RepurposeDrugs: an interactive web-portal and predictive platform for repurposing mono- and combination therapies.

RepurposeDrugs (https://repurposedrugs.org/) is a comprehensive web-portal that combines a unique drug indication database with a machine learning (ML) predictor to discover new drug-indication associations for approved as well as investigational mono and combination therapies. The platform provides detailed information on treatment status, disease indications and clinical trials across 25 indication categories, including neoplasms and cardiovascular conditions. The current version comprises 4314 compounds (approved, terminated or investigational) and 161 drug combinations linked to 1756 indications/conditions, totaling 28 148 drug-disease pairs. By leveraging data on both approved and failed indications, RepurposeDrugs provides ML-based predictions for the approval potential of new drug-disease indications, both for mono- and combinatorial therapies, demonstrating high predictive accuracy in cross-validation. The validity of the ML predictor is validated through a number of real-world case studies, demonstrating its predictive power to accurately identify repurposing candidates with a high likelihood of future approval. To our knowledge, RepurposeDrugs web-portal is the first integrative database and ML-based predictor for interactive exploration and prediction of both single-drug and combination approval likelihood across indications. Given its broad coverage of indication areas and therapeutic options, we expect it accelerates many future drug repurposing projects.

HANSynergy: Heterogeneous Graph Attention Network for Drug Synergy Prediction.

Drug synergy therapy is a promising strategy for cancer treatment. However, the extensive variety of available drugs and the time-intensive process of determining effective drug combinations through clinical trials pose significant challenges. It requires a reliable method for the rapid and precise selection of drug synergies. In response, various computational strategies have been developed for predicting drug synergies, yet the exploitation of heterogeneous biological network features remains underexplored. In this study, we construct a heterogeneous graph that encompasses diverse biological entities and interactions, utilizing rich data sets from sources, such as DrugCombDB, PubChem, UniProt, and cancer cell line encyclopedia (CCLE). We initialize node feature representations and introduce a novel virtual node to enhance drug representation. Our proposed method, the heterogeneous graph attention network for drug-drug synergy prediction (HANSynergy), has been experimentally validated to demonstrate that the heterogeneous graph attention network can extract key node features, efficiently harness the diversity of information, and further enhance network functionality through the incorporation of a multihead attention mechanism. In the comparative experiment, the highest accuracy (Acc) and area under the curve (AUC) are 0.877 and 0.947, respectively, in DrugCombDB_early data set, demonstrating the superiority of HANSynergy over the competing methods. Moreover, protein-protein interactions are important in understanding the mechanism of action of drugs. The heterogeneous attention mechanism facilitates protein-protein interaction analysis. By analyzing the changes of attention weight before and after heterogeneous network training, we investigated proteins that may be associated with drug combinations. Additionally, case studies align our findings with existing research, underscoring the potential of HANSynergy in drug synergy prediction. This advancement not only contributes to the burgeoning field of drug synergy prediction but also holds the potential to provide valuable insights and uncover new drug synergies for combating cancer.

ADCdb: the database of antibody-drug conjugates.

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/.

DGIdb 5.0: rebuilding the drug-gene interaction database for precision medicine and drug discovery platforms.

The Drug-Gene Interaction Database (DGIdb, https://dgidb.org) is a publicly accessible resource that aggregates genes or gene products, drugs and drug-gene interaction records to drive hypothesis generation and discovery for clinicians and researchers. DGIdb 5.0 is the latest release and includes substantial architectural and functional updates to support integration into clinical and drug discovery pipelines. The DGIdb service architecture has been split into separate client and server applications, enabling consistent data access for users of both the application programming interface (API) and web interface. The new interface was developed in ReactJS, and includes dynamic visualizations and consistency in the display of user interface elements. A GraphQL API has been added to support customizable queries for all drugs, genes, annotations and associated data. Updated documentation provides users with example queries and detailed usage instructions for these new features. In addition, six sources have been added and many existing sources have been updated. Newly added sources include ChemIDplus, HemOnc, NCIt (National Cancer Institute Thesaurus), Drugs@FDA, HGNC (HUGO Gene Nomenclature Committee) and RxNorm. These new sources have been incorporated into DGIdb to provide additional records and enhance annotations of regulatory approval status for therapeutics. Methods for grouping drugs and genes have been expanded upon and developed as independent modular normalizers during import. The updates to these sources and grouping methods have resulted in an improvement in FAIR (findability, accessibility, interoperability and reusability) data representation in DGIdb.

