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Drug repurposing is a new method for disease treatments, which accelerates the identification of new uses for existing drugs with minimal side effects for patients. MicroRNA-based therapeutics are a class of drugs that have been used in gene therapy following the FDA’s approval of the first anti-sense therapy. This study examines the effects of oxLDL on vascular smooth muscle cells (VSMCs) and identifies potential drugs and antimiRs for treating VSMC-associated diseases. The Connectivity Map (cMap) database is utilized to identify potential new uses of existing drugs. The success of the identifications was supported by MTT assay, clonogenic assay and clinical trial data. Specifically, 37 drugs, some of which are undergoing clinical trials, were identified. Three of the identified drugs exhibit IC50 activities. Among the 37 drugs’ targets, three differentially expressed genes (DEGs) are identified as drug targets by using both the DrugBank and the NCBI PubChem Compound databases. Also, one DEG, DNMT1, which is regulated by 17 miRNAs, where these miRNAs are potential targets for developing antimiR-based miRNA therapy, is found.

Over the past decades, many existing drugs and clinical/preclinical compounds have been repositioned as new therapeutic indication from which they were originally intended and to treat off-target diseases by targeting their noncognate protein receptors, such as Sildenafil and Paxlovid, termed drug repurposing (DRP). Despite its significant attraction in the current medicinal community, the DRP is usually considered as a matter of accidents that cannot be fulfilled reliably by traditional drug discovery protocol. In this study, we proposed an integrated computational/experimental (iC/E) strategy to facilitate the DRP within a framework of rational drug design, which was practiced on the identification of new neuronal nitric oxide synthase (nNOS) inhibitors from a structurally diverse, functionally distinct drug pool. We demonstrated that the iC/E strategy is very efficient and readily feasible, which confirmed that the phosphodiesterase inhibitor DB06237 showed a high inhibitory potency against nNOS synthase domain, while other two general drugs, i.e. DB02302 and DB08258, can also inhibit the synthase at nanomolar level. Structural bioinformatics analysis revealed diverse noncovalent interactions such as hydrogen bonds, hydrophobic forces and van der Waals contacts across the complex interface of nNOS active site with these identified drugs, conferring both stability and specificity for the complex recognition and association.

Sirtuin 2 (SIRT2) is a nicotinamide adenine dinucleotide (NAD+)-dependent deacetylase that has been identified as a target for many diseases, including Parkinson’s disease (PD) and leukemia. Using 234 SIRT2 inhibitors from the ZINC15 database, we generated molecular descriptors with PaDEL and constructed a machine-learning (ML) model for the binary classification of SIRT2 inhibitors. To predict compounds with novel inhibitory mechanisms, we then applied the model on the ZINC15/FDA subset, yielding 107 potential SIRT2 inhibitors. For validation of these substances, we employed the binding analysis software AutoDock Vina to perform virtual screening, with which 43 compounds were considered best inhibitors at the $-10$kcal/mol binding affinity threshold. Our results demonstrate the potential of ligand-based (LB) ML techniques in conjunction with receptor-based virtual screening (RBVS) to facilitate the drug discovery or repurposing.

The global spread of COVID-19 caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) originated in Wuhan in December 2019, created a massive health crisis, and disrupted the world economy. Much research has been conducted to discover drugs, develop vaccines, and find repurposable drugs against the disease. Computational drug repurposing, the process of determining new uses for approved drugs through computational techniques, becomes an effective solution to fight the COVID-19 pandemic. This study aims to investigate and prioritize potential drugs against SARS-CoV-2 through an integrated network-based approach. We propose an ensemble approach based on network inference and inductive matrix completion (NIMCVDA) for virus–drug association prediction to identify antivirals against COVID-19. We constructed a heterogeneous drug–virus network using intra-similarities of virus genomic sequences and drug chemical structures and existing associations between viruses and drugs. A network inference method is used to infer missing drug–virus edges. Based on this, existing drug–virus association matrix is reconstructed. Finally, more accurate association scores between drugs and viruses are computed using the inductive matrix completion algorithm. The proposed method achieved an AUC of 0.9020 on five-fold cross-validation and 0.8786 on leave-one-out cross-validation. We compared the performance of the model with related approaches. In addition, we carried out case studies on the top-predicted drugs and implemented our model with other datasets to verify prediction performance. Our work prioritized repurposable drugs to battle with COVID-19 epidemic. The cross-validation results and case studies illustrate that the top-predicted drugs are strong candidates for further biological tests.

