234 research outputs found
Drugst.One -- A plug-and-play solution for online systems medicine and network-based drug repurposing
In recent decades, the development of new drugs has become increasingly
expensive and inefficient, and the molecular mechanisms of most pharmaceuticals
remain poorly understood. In response, computational systems and network
medicine tools have emerged to identify potential drug repurposing candidates.
However, these tools often require complex installation and lack intuitive
visual network mining capabilities. To tackle these challenges, we introduce
Drugst.One, a platform that assists specialized computational medicine tools in
becoming user-friendly, web-based utilities for drug repurposing. With just
three lines of code, Drugst.One turns any systems biology software into an
interactive web tool for modeling and analyzing complex protein-drug-disease
networks. Demonstrating its broad adaptability, Drugst.One has been
successfully integrated with 21 computational systems medicine tools. Available
at https://drugst.one, Drugst.One has significant potential for streamlining
the drug discovery process, allowing researchers to focus on essential aspects
of pharmaceutical treatment research.Comment: 45 pages, 6 figures, 7 table
Transcriptome-Guided Drug Repositioning
Drug repositioning can save considerable time and resources and significantly speed up
the drug development process. The increasing availability of drug action and disease-associated
transcriptome data makes it an attractive source for repositioning studies. Here, we have developed a
transcriptome-guided approach for drug/biologics repositioning based on multi-layer self-organizing
maps (ml-SOM). It allows for analyzing multiple transcriptome datasets by segmenting them into
layers of drug action- and disease-associated transcriptome data. A comparison of expression changes
in clusters of functionally related genes across the layers identifies “drug target” spots in disease layers
and evaluates the repositioning possibility of a drug. The repositioning potential for two approved
biologics drugs (infliximab and brodalumab) confirmed the drugs’ action for approved diseases
(ulcerative colitis and Crohn’s disease for infliximab and psoriasis for brodalumab). We showed
the potential efficacy of infliximab for the treatment of sarcoidosis, but not chronic obstructive
pulmonary disease (COPD). Brodalumab failed to affect dysregulated functional gene clusters in
Crohn’s disease (CD) and systemic juvenile idiopathic arthritis (SJIA), clearly indicating that it may
not be effective in the treatment of these diseases. In conclusion, ml-SOM offers a novel approach for
transcriptome-guided drug repositioning that could be particularly useful for biologics drugs
A Dietary Cholesterol-Based Intestinal Inflammation Assay for Improving Drug-Discovery on Inflammatory Bowel Diseases
Funding: This work was funded by the Fundação para a Ciência e a Tecnologia (FCT; PTDC/BTM-SAL/29377/2017 to CC and AJ. Zebrafish were reproduced and maintained by the CEDOC Fish Facility, supported by Congento LISBOA-01-0145- FEDER-022170, co-financed by FCT (Portugal) and Lisboa2020, under the PORTUGAL2020 agreement (European Regional Development Fund).Inflammatory bowel diseases (IBD) with chronic infiltration of immune cells in the gastrointestinal tract are common and largely incurable. The therapeutic targeting of IBD has been hampered by the complex causality of the disease, with environmental insults like cholesterol-enriched Western diets playing a critical role. To address this drug development challenge, we report an easy-to-handle dietary cholesterol-based in vivo assay that allows the screening of immune-modulatory therapeutics in transgenic zebrafish models. An improvement in the feeding strategy with high cholesterol diet (HCD) selectively induces a robust and consistent infiltration of myeloid cells in larvae intestines that is highly suitable for compound discovery efforts. Using transgenics with fluorescent reporter expression in neutrophils, we take advantage of the unique zebrafish larvae clarity to monitor an acute inflammatory response in a whole organism context with a fully functional innate immune system. The use of semi-automated image acquisition and processing combined with quantitative image analysis allows categorizing anti- or pro-inflammatory compounds based on a leukocytic inflammation index. Our HCD gut inflammation (HCD-GI) assay is simple, cost- and time-effective as well as highly physiological which makes it unique when compared to chemical-based zebrafish models of IBD. Besides, diet is a highly controlled, selective and targeted trigger of intestinal inflammation that avoids extra-intestinal outcomes and reduces the chances of chemical-induced toxicity during screenings. We show the validity of this assay for a screening platform by testing two dietary phenolic acids, namely gallic acid (GA; 3,4,5-trihydroxybenzoic acid) and ferulic acid (FA; 4-hydroxy-3-methoxycinnamic acid), with well described anti-inflammatory actions in animal models of IBD. Analysis of common IBD therapeutics (Prednisolone and Mesalamine) proved the fidelity of our IBD-like intestinal inflammation model. In conclusion, the HCD-GI assay can facilitate and accelerate drug discovery efforts on IBD, by identification of novel lead molecules with immune modulatory action on intestinal neutrophilic inflammation. This will serve as a jumping-off point for more profound analyses of drug mechanisms and pathways involved in early IBD immune responses.publishersversionpublishe