Mortality and years of life lost related to adverse drug events in Brazil

ABSTRACT OBJECTIVE To assess regional and national mortality and years of life lost (YLL) related to adverse drug events in Brazil. METHODS This is an ecological study in which death records from 2009 to 2018 from the Mortality Information System were analyzed. Codes from the International Classification of Diseases 10th revision (ICD-10) that indicated drugs as the cause of death were identified. The number of deaths and the YLL due to adverse drug events were obtained. Crude, age- and gender-specific, and age-adjusted mortality rates and YLL rates per 100,000 inhabitants were formed by year, age group, gender, and Brazilian Federative Unit. Rate ratios were calculated by comparing rates from 2009 to 2018. A joinpoint regression model was applied for temporal analysis. RESULTS For the selected ICD-10 codes, a total of 95,231 deaths and 2,843,413 YLL were recorded. Mortality rates from adverse drug events increased by a mean of 2.5% per year, and YLL rates increased by 3.7%. Increases in rates were observed in almost all age groups for both genders. Variations in rates were found between Federative Units, with the highest age-adjusted mortality and YLL rates occurring in the Distrito Federal. CONCLUSIONS The numbers and rates of deaths and YLL increased during the study period, and variations in rates of deaths and YLL were observed between Brazilian Federative Units. Information on multiple causes of death from death certificates can be useful for quantifying adverse drug events and analyzing them geographically, by age and by gender.

Kalium 3.0 is a comprehensive depository of natural, artificial, and labeled polypeptides acting on potassium channels.

Here, we introduce the third release of Kalium database (http://kaliumdb.org/), a manually curated comprehensive depository that accumulates data on polypeptide ligands of potassium channels. The major goal of this amplitudinous update is to summarize findings for natural polypeptide ligands of K+ channels, as well as data for the artificial derivatives of these substances obtained over the decades of exploration. We manually analyzed more than 700 original manuscripts and systematized the information on mutagenesis, production of radio- and fluorescently labeled derivatives, and the molecular pharmacology of K+ channel ligands. As a result, data on more than 1200 substances were processed and added enriching the database content fivefold. We also included the electrophysiological data obtained on the understudied and neglected K+ channels including the heteromeric and concatenated channels. We associated target channels in Kalium with corresponding entries in the official database of the International Union of Basic and Clinical Pharmacology. Kalium was supplemented with an adaptive Statistics page, where users are able to obtain actual data output. Several other improvements were introduced, such as a color code to distinguish the range of ligand activity concentrations and advanced tools for filtration and sorting. Kalium is a fully open-access database, crosslinked to other databases of interest. It can be utilized as a convenient resource containing ample up-to-date information about polypeptide ligands of K+ channels.

Predicting lifespan-extending chemical compounds for C. elegans with machine learning and biologically interpretable features.

Recently, there has been a growing interest in the development of pharmacological interventions targeting ageing, as well as in the use of machine learning for analysing ageing-related data. In this work, we use machine learning methods to analyse data from DrugAge, a database of chemical compounds (including drugs) modulating lifespan in model organisms. To this end, we created four types of datasets for predicting whether or not a compound extends the lifespan of C. elegans (the most frequent model organism in DrugAge), using four different types of predictive biological features, based on: compound-protein interactions, interactions between compounds and proteins encoded by ageing-related genes, and two types of terms annotated for proteins targeted by the compounds, namely Gene Ontology (GO) terms and physiology terms from the WormBase's Phenotype Ontology. To analyse these datasets, we used a combination of feature selection methods in a data pre-processing phase and the well-established random forest algorithm for learning predictive models from the selected features. In addition, we interpreted the most important features in the two best models in light of the biology of ageing. One noteworthy feature was the GO term "Glutathione metabolic process", which plays an important role in cellular redox homeostasis and detoxification. We also predicted the most promising novel compounds for extending lifespan from a list of previously unlabelled compounds. These include nitroprusside, which is used as an antihypertensive medication. Overall, our work opens avenues for future work in employing machine learning to predict novel life-extending compounds.

