Cancers (Basel). 2021 Jun 9;13(12):2877. doi: 10.3390/cancers13122877.

ABSTRACT

Aberrant angiogenesis is a hallmark for cancer and inflammation, a key notion in drug repurposing efforts. To delineate the anti-angiogenic properties of amifostine in a human adult angiogenesis model via 3D cell metabolomics and upon a stimulant-specific manner, a 3D cellular angiogenesis assay that recapitulates cell physiology and drug action was coupled to untargeted metabolomics by liquid chromatography-mass spectrometry and nuclear magnetic resonance spectroscopy. The early events of angiogenesis upon its most prominent stimulants (vascular endothelial growth factor-A or deferoxamine) were addressed by cell sprouting measurements. Data analyses consisted of a series of supervised and unsupervised methods as well as univariate and multivariate approaches to shed light on mechanism-specific inhibitory profiles. The 3D untargeted cell metabolomes were found to grasp the early events of angiogenesis. Evident of an initial and sharp response, the metabolites identified primarily span amino acids, sphingolipids, and nucleotides. Profiles were pathway or stimulant specific. The amifostine inhibition profile was rather similar to that of sunitinib, yet distinct, considering that the latter is a kinase inhibitor. Amifostine inhibited both. The 3D cell metabolomics shed light on the anti-angiogenic effects of amifostine against VEGF-A- and deferoxamine-induced angiogenesis. Amifostine may serve as a dual radioprotective and anti-angiogenic agent in radiotherapy patients.

PMID:34207535 | DOI:10.3390/cancers13122877

Cancers (Basel). 2021 Jun 26;13(13):3193. doi: 10.3390/cancers13133193.

ABSTRACT

The substantial costs of clinical trials, the lengthy timelines of new drug discovery and development, along the high attrition rates underscore the need for alternative strategies for finding quickly suitable therapeutics agents. Given that most approved drugs possess more than one target tightly linked to other diseases, it encourages promptly testing these drugs in patients. Over the past decades, this has led to considerable attention for drug repurposing, which relies on identifying new uses for approved or investigational drugs outside the scope of the original medical indication. The known safety of approved drugs minimizes the possibility of failure for adverse toxicology, making them attractive de-risked compounds for new applications with potentially lower overall development costs and shorter development timelines. This latter case is an exciting opportunity, specifically in oncology, due to increased resistance towards the current therapies. Indeed, a large body of evidence shows that a wealth of non-cancer drugs has beneficial effects against cancer. Interestingly, 335 drugs are currently being evaluated in different clinical trials for their potential activities against various cancers (Redo database). This review aims to provide an extensive discussion about the anti-cancer activities exerted by antimicrobial agents and presents information about their mechanism(s) of action and stage of development/evaluation.

PMID:34206772 | DOI:10.3390/cancers13133193

Viruses. 2021 Jun 22;13(7):1187. doi: 10.3390/v13071187.

ABSTRACT

Mammarenaviruses are prevalent pathogens distributed worldwide, and several strains cause severe cases of human infections with high morbidity and significant mortality. Currently, there is no FDA-approved antiviral drugs and vaccines against mammarenavirus and the potential treatment option is limited to an off-label use of ribavirin that shows only partial protective effect and associates with side effects. For the past few decades, extensive research has reported potential anti-mammarenaviral drugs and their mechanisms of action in host as well as vaccine candidates. This review describes current knowledge about mammarenavirus virology, progress of antiviral drug development, and technical strategies of drug screening.

PMID:34206216 | DOI:10.3390/v13071187

Molecules. 2021 Jun 21;26(12):3772. doi: 10.3390/molecules26123772.

