In-vivo neuronal dysfunction by Aß and tau overlaps with brain-wide inflammatory mechanisms in Alzheimer's disease.

The molecular mechanisms underlying neuronal dysfunction in Alzheimer's disease (AD) remain uncharacterized. Here, we identify genes, molecular pathways and cellular components associated with whole-brain dysregulation caused by amyloid-beta (Aß) and tau deposits in the living human brain. We obtained in-vivo resting-state functional MRI (rs-fMRI), Aß- and tau-PET for 47 cognitively unimpaired and 16 AD participants from the Translational Biomarkers in Aging and Dementia cohort. Adverse neuronal activity impacts by Aß and tau were quantified with personalized dynamical models by fitting pathology-mediated computational signals to the participant's real rs-fMRIs. Then, we detected robust brain-wide associations between the spatial profiles of Aß-tau impacts and gene expression in the neurotypical transcriptome (Allen Human Brain Atlas). Within the obtained distinctive signature of in-vivo neuronal dysfunction, several genes have prominent roles in microglial activation and in interactions with Aß and tau. Moreover, cellular vulnerability estimations revealed strong association of microglial expression patterns with Aß and tau's synergistic impact on neuronal activity (q < 0.001). These results further support the central role of the immune system and neuroinflammatory pathways in AD pathogenesis. Neuronal dysregulation by AD pathologies also associated with neurotypical synaptic and developmental processes. In addition, we identified drug candidates from the vast LINCS library to halt or reduce the observed Aß-tau effects on neuronal activity. Top-ranked pharmacological interventions target inflammatory, cancer and cardiovascular pathways, including specific medications undergoing clinical evaluation in AD. Our findings, based on the examination of molecular-pathological-functional interactions in humans, may accelerate the process of bringing effective therapies into clinical practice.

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.

Effective holistic characterization of small molecule effects using heterogeneous biological networks.

The two most common reasons for attrition in therapeutic clinical trials are efficacy and safety. We integrated heterogeneous data to create a human interactome network to comprehensively describe drug behavior in biological systems, with the goal of accurate therapeutic candidate generation. The Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multiscale therapeutic discovery, repurposing, and design was enhanced by integrating drug side effects, protein pathways, protein-protein interactions, protein-disease associations, and the Gene Ontology, and complemented with its existing drug/compound, protein, and indication libraries. These integrated networks were reduced to a "multiscale interactomic signature" for each compound that describe its functional behavior as vectors of real values. These signatures are then used for relating compounds to each other with the hypothesis that similar signatures yield similar behavior. Our results indicated that there is significant biological information captured within our networks (particularly via side effects) which enhance the performance of our platform, as evaluated by performing all-against-all leave-one-out drug-indication association benchmarking as well as generating novel drug candidates for colon cancer and migraine disorders corroborated via literature search. Further, drug impacts on pathways derived from computed compound-protein interaction scores served as the features for a random forest machine learning model trained to predict drug-indication associations, with applications to mental disorders and cancer metastasis highlighted. This interactomic pipeline highlights the ability of Computational Analysis of Novel Drug Opportunities to accurately relate drugs in a multitarget and multiscale context, particularly for generating putative drug candidates using the information gleaned from indirect data such as side effect profiles and protein pathway information.

Computational drug repurposing by exploiting large-scale gene expression data: Strategy, methods and applications.

De novo drug development is an extremely complex, time-consuming and costly task. Urgent needs for therapies of various diseases have greatly accelerated searches for more effective drug development methods. Luckily, drug repurposing provides a new and effective perspective on disease treatment. Rapidly increased large-scale transcriptome data paints a detailed prospect of gene expression during disease onset and thus has received wide attention in the field of computational drug repurposing. However, how to efficiently mine transcriptome data and identify new indications for old drugs remains a critical challenge. This review discussed the irreplaceable role of transcriptome data in computational drug repurposing and summarized some representative databases, tools and strategies. More importantly, it proposed a practical guideline through establishing the correspondence between three gene expression data types and five strategies, which would facilitate researchers to adopt appropriate strategies to deeply mine large-scale transcriptome data and discover more effective therapies.

