In silico drug repositioning: what we need to know

FMCA-DTI: a fragment-oriented method based on a multihead cross attention mechanism to improve drug-target interaction prediction

MOTIVATION

Identifying drug-target interactions (DTI) is crucial in drug discovery. Fragments are less complex and can accurately characterize local features, which is important in DTI prediction. Recently, deep learning (DL)-based methods predict DTI more efficiently. However, two challenges remain in existing DL-based methods: (i) some methods directly encode drugs and proteins into integers, ignoring the substructure representation; (ii) some methods learn the features of the drugs and proteins separately instead of considering their interactions.

RESULTS

In this article, we propose a fragment-oriented method based on a multihead cross attention mechanism for predicting DTI, named FMCA-DTI. FMCA-DTI obtains multiple types of fragments of drugs and proteins by branch chain mining and category fragment mining. Importantly, FMCA-DTI utilizes the shared-weight-based multihead cross attention mechanism to learn the complex interaction features between different fragments. Experiments on three benchmark datasets show that FMCA-DTI achieves significantly improved performance by comparing it with four state-of-the-art baselines.

AVAILABILITY AND IMPLEMENTATION

The code for this workflow is available at: https://github.com/jacky102022/FMCA-DTI.

System biology approaches for drug repurposing

Drug repurposing, or drug repositioning, refers to the identification of alternative therapeutic applications for established medications that go beyond their initial indications. This strategy has becoming increasingly popular since it has the potential to significantly reduce the overall costs of drug development by around $300 million. System biology methodologies have been employed to facilitate medication repurposing, encompassing computational techniques such as signature matching and network-based strategies. These techniques utilize pre-existing drug-related data types and databases to find prospective repurposed medications that have minimal or acceptable harmful effects on patients. The primary benefit of medication repurposing in comparison to drug development lies in the fact that approved pharmaceuticals have already undergone multiple phases of clinical studies, thereby possessing well-established safety and pharmacokinetic properties. Utilizing system biology methodologies in medication repurposing offers the capacity to expedite the discovery of viable candidates for drug repurposing and offer novel perspectives for structure-based drug design.

Novel strategies for drug repurposing

Synthetic biology, precision medicine, and nanobiotechnology are the three main emerging areas that drive translational innovation toward commercialization. There are several strategies used in precision medicine and drug repurposing is one of the key approaches as it addresses the challenges in drug discovery (high cost and time). Here, we provide a perspective on various new approaches to drug repurposing for cancer precision medicine. We report here our optimized wound healing methodology that can be used to validate drug sensitivity and drug repurposing. Using HeLa as our benchmark, we demonstrated that the assay can be applied to identify drugs that limit cell proliferation. From a future perspective, this assay can be expanded to ex vivo culturing of solid tumors in 2D culture and leukemia in 3D culture.

Drug-target affinity prediction with extended graph learning-convolutional networks

BACKGROUND

High-performance computing plays a pivotal role in computer-aided drug design, a field that holds significant promise in pharmaceutical research. The prediction of drug-target affinity (DTA) is a crucial stage in this process, potentially accelerating drug development through rapid and extensive preliminary compound screening, while also minimizing resource utilization and costs. Recently, the incorporation of deep learning into DTA prediction and the enhancement of its accuracy have emerged as key areas of interest in the research community. Drugs and targets can be characterized through various methods, including structure-based, sequence-based, and graph-based representations. Despite the progress in structure and sequence-based techniques, they tend to provide limited feature information. Conversely, graph-based approaches have risen to prominence, attracting considerable attention for their comprehensive data representation capabilities. Recent studies have focused on constructing protein and drug molecular graphs using sequences and SMILES, subsequently deriving representations through graph neural networks. However, these graph-based approaches are limited by the use of a fixed adjacent matrix of protein and drug molecular graphs for graph convolution. This limitation restricts the learning of comprehensive feature representations from intricate compound and protein structures, consequently impeding the full potential of graph-based feature representation in DTA prediction. This, in turn, significantly impacts the models' generalization capabilities in the complex realm of drug discovery.

RESULTS

To tackle these challenges, we introduce GLCN-DTA, a model specifically designed for proficiency in DTA tasks. GLCN-DTA innovatively integrates a graph learning module into the existing graph architecture. This module is designed to learn a soft adjacent matrix, which effectively and efficiently refines the contextual structure of protein and drug molecular graphs. This advancement allows for learning richer structural information from protein and drug molecular graphs via graph convolution, specifically tailored for DTA tasks, compared to the conventional fixed adjacent matrix approach. A series of experiments have been conducted to validate the efficacy of the proposed GLCN-DTA method across diverse scenarios. The results demonstrate that GLCN-DTA possesses advantages in terms of robustness and high accuracy.

CONCLUSIONS

The proposed GLCN-DTA model enhances DTA prediction performance by introducing a novel framework that synergizes graph learning operations with graph convolution operations, thereby achieving richer representations. GLCN-DTA does not distinguish between different protein classifications, including structurally ordered and intrinsically disordered proteins, focusing instead on improving feature representation. Therefore, its applicability scope may be more effective in scenarios involving structurally ordered proteins, while potentially being limited in contexts with intrinsically disordered proteins.

OTTM: an automated classification tool for translational drug discovery from omics data

Omics data from clinical samples are the predominant source of target discovery and drug development. Typically, hundreds or thousands of differentially expressed genes or proteins can be identified from omics data. This scale of possibilities is overwhelming for target discovery and validation using biochemical or cellular experiments. Most of these proteins and genes have no corresponding drugs or even active compounds. Moreover, a proportion of them may have been previously reported as being relevant to the disease of interest. To facilitate translational drug discovery from omics data, we have developed a new classification tool named Omics and Text driven Translational Medicine (OTTM). This tool can markedly narrow the range of proteins or genes that merit further validation via drug availability assessment and literature mining. For the 4489 candidate proteins identified in our previous proteomics study, OTTM recommended 40 FDA-approved or clinical trial drugs. Of these, 15 are available commercially and were tested on hepatocellular carcinoma Hep-G2 cells. Two drugs-tafenoquine succinate (an FDA-approved antimalarial drug targeting CYC1) and branaplam (a Phase 3 clinical drug targeting SMN1 for the treatment of spinal muscular atrophy)-showed potent inhibitory activity against Hep-G2 cell viability, suggesting that CYC1 and SMN1 may be potential therapeutic target proteins for hepatocellular carcinoma. In summary, OTTM is an efficient classification tool that can accelerate the discovery of effective drugs and targets using thousands of candidate proteins identified from omics data. The online and local versions of OTTM are available at http://otter-simm.com/ottm.html.

Finding Lead Compounds for Dengue Antivirals from a Collection of Old Drugs through In Silico Target Prediction and Subsequent In Vitro Validation

Dengue virus (DENV) infection is one of the most widely spread flavivirus infections. Despite the fatality it could cause, no antiviral treatment is currently available to treat the disease. Hence, this study aimed to repurpose old drugs as novel DENV NS3 inhibitors. Ligand-based (L-B) and proteochemometric (PCM) prediction models were built using 62,354 bioactivity data to screen for potential NS3 inhibitors. Selected drugs were then subjected to the foci forming unit reduction assay (FFURA) and protease inhibition assay. Finally, molecular docking was performed to validate these results. The in silico studies revealed that both models performed well in the internal and external validations. However, the L-B model showed better accuracy in the external validation in terms of its sensitivity (0.671). In the in vitro validation, all drugs (zileuton, trimethadione, and linalool) were able to moderately inhibit the viral activities at the highest concentration tested. Zileuton showed comparable results with linalool when tested at 2 mM against the DENV NS3 protease, with a reduction of protease activity at 17.89 and 18.42%, respectively. Two new compounds were also proposed through the combination of the selected drugs, which are ziltri (zilueton + trimethadione) and zilool (zileuton + linalool). The molecular docking study confirms the in vitro observations where all drugs and proposed compounds were able to achieve binding affinity ≥ -4.1 kcal/mol, with ziltri showing the highest affinity at -7.7 kcal/mol, surpassing the control, panduratin A. The occupation of both S1 and S2 subpockets of NS2B-NS3 may be essential and a reason for the lower binding energy shown by the proposed compounds compared to the screened drugs. Based on the results, this study provided five potential new lead compounds (ziltri, zilool, zileuton, linalool, and trimethadione) for DENV that could be modified further.