Understanding Health Records in West Slavic Languages: Available Resources, Case Study in Oncology.

Currently, there is very little research aimed at developing medical knowledge extraction tools for major West Slavic languages (Czech, Polish, and Slovak). This project lays the groundwork for a general medical knowledge extraction pipeline, introducing the resource vocabularies available for the respective languages (UMLS resources, ICD-10 translations and national drug databases). It demonstrates the utility of this approach on a case study using a large proprietary corpus of Czech oncology records consisting of more than 40 million words written about more than 4,000 patients. After correlating MedDRA terms found in patients' records with drugs prescribed to them, significant non-obvious associations were found between selected medical conditions being mentioned and the probability of certain drugs being prescribed over the course of the patient's treatment, in some cases increasing the probability of prescriptions by over 250%. This direction of research, producing large amounts of annotated data, is a prerequisite for training deep learning models and predictive systems.

A Molecular Modeling Investigation of the Therapeutic Potential of Marine Compounds as DPP-4 Inhibitors.

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder characterized by elevated levels of blood glucose due to insulin resistance or insulin-secretion defects. The development of diabetes is mainly attributed to the interaction of several complex pathogenic, genetic, environmental and metabolic processes. Dipeptidyl peptidase-4 (DPP-4) is a serine protease that cleaves X-proline dipeptides from the N-terminus of several polypeptides, including natural hypoglycemic incretin hormones. Inhibition of this enzyme restores and maintains glucose homeostasis, making it an attractive drug target for the management of T2DM. Natural products are important sources of bioactive agents for anti-T2DM drug discovery. Marine ecosystems are a rich source of bioactive products and have inspired the development of drugs for various human disorders, including diabetes. Here, structure-based virtual screening and molecular docking were performed to identify antidiabetic compounds from the Comprehensive Marine Natural Products Database (CMNPD). The binding characteristics of two shortlisted compounds, CMNPD13046 and CMNPD17868, were assessed using molecular dynamics simulations. Thus, this study provides insights into the potential antidiabetic activity and the underlying molecular mechanism of two compounds of marine origin. These compounds could be investigated further for the development of potent DPP-4 inhibitors.

Design and application of a knowledge network for automatic prioritization of drug mechanisms.

MOTIVATION: Drug repositioning is an attractive alternative to de novo drug discovery due to reduced time and costs to bring drugs to market. Computational repositioning methods, particularly non-black-box methods that can account for and predict a drug's mechanism, may provide great benefit for directing future development. By tuning both data and algorithm to utilize relationships important to drug mechanisms, a computational repositioning algorithm can be trained to both predict and explain mechanistically novel indications. RESULTS: In this work, we examined the 123 curated drug mechanism paths found in the drug mechanism database (DrugMechDB) and after identifying the most important relationships, we integrated 18 data sources to produce a heterogeneous knowledge graph, MechRepoNet, capable of capturing the information in these paths. We applied the Rephetio repurposing algorithm to MechRepoNet using only a subset of relationships known to be mechanistic in nature and found adequate predictive ability on an evaluation set with AUROC value of 0.83. The resulting repurposing model allowed us to prioritize paths in our knowledge graph to produce a predicted treatment mechanism. We found that DrugMechDB paths, when present in the network were rated highly among predicted mechanisms. We then demonstrated MechRepoNet's ability to use mechanistic insight to identify a drug's mechanistic target, with a mean reciprocal rank of 0.525 on a test set of known drug-target interactions. Finally, we walked through repurposing examples of the anti-cancer drug imatinib for use in the treatment of asthma, and metolazone for use in the treatment of osteoporosis, to demonstrate this method's utility in providing mechanistic insight into repurposing predictions it provides. AVAILABILITY AND IMPLEMENTATION: The Python code to reproduce the entirety of this analysis is available at: https://github.com/SuLab/MechRepoNet (archived at https://doi.org/10.5281/zenodo.6456335). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

New Approach for Targeted Treatment of Mild COVID-19 by Honeysuckle through Network Pharmacology Analysis.

