Methods Mol Biol. 2022;2496:123-140. doi: 10.1007/978-1-0716-2305-3_7.

ABSTRACT

The major outcomes and insights of scientific research and clinical study end up in the form of publication or clinical record in an unstructured text format. Due to advancements in biomedical research, the growth of published literature is getting tremendous large in recent years. The scientists and clinical researchers are facing a big challenge to stay current with the knowledge and to extract hidden information from this sheer quantity of millions of published biomedical literature. The potential one-stop automated solution to this problem is biomedical literature mining. One of the long-standing goals in biology is to discover the disease-causing genes and their specific roles in personalized precision medicine and drug repurposing. However, the empirical approaches and clinical affirmation are expensive and time-consuming. In silico approach using text mining to identify the disease causing genes can contribute towards biomarker discovery. This chapter presents a protocol on combining literature mining and machine learning for predicting biomedical discoveries with a special emphasis on gene-disease relation based discovery. The protocol is presented as a literature based discovery (LBD) pipeline for gene-disease based discovery. The protocol includes our web based tools: (1) DNER (Disease Named Entity Recognizer) for disease entity recognition, (2) BCCNER (Bidirectional, Contextual clues Named Entity Tagger) for gene/protein entity recognition, (3) DisGeReExT (Disease-Gene Relation Extractor) for statistically validated results and visualization, and (4) a newly introduced deep learning based method for association discovery. Our proposed deep learning based method can be generalized and applied to other important biomedical discoveries focusing on entities such as drug/chemical, or miRNA.

PMID:35713862 | DOI:10.1007/978-1-0716-2305-3_7

Front Genet. 2022 May 31;13:868015. doi: 10.3389/fgene.2022.868015. eCollection 2022.

ABSTRACT

Target prioritization is essential for drug discovery and repositioning. Applying computational methods to analyze and process multi-omics data to find new drug targets is a practical approach for achieving this. Despite an increasing number of methods for generating datasets such as genomics, phenomics, and proteomics, attempts to integrate and mine such datasets remain limited in scope. Developing hybrid intelligence solutions that combine human intelligence in the scientific domain and disease biology with the ability to mine multiple databases simultaneously may help augment drug target discovery and identify novel drug-indication associations. We believe that integrating different data sources using a singular numerical scoring system in a hybrid intelligent framework could help to bridge these different omics layers and facilitate rapid drug target prioritization for studies in drug discovery, development or repositioning. Herein, we describe our prototype of the StarGazer pipeline which combines multi-source, multi-omics data with a novel target prioritization scoring system in an interactive Python-based Streamlit dashboard. StarGazer displays target prioritization scores for genes associated with 1844 phenotypic traits, and is available via https://github.com/AstraZeneca/StarGazer.

PMID:35711912 | PMC:PMC9197487 | DOI:10.3389/fgene.2022.868015

BMC Bioinformatics. 2022 Jun 16;23(1):232. doi: 10.1186/s12859-022-04753-4.

ABSTRACT

BACKGROUND: The Open Targets (OT) Platform integrates a wide range of data sources on target-disease associations to facilitate identification of potential therapeutic drug targets to treat human diseases. However, due to the complexity that targets are usually functionally pleiotropic and efficacious for multiple indications, challenges in identifying novel target to indication associations remain. Specifically, persistent need exists for new methods for integration of novel target-disease association evidence and biological knowledge bases via advanced computational methods. These offer promise for increasing power for identification of the most promising target-disease pairs for therapeutic development. Here we introduce a novel approach by integrating additional target-disease features with machine learning models to further uncover druggable disease to target indications.

RESULTS: We derived novel target-disease associations as supplemental features to OT platform-based associations using three data sources: (1) target tissue specificity from GTEx expression profiles; (2) target semantic similarities based on gene ontology; and (3) functional interactions among targets by embedding them from protein-protein interaction (PPI) networks. Machine learning models were applied to evaluate feature importance and performance benchmarks for predicting targets with known drug indications. The evaluation results show the newly integrated features demonstrate higher importance than current features in OT. In addition, these also show superior performance over association benchmarks and may support discovery of novel therapeutic indications for highly pursued targets.