Optimal COVID-19 therapeutic candidate discovery using the CANDO platform.

The worldwide outbreak of SARS-CoV-2 in early 2020 caused numerous deaths and unprecedented measures to control its spread. We employed our Computational Analysis of Novel Drug Opportunities (CANDO) multiscale therapeutic discovery, repurposing, and design platform to identify small molecule inhibitors of the virus to treat its resulting indication, COVID-19. Initially, few experimental studies existed on SARS-CoV-2, so we optimized our drug candidate prediction pipelines using results from two independent high-throughput screens against prevalent human coronaviruses. Ranked lists of candidate drugs were generated using our open source cando.py software based on viral protein inhibition and proteomic interaction similarity. For the former viral protein inhibition pipeline, we computed interaction scores between all compounds in the corresponding candidate library and eighteen SARS-CoV proteins using an interaction scoring protocol with extensive parameter optimization which was then applied to the SARS-CoV-2 proteome for prediction. For the latter similarity based pipeline, we computed interaction scores between all compounds and human protein structures in our libraries then used a consensus scoring approach to identify candidates with highly similar proteomic interaction signatures to multiple known anti-coronavirus actives. We published our ranked candidate lists at the very beginning of the COVID-19 pandemic. Since then, 51 of our 276 predictions have demonstrated anti-SARS-CoV-2 activity in published clinical and experimental studies. These results illustrate the ability of our platform to rapidly respond to emergent pathogens and provide greater evidence that treating compounds in a multitarget context more accurately describes their behavior in biological systems.

GCMM: graph convolution network based on multimodal attention mechanism for drug repurposing.

BACKGROUND: The main focus of in silico drug repurposing, which is a promising area for using artificial intelligence in drug discovery, is the prediction of drug-disease relationships. Although many computational models have been proposed recently, it is still difficult to reliably predict drug-disease associations from a variety of sources of data. RESULTS: In order to identify potential drug-disease associations, this paper introduces a novel end-to-end model called Graph convolution network based on a multimodal attention mechanism (GCMM). In particular, GCMM incorporates known drug-disease relations, drug-drug chemical similarity, drug-drug therapeutic similarity, disease-disease semantic similarity, and disease-disease target-based similarity into a heterogeneous network. A Graph Convolution Network encoder is used to learn how diseases and drugs are embedded in various perspectives. Additionally, GCMM can enhance performance by applying a multimodal attention layer to assign various levels of value to various features and the inputting of multi-source information. CONCLUSION: 5 fold cross-validation evaluations show that the GCMM outperforms four recently proposed deep-learning models on the majority of the criteria. It shows that GCMM can predict drug-disease relationships reliably and suggests improvement in the desired metrics. Hyper-parameter analysis and exploratory ablation experiments are also provided to demonstrate the necessity of each module of the model and the highest possible level of prediction performance. Additionally, a case study on Alzheimer's disease (AD). Four of the five medications indicated by GCMM to have the highest potential correlation coefficient with AD have been demonstrated through literature or experimental research, demonstrating the viability of GCMM. All of these results imply that GCMM can provide a strong and effective tool for drug development and repositioning.

The Anti-Histamine Azelastine, Identified by Computational Drug Repurposing, Inhibits Infection by Major Variants of SARS-CoV-2 in Cell Cultures and Reconstituted Human Nasal Tissue.

