Front Artif Intell. 2023 Sep 1;6:1237542. doi: 10.3389/frai.2023.1237542. eCollection 2023.

ABSTRACT

MOTIVATION: Tensor decomposition (TD)-based unsupervised feature extraction (FE) has proven effective for a wide range of bioinformatics applications ranging from biomarker identification to the identification of disease-causing genes and drug repositioning. However, TD-based unsupervised FE failed to gain widespread acceptance due to the lack of user-friendly tools for non-experts.

RESULTS: We developed two bioconductor packages-TDbasedUFE and TDbasedUFEadv-that enable researchers unfamiliar with TD to utilize TD-based unsupervised FE. The packages facilitate the identification of differentially expressed genes and multiomics analysis. TDbasedUFE was found to outperform two state-of-the-art methods, such as DESeq2 and DIABLO.

AVAILABILITY AND IMPLEMENTATION: TDbasedUFE and TDbasedUFEadv are freely available as R/Bioconductor packages, which can be accessed at https://bioconductor.org/packages/TDbasedUFE and https://bioconductor.org/packages/TDbasedUFEadv, respectively.

PMID:37719083 | PMC:PMC10503044 | DOI:10.3389/frai.2023.1237542

Biochem Pharmacol. 2023 Sep 15:115809. doi: 10.1016/j.bcp.2023.115809. Online ahead of print.

ABSTRACT

Although a great cure rate has been achieved for pediatric BCP-ALL, approximately 15% of patients do not respond to conventional chemotherapy and experience disease relapse. A major effort to improve the cure rates by treatment intensification would result in an undesirable increase in treatment-related toxicity and mortality, raising the need to identify novel therapeutic approaches. High-throughput (HTP) drug screening enables the profiling of patients' responses in vitro and allows the repurposing of compounds currently used for other diseases, which can be immediately available for clinical application. The aim of this study was to apply HTP drug screening to identify potentially effective compounds for the treatment of pediatric BCP-ALL patients with poor prognosis, such as patients with Down Syndrome (DS) or carrying rearrangements involving PAX5 or KMT2A/MLL genes. Patient-derived Xenografts (PDX) samples from 34 BCP-ALL patients (9 DS CRLF2r, 15 PAX5r, 10 MLLr), 7 human BCP-ALL cell lines and 14 hematopoietic healthy donor samples were screened on a semi-automated HTP drug screening platform using a 174 compound library (FDA/EMA-approved or in preclinical studies). We identified 9 compounds active against BCP-ALL (ABT-199/venetoclax, AUY922/luminespib, dexamethasone, EC144, JQ1, NVP-HSP990, paclitaxel, PF-04929113 and vincristine), but sparing normal cells. Ex vivo validations confirmed that the BCL2 inhibitor venetoclax exerts an anti-leukemic effect against all three ALL subgroups at nanomolar concentrations. Overall, this study points out the benefit of HTP screening application for drug repurposing to allow the identification of effective and clinically translatable therapeutic agents for difficult-to-treat childhood BCP-ALL subgroups.

PMID:37717691 | DOI:10.1016/j.bcp.2023.115809

Sci Data. 2023 Sep 16;10(1):632. doi: 10.1038/s41597-023-02534-z.

ABSTRACT

Computational drug repositioning methods have emerged as an attractive and effective solution to find new candidates for existing therapies, reducing the time and cost of drug development. Repositioning methods based on biomedical knowledge graphs typically offer useful supporting biological evidence. This evidence is based on reasoning chains or subgraphs that connect a drug to a disease prediction. However, there are no databases of drug mechanisms that can be used to train and evaluate such methods. Here, we introduce the Drug Mechanism Database (DrugMechDB), a manually curated database that describes drug mechanisms as paths through a knowledge graph. DrugMechDB integrates a diverse range of authoritative free-text resources to describe 4,583 drug indications with 32,249 relationships, representing 14 major biological scales. DrugMechDB can be employed as a benchmark dataset for assessing computational drug repositioning models or as a valuable resource for training such models.

PMID:37717042 | DOI:10.1038/s41597-023-02534-z

Oncotarget. 2023 Sep 15;14:807-808. doi: 10.18632/oncotarget.28463.

NO ABSTRACT

PMID:37713333 | DOI:10.18632/oncotarget.28463

Genes Genomics. 2023 Sep 15. doi: 10.1007/s13258-023-01449-8. Online ahead of print.