The novel coronavirus disease 19 (COVID-19) has resulted in an estimated 20 million excess deaths and the recent resurgence of COVID-19 in China is predicted to result in up to 1 million deaths over the next few months. With vaccines being ineffective in the case of immunocompromised patients, it is important to continue our quest for safe, effective and affordable drugs that will be available to all countries. Drug repurposing is one of the strategies being explored in this context. Recently, out of the 7817 drugs approved worldwide, 214 candidates were systematically down-selected using a combination of 11 filters including FDA/TGA approval status, assay data against SARS-CoV-2, pharmacokinetic, pharmacodynamic and toxicity profiles. These down-selected drugs were subjected in this study to virtual screening against various SARS-CoV-2 targets followed by molecular dynamics studies of the best scoring ligands against each target. The chosen molecular targets were spike receptor binding domain, nucleocapsid protein RNA binding domain and key nonstructural proteins 3, 5 and 12–14. Four drugs approved for other indications — alendronate, cromolyn, natamycin and treprostinil — look sufficiently promising from our in-silico studies to warrant further in-vitro and in-vivo investigations as appropriate to ascertain their extent of antiviral activities.

Millions of Americans are affected by rare diseases, many of which have poor survival rates. However, the small market size of individual rare diseases, combined with the time and capital requirements of pharmaceutical R&D, have hindered the development of new drugs for these cases. A promising alternative is drug repurposing, whereby existing FDA-approved drugs might be used to treat diseases different from their original indications. In order to generate drug repurposing hypotheses in a systematic and comprehensive fashion, it is essential to integrate information from across the literature of pharmacology, genetics, and pathology. To this end, we leverage a newly developed knowledge graph, the Global Network of Biomedical Relationships (GNBR). GNBR is a large, heterogeneous knowledge graph comprising drug, disease, and gene (or protein) entities linked by a small set of semantic themes derived from the abstracts of biomedical literature. We apply a knowledge graph embedding method that explicitly models the uncertainty associated with literature-derived relationships and uses link prediction to generate drug repurposing hypotheses. This approach achieves high performance on a gold-standard test set of known drug indications (AUROC = 0.89) and is capable of generating novel repurposing hypotheses, which we independently validate using external literature sources and protein interaction networks. Finally, we demonstrate the ability of our model to produce explanations of its predictions.

On 30 January 2020, the World Health Organization (WHO) characterized the novel severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) outbreak as a Public Health Emergency of International Concern. Subsequently, on 11 March 2020, WHO declared the global spread of Coronavirus disease 2019 (COVID-19) as a pandemic triggered by this causative virus. This COVID-19 pandemic has impacted lives and livelihoods worldwide, resulting in unprecedented social disruption and economic losses. In order to design and develop effective diagnostics, vaccines and therapeutic interventions against SARS-CoV-2, it is imperative to understand the molecular and cellular mechanisms underpinning the complex interactions between this virus, its variants, and its infected hosts. This chapter provides an overview on the classification, genomic organization and evolution of SARS-CoV-2 (including the emergence of variants from Alpha to Omicron), and summarizes existing and emerging testing strategies. With unprecedented speed, an array of conventional and new COVID-19 vaccines has been developed, evaluated in clinical trials, and administered to billions worldwide. Current and novel antiviral drugs and immunomodulatory approaches are discussed for the therapeutic and prophylactic management of SARS-CoV-2 infections. Finally, much remains for humanity to discover and learn as the world must continue to adapt and live with endemic COVID-19 and SARS-CoV-2 evolution.

This PSB 2024 session discusses the many broad biological, computational, and statistical approaches currently being used for therapeutic drug target identification and repurposing of existing treatments. Drug repurposing efforts have the potential to dramatically improve the treatment landscape by more rapidly identifying drug targets and alternative strategies for untreated or poorly managed diseases. The overarching theme for this session is the use and integration of real-world data to identify drug-disease pairs with potential therapeutic use. These drug-disease pairs may be identified through genomic, proteomic, biomarkers, protein interaction analyses, electronic health records, and chemical profiling. Taken together, this session combines novel applications of methods and innovative modeling strategies with diverse real-world data to suggest new pharmaceutical treatments for human diseases.

Drug repurposing (DR) intends to identify new uses for approved medications outside their original indication. Computational methods for finding DR candidates usually rely on prior biological and chemical information on a specific drug or target but rarely utilize real-world observations. In this work, we propose a simple and effective systematic screening approach to measure medication impact on hospitalization risk based on large-scale observational data. We use common classification systems to group drugs and diseases into broader functional categories and test for non-zero effects in each drug-disease category pair. Treatment effects on the hospitalization risk of an individual disease are obtained by combining widely used methods for causal inference and time-to-event modelling. 6468 drug-disease pairs were tested using data from the UK Biobank, focusing on cardiovascular, metabolic, and respiratory diseases. We determined key parameters to reduce the number of spurious correlations and identified 7 statistically significant associations of reduced hospitalization risk after correcting for multiple testing. Some of these associations were already reported in other studies, including new potential applications for cardioselective beta-blockers and thiazides. We also found evidence for proton pump inhibitor side effects and multiple possible associations for anti-diabetic drugs. Our work demonstrates the applicability of the present screening approach and the utility of real-world data for identifying potential DR candidates.