OBJECTIVE: To investigate the potential pharmacological value of extracts from honeysuckle on patients with mild coronavirus disease 2019 (COVID-19) infection. METHODS: The active components and targets of honeysuckle were screened by Traditional Chinese Medicine Database and Analysis Platform (TCMSP). SwissADME and pkCSM databases predict pharmacokinetics of ingredients. The Gene Expression Omnibus (GEO) database collected transcriptome data for mild COVID-19. Data quality control, differentially expressed gene (DEG) identification, enrichment analysis, and correlation analysis were implemented by R toolkit. CIBERSORT evaluated the infiltration of 22 immune cells. RESULTS: The seven active ingredients of honeysuckle had good oral absorption and medicinal properties. Both the active ingredient targets of honeysuckle and differentially expressed genes of mild COVID-19 were significantly enriched in immune signaling pathways. There were five overlapping immunosignature genes, among which RELA and MAP3K7 expressions were statistically significant (P < 0.05). Finally, immune cell infiltration and correlation analysis showed that RELA, MAP3K7, and natural killer (NK) cell are with highly positive correlation and highly negatively correlated with hematopoietic stem cells. CONCLUSION: Our analysis suggested that honeysuckle extract had a safe and effective protective effect against mild COVID-19 by regulating a complex molecular network. The main mechanism was related to the proportion of infiltration between NK cells and hematopoietic stem cells.

Assisting Multitargeted Ligand Affinity Prediction of Receptor Tyrosine Kinases Associated Nonsmall Cell Lung Cancer Treatment with Multitasking Principal Neighborhood Aggregation.

A multitargeted therapeutic approach with hybrid drugs is a promising strategy to enhance anticancer efficiency and overcome drug resistance in nonsmall cell lung cancer (NSCLC) treatment. Estimating affinities of small molecules against targets of interest typically proceeds as a preliminary action for recent drug discovery in the pharmaceutical industry. In this investigation, we employed machine learning models to provide a computationally affordable means for computer-aided screening to accelerate the discovery of potential drug compounds. In particular, we introduced a quantitative structure-activity-relationship (QSAR)-based multitask learning model to facilitate an in silico screening system of multitargeted drug development. Our method combines a recently developed graph-based neural network architecture, principal neighborhood aggregation (PNA), with a descriptor-based deep neural network supporting synergistic utilization of molecular graph and fingerprint features. The model was generated by more than ten-thousands affinity-reported ligands of seven crucial receptor tyrosine kinases in NSCLC from two public data sources. As a result, our multitask model demonstrated better performance than all other benchmark models, as well as achieving satisfying predictive ability regarding applicable QSAR criteria for most tasks within the model's applicability. Since our model could potentially be a screening tool for practical use, we have provided a model implementation platform with a tutorial that is freely accessible hence, advising the first move in a long journey of cancer drug development.

Computational Drug Repurposing Based on a Recommendation System and Drug-Drug Functional Pathway Similarity.

Drug repurposing identifies new clinical indications for existing drugs. It can be used to overcome common problems associated with cancers, such as heterogeneity and resistance to established therapies, by rapidly adapting known drugs for new treatment. In this study, we utilized a recommendation system learning model to prioritize candidate cancer drugs. We designed a drug-drug pathway functional similarity by integrating multiple genetic and epigenetic alterations such as gene expression, copy number variation (CNV), and DNA methylation. When compared with other similarities, such as SMILES chemical structures and drug targets based on the protein-protein interaction network, our approach provided better interpretable models capturing drug response mechanisms. Furthermore, our approach can achieve comparable accuracy when evaluated with other learning models based on large public datasets (CCLE and GDSC). A case study about the Erlotinib and OSI-906 (Linsitinib) indicated that they have a synergistic effect to reduce the growth rate of tumors, which is an alternative targeted therapy option for patients. Taken together, our computational method characterized drug response from the viewpoint of a multi-omics pathway and systematically predicted candidate cancer drugs with similar therapeutic effects.

Trends in Opioid Use Among Cancer Patients in the United States: 2013-2018.