CONCLUSION: Our newly generated features can be used to represent additional underlying biological relatedness among targets and diseases to further empower improved performance for predicting novel indications for drug targets through advanced machine learning models. The proposed methodology enables a powerful new approach for systematic evaluation of drug targets with novel indications.

PMID:35710324 | DOI:10.1186/s12859-022-04753-4

Life Sci. 2022 Jun 13:120716. doi: 10.1016/j.lfs.2022.120716. Online ahead of print.

ABSTRACT

INTRODUCTION: Malignant pleural mesothelioma (MPM) is a rare malignancy affecting the mesothelial cells in the pleural lining surrounding the lungs. First approved chemotherapy against MPM was a platinum/antifolate (cisplatin/pemetrexed) (2003). Since then, no USFDA approvals have gone through for small molecules as these molecules have not been proven to be therapeutically able in later stages of clinical studies. An alternative to conventional chemotherapy can be utilization of monoclonal antibodies, which are proven to improve patient survival significantly as compared to conventional chemotherapy (Nivolumab + Ipilimumab, 2020).

AREA COVERED: Drug repurposing has been instrumental in drug discovery for rare diseases such as MPM and multiple repositioned small molecule therapies and immunotherapies are currently being tested for its applicability in MPM management. This article summarizes essential breakthroughs along the pre-clinical and clinical developmental stages of small molecules and monoclonal antibodies for MPM management.

EXPERT OPINION: For rare diseases such as malignant pleural mesothelioma, a drug repurposing strategy can be adapted as it eases the financial burden on pharmaceutical companies along with fast-tracking development. With the rise of multiple small molecule repurposed therapies and innovations in localized treatment, MPM therapeutics are bound to be more effective in this decade.

PMID:35709894 | DOI:10.1016/j.lfs.2022.120716

Adv Sci (Weinh). 2022 Jun 15:e2200222. doi: 10.1002/advs.202200222. Online ahead of print.

ABSTRACT

Current therapeutic strategies against bacterial infections focus on reduction of pathogen load using antibiotics; however, stimulation of host tolerance to infection in the presence of pathogens might offer an alternative approach. Computational transcriptomics and Xenopus laevis embryos are used to discover infection response pathways, identify potential tolerance inducer drugs, and validate their ability to induce broad tolerance. Xenopus exhibits natural tolerance to Acinetobacter baumanii, Klebsiella pneumoniae, Staphylococcus aureus, and Streptococcus pneumoniae bacteria, whereas Aeromonas hydrophila and Pseudomonas aeruginosa produce lethal infections. Transcriptional profiling leads to definition of a 20-gene signature that discriminates between tolerant and susceptible states, as well as identification of a more active tolerance response to gram negative compared to gram positive bacteria. Gene pathways associated with active tolerance in Xenopus, including some involved in metal ion binding and hypoxia, are found to be conserved across species, including mammals, and administration of a metal chelator (deferoxamine) or a HIF-1α agonist (1,4-DPCA) in embryos infected with lethal A. hydrophila increased survival despite high pathogen load. These data demonstrate the value of combining the Xenopus embryo infection model with computational multiomics analyses for mechanistic discovery and drug repurposing to induce host tolerance to bacterial infections.

PMID:35706367 | DOI:10.1002/advs.202200222

Eur J Cancer Prev. 2021 Dec 1;31(Suppl 1):S9. doi: 10.1097/01.cej.0000816688.51872.f3. Epub 2021 Dec 30.

NO ABSTRACT

PMID:35704004 | DOI:10.1097/01.cej.0000816688.51872.f3

Med Cannabis Cannabinoids. 2022 Feb 25;5(1):32-35. doi: 10.1159/000522472. eCollection 2022.

NO ABSTRACT

PMID:35702401 | PMC:PMC9149510 | DOI:10.1159/000522472

NPJ Digit Med. 2022 Jun 14;5(1):77. doi: 10.1038/s41746-022-00617-6.