A survey of drug-target interaction and affinity prediction methods via graph neural networks

The tasks of drug-target interaction (DTI) and drug-target affinity (DTA) prediction play important roles in the field of drug discovery. However, biological experiment-based methods are time-consuming and expensive. Recently, computational-based approaches have accelerated the process of drug-target relationship prediction. Drug and target features are represented in structure-based, sequence-based, and graph-based ways. Although some achievements have been made regarding structure-based representations and sequence-based representations, the acquired feature information is not sufficiently rich. Molecular graph-based representations are some of the more popular approaches, and they have also generated a great deal of interest. In this article, we provide an overview of the DTI prediction and DTA prediction tasks based on graph neural networks (GNNs). We briefly discuss the molecular graphs of drugs, the primary sequences of target proteins, and the graph reSLBpresentations of target proteins. Meanwhile, we conducted experiments on various fundamental datasets to substantiate the plausibility of DTI and DTA utilizing graph neural networks.

DTSEA: A network-based drug target set enrichment analysis method for drug repurposing against COVID-19

The Coronavirus Disease 2019 (COVID-19) pandemic is still wreaking havoc worldwide. Therefore, the urgent need for efficient treatments pushes researchers and clinicians into screening effective drugs. Drug repurposing may be a promising and time-saving strategy to identify potential drugs against this disease. Here, we developed a novel computational approach, named Drug Target Set Enrichment Analysis (DTSEA), to identify potent drugs against COVID-19. DTSEA first mapped the disease-related genes into a gene functional interaction network, and then it used a network propagation algorithm to rank all genes in the network by calculating the network proximity of genes to disease-related genes. Finally, an enrichment analysis was performed on drug target sets to prioritize disease-candidate drugs. It was shown that the top three drugs predicted by DTSEA, including Ataluren, Carfilzomib, and Aripiprazole, were significantly enriched in the immune response pathways indicating the potential for use as promising COVID-19 inhibitors. In addition to these drugs, DTSEA also identified several drugs (such as Remdesivir and Olumiant), which have obtained emergency use authorization (EUA) for COVID-19. These results indicated that DTSEA could effectively identify the candidate drugs for COVID-19, which will help to accelerate the development of drugs for COVID-19. We then performed several validations to ensure the reliability and validity of DTSEA, including topological analysis, robustness analysis, and prediction consistency. Collectively, DTSEA successfully predicted candidate drugs against COVID-19 with high accuracy and reliability, thus making it a formidable tool to identify potential drugs for a specific disease and facilitate further investigation.

Drug repurposing of propafenone to discover novel anti-tumor agents by impairing homologous recombination to delay DNA damage recovery of rare disease conjunctival melanoma

Conjunctival melanoma (CM), a rare and fatal malignant ocular tumor, lacks proper diagnostic biomarkers and therapy. Herein, we revealed the novel application of propafenone, an FDA-approved antiarrhythmic medication, which was identified effective in inhibiting CM cells viability and homologous recombination pathway. Detailed structure-activity relationships generated D34 as one of the most promising derivatives, which strongly suppressed the proliferation, viability, and migration of CM cells at submicromolar concentrations. Mechanically, D34 had the potential to increase γ-H2AX nuclear foci and aggravated DNA damage by suppressing homologous recombination pathway and its factors, particularly the complex of MRE11-RAD50-NBS1. D34 bound to human recombinant MRE11 protein and inhibited its endonuclease activity. Moreover, D34 dihydrochloride significantly suppressed tumor growth in the CRMM1 NCG xenograft model without obvious toxicity. Our finding shows that propafenone derivatives modulating the MRE11-RAD50-NBS1 complex will most likely provide an approach for CM targeted therapy, especially for improving chemo- and radio-sensitivity for CM patients.

Identification of potential novel inhibitors against glutamine synthetase enzyme of Leishmania major by using computational tools

Glutamine Synthetase (GS) is functionally important in many pathogens, so its viability as a drug target has been widely investigated. We identified Leishmania major glutamine synthetase (Lm-GS) as an appealing target for developing potential leishmaniasis inhibitors. Comparative modeling, virtual screening, MD simulations along with MM-PBSA analyses were performed and two FDA approved compounds namely Chlortalidone (id ZINC00020253) and Ciprofloxacin (id ZINC00020220) were identified as potential inhibitor among the screened library. These compounds may be used as a lead molecule, although additional in vitro and in vivo testing is required to establish its anti-leishmanial effect. Hence, the goal of this study was to locate and identify certain medications that were previously FDA-approved for definite disorders and that might show anti-leishmanial effect. Due to GS's presence in additional Leishmania species, a novel medication docked with Lm-GS may have broad anti-leishmania efficacy.Communicated by Ramaswamy H. Sarma.

Potential effect of luteolin, epiafzelechin, and albigenin on rats under cadmium-induced inflammatory insult: In silico and in vivo approach

Introduction: Cadmium(Cd) an industrial poison present abundantly in the environment, causes human toxicity by an inflammatory process. Chronic exposure of cadmium can cause a number of molecular lesions that could be relevant to oncogenesis, through indirect or epigenetic mechanisms, potentially including abnormal activation of oncogenes and suppression of apoptosis by depletion of antioxidants. As induction of cyclooxygenase (COX)-2 is linked to inflammatory processes, use of luteolin, epiafzelechin, and albigenin alone or in different combinations may be used as anti-inflammatory therapeutic agents. Methods: We, herein, performed in silico experiments to check the binding affinity of phytochemicals and their therapeutic effect against COX-2 in cadmium administered rats. Wistar albino rats were given phytochemicals in different combinations to check their anti-inflammatory activities against cadmium intoxication. The level of alanine aminotransferases (ALT), 4-hydroxynonenal (4HNE), 8-hydroxy-2-deoxyguanosine (8-OHdG), tumor necrosis factor-alpha (TNF-α), isoprostanes (IsoP-2α), COX-2, and malondialdehyde (MDA) were estimated with their respective ELISA and spectrophotometric methods. Results: The generated results show that phytocompounds possessed good binding energy potential against COX-2, and common interactive behavior was observed in all docking studies. Moreover, the level of ALT, 4HNE, 8-OHdG, TNF-α, IsoP-2α, malondialdehyde, and COX-2 were significantly increased in rats with induced toxicity compared to the control group, whereas in combinational therapy of phytocompounds, the levels were significantly decreased in the group. Discussion: Taken together, luteolin, epiafzelechin, and albigenin can be used as anti-inflammatory therapeutic agents for future novel drug design, and thus it may have therapeutic importance against cadmium toxicity.