ABSTRACT

The discovery of drugs capable of inhibiting SARS-CoV-2 is a priority for human beings due to the severity of the global health pandemic caused by COVID-19. To this end, repurposing of FDA-approved drugs such as NSAIDs against COVID-19 can provide therapeutic alternatives that could be utilized as an effective safe treatment for COVID-19. The anti-inflammatory activity of NSAIDs is also advantageous in the treatment of COVID-19, as it was found that SARS-CoV-2 is responsible for provoking inflammatory cytokine storms resulting in lung damage. In this study, 40 FDA-approved NSAIDs were evaluated through molecular docking against the main protease of SARS-CoV-2. Among the tested compounds, sulfinpyrazone 2, indomethacin 3, and auranofin 4 were proposed as potential antagonists of COVID-19 main protease. Molecular dynamics simulations were also carried out for the most promising members of the screened NSAID candidates (2, 3, and 4) to unravel the dynamic properties of NSAIDs at the target receptor. The conducted quantum mechanical study revealed that the hybrid functional B3PW91 provides a good description of the spatial parameters of auranofin 4. Interestingly, a promising structure-activity relationship (SAR) was concluded from our study that could help in the future design of potential SARS-CoV-2 main protease inhibitors with expected anti-inflammatory effects as well. NSAIDs may be used by medicinal chemists as lead compounds for the development of potent SARS-CoV-2 (Mpro) inhibitors. In addition, some NSAIDs can be selectively designated for treatment of inflammation resulting from COVID-19.

PMID:34205704 | DOI:10.3390/molecules26123772

J Pers Med. 2021 Jun 21;11(6):585. doi: 10.3390/jpm11060585.

ABSTRACT

Enhanced permeation retention (EPR) was a significant milestone discovery by Maeda et al. paving the path for the emerging field of nanomedicine to become a powerful tool in the fight against cancer. Sildenafil is a potent inhibitor of phosphodiesterase 5 (PDE-5) used for the treatment of erectile dysfunction (ED) through the relaxation of smooth muscles and the modulation of vascular endothelial permeability. Overexpression of PDE-5 has been reported in lung, colon, metastatic breast cancers, and bladder squamous carcinoma. Moreover, sildenafil has been reported to increase the sensitivity of tumor cells of different origins to the cytotoxic effect of chemotherapeutic agents with augmented apoptosis mediated through inducing the downregulation of Bcl-xL and FAP-1 expression, enhancing reactive oxygen species (ROS) generation, phosphorylating BAD and Bcl-2, upregulating caspase-3,8,9 activities, and blocking cells at G0/G1 cell cycle phase. Sildenafil has also demonstrated inhibitory effects on the efflux activity of ATP-binding cassette (ABC) transporters such as ABCC4, ABCC5, ABCB1, and ABCG2, ultimately reversing multidrug resistance. Accordingly, there has been a growing interest in using sildenafil as monotherapy or chemoadjuvant in EPR augmentation and management of different types of cancer. In this review, we critically examine the basic molecular mechanism of sildenafil related to cancer biology and discuss the overall potential of sildenafil in enhancing EPR-based anticancer drug delivery, pointing to the outcomes of the most important related preclinical and clinical studies.

PMID:34205602 | DOI:10.3390/jpm11060585

IEEE/ACM Trans Comput Biol Bioinform. 2021 Jul 1;PP. doi: 10.1109/TCBB.2021.3094217. Online ahead of print.

ABSTRACT

Predicting the interaction between a compound and a target is crucial for rapid drug repurposing. Deep learning has been successfully applied in drug-target affinity (DTA) problem. However, previous deep learning-based methods ignore modeling the direct interactions between drug and protein residues. This would lead to inaccurate learning of target representation which may change due to the drug binding effects. In addition, previous DTA methods learn protein representation solely based on a small number of protein sequences in DTA datasets while neglecting the use of proteins outside of the DTA datasets. We propose GEFA (Graph Early Fusion Affinity), a novel graph-in-graph neural network with attention mechanism to address the changes in target representation because of the binding effects. Specifically, a drug is modeled as a graph of atoms, which then serves as a node in a larger graph of residues-drug complex. The resulting model is an expressive deep nested graph neural network. We also use pre-trained protein representation powered by the recent effort of learning contextualized protein representation. The experiments are conducted under different settings to evaluate scenarios such as novel drugs or targets. The results demonstrate the effectiveness of the pre-trained protein embedding and the advantages our GEFA in modeling the nested graph for drug-target interaction.