ABSTRACT

BACKGROUND: Monkeypox is endemic to African region and has become of Global concern recently due to its outbreaks in non-endemic countries. Although, the disease was first recorded in 1970, no monkeypox specific drug or vaccine exists as of now.

METHODS: We applied drug repositioning method, testing effectiveness of currently approved drugs against emerging disease, as one of the most affordable approaches for discovering novel treatment measures. Techniques such as virtual ligand-based and structure-based screening were applied to identify potential drug candidates against monkeypox.

RESULTS: We narrowed down our results to 6 antiviral and 20 anti-tumor drugs that exhibit theoretically higher potency than tecovirimat, the currently approved drug for monkeypox disease.

CONCLUSIONS: Our results indicated that selected drug compounds displayed strong binding affinity for p37 receptor of monkeypox virus and therefore can potentially be used in future studies to confirm their effectiveness against the disease.

PMID:37713070 | DOI:10.1007/s13258-023-01449-8

Drug Dev Res. 2023 Sep 15. doi: 10.1002/ddr.22115. Online ahead of print.

ABSTRACT

By harnessing artificial intelligence (AI) algorithms and machine learning techniques, the entire drug discovery process stands to undergo a profound transformation, offering a myriad of advantages. Foremost among these is the ability of AI to conduct swift and efficient screenings of expansive compound libraries, significantly augmenting the identification of potential drug candidates. Moreover, AI algorithms can prove instrumental in predicting the efficacy and safety profiles of candidate compounds, thus endowing invaluable insights and reducing reliance on extensive preclinical and clinical testing. This predictive capacity of AI has the potential to streamline the drug development pipeline and enhance the success rate of clinical trials, ultimately resulting in the emergence of more efficacious and safer therapeutic agents. However, the deployment of AI in drug discovery introduces certain challenges that warrant attention. A primary hurdle entails the imperative acquisition of high-quality and diverse data. Furthermore, ensuring the interpretability of AI models assumes critical importance in securing regulatory endorsement and cultivating trust within scientific and medical communities. Addressing ethical considerations, including data privacy and mitigating bias, represents an additional momentous challenge, requiring assiduous navigation. In this review, we provide an intricate and comprehensive overview of the multifaceted challenges intrinsic to conventional drug development paradigms, while simultaneously interrogating the efficacy of AI in effectively surmounting these formidable obstacles.

PMID:37712494 | DOI:10.1002/ddr.22115

Cancer Biol Ther. 2023 Dec 31;24(1):2253586. doi: 10.1080/15384047.2023.2253586.

ABSTRACT

Lung adenocarcinoma is one of the leading causes of cancer-related mortality globally. Various treatment approaches and drugs had little influence on overall survival; thus, new drugs and treatment strategies are needed. Drug repositioning (repurposing) seems a favorable approach considering that developing new drugs needs much more time and costs. We performed a meta-analysis on 6 microarray datasets to obtain the main genes with significantly altered expression in lung adenocarcinoma. Following that, we found major gene clusters and hub genes. We assessed their enrichment in biological pathways to get insight into the underlying biological process involved in lung adenocarcinoma pathogenesis. The L1000 database was explored for drug perturbations that might reverse the expression of differentially expressed genes in lung adenocarcinoma. We evaluated the potential drug combinations that interact the most with hub genes and hence have the most potential to reverse the disease process. A total of 2148 differentially expressed genes were identified. Six main gene clusters and 27 significant hub genes mainly involved in cell cycle regulation have been identified. By assessing the interaction between 3 drugs and hub genes and information gained from previous clinical investigations, we suggested the three possible repurposed drug combinations, Vorinostat - Dorsomorphin, PP-110 - Dorsomorphin, and Puromycin - Vorinostat with a high chance of success in clinical trials.

PMID:37710391 | DOI:10.1080/15384047.2023.2253586

Pharmacol Ther. 2023 Sep 12:108530. doi: 10.1016/j.pharmthera.2023.108530. Online ahead of print.