Literature-based drug-drug similarity for drug repurposing: impact of Medical Subject Headings term refinement and hierarchical clustering

Background: We describe herein, an improved procedure for drug repurposing based on refined Medical Subject Headings (MeSH) terms and hierarchical clustering method. Materials & methods: In the present study, we have employed MeSH data from MEDLINE (2019), 1669 US FDA approved drugs from Open FDA and a refined set of MeSH terms. Refinement of MeSH terms was performed to include terms related to mechanistic information of drugs and diseases. Results and Conclusions: In-depth analysis of the results obtained, demonstrated greater efficiency of the proposed approach, based on refined MeSH terms and hierarchical clustering, in terms of number of selected drug candidates for repurposing. Further, analysis of misclustering and size of noise clusters suggest that the proposed approach is reliable and can be employed in drug repurposing.

Articulating target-mining techniques to disinter Alzheimer's specific targets for drug repurposing

BACKGROUND AND OBJECTIVES

Alzheimer's Disease (AD), an extremely progressive neurodegenerative disorder is an amalgamation of numerous intricate pathological networks. This century old disease is still an unmet medical condition owing to the modest efficacy of existing therapeutic agents in antagonizing the multi-targeted pathological pathways underlying AD. Given the paucity in AD specific drugs, fabricating comprehensive research strategies to envision disease specific targets to channelize and expedite drug discovery are mandated. However, the dwindling approval rates and stringent regulatory constraints concerning the approval of a new chemical entity is daunting the pharmaceutical industries from effectuating de novo research. To bridge the existing gaps in AD drug research, a promising contemporary way out could be drug repurposing. This drug repurposing investigation is intended to envisage AD specific targets and create drug libraries pertinent to the shortlisted targets via a series of avant-garde bioinformatics and computational strategies.

METHODS

Transcriptomic analysis of three AD specific datasets viz., GSE122063, GSE15222 and GSE5281 revealed significant Differentially Expressed Genes (DEGs) and subsequent Protein-Protein Interactions (PPI) network analysis captured crucial AD targets. Later, homology model was constructed through I-TASSER for a shortlisted target protein which lacked X-ray crystallographic structure and the built protein model was validated by molecular dynamic simulations. Further, drug library was created for the shortlisted target based on structural and side effect similarity with respective standard drugs. Finally, molecular docking, binding energy calculations and molecular dynamics studies were carried out to unravel the interactions exhibited by drugs from the created library with amino acids in active binding pocket of RGS4.

RESULTS

SST and RGS4 were shortlisted as potentially significant AD specific targets, however, the less explored target RGS4 was considered for further sequential analysis. Homology model constructed for RGS4 displayed best quality when validated through Ramachandran plot and ERRAT plot. Subsequent docking and molecular dynamics studies showcased substantial affinity demonstrated by three drugs viz., Ziprasidone, Melfoquine and Metaxalone from the created drug libraries, towards RGS4.

CONCLUSION

This virtual analysis forecasted the repurposable potential of Ziprasidone, Melfoquine and Metaxalone against AD based on their affinity towards RGS4, a key AD-specific target.

Computational Analysis of Pathogenetic Pathways in Alzheimer’s Disease and Prediction of Potential Therapeutic Drugs

Background. Alzheimer’s disease (AD) is a chronic and progressive neurodegenerative disease which affects more than 50 million patients and represents 60–80% of all cases of dementia. Mutations in the APP gene, mostly affecting the γ-secretase site of cleavage and presenilin mutations, have been identified in inherited forms of AD. Methods. In the present study, we performed a meta-analysis of the transcriptional signatures that characterize two familial AD mutations (APP^V7171F and PSEN1^M146V) in order to characterize the common altered biomolecular pathways affected by these mutations. Next, an anti-signature perturbation analysis was performed using the AD meta-signature and the drug meta-signatures obtained from the L1000 database, using cosine similarity as distance metrics. Results. Overall, the meta-analysis identified 1479 differentially expressed genes (DEGs), 684 downregulated genes, and 795 upregulated genes. Additionally, we found 14 drugs with a significant anti-similarity to the AD signature, with the top five drugs being naftifine, moricizine, ketoconazole, perindopril, and fexofenadine. Conclusions. This study aimed to integrate the transcriptional profiles associated with common familial AD mutations in neurons in order to characterize the pathogenetic mechanisms involved in AD and to find more effective drugs for AD.

Characterization of Altered Molecular Pathways in the Entorhinal Cortex of Alzheimer’s Disease Patients and In Silico Prediction of Potential Repurposable Drugs

Alzheimer’s disease (AD) is the most common cause of dementia worldwide and is characterized by a progressive decline in cognitive functions. Accumulation of amyloid-β plaques and neurofibrillary tangles are a typical feature of AD neuropathological changes. The entorhinal cortex (EC) is the first brain area associated with pathologic changes in AD, even preceding atrophy of the hippocampus. In the current study, we have performed a meta-analysis of publicly available expression data sets of the entorhinal cortex (EC) in order to identify potential pathways underlying AD pathology. The meta-analysis identified 1915 differentially expressed genes (DEGs) between the EC from normal and AD patients. Among the downregulated DEGs, we found a significant enrichment of biological processes pertaining to the “neuronal system” (R-HSA-112316) and the “synaptic signaling” (GO:0099536), while the “regulation of protein catabolic process” (GO:00042176) and “transport of small molecules” (R-HSA-382551) resulted in enrichment among both the upregulated and downregulated DEGs. Finally, by means of an in silico pharmacology approach, we have prioritized drugs and molecules potentially able to revert the transcriptional changes associated with AD pathology. The drugs with a mostly anti-correlated signature were: efavirenz, an anti-retroviral drug; tacrolimus, a calcineurin inhibitor; and sirolimus, an mTOR inhibitor. Among the predicted drugs, those potentially able to cross the blood-brain barrier have also been identified. Overall, our study found a disease-specific set of dysfunctional biological pathways characterizing the EC in AD patients and identified a set of drugs that could in the future be exploited as potential therapeutic strategies. The approach used in the current study has some limitations, as it does not account for possible post-transcriptional events regulating the cellular phenotype, and also, much clinical information about the samples included in the meta-analysis was not available. However, despite these limitations, our study sets the basis for future investigations on the pathogenetic processes occurring in AD and proposes the repurposing of currently used drugs for the treatment of AD patients.

A survey of optimal strategy for signature-based drug repositioning and an application to liver cancer

Pharmacologic perturbation projects, such as Connectivity Map (CMap) and Library of Integrated Network-based Cellular Signatures (LINCS), have produced many perturbed expression data, providing enormous opportunities for computational therapeutic discovery. However, there is no consensus on which methodologies and parameters are the most optimal to conduct such analysis. Aiming to fill this gap, new benchmarking standards were developed to quantitatively evaluate drug retrieval performance. Investigations of potential factors influencing drug retrieval were conducted based on these standards. As a result, we determined an optimal approach for LINCS data-based therapeutic discovery. With this approach, homoharringtonine (HHT) was identified to be a candidate agent with potential therapeutic and preventive effects on liver cancer. The antitumor and antifibrotic activity of HHT was validated experimentally using subcutaneous xenograft tumor model and carbon tetrachloride (CCL₄)-induced liver fibrosis model, demonstrating the reliability of the prediction results. In summary, our findings will not only impact the future applications of LINCS data but also offer new opportunities for therapeutic intervention of liver cancer.