PMID:34197324 | DOI:10.1109/TCBB.2021.3094217

ACS Infect Dis. 2021 Jul 1. doi: 10.1021/acsinfecdis.1c00195. Online ahead of print.

ABSTRACT

Tuberculosis (TB), caused by Mycobacterium tuberculosis, is a major global health concern given the increase in multiple forms of drug-resistant TB. This underscores the importance of a continuous pipeline of new anti-TB agents. Drug repurposing has shown promise in expanding the therapeutic options for TB chemotherapy. Fusidic acid (FA), a natural product-derived antibiotic, is one such candidate for repurposing. The present study aimed to understand the mechanism of action of FA and its selected analogs in M. tuberculosis. By using chemical biology and genetics, we identified elongation factor G as the target of FA in M. tuberculosis. We showed essentiality of its encoding gene fusA1 in M. tuberculosis by demonstrating that the transcriptional silencing of fusA1 is bactericidal in vitro and in macrophages. Thus, this work validated a novel drug target FusA1 in M. tuberculosis.

PMID:34196521 | DOI:10.1021/acsinfecdis.1c00195

Heliyon. 2021 Jun 16;7(6):e07320. doi: 10.1016/j.heliyon.2021.e07320. eCollection 2021 Jun.

ABSTRACT

Waddlia chondrophila is an emerging pathogen that has been implicated in numerous unpropitious pregnancy events in humans and ruminants. Taking into account its association with abortigenic events, possible modes of transmission, and future risk, immediate clinical measures are required to prevent widespread damage caused by this organism and hence this study. Here, a subtractive proteomics approach was employed to identify druggable proteins of W. chondrophila. Considering the essential genes, antibiotic resistance proteins, and virulence factors, 676 unique important proteins were initially identified for this bacterium. Afterward, NCBI BLASTp performed against human proteome identified 223 proteins that were further pushed into KEGG Automatic Annotation Server (KAAS) for automatic annotation. Using the information from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database 14 Waddlia specific metabolic pathways were identified with respect to humans. Analyzing the data from KAAS and KEGG databases, forty-eight metabolic pathway-dependent, and seventy metabolic pathway independent proteins were identified. Standalone BLAST search against DrugBank FDA approved drug targets revealed eight proteins that are finally considered druggable proteins. Prediction of three-dimensional structures was done for the eight proteins through homology modeling and the Ramachandran plot model showed six models as a valid prediction. Finally, virtual screening against MurB protein was performed using FDA approved drugs to employ the drug repositioning strategy. Three drugs showed promising docking results that can be used for therapeutic purposes against W. chondrophila following the clinical validation of the study.

PMID:34195427 | PMC:PMC8239728 | DOI:10.1016/j.heliyon.2021.e07320

BMJ Open. 2021 Jun 30;11(6):e051343. doi: 10.1136/bmjopen-2021-051343.

ABSTRACT

INTRODUCTION: Alzheimer's disease (AD) is one of the most common causes of dementia. Pathogenic variants in the presenilin 1 (PSEN1) gene are the most frequent cause of early-onset AD. Medications for patients with AD bearing PSEN1 mutation (PSEN1-AD) are limited to symptomatic therapies and no established radical treatments are available. Induced pluripotent stem cell (iPSC)-based drug repurposing identified bromocriptine as a therapeutic candidate for PSEN1-AD. In this study, we used an enrichment strategy with iPSCs to select the study population, and we will investigate the safety and efficacy of an orally administered dose of bromocriptine in patients with PSEN1-AD.