Background and purpose: The COVID-19 pandemic continues to pose challenges, especially with the emergence of new SARS-CoV-2 variants that are associated with higher infectivity and/or compromised protection afforded by the current vaccines. There is a high demand for additional preventive and therapeutic strategies effective against this changing virus. Repurposing of approved or clinically tested drugs can provide an immediate solution. Experimental Approach: We applied a novel computational approach to search among approved and commercially available drugs. Antiviral activity of a predicted drug, azelastine, was tested in vitro in SARS-CoV-2 infection assays with Vero E6 cells, Vero cells stably overexpressing the human TMPRSS2 and ACE2 proteins as well as on reconstituted human nasal tissue using the predominant variant circulating in Europe in summer 2020, B.1.177 (D614G variant), and its emerging variants of concern; B.1.1.7 (alpha), B.1.351 (beta) and B.1.617.2 (delta) variants. The effect of azelastine on viral replication was assessed by quantification of viral genomes by droplet digital PCR or qPCR. Key results: The computational approach identified major drug families, such as anti-infective, anti-inflammatory, anti-hypertensive, antihistamine, and neuroactive drugs. Based on its attractive safety profile and availability in nasal formulation, azelastine, a histamine 1 receptor-blocker was selected for experimental testing. Azelastine reduced the virus-induced cytopathic effect and SARS-CoV-2 copy numbers both in preventive and treatment settings upon infection of Vero cells with an EC50 of 2.2-6.5 µM. Comparable potency was observed with the alpha, beta and delta variants. Furthermore, five-fold dilution (containing 0.02% azelastine) of the commercially available nasal spray formulation was highly potent in inhibiting viral propagation in reconstituted human nasal tissue. Conclusion and Implications: Azelastine, an antihistamine available as nasal sprays developed against allergic rhinitis may be considered as a topical prevention or treatment of nasal colonization by SARS-CoV-2. A Phase 2 efficacy indicator study with azelastine-containing nasal spray that was designed based on the findings reported here has been concluded recently, confirming accelerated viral clearance in SARS-CoV-2 positive subjects.

An Integrative Heterogeneous Graph Neural Network-Based Method for Multi-Labeled Drug Repurposing.

Drug repurposing is the process of discovering new indications (i.e., diseases or conditions) for already approved drugs. Many computational methods have been proposed for predicting new associations between drugs and diseases. In this article, we proposed a new method, called DR-HGNN, an integrative heterogeneous graph neural network-based method for multi-labeled drug repurposing, to discover new indications for existing drugs. For this purpose, we first used the DTINet dataset to construct a heterogeneous drug-protein-disease (DPD) network, which is a graph composed of four types of nodes (drugs, proteins, diseases, and drug side effects) and eight types of edges. Second, we labeled each drug-protein edge, dp i,j = (d i , p j ), of the DPD network with a set of diseases, {δ i,j,1, … , δ i,j,k } associated with both d i and p j and then devised multi-label ranking approaches which incorporate neural network architecture that operates on the heterogeneous graph-structured data and which leverages both the interaction patterns and the features of drug and protein nodes. We used a derivative of the GraphSAGE algorithm, HinSAGE, on the heterogeneous DPD network to learn low-dimensional vector representation of features of drugs and proteins. Finally, we used the drug-protein network to learn the embeddings of the drug-protein edges and then predict the disease labels that act as bridges between drugs and proteins. The proposed method shows better results than existing methods applied to the DTINet dataset, with an AUC of 0.964.

Considerations and challenges for sex-aware drug repurposing.

Sex differences are essential factors in disease etiology and manifestation in many diseases such as cardiovascular disease, cancer, and neurodegeneration [33]. The biological influence of sex differences (including genomic, epigenetic, hormonal, immunological, and metabolic differences between males and females) and the lack of biomedical studies considering sex differences in their study design has led to several policies. For example, the National Institute of Health's (NIH) sex as a biological variable (SABV) and Sex and Gender Equity in Research (SAGER) policies to motivate researchers to consider sex differences [204]. However, drug repurposing, a promising alternative to traditional drug discovery by identifying novel uses for FDA-approved drugs, lacks sex-aware methods that can improve the identification of drugs that have sex-specific responses [7, 11, 14, 33]. Sex-aware drug repurposing methods either select drug candidates that are more efficacious in one sex or deprioritize drug candidates based on if they are predicted to cause a sex-bias adverse event (SBAE), unintended therapeutic effects that are more likely to occur in one sex. Computational drug repurposing methods are encouraging approaches to develop for sex-aware drug repurposing because they can prioritize sex-specific drug candidates or SBAEs at lower cost and time than traditional drug discovery. Sex-aware methods currently exist for clinical, genomic, and transcriptomic information [1, 7, 155]. They have not expanded to other data types, such as DNA variation, which has been beneficial in other drug repurposing methods that do not consider sex [114]. Additionally, some sex-aware methods suffer from poorer performance because a disproportionate number of male and female samples are available to train computational methods [7]. However, there is development potential for several different categories (i.e., data mining, ligand binding predictions, molecular associations, and networks). Low-dimensional representations of molecular association and network approaches are also especially promising candidates for future sex-aware drug repurposing methodologies because they reduce the multiple hypothesis testing burden and capture sex-specific variation better than the other methods [151, 159]. Here we review how sex influences drug response, the current state of drug repurposing including with respect to sex-bias drug response, and how model organism study design choices influence drug repurposing validation.