ABSTRACT

Computational drug repurposing methods adapt Artificial intelligence (AI) algorithms for the discovery of new applications of approved or investigational drugs. Among the heterogeneous datasets, electronic health records (EHRs) datasets provide rich longitudinal and pathophysiological data that facilitate the generation and validation of drug repurposing. Here, we present an appraisal of recently published research on computational drug repurposing utilizing the EHR. Thirty-three research articles, retrieved from Embase, Medline, Scopus, and Web of Science between January 2000 and January 2022, were included in the final review. Four themes, (1) publication venue, (2) data types and sources, (3) method for data processing and prediction, and (4) targeted disease, validation, and released tools were presented. The review summarized the contribution of EHR used in drug repurposing as well as revealed that the utilization is hindered by the validation, accessibility, and understanding of EHRs. These findings can support researchers in the utilization of medical data resources and the development of computational methods for drug repurposing.

PMID:35701544 | DOI:10.1038/s41746-022-00617-6

3 Biotech. 2022 Jul;12(7):146. doi: 10.1007/s13205-022-03207-1. Epub 2022 Jun 10.

ABSTRACT

The interrelation of cancer and Alzheimer's disorder (AD)-associated molecular mechanisms, reported last decade, paved the path for drug discoveries. In this direction, while chemotherapy is well established for breast cancer (BC), the detection and targeted therapy for AD is not advanced due to a lack of recognized peripheral biomarkers. The present study aimed to find diagnostic and prognostic molecular signature markers common to both BC and AD for possible drug targeting and repurposing. For these disorders, two corresponding microarray datasets (GSE42568, GSE33000) were used for identifying the differentially expressed genes (DEGs), resulting in recognition of CD209 and MCM7 as the two common players. While the CD209 gene was upregulated in both disorders and has been studied vastly, the MCM7 gene showed a strikingly reverse pattern of expression level, downregulated in the case of BC while upregulated in the case of AD. Thus, the MCM7 gene was further analyzed for expression, predictions, and validations of its structure and protein-protein interaction (PPI) for the possible development of new treatment methods for AD. The study concluded with indicative drug repurposing studies to check the effect of existing clinically approved drugs for BC for rectifying the expression levels of the mutated MCM7 gene in AD.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13205-022-03207-1.

PMID:35698583 | PMC:PMC9187790 | DOI:10.1007/s13205-022-03207-1

Cell Mol Life Sci. 2022 Jun 13;79(7):361. doi: 10.1007/s00018-022-04390-3.

ABSTRACT

COVID-19 is a complex disease with short- and long-term respiratory, inflammatory and neurological symptoms that are triggered by the infection with SARS-CoV-2. Invasion of the brain by SARS-CoV-2 has been observed in humans and is postulated to be involved in post-COVID state. Brain infection is particularly pronounced in the K18-hACE2 mouse model of COVID-19. Prevention of brain infection in the acute phase of the disease might thus be of therapeutic relevance to prevent long-lasting symptoms of COVID-19. We previously showed that melatonin or two prescribed structural analogs, agomelatine and ramelteon delay the onset of severe clinical symptoms and improve survival of SARS-CoV-2-infected K18-hACE2 mice. Here, we show that treatment of K18-hACE2 mice with melatonin and two melatonin-derived marketed drugs, agomelatine and ramelteon, prevents SARS-CoV-2 entry in the brain, thereby reducing virus-induced damage of small cerebral vessels, immune cell infiltration and brain inflammation. Molecular modeling analyses complemented by experimental studies in cells showed that SARS-CoV-2 entry in endothelial cells is prevented by melatonin binding to an allosteric-binding site on human angiotensin-converting enzyme 2 (ACE2), thus interfering with ACE2 function as an entry receptor for SARS-CoV-2. Our findings open new perspectives for the repurposing of melatonergic drugs and its clinically used analogs in the prevention of brain infection by SARS-CoV-2 and COVID-19-related long-term neurological symptoms.

PMID:35697820 | DOI:10.1007/s00018-022-04390-3

Sci Rep. 2022 Jun 13;12(1):9726. doi: 10.1038/s41598-022-13460-2.

NO ABSTRACT

PMID:35697726 | DOI:10.1038/s41598-022-13460-2

J Neural Transm (Vienna). 2022 Jun 13. doi: 10.1007/s00702-022-02520-6. Online ahead of print.