Identification of clinically relevant genetic variation in immune-mediated inflammatory diseases using genome-wide approaches
Rheumatoid arthritis, psoriasis, psoriatic arthritis, systemic lupus erythematosus, Crohn’s disease and ulcerative colitis are six of the most prevalent immune-mediated inflammatory diseases (IMIDs) and are associated with a high socio-economic impact. There is compelling evidence that IMIDs are genetically complex diseases. To date, however, the genetic component of IMIDs has been only partially explained. Identifying new clinically relevant variation is therefore of major clinical interest. The objective of the present thesis was to identify new genetic variation underlying IMIDs. The research activity here presented is the result of analyzing high-throughput genomic data from a large cohort of IMID patients collected by the IMID Consortium. Using genome-wide approaches and functional analyses, we have identified new genetic variants associated to IMID susceptibility, IMID clinical phenotypes and specific treatment outcomes. Taken together, these findings contribute to better understanding the genetic basis of IMIDs and suggest more specific and preventive therapeutic strategies.L’artritis reumatoide, la psoriasis, l’artritis psoriĂ sica, el lupus eritematĂłs sistèmic, la malaltia de Crohn i la colitis ulcerosa sĂłn sis malalties inflamatòries mediades per immunitat (IMIDs) d’elevada prevalença i amb un fort impacte socioeconòmic. Totes elles comparteixen un component genètic important. No obstant, a dia d’avui, nomĂ©s s’ha caracteritzat una part dels factors genètics de les IMIDs. La identificaciĂł de factors genètics clĂnicament rellevants presenta doncs un gran interès clĂnic per tal d’incorporar la informaciĂł genètica a la prĂ ctica mèdica. L’objectiu d’aquesta tesi Ă©s identificar noves variants genètiques associades a les IMIDs. La recerca que es presenta Ă©s el resultat d’analitzar dades genòmiques d’una gran cohort de pacients amb IMIDs, els quals es van obtenir a travĂ©s del consorci IMID Consortium. Mitjançant estratègies d’anĂ lisi de genoma complet i estudis funcionals, en aquesta tesi s’han identificat noves variants genètiques associades al risc de desenvolupar IMIDs aixĂ com als seus fenotips clĂnics i tractament. Aquesta tesi contribueix significativament a la caracteritzaciĂł del component genètic de les IMIDs i, des d’un punt de vista clĂnic, suggereix noves estratègies terapèutiques
Recent Trends in Computational Research on Diseases
Recent advances in information technology have brought forth a paradigm shift in science, especially in the biology and medical fields. Statistical methodologies based on high-performance computing and big data analysis are now indispensable for the qualitative and quantitative understanding of experimental results. In fact, the last few decades have witnessed drastic improvements in high-throughput experiments in health science, for example, mass spectrometry, DNA microarray, next generation sequencing, etc. Those methods have been providing massive data involving four major branches of omics (genomics, transcriptomics, proteomics, and metabolomics). Information about amino acid sequences, protein structures, and molecular structures are fundamental data for the prediction of bioactivity of chemical compounds when screening drugs. On the other hand, cell imaging, clinical imaging, and personal healthcare devices are also providing important data concerning the human body and disease. In parallel, various methods of mathematical modelling such as machine learning have developed rapidly. All of these types of data can be utilized in computational approaches to understand disease mechanisms, diagnosis, prognosis, drug discovery, drug repositioning, disease biomarkers, driver mutations, copy number variations, disease pathways, and much more. In this Special Issue, we have published 8 excellent papers dedicated to a variety of computational problems in the biomedical field from the genomic level to the whole-person physiological level
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Understanding Disease and Disease Relationships Using Transcriptomic Data
As the volume of transcriptomic data continues to increase, so too does its potential to deepen our understanding of disease; for example, by revealing gene expression patterns shared between diseases. However, key questions remain around the strength of the transcriptomic signal of disease and the identification of meaningful commonalities between datasets, which are addressed in this thesis as follows.
The first chapter, Concordance of Microarray Studies of Parkinson’s Disease, examines the agreement between differential expression signatures across 33 studies of Parkinson’s disease. Comparison of these studies, which cover a range of microarray platforms, tissues, and disease models, reveals a characteristic pattern of differential expression in the most highly-affected tissues in human patients. Using correlation and clustering analyses to measure the representativeness of different study designs to human disease, the work described acts as a guideline for the comparison of microarray studies in the following chapters.
In the next chapter, Using Dysregulated Signalling Paths to Understand Disease, gene expression changes are linked on the human signalling network, enabling identification of network regions dysregulated in disease. Applying this method across a large dataset of 141 common and rare diseases identifies dysregulated processes shared between diverse conditions, which relate to known disease- and drug-sharing-relationships.
