Computational Drug Repurposing Research Articles

Repurposing of phytomedicine-derived bioactive compounds with promising anti-SARS-CoV-2 potential: Molecular docking, MD simulation and drug-likeness/ADMET studies

In view of the potential of traditional plant-based remedies (or phytomedicines) in the management of COVID-19, the present investigation was aimed at finding novel anti-SARS-CoV-2 molecules by in silico screening of bioactive phytochemicals (database) using computational methods and drug repurposing approach. A total of 160 compounds belonging to various phytochemical classes (flavonoids, limonoids, saponins, triterpenoids, steroids etc.) were selected (as initial hits) and screened against three specific therapeutic targets (Mpro/3CLpro, PLpro and RdRp) of SARS-CoV-2 by docking, molecular dynamics simulation and drug-likeness/ADMET studies. From our studies, six phytochemicals were identified as notable ant-SARS-CoV-2 agents (best hit molecules) with promising inhibitory effects effective against protease (Mpro and PLpro) and polymerase (RdRp) enzymes. These compounds are namely, ginsenoside Rg2, saikosaponin A, somniferine, betulinic acid, soyasapogenol C and azadirachtin A. On the basis of binding modes and dynamics studies of protein–ligand intercations, ginsenoside Rg2, saikosaponin A, somniferine were found to be the most potent (in silico) inhibitors potentially active against Mpro, PLpro and RdRp, respectively. The present investigation can be directed towards further experimental studies in order to confirm the anti-SARS-CoV-2 efficacy along with toxicities of identified phytomolecules.

Computational drug repurposing of bethanidine for SENP1 inhibition in cardiovascular diseases treatment

AimsBethanidine (BW467C60) is a newly presented strong adrenergic neuron blocking factor which has a hypotensive operation in man. SENPs are essential for maintaining a balance between SUMOylation and deSUMOylation which can be disturbed by changing the expression of (sentrin-specific proteases) SENPs. SENP1 is the most studied isoform of SENPs. Hypertrophic stimuli can increase SENP1 expression using calcium/calcineurin-NFAT3 signaling in heart. Moreover, SENP1 expression may positively relate to the expression of mitochondrial genes of the heart, and can cause the heart and mitochondrial dysfunction. Materials and methodsIn order to inhibit SENP1 using Bethanidine, molecular docking and molecular dynamics (MD) simulation of SENP1 with Bethanidine were performed. Molecular docking showed that Bethanidine can inhibit SENP1. Key findings: MD Simulation showed that Bethanidine constitutes a stable complex with SENP1 as was evident from RMSD, RMSF, H-bond and DSSP plots. Free binding energy and the interaction patterns were obtained from molecular docking, and MD trajectory exhibited Bethanidine can be a potential drug candidate for SENP1 inhibition. Significance: This study supplies enough evidences that Bethanidine is a potential inhibitor of SENP1 and can be applied for the treatment of cardiovascular diseases.

Computational Drug Repurposing for Antituberculosis Therapy: Discovery of Multi-Strain Inhibitors.

Tuberculosis remains the most afflicting infectious disease known by humankind, with one quarter of the population estimated to have it in the latent state. Discovering antituberculosis drugs is a challenging, complex, expensive, and time-consuming task. To overcome the substantial costs and accelerate drug discovery and development, drug repurposing has emerged as an attractive alternative to find new applications for “old” drugs and where computational approaches play an essential role by filtering the chemical space. This work reports the first multi-condition model based on quantitative structure–activity relationships and an ensemble of neural networks (mtc-QSAR-EL) for the virtual screening of potential antituberculosis agents able to act as multi-strain inhibitors. The mtc-QSAR-EL model exhibited an accuracy higher than 85%. A physicochemical and fragment-based structural interpretation of this model was provided, and a large dataset of agency-regulated chemicals was virtually screened, with the mtc-QSAR-EL model identifying already proven antituberculosis drugs while proposing chemicals with great potential to be experimentally repurposed as antituberculosis (multi-strain inhibitors) agents. Some of the most promising molecules identified by the mtc-QSAR-EL model as antituberculosis agents were also confirmed by another computational approach, supporting the capabilities of the mtc-QSAR-EL model as an efficient tool for computational drug repurposing.

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.

Repurposing statins as a potential ligand for estrogen receptor alpha via molecular docking

Computational drug repurposing is the strategy for drug development which remarkably reduces the cost and development time. Research suggests that breast cancer development in women have been associated with cholesterol and its transporters. Cholesterol lowering drugs can be repurposed as potential therapeutic agents to prevent high cholesterol in estrogen receptor positive- breast cancer. The objective of this study was to carryout in-silico molecular docking of HMG-CoA reductase inhibitors (statins) with estrogen α receptor (3ERT) to repurpose the statins as breast cancer inhibitors. Molecular docking studies were performed to explore the mechanism of interactions between the statins and human estrogen α receptor. Docking results revealed that statins bind to the hydrophobic pocket of the estrogen α receptor with high binding affinity. The docking scores were compared with the standard drug 4- hydroxy tamoxifen. The study helped to compare the interactions amongst different statins with the receptor and the energy values produced were ranging from -8.5 to -5.5 kcal/mol. Molinspiration web servers was used to calculate the physiochemical properties and ADMET of the statins. Simvastatin showed better interaction amongst the docked statins with best protein ligand interactions, it was found to exhibit higher docking score of -8.5 kcal/mol. Therefore, we conclude that statins can be employed as an alternative drug for treatment of breast cancer.