METHODS AND ANALYSIS: This is a multicentre, randomised, placebo-controlled trial. AD patients with PSEN1 mutations and a Mini Mental State Examination-Japanese score of ≤25 will be randomly assigned, at a 2:1 ratio, to the trial drug or placebo group (≥4 patients in TW-012R and ≥2 patients in placebo). This clinical trial consists of a screening period, double-blind phase (9 months) and extension phase (3 months). The double-blind phase for evaluating the efficacy and safety is composed of the low-dose maintenance period (10 mg/day), high-dose maintenance period (22.5 mg/day) and tapering period of the trial drug. Additionally, there is an open-labelled active drug extension period for evaluating long-term safety. Primary outcomes are safety and efficacy in cognitive and psychological function. Also, exploratory investigations for the efficacy of bromocriptine by neurological scores and biomarkers will be conducted.

ETHICS AND DISSEMINATION: The proposed trial is conducted according to the Declaration of Helsinki, and was approved by the Institutional Review Board (K070). The study results are expected to be disseminated at international or national conferences and published in international journals following the peer-review process.

TRIAL REGISTRATION NUMBER: jRCT2041200008, NCT04413344.

PMID:34193504 | DOI:10.1136/bmjopen-2021-051343

Sci Adv. 2021 Jun 30;7(27):eabh3032. doi: 10.1126/sciadv.abh3032. Print 2021 Jun.

ABSTRACT

The global outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) necessitates the rapid development of new therapies against coronavirus disease 2019 (COVID-19) infection. Here, we present the identification of 200 approved drugs, appropriate for repurposing against COVID-19. We constructed a SARS-CoV-2-induced protein network, based on disease signatures defined by COVID-19 multiomics datasets, and cross-examined these pathways against approved drugs. This analysis identified 200 drugs predicted to target SARS-CoV-2-induced pathways, 40 of which are already in COVID-19 clinical trials, testifying to the validity of the approach. Using artificial neural network analysis, we classified these 200 drugs into nine distinct pathways, within two overarching mechanisms of action (MoAs): viral replication (126) and immune response (74). Two drugs (proguanil and sulfasalazine) implicated in viral replication were shown to inhibit replication in cell assays. This unbiased and validated analysis opens new avenues for the rapid repurposing of approved drugs into clinical trials.

PMID:34193418 | DOI:10.1126/sciadv.abh3032

Br J Clin Pharmacol. 2021 Jun 30. doi: 10.1111/bcp.14964. Online ahead of print.

ABSTRACT

Repurposing the large arsenal of existing non-cancer drugs is an attractive proposition to expand the clinical pipelines for cancer therapeutics. The earlier successes in repurposing resulted primarily from serendipitous findings but more recently, drug or target-centric systematic identification of repurposing opportunities continues to rise. Kinases are one of the most sought-after cancer drug targets over the last three decades. There are many non-cancer approved drugs that can inhibit kinases as "off-targets" as well as many existing kinase inhibitors that can target new additional kinases in cancer. Identifying cancer-associated kinase inhibitors through mining commercial drug databases or new kinase targets for existing inhibitors through comprehensive kinome profiling can offer more effective trial-ready options to rapidly advance drugs for clinical validation. In this review, we argue that drug repurposing is an important approach in modern drug development for cancer therapeutics. We have summarized the advantages of repurposing, the rationale behind this approach together with key barriers and opportunities in cancer drug development. We have also included examples of non-cancer drugs that inhibit kinases or are associated with kinase signalling as a basis for their anti-cancer action.

PMID:34192364 | DOI:10.1111/bcp.14964

Brief Funct Genomics. 2021 Jun 29:elab031. doi: 10.1093/bfgp/elab031. Online ahead of print.

ABSTRACT

Drug-target interaction prediction is important for drug development and drug repurposing. Many computational methods have been proposed for drug-target interaction prediction due to their potential to the time and cost reduction. In this review, we introduce the molecular docking and machine learning-based methods, which have been widely applied to drug-target interaction prediction. Particularly, machine learning-based methods are divided into different types according to the data processing form and task type. For each type of method, we provide a specific description and propose some solutions to improve its capability. The knowledge of heterogeneous network and learning to rank are also summarized in this review. As far as we know, this is the first comprehensive review that summarizes the knowledge of heterogeneous network and learning to rank in the drug-target interaction prediction. Moreover, we propose three aspects that can be explored in depth for future research.