Background: In response to the US opioid epidemic, the Centers for Disease Control and Prevention updated their guideline on prescription opioids for chronic pain management in March 2016. The aim of this study was to provide detailed analysis of trends in opioid claims among cancer patients in the United States during 2013-2018. Methods: We analyzed pharmaceutical dispensing data from Symphony Health's Integrated Dataverse database, which covers approximately 80% of the US population. We examined annual trends in dispensed opioids in cancer patients during 2013-2018. We examined quarterly trends of the prevalence, mean number of days, and dose (stated as morphine milligram equivalents) of opioid dispensing in cancer patients. Results: Dispensing records of an average of over 3.7 million cancer patients contributed to the study annually in 2013-2018. The annual prevalence of opioid dispensing claims declined from 40.2% in 2013 to 34.5% in 2018. Annual declines occurred across cancer sites, and particularly among patients with metastatic cancer (decline of 19.8%), breast cancer (18.2%), and lung cancer (13.8%). By quarter, the prevalence of opioid claims declined statistically significantly from 26.6% in Q1 2013 to 21.2% in Q4 2018; this decline was more pronounced after Q3 2016 (2-sided P = .004). Both quarterly trends in mean days and morphine milligram equivalents of opioids supplied showed a gradual decline from 2013 to 2018, with a slightly larger decline after 2016. Conclusions: We observed a decline in opioid use among cancer patients, particularly after 2016, coinciding with the publication of the Centers for Disease Control and Prevention's guideline on prescription opioids for chronic pain management.

Virtual Screening and Molecular Docking to Study the Mechanism of Chinese Medicines in the Treatment of Coronavirus Infection.

BACKGROUND Heat-clearing and detoxifying herbs (HDHs) play an important role in the prevention and treatment of coronavirus infection. However, their mechanism of action needs further study. This study aimed to explore the anti-coronavirus basis and mechanism of HDHs. MATERIAL AND METHODS Database mining was performed on 7 HDHs. Core ingredients and targets were screened according to ADME rules combined with Neighborhood, Co-occurrence, Co-expression, and other algorithms. GO enrichment and KEGG pathway analyses were performed using the R language. Finally, high-throughput molecular docking was used for verification. RESULTS HDHs mainly acts on NOS3, EGFR, IL-6, MAPK8, PTGS2, MAPK14, NFKB1, and CASP3 through quercetin, luteolin, wogonin, indirubin alkaloids, ß-sitosterol, and isolariciresinol. These targets are mainly involved in the regulation of biological processes such as inflammation, activation of MAPK activity, and positive regulation of NF-kappaB transcription factor activity. Pathway analysis further revealed that the pathways regulated by these targets mainly include: signaling pathways related to viral and bacterial infections such as tuberculosis, influenza A, Ras signaling pathways; inflammation-related pathways such as the TLR, TNF, MAPK, and HIF-1 signaling pathways; and immune-related pathways such as NOD receptor signaling pathways. These pathways play a synergistic role in inhibiting lung inflammation and regulating immunity and antiviral activity. CONCLUSIONS HDHs play a role in the treatment of coronavirus infection by regulating the body's immunity, fighting inflammation, and antiviral activities, suggesting a molecular basis and new strategies for the treatment of COVID-19 and a foundation for the screening of new antiviral drugs.

In Silico Exploration of Novel Tubulin Inhibitors: A Combination of Docking and Molecular Dynamics Simulations, Pharmacophore Modeling, and Virtual Screening.

Microtubules play a critical role in mitosis and cell division and are regarded as an excellent target for anticancer therapy. Although microtubule-targeting agents have been widely used in the clinical treatment of different human cancers, their clinical application in cancer therapy is limited by both intrinsic and acquired drug resistance and adverse toxicities. In a previous work, we synthesized compound 9IV-c, ((E)-2-(3,4-dimethoxystyryl)-6,7,8-trimethoxy-N-(3,4,5-trimethoxyphenyl)quinoline-4-amine) that showed potent activity against multiple human tumor cell lines, by targeting spindle formation and/or the microtubule network. Accordingly, in this study, to identify potent tubulin inhibitors, at first, molecular docking and molecular dynamics studies of compound 9IV-c were performed into the colchicine binding site of tubulin; then, a pharmacophore model of the 9IV-c-tubulin complex was generated. The pharmacophore model was then validated by Güner-Henry (GH) scoring methods and receiver operating characteristic (ROC) analysis. The IBScreen database was searched by using this pharmacophore model as a screening query. Finally, five retrieved compounds were selected for molecular docking studies. These efforts identified two compounds (b and c) as potent tubulin inhibitors. Investigation of pharmacokinetic properties of these compounds (b and c) and compound 9IV-c displayed that ligand b has better drug characteristics compared to the other two ligands.