Repurposing – second life for drugs

Drug repurposing refers to finding new indications for existing drugs. The paradigm shift from traditional drug discovery to drug repurposing is driven by the fact that new drug pipelines are getting dried up because of mounting Research & Development (R&D) costs, long timeline for new drug development, low success rate for new molecular entities, regulatory hurdles coupled with revenue loss from patent expiry and competition from generics. Anaemic drug pipelines along with increasing demand for newer effective, cheaper, safer drugs and unmet medical needs call for new strategies of drug discovery and, drug repurposing seems to be a promising avenue for such endeavours. Drug repurposing strategies have progressed over years from simple serendipitous observations to more complex computational methods in parallel with our ever-growing knowledge on drugs, diseases, protein targets and signalling pathways but still the knowledge is far from complete. Repurposed drugs too have to face many obstacles, although lesser than new drugs, before being successful.

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.

System and network biology-based computational approaches for drug repositioning

Recent advances in computational biology have not only fastened the drug discovery process but have also proven to be a powerful tool for the search of existing molecules of therapeutic value for drug repurposing. The system biology-based drug repurposing approaches shorten the time and reduced the cost of the whole process when compared to de novo drug discovery. In the present pandemic situation, these computational approaches have emerged as a boon to tackle the COVID-19 associated morbidities and mortalities. In this chapter, we present the overview of system biology-based network system approaches which can be exploited for the drug repurposing of disease. Besides, we have included information on relevant repurposed drugs which are currently used for the treatment of COVID-19.

AI-powered drug repurposing for developing COVID-19 treatments

Emerging infectious diseases are an ever-present threat to public health, and COVID-19 is the most recent example. There is an urgent need to develop a robust framework to combat the disease with safe and effective therapeutic options. Compared to de novo drug discovery, drug repurposing may offer a lower-cost and faster drug discovery paradigm to explore potential treatment options of existing drugs. This chapter elucidates the advantages of artificial intelligence (AI) in enhancing the drug repurposing process from a data science perspective, using COVID-19 as an example. First, we elaborate on how AI-powered drug repurposing benefits from the accumulated data and knowledge of COVID-19 natural history and pathogenesis. Second, we summarize the pros and cons of AI-powered drug repurposing strategies to facilitate fit-for-purpose selection. Finally, we outline challenges of AI-powered drug repurposing from a regulatory perspective and suggest some potential solutions.

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AI-powered drug purposing is promising for emerging treatments for COVID-19 infection.

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Accumulated biological data profiles facilitate AI-based drug repurposing efforts for development of COVID-19 therapies.

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The ‘fit-for-purpose selection of AI-powered drug repurposing strategies is key to uncovering hidden information among drugs, targets, and diseases.

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Efforts from different stakeholders boost the adoption of AI-powered drug repurposing in the regulatory setting.

A Review of the Current Landscape of SARS-CoV-2 Main Protease Inhibitors: Have We Hit the Bullseye Yet?

In this review, we collected 1765 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) M-pro inhibitors from the bibliography and other sources, such as the COVID Moonshot project and the ChEMBL database. This set of inhibitors includes only those compounds whose inhibitory capacity, mainly expressed as the half-maximal inhibitory concentration (IC50) value, against M-pro from SARS-CoV-2 has been determined. Several covalent warheads are used to treat covalent and non-covalent inhibitors separately. Chemical space, the variation of the IC50 inhibitory activity when measured by different methods or laboratories, and the influence of 1,4-dithiothreitol (DTT) are discussed. When available, we have collected the values of inhibition of viral replication measured with a cellular antiviral assay and expressed as half maximal effective concentration (EC50) values, and their possible relationship to inhibitory potency against M-pro is analyzed. Finally, the most potent covalent and non-covalent inhibitors that simultaneously inhibit the SARS-CoV-2 M-pro and the virus replication in vitro are discussed.

A data-driven methodology towards evaluating the potential of drug repurposing hypotheses

Drug repurposing has become a widely used strategy to accelerate the process of finding treatments. While classical de novo drug development involves high costs, risks, and time-consuming paths, drug repurposing allows to reuse already-existing and approved drugs for new indications. Numerous research has been carried out in this field, both in vitro and in silico. Computational drug repurposing methods make use of modern heterogeneous biomedical data to identify and prioritize new indications for old drugs. In the current paper, we present a new complete methodology to evaluate new potentially repurposable drugs based on disease-gene and disease-phenotype associations, identifying significant differences between repurposing and non-repurposing data. We have collected a set of known successful drug repurposing case studies from the literature and we have analysed their dissimilarities with other biomedical data not necessarily participating in repurposing processes. The information used has been obtained from the DISNET platform. We have performed three analyses (at the genetical, phenotypical, and categorization levels), to conclude that there is a statistically significant difference between actual repurposing-related information and non-repurposing data. The insights obtained could be relevant when suggesting new potential drug repurposing hypotheses.

Computational Drug Repurposing: Classification of the Research Opportunities and Challenges

BACKGROUND

Drug repurposing has grown significantly in recent years. Research and innovation in drug repurposing are extremely popular due to its practical and explicit advantages. However, its adoption into practice is slow because researchers and industries have to face various challenges.

OBJECTIVE

As this field, there is a lack of a comprehensive platform for systematic identification for removing development limitations. This paper deals with a comprehensive classification of challenges in drug repurposing.

METHODS

Initially, a classification of various existing repurposing models is propounded. Next, the benefits of drug repurposing are summarized. Further, a categorization for computational drug repurposing shortcomings is presented. Finally, the methods are evaluated based on their strength to addressing the drawbacks.

RESULTS

This work can offer a desirable platform for comparing the computational repurposing methods by measuring the methods in light of these challenges.

CONCLUSION

A proper comparison could prepare guidance for a genuine understanding of methods. Accordingly, this comprehension of the methods will help researchers eliminate the barriers thereby developing and improving methods. Furthermore, in this study, we conclude why despite all the benefits of drug repurposing, it is not being done anymore.

Drug repurposing and computational modeling for discovery of inhibitors of the main protease (Mpro) of SARS-CoV-2

The main protease (Mpro or 3CLpro) is a conserved cysteine protease from the coronaviruses and started to be considered an important drug target for developing antivirals, as it produced a deadly outbreak of COVID-19. Herein, we used a combination of drug reposition and computational modeling approaches including molecular docking, molecular dynamics (MD) simulations, and the calculated binding free energy to evaluate a set of drugs in complex with the Mpro enzyme. Particularly, our results show that darunavir and triptorelin drugs have favorable binding free energy (-63.70 and -77.28 kcal mol-1, respectively) in complex with the Mpro enzyme. Based on the results, the structural and energetic features that explain why some drugs can be repositioned to inhibit Mpro from SARS-CoV-2 were exposed. These features should be considered for the design of novel Mpro inhibitors.

Drug Repurposing Using Biological Networks

Drug repositioning is a strategy to identify new uses for existing, approved, or research drugs that are outside the scope of its original medical indication. Drug repurposing is based on the fact that one drug can act on multiple targets or that two diseases can have molecular similarities, among others. Currently, thanks to the rapid advancement of high-performance technologies, a massive amount of biological and biomedical data is being generated. This allows the use of computational methods and models based on biological networks to develop new possibilities for drug repurposing. Therefore, here, we provide an in-depth review of the main applications of drug repositioning that have been carried out using biological network models. The goal of this review is to show the usefulness of these computational methods to predict associations and to find candidate drugs for repositioning in new indications of certain diseases.

A Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning

Identification of drug-target interactions (DTIs) is a significant step in the drug discovery or repositioning process. Compared with the time-consuming and labor-intensive in vivo experimental methods, the computational models can provide high-quality DTI candidates in an instant. In this study, we propose a novel method called LGDTI to predict DTIs based on large-scale graph representation learning. LGDTI can capture the local and global structural information of the graph. Specifically, the first-order neighbor information of nodes can be aggregated by the graph convolutional network (GCN); on the other hand, the high-order neighbor information of nodes can be learned by the graph embedding method called DeepWalk. Finally, the two kinds of feature are fed into the random forest classifier to train and predict potential DTIs. The results show that our method obtained area under the receiver operating characteristic curve (AUROC) of 0.9455 and area under the precision-recall curve (AUPR) of 0.9491 under 5-fold cross-validation. Moreover, we compare the presented method with some existing state-of-the-art methods. These results imply that LGDTI can efficiently and robustly capture undiscovered DTIs. Moreover, the proposed model is expected to bring new inspiration and provide novel perspectives to relevant researchers.

Explainable artificial intelligence in high-throughput drug repositioning for subgroup stratifications with interventionable potential

Enabling precision medicine requires developing robust patient stratification methods as well as drugs tailored to homogeneous subgroups of patients from a heterogeneous population. Developing de novo drugs is expensive and time consuming with an ultimately low FDA approval rate. These limitations make developing new drugs for a small portion of a disease population unfeasible. Therefore, drug repositioning is an essential alternative for developing new drugs for a disease subpopulation. This shows the importance of developing data-driven approaches that find druggable homogeneous subgroups within the disease population and reposition the drugs for these subgroups. In this study, we developed an explainable AI approach for patient stratification and drug repositioning. Contrast pattern mining and network analysis were used to discover homogeneous subgroups within a disease population. For each subgroup, a biomedical network analysis was done to find the drugs that are most relevant to a given subgroup of patients. The set of candidate drugs for each subgroup was ranked using an aggregated drug score assigned to each drug. The proposed method represents a human-in-the-loop framework, where medical experts use the data-driven results to generate hypotheses and obtain insights into potential therapeutic candidates for patients who belong to a subgroup. Colorectal cancer (CRC) was used as a case study. Patients' phenotypic and genotypic data was utilized with a heterogeneous knowledge base because it gives a multi-view perspective for finding new indications for drugs outside of their original use. Our analysis of the top candidate drugs for the subgroups identified by medical experts showed that most of these drugs are cancer-related, and most of them have the potential to be a CRC regimen based on studies in the literature.

Computational Drug Repositioning and Experimental Validation of Ivermectin in Treatment of Gastric Cancer

Objective: The aim of the present study was repositioning of ivermectin in treatment of gastric cancer (GC) by computational prediction based on gene expression profiles of human and mouse model of GC and validations with in silico, in vitro and in vivo approaches.

Methods: Computational drug repositioning was performed using connectivity map (cMap) and data/pathway mining with the Ingenuity Knowledge Base. Tissue samples of GC were collected from 16 patients and 57 mice for gene expression profiling. Additional seven independent datasets of gene expression of human GC from the TCGA database were used for validation. In silico testing was performed by constructing interaction networks of ivermectin and the downstream effects in targeted signaling pathways. In vitro testing was carried out in human GC cell lines (MKN74 and KATO-III). In vivo testing was performed in a transgenic mouse model of GC (INS-GAS mice).

Results: GC gene expression “signature” and data/pathway mining but not cMAP revealed nine molecular targets of ivermectin in both human and mouse GC associated with WNT/β-catenin signaling as well as cell proliferation pathways. In silico inhibition of the targets of ivermectin and concomitant activation of ivermectin led to the inhibition of WNT/β-catenin signaling pathway in “dose-depended” manner. In vitro, ivermectin inhibited cell proliferation in time- and concentration-depended manners, and cells were arrested in the G₁ phase at IC₅₀ and shifted to S phase arrest at >IC₅₀. In vivo, ivermectin reduced the tumor size which was associated with inactivation of WNT/β-catenin signaling and cell proliferation pathways and activation of cell death signaling pathways.

Conclusion: Ivermectin could be recognized as a repositioning candidate in treatment of gastric cancer.

Drug Repurposing: An Emerging Tool for Drug Reuse, Recycling and Discovery

Drug repositioning or repurposing is a revolutionary breakthrough in drug development that focuses on rediscovering new uses for old therapeutic agents. Drug repositioning can be defined more precisely as the process of exploring new indications for an already approved drug while drug repurposing includes overall re-development approaches grounded in the identical chemical structure of the active drug moiety as in the original product. The repositioning approach accelerates the drug development process, curtails the cost and risk inherent to drug development. The strategy focuses on the polypharmacology of drugs to unlocks novel opportunities for logically designing more efficient therapeutic agents for unmet medical disorders. Drug repositioning also expresses certain regulatory challenges that hamper its further utilization. The review outlines the eminent role of drug repositioning in new drug discovery, methods to predict the molecular targets of a drug molecule, advantages that the strategy offers to the pharmaceutical industries, explaining how the industrial collaborations with academics can assist in the discovering more repositioning opportunities. The focus of the review is to highlight the latest applications of drug repositioning in various disorders. The review also includes a comparison of old and new therapeutic uses of repurposed drugs, assessing their novel mechanisms of action and pharmacological effects in the management of various disorders. Various restrictions and challenges that repurposed drugs come across during their development and regulatory phases are also highlighted.

Repurposing of renin inhibitors as SARS-COV-2 main protease inhibitors: A computational study

The COVID-19 pandemic has urged for the repurposing of existing drugs for rapid management and treatment. Renin inhibitors down regulation of ACE2, which is an essential receptor for SARS-CoV-2 infection that is responsible for COVID-19, in addition to their ability to act as protease inhibitors were encouraging aspects for their investigation as possible inhibitors of main protease of SARS-CoV-2 via computational studies. A Pharmacophore model was generated using the newly released SARS-COV-2 main protease inhibitors. Virtual screening was performed on renin inhibitors, and Drug likeness filter identified remikiren and 0IU as hits. Molecular docking for both compounds showed that the orally active renin inhibitor remikiren (Ro 42–5892) of Hoffmann–La Roche exhibited good molecular interaction with Cys145 and His41 in the catalytic site of SARS-CoV-2 main protease. Molecular dynamics simulation suggested that the drug is stable in the active site of the enzyme.

Searching for target-specific and multi-targeting organics for Covid-19 in the Drugbank database with a double scoring approach

The current outbreak of Covid-19 infection due to SARS-CoV-2, a virus from the coronavirus family, has become a major threat to human healthcare. The virus has already infected more than 44 M people and the number of deaths reported has reached more than 1.1 M which may be attributed to lack of medicine. The traditional drug discovery approach involves many years of rigorous research and development and demands for a huge investment which cannot be adopted for the ongoing pandemic infection. Rather we need a swift and cost-effective approach to inhibit and control the viral infection. With the help of computational screening approaches and by choosing appropriate chemical space, it is possible to identify lead drug-like compounds for Covid-19. In this study, we have used the Drugbank database to screen compounds against the most important viral targets namely 3C-like protease (3CLpro), papain-like protease (PLpro), RNA-dependent RNA polymerase (RdRp) and the spike (S) protein. These targets play a major role in the replication/transcription and host cell recognition, therefore, are vital for the viral reproduction and spread of infection. As the structure based computational screening approaches are more reliable, we used the crystal structures for 3C-like main protease and spike protein. For the remaining targets, we used the structures based on homology modeling. Further, we employed two scoring methods based on binding free energies implemented in AutoDock Vina and molecular mechanics-generalized Born surface area approach. Based on these results, we propose drug cocktails active against the three viral targets namely 3CLpro, PLpro and RdRp. Interestingly, one of the identified compounds in this study i.e. Baloxavir marboxil has been under clinical trial for the treatment of Covid-19 infection. In addition, we have identified a few compounds such as Phthalocyanine, Tadalafil, Lonafarnib, Nilotinib, Dihydroergotamine, R-428 which can bind to all three targets simultaneously and can serve as multi-targeting drugs. Our study also included calculation of binding energies for various compounds currently under drug trials. Among these compounds, it is found that Remdesivir binds to targets, 3CLpro and RdRp with high binding affinity. Moreover, Baricitinib and Umifenovir were found to have superior target-specific binding while Darunavir is found to be a potential multi-targeting drug. As far as we know this is the first study where the compounds from the Drugbank database are screened against four vital targets of SARS-CoV-2 and illustrates that the computational screening using a double scoring approach can yield potential drug-like compounds against Covid-19 infection.