Evaluating the performance of drug-repurposing technologies.

Drug-repurposing technologies are growing in number and maturing. However, comparisons to each other and to reality are hindered because of a lack of consensus with respect to performance evaluation. Such comparability is necessary to determine scientific merit and to ensure that only meaningful predictions from repurposing technologies carry through to further validation and eventual patient use. Here, we review and compare performance evaluation measures for these technologies using version 2 of our shotgun repurposing Computational Analysis of Novel Drug Opportunities (CANDO) platform to illustrate their benefits, drawbacks, and limitations. Understanding and using different performance evaluation metrics ensures robust cross-platform comparability, enabling us to continue to strive toward optimal repurposing by decreasing the time and cost of drug discovery and development.

DrugSim2DR: systematic prediction of drug functional similarities in the context of specific disease for drug repurposing.

BACKGROUND: Traditional approaches to drug development are costly and involve high risks. The drug repurposing approach can be a valuable alternative to traditional approaches and has therefore received considerable attention in recent years. FINDINGS: Herein, we develop a previously undescribed computational approach, called DrugSim2DR, which uses a network diffusion algorithm to identify candidate anticancer drugs based on a drug functional similarity network. The innovation of the approach lies in the drug-drug functional similarity network constructed in a manner that implicitly links drugs through their common biological functions in the context of a specific disease state, as the similarity relationships based on general states (e.g., network proximity or Jaccard index of drug targets) ignore disease-specific molecular characteristics. The drug functional similarity network may provide a reference for prediction of drug combinations. We describe and validate the DrugSim2DR approach through analysis of data on breast cancer and lung cancer. DrugSim2DR identified some US Food and Drug Administration-approved anticancer drugs, as well as some candidate drugs validated by previous studies in the literature. Moreover, DrugSim2DR showed excellent predictive performance, as evidenced by receiver operating characteristic analysis and multiapproach comparisons in various cancer datasets. CONCLUSIONS: DrugSim2DR could accurately assess drug-drug functional similarity within a specific disease context and may more effectively prioritize disease candidate drugs. To increase the usability of our approach, we have developed an R-based software package, DrugSim2DR, which is freely available on CRAN (https://CRAN.R-project.org/package=DrugSim2DR).

Identification of Mpro inhibitors of SARS-CoV-2 using structure based computational drug repurposing.

The recent outbreak of COVID-19, caused by the novel pathogen SARS-coronavirus 2 (SARS-CoV-2) is a severe health emergency. In this pandemic, drug repurposing seems to be the most promising alternative to identify effective therapeutic agents for immediate treatment of infected patients. The present study aimed to evaluate all the drugs present in drug bank as potential novel SARS-CoV-2 inhibitors, using computational drug repurposing studies. Docking-based virtual screening and binding energy prediction were performed, followed by Absorption Distribution Metabolism Excretion calculation. Hydroxychloroquine and Nelfinavir have been identified as the best potential inhibitor against the SARS-CoV-2, therefore, they were used as reference compounds in computational DR studies. The docking study revealed 13 best compounds based on their highest binding affinity, binding energy, and dock score concerning the other screened compounds. Out of 13, only 4 compounds were further shortlisted based on their binding energy and best ADME properties. The hierarchical virtual screening yielded the best 04 drugs, DB07042 (compound 2), DB13035 (compound 3), DB13604 (compound 5) and DB08253 (compound 6), with commendable binding energies in kcal/mol, i.e. -65.45, -62.01, -52.09 and -51.70 respectively. Further, Molecular dynamics simulation with 04 best-retrieved hits has confirmed stable trajectories in protein in terms of root mean square deviation and root mean square fluctuation. During 30 ns simulation, the interactions were also found similar to the docking-based studies. However, clinical studies are necessary to investigate their therapeutic use against this outbreak.

