Novel graph attention autoencoder framework with multilayered validation identifies drug repurposing candidates for COVID-19 treatment

Novel graph attention autoencoder framework with multilayered validation identifies drug repurposing candidates for COVID-19 treatment | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Novel graph attention autoencoder framework with multilayered validation identifies drug repurposing candidates for COVID-19 treatment Yusuke Nakayama, Shingo Tsuji, Koji Yamamoto, Juran Kato-Suzuki, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8274293/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background The COVID-19 pandemic highlighted the critical need for drug repurposing to rapidly identify therapeutic options. While computational graph-based approaches show promise, conventional single analytical methods often fail to capture the complex pathological mechanisms needed for clinical translation. Methods We developed a Graph Attention Autoencoder (GATE) framework with multilayered validation integrating computational prediction, real-world data, and phenome analyses. Network-based embeddings identified drug candidates through latent space similarity analysis within biomedical knowledge graphs. Clinical potential was assessed via disproportionality analysis (DPA) using adverse event reporting system, and biological plausibility was evaluated through gene expression profiling and pathway analyses. Results Our GATE framework identified 17 drug candidates, including 13 with known clinical effects on COVID-19 symptoms, validating model performance. Four novel candidates (cilastatin, megestrol, drotrecogin alfa, and ethacrynic acid) with minimal prior COVID-19 associations were identified. DPA further narrowed these to two promising candidates with significant protective signals: cilastatin (reporting odds ratio [ROR]: 0.19, 95% confidence interval [CI]: 0.09–0.43) and megestrol (ROR: 0.63, 95% CI: 0.42–0.94). Pathway profiling confirmed that both drugs share molecular signatures with drugs investigated in COVID-19 clinical trials. Gene expression analyses suggest that cilastatin may have anti-viral and anti-inflammatory effects via suppression of splicing, ribosomal function and mitochondrial pathway, through DPEP1 inhibition. Conclusions This study presents the first systematic integration of GATE-based computational prediction, real-world evidence from adverse event databases, and phenome-level pathway profiling for COVID-19 drug repurposing. This multilayered approach enabled multidimensional candidate validation unattainable by single analytical methods alone. The identification of clinically approved drugs with established safety profiles may facilitate accelerated clinical evaluation. Furthermore, the pathway and gene expression analyses provide mechanistic working hypotheses that can streamline subsequent preclinical validation. Although preclinical and clinical validation remain essential, this framework offers a generalizable strategy for rapid candidate identification during pandemics and for diseases with unmet therapeutic needs. drug repurposing drug repositioning COVID-19 SARS-CoV-2 graph neural networks real world evidence gene expression hypothesis generation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The spread of infectious diseases and the emergence of new pathologies have particularly increased the demand for treatments targeting rare diseases. The COVID-19 pandemic highlighted critical gaps in rapid therapeutic development, demonstrating the urgent need for drug repurposing strategies [ 1 ]. While traditional drug development requires over 10 years and billions of dollars and enormous time commitments [ 2 – 4 ], with approximately 10% of success rates in clinical development [ 5 ], drug repurposing offers accelerated timelines and higher success probabilities due to established safety and pharmacological profiles [ 6 , 7 ]. Indeed, several computational approaches have successfully identified COVID-19 therapeutics, including Baricitinib through network-based repositioning [ 8 ], validating the potential of systematic computational drug discovery. Computational drug repurposing leverages public databases and bioinformatics to propose novel therapeutic candidates [ 6 ]. Graph structure-based approaches – which model relationships between diseases, drugs, and genes as interconnected networks – have emerged as powerful tools for capturing this biological complexity. Ritonavir (a component of Paxlovid), and Atorvastatin were successfully identified as potential COVID-19 treatments through network-derived relationships analysis, highlighting how graph-based representations can serve as powerful foundation for systematically uncovering repurposing opportunities [ 9 ]. In graph structure-based drug repurposing, it is common to convert graph nodes and edges into vector representations through embedding. Graph embedding methods have been applied to recommendation systems in web stores and other applications [ 10 ]. Building upon these foundations, Graph Neural Networks (GNNs), which employ neural networks for graph embedding, have been proposed [ 11 ] and have become widely used. While various GNN methods exist, Graph Attention Networks (GATs) that utilize attention mechanisms have demonstrated superiority [ 12 ]. The attention mechanism is a technology used in Transformer Architecture, which forms the foundation of generative AI using large language models such as ChatGPT [ 13 ], and supports today's revolutionary AI evolution. Recently, these GATs have increasingly been applied to drug repurposing research [ 14 ]. However, existing GNN approaches face critical limitations when confronting complex pathological mechanisms that constrain their therapeutic discovery potential. In fact, COVID-19 pathogenesis involves complex, multifaceted mechanisms encompassing viral replication, host immune responses, and cellular metabolic processes. Approximately 200 existing drugs may target SARS-CoV-2-associated pathways, spanning diverse targets from viral proteins (spike protein, papain-like protease (PLpro), 3-chymotrypsin-like protease (3CLpro), RNA-dependent RNA polymerase (RdRp), and helicase [ 15 ]) and host cellular machinery (mTOR-PI3K pathway [ 16 ], autophagy [ 17 ], vesicular transport systems [ 18 ], and immune response [ 19 ]). This molecular complexity presents a fundamental challenge; single analytical approaches may be insufficient for adequate exploration of therapeutic candidates. To address this challenge, we developed a comprehensive multilayered analytical framework integrating Graph Attention Autoencoder (GATE) with validation using real-world evidence and phenome data. This represents the first systematic combination of these approaches for drug repurposing, enabling identification of candidate drugs such as cilastatin and megestrol that would remain invisible to conventional computational discovery methods while providing actionable mechanistic insights for therapeutic development. Materials and Methods [Graph Attention Autoencoder (GATE)] We constructed a knowledge graph comprising three types of nodes: disease, drug, and gene (target). Data were primarily obtained from Open Targets (version 24.06) [ 20 ]. Open Targets aggregates target and disease association data from multiple public sources. Edges were defined using the following four types of relationship information: (i) "Molecular interactions" for target-target relationships, (ii) "Associations - direct (overall score)" for target-disease relationships, (iii) "Drug - mechanism of action" for drug-target relationships, (iv) "Drug - indications" for drug-disease relationships. Initial attribute vectors for nodes were generated using Large Language Models (LLMs) to create vector representations reflecting the characteristics of each node. Using this graph and a Graph Attention Autoencoder (GATE), we computed vector representations for each node and identified drug nodes proximal to the COVID-19 node by calculating cosine distances. The implementation was performed using the Python programming language with Spektral [ 21 ] utilized for GATE construction. [Disproportionality Analysis with Spontaneous Reporting Systems] We utilized a spontaneous adverse event (AE) reporting database constructed by U.S. Food and Drug Administration (FDA). The FDA Adverse Event Reporting System (FAERS) contains reports of spontaneous AEs reported by consumers, pharmaceutical companies, and medical professionals worldwide. FAERS consists of seven datasets: DEMO (patient demographic and administrative information), DRUG (drug information), REAC (AEs), INDI (indications for the use of reported drugs), THER (start and end dates for the reported drugs), OUTC (outcomes for the event) and RPSR (sources for the event). The present study used FAERS data from January 2020 through March 2022, representing 3,617,885 reports. This data was obtained through JAPIC-AERS (Japan Pharmaceutical Information Center (JAPIC), Tokyo, Japan), a commercially processed FAERS database. AEs in the FAERS database are coded using Medical Dictionary for Regulatory Activities (MedDRA) Preferred Terms (PTs), which are grouped according to defined medical conditions or areas of interest. We used the Standardized MedDRA Queries to identify PTs related to COVID-19. The search terms used for “COVID-19” are described in Table S1 . AE risk was evaluated by calculating the reporting odds ratio (ROR) and corresponding 95% confidence intervals (CI) . The ROR and 95% CI were defined as follows: ROR = (a/c) / (b/d) 95% CI = exp [ln(ROR) ± 1.96√(1/a + 1/b + 1/c + 1/d)] where a, b, c and d refer to the number of individuals in the FAERS database as divided into the following four groups: (a) individuals who received the drug of interest (i.e., cilastatin) and exhibited COVID-19-related AEs; (b) individuals who received the drug of interest but did not exhibit COVID-19-related AEs; (c) individuals who did not receive the drug of interest and exhibited COVID-19-related AEs; and (d) individuals who did not receive the drug of interest and did not exhibit COVID-19-related AEs. If the upper limit of the 95% CI was < 1, a significant association was assumed between use of the drug of interest and the decreased occurrence of COVID-19-related AEs. [Pathway Profiling] To explore potential therapeutic agents at the phenome level, we conducted analyses using pathway alteration profiles following drug treatment. We utilized gene expression profile data from the Expanded CMAP (Connectivity Map) LINCS (Library of Integrated Network-Based Cellular Signatures) Resource 2020 [ 22 ], which contains transcriptome data generated from cell lines treated with various compounds. To mitigate noise inherent in high-throughput methodologies, we employed a quality control process based on gene expression distribution in duplicate samples, retaining only high-confidence data. Plate-to-plate normalization was performed to eliminate assay bias. Furthermore, we applied distribution analysis to determine gene expression alteration levels, selecting only samples with gene alterations at a Q-value threshold of < 0.2. Gene Set Enrichment Analysis (GSEA) [ 23 , 24 ] was conducted on filtered samples to identify significantly altered pathways for each sample, using a false discovery rate (FDR) cutoff of < 0.20. For the GSEA analysis, we utilized ontology gene sets (C5) [ 25 ] from the Molecular Signatures Database (MSigDB) [ 26 , 27 ]. Correlations between pathway profiles across samples were evaluated using Fisher's exact test to identify compounds with similar phenotypic effects. Statistical analysis was implemented in C and Ruby, ensuring computational efficiency for large-scale comparisons. Multiple testing correction was applied using the Benjamini-Hochberg method, with a FDR threshold of 0.2 to determine statistical significance. Pathway enrichment visualization was performed using Enrichment Map (v3.4.0) [ 28 ], a plugin for Cytoscape software (Version 3.10.1) [ 29 ]. Significantly enriched pathways with FDR < 0.20 were included in the analysis. The pathway network was constructed with each node representing an enriched gene set, and edges connecting pathways with shared genes, where the edge weight corresponds to the overlap coefficient between the gene sets. To improve interpretability of the resulting network, we utilized the AutoAnnotate plugin (v1.5.1) [ 30 ] to cluster and label groups of functionally related pathways based on their shared gene content. This approach allowed for the identification of major biological themes among the differentially regulated pathways. Clusters were manually reviewed and annotated to ensure biological relevance. [Use of Generative Artificial Intelligence Tools] During the preparation of this manuscript, we used large language models (ChatGPT, OpenAI; Claude, Anthropic) to assist with English translation, improve readability and clarity, and support literature search strategies. All AI-assisted outputs were carefully reviewed and edited by the authors, who take full responsibility for the accuracy and integrity of the final manuscript. Results and Discussion [GATE Approach Reveals Novel Candidate Drugs for COVID-19 Treatment] To systematically identify and validate novel drug repurposing candidates we developed a comprehensive analytical framework that addresses the limitations of standalone computational approaches. Our methodology consists of three integrated phases: (1) computational identification of candidate drugs through state-of-the-art GATE approaches utilizing attention mechanisms and data obtained from Open Targets [ 20 ], (2) concept validation of clinical potential using real-world evidence, and (3) molecular biological assessment for mechanism of action (MOA) hypotheses generation using phenome data (Fig. 1 ). This multilayered evaluation strategy enables us to move beyond simple similarity predictions to provide actionable insights for therapeutic development. To demonstrate the utility of this system, we applied it to identify and validate novel drug repurposing candidates for COVID-19. AI network analysis (GATE) selects candidate drugs for COVID-19, real-world data analysis (signal detection) narrows them to drugs with protective signals, and phenome analysis (pathway profiling) evaluates their molecular plausibility and mechanisms of action. Based on the latent space representation of GATE, we identified drug nodes showing high similarity to the COVID-19 node (Table 1 ). In interpreting the results, we considered different salt or derivative forms of the same compound (e.g., cilastatin and cilastatin sodium) as representing a single drug entity. Among the 17 drugs identified, 13 drugs were already known to have therapeutic effects against COVID-19 symptoms, indicating that our model identifies clinically meaningful drug-disease associations. The remaining 4 drugs (cilastatin, megestrol, drotrecogin alfa, ethacrynic acid) showed minimal prior associations with COVID-19 in the literature, revealing hidden therapeutic opportunities that would have remained invisible to conventional computational discovery approaches. Importantly, these candidate drugs and COVID-19 were not directly connected in the original knowledge graph (Fig. 2 , Figure S1 -3), demonstrating our GATE’s ability to discover latent associations implicit in network relationships. Table 1 Top-ranked drug candidates based on embedding similarity to COVID-19 in the latent space learned by GATE model ID Name Description Cosine Similarity OpenTargets Association Score OpenTargets Rank of Score CHEMBL1201057 CILASTATIN SODIUM Antibiotic, Dipeptidyl Peptidase I Inhibitor 0.86067 0.01 2672 CHEMBL766 CILASTATIN Antibiotic, Dipeptidyl Peptidase I Inhibitor 0.86006 0.01 2672 CHEMBL1201014 PREDNISOLONE SODIUM PHOSPHATE Steroid Drug 0.85693 0.609 4 CHEMBL1201231 PREDNISOLONE PHOSPHORIC ACID Steroid Drug 0.85526 0.609 4 CHEMBL679 EPINEPHRINE Adrenergic Receptor Agonist 0.85111 0.372 169 CHEMBL2134724 IPRATROPIUM BROMIDE Anticholinergic Drug, COPD and Bronchial Asthma Treatment 0.84696 0.076 737 CHEMBL1621597 IPRATROPIUM Anticholinergic Drug, COPD and Bronchial Asthma Treatment 0.84621 0.076 737 CHEMBL1200689 NITRIC OXIDE Nitric Oxide, Pulmonary Hypertension Treatment 0.84535 0.345 397 CHEMBL1201335 GLYCOPYRRONIUM Anticholinergic Drug, COPD 0.84439 0.076 737 CHEMBL1437 NOREPINEPHRINE Adrenergic Receptor Agonist 0.84266 0.372 169 CHEMBL1215 PHENYLEPHRINE Adrenergic Receptor Agonist 0.84121 0.372 169 CHEMBL2108429 MEPOLIZUMAB Anti-IL-5 Antibody 0.83772 0.068 799 CHEMBL1201139 MEGESTROL ACETATE Antineoplastic Drug, Contraceptive, Progesterone Receptor Agonist 0.83667 0.086 636 CHEMBL1370 BUDESONIDE Steroid Drug 0.83639 0.609 4 CHEMBL1364144 METHYLPREDNISOLONE ACETATE Steroid Drug 0.83551 0.609 4 CHEMBL1201027 GLYCOPYRROLATE Anticholinergic Drug 0.83493 0.076 737 CHEMBL2109065 DROTRECOGIN ALFA (ACTIVATED) Antithrombotic, Anti-inflammatory, Fibrinolysis-promoting Human Activated Protein C 0.83448 0.026 1339 CHEMBL52440 DEXTROMETHORPHAN Cough Suppressant and Expectorant (Medicon) 0.83335 0.365 334 CHEMBL1473 FLUTICASONE PROPIONATE Steroid Drug 0.83256 0.609 4 CHEMBL3707243 GLYCOPYRRONIUM TOSYLATE Anticholinergic Drug, Primary Axillary Hyperhidrosis