The final chapter, Understanding and Predicting Disease Relationships Through Similarity Fusion, explores the integration of gene expression with other data types – in this case, ontological, phenotypic, literature co-occurrence, genetic, and drug data – to understand relationships between diseases. A similarity fusion approach is proposed to overcome the differences in data type properties between each space, resulting in the identification of novel disease relationships spanning multiple bioinformatic levels. The similarity of disease relationships between each data type is considered, revealing that relationships in differential expression space are distinct from those in other molecular and clinical spaces.
In summary, the work described in this thesis sets out a framework for the comparative analysis of transcriptomic data in disease, including the integration of biological networks and other bioinformatic data types, in order to further our knowledge of diseases and the relationships between them.PhD funded by the Biotechnology and Biological Sciences Research Council Doctoral Training Partnershi
In Silico Drug Repurposing: An Effective Tool to Accelerate the Drug Discovery Process
Repurposing “old” drugs to treat both common and rare diseases is increasingly emerging as an attractive proposition due to the use of de-risked compounds, with potential for lower overall development costs and shorter development timelines. This is due to the high attrition rates, significant costs, and slow pace of new drug discovery and development. Drug repurposing is the process of finding new, more efficient uses for already-available medications. Numerous computational drug repurposing techniques exist, there are three main types of computational drug-repositioning methods used on COVID-19 are network-based models, structure-based methods and artificial intelligence (AI) methods used to discover novel drug–target relationships useful for new therapies. In order to assess how a chemical molecule can interact with its biological counterpart and try to find new uses for medicines already on the market, structure-based techniques made it possible to identify small chemical compounds capable of binding macromolecular targets. In this chapter, we explain strategies for drug repurposing, discuss about difficulties encountered by the repurposing community, and suggest reported drugs through the drug repurposing. Moreover, metabolic and drug discovery network resources, tools for network construction, analysis and protein–protein interaction analysis to enable drug repurposing to reach its full potential
Using systems medicine to identify a therapeutic agent with potential for repurposing in inflammatory bowel disease
ObjectiveInflammatory bowel diseases cause significant morbidity and mortality. Aberrant NF-ÎşB signalling is strongly associated with these conditions, and several established drugs influence the NF-ÎşB signalling network to exert their effect. This study aimed to identify drugs which alter NF-ÎşB signalling and may be repositioned for use in inflammatory bowel disease.DesignThe SysmedIBD consortium established a novel drug-repurposing pipeline based on a combination of in-silico drug discovery and biological assays targeted at demonstrating an impact on NF-kappaB signalling, and a murine model of IBD.ResultsThe drug discovery algorithm identified several drugs already established in IBD, including corticosteroids. The highest-ranked drug was the macrolide antibiotic Clarithromycin, which has previously been reported to have anti-inflammatory effects in aseptic conditions. Clarithromycin's effects were validated in several experiments: it influenced NF-ÎşB mediated transcription in murine peritoneal macrophages and intestinal enteroids; it suppressed NF-ÎşB protein shuttling in murine reporter enteroids; it suppressed NF-ÎşB (p65) DNA binding in the small intestine of mice exposed to LPS, and it reduced the severity of dextran sulphate sodium-induced colitis in C57BL/6 mice. Clarithromycin also suppressed NF-ÎşB (p65) nuclear translocation in human intestinal enteroids.ConclusionsThese findings demonstrate that in-silico drug repositioning algorithms can viably be allied to laboratory validation assays in the context of inflammatory bowel disease; and that further clinical assessment of clarithromycin in the management of inflammatory bowel disease is required
Elucidation of bioinformatic-guided high-prospect drug repositioning candidates for DMD via Swanson linking of target-focused latent knowledge from text-mined categorical metadata
Duchenne Muscular Dystrophy (DMD)’s complex multi-system pathophysiology, coupled with the cost-prohibitive logistics of multi-year drug screening and follow-up, has hampered the pursuit of new therapeutic approaches. Here we conducted a systematic historical and text mining-based pilot feasibility study to explore the potential of established or previously tested drugs as prospective DMD therapeutic agents. Our approach utilized a Swanson linking-inspired method to uncover meaningful yet largely hidden deep semantic connections between pharmacologically significant DMD targets and drugs developed for unrelated diseases. Specifically, we focused on molecular target-based MeSH terms and categories as high-yield bioinformatic proxies, effectively tagging relevant literature with categorical metadata. To identify promising leads, we comprehensively assembled published reports from 2011 and sampling from subsequent years. We then determined the earliest year when distinct MeSH terms or category labels of the relevant cellular target were referenced in conjunction with the drug, as well as when the pertinent target itself was first conclusively identified as holding therapeutic value for DMD. By comparing the earliest year when the drug was identifiable as a DMD treatment candidate with that of the first actual report confirming this, we computed an Index of Delayed Discovery (IDD), which serves as a metric of Swanson-linked latent knowledge. Using these findings, we identified data from previously unlinked articles subsetted via MeSH-derived Swanson linking or from target classes within the DrugBank repository. This enabled us to identify new but untested high-prospect small-molecule candidates that are of particular interest in repurposing for DMD and warrant further investigations
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