PMID:34189559 | DOI:10.1093/bfgp/elab031

Drug Des Devel Ther. 2021 Jun 22;15:2747-2767. doi: 10.2147/DDDT.S308973. eCollection 2021.

ABSTRACT

Drug repurposing is a feasible strategy in developing novel medications. Regarding the cancer field, scientists are continuously making efforts to redirect conventional drugs into cancer treatment. This approach aims at exploring new applications in the existing agents. Antiparasitic medications, including artemisinin derivatives (ARTs), quinine-related compounds, niclosamide, ivermectin, albendazole derivatives, nitazoxanide and pyrimethamine, have been deeply investigated and widely applied in treating various parasitic diseases for a long time. Generally, their pharmacokinetic and pharmacodynamic properties are well understood, while the side effects are roughly acceptable. Scientists noticed that some of these agents have anticancer potentials and explored the underlying mechanisms to achieve drug repurposing. Recent studies show that these agents inhibit cancer progression via multiple interesting ways, inducing ferroptosis induction, autophagy regulation, mitochondrial disturbance, immunoregulation, and metabolic disruption. In this review, we summarize the recent advancement in uncovering antiparasitic drugs' anticancer properties from the perspective of their pharmacological targets. Instead of paying attention to the previously discovered mechanisms, we focus more on newly emerging ones that are worth noticing. While most investigations are focusing on the mechanisms of their antiparasitic effect, more in vivo exploration in clinical trials in the future is necessary. Moreover, we also paid attention to what limits the clinical application of these agents. For some of these agents like ARTs and niclosamide, drug modification, novel delivery system invention, or drug combination are strongly recommended for future exploration.

PMID:34188451 | PMC:PMC8235938 | DOI:10.2147/DDDT.S308973

Sci Rep. 2021 Jun 29;11(1):13540. doi: 10.1038/s41598-021-92903-8.

ABSTRACT

Computational methods accelerate the drug repurposing pipelines that are a quicker and cost-effective alternative to discovering new molecules. However, there is a paucity of web servers to conduct fast, focussed, and customized investigations for identifying new uses of old drugs. We present the NOD web server, which has the mentioned characteristics. NOD uses a sensitive sequence-guided approach to identify close and distant homologs of a protein of interest. NOD then exploits this evolutionary information to suggest potential compounds from the DrugBank database that can be repurposed against the input protein. NOD also allows expansion of the chemical space of the potential candidates through similarity searches. We have validated the performance of NOD against available experimental and/or clinical reports. In 65.6% of the investigated cases in a control study, NOD is able to identify drugs more effectively than the searches made in DrugBank. NOD is freely-available at http://pauling.mbu.iisc.ac.in/NOD/NOD/ .

PMID:34188160 | DOI:10.1038/s41598-021-92903-8

Diabetes Metab Syndr. 2021 Jun 18;15(4):102180. doi: 10.1016/j.dsx.2021.06.017. Online ahead of print.

ABSTRACT

BACKGROUND AND AIM: Objectives are to explore recent advances in discovery of new antidiabetic agents using repurposing strategies and to discuss modern technologies used for drug repurposing highlighting diabetic specific web portal.

METHODS: Recent literature were studied and analyzed from various sources such as Scopus, PubMed, and IEEE Xplore databases.

RESULTS: Drugs like Niclosamideethanolamine, Methazolamide, Diacerein, Berberine, Clobetasol, etc. with possibility of repurposing to curb diabetes can be potential late-stage clinical candidates, providing access to information on pharmacology, formulation, and probable toxicity if any.

CONCLUSIONS: With collaboration of artificial intelligence (AI) with pharmacology, the efficiency of drug repurposing can improve significantly.

PMID:34186343 | DOI:10.1016/j.dsx.2021.06.017

Genet Med. 2021 Jun 28. doi: 10.1038/s41436-021-01243-5. Online ahead of print.

ABSTRACT

PURPOSE: It is critical to identify putative causal targets for SARS coronavirus 2, which may guide drug repurposing options to reduce the public health burden of COVID-19.