Treatment 0.83239 0.076 737 CHEMBL456 ETHACRYNIC ACID Loop Diuretic Drug 0.83171 0.004 4822 Drug candidates are ranked by cosine similarity scores in GATE latent space. Rows shown in bold indicate drugs not previously used for COVID-19 treatment, representing novel repositioning candidates. The OpenTargets association scores range from 0 to 1, with higher values indicating stronger relevance. Subnetwork extracted from a multimodal knowledge graph consisting of drugs, diseases, and genes. Nodes are colored by type: orange for diseases, blue for drugs, and green for genes. To further evaluate the findings, we investigated the association scores between COVID-19 and the target genes of each candidate drug using the Open Targets Platform, an integrated drug discovery database. Association scores are comprehensive relatedness indicators based on literature information, genetic evidence, experimental data, and other sources. This analysis revealed that all four candidate drugs showed low association scores with COVID-19 (Table 1 ), suggesting that these drugs would not be visible through existing knowledge bases. In particular, our top candidate, cilastatin, had an association score of 0.01, which is significantly lower than scores (0.068–0.609) of drugs that are known to have effects on COVID-19. To benchmark against other approaches, we investigated whether these four drugs had been reported as COVID-19 repurposing candidates in existing studies. Cilastatin was reported as one of 78 candidates for SARS-CoV-2 3CLpro inhibition through docking simulation approaches, but detailed validation was not performed [ 31 ]. Megestrol was proposed as one of 34 COVID-19 therapeutic candidates through network-based approaches, though detailed investigation was not conducted [ 32 ]. Drotrecogin alfa has no existing reports, representing the first identification of this drug as a COVID-19 candidate. Ethacrynic acid was reported as a promising candidate for SARS-CoV-2 main protease (Mpro) inhibition through docking simulation approaches, with experimental validation also performed [ 33 ]. Although some candidate drugs identified by the GATE approach have been previously reported by other computational methods, these studies provided only preliminary identification without comprehensive evaluation of therapeutic potential. Importantly, three drugs (cilastatin, drotrecogin alfa, ethacrynic acid) excluding megestrol were not identified by conventional network-based approaches and were first detected by the GATE approach. This indicates that GATE can identify complex associations that remain undetected by conventional network analysis, exemplified by novel identification of drotrecogin alfa. These results suggest that the GATE approach is complementary to conventional methods and has the potential to efficiently discover novel relationships between unknown drug-disease pairs. Furthermore, the identification of candidates such as ethacrynic acid, for which experimental validation has been conducted, provides additional evidence supporting the reliability of our approach. However, these remain computational predictions that require experimental validation. To provide mechanistic insights that could guide future preclinical studies, we next analyzed the potential mechanisms of action of these candidate drugs. [Evaluation of Clinical Potential of Candidate Drugs through FAERS Disproportionality Analysis (DPA)] We further analyzed the four drugs identified by the GATE approach using real-world adverse events data from FAERS to examine potential clinical associations with COVID-19 by DPA. In DPA, ROR and 95% CI are used to evaluate associations between drugs and diseases. An inverse association (ROR < 1) is considered protective and a 95% CI range with the upper limit < 1 indicates statistical significance. This analysis revealed striking protective signals for cilastatin and megestrol, with ROR for COVID-19 infection of 0.19 (95% CI: 0.09–0.43) and 0.63 (95% CI: 0.42–0.94), respectively. (Fig. 3 ). Notably, cilastatin demonstrated an exceptionally strong protective association, with patients taking this medication showing approximately 5-fold lower odds of COVID-19 infection compared to non-cilastatin users. Megestrol also exhibited a significant protective effect, with approximately 40% reduction in infection odds. These findings suggest substantial clinical potential for both drugs in COVID-19 infection, with cilastatin showing one of the most pronounced protective signals observed in real-world data. (A) Reporting odds ratios (ROR) and 95% confidential intervals (CI) for candidate drugs through GATE-based analysis. “NA” indicates values that could not be calculated due to insufficient sample size. (B) Forest plot visualizing RORs for the same set of drugs. The horizontal bars represent 95% CIs. RORs for drotrecogin alfa and ethacrynic acid were not calculable and are indicated with asterisks (*) To examine whether the observed inverse associations were specific to each drug, we conducted DPA for drugs in the same class as each candidate drug or for drugs that are often co-administered with the candidate drug. Cilastatin is often co-administered with imipenem, a β-lactamase inhibitor. DPA for β-lactamase inhibitors showed ROR values around 1.0, and the remarkable inverse associations observed with cilastatin were not observed (Figure S4 ). This result suggests that the observed COVID-19 infection suppression effect may be specific to cilastatin. Megestrol is a progesterone receptor agonist. Progesterone preparations classified as progesterone receptor agonists showed RORs around 0.5 with 95% CI upper limit less than 1, indicating inverse association trends similar to megestrol (Figure S5). This result suggests that the observed effect represents a pharmacological class effect mediated through progesterone receptors rather than a mechanism that is specific to megestrol. However, more detailed analysis is needed to clearly distinguish the influence of class effects. The remarkable inverse association observed for cilastatin is consistent with previous studies demonstrating that this drug may attenuate COVID-19 pathology by reducing SARS-CoV-2 viral replication and providing protection from the cytokine storm observed in severe COVID-19 [ 34 ]. Based on these findings, cilastatin warrants further investigation as potential therapeutic candidate for COVID-19. Regarding megestrol, no literature has reported direct associations with COVID-19. However, progesterone, a drug in the same class that our DPA has indicated to be protective against COVID-19, has anti-inflammatory properties, suggesting potential as a COVID-19 treatment [ 35 – 37 ]. Clinical studies on COVID-19 treatment using progesterone have been conducted (NCT04365127, NCT04865029) and in the NCT04865029 trial, progesterone administration in moderate to severe COVID-19 patients resulted in significant improvement in clinical status (a score of 1.5 points, corresponding to a 3-day reduction in oxygen supplementation and 2.5-day reduction in hospitalization duration) [ 38 ]. Given the observed class effect of progesterone receptor agonists and the clinical evidence supporting progesterone, megestrol merits evaluation to determine whether it confers similar protective effects. Note that drotrecogin alfa and ethacrynic acid could not be evaluated using this DPA due to insufficient co-occurrence reports with COVID-19-related AEs in the FAERS database during the analysis period. [Evaluation of Molecular Potential of Cilastatin and Megestrol for COVID-19 Treatment] To further explore these findings at the phenome level, we conducted correlation analysis using pathway profiling with LINCS 2020 for cilastatin and megestrol, the two drugs that showed statistically significant inverse associations in DPA. LINCS 2020 is the successor database to the Connectivity Map (CMAP), an established platform for comparing phenomic signatures of any given drug with those associated with specific diseases or disease therapeutics [ 22 ]. For each drug, we explored compounds with correlated pathway profiles from 17,170 compounds (241,597 profiles) using Fisher's exact test. The compounds showing the strongest correlations with cilastatin (Table 2 ) included artesunate and sirolimus, which are drugs that have undergone clinical trials for COVID-19. The analysis also revealed ipidacrine, ursolic acid, wortmannin, and thapsigargin, which have been reported in the literature as COVID-19 therapeutic candidates. These results suggest that cilastatin has potential as a therapeutic candidate at the phenome level. Table 2 Top-ranked compounds positively correlated with the pathway profile of cilastatin PertID Name MOA Cell Conc Time Adj. P-value COVID-19 relation BRD-K79759031 artesunate HT29 10uM 6h 4.4E-42 10 clinical studies BRD-K66896231 ipidacrine Acetylcholinesterase inhibitor HT29 10uM 6h 1.0E-40 1 report BRD-K60067222 BRD-K60067222 PC3 10uM 24h 8.7E-40 BRD-K68185022 ursolic-acid 11-beta hydroxysteroid dehydrogenase inhibitor PC3 70uM 24h 4.2E-39 4 reports BRD-A19037878 BRD-A19037878 K562 2.5uM 24h 8.0E-39 BRD-A75409952 wortmannin PI3K inhibitor PC3 10uM 6h 2.6E-38 2 reports BRD-K81855038 roxatidine HT29 10uM 6h 4.9E-38 BRD-A79768653 sirolimus MTOR inhibitor PC3 3.33uM 24h 1.5E-37 9 clinical studies BRD-K69023402 thapsigargin ATPase inhibitor JURKAT 0.37uM 24h 1.5E-37 3 reports BRD-K56032964 AP-26113 HT29 0.37uM 24h 1.6E-37 The "COVID-19 relation" column indicates whether each compound has been previously associated with COVID-19 through publications or clinical trials. Details of the corresponding reports and studies are provided in Supporting Information 1 and 2. Similarly, the compounds showing the strongest correlations with megestrol (Table 3 ) included tianeptine, fedratinib (TG-101348), AZD-6482 and vorinostat. Tianeptine and fedratinib (TG-101348) have undergone clinical trials for COVID-19. AZD-6482 and vorinostat have been reported as COVID-19 therapeutic candidates. These results suggest that megestrol has potential as a COVID-19 therapeutic candidate at the phenome level. Table 3 Top-ranked compounds positively correlated with the pathway profile of megestrol PertID Name MOA Cell Conc Time Adj. P-value COVID-19 relation BRD-K37142460 MI-2 OCILY3 10uM 24h 1.3E-08 BRD-A81370665 BI-D1870 Ribosomal protein inhibitor HBL1 10uM 24h 1.3E-08 BRD-K01436366 XMD-1150 Leucine rich repeat kinase inhibitor BJAB 10uM 24h 3.4E-08 BRD-K36363294 I-BET-151 HIMG002 2.5uM 24h 3.4E-08 BRD-A53077924 tianeptine Selective serotonin reuptake enhancer HIMG001 15uM 24h 4.1E-08 1 clinical study BRD-K12502280 TG-101348 FLT3 inhibitor OCILY3 10uM 4h 2.5E-07 1 clinical study BRD-K58772419 AZD-6482 PI3K inhibitor MCF7 1.11uM 24h 3.0E-07 1 report BRD-K12502280 TG-101348 FLT3 inhibitor TMD8 2.5uM 4h 5.7E-07 1 clinical study BRD-K81418486 vorinostat HDAC inhibitor JURKAT 10uM 24h 5.7E-07 3 reports BRD-K54606188 BRD-K54606188 TMD8 0.66uM 4h 1.9E-06 The "COVID-19 relation" column indicates whether each compound has been previously associated with COVID-19 through publications or clinical trials. Details of the corresponding reports and studies are provided in Supporting Information 1 and 2. [Hypothesized Mechanisms of Action of Cilastatin for COVID-19 Treatment] The correlation with established COVID-19 therapeutic candidates provided substantial evidence for the intrinsic potential of cilastatin for clinical application. To further strengthen this rationale and evaluate whether cilastatin possesses sufficient mechanistic basis to incentivize clinical development, we sought to elucidate the underlying biological mechanisms based on pathway profiling data. We argued that because pathway-level information represents a functional layer closer to biological responses than individual genes, a detailed analysis of the LINCS 2020 data can facilitate hypothesis generation regarding mechanisms of action. In the LINCS 2020 data, cilastatin treatment significantly decreased the expression of 42 pathways in the HT29 cell line (Table S2 ). Enrichment Map analysis revealed that gene sets related to splicing, ribosome, mitochondria, telomere, and ubiquitin pathways were altered (Fig. 4 ). Based on these pathway alterations, we constructed a mechanistic hypothesis for how cilastatin might intervene in the pathophysiological processes of COVID-19 induced by SARS-CoV-2 infection (Fig. 5 ). This network visualizes gene ontology (GO) biological process terms significantly down-regulated by cilastatin treatment. Each node represents a gene set corresponding to a GO term, with node size proportional to the number of genes in the set and node color reflecting statistical significance (log₁₀-transformed p-value). Edges indicate gene overlap between gene sets. Functionally related clusters are annotated. This schematic illustrates potential therapeutic mechanisms by which cilastatin may alleviate COVID-19 pathology, inferred from pathway-level gene expression changes. Cilastatin-mediated inhibition of DPEP1 is proposed to influence multiple downstream biological processes, including ribosome biogenesis, RNA splicing, and mitochondrial metabolism. These changes converge on antiviral and anti-inflammatory outcomes such as suppression of viral replication and inflammation. Pathways shaded in blue indicate those with down-regulated gene expression following cilastatin treatment. Cilastatin is clinically used as a dipeptidase-1 (DPEP1) inhibitor in combination with imipenem to prevent renal degradation of the antibiotic by DPEP1. Beyond this established role in antibiotic protection, our analysis of LINCS2020 data revealed that cilastatin modulatesmultiple cellular pathways related to splicing, ribosomal function, and mitochondrial processes in cells. DPEP1, in addition to its primary function of dipeptide hydrolysis in the kidney, also functions as a neutrophil adhesion receptor on vascular endothelial cells and is known to promote neutrophil recruitment during inflammation [ 39 ]. DPEP1 may restrict host ribosomes utilized by SARS-CoV-2 by controlling the PI3K/Akt/mTOR pathway [ 40 ], which regulates cellular protein synthesis and metabolism [ 41 ]. This may suppress viral replication through a mechanism similar to that of mTOR inhibitors such as sirolimus (rapamycin) [ 42 ]. Of particular interest, it is known that the nonstructural protein 16 (NSP16) of SARS-CoV-2 binds to U1/U2 snRNA and interferes with the host splicing mechanism [ 43 ]. Suppression of splicing-related pathways may function as a cellular stress response that limits host gene expression and potentially suppresses viral replication by restricting protein synthesis. Moreover, splicing abnormalities may activate antiviral responses via RIG-I-like receptors [ 44 ]. Beyond the splicing mechanism, cilastatin also affects mitochondrial pathways. Decreased expression of mitochondria-related genes suggests changes in cellular metabolic activity and immune signaling. These changes are expected to achieve balanced immune regulation by limiting energy available for viral replication while maintaining type I interferon responses through mitochondrial antiviral signaling protein (MAVS) [ 45 ]. [Study Limitations] While our multilayered computational approach successfully identified several promising therapeutic candidates and elucidated potential mechanisms of action, there are several important limitations that must be addressed. Limitations of the GATE approach The predictive performance of our knowledge graph-based method heavily depends on the comprehensiveness and quality of the base knowledge graph being used. In biomedical knowledge graphs, data bias tends to occur based on research progress and attention levels, with particularly insufficient information regarding rare diseases and drugs in early development stages. This deficiency may limit the discovery of novel drug-disease associations and reduce predictability. Therefore, developing comprehensive knowledge graphs that systematically incorporate data on rare diseases and emerging therapeutics will be essential for improving comprehensiveness and reducing bias in future studies. Limitations of real-world data analysis Since FAERS is a spontaneous AE reporting system, it inherently contains problems such as reporting bias, underreporting, and selective reporting. A critical limitation emerged from our analysis of drotrecogin alfa and ethacrynic acid, which revealed that the method cannot analyze drugs with insufficient data in the database. This particularly affects newer drugs that lack extensive real-world usage data, limiting our ability to detect potential associations. Conversely, this suggests that classical, widely used drugs may have higher detection sensitivity due to larger volumes of available data. Furthermore, inferring causal relationships from observed associations remains challenging, and the influence of confounding factors cannot be completely eliminated. Limitations of phenome analysis The LINCS database used is primarily based on drug responses in cancer cell lines, which exhibit different characteristics from alveolar epithelial cells, vascular endothelial cells, and immune cells, which are important in COVID-19 pathogenesis. Additionally, drug responses under the inflammatory