditures are being pursued. Computer-aided virtual screening and repurposing approved drugs are two such strategies that have shown recent success. Herein, we report the creation of a highly-curated in silico database of chemical structures representing approved drugs, chemical isolates from traditional medicinal herbs, and regulated chemicals, termed the SWEETLEAD database. The motivation for SWEETLEAD stems from the observance of conflicting information in publicly available chemical databases and the lack of a highly curated database of chemical structures for the globally approved drugs. A consensus building scheme surveying information from several publicly accessible databases was employed to identify the correct structure for each chemical. Resulting structures are filtered for the active pharmaceutical ingredient, standardized, and differing formulations of the same drug were combined in the final database. The publically available release of SWEETLEAD (https://simtk.org/home/sweetlead) provides an important tool to enable the successful completion of computer-aided repurposing and drug discovery campaigns.     

Predicting and characterizing selective multiple drug treatments for metabolic diseases and cancer

ABSTRACT: BACKGROUND: In the field of drug discovery, assessing the potential of multidrug therapies is a difficult task because of the combinatorial complexity (both theoretical and experimental) and because of the requirements on the selectivity of the therapy. To cope with this problem, we have developed a novel method for the systematic in silico investigation of synergistic effects of currently available drugs on genome-scale metabolic networks. The algorithm finds the optimal combination of drugs which guarantees the inhibition of an objective function, while minimizing the side effect on the overall network. RESULTS: Two different applications are considered: finding drug synergisms for human metabolic diseases (like diabetes, obesity and hypertension) and finding antitumoral drug combinations with minimal side effect on the normal human metabolism. The results we obtain are consistent with some of the available therapeutic indications and predict some new multiple drug treatments. A cluster analysis on all possible interactions among the currently available drugs indicates a limited variety on the metabolic targets for the approved drugs. CONCLUSION: The in silico prediction of drug synergism can represent an important tool for the repurposing of drug in a realistic perspective which considers also the selectivty of the therapy. Moreover, for a more profitable exploitation of drug-drug interactions, also drugs which show a too low efficacy but which have a non-common mechanism of action, can be reconsider as potential ingredients of new multicompound therapeutic indications. Needless to say the clues provided by a computational study like ours need in any case to be thoroughly evaluated experimentally.     

BioGPS: Navigating biological space to predict polypharmacology,off‐targeting,and selectivity

The structural comparison of protein binding sites is increasingly important in drug design; identifying structurally similar sites can be useful for techniques such as drug repurposing, and also in a polypharmacological approach to deliberately affect multiple targets in a disease pathway, or to explain unwanted off‐target effects. Once similar sites are identified, identifying local differences can aid in the design of selectivity. Such an approach moves away from the classical “one target one drug” approach and toward a wider systems biology paradigm. Here, we report a semiautomated approach, called BioGPS, that is based on the software FLAP which combines GRID Molecular Interactions Fields (MIFs) and pharmacophoric fingerprints. BioGPS comprises the automatic preparation of protein structure data, identification of binding sites, and subsequent comparison by aligning the sites and directly comparing the MIFs. Chemometric approaches are included to reduce the complexity of the resulting data on large datasets, enabling focus on the most relevant information. Individual site similarities can be analyzed in terms of their Pharmacophoric Interaction Field (PIF) similarity, and importantly the differences in their PIFs can be extracted. Here we describe the BioGPS approach, and demonstrate its applicability to rationalize off‐target effects (ERα and SERCA), to classify protein families and explain polypharmacology (ABL1 kinase and NQO2), and to rationalize selectivity between subfamilies (MAP kinases p38α/ERK2 and PPARδ/PPARγ). The examples shown demonstrate a significant validation of the method and illustrate the effectiveness of the approach. Proteins 2015; 83:517–532. © 2015 Wiley Periodicals, Inc.     

Bias in vegetation databases? A comparison of stratified‐random and preferential sampling

Aim: Vegetation plots collected since the early 20th century and stored in large vegetation databases are an important source of ecological information. These databases are used for analyses of vegetation diversity and estimation of vegetation parameters, however such analyses can be biased due to preferential sampling of the original data. In contrast, modern vegetation survey increasingly uses stratified‐random instead of preferential sampling. To explore how these two sampling schemes affect vegetation analyses, we compare parameters of vegetation diversity based on preferentially sampled plots from a large vegetation database with those based on stratified‐random sampling. Location: Moravian Karst and Silesia, Czech Republic. Methods: We compared two parallel analyses of forest vegetation, one based on preferentially sampled plots taken from a national vegetation database and the other on plots sampled in the field according to a stratified‐random design. We repeated this comparison for two different regions in the Czech Republic. We focussed on vegetation properties commonly analysed using data from large vegetation databases, including alpha (within‐plot) diversity, cover and participation of different species groups, such as endangered and alien species within plots, total species richness of data sets, beta diversity and ordination patterns. Results: The preferentially sampled data sets obtained from the database contained more endangered species and had higher beta diversity, whereas estimates of alpha diversity and representation of alien species were not consistently different between preferentially and stratified‐randomly sampled data sets. In ordinations, plots from the preferential samples tended to be more common at margins of plot scatters. Conclusions: Vegetation data stored in large databases are influenced by researcher subjectivity in plot positioning, but we demonstrated that not all of their properties necessarily differ from data sets obtained by stratified‐random sampling. This indicates the value of vegetation databases for use in biodiversity studies; however, some analyses based on these databases are clearly biased and their results must be interpreted with caution.     