Uncovering New Drug Properties in Target-Based Drug-Drug Similarity Networks

Despite recent advances in bioinformatics, systems biology, and machine learning, the accurate prediction of drug properties remains an open problem. Indeed, because the biological environment is a complex system, the traditional approach—based on knowledge about the chemical structures—can not fully explain the nature of interactions between drugs and biological targets. Consequently, in this paper, we propose an unsupervised machine learning approach that uses the information we know about drug–target interactions to infer drug properties. To this end, we define drug similarity based on drug–target interactions and build a weighted Drug–Drug Similarity Network according to the drug–drug similarity relationships. Using an energy-model network layout, we generate drug communities associated with specific, dominant drug properties. DrugBank confirms the properties of 59.52% of the drugs in these communities, and 26.98% are existing drug repositioning hints we reconstruct with our DDSN approach. The remaining 13.49% of the drugs seem not to match the dominant pharmacologic property; thus, we consider them potential drug repurposing hints. The resources required to test all these repurposing hints are considerable. Therefore we introduce a mechanism of prioritization based on the betweenness/degree node centrality. Using betweenness/degree as an indicator of drug repurposing potential, we select Azelaic acid and Meprobamate as a possible antineoplastic and antifungal, respectively. Finally, we use a test procedure based on molecular docking to analyze Azelaic acid and Meprobamate’s repurposing.

Exploring the SARS-CoV-2 Proteome in the Search of Potential Inhibitors via Structure-Based Pharmacophore Modeling/Docking Approach

To date, SARS-CoV-2 infectious disease, named COVID-19 by the World Health Organization (WHO) in February 2020, has caused millions of infections and hundreds of thousands of deaths. Despite the scientific community efforts, there are currently no approved therapies for treating this coronavirus infection. The process of new drug development is expensive and time-consuming, so that drug repurposing may be the ideal solution to fight the pandemic. In this paper, we selected the proteins encoded by SARS-CoV-2 and using homology modeling we identified the high-quality model of proteins. A structure-based pharmacophore modeling study was performed to identify the pharmacophore features for each target. The pharmacophore models were then used to perform a virtual screening against the DrugBank library (investigational, approved and experimental drugs). Potential inhibitors were identified for each target using XP docking and induced fit docking. MM-GBSA was also performed to better prioritize potential inhibitors. This study will provide new important comprehension of the crucial binding hot spots usable for further studies on COVID-19. Our results can be used to guide supervised virtual screening of large commercially available libraries.

Computational Chemogenomics Drug Repositioning Strategy Enables the Discovery of Epirubicin as a New Repurposed Hit for Plasmodium falciparum and P. vivax

Widespread resistance against antimalarial drugs thwarts current efforts for controlling the disease and urges the discovery of new effective treatments. Drug repositioning is increasingly becoming an attractive strategy since it can reduce costs, risks, and time-to-market. Herein, we have used this strategy to identify novel antimalarial hits. We used a comparative in silico chemogenomics approach to select Plasmodium falciparum and Plasmodium vivax proteins as potential drug targets and analyzed them using a computer-assisted drug repositioning pipeline to identify approved drugs with potential antimalarial activity.

Widespread resistance against antimalarial drugs thwarts current efforts for controlling the disease and urges the discovery of new effective treatments. Drug repositioning is increasingly becoming an attractive strategy since it can reduce costs, risks, and time-to-market. Herein, we have used this strategy to identify novel antimalarial hits. We used a comparative in silico chemogenomics approach to select Plasmodium falciparum and Plasmodium vivax proteins as potential drug targets and analyzed them using a computer-assisted drug repositioning pipeline to identify approved drugs with potential antimalarial activity. Among the seven drugs identified as promising antimalarial candidates, the anthracycline epirubicin was selected for further experimental validation. Epirubicin was shown to be potent in vitro against sensitive and multidrug-resistant P. falciparum strains and P. vivax field isolates in the nanomolar range, as well as being effective against an in vivo murine model of Plasmodium yoelii. Transmission-blocking activity was observed for epirubicin in vitro and in vivo. Finally, using yeast-based haploinsufficiency chemical genomic profiling, we aimed to get insights into the mechanism of action of epirubicin. Beyond the target predicted in silico (a DNA gyrase in the apicoplast), functional assays suggested a GlcNac-1-P-transferase (GPT) enzyme as a potential target. Docking calculations predicted the binding mode of epirubicin with DNA gyrase and GPT proteins. Epirubicin is originally an antitumoral agent and presents associated toxicity. However, its antiplasmodial activity against not only P. falciparum but also P. vivax in different stages of the parasite life cycle supports the use of this drug as a scaffold for hit-to-lead optimization in malaria drug discovery.

Repurposing of Drugs Is a Viable Approach to Develop Therapeutic Strategies against Central Nervous System Related Pathogenic Amoebae

Brain-eating amoebae including Acanthamoeba spp., Naegleria fowleri, and Balamuthia mandrillaris cause rare infections of the central nervous system that almost always result in death. The high mortality rate, lack of interest for drug development from pharmaceutical industries, and no available effective drugs present an alarming challenge. The current drugs employed in the management and therapy of these devastating diseases are amphotericin B, miltefosine, chlorhexidine, pentamidine, and voriconazole which are generally used in combination. However, clinical evidence shows that these drugs have limited efficacy and high host cell cytotoxicity. Repurposing of drugs is a practical approach to utilize commercially available, U.S. Food and Drug Administration approved drugs for one disease against rare diseases caused by brain-eating amoebae. In this Perspective, we highlight some of the success stories of drugs repositioned against neglected parasitic diseases and identify future potential for effective and sustainable drug development against brain-eating amoebae infections.

Network perturbation analysis in human bronchial epithelial cells following SARS-CoV2 infection

Background

SARS-CoV2, the agent responsible for the current pandemic, is also causing respiratory distress syndrome (RDS), hyperinflammation and high mortality. It is critical to dissect the pathogenetic mechanisms in order to reach a targeted therapeutic approach.

Methods

In the present investigation, we evaluated the effects of SARS-CoV₂ on human bronchial epithelial cells (HBEC). We used RNA-seq datasets available online for identifying SARS-CoV₂ potential genes target on human bronchial epithelial cells. RNA expression levels and potential cellular gene pathways have been analyzed. In order to identify possible common strategies among the main pandemic viruses, such as SARS-CoV₂, SARS-CoV1, MERS-CoV, and H1N1, we carried out a hypergeometric test of the main genes transcribed in the cells of the respiratory tract exposed to these viruses.

Results

The analysis showed that two mechanisms are highly regulated in HBEC: the innate immunity recruitment and the disassembly of cilia and cytoskeletal structure. The granulocyte colony-stimulating factor (CSF3) and dynein heavy chain 7, axonemal (DNAH7) represented respectively the most upregulated and downregulated genes belonging to the two mechanisms highlighted above. Furthermore, the carcinoembryonic antigen-related cell adhesion molecule 7 (CEACAM7) that codifies for a surface protein is highly specific of SARS-CoV₂ and not for SARS-CoV1, MERS-CoV, and H1N1, suggesting a potential role in viral entry. In order to identify potential new drugs, using a machine learning approach, we highlighted Flunisolide, Thalidomide, Lenalidomide, Desoximetasone, xylazine, and salmeterol as potential drugs against SARS-CoV₂ infection.