environment in patients during SARS-CoV-2 infection may differ significantly from normal cell culture conditions. Furthermore, it is difficult to capture host-virus interactions and other complex biological processes that can only be detected in in vivo conditions using cell line-based phenome data. While extensive animal data have been accumulated and could potentially be curated and integrated into such databases, comprehensive incorporation remains challenging due to species differences in drug target molecules, including variations in binding site homology and pharmacological responses between species. Need for experimental validation This study primarily focused on computational predictions and analysis of existing databases to systematically identify therapeutic candidates and elucidate potential mechanisms. While this approach provided valuable insights into drug repurposing opportunities for COVID-19 treatment, future studies should include direct validation using SARS-CoV-2-infected models and/or clinical evaluation. Conclusions In this study, we systematically evaluated the applicability of existing drugs for COVID-19 treatment using the GATE-based latent space analysis with real-world evidence and phenome-level validation. This represents the first systematic combination of these approaches for drug repurposing. We identified two candidate drugs for further investigation: cilastatin and megestrol, both clinically approved medications with established safety profiles, which may be amenable to accelerated clinical evaluation. Our multilayered validation approach achieved high-confidence candidate drug selection by comprehensively validating clinical and molecular biological plausibility. Integration of these heterogeneous data sources – static knowledge graphs, real-world clinical-observations, and dynamic gene expression data – enabled multi-dimensional insights unattainable by single analytical method based approaches. Real-world data analysis confirmed clinical potential, while phenome data analysis established molecular biological validity and mechanistic hypotheses for both drugs. This integrated framework establishes a new paradigm for computational drug discovery that bridges the gap between in silico prediction and clinical translation. This multimodal methodology has applications for rapid candidate identification during pandemics and for diseases and conditions with limited therapeutic options. Future developments in large language models (LLMs) and agentic AI may further enhance this GATE approach by improving knowledge graph comprehensiveness. Future research should focus on in vivo validation and clinical trials of these repurposed drug candidates. It should also explore the applicability of this methodology to other diseases and establish selection criteria for optimal computational methods based on data scale and intended use. Data Availability The datasets used and analyzed in this study were obtained from publicly accessible resources, including the Open Targets Platform datasets ( https://platform.opentargets.org/downloads ), the CMAP LINCS 2020 collection on the CLUE platform ( https://clue.io/data/CMap2020#LINCS2020 ), and the MSigDB C5 collection ( https://www.gsea-msigdb.org/gsea/msigdb/ ). Spontaneous adverse event data were obtained from JAPIC-AERS, a licensed database derived from FAERS. Due to licensing and commercial restrictions, the JAPIC-AERS data and other processed datasets used in this study are not publicly available and cannot be shared. Abbreviations AE adverse event CI confidence interval CMAP Connectivity Map DPA disproportionality analysis DPEP1 dipeptidase-1 FAERS FDA Adverse Event Reporting System FDA Food and Drug Administration GAT Graph Attention Network GATE Graph Attention Autoencoder GNN Graph Neural Network LINCS Library of Integrated Network-Based Cellular Signatures LLM Large Language Model MOA mechanism of action ROR reporting odds ratio Declarations Data Availability The datasets used and analyzed in this study were obtained from publicly accessible resources, including the Open Targets Platform datasets (https://platform.opentargets.org/downloads), the CMAP LINCS 2020 collection on the CLUE platform (https://clue.io/data/CMap2020#LINCS2020), and the MSigDB C5 collection (https://www.gsea-msigdb.org/gsea/msigdb/). Spontaneous adverse event data were obtained from JAPIC-AERS, a licensed database derived from FAERS. Due to licensing and commercial restrictions, the JAPIC-AERS data and other processed datasets used in this study are not publicly available and cannot be shared. Acknowledgments The authors gratefully acknowledge the valuable discussions and technical advice provided by K. Matsubara, T. Morimoto, N. Hirashima, Dr. K. Shimada, and T. Naito. We would like to thank K. Hashizume for collaboration on this work. We also thank Dr. J. Urano for insightful comments and critical suggestions on the manuscript. In addition, a preliminary version of this work was presented as an oral presentation at the 145th Annual Meeting of the Pharmaceutical Society of Japan (Fukuoka, March 26–29, 2025), and the present manuscript has been substantially expanded to include further analyses and more in-depth mechanistic discussion. Funding This work and its publication were supported by GEXVal Inc. through its internal research and development budget; and the Business Restructuring Subsidy Program of the Ministry of Economy, Trade and Industry (METI), Japan [grant number R2148U00245-000]. The funders provided financial support in the form of salaries and research expenses but had no additional role in study design, data collection and analysis, manuscript preparation, or the decision to publish. Author Information Authors and Affiliations GEXVal Inc., Shonan iPark 2-26-1 Muraoka Higashi, Fujisawa-shi, Kanagawa, 251-8555, Japan Yusuke Nakayama & Juran Kato-Suzuki Research Center for Advanced Science and Technology (RCAST), The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, 153-8904, Japan. Shingo Tsuji Independent Researcher, Ibaraki, Japan Koji Yamamoto Faculty of Pharmacy, Kindai University, 3-4-1, Kowakae, Higashi-osaka, Osaka 577-8502, Japan. Kouichi Hosomi Contributions Conceptualization, Y.N., S.T., J.K-S. and K.H.; Methodology, S.T., K.H. and K.Y.; Data curation, S.T. and K.Y.; Formal analysis, Y.N. and K.H.; Investigation, Y.N. and K.H.; Writing—original draft preparation, Y.N.; Writing—review and editing, S.T., J.K-S. and K.H.; Supervision, Y.N., S.T., J.K-S. and K.H. All authors have read and agreed to the published version of the manuscript. Corresponding author Correspondence to Yusuke Nakayama Ethics declarations Ethics approval and consent to participate This study used only secondary, de-identified data from publicly available databases and did not involve human participants, animals, or human tissue. Ethics approval and consent to participate were therefore not required. All research was conducted in accordance with ICMJE, MEXT, and JSPS ethical guidelines. Consent for publication All authors have agreed to the publication. Competing interests J.K. is the founder representative director of GEXVal Inc. Y.N. is an employee of GEXVal Inc. S.T., K.Y. and K.H. are external consultants providing scientific and technical advice under service agreements with GEXVal Inc. for this academic study. The algorithms and methodologies reported in this study will be utilized by GEXVal Inc. for internal research and development activities as well as external collaborations. All authors have reviewed and approved this statement. References Serafin MB, et al. Drug repositioning is an alternative for the treatment of coronavirus COVID-19. Int J Antimicrob Agents. 2020;55(6):105969. Thomas D et al. Clinical Development Success Rates and Contributing Factors 2011–2020 (Biotechnology Innovation Organization, 2021) . 2021; Available from: https://go.bio.org/rs/490-EHZ-999/images/ClinicalDevelopmentSuccessRates2011_2020.pdf . Accessed 27 November 2025. Simoens S, Huys I. R&D Costs of New Medicines: A Landscape Analysis. Front Med (Lausanne). 2021;8:760762. Sertkaya A, et al. Costs of Drug Development and Research and Development Intensity in the US, 2000–2018. JAMA Netw Open. 2024;7(6):e2415445. Wong CH, Siah KW, Lo AW. Estimation of clinical trial success rates and related parameters. Biostatistics. 2018;20(2):273–86. Pushpakom S, et al. Drug repurposing: progress, challenges and recommendations. Nat Rev Drug Discov. 2019;18(1):41–58. Nosengo N. Can you teach old drugs new tricks? Nature. 2016;534(7607):314–6. Saber-Ayad M et al. Current Status of Baricitinib as a Repurposed Therapy for COVID-19. Pharmaceuticals (Basel), 2021. 14(7). Morselli Gysi D et al. Network medicine framework for identifying drug-repurposing opportunities for COVID-19. Proc Natl Acad Sci U S A, 2021. 118(19). Cai H, Zheng VW, Chang KC-C. A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications. IEEE Trans Knowl Data Eng. 2018;30(9):1616–37. Cao S, Lu W, Xu Q. Deep Neural Networks for Learning Graph Representations. Proceedings of the AAAI Conference on Artificial Intelligence, 2016. 30(1). Salehi A, Davulcu H. Graph Attention Auto-Encoders . IEEE. Vaswani AS, Parmar N, Uszkoreit N, Jones J, Gomez L, Kaiser AN, Polosukhin Łukasz. Illia, Attention Is All You Need. Advances in Neural Information Processing Systems. Curran Associates, Inc.; 2017. p. 30. Li W et al. Drug repurposing based on the DTD-GNN graph neural network: revealing the relationships among drugs, targets and diseases. BMC Genomics, 2024. 25(1). Muhammed Y. Molecular targets for COVID-19 drug development: Enlightening Nigerians about the pandemic and future treatment. Biosaf Health. 2020;2(4):210–6. Kindrachuk J, et al. Antiviral potential of ERK/MAPK and PI3K/AKT/mTOR signaling modulation for Middle East respiratory syndrome coronavirus infection as identified by temporal kinome analysis. Antimicrob Agents Chemother. 2015;59(2):1088–99. Gassen NC, et al. SKP2 attenuates autophagy through Beclin1-ubiquitination and its inhibition reduces MERS-Coronavirus infection. Nat Commun. 2019;10(1):5770. Sureda A, et al. Endoplasmic reticulum as a potential therapeutic target for covid-19 infection management? Eur J Pharmacol. 2020;882:173288. Han N et al. Identification of SARS-CoV-2-induced pathways reveals drug repurposing strategies. Sci Adv, 2021. 7(27). Open Targets Platform Datasets . Available from: https://platform.opentargets.org/downloads . Accessed 27 November 2025. Grattarola D, Alippi C. Graph Neural Networks in TensorFlow and Keras with Spektral . 2020. arXiv:2006.12138 DOI: 10.48550/arXiv.2006.12138. CMAP LINCS. 2020. Available from: https://clue.io/data/CMap2020#LINCS2020 . Accessed 27 November 2025. Subramanian A et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 2005. 102(43): pp. 15545–15550. Mootha VK, et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 2003;34(3):267–73. MSigDB. : c5 . Available from: https://www.gsea-msigdb.org/gsea/msigdb/human/genesets.jsp?collection=C5 . Accessed 27 November 2025. MSigDB. Available from: https://www.gsea-msigdb.org/gsea/msigdb/ . Accessed 27 November 2025. Liberzon A, et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics. 2011;27(12):1739–40. Merico D, et al. Enrichment Map: A Network-Based Method for Gene-Set Enrichment Visualization and Interpretation. PLoS ONE. 2010;5(11):e13984. Shannon P, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504. Kucera M et al. AutoAnnotate: A Cytoscape app for summarizing networks with semantic annotations. F1000Res, 2016. 5: p. 1717. Wu C, et al. Analysis of therapeutic targets for SARS-CoV-2 and discovery of potential drugs by computational methods. Acta Pharm Sinica B. 2020;10(5):766–88. Zhou Y, et al. A network medicine approach to investigation and population-based validation of disease manifestations and drug repurposing for COVID-19. PLoS Biol. 2020;18(11):e3000970. Isgrò C, Sardanelli AM, Palese LL. Systematic Search for SARS-CoV-2 Main Protease Inhibitors for Drug Repurposing: Ethacrynic Acid as a Potential Drug. Viruses. 2021;13(1):106. González-Nicolás M, et al. Cilastatin: a potential treatment strategy against COVID-19 that may decrease viral replication and protect from the cytokine storm. Clin Kidney J. 2020;13(5):903–5. Shilpa. COVID-19 and Progesterone: Part 1. SARS-CoV-2, Progesterone and its potential clinical use. Endocr Metabolic Sci. 2021;5:100109. Su S et al. Modulation of innate immune response to viruses including SARS-CoV-2 by progesterone. Signal Transduct Target Therapy, 2022. 7(1). Al-Kuraishy HM et al. New insights on the potential effect of progesterone in Covid‐19: Anti‐inflammatory and immunosuppressive effects. Immunity, Inflammation and Disease, 2023. 11(11). Ghandehari S, et al. Progesterone in Addition to Standard of Care vs Standard of Care Alone in the Treatment of Men Hospitalized With Moderate to Severe COVID-19: A Randomized, Controlled Pilot Trial. Chest. 2021;160(1):74–84. Choudhury SR, et al. Dipeptidase-1 Is an Adhesion Receptor for Neutrophil Recruitment in Lungs and Liver. Cell. 2019;178(5):1205–e122117. Cui X, et al. DPEP1 is a direct target of miR-193a-5p and promotes hepatoblastoma progression by PI3K/Akt/mTOR pathway. Cell Death Dis. 2019;10(10):701. Appelberg S, et al. Dysregulation in Akt/mTOR/HIF-1 signaling identified by proteo-transcriptomics of SARS-CoV-2 infected cells. Emerg Microbes Infect. 2020;9(1):1748–60. Khan N. mTOR: A possible therapeutic target against SARS-CoV-2 infection. Arch Stem Cell Ther. 2021;2(1):5–7. Banerjee AK, et al. SARS-CoV-2 Disrupts Splicing, Translation, and Protein Trafficking to Suppress Host Defenses. Cell. 2020;183(5):1325–e133921. Chang AY, et al. Modulation of SF3B1 in the pre-mRNA spliceosome induces a RIG-I-dependent type I IFN response. J Biol Chem. 2021;297(5):101277. Hanada Y, et al. MAVS is energized by Mff which senses mitochondrial metabolism via AMPK for acute antiviral immunity. Nat Commun. 2020;11(1):5711. Additional Declarations Competing interest reported. J.K. is the founder representative director of GEXVal Inc. Y.N. is an employee of GEXVal Inc. S.T., K.Y. and K.H. are external consultants providing scientific and technical advice under service agreements with GEXVal Inc. for this academic study. The algorithms and methodologies reported in this study will be utilized by GEXVal Inc. for internal research and development activities as well as external collaborations. All authors have reviewed and approved this statement. Supplementary Files SupplementalFigures.pdf SupplementalTables.pdf SupportingInformation1COVID19clinicaltrials.xlsx SupportingInformation2COVID19reports.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 20 Jan, 2026 Reviewers agreed at journal 17 Jan, 2026 Reviews received at journal 12 Jan, 2026 Reviewers agreed at journal 12 Jan, 2026 Reviewers agreed at journal 11 Jan, 2026 Reviewers invited by journal 07 Jan, 2026 Editor invited by journal 10 Dec, 2025 Editor assigned by journal 04 Dec, 2025 Submission checks completed at journal 04 Dec, 2025 First submitted to journal 03 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8274293","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":571046216,"identity":"d58591d9-b2ae-4577-83cc-2c64305bfa77","order_by":0,"name":"Yusuke Nakayama","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIiWNgGAWjYJACxgYQyQPhyEGoAhK0GEMoAxK0JDYQ0mJwI/2Z5IwKhjz+nsPPJH7m2KXPd+8xk/hhwCDPL3YAh5YcM8kNZxiKJc62mUn2bkvO3XjmWJpkjwGD4czZCbi0sEk+bAO65zyDmQTvNubcjTOSDxvwGDAkGNzGpQXosIf/GBLnn2f/Jvl3W3264fyHzYZ/8GpJMJPc2MCQuOFsj5k077bDCfISzAcf47NF8swbY8sZxyQSN545U2wtu+244QaetMTHMgYSOP3Cdzz94c2eGpvEeWfSN958u61aXr79jMHBNxU28vzS2LUoHABTEiCCBUwaIIlgBfINCDbzB3SRUTAKRsEoGAUgAAD8J2KhbstXCQAAAABJRU5ErkJggg==","orcid":"","institution":"GEXVal Inc.","correspondingAuthor":true,"prefix":"","firstName":"Yusuke","middleName":"","lastName":"Nakayama","suffix":""},{"id":571046218,"identity":"95cf3e77-1695-4803-bcf8-a7d1e41d9c42","order_by":1,"name":"Shingo Tsuji","email":"","orcid":"","institution":"The University of Tokyo","correspondingAuthor":false,"prefix":"","firstName":"Shingo","middleName":"","lastName":"Tsuji","suffix":""},{"id":571046220,"identity":"d4cc96b9-c260-46a2-863a-f4d6f4f5d435","order_by":2,"name":"Koji Yamamoto","email":"","orcid":"","institution":"Independent Researcher","correspondingAuthor":false,"prefix":"","firstName":"Koji","middleName":"","lastName":"Yamamoto","suffix":""},{"id":571046225,"identity":"7a419295-0b1b-466c-8339-6e9182cc8228","order_by":3,"name":"Juran Kato-Suzuki","email":"","orcid":"","institution":"GEXVal Inc.","correspondingAuthor":false,"prefix":"","firstName":"Juran","middleName":"","lastName":"Kato-Suzuki","suffix":""},{"id":571046227,"identity":"94ddd2ed-cf3f-4268-9165-41ec2a6dfa0a","order_by":4,"name":"Kouichi Hosomi","email":"","orcid":"","institution":"Kindai University","correspondingAuthor":false,"prefix":"","firstName":"Kouichi","middleName":"","lastName":"Hosomi","suffix":""}],"badges":[],"createdAt":"2025-12-04 01:23:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8274293/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8274293/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99799460,"identity":"e750ea94-a4b4-4d48-876e-41b72bd4abce","added_by":"auto","created_at":"2026-01-08 13:49:36","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1687661,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptRePhaINDCOVID19BMCPharmToxsubmission.docx","url":"https://assets-eu.researchsquare.com/files/rs-8274293/v1/cf97083056353db59809d531.docx"},{"id":99756191,"identity":"7e6b7dd9-99eb-40fc-a228-a5fcb8cef0a5","added_by":"auto","created_at":"2026-01-08 05:40:34","extension":"json","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9348,"visible":true,"origin":"","legend":"","description":"","filename":"2de1a0127616415ea841f6211fe002b1.json","url":"https://assets-eu.researchsquare.com/files/rs-8274293/v1/da755ecd2a84fa04b9e9a163.json"},{"id":99756206,"identity":"2bd78513-43f3-41c7-9fd1-0b9d20298f54","added_by":"auto","created_at":"2026-01-08 05:40:35","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1705149,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8274293/v1/a18b4b9495384a168bc579df.pdf"},{"id":99756203,"identity":"396f9fb9-335c-48ff-ab57-769b71565895","added_by":"auto","created_at":"2026-01-08 05:40:35","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":57199,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTables.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8274293/v1/f031944718d3967b3dac8801.pdf"},{"id":99756175,"identity":"cc6e3e17-58b1-49f8-9dc8-1e7b28112671","added_by":"auto","created_at":"2026-01-08 05:40:33","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":16430,"visible":true,"origin":"","legend":"","description":"","filename":"SupportingInformation1COVID19clinicaltrials.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8274293/v1/0b88d9a3f2d7d53eefd48875.xlsx"},{"id":99756186,"identity":"721ca1d5-2878-4b86-bc89-e669d6465b62","added_by":"auto","created_at":"2026-01-08 05:40:33","extension":"pdf","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":41551,"visible":true,"origin":"","legend":"","description":"","filename":"SupportingInformation2COVID19reports.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8274293/v1/c979e492a67e59c13aa1a471.pdf"},{"id":99756177,"identity":"b6cf6f42-bf49-46dc-b866-6d2a925c87fd","added_by":"auto","created_at":"2026-01-08 05:40:33","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":132414,"visible":true,"origin":"","legend":"","description":"","filename":"2de1a0127616415ea841f6211fe002b11enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8274293/v1/e8abcbd466ee51a0f16fd45b.xml"},{"id":99797527,"identity":"78fbf3b6-5723-41be-9fc4-e330d5d3f5a8","added_by":"auto","created_at":"2026-01-08 13:45:57","extension":"pdf","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":121258,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8274293/v1/857021786d5fa2d769173294.pdf"},{"id":99756197,"identity":"06221c36-459e-4158-a021-9952ed1953f3","added_by":"auto","created_at":"2026-01-08 05:40:34","extension":"pdf","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":945671,"visible":true,"origin":"","legend":"","description":"","filename":"Figure2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8274293/v1/329af081dc68221a2705815c.pdf"},{"id":99798110,"identity":"9722d180-7583-4350-8c9d-8c536cfd30d8","added_by":"auto","created_at":"2026-01-08 13:47:17","extension":"pdf","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":44466,"visible":true,"origin":"","legend":"","description":"","filename":"Figure3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8274293/v1/98d2fe035f08e1d199decdb9.pdf"},{"id":99756179,"identity":"5f36dbe0-566d-42fd-8f1a-b34f29f0cf51","added_by":"auto","created_at":"2026-01-08 05:40:33","extension":"pdf","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":85733,"visible":true,"origin":"","legend":"","description":"","filename":"Figure4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8274293/v1/3795522c1397663e18361c52.pdf"},{"id":99756192,"identity":"589cbbfb-8f1f-4e28-9093-439a8f549153","added_by":"auto","created_at":"2026-01-08 05:40:34","extension":"pdf","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":41672,"visible":true,"origin":"","legend":"","description":"","filename":"Figure5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8274293/v1/3514aebfda5c2b16ea550be0.pdf"},{"id":99756199,"identity":"13ebfe03-aafe-4a16-9ff3-2abf1eaf3bf6","added_by":"auto","created_at":"2026-01-08 05:40:34","extension":"emf","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":877872,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.emf","url":"https://assets-eu.researchsquare.com/files/rs-8274293/v1/572f6c08c7ecc9bcfdf82338.emf"},{"id":99756193,"identity":"1bfc18d5-4d56-439c-91c3-292aebbfd5a5","added_by":"auto","created_at":"2026-01-08 05:40:34","extension":"emf","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1802644,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.emf","url":"https://assets-eu.researchsquare.com/files/rs-8274293/v1/2c1bfacd4cfa072d056f5f69.emf"},{"id":99756195,"identity":"6a8ca492-96a4-488e-8d03-5c9e7a74e341","added_by":"auto","created_at":"2026-01-08 05:40:34","extension":"emf","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":567388,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.emf","url":"https://assets-eu.researchsquare.com/files/rs-8274293/v1/5edfcbe0424d36313c828651.emf"},{"id":99756198,"identity":"f3203558-c7a0-4be7-b09a-b29704a754b1","added_by":"auto","created_at":"2026-01-08 05:40:34","extension":"emf","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":744192,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.emf","url":"https://assets-eu.researchsquare.com/files/rs-8274293/v1/2cae2e145e5a3ebf30f09058.emf"},{"id":99799102,"identity":"069fbee2-23e8-4ad3-b02c-64bbf58e5947","added_by":"auto","created_at":"2026-01-08 13:49:13","extension":"emf","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":939244,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.emf","url":"https://assets-eu.researchsquare.com/files/rs-8274293/v1/77da5d76d15c8413b8058587.emf"},{"id":99756205,"identity":"d17b4757-8e10-40e3-83eb-7daf5170436a","added_by":"auto","created_at":"2026-01-08 05:40:35","extension":"png","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":17912,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8274293/v1/2749f17ec8862193f264de31.png"},{"id":99756183,"identity":"c41be5f4-5216-442e-b7b3-3ad08ca741e6","added_by":"auto","created_at":"2026-01-08 05:40:33","extension":"png","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":35657,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8274293/v1/34dbaed00bb531ba5dd10bfa.png"},{"id":99756208,"identity":"1b2b9b6d-e9c2-4c2a-b8de-ab96075eb886","added_by":"auto","created_at":"2026-01-08 05:40:35","extension":"png","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6522,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8274293/v1/1c922cc2c1734a406c18de57.png"},{"id":99756204,"identity":"7558e956-be1a-420d-9f1b-cc3a80dc7e3e","added_by":"auto","created_at":"2026-01-08 05:40:35","extension":"png","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":17229,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8274293/v1/49425fb45bbf922730883b10.png"},{"id":99756180,"identity":"12af0446-81f9-4d93-a42e-e94ef9e3bc09","added_by":"auto","created_at":"2026-01-08 05:40:33","extension":"png","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":11644,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8274293/v1/b6f2000523aef18784b905f9.png"},{"id":99756201,"identity":"82e2d678-aeff-42a1-9f79-31912cff792d","added_by":"auto","created_at":"2026-01-08 05:40:35","extension":"xml","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":129327,"visible":true,"origin":"","legend":"","description":"","filename":"2de1a0127616415ea841f6211fe002b11structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8274293/v1/60ade61d8c139743cd236660.xml"},{"id":99799046,"identity":"0955588f-01cd-41a0-8f8c-921fbb82283c","added_by":"auto","created_at":"2026-01-08 13:49:10","extension":"html","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":145640,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8274293/v1/60886f2c79a4f20e6f0cf61e.html"},{"id":99756200,"identity":"2fb6ff9d-423c-4f06-8889-6c4cb30b6c4c","added_by":"auto","created_at":"2026-01-08 05:40:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":50882,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow of analytical framework for COVID-19\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8274293/v1/002a6a4f1786c10f19e227ae.png"},{"id":99756184,"identity":"0a6b9a9c-f0cd-4eb7-9e24-332945d1476a","added_by":"auto","created_at":"2026-01-08 05:40:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":221583,"visible":true,"origin":"","legend":"\u003cp\u003eSubnetwork connecting COVID-19 and cilastatin in multimodal knowledge graph\u003c/p\u003e\n\u003cp\u003eSubnetwork extracted from a multimodal knowledge graph consisting of drugs, diseases, and genes. Nodes are colored by type: orange for diseases, blue for drugs, and green for genes.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8274293/v1/452af465a4faf25daeb8c443.png"},{"id":99756181,"identity":"5e7cb200-8e75-4a5e-bb4f-030247d4142a","added_by":"auto","created_at":"2026-01-08 05:40:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":17525,"visible":true,"origin":"","legend":"\u003cp\u003eDPA of repurposing candidate drugs and coronavirus infections\u003c/p\u003e\n\u003cp\u003e(A) Reporting odds ratios (ROR) and 95% confidential intervals (CI) for candidate drugs through GATE-based analysis. “NA” indicates values that could not be calculated due to insufficient sample size. (B) Forest plot visualizing RORs for the same set of drugs. The horizontal bars represent 95% CIs. RORs for drotrecogin alfa and ethacrynic acid were not calculable and are indicated with asterisks (*)\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8274293/v1/3a080c88918468d2ac009c0c.png"},{"id":99797673,"identity":"b4ad7cfe-bdde-4da0-9551-1faaa225c6b3","added_by":"auto","created_at":"2026-01-08 13:46:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":51987,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment map of down-regulated pathways following cilastatin treatment\u003c/p\u003e\n\u003cp\u003eEnrichment map of down-regulated pathways following cilastatin treatment\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8274293/v1/d384828664973a67ba98167d.png"},{"id":99756187,"identity":"73f95c36-7c9c-410d-be81-85317d470c42","added_by":"auto","created_at":"2026-01-08 05:40:34","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":40944,"visible":true,"origin":"","legend":"\u003cp\u003eHypothesized mechanisms of action of cilastatin in COVID-19\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8274293/v1/534a79d43ecb633b6aec805a.png"},{"id":99805444,"identity":"06766bfa-72d2-4709-9ffa-1e45f7ac82c5","added_by":"auto","created_at":"2026-01-08 14:16:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1702396,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8274293/v1/6377c3b7-9a14-47ea-818f-d6ea544d2ea7.pdf"},{"id":99756188,"identity":"1ce00aa6-db18-4b3b-b77d-6592c4addca5","added_by":"auto","created_at":"2026-01-08 05:40:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1705149,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8274293/v1/b8f11a0f2333a38845928b16.pdf"},{"id":99797470,"identity":"d254d98b-5b83-4c21-a305-a385a0774688","added_by":"auto","created_at":"2026-01-08 13:45:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":57199,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTables.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8274293/v1/c02850651f7c3515a67e03ec.pdf"},{"id":99756176,"identity":"bddc95a5-9602-42e4-916d-adb06c4fd479","added_by":"auto","created_at":"2026-01-08 05:40:33","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16430,"visible":true,"origin":"","legend":"","description":"","filename":"SupportingInformation1COVID19clinicaltrials.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8274293/v1/c17978638a1b335656372ed8.xlsx"},{"id":99799464,"identity":"f191ed42-69de-49eb-b534-fd439642cac8","added_by":"auto","created_at":"2026-01-08 13:49:37","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":41551,"visible":true,"origin":"","legend":"","description":"","filename":"SupportingInformation2COVID19reports.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8274293/v1/77ac579b707d9fbe1defb39c.pdf"}],"financialInterests":"Competing interest reported. J.K. is the founder representative director of GEXVal Inc. Y.N. is an employee of GEXVal Inc. S.T., K.Y. and K.H. are external consultants providing scientific and technical advice under service agreements with GEXVal Inc. for this academic study. The algorithms and methodologies reported in this study will be utilized by GEXVal Inc. for internal research and development activities as well as external collaborations. All authors have reviewed and approved this statement.","formattedTitle":"Novel graph attention autoencoder framework with multilayered validation identifies drug repurposing candidates for COVID-19 treatment","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe spread of infectious diseases and the emergence of new pathologies have particularly increased the demand for treatments targeting rare diseases. The COVID-19 pandemic highlighted critical gaps in rapid therapeutic development, demonstrating the urgent need for drug repurposing strategies [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. While traditional drug development requires over 10 years and billions of dollars and enormous time commitments [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], with approximately 10% of success rates in clinical development [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], drug repurposing offers accelerated timelines and higher success probabilities due to established safety and pharmacological profiles [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Indeed, several computational approaches have successfully identified COVID-19 therapeutics, including Baricitinib through network-based repositioning [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], validating the potential of systematic computational drug discovery.\u003c/p\u003e \u003cp\u003eComputational drug repurposing leverages public databases and bioinformatics to propose novel therapeutic candidates [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Graph structure-based approaches \u0026ndash; which model relationships between diseases, drugs, and genes as interconnected networks \u0026ndash; have emerged as powerful tools for capturing this biological complexity. Ritonavir (a component of Paxlovid), and Atorvastatin were successfully identified as potential COVID-19 treatments through network-derived relationships analysis, highlighting how graph-based representations can serve as powerful foundation for systematically uncovering repurposing opportunities [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn graph structure-based drug repurposing, it is common to convert graph nodes and edges into vector representations through embedding. Graph embedding methods have been applied to recommendation systems in web stores and other applications [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Building upon these foundations, Graph Neural Networks (GNNs), which employ neural networks for graph embedding, have been proposed [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and have become widely used.\u003c/p\u003e \u003cp\u003eWhile various GNN methods exist, Graph Attention Networks (GATs) that utilize attention mechanisms have demonstrated superiority [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The attention mechanism is a technology used in Transformer Architecture, which forms the foundation of generative AI using large language models such as ChatGPT [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and supports today's revolutionary AI evolution. Recently, these GATs have increasingly been applied to drug repurposing research [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, existing GNN approaches face critical limitations when confronting complex pathological mechanisms that constrain their therapeutic discovery potential.\u003c/p\u003e \u003cp\u003eIn fact, COVID-19 pathogenesis involves complex, multifaceted mechanisms encompassing viral replication, host immune responses, and cellular metabolic processes. Approximately 200 existing drugs may target SARS-CoV-2-associated pathways, spanning diverse targets from viral proteins (spike protein, papain-like protease (PLpro), 3-chymotrypsin-like protease (3CLpro), RNA-dependent RNA polymerase (RdRp), and helicase [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]) and host cellular machinery (mTOR-PI3K pathway [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], autophagy [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], vesicular transport systems [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and immune response [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]). This molecular complexity presents a fundamental challenge; single analytical approaches may be insufficient for adequate exploration of therapeutic candidates.