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DrugCentral monitors new drug approvals and standardizes drug information. The current update contains 285 drugs (131 for human use). New additions include: (i) the integration of veterinary drugs (154 for animal use only), (ii) the addition of 66 documented off-label uses and iii) the identification of adverse drug events from pharmacovigilance da...
Contexts in source publication
Context 1
... are 107 targets defined as mechanism-of-action (MoA) targets for 112 new drugs. Only 47 (briefly described in Table 2) of these are new targets compared to the previous DrugCentral version. According to the Target Development Level classification system of human proteins (31) adopted within the IDG consortium, 39 new targets were annotated as Tclin, i.e. ...Context 2
... analysis of novel MoA drug targets (targets that an approved drug had not previously perturbed) published annually in the Nature Reviews Drug Discovery series (33,34,41,42) with data pulled from DrugCentral, shows, on average, an enrichment rate of 15 novel drug targets per year (some of the data overlap with Table 2 -in accordance to the time frame reported). These targets are modulated by increasing numbers of mAb and ADC, mainly directed toward cytokines, surface antigens, and membrane receptors. ...Citations
... Data on the FDA approval years of chemical substances were obtained from the DrugCentral 2023 database(Avram et al., 2023). ...
... Drug related databases. DrugBank [1] and DrugCentral [36] are comprehensive platforms for drugs and drug-related entities. BioSNAP [37] contains drug-disease information. ...
In biomedical research, the utilization of Knowledge Graph (KG) has proven valuable in gaining deep understanding of various processes. In this study, we constructed a comprehensive biomedical KG, named as MegaKG, by integrating a total of 23 primary data sources, which finally consisted of 188, 844 nodes/entities and 9, 165, 855 edges/relations after stringent data processing. Such a massive KG can not only provide a holistic view of the entities of interest, but also generate insightful hypotheses on unknown relations by applying AI computations. We focused on the interplay of the key elements in drug development, such as genes, diseases and drugs, and aimed to facilitate practical applications that could benefit early drug development in industries. More importantly, we placed much emphasis on the exploitability of the predictions generated by MegaKG. This may greatly help researchers to assess the feasibility or design appropriate downstream validation experiments, making AI techniques more than just black-box models. In this regard, NBFNet was adopted, which combines the advantages of both traditional path-based methods and more recently developed GNN-based ones. Performance evaluation experiments indicated superior results by MegaKG. We also conducted real case studies to validate its practical utility in various scenarios, including target prediction, indication extension and drug repurposing. All these experiments highlighted the potential of MegaKG as a valuable tool in driving innovation and accelerating drug development in pharmaceutical industry.
... org/) [22] and the database DrugCentral (https:// drugc entral. org/) [23] forecasted the association of interactions between DERATGs and medicinal compounds. The microRNA (miRNA)-mRNA regulatory networks were constructed using the TargetScan database (http:// www. ...
Objective
Rheumatoid arthritis (RA) is a chronic inflammatory arthritis. This study aimed to identify potential biomarkers and possible pathogenesis of RA using various bioinformatics analysis tools.
Methods
The GMrepo database provided a visual representation of the analysis of intestinal flora. We selected the GSE55235 and GSE55457 datasets from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs) separately. With the intersection of these DEGs with the target genes associated with RA found in the GeneCards database, we obtained the DEGs targeted by RA (DERATGs). Subsequently, Disease Ontology, Gene Ontology, and the Kyoto Encyclopedia of Genes and Genomes were used to analyze DERATGs functionally. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were performed on the data from the gene expression matrix. Additionally, the protein-protein interaction network, transcription factor (TF)-targets, target-drug, microRNA (miRNA)-mRNA networks, and RNA-binding proteins (RBPs)-DERATGs correlation analyses were built. The CIBERSORT was used to evaluate the inflammatory immune state. The single-sample GSEA (ssGSEA) algorithm and differential analysis of DERATGs were used among the infiltration degree subtypes.
Results
There were some correlations between the abundance of gut flora and the prevalence of RA. A total of 54 DERATGs were identified, mainly related to immune and inflammatory responses and immunodeficiency diseases. Through GSEA and GSVA analysis, we found pathway alterations related to metabolic regulations, autoimmune diseases, and immunodeficiency-related disorders. We obtained 20 hub genes and 2 subnetworks. Additionally, we found that 39 TFs, 174 drugs, 2310 miRNAs, and several RBPs were related to DERATGs. Mast, plasma, and naive B cells differed during immune infiltration. We discovered DERATGs’ differences among subtypes using the ssGSEA algorithm and subtype grouping.
Conclusions
The findings of this study could help with RA diagnosis, prognosis, and targeted molecular treatment.