Network Pharmacology Prediction: The Possible Mechanisms of Cinobufotalin against Osteosarcoma.

OBJECTIVE: To explore the active compounds and targets of cinobufotalin (huachansu) compared with the osteosarcoma genes to obtain the potential therapeutic targets and pharmacological mechanisms of action of cinobufotalin on osteosarcoma through network pharmacology. METHODS: The composition of cinobufotalin was searched by literature retrieval, and the target was selected from the CTD and TCMSP databases. The osteosarcoma genes, found from the GeneCards, OMIM, and other databases, were compared with the cinobufotalin targets to obtain potential therapeutic targets. The protein-protein interaction (PPI) network of potential therapeutic targets, constructed through the STRING database, was inputted into Cytoscape software to calculate the hub genes, using the NetworkAnalyzer. The hub genes were inputted into the Kaplan-Meier Plotter online database for exploring the survival curve. Functional enrichment analysis was identified using the DAVID database. RESULTS: 28 main active compounds of cinobufotalin were explored, including bufalin, adenosine, oleic acid, and cinobufagin. 128 potential therapeutic targets on osteosarcoma are confirmed among 184 therapeutic targets form cinobufotalin. The hub genes included TP53, ACTB, AKT1, MYC, CASP3, JUN, TNF, VEGFA, HSP90AA1, and STAT3. Among the hub genes, TP53, ACTB, MYC, TNF, VEGFA, and STAT3 affect the patient survival prognosis of sarcoma. Through function enrichment analysis, it is found that the main mechanisms of cinobufotalin on osteosarcoma include promoting sarcoma apoptosis, regulating the cell cycle, and inhibiting proliferation and differentiation. CONCLUSION: The possible mechanisms of cinobufotalin against osteosarcoma are preliminarily predicted through network pharmacology, and further experiments are needed to prove these predictions.

Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2022.

The National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB), provides a family of database resources to support global research in both academia and industry. With the explosively accumulated multi-omics data at ever-faster rates, CNCB-NGDC is constantly scaling up and updating its core database resources through big data archive, curation, integration and analysis. In the past year, efforts have been made to synthesize the growing data and knowledge, particularly in single-cell omics and precision medicine research, and a series of resources have been newly developed, updated and enhanced. Moreover, CNCB-NGDC has continued to daily update SARS-CoV-2 genome sequences, variants, haplotypes and literature. Particularly, OpenLB, an open library of bioscience, has been established by providing easy and open access to a substantial number of abstract texts from PubMed, bioRxiv and medRxiv. In addition, Database Commons is significantly updated by cataloguing a full list of global databases, and BLAST tools are newly deployed to provide online sequence search services. All these resources along with their services are publicly accessible at https://ngdc.cncb.ac.cn.

GRAND: a database of gene regulatory network models across human conditions.

Gene regulation plays a fundamental role in shaping tissue identity, function, and response to perturbation. Regulatory processes are controlled by complex networks of interacting elements, including transcription factors, miRNAs and their target genes. The structure of these networks helps to determine phenotypes and can ultimately influence the development of disease or response to therapy. We developed GRAND (https://grand.networkmedicine.org) as a database for computationally-inferred, context-specific gene regulatory network models that can be compared between biological states, or used to predict which drugs produce changes in regulatory network structure. The database includes 12 468 genome-scale networks covering 36 human tissues, 28 cancers, 1378 unperturbed cell lines, as well as 173 013 TF and gene targeting scores for 2858 small molecule-induced cell line perturbation paired with phenotypic information. GRAND allows the networks to be queried using phenotypic information and visualized using a variety of interactive tools. In addition, it includes a web application that matches disease states to potentially therapeutic small molecule drugs using regulatory network properties.

Repurposing existing therapeutics, its importance in oncology drug development: Kinases as a potential target.

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