\u003c/p\u003e \u003cp\u003eTo address this challenge, we developed a comprehensive multilayered analytical framework integrating Graph Attention Autoencoder (GATE) with validation using real-world evidence and phenome data. This represents the first systematic combination of these approaches for drug repurposing, enabling identification of candidate drugs such as cilastatin and megestrol that would remain invisible to conventional computational discovery methods while providing actionable mechanistic insights for therapeutic development.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e[Graph Attention Autoencoder (GATE)]\u003c/h2\u003e \u003cp\u003eWe constructed a knowledge graph comprising three types of nodes: disease, drug, and gene (target). Data were primarily obtained from Open Targets (version 24.06) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Open Targets aggregates target and disease association data from multiple public sources. Edges were defined using the following four types of relationship information: (i) \"Molecular interactions\" for target-target relationships, (ii) \"Associations - direct (overall score)\" for target-disease relationships, (iii) \"Drug - mechanism of action\" for drug-target relationships, (iv) \"Drug - indications\" for drug-disease relationships.\u003c/p\u003e \u003cp\u003eInitial attribute vectors for nodes were generated using Large Language Models (LLMs) to create vector representations reflecting the characteristics of each node. Using this graph and a Graph Attention Autoencoder (GATE), we computed vector representations for each node and identified drug nodes proximal to the COVID-19 node by calculating cosine distances. The implementation was performed using the Python programming language with Spektral [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] utilized for GATE construction.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e[Disproportionality Analysis with Spontaneous Reporting Systems]\u003c/h3\u003e\n\u003cp\u003eWe utilized a spontaneous adverse event (AE) reporting database constructed by U.S. Food and Drug Administration (FDA). The FDA Adverse Event Reporting System (FAERS) contains reports of spontaneous AEs reported by consumers, pharmaceutical companies, and medical professionals worldwide. FAERS consists of seven datasets: DEMO (patient demographic and administrative information), DRUG (drug information), REAC (AEs), INDI (indications for the use of reported drugs), THER (start and end dates for the reported drugs), OUTC (outcomes for the event) and RPSR (sources for the event). The present study used FAERS data from January 2020 through March 2022, representing 3,617,885 reports. This data was obtained through JAPIC-AERS (Japan Pharmaceutical Information Center (JAPIC), Tokyo, Japan), a commercially processed FAERS database.\u003c/p\u003e \u003cp\u003eAEs in the FAERS database are coded using Medical Dictionary for Regulatory Activities (MedDRA) Preferred Terms (PTs), which are grouped according to defined medical conditions or areas of interest. We used the Standardized MedDRA Queries to identify PTs related to COVID-19. The search terms used for \u0026ldquo;COVID-19\u0026rdquo; are described in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAE risk was evaluated by calculating the reporting odds ratio (ROR) and corresponding 95% confidence intervals (CI) .\u003c/p\u003e \u003cp\u003eThe ROR and 95% CI were defined as follows:\u003c/p\u003e \u003cp\u003eROR = (a/c) / (b/d)\u003c/p\u003e \u003cp\u003e95% CI\u0026thinsp;=\u0026thinsp;exp [ln(ROR)\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96\u0026radic;(1/a\u0026thinsp;+\u0026thinsp;1/b\u0026thinsp;+\u0026thinsp;1/c\u0026thinsp;+\u0026thinsp;1/d)]\u003c/p\u003e \u003cp\u003ewhere a, b, c and d refer to the number of individuals in the FAERS database as divided into the following four groups: (a) individuals who received the drug of interest (i.e., cilastatin) and exhibited COVID-19-related AEs; (b) individuals who received the drug of interest but did not exhibit COVID-19-related AEs; (c) individuals who did not receive the drug of interest and exhibited COVID-19-related AEs; and (d) individuals who did not receive the drug of interest and did not exhibit COVID-19-related AEs. If the upper limit of the 95% CI was \u0026lt;\u0026thinsp;1, a significant association was assumed between use of the drug of interest and the decreased occurrence of COVID-19-related AEs.\u003c/p\u003e\n\u003ch3\u003e[Pathway Profiling]\u003c/h3\u003e\n\u003cp\u003eTo explore potential therapeutic agents at the phenome level, we conducted analyses using pathway alteration profiles following drug treatment. We utilized gene expression profile data from the Expanded CMAP (Connectivity Map) LINCS (Library of Integrated Network-Based Cellular Signatures) Resource 2020 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], which contains transcriptome data generated from cell lines treated with various compounds.\u003c/p\u003e \u003cp\u003eTo mitigate noise inherent in high-throughput methodologies, we employed a quality control process based on gene expression distribution in duplicate samples, retaining only high-confidence data. Plate-to-plate normalization was performed to eliminate assay bias. Furthermore, we applied distribution analysis to determine gene expression alteration levels, selecting only samples with gene alterations at a Q-value threshold of \u0026lt;\u0026thinsp;0.2.\u003c/p\u003e \u003cp\u003eGene Set Enrichment Analysis (GSEA) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] was conducted on filtered samples to identify significantly altered pathways for each sample, using a false discovery rate (FDR) cutoff of \u0026lt;\u0026thinsp;0.20. For the GSEA analysis, we utilized ontology gene sets (C5) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] from the Molecular Signatures Database (MSigDB) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Correlations between pathway profiles across samples were evaluated using Fisher's exact test to identify compounds with similar phenotypic effects.\u003c/p\u003e \u003cp\u003eStatistical analysis was implemented in C and Ruby, ensuring computational efficiency for large-scale comparisons. Multiple testing correction was applied using the Benjamini-Hochberg method, with a FDR threshold of 0.2 to determine statistical significance.\u003c/p\u003e \u003cp\u003ePathway enrichment visualization was performed using Enrichment Map (v3.4.0) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], a plugin for Cytoscape software (Version 3.10.1) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Significantly enriched pathways with FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.20 were included in the analysis. The pathway network was constructed with each node representing an enriched gene set, and edges connecting pathways with shared genes, where the edge weight corresponds to the overlap coefficient between the gene sets.\u003c/p\u003e \u003cp\u003eTo improve interpretability of the resulting network, we utilized the AutoAnnotate plugin (v1.5.1) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] to cluster and label groups of functionally related pathways based on their shared gene content. This approach allowed for the identification of major biological themes among the differentially regulated pathways. Clusters were manually reviewed and annotated to ensure biological relevance.\u003c/p\u003e\n\u003ch3\u003e[Use of Generative Artificial Intelligence Tools]\u003c/h3\u003e\n\u003cp\u003eDuring the preparation of this manuscript, we used large language models (ChatGPT, OpenAI; Claude, Anthropic) to assist with English translation, improve readability and clarity, and support literature search strategies. All AI-assisted outputs were carefully reviewed and edited by the authors, who take full responsibility for the accuracy and integrity of the final manuscript.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e[GATE Approach Reveals Novel Candidate Drugs for COVID-19 Treatment]\u003c/h2\u003e \u003cp\u003eTo systematically identify and validate novel drug repurposing candidates we developed a comprehensive analytical framework that addresses the limitations of standalone computational approaches. Our methodology consists of three integrated phases: (1) computational identification of candidate drugs through state-of-the-art GATE approaches utilizing attention mechanisms and data obtained from Open Targets [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], (2) concept validation of clinical potential using real-world evidence, and (3) molecular biological assessment for mechanism of action (MOA) hypotheses generation using phenome data (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This multilayered evaluation strategy enables us to move beyond simple similarity predictions to provide actionable insights for therapeutic development. To demonstrate the utility of this system, we applied it to identify and validate novel drug repurposing candidates for COVID-19.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAI network analysis (GATE) selects candidate drugs for COVID-19, real-world data analysis (signal detection) narrows them to drugs with protective signals, and phenome analysis (pathway profiling) evaluates their molecular plausibility and mechanisms of action.\u003c/p\u003e \u003cp\u003eBased on the latent space representation of GATE, we identified drug nodes showing high similarity to the COVID-19 node (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In interpreting the results, we considered different salt or derivative forms of the same compound (e.g., cilastatin and cilastatin sodium) as representing a single drug entity. Among the 17 drugs identified, 13 drugs were already known to have therapeutic effects against COVID-19 symptoms, indicating that our model identifies clinically meaningful drug-disease associations. The remaining 4 drugs (cilastatin, megestrol, drotrecogin alfa, ethacrynic acid) showed minimal prior associations with COVID-19 in the literature, revealing hidden therapeutic opportunities that would have remained invisible to conventional computational discovery approaches. Importantly, these candidate drugs and COVID-19 were not directly connected in the original knowledge graph (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-3), demonstrating our GATE\u0026rsquo;s ability to discover latent associations implicit in network relationships.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop-ranked drug candidates based on embedding similarity to COVID-19 in the latent space learned by GATE model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCosine Similarity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpenTargets\u003c/p\u003e \u003cp\u003eAssociation Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOpenTargets\u003c/p\u003e \u003cp\u003eRank of Score\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCHEMBL1201057\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCILASTATIN SODIUM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eAntibiotic, Dipeptidyl Peptidase I Inhibitor\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.86067\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2672\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCHEMBL766\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCILASTATIN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eAntibiotic, Dipeptidyl Peptidase I Inhibitor\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.86006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2672\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHEMBL1201014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePREDNISOLONE SODIUM PHOSPHATE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSteroid Drug\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.85693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHEMBL1201231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePREDNISOLONE PHOSPHORIC ACID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSteroid Drug\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.85526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHEMBL679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEPINEPHRINE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdrenergic Receptor Agonist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.85111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHEMBL2134724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIPRATROPIUM BROMIDE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnticholinergic Drug, COPD and Bronchial Asthma Treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.84696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e737\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHEMBL1621597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIPRATROPIUM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnticholinergic Drug, COPD and Bronchial Asthma Treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.84621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e737\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHEMBL1200689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNITRIC OXIDE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNitric Oxide, Pulmonary Hypertension Treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.84535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e397\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHEMBL1201335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGLYCOPYRRONIUM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnticholinergic Drug, COPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.84439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e737\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHEMBL1437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNOREPINEPHRINE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdrenergic Receptor Agonist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.84266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHEMBL1215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePHENYLEPHRINE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdrenergic Receptor Agonist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.84121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHEMBL2108429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMEPOLIZUMAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnti-IL-5 Antibody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.83772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e799\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCHEMBL1201139\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMEGESTROL ACETATE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eAntineoplastic Drug, Contraceptive, Progesterone Receptor Agonist\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.83667\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.086\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e636\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHEMBL1370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBUDESONIDE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSteroid Drug\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.83639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHEMBL1364144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMETHYLPREDNISOLONE ACETATE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSteroid Drug\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.83551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHEMBL1201027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGLYCOPYRROLATE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnticholinergic Drug\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.83493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e737\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCHEMBL2109065\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eDROTRECOGIN ALFA (ACTIVATED)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eAntithrombotic, Anti-inflammatory, Fibrinolysis-promoting Human Activated Protein C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.83448\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.026\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1339\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHEMBL52440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDEXTROMETHORPHAN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCough Suppressant and Expectorant (Medicon)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.83335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e334\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHEMBL1473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFLUTICASONE PROPIONATE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSteroid Drug\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.83256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHEMBL3707243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGLYCOPYRRONIUM TOSYLATE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnticholinergic Drug, Primary Axillary Hyperhidrosis Treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.83239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e737\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCHEMBL456\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eETHACRYNIC ACID\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eLoop Diuretic Drug\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.83171\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e4822\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDrug candidates are ranked by cosine similarity scores in GATE latent space. Rows shown in bold indicate drugs not previously used for COVID-19 treatment, representing novel repositioning candidates. The OpenTargets association scores range from 0 to 1, with higher values indicating stronger relevance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSubnetwork extracted from a multimodal knowledge graph consisting of drugs, diseases, and genes. Nodes are colored by type: orange for diseases, blue for drugs, and green for genes.