Modeling drug response using network-based personalized treatment prediction (NetPTP) with applications to inflammatory bowel disease

For many prevalent complex diseases, treatment regimens are frequently ineffective. For example, despite multiple available immunomodulators and immunosuppressants, inflammatory bowel disease (IBD) remains difficult to treat. Heterogeneity in the disease across patients makes it challenging to select the optimal treatment regimens, and some patients do not respond to any of the existing treatment choices. Drug repurposing strategies for IBD have had limited clinical success and have not typically offered individualized patient-level treatment recommendations. In this work, we present NetPTP, a Network-based Personalized Treatment Prediction framework which models measured drug effects from gene expression data and applies them to patient samples to generate personalized ranked treatment lists. To accomplish this, we combine publicly available network, drug target, and drug effect data to generate treatment rankings using patient data. These ranked lists can then be used to prioritize existing treatments and discover new therapies for individual patients. We demonstrate how NetPTP captures and models drug effects, and we apply our framework to individual IBD samples to provide novel insights into IBD treatment.     

The quest to deduce protein function from sequence: the role of pattern databases

In the wake of the numerous now-fruitful genome projects, we have witnessed a 'tsunami' of sequence data and with it the birth of the field of bioinformatics. Bioinformatics involves the application of information technology to the management and analysis of biological data. For many of us, this means that databases and their search tools have become an essential part of the research environment. However, the rate of sequence generation and the haphazard proliferation of databases have made it difficult to keep pace with developments, even for the cognoscenti. Moreover, increasing amounts of sequence information do not necessarily equate with an increase in knowledge, and in the panic to automate the route from raw data to biological insight, we may be generating and propagating innumerable errors in our precious databases. In the genome era upon us, researchers want rapid, easy-to-use, reliable tools for functional characterisation of newly determined sequences. For the pharmaceutical industry in particular, the Pandora's box of bioinformatics harbours an information-rich nugget, ripe with potential drug targets and possible new avenues for the development of therapeutic agents. This review outlines the current status of the major pattern databases now used routinely in the analysis of protein sequences. The review is divided into three main sections. In the first, commonly used terms are defined and the methods behind the databases are briefly described; in the second, the structure and content of the principal pattern databases are discussed; and in the final part, several alignment databases, which are frequently confused with pattern databases, are mentioned. For the new-comer, the array of resources, the range of methods behind them and the different tools required to search them can be confusing. The review therefore also briefly mentions a current international endeavour to integrate the diverse databases, which effort should facilitate sequence analysis in the future. This is particularly important for target-discovery programmes, where the challenge is to rationalise the enormous numbers of potential targets generated by sequence database searches. This problem may be addressed, at least in part, by reducing search outputs to the more focused and manageable subsets suggested by searches of integrated groups of family-specific pattern databases.     

Gene expression‐based drug repurposing to target aging

Aging is the largest risk factor for a variety of noncommunicable diseases. Model organism studies have shown that genetic and chemical perturbations can extend both lifespan and healthspan. Aging is a complex process, with parallel and interacting mechanisms contributing to its aetiology, posing a challenge for the discovery of new pharmacological candidates to ameliorate its effects. In this study, instead of a target‐centric approach, we adopt a systems level drug repurposing methodology to discover drugs that could combat aging in human brain. Using multiple gene expression data sets from brain tissue, taken from patients of different ages, we first identified the expression changes that characterize aging. Then, we compared these changes in gene expression with drug‐perturbed expression profiles in the Connectivity Map. We thus identified 24 drugs with significantly associated changes. Some of these drugs may function as antiaging drugs by reversing the detrimental changes that occur during aging, others by mimicking the cellular defence mechanisms. The drugs that we identified included significant number of already identified prolongevity drugs, indicating that the method can discover de novo drugs that meliorate aging. The approach has the advantages that using data from human brain aging data, it focuses on processes relevant in human aging and that it is unbiased, making it possible to discover new targets for aging studies.     