ABSTRACT

To date, the diagnoses of Parkinson syndromes are based on clinical examination. Therefore, these specific diagnoses are made, when the neuropathological process is already advanced. However, disease modification or neuroprotection, is considered to be most effective before marked neurodegeneration has occurred. In recent years, early clinical or prodromal stages of Parkinson syndromes came into focus. Moreover, subtypes of distinct diseases will allow predictions of the individual course of the diseases more precisely. Thereby, patients will be enrolled into clinical trials with more specific disease entities and endpoints. Furthermore, novel fluid and imaging biomarkers that allow biochemical diagnoses are under development. These will lead to earlier diagnoses and earlier therapy in the future as consequence. Furthermore, therapeutic approaches will take the underlying neuropathological process of neurodegenerative Parkinson syndromes more specific into account. Specifically, future therapies will target the aggregation of aggregation-prone proteins such as alpha-synuclein and tau, the degradation of pathological aggregates, and the spreading of pathological protein aggregates throughout the brain. Many of these approaches are already in (pre)clinical development. In addition, anti-inflammatory approaches are in development. Furthermore, drug-repurposing is a feasible approach to shorten the developmental process of new drugs.

PMID:35695938 | DOI:10.1007/s00702-022-02520-6

Naunyn Schmiedebergs Arch Pharmacol. 2022 Jun 13. doi: 10.1007/s00210-022-02263-x. Online ahead of print.

ABSTRACT

Cancer is a complex disease affecting millions of people around the world. Despite advances in surgical and radiation therapy, chemotherapy continues to be an important therapeutic option for the treatment of cancer. The current treatment is expensive and has several side effects. Also, over time, cancer cells develop resistance to chemotherapy, due to which there is a demand for new drugs. Drug repurposing is a novel approach that focuses on finding new applications for the old clinically approved drugs. Current advances in the high-dimensional multiomics landscape, especially proteomics, genomics, and computational omics-data analysis, have facilitated drug repurposing. The drug repurposing approach provides cheaper, effective, and safe drugs with fewer side effects and fastens the process of drug development. The review further delineates each repurposed drug's original indication and mechanism of action in cancer. Along with this, the article also provides insight upon artificial intelligence and its application in drug repurposing. Clinical trials are vital for determining medication safety and effectiveness, and hence the clinical studies for each repurposed medicine in cancer, including their stages, status, and National Clinical Trial (NCT) identification, are reported in this review article. Various emerging evidences imply that repurposing drugs is critical for the faster and more affordable discovery of anti-cancerous drugs, and the advent of artificial intelligence-based computational tools can accelerate the translational cancer-targeting pipeline.

PMID:35695911 | DOI:10.1007/s00210-022-02263-x

Pharmacoepidemiol Drug Saf. 2022 Jun 10. doi: 10.1002/pds.5491. Online ahead of print.

ABSTRACT

With availability of voluminous sets of observational data, an empirical paradigm to screen for drug repurposing opportunities (i.e., beneficial effects of drugs on non-indicated outcomes) is feasible. In this paper, we use a linked claims and electronic health record database to comprehensively explore repurposing effects of anti-hypertensive drugs. We follow a target trial emulation framework for causal inference to emulate randomized controlled trials estimating confounding adjusted effects of anti-hypertensives on each of 262 outcomes of interest. We then fit hierarchical models to the results as a form of post-processing to account for multiple comparisons and to sift through the results in a principled way. Our motivation is twofold. We seek both to surface genuinely intriguing drug repurposing opportunities and to elucidate through a real application some study design decisions and potential biases that arise in this context. This article is protected by copyright. All rights reserved.

PMID:35689299 | DOI:10.1002/pds.5491

Front Nutr. 2022 May 23;9:847320. doi: 10.3389/fnut.2022.847320. eCollection 2022.