Advances in the computational landscape for repurposed drugs against COVID-19.

The COVID-19 pandemic has caused millions of deaths and massive societal distress worldwide. Therapeutic solutions are urgently needed, but de novo drug development remains a lengthy process. One promising alternative is computational drug repurposing, which enables the prioritization of existing compounds through fast in silico analyses. Recent efforts based on molecular docking, machine learning, and network analysis have produced actionable predictions. Some predicted drugs, targeting viral proteins and pathological host pathways are undergoing clinical trials. Here, we review this work, highlight drugs with high predicted efficacy and classify their mechanisms of action. We discuss the strengths and limitations of the published methodologies and outline possible future directions. Finally, we curate a list of COVID-19 data portals and other repositories that could be used to accelerate future research.

An Ensemble Matrix Completion Model for Predicting Potential Drugs Against SARS-CoV-2.

Because of the catastrophic outbreak of global coronavirus disease 2019 (COVID-19) and its strong infectivity and possible persistence, computational repurposing of existing approved drugs will be a promising strategy that facilitates rapid clinical treatment decisions and provides reasonable justification for subsequent clinical trials and regulatory reviews. Since the effects of a small number of conditionally marketed vaccines need further clinical observation, there is still an urgent need to quickly and effectively repurpose potentially available drugs before the next disease peak. In this work, we have manually collected a set of experimentally confirmed virus-drug associations through the publicly published database and literature, consisting of 175 drugs and 95 viruses, as well as 933 virus-drug associations. Then, because the samples are extremely sparse and unbalanced, negative samples cannot be easily obtained. We have developed an ensemble model, EMC-Voting, based on matrix completion and weighted soft voting, a semi-supervised machine learning model for computational drug repurposing. Finally, we have evaluated the prediction performance of EMC-Voting by fivefold crossing-validation and compared it with other baseline classifiers and prediction models. The case study for the virus SARS-COV-2 included in the dataset demonstrates that our model achieves the outperforming AUPR value of 0.934 in virus-drug association's prediction.

Virtual screening, ADME/Tox predictions and the drug repurposing concept for future use of old drugs against the COVID-19.

The new Coronavirus (SARS-CoV-2) is the cause of a serious infection in the respiratory tract called COVID-19. Structures of the main protease of SARS-CoV-2 (Mpro), responsible for the replication of the virus, have been solved and quickly made available, thus allowing the design of compounds that could interact with this protease and thus to prevent the progression of the disease by avoiding the viral peptide to be cleaved, so that smaller viral proteins can be released into the host's plasma. These structural data are extremely important for in silico design and development of compounds as well, being possible to quick and effectively identify potential inhibitors addressed to such enzyme's structure. Therefore, in order to identify potential inhibitors for Mpro, we used virtual screening approaches based with the structure of the enzyme and two compounds libraries, targeted to SARS-CoV-2, containing compounds with predicted activity against Mpro. In this way, we selected, through docking studies, the 100 top-ranked compounds, which followed to subsequent studies of pharmacokinetic and toxicity predictions. After all the simulations and predictions here performed, we obtained 10 top-ranked compounds that were again in silico analyzed inside the Mpro catalytic site, together some drugs that are being currently investigated for treatment of COVID-19. After proposing and analyzing the interaction modes of these compounds, we submitted one molecule then selected as template to a 2D similarity study in a database containing drugs approved by FDA and we have found and indicated Apixaban as a potential drug for future treatment of COVID-19.