ABSTRACT

Neurodevelopmental disorders (NDDs) impact multiple aspects of an individual's functioning, including social interactions, communication, and behaviors. The underlying biological mechanisms of NDDs are not yet fully understood, and pharmacological treatments have been limited in their effectiveness, in part due to the complex nature of these disorders and the heterogeneity of symptoms across individuals. Identifying genetic loci associated with NDDs can help in understanding biological mechanisms and potentially lead to the development of new treatments. However, the polygenic nature of these complex disorders has made identifying new treatment targets from genome-wide association studies (GWAS) challenging. Recent advances in the fields of big data and high-throughput tools have provided radically new insights into the underlying biological mechanism of NDDs. This paper reviews various big data approaches, including classical and more recent techniques like deep learning, which can identify potential treatment targets from GWAS and other omics data, with a particular emphasis on NDDs. We also emphasize the increasing importance of explainable and causal machine learning (ML) methods that can aid in identifying genes, molecular pathways, and more complex biological processes that may be future targets of intervention in these disorders. We conclude that these new developments in genetics and ML hold promise for advancing our understanding of NDDs and identifying novel treatment targets.

PMID:37708996 | DOI:10.1016/j.pharmthera.2023.108530

ACS Chem Biol. 2023 Sep 14. doi: 10.1021/acschembio.3c00339. Online ahead of print.

ABSTRACT

Despite the well-established role of oxidative stress in the pathogenesis of age-related macular degeneration (AMD), the mechanism underlying phototoxicity remains unclear. Herein, we used a drug repurposing approach to isolate an FDA-approved drug that blocks the aggregation of the photoinducible major fluorophore of lipofuscin, the bis-retinoid N-retinylidene-N-retinylethanolamine (A2E). Our fluorescence-based screening combined with dynamic light scattering (DLS) analysis led to the identification of entacapone as a potent inhibitor of A2E fluorescence and aggregation. The entacapone-mediated inhibition of A2E aggregation blocks its photodegradation and offers photoprotection in A2E-loaded retinal pigment epithelial (RPE) cells exposed to blue light. In-depth mechanistic analysis suggests that entacapone prevents the conversion of toxic aggregates by redirecting A2E into off-pathway oligomers. These findings provide evidence that aggregation contributes to the phototoxicity of A2E.

PMID:37708070 | DOI:10.1021/acschembio.3c00339

J Comput Aided Mol Des. 2023 Sep 14. doi: 10.1007/s10822-023-00529-x. Online ahead of print.

ABSTRACT

DrugCentral, accessible at https://drugcentral.org , is an open-access online drug information repository. It covers over 4950 drugs, incorporating structural, physicochemical, and pharmacological details to support drug discovery, development, and repositioning. With around 20,000 bioactivity data points, manual curation enhances information from several major digital sources. Approximately 724 mechanism-of-action (MoA) targets offer updated drug target insights. The platform captures clinical data: over 14,300 on- and off-label uses, 27,000 contraindications, and around 340,000 adverse drug events from pharmacovigilance reports. DrugCentral encompasses information from molecular structures to marketed formulations, providing a comprehensive pharmaceutical reference. Users can easily navigate basic drug information and key features, making DrugCentral a versatile, unique resource. Furthermore, we present a use-case example where we utilize experimentally determined data from DrugCentral to support drug repurposing. A minimum activity threshold t should be considered against novel targets to repurpose a drug. Analyzing 1156 bioactivities for human MoA targets suggests a general threshold of 1 µM: t = 6 when expressed as - log[Activity(M)]). This applies to 87% of the drugs. Moreover, t can be refined empirically based on water solubility (S): t = 3 - logS, for logS < - 3. Alongside the drug repurposing classification scheme, which considers intellectual property rights, market exclusivity protections, and market accessibility, DrugCentral provides valuable data to prioritize candidates for drug repurposing programs efficiently.

PMID:37707619 | DOI:10.1007/s10822-023-00529-x

Bioinformatics. 2023 Sep 14:btad570. doi: 10.1093/bioinformatics/btad570. Online ahead of print.

ABSTRACT

SUMMARY: Knowledge graphs are an increasingly common data structure for representing biomedical information. These knowledge graphs can easily represent heterogeneous types of information, and many algorithms and tools exist for querying and analyzing graphs. Biomedical knowledge graphs have been used in a variety of applications, including drug repurposing, identification of drug targets, prediction of drug side effects, and clinical decision support. Typically, knowledge graphs are constructed by centralization and integration of data from multiple disparate sources. Here, we describe BioThings Explorer, an application that can query a virtual, federated knowledge graph derived from the aggregated information in a network of biomedical web services. BioThings Explorer leverages semantically precise annotations of the inputs and outputs for each resource, and automates the chaining of web service calls to execute multi-step graph queries. Because there is no large, centralized knowledge graph to maintain, BioThings Explorer is distributed as a lightweight application that dynamically retrieves information at query time.