The drug development pipeline for a new compound can last 10-20 years and cost over $10 billion. Drug repurposing offers a more time- and cost-effective alternative. Computational approaches based on network graph representations, comprising a mixture of disease nodes and their interactions, have recently yielded new drug repurposing hypotheses, including suitable candidates for COVID-19. However, these interactomes remain aggregate by design and often lack disease specificity. This dilution of information may affect the relevance of drug node embeddings to a particular disease, the resulting drug-disease and drug-drug similarity scores, and therefore our ability to identify new targets or drug synergies. To address this problem, we propose constructing and learning disease-specific hypergraphs in which hyperedges encode biological pathways of various lengths. We use a modified node2vec algorithm to generate pathway embeddings. We evaluate our hypergraph’s ability to find repurposing targets for an incurable but prevalent disease, Alzheimer’s disease (AD), and compare our ranked-ordered recommendations to those derived from a state-of-the-art knowledge graph, the multiscale interactome. Using our method, we successfully identified 7 promising repurposing candidates for AD that were ranked as unlikely repurposing targets by the multiscale interactome but for which the existing literature provides supporting evidence. Additionally, our drug repositioning suggestions are accompanied by explanations, eliciting plausible biological pathways. In the future, we plan on scaling our proposed method to 800+ diseases, combining single-disease hypergraphs into multi-disease hypergraphs to account for subpopulations with risk factors or encode a given patient’s comorbidities to formulate personalized repurposing recommendations.

Supplementary materials and code: https://github.com/ayujain04/psb_supplement

Recently, drug repurposing has emerged as an effective and resource-efficient paradigm for AD drug discovery. Among various methods for drug repurposing, network-based methods have shown promising results as they are capable of leveraging complex networks that integrate multiple interaction types, such as protein-protein interactions, to more effectively identify candidate drugs. However, existing approaches typically assume paths of the same length in the network have equal importance in identifying the therapeutic effect of drugs. Other domains have found that same length paths do not necessarily have the same importance. Thus, relying on this assumption may be deleterious to drug repurposing attempts. In this work, we propose MPI (Modeling Path Importance), a novel network-based method for AD drug repurposing. MPI is unique in that it prioritizes important paths via learned node embeddings, which can effectively capture a network’s rich structural information. Thus, leveraging learned embeddings allows MPI to effectively differentiate the importance among paths. We evaluate MPI against a commonly used baseline method that identifies anti-AD drug candidates primarily based on the shortest paths between drugs and AD in the network. We observe that among the top-50 ranked drugs, MPI prioritizes 20.0% more drugs with anti-AD evidence compared to the baseline. Finally, Cox proportional-hazard models produced from insurance claims data aid us in identifying the use of etodolac, nicotine, and BBB-crossing ACE-INHs as having a reduced risk of AD, suggesting such drugs may be viable candidates for repurposing and should be explored further in future studies.

Repurposing existing drugs for new uses has attracted considerable attention over the past years. To identify potential candidates that could be repositioned for a new indication, many studies make use of chemical, target, and side effect similarity between drugs to train classifiers. Despite promising prediction accuracies of these supervised computational models, their use in practice, such as for rare diseases, is hindered by the assumption that there are already known and similar drugs for a given condition of interest. In this study, using publicly available data sets, we question the prediction accuracies of supervised approaches based on drug similarity when the drugs in the training and the test set are completely disjoint. We first build a Python platform to generate reproducible similarity-based drug repurposing models. Next, we show that, while a simple chemical, target, and side effect similarity based machine learning method can achieve good performance on the benchmark data set, the prediction performance drops sharply when the drugs in the folds of the cross validation are not overlapping and the similarity information within the training and test sets are used independently. These intriguing results suggest revisiting the assumptions underlying the validation scenarios of similarity-based methods and underline the need for unsupervised approaches to identify novel drug uses inside the unexplored pharmacological space. We make the digital notebook containing the Python code to replicate our analysis that involves the drug repurposing platform based on machine learning models and the proposed disjoint cross fold generation method freely available at github.com/emreg00/repurpose.

Gene expression profiling of in vitro drug perturbations is useful for many biomedical discovery applications including drug repurposing and elucidation of drug mechanisms. However, limited data availability across cell types has hindered our capacity to leverage or explore the cell-specificity of these perturbations. While recent efforts have generated a large number of drug perturbation profiles across a variety of human cell types, many gaps remain in this combinatorial drug-cell space. Hence, we asked whether it is possible to fill these gaps by predicting cell-specific drug perturbation profiles using available expression data from related conditions--i.e. from other drugs and cell types. We developed a computational framework that first arranges existing profiles into a three-dimensional array (or tensor) indexed by drugs, genes, and cell types, and then uses either local (nearest-neighbors) or global (tensor completion) information to predict unmeasured profiles. We evaluate prediction accuracy using a variety of metrics, and find that the two methods have complementary performance, each superior in different regions in the drug-cell space. Predictions achieve correlations of 0.68 with true values, and maintain accurate differentially expressed genes (AUC 0.81). Finally, we demonstrate that the predicted profiles add value for making downstream associations with drug targets and therapeutic classes.