Conclusions

Overall, lung involvement and RDS could be generated by the activation and down regulation of diverse gene pathway involving respiratory cilia and muscle contraction, apoptotic phenomena, matrix destructuration, collagen deposition, neutrophil and macrophages recruitment.

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SARS-CoV2 causing respiratory distress syndrome, hyperinflammation and high mortality.

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In NHBEC, SARS-CoV2 highly regulated the innate immunity recruitment and the disassembly of cilia and cytoskeletal structure.

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The granulocyte colony-stimulating factor (CSF3) is the most upregulated gene by SARS-CoV2.

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The dynein heavy chain 7, axonemal (DNAH7) represented the most downregulated genes by SARS-CoV2.

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Flunisolide, Thalidomide, Lenalidomide, Desoximetasone, xylazine, and salmeterol as potential drugs against SARS-CoV-2.

Turning genome-wide association study findings into opportunities for drug repositioning

Drug development is a very costly and lengthy process, while repositioned or repurposed drugs could be brought into clinical practice within a shorter time-frame and at a much reduced cost. Numerous computational approaches to drug repositioning have been developed, but methods utilizing genome-wide association studies (GWASs) data are less explored.

The past decade has observed a massive growth in the amount of data from GWAS; the rich information contained in GWAS has great potential to guide drug repositioning or discovery. While multiple tools are available for finding the most relevant genes from GWAS hits, searching for top susceptibility genes is only one way to guide repositioning, which has its own limitations.

Here we provide a comprehensive review of different computational approaches that employ GWAS data to guide drug repositioning. These methods include selecting top candidate genes from GWAS as drug targets, deducing drug candidates based on drug-drug and disease-disease similarities, searching for reversed expression profiles between drugs and diseases, pathway-based methods as well as approaches based on analysis of biological networks. Each method is illustrated with examples, and their respective strengths and limitations are discussed. We also discussed several areas for future research.

Prediction of Novel Inhibitors of the Main Protease (M-pro) of SARS-CoV-2 through Consensus Docking and Drug Reposition

Since the outbreak of the COVID-19 pandemic in December 2019 and its rapid spread worldwide, the scientific community has been under pressure to react and make progress in the development of an effective treatment against the virus responsible for the disease. Here, we implement an original virtual screening (VS) protocol for repositioning approved drugs in order to predict which of them could inhibit the main protease of the virus (M-pro), a key target for antiviral drugs given its essential role in the virus' replication. Two different libraries of approved drugs were docked against the structure of M-pro using Glide, FRED and AutoDock Vina, and only the equivalent high affinity binding modes predicted simultaneously by the three docking programs were considered to correspond to bioactive poses. In this way, we took advantage of the three sampling algorithms to generate hypothetic binding modes without relying on a single scoring function to rank the results. Seven possible SARS-CoV-2 M-pro inhibitors were predicted using this approach: Perampanel, Carprofen, Celecoxib, Alprazolam, Trovafloxacin, Sarafloxacin and ethyl biscoumacetate. Carprofen and Celecoxib have been selected by the COVID Moonshot initiative for in vitro testing; they show 3.97 and 11.90% M-pro inhibition at 50 µM, respectively.

Drug rechanneling: A novel paradigm for cancer treatment

Cancer continues to be one of the leading contributors towards global disease burden. According to NIH, cancer incidence rate per year will increase to 23.6 million by 2030. Even though cancer continues to be a major proportion of the disease burden worldwide, it has the lowest clinical trial success rate amongst other diseases. Hence, there is an unmet need for novel, affordable and effective anti-neoplastic medications. As a result, a growing interest has sparkled amongst researchers towards drug repurposing. Drug repurposing follows the principle of polypharmacology, which states, "any drug with multiple targets or off targets can present several modes of action". Drug repurposing also known as drug rechanneling, or drug repositioning is an economic and reliable approach that identifies new disease treatment of already approved drugs. Repurposing guarantees expedited access of drugs to the patients as these drugs are already FDA approved and their safety and toxicity profile is completely established. Epidemiological studies have identified the decreased occurrence of oncological or non-oncological conditions in patients undergoing treatment with FDA approved drugs. Data from multiple experimental studies and clinical observations have depicted that several non-neoplastic drugs have potential anticancer activity. In this review, we have summarized the potential anti-cancer effects of anti-psychotic, anti-malarial, anti-viral and anti-emetic drugs with a brief overview on their mechanism and pathways in different cancer types. This review highlights promising evidences for the repurposing of drugs in oncology.

N-p-Tosyl-L-phenylalanine chloromethyl ketone (TPCK) inhibits HIV-1 by suppressing the activity of viral protease

Human Immunodeficiency Virus (HIV), the etiological agent for Acquired Immunodeficiency Syndrome (AIDS), continues to kill humans despite stupendous advances in antiviral research. With the presently available combination antiretroviral therapeutic arsenal, AIDS is now a manageable disease but with no cure available till date. The development of novel antivirals consumes an extensive amount of time and resources. Hence, repurposing of the established gold standard molecules for their anti-HIV application is enormously advantageous. In this study, we report that N-p-Tosyl-L-phenylalanine chloromethyl ketone (TPCK) inhibits HIV-1 replication in a highly-conserved manner. Further, TPCK inhibits HIV-1 replication at the late stages of its life cycle by impeding viral protease (PR) enzyme activity. Additionally, our results demonstrate that the combination of TPCK with established HIV-1 PR inhibitors shows significant synergistic inhibitory potential, suggesting the potential use of TPCK in cART regimen. Collectively, we report the anti-HIV activity of TPCK, which should be further characterized for its translational applications.

Rational drug repurposing for cancer by inclusion of the unbiased molecular dynamics simulation in the structure-based virtual screening approach: Challenges and breakthroughs

Managing cancer is now one of the biggest concerns of health organizations. Many strategies have been developed in drug discovery pipelines to help rectify this problem and two of the best ones are drug repurposing and computational methods. The combination of these approaches can have immense impact on the course of drug discovery. In silico drug repurposing can significantly reduce the time, the cost and the effort of drug development. Computational methods such as structure-based drug design (SBDD) and virtual screening can predict the potentials of small molecule binders, such as drugs, for having favorable effect on a particular molecular target. However, the demand for accuracy and efficiency of SBDD requires more sophisticated and complicated approaches such as unbiased molecular dynamics (UMD) simulation that has been recently introduced. As a complementary strategy, the knowledge acquired from UMD simulations can increase the chance of finding the right candidates and the pipeline of its administration is introduced and discussed in this review. An elaboration of this pipeline is also made by detailing an example, the binding and unbinding pathways of dasatinib-c-Src kinase complex, which shows that how influential this method can be in rational drug repurposing in cancer treatment.

Drug repurposing against Parkinson's disease by text mining the scientific literature

Purpose

Drug repurposing involves the identification of new applications for existing drugs. Owing to the enormous rise in the costs of pharmaceutical R&D, several pharmaceutical companies are leveraging repurposing strategies. Parkinson's disease is the second most common neurodegenerative disorder worldwide, affecting approximately 1–2 percent of the human population older than 65 years. This study proposes a literature-based drug repurposing strategy in Parkinson's disease.