... In addition to its core functionalities, the KMC has worked to expand the utility of the knowledge it manages. For example, the development of DrugCentral stands out, 3 offering high-quality annotations of drugs, including those used in veterinary practice, and providing data regarding their mechanisms of action. Complementing this, the KMC has also developed data visualization tools, such as the Target Importance and Novelty Explorer (TIN-X) 4 and the Target Illumination GWAW Analytics (TIGA) tools, 5 specifically designed to explore target importance and novelty and to illuminate targets through genome-wide association studies (GWAS) analytics, respectively. ...
... The 2023 version of DrugCentral adds 285 new drugs to the 2021 publication. 3 Of these, 131 were approved only for human use and 154 only for veterinary use (Figure 2a). Thus, 242 drugs already stored in DrugCentral are now associated with human and veterinary approvals (Figure 2b). ...
... Many drugs from these groups are controlled substances, which increases the responsibility of the veterinarian for their proper and safe use (Anand & Hosanagar, 2021). It should also be taken into consideration that only 3% of such veterinary drugs are intended for use only in animals and more than 61% are also approved for use in human medicine (Avram, 2023). The clinical assessment of affective states, which mainly determine the course of behavioral reactions in animals, is a difficult but important task for a clinician when developing treatment protocols for animals with behavioral disorders (Schmidt et al., 1998;Irimajiri et al., 2009;Rutherford et al., 2012). ...
The issue of stress and behavioral disorders are growing significantly in the contemporary word in humans and animals alike. Various drugs are used to modify affected behavior, including psychotropic, anticonvulsant, antihistamines, hormones, analgesics, and neuroleptics. Psychotropic drugs are prescribed for animals with behavioral disorders, signs of anxiety or hypersensitivity. Improving the methods of diagnosing and treating behavioral disorders in animals can enhance animal welfare and optimize animal husbandry technology. Future research should be aimed at improving and optimizing the use of psychotropic drugs for behavioral disorders of various animal species. The main indication for the use of anxiolytic drugs is behavioral disorders associated with anxiety in wild and domestic animals. When anxiolytic drugs are used in mammals their pharmacological properties, the dependence of their action on the route of administration, age and species of the animal, and the ability to selectively affect the central nervous system should be taken into account. The most commonly used drugs for the treatment of behavioral disorders in animals are fluoxetine, amitriptyline, escitalopram, haloperidol, zuclopentixol and azaperone. Fluoxetine is an effective drug for the treatment of dogs with behavioral disorders associated with psychological changes. An important component of escitalopram's pharmacological effect is the psychomotor influence, when the animal's behavior changes are due to improved motor activity. Zuclopentixol has a wide range of anxiolytic, sedative and analgesic effects when used in wild cloven-hoofed animals. Amitriptyline along with antidepressant properties, has a local analgesic impact. Azaperone has a pronounced anxiolytic and sedative effect on animals. It is widely used as an anti-stress agent to overcome anxiety caused by weaning, regrouping or veterinary manipulations. Azaperone is often used to control aggressive behavior in group housing, especially in the pig industry. The psychotropic drugs surveyed in this paper, along with direct anxiolytic action, are able to manifest additional physiological effects, which should be taken into account when developing treatment protocols for animals with behavioral problems. Further targeted studies are required to assess the pharmacological effects of anxiolytic drugs in animals.
... So far, a variety of knowledgebases have been developed to provide the ADC-related information. Some of them offer the general data of interacting network, disease indication, therapy types for very limited number ( < 60) of ADCs as part of a broader collection of biological / chemical information, such as Drugs@FDA ( 5 ), NCATS Inxights Drugs ( 6 ), DrugMAP ( 7 ), ChEMBL ( 8 ), DrugBank ( 9 ) and so on; some others aim at describing the particular components of each ADC, which include (a) those offering mAb moiety, such as: IMGT / mAb-DB ( 10 ), Thera-SAbDab ( 11 ) and PDB ( 12 ), and (b) those showing the physicochemical properties of either linkers or payloads, such as: PubChem ( 13 ) and DrugCentral ( 14 ). These databases above have attracted considerable attention from the related research communities. ...
Antibody-drug conjugates (ADCs) are a class of innovative biopharmaceutical drugs, which, via their antibody (mAb) component, deliver and release their potent warhead (a.k.a. payload) at the disease site, thereby simultaneously improving the efficacy of delivered therapy and reducing its off-target toxicity. To design ADCs of promising efficacy, it is crucial to have the critical data of pharma-information and biological activities for each ADC. However, no such database has been constructed yet. In this study, a database named ADCdb focusing on providing ADC information (especially its pharma-information and biological activities) from multiple perspectives was thus developed. Particularly, a total of 6572 ADCs (359 approved by FDA or in clinical trial pipeline, 501 in preclinical test, 819 with in-vivo testing data, 1868 with cell line/target testing data, 3025 without in-vivo/cell line/target testing data) together with their explicit pharma-information was collected and provided. Moreover, a total of 9171 literature-reported activities were discovered, which were identified from diverse clinical trial pipelines, model organisms, patient/cell-derived xenograft models, etc. Due to the significance of ADCs and their relevant data, this new database was expected to attract broad interests from diverse research fields of current biopharmaceutical drug discovery. The ADCdb is now publicly accessible at: https://idrblab.org/adcdb/.