AVAILABILITY AND IMPLEMENTATION: More information can be found at https://explorer.biothings.io, and code is available at https://github.com/biothings/biothings_explorer.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:37707514 | DOI:10.1093/bioinformatics/btad570

J Med Microbiol. 2023 Sep;72(9). doi: 10.1099/jmm.0.001751.

ABSTRACT

Introduction. Antibiotic resistance is a major threat to public health, particularly with methicillin-resistant Staphylococcus aureus (MRSA) being a leading cause of antimicrobial resistance. To combat this problem, drug repurposing offers a promising solution for the discovery of new antibacterial agents.Hypothesis. Menadione exhibits antibacterial activity against methicillin-sensitive and methicillin-resistant S. aureus strains, both alone and in combination with oxacillin. Its primary mechanism of action involves inducing oxidative stress.Methodology. Sensitivity assays were performed using broth microdilution. The interaction between menadione, oxacillin, and antioxidants was assessed using checkerboard technique. Mechanism of action was evaluated using flow cytometry, fluorescence microscopy, and in silico analysis.Aim. The aim of this study was to evaluate the in vitro antibacterial potential of menadione against planktonic and biofilm forms of methicillin-sensitive and resistant S. aureus strains. It also examined its role as a modulator of oxacillin activity and investigated the mechanism of action involved in its activity.Results. Menadione showed antibacterial activity against planktonic cells at concentrations ranging from 2 to 32 µg ml-1, with bacteriostatic action. When combined with oxacillin, it exhibited an additive and synergistic effect against the tested strains. Menadione also demonstrated antibiofilm activity at subinhibitory concentrations and effectively combated biofilms with reduced sensitivity to oxacillin alone. Its mechanism of action involves the production of reactive oxygen species (ROS) and DNA damage. It also showed interactions with important targets, such as DNA gyrase and dehydroesqualene synthase. The presence of ascorbic acid reversed its effects.Conclusion. Menadione exhibited antibacterial and antibiofilm activity against MRSA strains, suggesting its potential as an adjunct in the treatment of S. aureus infections. The main mechanism of action involves the production of ROS, which subsequently leads to DNA damage. Additionally, the activity of menadione can be complemented by its interaction with important virulence targets.

PMID:37707372 | DOI:10.1099/jmm.0.001751

J Phys Chem Lett. 2023 Sep 14:8385-8396. doi: 10.1021/acs.jpclett.3c01440. Online ahead of print.

ABSTRACT

Open reading frame 6 (ORF6), the accessory protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that suppresses host type-I interferon signaling, possesses amyloidogenic sequences. ORF6 amyloidogenic peptides self-assemble to produce cytotoxic amyloid fibrils. Currently, the molecular properties of the ORF6 remain elusive. Here, we investigate the structural dynamics of the full-length ORF6 protein in a near-physiological environment using high-speed atomic force microscopy. ORF6 oligomers were ellipsoidal and readily assembled into ORF6 protofilaments in either a circular or a linear pattern. The formation of ORF6 protofilaments was enhanced at higher temperatures or on a lipid substrate. ORF6 filaments were sensitive to aliphatic alcohols, urea, and SDS, indicating that the filaments were predominantly maintained by hydrophobic interactions. In summary, ORF6 self-assembly could be necessary to sequester host factors and causes collateral damage to cells via amyloid aggregates. Nanoscopic imaging unveiled the innate molecular behavior of ORF6 and provides insight into drug repurposing to treat amyloid-related coronavirus disease 2019 complications.

PMID:37707320 | DOI:10.1021/acs.jpclett.3c01440

Int J Immunopathol Pharmacol. 2023 Jan-Dec;37:3946320231202349. doi: 10.1177/03946320231202349.