Mycobacterium tuberculosis Cell Wall Permeability Model Generation Using Chemoinformatics and Machine Learning Approaches.

The drug-resistant strains of Mycobacterium tuberculosis (M.tb) are evolving at an alarming rate, and this indicates the urgent need for the development of novel antitubercular drugs. However, genetic mutations, complex cell wall system of M.tb, and influx–efflux transporter systems are the major permeability barriers that significantly affect the M.tb drugs activity. Thus, most of the small molecules are ineffective to arrest the M.tb cell growth, even though they are effective at the cellular level. To address the permeability issue, different machine learning models that effectively distinguish permeable and impermeable compounds were developed. The enzyme-based (IC50) and cell-based (minimal inhibitory concentration) data were considered for the classification of M.tb permeable and impermeable compounds. It was assumed that the compounds that have high activity in both enzyme-based and cell-based assays possess the required M.tb cell wall permeability. The XGBoost model was outperformed when compared to the other models generated from different algorithms such as random forest, support vector machine, and naïve Bayes. The XGBoost model was further validated using the validation data set (21 permeable and 19 impermeable compounds). The obtained machine learning models suggested that various descriptors such as molecular weight, atom type, electrotopological state, hydrogen bond donor/acceptor counts, and extended topochemical atoms of molecules are the major determining factors for both M.tb cell permeability and inhibitory activity. Furthermore, potential antimycobacterial drugs were identified using computational drug repurposing. All the approved drugs from DrugBank were collected and screened using the developed permeability model. The screened compounds were given as input in the PASS server for the identification of possible antimycobacterial compounds. The drugs that were retained after two filters were docked to the active site of 10 different potential antimycobacterial drug targets. The results obtained from this study may improve the understanding of M.tb permeability and activity that may aid in the development of novel antimycobacterial drugs.

Ensemble-based screening of natural products and FDA-approved drugs identified potent inhibitors of SARS-CoV-2 that work with two distinct mechanisms

The recent outbreak of SARS-CoV-2 is responsible for high morbidity and mortality rate across the globe. This requires an urgent identification of drugs and other interventions to overcome this pandemic. Computational drug repurposing represents an alternative approach to provide a more effective approach in search for COVID-19 drugs. Selected natural product known to have antiviral activities were screened, and based on their hits; a similarity search with FDA approved drugs was performed using computational methods. Obtained drugs from similarity search were assessed for their stability and inhibition against SARS-CoV-2 targets. Diosmin (DB08995) was found to be a promising drug that works with two distinct mechanisms, preventing viral replication and viral fusion into the host cell. Isoquercetin (DB12665) and rutin (DB01698) work by inhibiting viral replication and preventing cell entry, respectively. Our analysis based on molecular dynamics simulation and MM-PBSA binding free energy calculation suggests that diosmin, isoquercetin, rutin and other similar flavone glycosides could serve as SARS-CoV-2 inhibitor, hence an alternative solution to treat COVID-19 upon further clinical validation.

Computational Drug Repurposing: Classification of the Research Opportunities and Challenges.

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

Repurposing metocurine as main protease inhibitor to develop novel antiviral therapy for COVID-19.

The outbreak of severe acute respiratory syndrome coronavirus-2 is causing a serious disaster through coronavirus disease-19 (COVID-19) around the globe. A large segment of the population from every corner of the world is already infected with this dreadful pathogen with a high mortality rate. These numbers are increasing drastically causing a situation of a global pandemic. Although after the continuous scientific efforts, we are still not having any specific drug or vaccine for the SARS-CoV-2 pathogen to date and there is an urgent need to develop a newer therapy to counter the COVID-19 global pandemic. Thus, in the current study, a framework for computational drug repurposing is established, and based on their safety profile, metocurine was chosen as a safe and effective drug candidate for developing therapy against the viral Mpro enzyme of SARS-CoV-2 for the treatment of COVID-19.

Computational target-based drug repurposing of elbasvir, an antiviral drug predicted to bind multiple SARS-CoV-2 proteins.