Design/methodology/approach

The literature-based drug repurposing strategy proposed herein combined natural language processing, network science and machine learning methods for analyzing unstructured text data and producing actional knowledge for drug repurposing. The approach comprised multiple computational components, including the extraction of biomedical entities and their relationships, knowledge graph construction, knowledge representation learning and machine learning-based prediction.

Findings

The proposed strategy was used to mine information pertaining to the mechanisms of disease treatment from known treatment relationships and predict drugs for repurposing against Parkinson's disease. The F1 score of the best-performing method was 0.97, indicating the effectiveness of the proposed approach. The study also presents experimental results obtained by combining the different components of the strategy.

Originality/value

The drug repurposing strategy proposed herein for Parkinson's disease is distinct from those existing in the literature in that the drug repurposing pipeline includes components of natural language processing, knowledge representation and machine learning for analyzing the scientific literature. The results of the study provide important and valuable information to researchers studying different aspects of Parkinson's disease.

Dipeptide Frequency of Word Frequency and Graph Convolutional Networks for DTA Prediction

Deep learning is an effective method to capture drug-target binding affinity, but low accuracy is still an obstacle to be overcome. Thus, we propose a novel predictor for drug-target binding affinity based on dipeptide frequency of word frequency encoding and a hybrid graph convolutional network. Word frequency characteristics of natural language are used to improve the frequency characteristics of peptides to express target proteins. For each drug molecules, the five different features of drug atoms and the atomic bond relationships are expressed as graphs. The obtained protein features and graph structure are used as the input of convolution neural network and the input of graph convolution neural network, respectively. A prediction model is established to predict the drug affinity by calculating the hidden relationship. In the KIBA data set test experiment, the consistency coefficient of the model is 0.901, which is 0.01 higher than the existing model, and the MSE (mean square error) of the model is 0.126, which is 5% lower than the existing model. In Davis data set test experiment, the consistency coefficient of the model is 0.895, which is 0.006 higher than the existing model, and the MSE of the model is 0.220, which is 4% lower than the existing model. These results show that our proposed method can not only predict the affinity better than those existing models, but also outperform unitary deep learning approaches.

Exploratory Analysis of iPSCS-Derived Neuronal Cells as Predictors of Diagnosis and Treatment of Alzheimer Disease

Alzheimer’s disease (AD) represents the most common neurodegenerative disorder, with 47 million affected people worldwide. Current treatment strategies are aimed at reducing the symptoms and do slow down the progression of the disease, but inevitably fail in the long-term. Induced pluripotent stem cells (iPSCs)-derived neuronal cells from AD patients have proven to be a reliable model for AD pathogenesis. Here, we have conducted an in silico analysis aimed at identifying pathogenic gene-expression profiles and novel drug candidates. The GSE117589 microarray dataset was used for the identification of Differentially Expressed Genes (DEGs) between iPSC-derived neuronal progenitor (NP) cells and neurons from AD patients and healthy donors. The Discriminant Analysis Module (DAM) algorithm was used for the identification of biomarkers of disease. Drugs with anti-signature gene perturbation profiles were identified using the L1000FWD software. DAM analysis was used to identify a list of potential biomarkers among the DEGs, able to discriminate AD patients from healthy people. Finally, anti-signature perturbation analysis identified potential anti-AD drugs. This study set the basis for the investigation of potential novel pharmacological strategies for AD. Furthermore, a subset of genes for the early diagnosis of AD is proposed.

Repurposing Clinical Drugs as AdoMetDC Inhibitors Using the SCAR Strategy

With the escalating costs in drug development, discovering new uses of approved drugs, i.e., drug repurposing, has attracted increasing interest. Spermidine and spermine are important polyamines for most cells and their biosynthesis are strictly regulated by the polyamine metabolic network. In cancerous cells and tumor environments, the concentrations of polyamines are much higher than in normal cells. During the synthesis of spermidine and spermine, an amino-propyl group is provided by decarboxylated S-adenosylmethionine, and the latter is generated from S-adenosylmethionine by AdoMetDC (AdoMet decarboxylase). Therefore, as a rate-limiting enzyme in the biosynthesis of spermidine and spermine, AdoMetDC has been an attractive drug target in cancer studies. In the last decades, many AdoMetDC inhibitors have been discovered, and several AdoMetDC inhibitors are under clinical trials, but unfortunately, none of them have been approved yet. To overcome the high costs in time and money for discovering de novo inhibitors, we set out to repurpose clinic drugs as AdoMetDC inhibitors. We used steric-clashes alleviating receptors (SCAR), a computer-aided drug discovery strategy developed by us recently for in silico screening. By combining computational screening and experimental validation, we successfully identified two approved drugs that have inhibitory potency on AdoMetDC's enzymatic activity. SCAR was previously shown to be suitable for the discovery of both covalent and non-covalent inhibitors, and this work further demonstrated the value of the SCAR strategy in drug repurposing.

Drug repositioning in cancer: The current situation in Japan

Cancer is a leading cause of death worldwide, and the incidence continues to increase. Despite major research aimed at discovering and developing novel and effective anticancer drugs, oncology drug development is a lengthy and costly process, with high attrition rates. Drug repositioning (DR, also referred to as drug repurposing), the process of finding new uses for approved noncancer drugs, has been gaining popularity in the past decade. DR has become a powerful alternative strategy for discovering and developing novel anticancer drug candidates from the existing approved drug space. Indeed, the availability of several large established libraries of clinical drugs and rapid advances in disease biology, genomics/transcriptomics/proteomics and bioinformatics has accelerated the pace of activity‐based, literature‐based and in silico DR, thereby improving safety and reducing costs. However, DR still faces financial obstacles in clinical trials, which could limit its practical use in the clinic. Here, we provide a brief review of DR in cancer and discuss difficulties in the development of DR for clinical use. Furthermore, we introduce some promising DR candidates for anticancer therapy in Japan.

ACID: a free tool for drug repurposing using consensus inverse docking strategy

Drug repurposing offers a promising alternative to dramatically shorten the process of traditional de novo development of a drug. These efforts leverage the fact that a single molecule can act on multiple targets and could be beneficial to indications where the additional targets are relevant. Hence, extensive research efforts have been directed toward developing drug based computational approaches. However, many drug based approaches are known to incur low successful rates, due to incomplete modeling of drug-target interactions. There are also many technical limitations to transform theoretical computational models into practical use. Drug based approaches may, thus, still face challenges for drug repurposing task. Upon this challenge, we developed a consensus inverse docking (CID) workflow, which has a ~ 10% enhancement in success rate compared with current best method. Besides, an easily accessible web server named auto in silico consensus inverse docking (ACID) was designed based on this workflow (http://chemyang.ccnu.edu.cn/ccb/server/ACID).

Exploring the new horizons of drug repurposing: A vital tool for turning hard work into smart work

Drug discovery and development are long and financially taxing processes. On an average it takes 12-15 years and costs 1.2 billion USD for successful drug discovery and approval for clinical use. Many lead molecules are not developed further and their potential is not tapped to the fullest due to lack of resources or time constraints. In order for a drug to be approved by FDA for clinical use, it must have excellent therapeutic potential in the desired area of target with minimal toxicities as supported by both pre-clinical and clinical studies. The targeted clinical evaluations fail to explore other potential therapeutic applications of the candidate drug. Drug repurposing or repositioning is a fast and relatively cheap alternative to the lengthy and expensive de novo drug discovery and development. Drug repositioning utilizes the already available clinical trials data for toxicity and adverse effects, at the same time explores the drug's therapeutic potential for a different disease. This review addresses recent developments and future scope of drug repositioning strategy.