Analysis of multiple compound–protein interactions reveals novel bioactive molecules

The discovery of novel bioactive molecules advances our systems‐level understanding of biological processes and is crucial for innovation in drug development. For this purpose, the emerging field of chemical genomics is currently focused on accumulating large assay data sets describing compound–protein interactions (CPIs). Although new target proteins for known drugs have recently been identified through mining of CPI databases, using these resources to identify novel ligands remains unexplored. Herein, we demonstrate that machine learning of multiple CPIs can not only assess drug polypharmacology but can also efficiently identify novel bioactive scaffold‐hopping compounds. Through a machine‐learning technique that uses multiple CPIs, we have successfully identified novel lead compounds for two pharmaceutically important protein families, G‐protein‐coupled receptors and protein kinases. These novel compounds were not identified by existing computational ligand‐screening methods in comparative studies. The results of this study indicate that data derived from chemical genomics can be highly useful for exploring chemical space, and this systems biology perspective could accelerate drug discovery processes.     

Rapid Identification of Antifungal Compounds against Exserohilum rostratum Using High Throughput Drug Repurposing Screens

A recent large outbreak of fungal infections by Exserohilum rostratum from contaminated compounding solutions has highlighted the need to rapidly screen available pharmaceuticals that could be useful in therapy. The present study utilized two newly-developed high throughput assays to screen approved drugs and pharmaceutically active compounds for identification of potential antifungal agents. Several known drugs were found that have potent effects against E. rostratum including the triazole antifungal posaconazole. Posaconazole is likely to be effective against infections involving septic joints and may provide an alternative for refractory central nervous system infections. The anti-E. rostratum activities of several other drugs including bithionol (an anti-parasitic drug), tacrolimus (an immunosuppressive agent) and floxuridine (an antimetabolite) were also identified from the drug repurposing screens. In addition, activities of other potential antifungal agents against E. rostratum were excluded, which may avoid unnecessary therapeutic trials and reveals the limited therapeutic alternatives for this outbreak. In summary, this study has demonstrated that drug repurposing screens can be quickly conducted within a useful time-frame. This would allow clinical implementation of identified alternative therapeutics and should be considered as part of the initial public health response to new outbreaks or rapidly-emerging microbial pathogens.     

Vesiclepedia: A Compendium for Extracellular Vesicles with Continuous Community Annotation

Extracellular vesicles (EVs) are membraneous vesicles released by a variety of cells into their microenvironment. Recent studies have elucidated the role of EVs in intercellular communication, pathogenesis, drug, vaccine and gene-vector delivery, and as possible reservoirs of biomarkers. These findings have generated immense interest, along with an exponential increase in molecular data pertaining to EVs. Here, we describe Vesiclepedia, a manually curated compendium of molecular data (lipid, RNA, and protein) identified in different classes of EVs from more than 300 independent studies published over the past several years. Even though databases are indispensable resources for the scientific community, recent studies have shown that more than 50% of the databases are not regularly updated. In addition, more than 20% of the database links are inactive. To prevent such database and link decay, we have initiated a continuous community annotation project with the active involvement of EV researchers. The EV research community can set a gold standard in data sharing with Vesiclepedia, which could evolve as a primary resource for the field.     

A hierarchical method for molecular docking using cloud computing

Discovering small molecules that interact with protein targets will be a key part of future drug discovery efforts. Molecular docking of drug-like molecules is likely to be valuable in this field; however, the great number of such molecules makes the potential size of this task enormous. In this paper, a method to screen small molecular databases using cloud computing is proposed. This method is called the hierarchical method for molecular docking and can be completed in a relatively short period of time. In this method, the optimization of molecular docking is divided into two subproblems based on the different effects on the protein–ligand interaction energy. An adaptive genetic algorithm is developed to solve the optimization problem and a new docking program (FlexGAsDock) based on the hierarchical docking method has been developed. The implementation of docking on a cloud computing platform is then discussed. The docking results show that this method can be conveniently used for the efficient molecular design of drugs.     

Influence of Postprandial Intragastric Pressures on Drug Release from Gastroretentive Dosage Forms

Despite extensive research in the field of gastroretentive dosage forms, this “holy grail” of oral drug delivery yet remained an unmet goal. Especially under fasting conditions, the reproducible retention of dosage forms in the stomach seems to be an impossible task. This is why such systems are often advised to be taken together with food. But also the postprandial motility can contribute significantly to the failure of gastroretentive dosage forms. To investigate the influence of postprandial pressure conditions on drug release from such systems, we used a novel in vitro dissolution tool, the dissolution stress test device. With the aid of this device, we simulated three different intragastric pressure profiles that may occur after postprandial intake. These transit scenarios were based on recently obtained, postprandial SmartPill® data. The tested systems, Glumetza® 1000 and Madopar® HBS 125, are marketed dosage forms that are based on different approaches to achieve proper gastric retention. All three transit scenarios revealed a highly pressure-sensitive drug release behavior, for both drugs. For Madopar® HBS 125, nearly complete drug release was observed even after early occurring pressures. Glumetza® 1000 seemed to be more resistant to these, most likely due to incomplete wetting of the system. On the contrary to these findings, data from standard dissolution tests using the paddle apparatus displayed controlled drug release for both systems for about 6 h. Based on these results, it can be doubted that established gastroretentive systems stay intact over a longer period of time, even under postprandial conditions.