ABSTRACT

Objectives: Our objective was to assess the effects and mechanisms of nifuratel on IgE-mediated mast cell (MC) degranulation and anaphylaxis in both in vitro and in vivo settings.Methods: The anti-allergic activity of nifuratel was evaluated in mast cell cultures and the passive cutaneous anaphylaxis (PCA) model. The effects of nifuratel on signaling pathways stimulated by antigen in mast cells were measured by immunoblotting, immunoprecipitation, in vitro protein tyrosine kinase assay, and other molecular biological methods.Results: Nifuratel reversibly inhibited antigen-induced degranulation of MCs (IC50, approximately 0.34 μM for RBL-2H3 cells; approximately 0.94 μM for BMMCs) and suppressed the secretion of inflammatory cytokines IL-4 (IC50, approximately 0.74 μM) and TNF-α (IC50, approximately 0.48 μM). Mechanism studies showed that nifuratel inhibited the phosphorylation of Syk by antigen via the inhibition of recruitment of cytosolic Syk to the ɣ subunit of FcεRI, and decreased the activation of Syk downstream signaling proteins LAT, Akt, and MAPKs. Finally, nifuratel dose-dependently suppressed the IgE-mediated passive cutaneous anaphylaxis in mice (ED50, approximately 22 mg/kg).Conclusion: Our findings suggest that nifuratel inhibits pathways essential for the activation of mast cells to suppress anaphylaxis, thereby indicating that the anti-microbial drug, nifuratel, could be a potential drug candidate for IgE-mediated allergic disorders.

PMID:37706235 | DOI:10.1177/03946320231202349

Expert Opin Ther Targets. 2023 Sep 13. doi: 10.1080/14728222.2023.2259097. Online ahead of print.

ABSTRACT

BACKGROUND: Drug repurposing is an attractive strategy for extending the arsenal of oncology therapies. Tolperisone is an old centrally acting muscle relaxant used for treatment of chronic pain conditions. In this study, we investigated the therapeutic effect and mechanism of tolperisone in human cancers and explored the combination strategy with proteasome inhibitor and immunotherapy.

RESEARCH DESIGN AND METHODS: The antitumor effect of tolperisone was evaluated by measuring half maximal inhibitory concentration, cell death and cell growth. RNA sequencing, western blotting, molecule docking, enzyme activity assay and ChIP-qPCR were performed to reveal the underlying mechanism. Xenograft models were used to evaluate the efficacy of tolperisone alone or in combination with proteasome inhibitor or immunotherapy.

RESULTS: Tolperisone inhibited cell growth and induced cell death in human cancer cell lines. Unfolded protein responses (UPR) pathway was hyperactivated in tolperisone-treated cells. We further identified histone lysine-specific demethylase 1 (LSD1) as a potential target of tolperisone, which directly demethylates UPR-related genes in H3K4me2. Tolperisone synergistically improved the efficacy of MG132 by enhancing UPR and sensitized tumors to immunotherapy by reprogramming M2 macrophages into M1 phenotype.

CONCLUSIONS: Tolperisone inhibits human cancer by targeting LSD1. Repurposing tolperisone in cancer therapy by combination strategy implies clinical potential.

PMID:37704953 | DOI:10.1080/14728222.2023.2259097

Methods Mol Biol. 2024;2716:223-240. doi: 10.1007/978-1-0716-3449-3_10.

ABSTRACT

Building and analyzing knowledge graphs (KGs) to aid drug discovery is a topical area of research. A salient feature of KGs is their ability to combine many heterogeneous data sources in a format that facilitates discovering connections. The utility of KGs has been exemplified in areas such as drug repurposing, with insights made through manual exploration and modeling of the data. In this chapter, we discuss promises and pitfalls of using natural language processing (NLP) to mine "unstructured text"- typically from scientific literature- as a data source for KGs. This draws on our experience of initially parsing "structured" data sources-such as ChEMBL-as the basis for data within a KG, and then enriching or expanding upon them using NLP. The fundamental promise of NLP for KGs is the automated extraction of data from millions of documents-a task practically impossible to do via human curation alone. However, there are many potential pitfalls in NLP-KG pipelines, such as incorrect named entity recognition and ontology linking, all of which could ultimately lead to erroneous inferences and conclusions.

PMID:37702942 | DOI:10.1007/978-1-0716-3449-3_10

Cornea. 2023 Sep 12. doi: 10.1097/ICO.0000000000003391. Online ahead of print.

ABSTRACT

Antibiotic resistance has emerged as a critical threat for the treatment of bacterial ocular infections. To address the critical need for novel therapeutics, antibiotic drug repurposing holds significant promise. As such, examples of existing FDA-approved drugs currently under development for new applications, novel combinations, and improved delivery systems are discussed.

PMID:37702607 | DOI:10.1097/ICO.0000000000003391

Infez Med. 2023 Sep 1;31(3):384-393. doi: 10.53854/liim-3103-12. eCollection 2023.