\u003c/p\u003e \u003cp\u003eTo further evaluate the findings, we investigated the association scores between COVID-19 and the target genes of each candidate drug using the Open Targets Platform, an integrated drug discovery database. Association scores are comprehensive relatedness indicators based on literature information, genetic evidence, experimental data, and other sources. This analysis revealed that all four candidate drugs showed low association scores with COVID-19 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), suggesting that these drugs would not be visible through existing knowledge bases. In particular, our top candidate, cilastatin, had an association score of 0.01, which is significantly lower than scores (0.068\u0026ndash;0.609) of drugs that are known to have effects on COVID-19.\u003c/p\u003e \u003cp\u003eTo benchmark against other approaches, we investigated whether these four drugs had been reported as COVID-19 repurposing candidates in existing studies. Cilastatin was reported as one of 78 candidates for SARS-CoV-2 3CLpro inhibition through docking simulation approaches, but detailed validation was not performed [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Megestrol was proposed as one of 34 COVID-19 therapeutic candidates through network-based approaches, though detailed investigation was not conducted [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Drotrecogin alfa has no existing reports, representing the first identification of this drug as a COVID-19 candidate. Ethacrynic acid was reported as a promising candidate for SARS-CoV-2 main protease (Mpro) inhibition through docking simulation approaches, with experimental validation also performed [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough some candidate drugs identified by the GATE approach have been previously reported by other computational methods, these studies provided only preliminary identification without comprehensive evaluation of therapeutic potential. Importantly, three drugs (cilastatin, drotrecogin alfa, ethacrynic acid) excluding megestrol were not identified by conventional network-based approaches and were first detected by the GATE approach. This indicates that GATE can identify complex associations that remain undetected by conventional network analysis, exemplified by novel identification of drotrecogin alfa.\u003c/p\u003e \u003cp\u003eThese results suggest that the GATE approach is complementary to conventional methods and has the potential to efficiently discover novel relationships between unknown drug-disease pairs. Furthermore, the identification of candidates such as ethacrynic acid, for which experimental validation has been conducted, provides additional evidence supporting the reliability of our approach. However, these remain computational predictions that require experimental validation. To provide mechanistic insights that could guide future preclinical studies, we next analyzed the potential mechanisms of action of these candidate drugs.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e[Evaluation of Clinical Potential of Candidate Drugs through FAERS Disproportionality Analysis (DPA)]\u003c/h3\u003e\n\u003cp\u003eWe further analyzed the four drugs identified by the GATE approach using real-world adverse events data from FAERS to examine potential clinical associations with COVID-19 by DPA. In DPA, ROR and 95% CI are used to evaluate associations between drugs and diseases. An inverse association (ROR\u0026thinsp;\u0026lt;\u0026thinsp;1) is considered protective and a 95% CI range with the upper limit\u0026thinsp;\u0026lt;\u0026thinsp;1 indicates statistical significance. This analysis revealed striking protective signals for cilastatin and megestrol, with ROR for COVID-19 infection of 0.19 (95% CI: 0.09\u0026ndash;0.43) and 0.63 (95% CI: 0.42\u0026ndash;0.94), respectively. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Notably, cilastatin demonstrated an exceptionally strong protective association, with patients taking this medication showing approximately 5-fold lower odds of COVID-19 infection compared to non-cilastatin users. Megestrol also exhibited a significant protective effect, with approximately 40% reduction in infection odds. These findings suggest substantial clinical potential for both drugs in COVID-19 infection, with cilastatin showing one of the most pronounced protective signals observed in real-world data.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(A) Reporting odds ratios (ROR) and 95% confidential intervals (CI) for candidate drugs through GATE-based analysis. \u0026ldquo;NA\u0026rdquo; indicates values that could not be calculated due to insufficient sample size. (B) Forest plot visualizing RORs for the same set of drugs. The horizontal bars represent 95% CIs. RORs for drotrecogin alfa and ethacrynic acid were not calculable and are indicated with asterisks (*)\u003c/p\u003e \u003cp\u003eTo examine whether the observed inverse associations were specific to each drug, we conducted DPA for drugs in the same class as each candidate drug or for drugs that are often co-administered with the candidate drug. Cilastatin is often co-administered with imipenem, a β-lactamase inhibitor. DPA for β-lactamase inhibitors showed ROR values around 1.0, and the remarkable inverse associations observed with cilastatin were not observed (Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). This result suggests that the observed COVID-19 infection suppression effect may be specific to cilastatin. Megestrol is a progesterone receptor agonist. Progesterone preparations classified as progesterone receptor agonists showed RORs around 0.5 with 95% CI upper limit less than 1, indicating inverse association trends similar to megestrol (Figure S5). This result suggests that the observed effect represents a pharmacological class effect mediated through progesterone receptors rather than a mechanism that is specific to megestrol. However, more detailed analysis is needed to clearly distinguish the influence of class effects.\u003c/p\u003e \u003cp\u003eThe remarkable inverse association observed for cilastatin is consistent with previous studies demonstrating that this drug may attenuate COVID-19 pathology by reducing SARS-CoV-2 viral replication and providing protection from the cytokine storm observed in severe COVID-19 [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Based on these findings, cilastatin warrants further investigation as potential therapeutic candidate for COVID-19.\u003c/p\u003e \u003cp\u003eRegarding megestrol, no literature has reported direct associations with COVID-19. However, progesterone, a drug in the same class that our DPA has indicated to be protective against COVID-19, has anti-inflammatory properties, suggesting potential as a COVID-19 treatment [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Clinical studies on COVID-19 treatment using progesterone have been conducted (NCT04365127, NCT04865029) and in the NCT04865029 trial, progesterone administration in moderate to severe COVID-19 patients resulted in significant improvement in clinical status (a score of 1.5 points, corresponding to a 3-day reduction in oxygen supplementation and 2.5-day reduction in hospitalization duration) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Given the observed class effect of progesterone receptor agonists and the clinical evidence supporting progesterone, megestrol merits evaluation to determine whether it confers similar protective effects.\u003c/p\u003e \u003cp\u003eNote that drotrecogin alfa and ethacrynic acid could not be evaluated using this DPA due to insufficient co-occurrence reports with COVID-19-related AEs in the FAERS database during the analysis period.\u003c/p\u003e\n\u003ch3\u003e[Evaluation of Molecular Potential of Cilastatin and Megestrol for COVID-19 Treatment]\u003c/h3\u003e\n\u003cp\u003eTo further explore these findings at the phenome level, we conducted correlation analysis using pathway profiling with LINCS 2020 for cilastatin and megestrol, the two drugs that showed statistically significant inverse associations in DPA. LINCS 2020 is the successor database to the Connectivity Map (CMAP), an established platform for comparing phenomic signatures of any given drug with those associated with specific diseases or disease therapeutics [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. For each drug, we explored compounds with correlated pathway profiles from 17,170 compounds (241,597 profiles) using Fisher's exact test.\u003c/p\u003e \u003cp\u003eThe compounds showing the strongest correlations with cilastatin (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) included artesunate and sirolimus, which are drugs that have undergone clinical trials for COVID-19. The analysis also revealed ipidacrine, ursolic acid, wortmannin, and thapsigargin, which have been reported in the literature as COVID-19 therapeutic candidates. These results suggest that cilastatin has potential as a therapeutic candidate at the phenome level.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop-ranked compounds positively correlated with the pathway profile of cilastatin\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePertID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMOA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCell\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eConc\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTime\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAdj. P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCOVID-19 relation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRD-K79759031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eartesunate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHT29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10uM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.4E-42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10 clinical studies\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRD-K66896231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eipidacrine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAcetylcholinesterase inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHT29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10uM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0E-40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 report\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRD-K60067222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBRD-K60067222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10uM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.7E-40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRD-K68185022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eursolic-acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11-beta hydroxysteroid dehydrogenase inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70uM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.2E-39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4 reports\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRD-A19037878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBRD-A19037878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eK562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.5uM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.0E-39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRD-A75409952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ewortmannin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePI3K inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10uM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.6E-38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2 reports\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRD-K81855038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eroxatidine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHT29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10uM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.9E-38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRD-A79768653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esirolimus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMTOR inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.33uM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.5E-37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9 clinical studies\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRD-K69023402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ethapsigargin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eATPase inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eJURKAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.37uM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.5E-37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3 reports\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRD-K56032964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAP-26113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHT29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.37uM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.6E-37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe \"COVID-19 relation\" column indicates whether each compound has been previously associated with COVID-19 through publications or clinical trials. Details of the corresponding reports and studies are provided in Supporting Information 1 and 2.\u003c/p\u003e \u003cp\u003eSimilarly, the compounds showing the strongest correlations with megestrol (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) included tianeptine, fedratinib (TG-101348), AZD-6482 and vorinostat. Tianeptine and fedratinib (TG-101348) have undergone clinical trials for COVID-19. AZD-6482 and vorinostat have been reported as COVID-19 therapeutic candidates. These results suggest that megestrol has potential as a COVID-19 therapeutic candidate at the phenome level.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop-ranked compounds positively correlated with the pathway profile of megestrol\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePertID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMOA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCell\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eConc\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTime\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAdj. P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCOVID-19 relation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRD-K37142460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMI-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOCILY3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10uM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.3E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRD-A81370665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBI-D1870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRibosomal protein inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHBL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10uM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.3E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRD-K01436366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eXMD-1150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLeucine rich repeat kinase inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBJAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10uM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.4E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRD-K36363294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI-BET-151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHIMG002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.5uM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.4E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRD-A53077924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etianeptine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSelective serotonin reuptake enhancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHIMG001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15uM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.1E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 clinical study\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRD-K12502280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTG-101348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFLT3 inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOCILY3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10uM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.5E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 clinical study\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRD-K58772419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAZD-6482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePI3K inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMCF7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.11uM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.0E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 report\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRD-K12502280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTG-101348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFLT3 inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTMD8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.5uM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.7E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 clinical study\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRD-K81418486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003evorinostat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHDAC inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eJURKAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10uM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.7E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3 reports\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRD-K54606188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBRD-K54606188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTMD8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.66uM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.9E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe \"COVID-19 relation\" column indicates whether each compound has been previously associated with COVID-19 through publications or clinical trials. Details of the corresponding reports and studies are provided in Supporting Information 1 and 2.