... Figure 1 demonstrates the main components and their links. Although some information is manually curated from scientific literature and drug labels, most data is aggregated from public online resources [1][2][3][4]. ...
... Here we describe DrugCentral content (related to 4959 drugs) with various features and functionalities (based on the entire 2022 release) added during the past years [1][2][3][4]. We briefly discuss drug properties, targets, clinical effects, drug products, pharmaceutical formulations, and data accessibility. ...
... DrugCentral comprises 4959 active drug substances approved for human (4805) and veterinary use (396) [4], with properties computed or sourced externally. Various classification schemes, such as drug types, pharmacological classes, market availability, and patent coverage, enable users to select specific data sets. ...
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.
... 22 Leveraging the publicly available nSPS source code, we independently evaluate nSPS trends for approved SMDs worldwide by drawing data from DrugCentral. 29 On the basis of 4276 SMDs, the median nSPS is 15.654 (data not shown), with 66% of the drugs having nSPS ≤ 20 (Table 1). We then examine nSPS distribution for 1725 FDA-approved SMDs from an intellectual property (IP) perspective. ...
Molecular complexity (MC) lacks a universal definition, but various studies address it in contexts ranging from ligand-receptor interactions to DNA sequencing, with the overarching emphasis being its significance in synthetic organic chemistry and pharmaceutical research. Efforts to quantify MC in drug discovery have been numerous, but a unified approach remains challenging. Strategies based on graph theory, information theory, and substructural feature counts employed to gauge MC are often correlated to molecular weight (MW). Herbert Waldmann and his team introduced a new MC metric called the spacial score (SPS), which is based on factors like atom hybridization and stereoisomeric considerations. While SPS and its normalized version, nSPS, correlate with the natural product likeness score, they do not align with traditional chemical properties. We examined nSPS trends for approved drugs and found no significant changes in MC over eight decades, nor did nSPS capture drug innovation during that period. Furthermore, our analysis indicates that while the majority of approved drugs have an nSPS value between 10 and 20, this metric does not correlate with key drug properties like target bioactivity and oral bioavailability. Mirroring a chemist's intuitive sense of chemical complexity, nSPS addresses the need for a precise empirical tool while a universal definition of MC remains elusive.
... Uses not stated on the package inserts are called "off-label" uses. As of January 2023, the DrugCentral database contains 2331 FDA-approved human drugs associated with 2644 drug indications and 866 off-label uses (47). Therapeutic intent, the rationale behind choosing a therapy and the context in which it is prescribed, is vital to medical practice. ...
... Sumitomo Dainippon Pharma progressed to phase I clinicals for treatment of Alzheimer's disease psychosis in May 2021. See Fig 3 for (155), (156) and DrugCentral for gepirone (47). CIDs are PubChem compound identifiers. ...
Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small molecule drugs. AI technologies, such as generative chemistry, machine learning, and multi-property optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.
... This selection was done by searching the literature for evidence of these chemicals in the olive oil matrix, a screening step that resulted in the identification of 117 EVOO "marker" phytochemicals. DrugBank [31] and DrugCentral [32] databases were used to compile a list of experimental and available FDAapproved drugs, and FoodDB [33] database was used to compile a list of known phytochemicals (as described previously [16]). The STITCH database [34] was then used to find human proteins with which the molecules could interact, resulting in 67 EVOO marker phytochemicals. ...
Alzheimer’s disease (AD) poses a profound human, social, and economic burden. Previous studies suggest that extra virgin olive oil (EVOO) may be helpful in preventing cognitive decline. Here, we present a network machine learning method for identifying bioactive phytochemicals in EVOO with the highest potential to impact the protein network linked to the development and progression of the AD. A balanced classification accuracy of 70.3 ± 2.6% was achieved in fivefold cross-validation settings for predicting late-stage experimental drugs targeting AD from other clinically approved drugs. The calibrated machine learning algorithm was then used to predict the likelihood of existing drugs and known EVOO phytochemicals to be similar in action to the drugs impacting AD protein networks. These analyses identified the following ten EVOO phytochemicals with the highest likelihood of being active against AD: quercetin, genistein, luteolin, palmitoleate, stearic acid, apigenin, epicatechin, kaempferol, squalene, and daidzein (in the order from the highest to the lowest likelihood). This in silico study presents a framework that brings together artificial intelligence, analytical chemistry, and omics studies to identify unique therapeutic agents. It provides new insights into how EVOO constituents may help treat or prevent AD and potentially provide a basis for consideration in future clinical studies.