Repurposing Drugs by In Silico Methods to Target BCR Kinase Domain in Chronic Myeloid Leukemia.

BACKGROUND: Targeted therapy in the form of highly selective tyrosine kinase inhibitors (TKIs) has transformed the treatment of chronic myeloid leukemia (CML). However, mutations in the kinase domain contribute to drug resistance against TKIs which compromises the treatment response. Our aim is to explore regions outside the BCR-ABL oncoprotein to identify potential therapeutic targets to curb drug resistance by targeting growth factor receptor-bound protein-2 (Grb-2) which binds to BCR-ABL at the phosphorylated tyrosine (Y177) thereby activating the Ras and PI3K/AKT signaling pathway. METHODS: We have used in silico methods to repurpose drugs for identifying their potential to inhibit the binding of Grb-2 with Y177 by occupying the active binding site of the BCR domain. RESULTS: Differentially expressed genes from GEO dataset were found to be associated with hematopoietic cell lineage, NK cell-mediated cytotoxicity, NF-κB and chemokine signaling, cytokine-cytokine receptor interaction, histidine metabolism and transcriptional misregulation in cancer. The fold recognition method of SPARKS-X tool was used to model the BCR domain (Z-score = 8.21). Connectivity Map generated a drug list based on the gene expression profile, which were docked with BCR. Schrodinger XP glide docking identified Diphosphopyridine nucleotide, Hesperidin, Butirosin, Ovoflavin, and Nor-dihydroguaiaretic acid to show strong interaction in close proximity to the active binding pocket containing Y177 of the target protein and was further validated using iGEMDOCK and Parallelized Open Babel and AutoDock suite Pipeline (POAP). CONCLUSION: Our study not only extends our current knowledge about repurposing drugs for newer indications but also provides a route towards combinatorial therapy with standard drugs used for CML treatment. However, the efficacy of these repurposed drugs needs to be further investigated using in vitro and in vivo studies.
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A review of computational drug repurposing.

Although sciences and technology have progressed rapidly, de novo drug development has been a costly and time-consuming process over the past decades. In view of these circumstances, 'drug repurposing' (or 'drug repositioning') has appeared as an alternative tool to accelerate drug development process by seeking new indications for already approved drugs rather than discovering de novo drug compounds, nowadays accounting for 30% of newly marked drugs in the U.S. In the meantime, the explosive and large-scale growth of molecular, genomic and phenotypic data of pharmacological compounds is enabling the development of new area of drug repurposing called computational drug repurposing. This review provides an overview of recent progress in the area of computational drug repurposing. First, it summarizes available repositioning strategies, followed by computational methods commonly used. Then, it describes validation techniques for repurposing studies. Finally, it concludes by discussing the remaining challenges in computational repurposing.

A Machine-Learning-Based Drug Repurposing Approach Using Baseline Regularization.

We present the baseline regularization model for computational drug repurposing using electronic health records (EHRs). In EHRs, drug prescriptions of various drugs are recorded throughout time for various patients. In the same time, numeric physical measurements (e.g., fasting blood glucose level) are also recorded. Baseline regularization uses statistical relationships between the occurrences of prescriptions of some particular drugs and the increase or the decrease in the values of some particular numeric physical measurements to identify potential repurposing opportunities.

A review of computational drug repurposing

Although sciences and technology have progressed rapidly, de novo drug development has been a costly and time-consuming process over the past decades. In view of these circumstances, ‘drug repurposing’ (or ‘drug repositioning’) has appeared as an alternative tool to accelerate drug development process by seeking new indications for already approved drugs rather than discovering de novo drug compounds, nowadays accounting for 30% of newly marked drugs in the U.S. In the meantime, the explosive and large-scale growth of molecular, genomic and phenotypic data of pharmacological compounds is enabling the development of new area of drug repurposing called computational drug repurposing. This review provides an overview of recent progress in the area of computational drug repurposing. First, it summarizes available repositioning strategies, followed by computational methods commonly used. Then, it describes validation techniques for repurposing studies. Finally, it concludes by discussing the remaining challenges in computational repurposing.