METHODS: We applied complementary methods and multiphased design to pinpoint the most likely causal genes for COVID-19 severity. First, we applied cross-methylome omnibus (CMO) test and leveraged data from the COVID-19 Host Genetics Initiative (HGI) comparing 9,986 hospitalized COVID-19 patients and 1,877,672 population controls. Second, we evaluated associations using the complementary S-PrediXcan method and leveraging blood and lung tissue gene expression prediction models. Third, we assessed associations of the identified genes with another COVID-19 phenotype, comparing very severe respiratory confirmed COVID versus population controls. Finally, we applied a fine-mapping method, fine-mapping of gene sets (FOGS), to prioritize putative causal genes.

RESULTS: Through analyses of the COVID-19 HGI using complementary CMO and S-PrediXcan methods along with fine-mapping, XCR1, CCR2, SACM1L, OAS3, NSF, WNT3, NAPSA, and IFNAR2 are identified as putative causal genes for COVID-19 severity.

CONCLUSION: We identified eight genes at five genomic loci as putative causal genes for COVID-19 severity.

PMID:34183789 | DOI:10.1038/s41436-021-01243-5

Biochim Biophys Acta Mol Cell Res. 2021 Jun 25:119087. doi: 10.1016/j.bbamcr.2021.119087. Online ahead of print.

ABSTRACT

Due to its potent anti-tumor activity, well-investigated pharmacokinetic properties and safety profile, disulfiram (DSF) has emerged as a promising candidate for drug repurposing in cancer therapy. Although several molecular mechanisms have been proposed for its anti-cancer effects, the precise underlying mechanisms remain unclear. In the present study, we showed that DSF inhibited proliferation of cancer cells by inducing reactive oxygen species (ROS) production, a G1 cell cycle arrest and autophagy. Moreover, DSF triggered apoptosis via suppression of the anti-apoptotic protein survivin. To elucidate the mechanisms for the anti-proliferative activities of DSF, we applied a 2-DE combined with MALDI-TOF-MS/MS analysis to identify differentially expressed proteins in breast cancer cells upon treatment with DSF. Nine differentially expressed proteins were identified among which, three candidates including calmodulin (CaM), peroxiredoxin 1 (PRDX1) and collagen type I alpha 1 (COL1A1) are involved in the regulation of the AKT signaling pathway. The results of western blot analysis confirmed that DSF inhibited p-AKT, suggesting that DSF induces its anti-tumor effects via AKT blockade. Moreover, we found that DSF increased the mRNA levels of FOXO1, FOXO3 and FOXO4, and upregulated the expression of their target genes involved in G1 cell cycle arrest, apoptosis and autophagy. Finally, DSF potentiated the anti-proliferative effects of well-known chemotherapeutic agents such as arsenic trioxide (ATO), doxorubicin, paclitaxel and cisplatin. Altogether, these findings provide mechanistic insights into the anti-growth activities of DSF.

PMID:34182011 | DOI:10.1016/j.bbamcr.2021.119087

Mol Divers. 2021 Jun 28. doi: 10.1007/s11030-021-10263-x. Online ahead of print.

ABSTRACT

Anterior Gradient 2 (AGR2) has recently been reported as a tumor biomarker in various cancers, i.e., breast, prostate and lung cancer. Predominantly, AGR2 exists as a homodimer via a dimerization domain (E60-K64); after it is self-dimerized, it helps FGF2 and VEGF to homo-dimerize and promotes the angiogenesis and the invasion of vascular endothelial cells and fibroblasts. Up till now, no small molecule has been discovered to inhibit the AGR2-AGR2 homodimer. Therefore, the present study was performed to prepare a validated 3D structure of AGR2 by homology modeling and discover a small molecule by screening the FDA-approved drugs library on AGR2 homodimer as a target protein. Thirteen different homology models of AGR2 were generated based on different templates which were narrowed down to 5 quality models sorted by their overall Z-scores. The top homology model based on PDB ID = 3PH9 was selected having the best Z-score and was further assessed by Verify-3D, ERRAT and RAMPAGE analysis. Structure-based virtual screening narrowed down the large library of FDA-approved drugs to ten potential AGR2-AGR2 homodimer inhibitors having FRED score lower than - 7.8 kcal/mol in which the top 5 drugs' binding stability was counter-validated by molecular dynamic simulation. To sum up, the present study prepared a validated 3D structure of AGR2 and, for the first time reported the discovery of 5 FDA-approved drugs to inhibit AGR2-AGR2 homodimer by using structure-based virtual screening. Moreover, the binding of the top 5 hits with AGR2 was also validated by molecular dynamic simulation. A validated 3D structure of Anterior Gradient 2 (AGR2) was prepared by homology modeling, which was used in virtual screening of FDA-approved drugs library for the discovery of prospective inhibitors of AGR2-AGR2 homodimer.