ABSTRACT

Scrub typhus is an acute febrile illness caused by Orientia tsutsugamushi, a Gram-negative bacillus, commonly occurring in the Asia-Pacific region. It is transmitted to humans by the bite of an infected Leptotrombidium mite and the bacterium causes endothelial dysfunction resulting in widespread vasculitis and the possible development of thrombocytopenia, meningitis, acute respiratory distress syndrome, and infrequently, myocarditis. Early diagnosis and prompt treatment are crucial in managing scrub typhus. Here, we present four cases of scrub typhus with a comprehensive literature review. This study highlights the significance of considering scrub typhus as a possible diagnosis in patients of all ages from endemic regions who exhibit symptoms such as fever, thrombocytopenia, or transaminitis, even in the absence of typical clinical features. Two cases exhibited the characteristic lesion of eschar at the site of mite feeding. One case involved a middle-aged woman who was diagnosed with typhus-induced myocarditis with left ventricular dysfunction. Another case involved a 23-day-old neonate with poor feeding and seizures, who was diagnosed with late-onset sepsis with meningitis. Scrub typhus was confirmed in all cases using a positive qualitative IgM ELISA. However, it is preferred to use paired (ELISA before and after antibiotic therapy) or quantitative titers for confirmation. Healthcare providers must consider the patient's exposure history and clinical presentation to diagnose and treat scrub typhus promptly.

PMID:37701392 | PMC:PMC10495056 | DOI:10.53854/liim-3103-12

Curr Res Microb Sci. 2023 Sep 7;5:100202. doi: 10.1016/j.crmicr.2023.100202. eCollection 2023.

ABSTRACT

Influenza virus infection, more commonly known as the 'cold flu', is an etiological agent that gives rise to recurrent annual flu and many pandemics. Dated back to the 1918- Spanish Flu, the influenza infection has caused the loss of many human lives and significantly impacted the economy and daily lives. Influenza virus can be classified into four different genera: influenza A-D, with the former two, influenza A and B, relevant to humans. The capacity of antigenic drift and shift in Influenza A has given rise to many novel variants, rendering vaccines and antiviral therapies useless. In light of the emergence of a novel betacoronavirus, the SARS-CoV-2, unravelling the underpinning mechanisms that support the recurrent influenza epidemics and pandemics is essential. Given the symptom similarities between influenza and covid infection, it is crucial to reiterate what we know about the influenza infection. This review aims to describe the origin and evolution of influenza infection. Apart from that, the risk factors entail the implication of co-infections, especially regarding the COVID-19 pandemic is further discussed. In addition, antiviral strategies, including the potential of drug repositioning, are discussed in this context. The diagnostic approach is also critically discussed in an effort to understand better and prepare for upcoming variants and potential influenza pandemics in the future. Lastly, this review encapsulates the challenges in curbing the influenza spread and provides insights for future directions in influenza management.

PMID:37700857 | PMC:PMC10493511 | DOI:10.1016/j.crmicr.2023.100202

Comput Biol Med. 2023 Sep 9;165:107465. doi: 10.1016/j.compbiomed.2023.107465. Online ahead of print.

ABSTRACT

Arsenic trioxide (ATO) is a great discovery in the treatment of acute promyelocytic leukemia (APL), which has been used in an increasing number of malignant diseases. Systematic integrative analysis will help to precisely understand the mechanism of ATO and find new combined drugs. Therefore, we developed a one-stop comprehensive database of ATO named ATOdb by manually compiling a wealth of experimentally supported ATO-related data from 3479 articles, and integrated analysis tools. The current version of ATOdb contains 8373 associations among 2300 ATO targets, 80 conditions and 262 combined drugs. Each entry in ATOdb contains detailed information on ATO targets, therapeutic/side effects, systems, cell names, cell types, regulations, detection methods, brief descriptions, references, etc. Furthermore, ATOdb also provides data visualization and analysis results such as the drug similarities, protein-protein interactions, and miRNA-mRNA relationships. An easy-to-use web interface was deployed in ATOdb for users to easily browse, search and download the data. In conclusion, ATOdb will serve as a valuable resource for in-depth study of the mechanism of ATO, discovery of new drug combination strategies, promotion of rational drug use and individualized treatments. ATOdb is freely available at http://bio-bigdata.hrbmu.edu.cn/ATOdb/index.jsp.

PMID:37699323 | DOI:10.1016/j.compbiomed.2023.107465