Coronavirus disease 19 (COVID-19) is a severe acute respiratory syndrome caused by SARS-CoV-2 (2019-nCoV). While no drugs have yet been approved to treat this disease, small molecules effective against other viral infections are under clinical evaluation for therapeutic abatement of SARS-CoV-2 infections. Ongoing clinical trials include Kaletra (a combination of two protease inhibitors approved for HIV treatment), remdesivir (an investigational drug targeting RNA-dependent RNA polymerase [RdRP] of SARS-CoV-2), and hydroxychloroquine (an approved anti-malarial and immuno-modulatory drug). Since SARS-CoV-2 replication depends on three virally encoded proteins (RdRP, papain-like proteinase, and helicase), we screened 54 FDA-approved antiviral drugs and ~3300 investigational drugs for binding to these proteins using targeted and unbiased docking simulations and computational modeling. Elbasvir, a drug approved for treating hepatitis C, is predicted to bind stably and preferentially to all three proteins. At the therapeutic dosage, elbasvir has low toxicity (liver enzymes transiently elevated in 1% of subjects) and well-characterized drug-drug interactions. We predict that treatment with elbasvir, alone or in combination with other drugs such as grazoprevir, could efficiently block SARS-CoV-2 replication. The concerted action of elbasvir on at least three targets essential for viral replication renders viral mutation to drug resistance extremely unlikely.

Computational Drug Discovery and Repurposing for the Treatment of COVID-19: A Systematic Review

Computational Drug Discovery and Repurposing for the Treatment of COVID-19: A Systematic Review

Computational Drug Repurposing: Current Trends.

Biomedical discovery has been reshaped upon the exploding digitization of data which can be retrieved from a number of sources, ranging from clinical pharmacology to cheminformatics-driven databases. Now, supercomputing platforms and publicly available resources such as biological, physicochemical, and clinical data, can all be integrated to construct a detailed map of signaling pathways and drug mechanisms of action in relation to drug candidates. Recent advancements in computer-aided data mining have facilitated analyses of 'big data' approaches and the discovery of new indications for pre-existing drugs has been accelerated. Linking gene-phenotype associations to predict novel drug-disease signatures or incorporating molecular structure information of drugs and protein targets with other kinds of data derived from systems biology provide great potential to accelerate drug discovery and improve the success of drug repurposing attempts. In this review, we highlight commonly used computational drug repurposing strategies, including bioinformatics and cheminformatics tools, to integrate large-scale data emerging from the systems biology, and consider both the challenges and opportunities of using this approach. Moreover, we provide successful examples and case studies that combined various in silico drug-repurposing strategies to predict potential novel uses for known therapeutics.

Recent Advances in the Development of Pharmaceutical Agents for Metabolic Disorders: A Computational Perspective.

Metabolic disorders comprise a set of different disorders varying from epidemic diseases such as diabetes mellitus to inborn metabolic orphan diseases such as phenylketonuria. Despite considerable evidence showing the importance of the computational methods in discovery and development of new pharmaceuticals, there are no systematic reviews outlining how they are utilized in the field of metabolic disorders. This review aims to discuss the necessity of the development of web-based tools and databases by integration of available information for solving Big Data problems in network pharmacology of metabolic disorders. We undertook a structured search of bibliographic databases for peer-reviewed research literature using a focused review question and inclusion/exclusion criteria. The quality of retrieved papers was appraised using standard tools. The alterations in metabolic pathways cause various cardiovascular, hematological, neurological, gastrointestinal, immune disorders and cancer. In this regard, informatics, Big Data and modeling techniques aid in the design of novel therapeutic agents for metabolic diseases by addressing various Big Data problems in the network polypharmacology (drugs/pharmaceutical agents, proteins, genes, diseases, bioassays, ADMET and metabolic pathways), identification of privileged scaffolds, developing new diagnostic biomarkers, understanding the pathophysiology of disease and progress in personalized medicine. The recent advances of developing pharmaceutical agents for various metabolic disorders by considering their pathogenesis, mechanisms of action, therapeutic and adverse effects have been summarized. We have highlighted the role of computational techniques, drug repurposing, and network-based polypharmacological approaches in the identification of new/existing medicines with improved drug-likeness properties for the rare metabolic disorders.

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.

Identifying aberrant pathways through integrated analysis of knowledge in pharmacogenomics.

Many complex diseases are the result of abnormal pathway functions instead of single abnormalities. Disease diagnosis and intervention strategies must target these pathways while minimizing the interference with normal physiological processes. Large-scale identification of disease pathways and chemicals that may be used to perturb them requires the integration of information about drugs, genes, diseases and pathways. This information is currently distributed over several pharmacogenomics databases. An integrated analysis of the information in these databases can reveal disease pathways and facilitate novel biomedical analyses. We demonstrate how to integrate pharmacogenomics databases through integration of the biomedical ontologies that are used as meta-data in these databases. The additional background knowledge in these ontologies can then be used to enable novel analyses. We identify disease pathways using a novel multi-ontology enrichment analysis over the Human Disease Ontology, and we identify significant associations between chemicals and pathways using an enrichment analysis over a chemical ontology. The drug-pathway and disease-pathway associations are a valuable resource for research in disease and drug mechanisms and can be used to improve computational drug repurposing. http://pharmgkb-owl.googlecode.com