PMID:34181147 | DOI:10.1007/s11030-021-10263-x

Bioinformatics. 2021 Jun 28:btab473. doi: 10.1093/bioinformatics/btab473. Online ahead of print.

ABSTRACT

MOTIVATION: Predicting new drug-target interactions is an important step in new drug development, understanding of its side effects, and drug repositioning. Heterogeneous data sources can provide comprehensive information and different perspectives for drug-target interaction prediction. Thus, there have been many calculation methods relying on heterogeneous networks. Most of them use graph-related algorithms to characterize nodes in heterogeneous networks for predicting new DTI. However, these methods can only make predictions in known heterogeneous network datasets, and cannot support the prediction of new chemical entities outside the heterogeneous network, which hinder further drug discovery and development.

RESULTS: To solve this problem, we proposed a multi-modal DTI prediction model named 'MultiDTI' which uses our proposed joint learning framework based on heterogeneous networks. It combines the interaction or association information of the heterogeneous network and the drug/target sequence information, and maps the drugs, targets, side effects and disease nodes in the heterogeneous network into a common space. In this way, 'MultiDTI' can map the new chemical entity to this learned common space based on the chemical structure of the new entity. That is, bridging the gap between new chemical entities and known heterogeneous network. Our model has strong predictive performance, and the area under the receiver operating characteristic curve(AUC) of the model is 0.961 and the area under the precision recall curve (AUPRC) is 0.947 with 10-fold cross validation. In addition, some predicted new DTIs have been confirmed by ChEMBL database. Our results indicate that 'MultiDTI' is a powerful and practical tool for predicting new DTI, which can promote the development of drug discovery or drug repositioning.

AVAILABILITY: Python codes and dataset are available at https://github.com/Deshan-Zhou/MultiDTI/.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:34180970 | DOI:10.1093/bioinformatics/btab473

Adv Ther (Weinh). 2021 May 20:2100055. doi: 10.1002/adtp.202100055. Online ahead of print.

ABSTRACT

Identifying effective drug treatments for COVID-19 is essential to reduce morbidity and mortality. Although a number of existing drugs have been proposed as potential COVID-19 treatments, effective data platforms and algorithms to prioritize drug candidates for evaluation and application of knowledge graph for drug repurposing have not been adequately explored. A COVID-19 knowledge graph by integrating 14 public bioinformatic databases containing information on drugs, genes, proteins, viruses, diseases, symptoms and their linkages is developed. An algorithm is developed to extract hidden linkages connecting drugs and COVID-19 from the knowledge graph, to generate and rank proposed drug candidates for repurposing as treatments for COVID-19 by integrating three scores for each drug: motif scores, knowledge graph PageRank scores, and knowledge graph embedding scores. The knowledge graph contains over 48 000 nodes and 13 37 000 edges, including 13 563 molecules in the DrugBank database. From the 5624 molecules identified by the motif-discovery algorithms, ranking results show that 112 drug molecules had the top 2% scores, of which 50 existing drugs with other indications approved by health administrations reported. The proposed drug candidates serve to generate hypotheses for future evaluation in clinical trials and observational studies.

PMID:34179346 | PMC:PMC8212091 | DOI:10.1002/adtp.202100055