ABSTRACT

Exposure to arsenic (As), an inorganic poison, may lead to skin lesions, including dermatitis. Vitamin A (VA), a fat-soluble vitamin essential for mucous membrane integrity, plays a key role in skin protection. Although the beneficial actions of VA are known, the anti-As-related dermatitis effects of VA action remain unclear. Hence, in this study, we aimed to interpret and identify the core target genes and therapeutic mechanisms of VA action in the treatment of As-related dermatitis through integrated in silico approaches of network pharmacology and molecular docking. We integrated the key VA-biological target-signaling pathway-As-related dermatitis networks for identifying core drug targets and interaction pathways associated with VA action. The network pharmacology data indicated that VA may possess potential activity for treating As-related dermatitis through the effective regulation of core target genes. An enrichment analysis in biological processes further revealed multiple immunoregulation-associated functions, including interferon-gamma production and negative regulation of T-cell activation and production of molecular mediator of immune response. An enrichment analysis in molecular pathways mainly uncovered multiple biological signaling, including natural killer cell mediated cytotoxicity, autophagy, apoptosis, necroptosis, platelet activation involved in cell fate, and immunity regulations. Molecular docking study was used to identify docked well core target proteins with VA, including Jun, tumor protein p53 (TP53), mitogen-activated protein kinase-3 (MAPK3), MAPK1, and MAPK14. In conclusion, the potential use of VA may suppress the inflammatory stress and enhance the immunity against As-related dermatitis. In the future, VA might be useful in the treatment of dermatitis associated with As through multi-targets and multi-pathways in clinical practice.

PMID:35685889 | PMC:PMC9171494 | DOI:10.3389/fnut.2022.847320

Int J Mol Sci. 2022 Jun 6;23(11):6347. doi: 10.3390/ijms23116347.

ABSTRACT

Multiple sclerosis is an autoimmune disease with a strong neuroinflammatory component that contributes to severe demyelination, neurodegeneration and lesions formation in white and grey matter of the spinal cord and brain. Increasing attention is being paid to the signaling of the biogenic amine histamine in the context of several pathological conditions. In multiple sclerosis, histamine regulates the differentiation of oligodendrocyte precursors, reduces demyelination, and improves the remyelination process. However, the concomitant activation of histamine H1-H4 receptors can sustain either damaging or favorable effects, depending on the specifically activated receptor subtype/s, the timing of receptor engagement, and the central versus peripheral target district. Conventional drug development has failed so far to identify curative drugs for multiple sclerosis, thus causing a severe delay in therapeutic options available to patients. In this perspective, drug repurposing offers an exciting and complementary alternative for rapidly approving some medicines already approved for other indications. In the present work, we have adopted a new network-medicine-based algorithm for drug repurposing called SAveRUNNER, for quantifying the interplay between multiple sclerosis-associated genes and drug targets in the human interactome. We have identified new histamine drug-disease associations and predicted off-label novel use of the histaminergic drugs amodiaquine, rupatadine, and diphenhydramine among others, for multiple sclerosis. Our work suggests that selected histamine-related molecules might get to the root causes of multiple sclerosis and emerge as new potential therapeutic strategies for the disease.

PMID:35683024 | DOI:10.3390/ijms23116347

Cancers (Basel). 2022 May 25;14(11):2608. doi: 10.3390/cancers14112608.

ABSTRACT

Based on the enhanced knowledge on the tumor microenvironment (TME), a more comprehensive treatment landscape for targeting the TME has emerged. This microenvironment provides multiple therapeutic targets due to its diverse characteristics, leading to numerous TME-targeted strategies. With multifaced activities targeting tumors and the TME, vitamin C is renown as a promising candidate for combination therapy. In this review, we present new advances in how vitamin C reshapes the TME in the immune, hypoxic, metabolic, acidic, neurological, mechanical, and microbial dimensions. These findings will open new possibilities for multiple therapeutic avenues in the fight against cancer. We also review the available preclinical and clinical evidence of vitamin C combined with established therapies, highlighting vitamin C as an adjuvant that can be exploited for novel therapeutics. Finally, we discuss unresolved questions and directions that merit further investigation.

PMID:35681589 | DOI:10.3390/cancers14112608

Cancers (Basel). 2022 May 25;14(11):2602. doi: 10.3390/cancers14112602.