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e[Hypothesized Mechanisms of Action of Cilastatin for COVID-19 Treatment]\u003c/h2\u003e \u003cp\u003eThe correlation with established COVID-19 therapeutic candidates provided substantial evidence for the intrinsic potential of cilastatin for clinical application. To further strengthen this rationale and evaluate whether cilastatin possesses sufficient mechanistic basis to incentivize clinical development, we sought to elucidate the underlying biological mechanisms based on pathway profiling data. We argued that because pathway-level information represents a functional layer closer to biological responses than individual genes, a detailed analysis of the LINCS 2020 data can facilitate hypothesis generation regarding mechanisms of action.\u003c/p\u003e \u003cp\u003eIn the LINCS 2020 data, cilastatin treatment significantly decreased the expression of 42 pathways in the HT29 cell line (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Enrichment Map analysis revealed that gene sets related to splicing, ribosome, mitochondria, telomere, and ubiquitin pathways were altered (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Based on these pathway alterations, we constructed a mechanistic hypothesis for how cilastatin might intervene in the pathophysiological processes of COVID-19 induced by SARS-CoV-2 infection (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis network visualizes gene ontology (GO) biological process terms significantly down-regulated by cilastatin treatment. Each node represents a gene set corresponding to a GO term, with node size proportional to the number of genes in the set and node color reflecting statistical significance (log₁₀-transformed p-value). Edges indicate gene overlap between gene sets. Functionally related clusters are annotated.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis schematic illustrates potential therapeutic mechanisms by which cilastatin may alleviate COVID-19 pathology, inferred from pathway-level gene expression changes. Cilastatin-mediated inhibition of DPEP1 is proposed to influence multiple downstream biological processes, including ribosome biogenesis, RNA splicing, and mitochondrial metabolism. These changes converge on antiviral and anti-inflammatory outcomes such as suppression of viral replication and inflammation. Pathways shaded in blue indicate those with down-regulated gene expression following cilastatin treatment.\u003c/p\u003e \u003cp\u003eCilastatin is clinically used as a dipeptidase-1 (DPEP1) inhibitor in combination with imipenem to prevent renal degradation of the antibiotic by DPEP1. Beyond this established role in antibiotic protection, our analysis of LINCS2020 data revealed that cilastatin modulatesmultiple cellular pathways related to splicing, ribosomal function, and mitochondrial processes in cells. DPEP1, in addition to its primary function of dipeptide hydrolysis in the kidney, also functions as a neutrophil adhesion receptor on vascular endothelial cells and is known to promote neutrophil recruitment during inflammation [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. DPEP1 may restrict host ribosomes utilized by SARS-CoV-2 by controlling the PI3K/Akt/mTOR pathway [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], which regulates cellular protein synthesis and metabolism [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. This may suppress viral replication through a mechanism similar to that of mTOR inhibitors such as sirolimus (rapamycin) [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOf particular interest, it is known that the nonstructural protein 16 (NSP16) of SARS-CoV-2 binds to U1/U2 snRNA and interferes with the host splicing mechanism [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Suppression of splicing-related pathways may function as a cellular stress response that limits host gene expression and potentially suppresses viral replication by restricting protein synthesis. Moreover, splicing abnormalities may activate antiviral responses via RIG-I-like receptors [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBeyond the splicing mechanism, cilastatin also affects mitochondrial pathways. Decreased expression of mitochondria-related genes suggests changes in cellular metabolic activity and immune signaling. These changes are expected to achieve balanced immune regulation by limiting energy available for viral replication while maintaining type I interferon responses through mitochondrial antiviral signaling protein (MAVS) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e[Study Limitations]\u003c/h2\u003e \u003cp\u003eWhile our multilayered computational approach successfully identified several promising therapeutic candidates and elucidated potential mechanisms of action, there are several important limitations that must be addressed.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eLimitations of the GATE approach\u003c/strong\u003e \u003cp\u003eThe predictive performance of our knowledge graph-based method heavily depends on the comprehensiveness and quality of the base knowledge graph being used. In biomedical knowledge graphs, data bias tends to occur based on research progress and attention levels, with particularly insufficient information regarding rare diseases and drugs in early development stages. This deficiency may limit the discovery of novel drug-disease associations and reduce predictability. Therefore, developing comprehensive knowledge graphs that systematically incorporate data on rare diseases and emerging therapeutics will be essential for improving comprehensiveness and reducing bias in future studies.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eLimitations of real-world data analysis\u003c/strong\u003e \u003cp\u003eSince FAERS is a spontaneous AE reporting system, it inherently contains problems such as reporting bias, underreporting, and selective reporting. A critical limitation emerged from our analysis of drotrecogin alfa and ethacrynic acid, which revealed that the method cannot analyze drugs with insufficient data in the database. This particularly affects newer drugs that lack extensive real-world usage data, limiting our ability to detect potential associations. Conversely, this suggests that classical, widely used drugs may have higher detection sensitivity due to larger volumes of available data. Furthermore, inferring causal relationships from observed associations remains challenging, and the influence of confounding factors cannot be completely eliminated.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eLimitations of phenome analysis\u003c/strong\u003e \u003cp\u003eThe LINCS database used is primarily based on drug responses in cancer cell lines, which exhibit different characteristics from alveolar epithelial cells, vascular endothelial cells, and immune cells, which are important in COVID-19 pathogenesis. Additionally, drug responses under the inflammatory environment in patients during SARS-CoV-2 infection may differ significantly from normal cell culture conditions. Furthermore, it is difficult to capture host-virus interactions and other complex biological processes that can only be detected in \u003cem\u003ein vivo\u003c/em\u003e conditions using cell line-based phenome data. While extensive animal data have been accumulated and could potentially be curated and integrated into such databases, comprehensive incorporation remains challenging due to species differences in drug target molecules, including variations in binding site homology and pharmacological responses between species.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNeed for experimental validation\u003c/strong\u003e \u003cp\u003eThis study primarily focused on computational predictions and analysis of existing databases to systematically identify therapeutic candidates and elucidate potential mechanisms. While this approach provided valuable insights into drug repurposing opportunities for COVID-19 treatment, future studies should include direct validation using SARS-CoV-2-infected models and/or clinical evaluation.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, we systematically evaluated the applicability of existing drugs for COVID-19 treatment using the GATE-based latent space analysis with real-world evidence and phenome-level validation. This represents the first systematic combination of these approaches for drug repurposing. We identified two candidate drugs for further investigation: cilastatin and megestrol, both clinically approved medications with established safety profiles, which may be amenable to accelerated clinical evaluation.\u003c/p\u003e \u003cp\u003eOur multilayered validation approach achieved high-confidence candidate drug selection by comprehensively validating clinical and molecular biological plausibility. Integration of these heterogeneous data sources \u0026ndash; static knowledge graphs, real-world clinical-observations, and dynamic gene expression data \u0026ndash; enabled multi-dimensional insights unattainable by single analytical method based approaches. Real-world data analysis confirmed clinical potential, while phenome data analysis established molecular biological validity and mechanistic hypotheses for both drugs.\u003c/p\u003e \u003cp\u003eThis integrated framework establishes a new paradigm for computational drug discovery that bridges the gap between \u003cem\u003ein silico\u003c/em\u003e prediction and clinical translation. This multimodal methodology has applications for rapid candidate identification during pandemics and for diseases and conditions with limited therapeutic options. Future developments in large language models (LLMs) and agentic AI may further enhance this GATE approach by improving knowledge graph comprehensiveness.\u003c/p\u003e \u003cp\u003eFuture research should focus on \u003cem\u003ein vivo\u003c/em\u003e validation and clinical trials of these repurposed drug candidates. It should also explore the applicability of this methodology to other diseases and establish selection criteria for optimal computational methods based on data scale and intended use.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eData Availability\u003c/h2\u003e \u003cp\u003eThe datasets used and analyzed in this study were obtained from publicly accessible resources, including the Open Targets Platform datasets (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://platform.opentargets.org/downloads\u003c/span\u003e\u003cspan address=\"https://platform.opentargets.org/downloads\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the CMAP LINCS 2020 collection on the CLUE platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://clue.io/data/CMap2020#LINCS2020\u003c/span\u003e\u003cspan address=\"https://clue.io/data/CMap2020#LINCS2020\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the MSigDB C5 collection (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/msigdb/\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/msigdb/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSpontaneous adverse event data were obtained from JAPIC-AERS, a licensed database derived from FAERS. Due to licensing and commercial restrictions, the JAPIC-AERS data and other processed datasets used in this study are not publicly available and cannot be shared.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eadverse event\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCMAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConnectivity Map\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDPA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edisproportionality analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDPEP1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edipeptidase-1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFAERS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFDA Adverse Event Reporting System\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFDA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFood and Drug Administration\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGAT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGraph Attention Network\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGATE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGraph Attention Autoencoder\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGNN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGraph Neural Network\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLINCS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLibrary of Integrated Network-Based Cellular Signatures\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLLM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLarge Language Model\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMOA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emechanism of action\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereporting odds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData Availability\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed in this study were obtained from publicly accessible resources, including the Open Targets Platform datasets (https://platform.opentargets.org/downloads), the CMAP LINCS 2020 collection on the CLUE platform (https://clue.io/data/CMap2020#LINCS2020), and the MSigDB C5 collection (https://www.gsea-msigdb.org/gsea/msigdb/).\u003c/p\u003e\n\u003cp\u003eSpontaneous adverse event data were obtained from JAPIC-AERS, a licensed database derived from FAERS. Due to licensing and commercial restrictions, the JAPIC-AERS data and other processed datasets used in this study are not publicly available and cannot be shared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgments\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the valuable discussions and technical advice provided by\u0026nbsp;K. Matsubara, T. Morimoto, N. Hirashima, Dr. K. Shimada, and T. Naito. We would like to thank K. Hashizume for collaboration on this work. We also thank Dr. J. Urano for insightful comments and critical suggestions on the manuscript.\u003c/p\u003e\n\u003cp\u003eIn addition, a preliminary version of this work was presented as an oral presentation at the 145th Annual Meeting of the Pharmaceutical Society of Japan (Fukuoka, March 26\u0026ndash;29, 2025), and the present manuscript has been substantially expanded to include further analyses and more in-depth mechanistic discussion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work and its publication were supported by GEXVal Inc.\u0026nbsp;through its internal research and development budget; and the\u0026nbsp;Business Restructuring Subsidy Program of the Ministry of Economy, Trade and Industry (METI), Japan [grant number R2148U00245-000]. The funders provided financial support in the form of salaries and research expenses but had no additional role in study design, data collection and analysis, manuscript preparation, or the decision to publish.