Repurposing existing drugs for new therapeutic indications can improve success rates and streamline development. Use of large-scale biomedical data repositories, including eQTL regulatory relationships and genome-wide disease risk associations, offers opportunities to propose novel indications for drugs targeting common or convergent molecular candidates associated to two or more diseases. This proposed novel computational approach scales across 262 complex diseases, building a multi-partite hierarchical network integrating (i) GWAS-derived SNP-to-disease associations, (ii) eQTL-derived SNP-to-eGene associations incorporating both cis- and trans-relationships from 19 tissues, (iii) protein target-to-drug, and (iv) drug-to-disease indications with (iv) Gene Ontology-based information theoretic semantic (ITS) similarity calculated between protein target functions. Our hypothesis is that if two diseases are associated to a common or functionally similar eGene - and a drug targeting that eGene/protein in one disease exists - the second disease becomes a potential repurposing indication. To explore this, all possible pairs of independently segregating GWAS-derived SNPs were generated, and a statistical network of similarity within each SNP-SNP pair was calculated according to scale-free overrepresentation of convergent biological processes activity in regulated eGenes (ITS_eGENE-eGENE) and scale-free overrepresentation of common eGene targets between the two SNPs (ITS_SNP-SNP). Significance of ITS_SNP-SNP was conservatively estimated using empirical scale-free permutation resampling keeping the node-degree constant for each molecule in each permutation. We identified 26 new drug repurposing indication candidates spanning 89 GWAS diseases, including a potential repurposing of the calcium-channel blocker Verapamil from coronary disease to gout. Predictions from our approach are compared to known drug indications using DrugBank as a gold standard (odds ratio=13.1, p-value=2.49x10^-8). Because of specific disease-SNPs associations to candidate drug targets, the proposed method provides evidence for future precision drug repositioning to a patient’s specific polymorphisms.

The investigation of phenotypes in model organisms has the potential to reveal the molecular mechanisms underlying disease. The large-scale comparative analysis of phenotypes across species can reveal novel associations between genotypes and diseases. We use the PhenomeNET network of phenotypic similarity to suggest genotype–disease association, combine them with drug–gene associations available from the PharmGKB database, and infer novel associations between drugs and diseases. We evaluate and quantify our results based on our method's capability to reproduce known drug–disease associations. We find and discuss evidence that levonorgestrel, tretinoin and estradiol are associated with cystic fibrosis (p < 2:65 · 10⁻⁶, p < 0:002 and p < 0:031, Wilcoxon signedrank test, Bonferroni correction) and that ibuprofen may be active in chronic lymphocytic leukemia (p < 2:63 p < 0:03110⁻²³ Wilcoxon signed-rank test, Bonferroni correction). To enable access to our results, we implement a web server and make our raw data freely available. Our results are the first steps in implementing an integrated system for the analysis and prediction of drug–disease associations for rare and orphan diseases for which the molecular basis is not known.

According to Cancer Research UK, cancer is a leading cause of death accounting for more than one in four of all deaths in 2011. The recent advances in experimental technologies in cancer research have resulted in the accumulation of large amounts of patient-specific datasets, which provide complementary information on the same cancer type. We introduce a versatile data fusion (integration) framework that can effectively integrate somatic mutation data, molecular interactions and drug chemical data to address three key challenges in cancer research: stratification of patients into groups having different clinical outcomes, prediction of driver genes whose mutations trigger the onset and development of cancers, and repurposing of drugs treating particular cancer patient groups. Our new framework is based on graph-regularised non-negative matrix tri-factorization, a machine learning technique for co-clustering heterogeneous datasets. We apply our framework on ovarian cancer data to simultaneously cluster patients, genes and drugs by utilising all datasets.We demonstrate superior performance of our method over the state-of-the-art method, Network-based Stratification, in identifying three patient subgroups that have significant differences in survival outcomes and that are in good agreement with other clinical data. Also, we identify potential new driver genes that we obtain by analysing the gene clusters enriched in known drivers of ovarian cancer progression. We validated the top scoring genes identified as new drivers through database search and biomedical literature curation. Finally, we identify potential candidate drugs for repurposing that could be used in treatment of the identified patient subgroups by targeting their mutated gene products. We validated a large percentage of our drug-target predictions by using other databases and through literature curation.