ABSTRACT

Repurposed drugs have demonstrated in vitro success against high-grade gliomas; however, their clinical success has been limited due to the in vitro model not truly representing the clinical scenario. In this study, we used two distinct patient-derived tumour fragments (tumour core (TC) and tumour margin (TM)) to generate a heterogeneous, clinically relevant in vitro model to assess if a combination of repurposed drugs (irinotecan, pitavastatin, disulfiram, copper gluconate, captopril, celecoxib, itraconazole and ticlopidine), each targeting a different growth promoting pathway, could successfully treat high-grade gliomas. To ensure the clinical relevance of our data, TC and TM samples from 11 different patients were utilized. Our data demonstrate that, at a concentration of 100µm or lower, all drug combinations achieved lower LogIC50 values than temozolomide, with one of the combinations almost eradicating the cancer by achieving cell viabilities below 4% in five of the TM samples 6 days after treatment. Temozolomide was unable to stop tumour growth over the 14-day assay, while combination 1 stopped tumour growth, with combinations 2, 3 and 4 slowing down tumour growth at higher doses. To validate the cytotoxicity data, we used two distinct assays, end point MTT and real-time IncuCyte life analysis, to evaluate the cytotoxicity of the combinations on the TC fragment from patient 3, with the cell viabilities comparable across both assays. The local administration of combinations of repurposed drugs that target different growth promoting pathways of high-grade gliomas have the potential to be translated into the clinic as a novel treatment strategy for high-grade gliomas.

PMID:35681582 | DOI:10.3390/cancers14112602

Curr Issues Mol Biol. 2022 May 19;44(5):2287-2299. doi: 10.3390/cimb44050155.

ABSTRACT

Drug-target interactions provide insight into the drug-side effects and drug repositioning. However, wet-lab biochemical experiments are time-consuming and labor-intensive, and are insufficient to meet the pressing demand for drug research and development. With the rapid advancement of deep learning, computational methods are increasingly applied to screen drug-target interactions. Many methods consider this problem as a binary classification task (binding or not), but ignore the quantitative binding affinity. In this paper, we propose a new end-to-end deep learning method called DeepMHADTA, which uses the multi-head self-attention mechanism in a deep residual network to predict drug-target binding affinity. On two benchmark datasets, our method outperformed several current state-of-the-art methods in terms of multiple performance measures, including mean square error (MSE), consistency index (CI), rm2, and PR curve area (AUPR). The results demonstrated that our method achieved better performance in predicting the drug-target binding affinity.

PMID:35678684 | DOI:10.3390/cimb44050155

ArXiv. 2022 Jun 7:arXiv:2206.02970v1. Preprint.

ABSTRACT

Network medicine has been pursued for Covid-19 drug repurposing. One such approach adopts structural controllability, a theory for controlling a network (the cell). Motivated to protect the cell from viral infections, we extended this theory to total controllability and introduced a new concept of control hubs. Perturbation to any control hub renders the cell uncontrollable by exogenous stimuli, e.g., viral infections, so control hubs are ideal drug targets. We developed an efficient algorithm for finding all control hubs and applied it to the largest homogenous human protein-protein interaction network. Our new method outperforms several popular gene-selection methods, including that based on structural controllability. The final 65 druggable control hubs are enriched with functions of cell proliferation, regulation of apoptosis, and responses to cellular stress and nutrient levels, revealing critical pathways induced by SARS-CoV-2. These druggable control hubs led to drugs in 4 major categories: antiviral and anti-inflammatory agents, drugs on central nerve systems, and dietary supplements and hormones that boost immunity. Their functions also provided deep insights into the therapeutic mechanisms of the drugs for Covid-19 therapy, making the new approach an explainable drug repurposing method. A remarkable example is Fostamatinib that has been shown to lower mortality, shorten the length of ICU stay, and reduce disease severity of hospitalized Covid-19 patients. The drug targets 10 control hubs, 9 of which are kinases that play key roles in cell differentiation and programmed death. One such kinase is RIPK1 that directly interacts with viral protein nsp12, the RdRp of the virus. The study produced many control hubs that were not targets of existing drugs but were enriched with proteins on membranes and the NF-$\kappa$B pathway, so are excellent candidate targets for new drugs.

PMID:35677421 | PMC:PMC9176652