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthor Information\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors and Affiliations\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGEXVal Inc., Shonan iPark 2-26-1 Muraoka Higashi, Fujisawa-shi, Kanagawa, 251-8555, Japan\u003c/p\u003e\n\u003cp\u003eYusuke Nakayama \u0026amp; Juran Kato-Suzuki\u003c/p\u003e\n\u003cp\u003eResearch Center for Advanced Science and Technology (RCAST), The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, 153-8904, Japan.\u003c/p\u003e\n\u003cp\u003eShingo Tsuji\u003c/p\u003e\n\u003cp\u003eIndependent Researcher, Ibaraki, Japan\u003c/p\u003e\n\u003cp\u003eKoji Yamamoto\u003c/p\u003e\n\u003cp\u003eFaculty of Pharmacy, Kindai University, 3-4-1, Kowakae, Higashi-osaka, Osaka 577-8502, Japan.\u003c/p\u003e\n\u003cp\u003eKouichi Hosomi\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eContributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, Y.N., S.T., J.K-S. and K.H.; Methodology, S.T., K.H. and K.Y.; Data curation, S.T. and K.Y.; Formal analysis, Y.N. and K.H.; Investigation, Y.N. and K.H.; Writing\u0026mdash;original draft preparation, Y.N.; Writing\u0026mdash;review and editing, S.T., J.K-S. and K.H.; Supervision, Y.N., S.T., J.K-S. and K.H. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCorresponding author\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Yusuke Nakayama\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics declarations\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used only secondary, de-identified data from publicly available databases and did not involve human participants, animals, or human tissue. Ethics approval and consent to participate were therefore not required. All research was conducted in accordance with ICMJE, MEXT, and JSPS ethical guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have agreed to the publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.K. is the founder representative director of GEXVal Inc. Y.N. is an employee of GEXVal Inc. S.T., K.Y. and K.H. are external consultants providing scientific and technical advice under service agreements with GEXVal Inc. for this academic study. The algorithms and methodologies reported in this study will be utilized by GEXVal Inc. for internal research and development activities as well as external collaborations. All authors have reviewed and approved this statement.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSerafin MB, et al. Drug repositioning is an alternative for the treatment of coronavirus COVID-19. Int J Antimicrob Agents. 2020;55(6):105969.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThomas D et al. \u003cem\u003eClinical Development Success Rates and Contributing Factors 2011\u0026ndash;2020 (Biotechnology Innovation Organization, 2021)\u003c/em\u003e. 2021; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://go.bio.org/rs/490-EHZ-999/images/ClinicalDevelopmentSuccessRates2011_2020.pdf\u003c/span\u003e\u003cspan address=\"https://go.bio.org/rs/490-EHZ-999/images/ClinicalDevelopmentSuccessRates2011_2020.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 27 November 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSimoens S, Huys I. R\u0026amp;D Costs of New Medicines: A Landscape Analysis. Front Med (Lausanne). 2021;8:760762.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSertkaya A, et al. Costs of Drug Development and Research and Development Intensity in the US, 2000\u0026ndash;2018. JAMA Netw Open. 2024;7(6):e2415445.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong CH, Siah KW, Lo AW. Estimation of clinical trial success rates and related parameters. Biostatistics. 2018;20(2):273\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePushpakom S, et al. Drug repurposing: progress, challenges and recommendations. Nat Rev Drug Discov. 2019;18(1):41\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNosengo N. Can you teach old drugs new tricks? Nature. 2016;534(7607):314\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaber-Ayad M et al. Current Status of Baricitinib as a Repurposed Therapy for COVID-19. Pharmaceuticals (Basel), 2021. 14(7).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorselli Gysi D et al. \u003cem\u003eNetwork medicine framework for identifying drug-repurposing opportunities for COVID-19.\u003c/em\u003e Proc Natl Acad Sci U S A, 2021. 118(19).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCai H, Zheng VW, Chang KC-C. A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications. IEEE Trans Knowl Data Eng. 2018;30(9):1616\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao S, Lu W, Xu Q. \u003cem\u003eDeep Neural Networks for Learning Graph Representations.\u003c/em\u003e Proceedings of the AAAI Conference on Artificial Intelligence, 2016. 30(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalehi A, Davulcu H. \u003cem\u003eGraph Attention Auto-Encoders\u003c/em\u003e. IEEE.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVaswani AS, Parmar N, Uszkoreit N, Jones J, Gomez L, Kaiser AN, Polosukhin Łukasz. Illia, \u003cem\u003eAttention Is All You Need.\u003c/em\u003e Advances in Neural Information Processing Systems. Curran Associates, Inc.; 2017. p. 30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi W et al. Drug repurposing based on the DTD-GNN graph neural network: revealing the relationships among drugs, targets and diseases. BMC Genomics, 2024. 25(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuhammed Y. Molecular targets for COVID-19 drug development: Enlightening Nigerians about the pandemic and future treatment. Biosaf Health. 2020;2(4):210\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKindrachuk J, et al. Antiviral potential of ERK/MAPK and PI3K/AKT/mTOR signaling modulation for Middle East respiratory syndrome coronavirus infection as identified by temporal kinome analysis. Antimicrob Agents Chemother. 2015;59(2):1088\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGassen NC, et al. SKP2 attenuates autophagy through Beclin1-ubiquitination and its inhibition reduces MERS-Coronavirus infection. Nat Commun. 2019;10(1):5770.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSureda A, et al. Endoplasmic reticulum as a potential therapeutic target for covid-19 infection management? Eur J Pharmacol. 2020;882:173288.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan N et al. Identification of SARS-CoV-2-induced pathways reveals drug repurposing strategies. Sci Adv, 2021. 7(27).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cem\u003eOpen Targets Platform Datasets\u003c/em\u003e. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://platform.opentargets.org/downloads\u003c/span\u003e\u003cspan address=\"https://platform.opentargets.org/downloads\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 27 November 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrattarola D, Alippi C. \u003cem\u003eGraph Neural Networks in TensorFlow and Keras with Spektral\u003c/em\u003e. 2020. arXiv:2006.12138 DOI: 10.48550/arXiv.2006.12138.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCMAP LINCS. 2020. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://clue.io/data/CMap2020#LINCS2020\u003c/span\u003e\u003cspan address=\"https://clue.io/data/CMap2020#LINCS2020\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 27 November 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSubramanian A et al. \u003cem\u003eGene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles.\u003c/em\u003e Proceedings of the National Academy of Sciences, 2005. 102(43): pp. 15545\u0026ndash;15550.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMootha VK, et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 2003;34(3):267\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMSigDB. : \u003cem\u003ec5\u003c/em\u003e. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/msigdb/human/genesets.jsp?collection=C5\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/msigdb/human/genesets.jsp?collection=C5\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 27 November 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMSigDB. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/msigdb/\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/msigdb/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 27 November 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiberzon A, et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics. 2011;27(12):1739\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMerico D, et al. Enrichment Map: A Network-Based Method for Gene-Set Enrichment Visualization and Interpretation. PLoS ONE. 2010;5(11):e13984.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShannon P, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498\u0026ndash;504.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKucera M et al. AutoAnnotate: A Cytoscape app for summarizing networks with semantic annotations. F1000Res, 2016. 5: p. 1717.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu C, et al. Analysis of therapeutic targets for SARS-CoV-2 and discovery of potential drugs by computational methods. Acta Pharm Sinica B. 2020;10(5):766\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou Y, et al. A network medicine approach to investigation and population-based validation of disease manifestations and drug repurposing for COVID-19. PLoS Biol. 2020;18(11):e3000970.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIsgr\u0026ograve; C, Sardanelli AM, Palese LL. Systematic Search for SARS-CoV-2 Main Protease Inhibitors for Drug Repurposing: Ethacrynic Acid as a Potential Drug. Viruses. 2021;13(1):106.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGonz\u0026aacute;lez-Nicol\u0026aacute;s M, et al. Cilastatin: a potential treatment strategy against COVID-19 that may decrease viral replication and protect from the cytokine storm. Clin Kidney J. 2020;13(5):903\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShilpa. COVID-19 and Progesterone: Part 1. SARS-CoV-2, Progesterone and its potential clinical use. Endocr Metabolic Sci. 2021;5:100109.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSu S et al. Modulation of innate immune response to viruses including SARS-CoV-2 by progesterone. Signal Transduct Target Therapy, 2022. 7(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl-Kuraishy HM et al. \u003cem\u003eNew insights on the potential effect of progesterone in Covid‐19: Anti‐inflammatory and immunosuppressive effects.\u003c/em\u003e Immunity, Inflammation and Disease, 2023. 11(11).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhandehari S, et al. Progesterone in Addition to Standard of Care vs Standard of Care Alone in the Treatment of Men Hospitalized With Moderate to Severe COVID-19: A Randomized, Controlled Pilot Trial. Chest. 2021;160(1):74\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoudhury SR, et al. Dipeptidase-1 Is an Adhesion Receptor for Neutrophil Recruitment in Lungs and Liver. Cell. 2019;178(5):1205\u0026ndash;e122117.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCui X, et al. DPEP1 is a direct target of miR-193a-5p and promotes hepatoblastoma progression by PI3K/Akt/mTOR pathway. Cell Death Dis. 2019;10(10):701.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAppelberg S, et al. Dysregulation in Akt/mTOR/HIF-1 signaling identified by proteo-transcriptomics of SARS-CoV-2 infected cells. Emerg Microbes Infect. 2020;9(1):1748\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhan N. mTOR: A possible therapeutic target against SARS-CoV-2 infection. Arch Stem Cell Ther. 2021;2(1):5\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBanerjee AK, et al. SARS-CoV-2 Disrupts Splicing, Translation, and Protein Trafficking to Suppress Host Defenses. Cell. 2020;183(5):1325\u0026ndash;e133921.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang AY, et al. Modulation of SF3B1 in the pre-mRNA spliceosome induces a RIG-I-dependent type I IFN response. J Biol Chem. 2021;297(5):101277.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHanada Y, et al. MAVS is energized by Mff which senses mitochondrial metabolism via AMPK for acute antiviral immunity. Nat Commun. 2020;11(1):5711.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pharmacology-and-toxicology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"phat","sideBox":"Learn more about [BMC Pharmacology and Toxicology](http://bmcpharmacoltoxicol.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/phat/Default.aspx","title":"BMC Pharmacology and Toxicology","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"drug repurposing, drug repositioning, COVID-19, SARS-CoV-2, graph neural networks, real world evidence, gene expression, hypothesis generation","lastPublishedDoi":"10.21203/rs.3.rs-8274293/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8274293/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe COVID-19 pandemic highlighted the critical need for drug repurposing to rapidly identify therapeutic options. While computational graph-based approaches show promise, conventional single analytical methods often fail to capture the complex pathological mechanisms needed for clinical translation.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe developed a Graph Attention Autoencoder (GATE) framework with multilayered validation integrating computational prediction, real-world data, and phenome analyses. Network-based embeddings identified drug candidates through latent space similarity analysis within biomedical knowledge graphs. Clinical potential was assessed via disproportionality analysis (DPA) using adverse event reporting system, and biological plausibility was evaluated through gene expression profiling and pathway analyses.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur GATE framework identified 17 drug candidates, including 13 with known clinical effects on COVID-19 symptoms, validating model performance. Four novel candidates (cilastatin, megestrol, drotrecogin alfa, and ethacrynic acid) with minimal prior COVID-19 associations were identified. DPA further narrowed these to two promising candidates with significant protective signals: cilastatin (reporting odds ratio [ROR]: 0.19, 95% confidence interval [CI]: 0.09\u0026ndash;0.43) and megestrol (ROR: 0.63, 95% CI: 0.42\u0026ndash;0.94). Pathway profiling confirmed that both drugs share molecular signatures with drugs investigated in COVID-19 clinical trials. Gene expression analyses suggest that cilastatin may have anti-viral and anti-inflammatory effects via suppression of splicing, ribosomal function and mitochondrial pathway, through DPEP1 inhibition.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study presents the first systematic integration of GATE-based computational prediction, real-world evidence from adverse event databases, and phenome-level pathway profiling for COVID-19 drug repurposing. This multilayered approach enabled multidimensional candidate validation unattainable by single analytical methods alone. The identification of clinically approved drugs with established safety profiles may facilitate accelerated clinical evaluation. Furthermore, the pathway and gene expression analyses provide mechanistic working hypotheses that can streamline subsequent preclinical validation. Although preclinical and clinical validation remain essential, this framework offers a generalizable strategy for rapid candidate identification during pandemics and for diseases with unmet therapeutic needs.\u003c/p\u003e","manuscriptTitle":"Novel graph attention autoencoder framework with multilayered validation identifies drug repurposing candidates for COVID-19 treatment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-08 05:40:26","doi":"10.21203/rs.3.rs-8274293/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-01-20T11:06:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"313741510287175694897171166218793553829","date":"2026-01-18T01:24:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-12T15:45:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3057259063958708331849526176934450030","date":"2026-01-12T07:52:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"53635263628912735760827263835273758294","date":"2026-01-11T18:03:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-07T18:48:38+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-10T14:19:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-04T08:38:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-04T08:35:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pharmacology and Toxicology","date":"2025-12-04T01:04:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pharmacology-and-toxicology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"phat","sideBox":"Learn more about [BMC Pharmacology and Toxicology](http://bmcpharmacoltoxicol.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/phat/Default.aspx","title":"BMC Pharmacology and Toxicology","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ee2f6fb4-442e-4c04-a157-99225da78335","owner":[],"postedDate":"January 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-08T05:40:27+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-08 05:40:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8274293","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8274293","identity":"rs-8274293","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}